spatiotemporal characteristics of extreme rainfall events over java
TRANSCRIPT
SPATIOTEMPORAL CHARACTERISTICS
OF EXTREME RAINFALL EVENTS OVER JAVA ISLAND,
INDONESIA Case: East Java Province
COVER
Thesis
submitted to the Double Degree M.Sc. Program, Gadjah Mada University
and Faculty of Geo-Information Science and Earth Observation, University of Twente
in partial fulfillment of the requirement for the degree of Master of Science in
Geo-Information for Spatial Planning and Risk Management
By:
S U P A R I
GMU: 10/307098/PMU/06742
ITC: 27678
Supervisor:
1. Prof. Dr. HA. Sudibyakto M.S (GMU)
2. Dr. Ir. Janneke Ettema (ITC)
3. Dr. Edvin Aldrian M.Sc (BMKG)
THE GRADUATE SCHOOL
GADJAH MADA UNIVERSITY
FACULTY OF GEO-INFORMATION AND EARTH OBSERVATION
UNIVERSITY OF TWENTE
2012
APPROVAL SHEET
ABSTRACT
Extreme rainfall event is one of natural events frequently generating
serious impact to many sectors. To date, its characteristic is expected being
changing due global climate change. The study was aimed to identify the spatio-
temporal characteristics of extreme rainfall events over Java Island, Indonesia by
focusing analysis to East Java Province.
Some extreme indices calculated as annual series, were generated from
rainfall record within period of 1981 – 2010. The maximum number of
consecutive wet days, number of days where daily rainfall is more than or equal to
20 mm, 50 mm and 90th
percentile were chosen to represent the frequency of
extreme rainfall events. Meanwhile the highest 1-day rainfall amount, the highest
5-day rainfall amount, annual total and daily rainfall intensity were selected to
represent the intensity of the events. A set of quality control procedures including
duplicated data check, spatial outliers check, missing value check and
homogeneity test was applied prior the analysis. The spatial characteristic of those
events was identified by mapping climatological mean of indices while temporal
characteristic was assessed using the non-parametric Mann-Kendal test.
The quality control procedures selected 84 stations as high quality data
from total of 461 rainfall stations. The spatial pattern of extreme rainfall events
over East Java Province is generally characterized by low frequency and intensity
in the coastal area, and high frequency and intensity in the mountainous area. The
dominant finding from trend assessment is not-significant trend. However, the
consistently significant trend was observed in some districts. Rain stations in
District of Ponorogo, Ngawi, Bojonegoro, Gresik and Sumenep showed
significant negative trend for almost all indices whereas significant positive trend
was found in District of Surabaya and Pasuruan.
Key words: spatio-temporal characteristics, extreme rainfall events, hydro-
meteorological disaster, threshold, Java Island
INTISARI
Hujan ekstrim merupakan salah satu fenomena alam yang seringkali
menyebabkan dampak negatif pada berbagai sektor. Saat ini frekuensi dan
intensitas hujan ekstrim diduga telah mengalami perubahan sebagai akibat dari
perubahan iklim. Penelitian ini bertujuan untuk mengetahui karakteristik dari
hujan ekstrim di Pulau Jawa, khususnya di Propinsi Jawa Timur.
Analisis dilakukan dengan menghitung indeks-indeks hujan ekstrim,
berdasarkan data hujan harian pada periode tahun 1981 – 2010. Indeks-indeks
tersebut mengandung informasi baik tentang intensitas maupun frequency hujan
ekstrim. Data hujan harian diuji kualitasnya sebelum digunakan untuk menghitung
indeks ekstrim. Pengecekan kualitas data meliputi pengecekan kode standard,
pengecekan data ganda, pengecekan data pencilan, pengecekan data kosong dan
pengecekan terhadap homogenitas data. Karakteristik keruangan dari hujan
ekstrim dinilai dengan cara memetakan rata-rata klimatologis dari masing-masing
indeks, sedangkan karakterisktik terkait perubahan terhadap waktu diuji dengan
metode Mann-Kendal test.
Pengujian kualitas data menyisakan 84 pos hujan sebagai data yang bagus
dan layak untuk dianalisis. Karakteristik hujan ekstrim di Propinsi Jawa Timur
umumnya bisa dikenali dengan ciri-ciri frequency dan intensitas yang rendah di
wilayah pantai dan tinggi di wilayah pegunungan. Secara umum hujan ekstrim
tidak mengalami perubahan yang sangat nyata, namun pada beberapa kabupaten,
teramati adanya pos-pos hujan yang menunjukkan perubahan yang nyata dan
terjadi tidak hanya pada satu indeks tapi konsisten pada beberapa indeks.
Penelitian ini menemukan bahwa pos hujan di Kabupaten Ponorogo, Ngawi,
Bojonegoro, Gresik dan Sumenep menunjukkan penurunan frequensi dan intesitas
hujan ekstrim. Sebaliknya, pos hujan di Kabupaten dan Kota Surabaya dan
Pasuruan menunjukkan peningkatan frequensi dan intensitas hujan ekstrim.
ACKNOWLEDGMENTS
The Master project detailed in this thesis was supervised by Prof. Dr. HA.
Sudibyakto, M.S (Gadjah Mada University), Dr. Ir. Janneke Ettema (Univ. of
Twente) and Dr. Edvin Aldrian M.Sc (BMKG). Their advices and comments were
very much appreciated. Special thanks are extended to Division of Climate Data
Analysis - Head office of BMKG, BMKG’s regional office of Malang and
Semarang from which the rainfall data were collected.
I would also like to thank to Indonesian People for providing the
scholarship of this master study through national budget given for Pusbindiklatren
Bappenas and Pusdiklat BMKG, and to my institution for permitting me to pursue
this master degree.
For my parents as well as my parents in law, my deepest regards to you for
giving valuable supports. Your blessing enabled me to finish this hard work. For Rey,
my wife and Humam-Hanif, my sons this is dedicated to you.
i
Table of Contents
COVER __________________________________________________________ 0
APPROVAL LETTER ______________________________________________ 0
ABSTRACT ______________________________________________________ 2
ACKNOWLEDGMENTS ___________________________________________ 3
Table of Contents __________________________________________________ i
List of Figures ____________________________________________________ iv
List of Tables_____________________________________________________ vi
Abbreviations ___________________________________________________ vii
I. INTRODUCTION ____________________________________________ 1
1.1. Background _______________________________________________ 1
1.2. Problem Statement __________________________________________ 2
1.3. Research Objective __________________________________________ 5
1.4. Research Questions _________________________________________ 5
1.5. Research Hypothesis ________________________________________ 6
1.6. Research Benefits ___________________________________________ 7
1.7. Research Limitation _________________________________________ 7
II. LITERATURE REVIEW_______________________________________ 8
2.1. Climate in Indonesia _________________________________________ 8
2.1.1. Rainfall Process and Cloud Formation __________________________________ 9
2.1.2. Rainfall Classification in Indonesia ___________________________________ 10
2.2. Extreme Value Analysis _____________________________________ 12
2.2.1. Extreme Rainfall Event _____________________________________________ 13
2.2.2. Indices of Extreme Rainfall Events ___________________________________ 14
2.2.3. Spatial Analysis for Rainfall Data ____________________________________ 14
2.2.4. Temporal Trend Analysis ___________________________________________ 15
2.3. Research on Extreme Rainfall Events over the World ______________ 16
2.3.1. America_________________________________________________________ 16
2.3.2. Africa __________________________________________________________ 16
2.3.3. Asia ____________________________________________________________ 17
2.3.4. Europe __________________________________________________________ 18
2.3.5. Australia ________________________________________________________ 18
ii
III. STUDY AREA AND DATA ___________________________________ 20
3.1. Study Area _______________________________________________ 20
3.1.1. Java Island ______________________________________________________ 20
3.1.2. East Java Province ________________________________________________ 21
3.2. Data ____________________________________________________ 22
IV. RESEARCH METHODS _____________________________________ 26
4.1. Research Framework _______________________________________ 26
4.2. Method for Quality Control __________________________________ 27
4.2.1. Checking for duplicated data ________________________________________ 27
4.2.2. Checking for outliers and missing values _______________________________ 28
4.2.3. Homogeneity test _________________________________________________ 29
4.3. Method for Identification of Extreme Indices ____________________ 33
4.3.1. Fix Threshold ____________________________________________________ 33
4.3.2. Site Specific Threshold _____________________________________________ 34
4.4. Method for Spatial Analysis __________________________________ 37
4.5. Method for Temporal Trend Analysis __________________________ 38
4.6. Method for Severity Analysis _________________________________ 40
V. SCREENING DAILY RAINFALL DATA ________________________ 43
5.1. Converting into Standard Format ______________________________ 43
5.2. Duplicate Data Check _______________________________________ 45
5.2.1. Procedures on Checking Duplicated Data_______________________________ 45
5.2.2. Result of Duplicated Data Check _____________________________________ 49
5.3. Outlier Check _____________________________________________ 53
5.4. Missing Value Check _______________________________________ 58
5.5. Homogeneity Test _________________________________________ 62
VI. RESULT ___________________________________________________ 67
6.1. Spatial Characteristic of Extreme Rainfall Events _________________ 67
6.1.1. Fix Threshold ____________________________________________________ 67
6.1.2. Site Specific Threshold _____________________________________________ 70
6.1.3. Climatological Mean of Annual Indices ________________________________ 77
6.1.4. Topography Effect ________________________________________________ 88
6.2. Temporal Trend of Extreme Rainfall Events _____________________ 90
6.2.1. Result of the Assessment ___________________________________________ 90
6.2.2. Spatial Pattern of Detected Trend _____________________________________ 97
iii
6.3. Severity Analysis _________________________________________ 109
VII. DISCUSSION _____________________________________________ 113
VIII. CONCLUSION AND RECOMMENDATION ____________________ 119
8.1. Conclusion ______________________________________________ 119
8.2. Recommendation _________________________________________ 120
IX. REFERENCES _____________________________________________ 122
APPENDICES __________________________________________________ 127
iv
List of Figures
Figure 1-1. Frequency of floods and landslides over Java Island _____________ 4
Figure 2-1. Geographic location of Indonesia ____________________________ 8
Figure 2-2. The illustration of three types of rainfall ______________________ 11
Figure 2-3. Illustration of two ways on analyzing extreme values ___________ 13
Figure 3-1. Java Island (red box) among the other islands in Indonesian
archipelagos______________________________________________________ 20
Figure 3-2. The three dominant rainfall regions in Indonesia _______________ 21
Figure 3-3. Topographic feature of East Java Province ____________________ 24
Figure 3-4. Distribution of 2580 rain gauges over Java Island ______________ 25
Figure 4.1. Research Framework _____________________________________ 26
Figure 4-2. Unadjusted and adjusted time series _________________________ 30
Figure 4-3. Probability distribution function of Gumbel, Frechet and Weibull __ 35
Figure 4-4. Trend of rainfall being larger than 95th
percentile over Europe ____ 38
Figure 4-5. Thiessen polygons of Australia _____________________________ 41
Figure 4-6. The procedure to generate severity index _____________________ 42
Figure 5-1. The example of digital data stored by Malang Climatological
Station __________________________________________________________ 44
Figure 5-2. The example of digital data stored by Semarang Climatological
Station __________________________________________________________ 44
Figure 5-3. The example of standard format of rainfall series _______________ 45
Figure 5-4. The example of detection process for duplicated data ___________ 46
Figure 5-5. The example of coincident similar total value _________________ 46
Figure 5-6. The example of duplication where not all daily data are copied ____ 47
Figure 5-7. The duplication case at Station Maelang, District of Banyuwangi __ 47
Figure 5-8. A technique to identify original-copied data ___________________ 48
Figure 5-9. Number of months containing duplicated data _________________ 49
Figure 5-10. The process of rainfall data collecting in Indonesia ____________ 51
Figure 5-11. Spatial distribution of registered gauges _____________________ 52
Figure 5-12. Spatial correlation function of daily rainfall over study area _____ 53
Figure 5-13. An example of replacing outliers by statistical threshold ________ 57
Figure 5-14. Scatter plot of original series of Station Gombal ______________ 57
Figure 5-15. Scatter plot of corrected series of Station Gombal _____________ 58
Figure 5-16. Percentage of missing values average from 10 districts _________ 60
Figure 5-17. Spatial distribution of selected gauges based on missing value
criteria __________________________________________________________ 61
Figure 5-18. An interface of tool of homogeneity test on xlstat Add-Ins
package _________________________________________________________ 62
Figure 5-19. A shift in series of Wagir rain station, District of Malang _______ 63
Figure 5-20. A break in series of Dam Sembah rain station, District of Jember _ 64
Figure 5-21. Spatial distribution of useful series _________________________ 65
v
Figure 5-22. Spatial distribution of high quality rainfall data passing all quality
control procedures _________________________________________________ 66
Figure 6-1. Histogram and box plot of rainfall correspond to disaster ________ 67
Figure 6-2. The affected area due to hydro-meteorological disaster __________ 69
Figure 6-3. Box plot of threshold based on 90th
percentile, 1-yr, 5-yr and 25-yr
return period _____________________________________________________ 71
Figure 6-4. Spatial distribution of threshold based on 90th
percentile _________ 73
Figure 6-5. As Figure 6-4 but for rainfall with 1-year return period, R1yr _____ 74
Figure 6-6. As Figure 6-4 but for rainfall with 5-year return period, R5yr _____ 75
Figure 6-7. As Figure 6-4 but for rainfall with 25-year return period, 25yr ____ 76
Figure 6-8. The climatological mean of R20mm _________________________ 80
Figure 6-9. As Figure 6-8 but for R50mm ______________________________ 81
Figure 6-10. As Figure 6-8 but for R90p _______________________________ 82
Figure 6-11. As Figure 6-8 but for CWD _______________________________ 83
Figure 6-12. As Figure 6-8 but for RX1d _______________________________ 84
Figure 6-13. As Figure 6-8 but for RX5d _______________________________ 85
Figure 6-14. As Figure 6-8 but for RTOT ______________________________ 86
Figure 6-15. As Figure 6-8 but for SDII _______________________________ 87
Figure 6-16. The scatter plot of index CWD versus log of elevation __________ 89
Figure 6-17. The example of significant temporal change of R20mm _________ 91
Figure 6-18. The example of significant temporal change of R50mm _________ 92
Figure 6-19. The example of significant temporal change of R90p ___________ 93
Figure 6-20. The example of significant temporal change of CWD __________ 93
Figure 6-21. The example of significant temporal change of RX1d __________ 95
Figure 6-22. The example of significant temporal change of RX5d __________ 95
Figure 6-23. The example of significant temporal change of RTOT __________ 96
Figure 6-24. The example of significant temporal change of SDII ___________ 96
Figure 6-25. Spatial pattern of detected trend for R20mm __________________ 99
Figure 6-26. As Figure 6-25 but for R50mm ___________________________ 100
Figure 6-27. As Figure 6-25 but for R90p _____________________________ 101
Figure 6-28. As Figure 6-25 but for CWD _____________________________ 102
Figure 6-29. As Figure 6-25 but for RX1d _____________________________ 105
Figure 6-30. As Figure 6-25 but for RX5d _____________________________ 106
Figure 6-31. As Figure 6-25 but for RTOT ____________________________ 107
Figure 6-32. As Figure 6-25 but for SDII _____________________________ 108
Figure 6-33. Thiessen polygons created for regional analysis ______________ 111
Figure 6-34. The severity map for extreme rainfall events ________________ 112
Figure 7-1. Spatial distribution of stations showing consistently significant
trend___________________________________________________________ 118
vi
List of Tables
Table 1-1. Areal daily rainfall observed in the Bengawan Solo Watershed during
2007 Java flood ___________________________________________________ 4 Table 1-2. List of research questions __________________________________ 6 Table 2-1. European trends per decade _______________________________ 18 Table 2-2. Numbers of stations in Australia with positive and negative trend _ 19 Table 3-1. The detail required data___________________________________ 23 Table 4-1. Correspondence between GEV and three basic extreme value
distribution _____________________________________________________ 36 Table 4-2. Detail extreme rainfall indices _____________________________ 37 Table 5-1. The detail result of cases of duplicated data check for East Java
Province________________________________________________________ 50 Table 5-2. Summary of spatial outliers check in respect of district __________ 56 Table 5-3. Summary of MVs check with regard to district ________________ 59 Table 6-1. List of disasters occurred in the last ten year __________________ 68 Table 6-2. Summary of trend assessment for all frequency indicators _______ 94 Table 6-3. Summary of trend assessment for all intensity indicators_________ 97 Table 7-1. Contingency table showing inter-index relation _______________ 115 Table 7-2. List of stations which are consistently increasing ______________ 116 Table 7-3. List of stations which are consistently decreasing _____________ 116
vii
Abbreviations
APN Asia Pacific Network
BBWS Balai Besar Wilayah Sungai, Regional Office for
Watershed Management, Ministry of Public Work
BMKG Badan Meteorologi Klimatologi dan Geofisika, National
Agency for Meteorology, Climatology and Geophysics
BNPB Badan Nasional Penanggulangan Bencana, National
Agency for Disaster Management
BP DAS Balai Pengelola Daerah Aliran Sungai, Watershed
Management Authority, Ministry of Forestry
BPS Badan Pusat Statistik, Statistics Indonesia
GEV Generalized Extreme Value
GHCN Global Historical Climatology Network
IPCC Intergovernmental Panel on Climate Change
IQR Inter-Quartile Range
ITCZ Inter Tropical Convergence Zone
MV Missing Value
NCDC National Climatic Data Centre - USA
SNHT Standard Normal Homogeneity Test
SRTM Shuttle Radar Topography Mission
TSE The Theil-Sen Estimator
WMO World Meteorological Organization
1
I. INTRODUCTION
1.1. Background
Extreme rainfall events are among the most devastating weather
phenomena since they are frequently followed by flash floods and sometimes
accompanied by severe weather such as lightning, hail, strong surface winds, and
intense vertical wind shear (Jones et al. 2004). Consequently, they generate large
economic, social and environmental impact (Manton et al. 2001; Carvalho et al.
2002; Jones et al. 2004). In rural area, the extreme rainfall events can damage
crops and livestock. While in urban area, these events often cause flood problem
due to inadequate drainage system to accommodate a sudden large amount of
rainfall (Carvalho et al. 2002). In global perspective, these events are also
supposed being responsible for rapidly rising costs of losses since the 1970s
(Rosenzweig et al. 2007).
Extremes of climate are an expression of the natural variability, actually
(Trenberth et al. 2007). However, these events become serious issue to date
because their frequency is expected being change. It is confirmed by the
Intergovernmental Panel on Climate Change (IPCC) that human influences on
climate lead to change in frequency and intensity of extreme weather events
(Trenberth et al. 2007). Some extremes are expected to become more frequent,
more widespread and/or more intense. It is logic then, when the demand for
information of extreme weather is growing (WMO, 2009).
Observational studies over some regions suggested evidence of change in
climate extremes. Using daily rainfall data from 1931 – 1996, Kunkel et al.
(1999), examined the trend of extreme rainfall events over the Conterminous
United States and Canada and found an indication of increasing trend in the
number of 7-day, 1-yr events. Even, some climate divisions have experienced
increases of 50% – 100%. Zang et al. (2001), found an upward trends in the
number of extreme rainfall events for the spring over eastern Canada when they
examined the characteristic of extreme rainfall events using site specific threshold
2
over whole country. Study on this event in the Alpine Region by Frei and Schar
(2001), also confirmed an increasing trend for autumn and winter season.
However, some other studies in tropical region reveal inconsistent result.
For example in Peninsular Malaysia, Suhaila et al. (2010) found that almost all
stations in the eastern region show a decreasing trend of frequency of extreme
rainfall events during southwest monsoon period. Nevertheless, the western
region even shows the contrast result, an increasing trend. Atsamon et al. (2009)
also found different trend between two regions in Thailand. On the Andaman Sea,
they characterized an overall decrease while on the Gulf of Thailand they detected
an increasing trend in magnitude and frequency of more intense rainfall events.
Regarding the statement at WMO guidelines on analysis of extremes
(WMO, 2009) that the sustainability of economic development and living
conditions depends on our ability to manage the risks associated with extreme
events, the study of extreme rainfall events in Indonesia is urgent. The present
study focus on analyzing the spatial and temporal characteristics of extreme
rainfall events using GIS tools. The result of this research is expected being able
to provide crucial inputs to manage the risk as mentioned before.
1.2. Problem Statement
Changing of probability of extreme rainfall events implies seriously to
many sectors such as engineering, regional planning and other activities which
traditionally assumed that climate is stationary (Suppiah and Hennessy, 1998).
This assumption states that climate is variable, but the variation tends to be
constant meaning it occurs around unchanging mean state (WMO, 2009).
As described in previous sub chapter, the different changing trend was
found in distinct region. This confirms us that regional scale perhaps has different
response to the global climate which is identified changing. So far to the author’s
knowledge, there is no regional scale study on extreme rainfall events in
Indonesia. Manton et al. (2000) have studied extreme rainfall events over
Indonesia but for the large region i.e. Asia Pacific Region. They found different
trend. There is an increasing trend for extreme rainfall in Fiji and French
3
Polynesia. However, Solomon Island, Philippines, New Zealand, Malaysia and
Japan show the decrease trend. The others country even show no significant trend.
Over Indonesia, they concluded that the trend of extreme rainfall events is
not significant. Unfortunately, they only used six rainfall stations which are
Pangkalpinang, Jakarta, Balikpapan, Manado, Ambon and Palu. Those stations
represent western, central and eastern region of Indonesia. Those six stations are
inadequate certainly to display climatic condition of the whole country. The
regional scale study using more rainfall stations is therefore needed to figure out
the actual trend.
Relating to disasters triggered by extreme rainfall event (maximum daily),
Java Island is chosen as an ideal area for this study. There were high frequency of
severe disaster events here related to extreme weather event such as flood and
landslide event. National Agency for Disaster Management, BNPB recorded that
more than 1,000 occurrences of floods and landslides strike the Island with
various intensity within 2002 - 2008. The frequency of those events for each
province is shown in the figure 1-1 where West and Central Java take place as
first and second province with frequency of those disasters being more than 300
events.
The recent example of those disasters is flood which occurred in the end of
2007. Expanding from Central to East Java, it caused hundreds of casualties and
damaged thousands houses. The flood was triggered by heavy rainfall event with
intensity more than 100 mm/day taking place simultaneously and intensively in
December 25, 2007 (Hidayat et al. 2008). The record from three regions showed
that rainfall which occurred during this disaster has return period ranging from 40
up to more than 100 year (Table 1-1). The maximum areal daily rainfall was more
than 100 mm. The similar disaster reoccurred at the beginning of 2009 (BNPB,
2011).
4
Figure 1-1. Frequency of floods and landslides over Java Island: 2002 – 2008 (Source: BNPB,
2011)
Table 1-1. Areal daily rainfall observed in the Bengawan Solo Watershed during 2007 Java flood
(Source: Nippon Koeico. Ltd cited in Hidayat, 2008)
PARAMETER UPPER SOLO BASIN MADIUN BASIN WONOGIRI DAM
Intensity 134 mm/day 141 mm/day 128 mm/day
Return Period 55 year 500 year 40 year
Broadening our understanding of extreme rainfall events is especially
relevant for Java Island because it is the most populous Island in Indonesia, with
more than 120 million people living there. For national perspective, Java is the
centre of economic activity, national government system and agricultural product.
We need to examine then whether the extreme rainfall events in java regional
scale is changing or not. The changing of these events either in frequency or
intensity will affect on formulating the policy in many fields such as agricultural
(agricultural product management) and infrastructural sectors (construction
management). Without studying this subject, we will never realize whether our
strategies at those sectors are still supported by recent extreme climate condition
or not.
5
1.3. Research Objective
The major objective of this study is to analyze pattern of extreme rainfall
events within last three decades (1981 - 2010) both spatially and temporally in the
context of hazard study. By documenting its spatio-temporal characteristic, we
can recognize well the potential places where and when the extreme rainfall
events occur. The output will be displayed on the maps and curves, for example
map of annual probability of rainfall with intensity more than or equal to 50 mm
and map of distribution of daily rainfall intensity with 25 year return period. The
minor objectives are as follows:
1. To define threshold value of extreme rainfall events for general application.
Fix threshold defined by BMKG (National Agency for Meteorology,
Climatology and Geophysics) was evaluated by correlating it to historical
data of disaster. Site specific threshold was calculated for each station based
on statistical parameter of rainfall data.
2. To characterize spatial characteristics of extreme rainfall events over study
area by mapping its indices for various intensity and recurrence interval.
3. To detect possible temporal trend of extreme rainfall events.
4. To identify districts recording high frequency of extreme rainfall events.
Based on its frequency, the extreme rainfall events were classified in to the
category of less severe, moderate and more severe.
1.4. Research Questions
To address those all objectives, here some questions are formulated as
shown in the table below:
6
Table 1-2. List of research questions
Research objectives
Research questions
To define threshold for
extreme rainfall events
How do we evaluate extreme threshold
defined by BMKG?
How do we define the threshold of
Extreme Rainfall Events using site specific
threshold?
What is the amount of rainfall at different
return period?
To document spatial
characteristics of extreme
rainfall events
What is the spatial characteristic of
Extreme Rainfall Events over Java Island?
What is the effect of topography to
extreme rainfall events?
To characterize temporal
trend
What is the temporal trend of Extreme
Rainfall Events within last three decades?
How significant is the trend?
To identify the district with
severe extreme rainfall
events
How to map the severity of extreme
rainfall events?
Where is the most severe extreme rainfall
events present?
1.5. Research Hypothesis
1. The site specific threshold will be more appropriate to express extreme
rainfall in certain area. The amount of rainfall varies spatially in respect of
return period.
2. The spatial pattern of extreme rainfall events is affected by topographical
feature.
3. There is significant trend of extreme rainfall events.
7
1.6. Research Benefits
By producing some maps which display indices of rainfall extreme events,
this research will be beneficial for some stakeholders for example:
1. Disaster management authorities. They can utilize the research output to
identify which area should be prioritized more related to risk of extreme
rainfall events.
2. Public work authorities. They can use the output to evaluate whether the
existing infrastructure still appropriate considering recent extreme climate
condition or not.
3. Agricultural management authorities. They can use the output to define the
best agricultural product regarding extreme rainfall characteristic in certain
area.
4. For general society. The output will give better understanding about various
return period and intensity of extreme rainfall events so that they are able to
cope with the possible effects.
1.7. Research Limitation
The research will be limited to analyze the characteristics of extreme
rainfall events without studying more to the related disaster. It means that the
individual extreme rainfall event will not be analyzed. Considering the available
digital data of daily rainfall, the record used for the study is within 1981 – 2010.
The trend identified from the study is expected being able to figure out the change
of extreme rainfall in the last three decades.
The extreme events analyzed in the study refer to maximum extremes, not
minimum extremes since the minimum extreme in daily resolution give no impact
to the human life. The severity of extreme rainfall events was identified based on
its frequency. A recommendation to the districts was designed with regard to
severity level of extreme rainfall events only, without looking at the
environmental condition of those districts.
8
II. LITERATURE REVIEW
2.1. Climate in Indonesia
Climate is, in general, an expression of weather average (Petterssen, 1958).
The main energy source for climate dynamic is solar energy. Climate in the world
is variable both spatial and temporally as a result of difference respond of earth
surface on receiving solar energy.
The archipelagos of Indonesia are located between Asian Continent and
Australian Continent stretching out along the equator line, in east-west direction
(Figure 2-1). Every place in Indonesia receives solar energy in similar amount
approximately. It is logic then if the spatial variation of temperature and pressure
is quite limited. The variation of those two variables is only on vertical manner
due to altitude variation.
Figure 2-1. Geographic location of Indonesia - yellow box (a) and general atmospheric circulation
over Indonesia (b). The two continents and two oceans control air mass movement there causing
variability of climate (Source: google earth and www.climate4you.com).
As a tropical country, Indonesia receives abundant incoming solar
radiation along the year so the solar energy is surplus here. Indonesia has also
plentiful source of water vapor because it is located between two oceans which are
9
Indian Ocean and Pacific Ocean. It is not surprising that the climate here is very
both dynamics and humid. Two big continents (Asia and Australia) flanking it
also persuades the climate pattern in Indonesia by intervening general atmospheric
circulation over Indonesia (Figure 2-1).
A clear illustration of climate in Indonesia is the different weather
condition between dry and wet season. In the dry season, the weather will be
sunny, humid and less rainfall whilst in the wet season it will be cloudy, humid
and much rainfall. Rainfall varies notably with respect to its frequency, duration,
intensity and spatial pattern, a common characteristic of rainfall (Barrett and
Martin, 1981) but in Indonesia its variation is more complex. Therefore climate
regime classification in Indonesia is primarily based on rainfall variation.
2.1.1. Rainfall Process and Cloud Formation
Rainfall process refers to a cycle where the parcel or sample of air
undergoes a process to be moist, grow to be a cloud cell and produce rainfall
finally. The factors governing the occurrence of rainfall are the motion of cloud
air and its aerosol properties, which determine the concentration, initial size
distribution and nature of cloud properties (Mason, 1971 cited in Barrett and
Martin, 1981).
Rainfall process determines the characteristic of rainfall that it produces. It
describes the mechanism which stimulates cooling and condensation process by
which the moist air starts to become cloud droplets. Principally, cloud will form if
there is a parcel of moist air lifted. Petterssen (1958) described in his book that the
rainfall process is started when the moist air ascends and cools by expansion. As it
cools, the relative humidity will increase. When the process continues, the air will
be saturated and cloud droplets form.
These droplets do not freeze until the temperature is far below freezing
point (less than -28 C). In this step, the cloud has already formed. As soon as
some of cloud elements have outgrown the others, the larger ones will fall through
the cloud and further growth will result from collisions among them. When the
10
cloud droplets are large enough they will fall as rain droplets since the lifting
force is less than gravity.
Rainfall probability is a function of cloud thickness, base and top
temperature (Barrett and Martin, 1981). In tropical region, the clouds will not
produce rain until their thickness reaches more than 6000 feet. They are almost
produce rain when their thickness achieves more than 12000 feet (Petterssen
1958).
2.1.2. Rainfall Classification in Indonesia
The most common approach to classify rainfall is based on its forming
mechanism or its process. Based on the mechanism how the moist air is lifted,
there are three rainfall types found in Indonesia i.e. convectional rainfall,
orographic rainfall and convergence rainfall. The illustration is presented in the
Figure 2-2 whereas the description is summarized in the following paragraph.
1. Convectional Rainfall.
Convectional rainfall most commonly results from the air which having
been warmed by conduction from a heated land surface, expands and rises in a
cold and dense surrounding air (Monkhouse, 1959; Linsley Jr et al. 1982). The
local heating starts the whole process and is therefore known as a trigger effect
(Figure 2-2). However, when the moist air ascends, it can carry on even when the
heating process ends.
In the equator region, Indonesia for instance, convectional rainfall occurs
throughout the year because of constant high temperature and humidity
(Monkhouse, 1959). It is characterized by spotty rainfall with intensity is ranging
from light shower up to rainstorm (Linsley Jr et al. 1982).
2. Orographic Rainfall.
The orographic rainfall occurs when moist air is forced to climb the side of
mountain range. It is commonly found in the area where hills lie parallel to the
coast over which moist air are blown by wind from the sea (Monkhouse, 1959).
11
In Indonesia, we can find the suitable area for producing orographic
rainfall, like the mountainous area stretching out along Sumatra Island and Java
Island. When the warm moist air from sea climbs the mountain range, it is cooled
and condensed. The cloud forms then and develops intensively as long as the
supply of warm moist air is still continuous (Figure 2-2).
If the mountainous area is high enough, the cloud producing rainfall often
forms in front of the summit only (windward sides) and remains the relative dry
area in the leeward sides known as “rain shadow area”.
Figure 2-2. The illustration of three types of rainfall i.e. convectional rainfall (left top), orographic
rainfall (right top) and convergence rainfall (bottom). In nature, the mechanism process is
interrelated and the produced rainfall cannot be identified as being of one type exactly (Source:
http://splashman.phoenix.wikispaces.net).
3. Convergence Rainfall.
Convergence rainfall is produced from the cloud resulting from the
convergence of two moist air masses. When those two air masses converge, as a
fluid, they will ascend to find less dense area. The ascending causes the air masses
being cooled and condensed and the clouds form finally.
12
The inter-tropical convergence zone (ITCZ) is a perfect example for the
area where convergence rainfall occurs. Over Indonesia, the ITCZ often forms as
a convergence of tropical maritime air masses from Asia and Australia, lying
some hundreds of miles, ranging from Indian Ocean until Pacific Ocean
(Monkhouse, 1959). A long line of massive cumulonimbus clouds with torrential
rain and thunderstorm are common appearance of the ITCZ.
2.2. Extreme Value Analysis
Extreme value analysis is a statistical analysis based on what we know as
Extreme Value Theory. It is the branch of statistics which describes the behavior
of the extreme data observations (Gilli, 2003; Naveau et al. 2005). The purpose of
extreme value analysis is to estimate what future extreme levels of a process
might be expected (Coles SG and RSJ Sparks, 2001) and what the likely
recurrence of these events is, based on a historical series of observations (Murphy,
1997).
Generally there are two ways of identifying extremes in real data. The first
approach considers the maximum (or minimum) of the variable taking in
successive periods, for example months or years. These selected observations
constitute the extreme events, also called block maxima (or per-period maxima).
The second approach focuses on the appearance of values exceeding a given
threshold. Figure 2-3 displays the difference of those two approaches. The block
maxima method is the traditional method used to analyze data with seasonality as
for instance hydrological data. However, threshold methods use data more
efficiently and, for that reason, seem to become the choice method in recent
applications (Gilli, 2003).
13
Figure 2-3. Illustration of two ways on analyzing extreme values. Left panel, block maxima
method, displays the observations X2, X5, X7 and X11 representing these block maxima for four
periods with three observations per period . Right panel shows threshold method, by which X1, X2,
X7, X8, X9 and X11 are categorized as extremes since they exceed a threshold µ (Source: Gilli,
2003).
2.2.1. Extreme Rainfall Event
Extreme value analysis is applied by scientists to study climate extremes as
for instance extreme of temperature and rainfall. In line with increasing attention
to climate change issue, study of extreme climate events has also been more
interesting because their characteristics can be used to indicate the change in
climate.
The two approaches of analyzing extremes, as mentioned in previous sub
chapter, are operated. In the context of extreme rainfall events researches,
identifying annual series of maximum daily rainfall is example of block maxima
method while calculating frequency of rain day with rainfall more than 20 mm is
case of threshold method.
Extreme rainfall events are defined as 24-hour accumulative rainfall
exceeding a certain threshold. There are some different ways by which
meteorologists determine this threshold. Goswami and Ramesh (2007) used a
daily rainfall depth of 250 mm as a threshold on analyzing vulnerability of Indian
Region due to extreme rainfall events. Bodini and Cossu (2010) used a daily
rainfall depth above 95th percentile over certain period on assessing vulnerability
of Central-East Sardinia to extreme rainfall events. It sounds as a site dependent
threshold.
14
Fu et al. (2010), which adopted the methodology used by Kunkel et al.
(1999, 2003), used also a site dependent threshold which is defined by recurrence
interval when they analyzed long-term temporal variation of extreme rainfall
events in Australia. Hernandez et al. (2009) registered that there are at least 23
different ways used in professional literatures to define threshold or indices
explaining extreme rainfall events. World meteorological organization has
published a set of standard indices expressing extreme rainfall events (WMO,
2009) and standard procedure how to investigate that.
2.2.2. Indices of Extreme Rainfall Events
There are many different ways by which scientists define the indices of
extreme rainfall. Hernandez et al. (2009) registered that there are at least 23
different ways used in professional literatures throughout the world to define
threshold or indices explaining extreme rainfall events. WMO, (2009) through
their guidelines on analysis of extremes in a changing climate define 11 extreme
rainfall indices.
The readers interested to the detail indices are recommended to refer the
guidelines directly. The other ways to define indices can be found such as in the
study of Kunkel et al. (1999), Hernandez et al. (2009) and Bodini and Cossu
(2010). All indices are calculated for annual number or annual value. The rainfall
value of 1 mm is commonly applied to define rain event or rain day.
2.2.3. Spatial Analysis for Rainfall Data
Rainfall, as the other natural phenomena, is a kind of regionalized variable.
It varies in space and time. Local atmospheric condition and topographical factors
affect the spatial distribution of rainfall (Subyani, 2004). However, the rainfall is
measured in point base using rain gauges network. The density of rain gauges
often depends on the accessibility to location. On the flat area we can find the
dense rain gauges but in the complex terrain, a sparse rain gauge network is
common situation.
15
On the other hand, areal rainfall value is more essential for any
applications such as hydrological model and weather prediction rather than point
value. Interpolation technique is hence applied to get areal value. The basic
principle of interpolation is the assumption that at short distance the values are
more similar than at further distance (Meijerink et al. 1994).
There are many choices of interpolation techniques for rainfall data
ranging from simple approaches such as thiessen polygons and inverse distance
weighted to more complex approaches such as krigging and genetic algorithm
(Subyani, 2004; Haberlandt, 2007).
2.2.4. Temporal Trend Analysis
The method to identify temporal trend of time series data is a lot but the
most frequently used by meteorologist is Man-Kendall test. The use of this test on
detecting trend is shown for instance in Zang et al. (2001), Fu et al. (2010) and
Penalba and Robleda (2010). Principally, the Man-Kendall test examines the
observation by calculating a gap between one observation data with earlier one.
The data surely should be arranged in time order. The next data are calculated
respectively. The null hypothesis is that the total of those gaps will be 0 (zero)
meaning that there is no change in the series.
Mason et al. (1999) used an alternative method to calculate the significance
of change so called a re-sampling method. It is a method being free from
distributional assumptions. A series of data is divided into two successive periods.
Those two successive periods should be balance in term of long of data period.
The beta- and beta –P distribution are fitted then for each of those two periods.
The change of those periods can be assessed then by comparing those beta- and
beta –P parameters. The limitation of this method is that the results can be
sensitive to the a priori definition of the sub-period and to the value of n (number
of selected extremes).
16
2.3. Research on Extreme Rainfall Events over the World
Observational studies of climate extremes are focused on characterizing the
possible change of the extreme events as it is shown by some models. Over the
world, there are some studies to examine the change of extreme rainfall events.
The summary of those study based on its region is given in the next paragraphs.
2.3.1. America
In America continent, Kunkel et al. (1999) have investigated trend of
extreme rainfall events particularly over United States and Canada. The data
involved are 1295 rainfall stations with 66 year observation data (1931 – 1996) for
United Sates and 63 rainfall stations with 43 year observation data (1951 – 1993)
for Canada.
They designed a procedure to define extreme rainfall events. Event
durations of 1, 3, and 7 days were examined. Two precipitation total thresholds
were used to screen events for use in the analysis, defined by recurrence intervals
of 1 and 5 year. For each station, the annual number of events for each duration
and recurrence interval was identified.
The linear trend analysis indicates that there has been a significant increase
in the number of 7-day, 1-yr events over the period of 1931 – 1996 over United
States. Some climate divisions have even experienced increases of 50% – 100%.
While over Canada, an upward trend is not significant.
2.3.2. Africa
South Africa is the region which was selected by Mason et al. (1999) on
detecting the change of extreme rainfall events. 60 year observational data (1931 –
1990) from 314 rainfall station is involved. They did not test the homogeneity
data due to lack of metadata, but the rainfall stations selected are only un-
relocated rain gauges. For each station, they calculated the intensity of 5, 10, 20,
30 and 50 year recurrence interval of extreme rainfall events.
17
The method they applied was different. They divided those 60 year data in
to two time windows e.g. 1931 – 1960 and 1961 – 1990. The null hypothesis for
those time windows is that there is no difference in the intensity of extreme
rainfall events between first and second window. The significance of changes was
then assessed by comparing the difference in the intensity of those events with the
difference expected under the null hypothesis.
Over much of the country, they found a significant evidence of increases in
the intensity of high rainfall events between 1931 – 1960 and 1961 – 1990.
Percentage increases in intensities are largest for the most extreme rainfall events.
Similar patterns of change are evident for the different return periods analyzed,
but the percentage changes are even larger for the more extreme rainfall events.
2.3.3. Asia
Rainfall extreme events over Asia particularly Southeast Asia have been
studied by Manton et al. (2001) under the Asia Pacific Network (APN) for Global
Change Research. Using relatively short data period (1961 – 1998) they examine
91 rainfall stations over Southeast Asian and South Pacific. The criteria they used
to calculate the extreme indices are:
1. Frequency of daily rainfall exceeding the 1961 – 1990 mean 99th percentile
(extreme frequency).
2. Average intensity of events greater than or equal to the 99th percentile each
year, i.e. in the four wettest events (extreme intensity).
3. Percentage of annual total rainfall from events greater than or equal to the
99th percentile, i.e. received in the four wettest events (extreme proportion).
4. Frequency of days with at least 2 mm of rain (rain days).
They found that the number of rain days, annual total rainfall and
frequency of extreme rainfall events have decreased within the study period at the
majority of stations. The decreasing trend of the number of rain days is significant
whereas that of frequency and intensity is not significant.
18
2.3.4. Europe
Study of Klein Tank and Konnen (2003) is outlined here to figure the trend
of daily rainfall extremes in Europe. Using seven indices of climate extremes for
precipitation which are agreed internationally i.e. highest 1-day (RX1d), highest 5-
day (consecutive, RX5d), heavy rainfall day (R10mm), very heavy rainfall day
(R20mm), moderate wet days (R75%), very wet days (R95%) and rainfall fraction
due to very wet days (R95%tot, see www.knmi.nl/samenw/eca), they tested 151
rainfall stations over whole of Europe.
The result revealed that, averaged over Europe, six out of those seven
indices significantly increase between 1946 – 1999 but the spatial pattern is not
really coherence (Table 2-1).
Table 2-1. European trends per decade (with 95% confidence intervals in brackets) in the indices
of extreme precipitation for the periods 1946–1999. Values significant at the 5% level (t test) are
set bold face (Source: Klein Tank and Konnen, 2003).
2.3.5. Australia
Study on extreme rainfall events in Australia presented here is work of Fu
et al. (2010). They used 97 years observation record from 191 rainfall stations to
investigate temporal changes in the number of extreme rainfall events by closely
following the method of Kunkel et al. (1999).
From the data series, event duration of 1, 5 10 and 30 days were examined.
The new series which are identified from first step was screened then using
recurrence interval of 1, 5 and 20 year. They conclude that more than half of
stations show negative trend but they are mostly not significant. Their result is
19
shown in the table below. The figure of global trend of extreme rainfall events
over other regions including Japan, Russia and Brazil can be found in Easterling
et al. (2000).
Table 2-2. Numbers of stations with positive and negative trend in the numbers of extreme events
during the period 1910–2006, and numbers of stations for which these trend are statistically
significant at one-sided = 0.05 (Source: Fu et al. 2010).
20
III. STUDY AREA AND DATA
3.1. Study Area
3.1.1. Java Island
Situated between 105 2’ – 114 6’ E and 5 8’ – 8 8’ S (Figure 3-1), Java is one
of the big islands in Indonesia whose area is 126,700 km². Its topography is
characterized by low land whose elevation is less than 30 meter in the coastal area
and mountainous area whose elevation could reach up to more than 3,500 meter.
Java is the densely populated island in Indonesia and is the centre of national
economic activity. Sixty percent out of total populations in Indonesia inhabit this
island. The Statistics Indonesia (BPS) reported that the population of Java Island
in 2005 was 128.5 million inhabitants, distributed in six provinces i.e. Banten,
Jakarta, West Java, Central Java, Yogyakarta and East Java Province.
Figure 3-1. Java Island (red box) among the other islands in Indonesian archipelagos (Source:
data processing).
The climate of Java is mainly controlled by monsoon system. There are two
monsoon systems influencing this area. The northwest (NW) monsoon is active
from November to March (NDJFM) and the southeast (SE) monsoon is working
from May to September (MJJAS) (Aldrian and Susanto, 2003). The characteristic
21
of those two monsoons is significantly different. The northwest monsoon is wet
and implies much rainfall while the southeast monsoon is dry and is responsible
for less rainfall period over Java. Consequently, there is significant difference of
rainfall amount between dry and wet season. Figure 3-2 present general climate of
Indonesia which is divided in to three regions namely region A, B and C. Region
A is mainly controlled by monsoon system with one rainy season and one dry
season, including Java Island. Region B is characterized by double rainy seasons
and Region C is classified as local climate.
Figure 3-2. Left panel shows the three dominant rainfall regions in. Right panel shows the annual
cycles of rainfall in the region of climate “A”. Solid line indicates rainfall average, dashed lines
indicates one standard deviation (σ) above and below of it (Source: Aldrian and Susanto, 2003).
3.1.2. East Java Province
East Java Province was selected for the case of analysis of extreme rainfall
analysis even though all data have been prepared for Java Island entirely. East
Java Province comprises of two main islands i.e. the eastern part of Java Island
and Madura Island. Administratively, the province is divided in to 29 districts and
9 municipalities.
For the current study, 29 districts and 1 municipality only were considered since
the others municipalities are too small and located in the centre part of districts for
instance Municipality of Madiun is located in the centre part of Madiun District .
For effective analysis, those small municipalities therefore were supposed as
single region with related districts (see Figure 3-3).
22
In the middle-south part of Province, there is mountainous area stretching
out in the east-west direction while in the north part, the area is dominated by low
land which is also known as lower part of Bengawan Solo watershed, the largest
watershed over Java Island.
3.2. Data
The main data for this research is records of daily rainfall collected from
regional office of BMKG. The indices of extreme rainfall events were derived
from this data series. Rainfall data for West Java and Banten Province were
collected from head office of BMKG. Rainfall data for Central Java and
Yogyakarta Province were obtained from Semarang regional office and that for
East Java Province were gathered from Malang regional office.
Data period is crucial for valid analysis of extremes (Frei and Schar, 2001).
For statistical reasons, as mentioned in WMO Guidelines, long time series is
needed to obtain reasonable estimates of the intensity and frequency of rare events
(WMO, 2009) and to ignore bias trend (Manton et al. 2001). These data should
also be high time resolution, at least daily data, to take into account the sub-
monthly nature of extremes (WMO, 2009). For the current study, 30 years (1981 –
2010) was considered as an ideal period expressing climate condition of last three
decades regarding the available data.
Catalogue of rain station mentions that there are thousands rain station
installed over Java Island. They are operated by some institutions such as
meteorological agency (BMKG), ministry of public work (BBWS) and ministry
of forestry (BP DAS). Figure 3-4 displays spatial distribution of rain stations
which have been installed. However, the records are not integrated in to single
data base. For East Java province, there are at least 931 rain stations.
Digital Elevation Model over study area which was derived from SRTM
DEM is supporting data for this study. This data was used as an external variable
on discussing spatial pattern of extreme indices.
23
Table 3-1. The detail required data and sources to obtain them
DATA SOURCE OBJECTIVE
Daily rainfall data Regional Office of BMKG To derive Extreme Rainfall
Events indices
DEM SRTM Global DEM As external factor on
interpreting spatial pattern
Administrative
Map
Bakosurtanal As spatial unit on severity
analysis
24
Figure 3-3. Topographic feature of East Java Province overlaid by District Boundary (Source: data processing)
MADURA ISLAND
25
Figure 3-4. Distribution of 2580 rain gauges over Java Island (Source: data processing)
26
Data Analysis
Product
Analysis Legend
IV. RESEARCH METHODS
4.1. Research Framework
The research was conducted through four steps i.e. preparation, data
processing, data analysis and report writing. The general task for each step is
displayed in the research framework as shown in Figure 4-1.
Figure 4.1. Research Framework
PR
EPA
RA
TIO
N
DA
TA P
RO
CES
SIN
G
DA
TA A
NA
LYSI
S
REPORT WRITING
Daily rainfall data
Quality Control: 1. Gross error check
2. Duplicated data check
3. Outliers and Missing value check
4. Homogeneity test
Extreme Value Analysis
Extreme Indices: 1. Frequency Indicator
2. Intensity Indicator
SRTM DEM
Spatial Analysis:
Point Pattern Analysis
Temporal Trend
Analysis:
Mann – Kendall Test
Spatial pattern map
Trend of Annual Indices
Spatio-temporal Characteristic
DEM
Severity analysis
Final analysis
Report / MSc Thesis
Administrative Map
27
The analysis techniques mentioned in the research framework are elaborated in
the next paragraphs.
4.2. Method for Quality Control
Assessing extreme rainfall event needs high quality of long time series
rainfall data. The data completeness is fundamental for this analysis (WMO,
2009). On the contrary, it is difficult to find complete data for long periods. Data
homogeneity is another important issue when analyzing time series data. To
warrant the data being ready to be analyzed, quality control procedure was applied
through these steps:
4.2.1. Checking for duplicated data
Duplicate data check is one of quality control procedures done by National
Climatic Data Centre, NCDC - USA, for daily data of Global Historical
Climatology Network, GHCN (Durre et al., 2010). It is applied for checking the
duplication case between entire years, different years in the same calendar month
and different months within the same year. Duplication case could happen either
in the series of single rain gauge or among rain gauges. The duplicate data surely
affect our trend assessment because the estimated trend will be vague. Thus,
duplication check should be run.
The procedure for detecting duplicated data in the current study is based on
sub-monthly total. Using sub-monthly sum, the technique is able to detect the
duplication case of some observations within a month. Meanwhile, monthly sum
can only detect the duplication case when all observations in a month are copied.
First, the total rainfall of first and second half of month (day 1 – 14 and day 15 -
28) was calculated. Since the days number per each month is different, only day 1
– 28 were involved in detection process. Those total values were sorted then from
the smallest to the largest so that we could group the data which have similar total.
This group is suspect of duplicated data. However, since similar value of sub-
28
monthly total does not mean always a case of duplication, the manual check was
done for the suspect group to flag the actual duplication cases.
The adjustment for the detected cases was distinguished in to two actions.
For the data which were copied for the same time (e.g. January 2010 of Station A
similar with January 2010 of Station B), all duplicated data were set to “no
observation” because it is not possible to conclude which data is original. For the
data which were copied for the different time either in single station or between
two stations (e.g. January 2010 of Station A similar with March 2010 of Station
A), one of two series was deleted after identifying the original series. The
identification process was done by comparing the duplicated data with neighbor
gauges. When the magnitude and temporal distribution is significantly different
the time series is identified as copied series and set to “no observation”.
4.2.2. Checking for outliers and missing values
Selecting the rain gauges with high quality data is the second step in this
quality control procedure. Those stations should have a complete data series
without artificial outliers. Simple statistical parameters like mean and standard
deviation or formula developed by linier regression are affected strongly by
outliers. The quality of data could be improved by removing outliers and the bias
of analysis result could be reduced.
Outliers are defined as observations which do not conform to the pattern
established by the other observations in the sample (Hersfield, 1973). Identifying
outlier values should be done carefully to make sure the outliers we found is truly
erroneous and is not naturally extreme values.
A statistical outlier threshold which is defined using parameter of inter-
quartile range (IQR) was used for the study adopting the work by Gonzalez-
Rouco et.al (2001), as defined by formula below:
Threshold = Q3 + ( 3 * IQR )
29
where Q3 is third quartile and IQR is inter-quartile range. The inter-
quartile range method is known as a technique which is resistant to outliers but
still keep the information of extremes (Barnett and Lewis 1994, Eischeid et al.
1995, cited in Gonzalez-Rouco et al, 2001). The detected outlier values were
removed and substituted by the outlier threshold.
As suggested for daily data of GHCN (Durre et al., 2010), spatial
consistency of daily rainfall was examined day by day. Important variable for
spatial outlier check is the distance or radius to the nearest neighbors that should
be selected. In NCDC, 75 km is chosen as maximum radius on searching selected
neighbors. If more than seven neighbors are present, only the nearest seven
neighbors are used (Durre et al., 2010). In Norway, six closest stations are
considered on checking and correcting precipitation data (Vejen et al., 2002).
For the current study, a spatial correlation function was run to define the
proper distance. The correlation coefficient of daily rainfall data were plotted for
any distance of gauges. The critical value of correlation coefficient depends on the
number of pairs which was defined using table of product-moment correlation.
Examination of critical value and scatter plot of correlation coefficient allows
establishment of the maximum radius.
Following study by Kunkel et al. (2003) and Ngongondo et al. (2011), the
maximum missing observations allowed for the analysis is 10 %. This criterion
was applied for every time series. The missing values were not filled, but they are
still kept in the series to avoid bias on assessing the trend.
4.2.3. Homogeneity test
Rainfall data homogenization is aimed to adjust the measurement values, if
necessary, so that the temporal variations in the adjusted data are caused by
climatic process, not an artificial variation. Inhomogeneities in meteorological
data are fatal for climate analysis. Station relocations, changes in measuring
techniques and changes in observing practices could responsible of
inhomogeneities in rainfall series. Figure 4-2 shows that homogeneity is important
30
for time series data. The climate analysis will produce different results if using
homogeneous series compared with that of in-homogeneous series.
Testing data homogeneity is difficult, moreover for daily rainfall data since
daily rainfall is highly variable in time and space. Most methods introduced are
mainly designed for monthly or annual data (see Peterson et al., 1998). There is no
method established specifically for daily data (WMO, 2009).
Figure 4-2. Unadjusted (in-homogeneous, symbolized by “.”) and adjusted time series
(homogenous, symbolized by “+”) of annual precipitation at Briksdal in western Norway (Source:
Hanssen-Bauer and Førland, 1994, cited in Peterson et al., 1998)
A hybrid method developed by Wijngaard et al. (2003) was adopted for
detecting inhomogeneities in daily series. Four statistical absolute tests were
applied: the Pettitt test, the Standard Normal Homogeneity Test (SNHT) for single
break, the Buishand Range test and the Von Neumann ratio test.
The motivation on using more than one absolute test is that they have
different sensitivity to a break. The SNHT test is more sensitive to detect a break
near the beginning and the end of series whereas Pettitt and Buishand range test
are more sensitive to a shift in the middle of series. The von Neumann ratio test
cannot detect the location of break but it is sensitive to departures of homogeneity
that are of a nature other than strict step-wise shifts. Those all absolute tests are
summarized following Wijngaard et al. (2003).
31
The Pettitt test is an adaptation of the rank-based Mann-Whitney test that
allows us to identify the time at which the break occurs. This non-parametric test
requires no assumption about data distribution. The ranks 𝑟𝑖 , . . . , 𝑟𝑛 of the 𝑌1, . . .
, 𝑌𝑛 are used to calculate the statistics:
𝑋𝑦 = 2 𝑟𝑖 − 𝑦(𝑛 + 1)
𝑦
𝑖=1
,𝑦 = 1, 2, 3,………… ,𝑛
The break is identified occurring in year k, when:
𝑋𝑘 = max1 ≤ y ≤ n
𝑋𝑦
The significance level and critical value for 𝑋𝑘 are given in the Appendix
1.
The SNHT test is usually performed to a series of ratio comparing the
observation with an average. The ratios are standardized then. In a series a statistic
Ty is used to compare the mean of the first y years with that of the last (n – y)
years.
𝑇𝑦 = 𝑦𝑧1 + 𝑛 − 𝑦 𝑧2 ,𝑦 = 1,2,3,………… ,𝑛
where:
𝑧1 =1
𝑦
(𝑌𝑖 − 𝑌 )
𝑠
𝑦
𝑖=1
𝑎𝑛𝑑 𝑧2 =1
𝑛 − 𝑦
(𝑌𝑖 − 𝑌 )
𝑠
𝑛
𝑖=𝑦+1
If the shift occurs at the year y, then the 𝑇𝑦 will appeas as a maximum
value near the year y. the statistic test, defined as:
𝑇0 = max1 ≤ y ≤ n
𝑇𝑦
Critical values for 𝑇0 are given in the Appendix 1.
Buishand range test can be run for data following any type of distribution.
This test utilizes the adjusted partial sums or cumulative deviations, defined as:
32
𝑆0∗ = 0 𝑎𝑛𝑑 𝑆𝑦
∗ = (𝑌𝑖 − 𝑌
𝑦
𝑖=1
),𝑦 = 1,2,3,………… , 𝑛
For a homogeneous record one may expect that the St's fluctuate around
zero since there is no systematic pattern in the deviations of the Yi from their
average value Y. If a break is present in year y, then 𝑆𝑦∗ reaches a maximum or
minimum value near the year y. The “rescaled adjusted range”, R, which is the gap
between maximum and minimum of 𝑆𝑦∗ scaled by the standard deviation of sample
(s) is used to examine the significance of break.
𝑅 =
max1 ≤ y ≤ n
𝑆𝑦∗ − min
1 ≤ y ≤ n𝑆𝑦∗
𝑠
The critical values for 𝑅 𝑛 given by Buishand (1982) are included in the
Appendix 1.
The von Neumann ratio is based on the sum of the squares differences
between each pair of following time measures. The test statistic of von Neumann
ratio is defined as follows:
𝑁 =
𝑌𝑖 − 𝑌𝑖+1 2𝑛−1
𝑖=1
𝑌𝑖 − 𝑌 2𝑛𝑖=1
If the series has a constant mean, the expected value of E(N) = 2. For non-
homogeneous series, the mean of N tends to be smaller than 2. The location of
break (time) cannot be detected by this test. The critical values of N for any
number of samples are displayed in the Appendix 1.
Instead of using annual total as a tested variable, Wijngaard et al. (2003)
proposed the number of wet days in a year as more appropriate variable because
its variability is lower than that of annual total but it still represents important
characteristic of variation at the daily scale. 1 mm threshold to define wet day was
chosen for their work within the European Climate Assessment (ECA) project.
33
For testing data homogeneity of the current study, 2 mm threshold was applied to
define wet day.
All four tests were run under the null hypothesis that the annual numbers
of wet day are independent and identically distributed. The alternative test for the
Pettitt, the SNHT and the Buishand range test assumes that a break is exist. The
von Neumann test assumes under the alternative hypothesis that the series of wet
day count is not randomly distributed.
A qualitative interpretation to the result of four homogeneity test is the
next step. Since there is no metadata to confirm the break, only series passing all
absolute tests will be included in the further analysis. The series with one or more
tests rejecting the null hypothesis should not be used in the trend analysis because
they are lacking credibility. Therefore, there were only two classes to interpret
qualitatively i.e. “useful” for homogenous series and “suspect” for series
containing break.
4.3. Method for Identification of Extreme Indices
Adopting research by Hernandez et al. (2009) and WMO guidelines
(2009), the analysis of extreme rainfall events was expressed by some indices
which are widely used for describing extreme weather events. The indices were
calculated as annual value for each station. To capture all possible temporal
changes, some indices was used rather than one index only.
A limit of 1 mm was operated to define a rainy day (WMO, 2009; Bodini
and Cossu, 2010). Those indices can generally be grouped in to three board
categories, which are frequency, intensity and proportion indicators. Only
indicators from the first two categories were used in this study. In respect of
characteristic of threshold, there are two kinds of threshold i.e. fix and site
specific threshold.
4.3.1. Fix Threshold
A fix threshold of 100 mm daily rainfall amount was chosen considering
the criteria developed by BMKG (BMKG, 2010). Instead of designing new
34
threshold, the study was directed of evaluating the BMKG’s fix threshold. The
operability of fix threshold was assessed by finding a link of threshold to disasters
which were generated by extreme rainfall events.
Data of floods and landslides disaster over East Java Province in the last
ten years were collected from BNPB data base system for this goal. The
considered disasters are only those affecting large area which cause casualty/s.
The fix threshold is said operable when the observed rainfall in the day of
disasters falls mostly larger than the fix threshold.
4.3.2. Site Specific Threshold
The method on defining site specific threshold closely adopts that of
Kunkel et al, (1999, 2003) and Fu et al, (2010). One day duration events were
examined and were screened using three precipitation thresholds which are
expressed by recurrence interval of 1, 5 and 25 year. Thresholds were determined
for each station.
Using 30 years daily rainfall record (1981 – 2010), threshold for 1 year
recurrence interval was defined by sorting daily rainfall from largest to smallest
observation. The largest 30 daily rainfall within period 1981 – 2010 were
extracted and the smallest of these 30 observations were selected as threshold.
For 5 and 25 year return period, the threshold value was calculated using
extreme value theory. It is a statistics method that deals with extreme events
which have low probability. There are three models widely used for analyzing
extreme value i.e. the Gumbel, Frechet, and Weibull distribution functions
(Coldwell, 2002). These models are derived from Fisher-Tippett theorem.
The Gumbel distribution, also known as the extreme value type I, is
unbounded. The Frechet distribution is bounded below while the Weibull
distribution is bounded above and commonly used for distribution of minima (see
Figure 4-3).
35
Figure 4-3. Probability distribution function of Gumbel, Frechet and Weibull (Source: Coles,
2001)
To determine which distribution model (Gumbel, Frechet or Weibull)
should be used for our data, a probability check is needed. Plotting our data versus
the computed value based on selected distribution allows us to assess whether our
data follows the tested distribution. A tested model is selected when our plot
produce points falling close to a straight line.
A flexible formulation combining those three types of distribution was
introduced by scientist, namely the generalized extreme value distribution, GEV
(Coles, 2001). The distribution of GEV follows
𝑓 𝑥 =
1
𝜎exp −(1 + 𝑘𝑧)−1/𝑘 (1 + 𝑘𝑧)−1−1/𝑘 𝑘 ≠ 0
1
𝜎exp −𝑧 − 𝑒𝑥𝑝(−𝑧) 𝑘 = 0
where z = (x - µ) / σ, and k, σ, µ are the shape, scale, and location
parameters respectively. The scale must be positive (σ > 0), the shape and location
can take on any real value. The range of definition of the GEV distribution
depends on k:
1 + 𝑘(𝑥 − 𝜇)
𝜎 > 0 𝑓𝑜𝑟 𝑘 ≠ 0
−∞ < 𝑥 < +∞ 𝑓𝑜𝑟 𝑘 = 0
Various values for the shape parameter (k) correspond to the Gumbel,
Frechet and Weibull distribution as shown in table 4-1.
36
Table 4-1. Correspondence between GEV and three basic extreme value distribution (Source:
Orjubin, 2008).
GEV shape parameter Type based on Fisher-Tippett Theorem Domain
k > 0 Frechet x > m – s/k
k < 0 Weibull x < m – s/k
k = o Gumbel - < x < +
The threshold for certain return period (or also popularly known as “return
level” in extreme value terminology) is determined using formula as follows
(Coles, 2001):
𝑋𝑇 = 𝜇 − 𝜎
𝑘 1 − − log 1 − (
1
𝑇)
−𝑘
𝑇 = 𝑟𝑒𝑡𝑢𝑟𝑛 𝑝𝑒𝑟𝑖𝑜𝑑
Some extreme indices suggested by WMO were also calculated i.e.
R20mm, R50mm, R90p, CWD, RX1d, RX5d, SDII, RTOT. The final detail extreme
indices applied in the present study are presented in Table 4-2. Each index was
calculated for each station and was expressed as annual series.
37
Table 4-2. Detail extreme rainfall indices which are used in the study.
NOTATION DESCRIPTION
Frequency Indicator (adapted from Hernandez et al. 2009; WMO, 2009; BMKG, 2010)
1 R20mm The number of rain day with rainfall larger than or equal to 20 mm
(moderate rain days)
2 R50mm The number of rain day with rainfall larger than or equal to 50 mm
(heavy rain days)
3 R90p The number of rain day with rainfall larger than or equal to 90th
percentile of 1981 – 2010 series
4 CWD Consecutive wet day; maximum length of wet spell
Intensity Indicator (Adapted from WMO, 2009)
5 RX1d Maximum daily rainfall
6 RX5d Maximum cumulative rainfall of 5 consecutive rain days
7 RTOT Total annual rainfall
8 SDII Simple daily intensity index; total annual divided by number rain
day
9 RI90p Daily rainfall value at 90th percentile of 1981 – 2010 series
10 R1yr Daily rainfall value for 1-year return period
11 R5yr Daily rainfall value for 5-year return period
12 R25yr Daily rainfall value for 25-year return period
4.4. Method for Spatial Analysis
Since rain gauges with high quality data are not proportionally distributed,
spatial point pattern analysis was selected as method to explore spatial
characteristic of extreme rainfall events. This method is commonly used to
analyze pattern of distributed point whether the variable are distributed at random
or represent a clustered or regular pattern (Pfeiffer, 1996).
The aim of point pattern analysis is to analyze the geometrical structure of
patterns formed by objects that are distributed randomly in one-, two- or three-
dimensional space. The variables are displayed in thematic map by points and
38
marks. The points describe the locations of the objects while the marks provide
additional information, thus characterizing the objects further, e.g. through their
type, size or shape (Illian et.al, 2008). For the current study, magnitude of site
specific threshold and trend of extreme indices were mapped at the point in which
rain gauges are located.
The application of point pattern analysis in studying extreme rainfall
events could be found in Kunkel et al (1999), Tank and Konnen (2003), Deni et al
(2010) and Fu et al (2010). Figure 4-4 present the point map found in Tank and
Konnen (2003) on assessing trend of daily temperature and precipitation Extremes
in Europe. Trend of rainfall within period 1946 – 1999 was assessed as a fraction
due to very wet day (> 95th
percentile).
Figure 4-4. Trend of rainfall being larger than 95th
percentile over Europe presented in point map
(Source: Tank and Konnen, 2003)
4.5. Method for Temporal Trend Analysis
The temporal trend was identified by using Mann-Kendal test. It is a
familiarly used technique of detecting trend for environmental series data which is
often not distributed normally (Hipel, 1994). WMO also recommends this method
39
for trend assessment in climatological data (WMO, 1988). Many studies used this
method to detect rainfall trend both for mean rainfall such as Aldrian and Djamil
(2007) in Indonesia, or for extreme rainfall events such as Zang et al. (2001) in
Canada, Fu et al. (2010) in Australia and Ngongondo et al. (2011) in Malawi.
Here, the procedure to operate Mann-Kendal analysis was quoted from
Yue and Wang (2004) and HydroGeoLogic, Inc (2005). The data values are
evaluated as an ordered time series. Each data value is compared afterward to all
subsequent data values. The detail procedures are:
1. Calculate the Mann-Kendall statistic.
The initial value of the Mann-Kendall statistic (S) is assumed to be 0 (e.g.
no trend). If a data value from a later time period is higher than a data value from
an earlier time period, S is incremented by 1. On the other hand, if the data value
from a later time period is lower than a data value sampled earlier, S is
decremented by 1. The net result of all such increments and decrements yields the
final value of S.
The formula to calculate S is given by:
𝑆 = 𝑠𝑖𝑔𝑛(𝑥𝑗 − 𝑥𝑘)𝑛
𝑗=𝑘+1
𝑛−1
𝑘=1
Where:
𝑠𝑖𝑔𝑛 𝑥𝑗 − 𝑥𝑘 =
1, if 𝑥𝑗 – 𝑥𝑘 > 0
0, if 𝑥𝑗 – 𝑥𝑘 = 0
−1, if 𝑥𝑗 – 𝑥𝑘 < 0
2. Compute a normalized test statistic Z as follows:
𝑍 =
𝑆 − 1
𝑉𝑎𝑟 (𝑆), 𝑖𝑓 𝑆 > 0
0 , 𝑖𝑓 𝑆 = 0𝑆 + 1
𝑉𝑎𝑟 (𝑆), 𝑖𝑓 𝑆 < 0
40
3. Compute the probability associated with this normalized test statistic
expressed in p-value.
The probability density function for a normal distribution with a mean of 0
and a standard deviation of 1 is given by the following equation:
𝑓 𝑧 = 1
2𝜋𝑒−
𝑧2
2
p-value = 1 – f(z)
4. Decide level of significance ( = 5 % typically).
Conclude the trend using this criteria; the trend is said to be decreasing if Z
is negative and is said to be increasing if the Z is positive. The trend is statistically
significant if p-value is less than otherwise it is not significant or no trend.
Slope of the trend was estimated using Theil-Sen slope estimator. The
Theil-Sen estimator (TSE) was proposed by Theil (1950) and was extended by
Sen (1968). It is a robust estimate of the magnitude of a trend with a high
breakdown point up to 28.9 % outliers in the data, has a bounded influence
function, and possesses a high asymptotic efficiency (Dang et al, -----). As a
robust method, it is not affected by single errors or outliers. The slope of trend
was calculated as the median of pair-wise slopes, given by following formula:
𝑏 = 𝑚𝑒𝑑𝑖𝑎𝑛 𝑥𝑗 − 𝑥𝑖
𝑗 − 𝑖 𝑓𝑜𝑟 𝑖 = 1,……… ,𝑛 𝑎𝑛𝑑 𝑖 < 𝑗
where b is the estimate of the slope of the trend and 𝑥𝑖 𝑥𝑗 and is the ith
and
jth
observation. It has been commonly used in identifying the slope of the trend in
the hydrological data series (Yeu et al, 2002 cited in Deni et al, 2010).
4.6. Method for Severity Analysis
As a final result of study, the severity analysis of extreme rainfall events
based on regional index at district level was run. Representing characteristic for
41
regional level, the analysis was done using Thiessen polygon weighted method as
studied by Fu et al (2010) when assessing national trend for Australia. In their
study, the index across 189 stations was calculated using thiessen polygon
weighted to produce single index. For the current study, the frequency of extreme
rainfall events per districts was calculated based on index R50mm as
representative of fix threshold and index R90p as representative of site specific
threshold.
Figure 4-5. Thiessen polygons of Australia from which the national trend was assessed (Source:
Fu et al, 2010).
First, stations value for index R50mm and R90p was weighted to generate
districts index. The districts index of R50mm and R90p was divided then in to
three classes. The class of index R50mm was overlaid with class of index R90p to
produce severity index. The procedure to produce severity index is described in
Figure 4-6.
42
Figure 4-6. The procedure to generate severity index.
43
V. SCREENING DAILY RAINFALL DATA
Rainfall data are collected by some institutions in Indonesia.
Meteorological agency, ministry of public work, ministry of forestry, ministry of
agriculture and some private companies are example of those institutions (list is
not available). Unfortunately, those all data are not stored in one data base centre.
Each institution has its own data base system which is not connected each other.
The quality control done by each of them is based on their function and is not
similar, consequently.
5.1. Converting into Standard Format
The data used for this study were acquired from regional offices of
meteorological agency (BMKG). Data of East Java Province were obtained from
Malang Climatological Station, regional office of BMKG. Data of Central Java
Province were gathered from Semarang Climatological Station while that of West
Java Province were collected from Head office of Meteorological Agency.
However, the data were not saved in single standard format and standard
code. An example is shown in Figure 5-1, where the missing value (MV) of East
Java rainfall data is coded by “X” and no rainfall days are coded by blank cells.
Unfortunately, this procedure is not applied consistently to all stations data series.
In Figure 5-2 the digitized data from Semarang Climatological Station
(Central Java) is displayed. In this data format no rainfall days are coded by “0”
(zero) but somehow are coded by blank cells. The months containing missing
value/s are not digitized into data base system (black box), e.g. rainfall data of
March, 1990. For effective analysis process, all those series data were converted
into standard format using standard coding procedure.
44
Figure 5-1. The example of digital data stored by Malang Climatological Station (East Java) using
MS Excel (Source: data processing)
Figure 5-2. The example of digital data stored by Semarang Climatological Station (Central Java,
Source: data processing)
In the new format, all data were stored using consistently single format and
fix procedures. The missing values were coded by “9999” and no rainfall days
were coded by “0” (Figure 5-3).
45
Figure 5-3. The example of standard format of rainfall series, District of Banyuwangi (Source:
data processing)
5.2. Duplicate Data Check
5.2.1. Procedures on Checking Duplicated Data
As described in Chapter 4, duplicated data could be detected using simple
technique. After calculating sub-monthly total and sorting those values, the series
which have similar total value can be separated in to specific group which is
suspected to be duplicated data (Figure 5-4).
The manual check was applied then to exclude the series where the total
value is similar but the series is not duplicated, coincident only as shown in Figure
5-5. The manual check also helps us to detect the potential duplicated data which
are not similar in total value. This case could happen if not all daily data in a
month are duplicated. Figure 5-6 shows an example of this case in which data of
day 26 and 27 are different while that of other days are exactly similar.
46
Figure 5-4. The example of detection process. The total rainfall for day 1-14 and 15-28 (last two
columns) were sorted to localize the data having similar total value, black box (Source: data
processing).
Figure 5-5. The example of coincident similar total value (black box), but not a case of
duplication (Source: data processing)
47
Figure 5-6. The example of duplication where not all daily data are copied, see box (Source: data
processing)
Figure 5-7. The highlighted line shows the duplication case in a series where rainfall data of
January 1997 are exactly similar with July 1997 at Station Maelang, District of Banyuwangi
(Source: data processing).
48
The duplication check was run for all stations per district to detect
duplicated done at observer and district level. Therefore the technique is able to
detect duplication cases not only for data in a station but also for cases between
two or more stations in a district as displayed in Figure 5-6.
When duplication was found for different time as shown in Figure 5-7
(January 1997 and July 1997), it is essential to quantify which data series are the
actual measurements. Comparing with data from other gauges located in the same
district was used to identify the copied data. When the magnitude and temporal
distribution is significantly different, the time series is identified as copied series
(see Figure 5-8) which were set then to Missing Value or 9999.
Figure 5-8. A technique to identify original-copied data. The data of July 1997 at Gauge of
Lijenjambu and Maelang were recognized copied while data of January 1997 from those two
gauges were categorized as original data, consequently (Source: data processing).
For duplication which was found for same time, they all were deleted since
the original data could not be recognized. Figure 5-7 gives an example of this case
where data of November, 2003 at Dadapan station are similar with that of at Mcn
Pth Tambong station. The data of November, 2003 from those two stations were
adjusted by deleting and setting to Missing Value.
49
5.2.2. Result of Duplicated Data Check
From 931 rain gauges registered in Malang Climatological Station (East
Java Province), the digital data of 461 stations are available and were collected for
the study. Data from rest gauges are not available. Even data in the hand-written
format are not found in the office. In total, 430 rain gauges were involved for
duplicated data check based on their coverage period (1981 - 2010).
The collected rainfall data for the study contain duplicated data. No district
is free from duplication case. Since the cases were detected in monthly base, the
number of duplication case elaborated below was calculated in monthly base also,
not in observation base. The result revealed the average of month containing
duplicated data per district is 4.6 %, ranging between 0.5 % up to 23.5% as shown
in Table 5-1. The percentage of duplication case was calculated relative to total
months per district, excluding months containing missing values which were
detected before duplicated data check. In respect of time, duplication cases mostly
occur in the year 1990s and decrease sharply after 2000 (Figure 5-9).
Figure 5-9. Number of months containing duplicated data compiled from 430 tested gauges given
in yearly series (Source: data processing).
50
Table 5-1. The detail result of cases of duplicated data check for East Java Province. Displayed
MVs were detected before duplication check, calculated relative to total months for given period
(Source: data processing).
ID DISTRICT GAUGES PERIOD MVs (%) CASES (%)
1 BANYUWANGI 17 1979 – 2010 17.4 1.7
2 SITUBONDO 13 1981 – 2010 32.3 3.0
3 BONDOWOSO 15 1981 – 2010 24.4 0.9
4 JEMBER 19 1979 – 2010 17.3 9.2
5 PROBOLINGGO 14 1979 – 2010 21.6 1.5
6 LUMAJANG 15 1981 – 2010 28.7 3.3
7 PASURUAN 14 1979 – 2010 10.8 3.8
8 MALANG 18 1979 – 2010 9.6 1.5
9 BLITAR 20 1981 – 2010 30.4 14.1
10 KEDIRI 20 1981 – 2010 28.8 4.9
11 TULUNGAGUNG 10 1981 – 2010 32.1 0.5
12 TRENGGALEK 7 1981 – 2010 30.0 3.3
13 PACITAN 14 1979 – 2010 27.7 23.5
14 PONOROGO 18 1979 – 2010 11.8 13.8
15 MAGETAN 19 1979 – 2010 20.1 8.3
16 MADIUN 15 1979 – 2010 16.3 3.4
17 NGAWI 18 1979 – 2010 23.6 5.3
18 BOJONEGORO 18 1979 – 2010 8.2 0.7
19 TUBAN 22 1979 – 2010 8.5 7.1
20 LAMONGAN 13 1979 – 2010 6.5 2.0
21 GRESIK 7 1981 – 2010 3.2 1.3
22 SURABAYA 8 1979 – 2010 8.0 2.1
23 SIDOARJO 15 1980 – 2010 16.5 3.0
24 MOJOKERTO 15 1980 – 2010 22.1 1.8
25 JOMBANG 15 1979 – 2010 25.1 0.7
26 NGANJUK 15 1981 – 2010 31.0 2.9
27 BANGKALAN 9 1981 – 2010 44.8 2.1
28 SAMPANG 8 1979 – 2010 23.8 2.7
29 PAMEKASAN 7 1979 – 2010 38.5 4.5
30 SUMENEP 12 1979 – 2010 18.1 3.8
Overall, 358 gauges contain duplication case (red dots) while 72 gauges
are categorized as clear series (blue dots) as no duplication case found in their
series (see Figure 5-11). Six gauges were found with duplication case more than
51
50 % in their series i.e. Pacitan, Pringkuku (District of Pacitan), Ngrayun,
Ponorogo (District of Ponorogo) and Kwasen and Laju (District of Tuban).
Three gauges (sukorejo-sukowono, sukorejo-balung and sumberrejo,
District of Jember) were excluded from the series based on duplicated data check.
Those three stations contain duplication cases of 54 %, 51 % and 41 %,
respectively making them less credible for next process.
Analyzing the process of rainfall data management in Indonesia, there are
three stages where the duplication cases as detected in the study, could potentially
occur (Figure 5-10). First, the duplication may occur when the rainfall event is
observed and recorded by observer. Since most of rain gauges operated in
Indonesia are manual system, the accuracy of measurement depends truly on the
observer.
Second, it may happen when the rainfall data are collected by office at
district level especially for data from Irrigation and Agricultural Office. Data from
all rain gauges per district are gathered and compiled here. Finally, it could be
added when rainfall data are digitized in climatological office, a regional office of
BMKG. Figure 5-11 illustrates process on managing rainfall data in Indonesia and
the potential points for duplicating data.
Figure 5-10. The process of rainfall data collecting in Indonesia. Dashed box outlines in which the
duplication probably occurs (Source: data processing).
Observers
(Irrigation Office)
Observers
(Agricultural Office)
Observers
(Meteorological Office)
Compiling Data
(District Level)
Digitizing Data
(Climatological Office)
52
Figure 5-11. Spatial distribution of registered gauges (yellow boxes, 931) and tested gauges (blue and red dots, 430) over East Java Province overlaid by
boundary of district. Blue dots indicate series without duplication cases, red dots indicate series containing duplication cases (Source: data processing).
53
5.3. Outlier Check
Following the procedure of quality control for daily data of GHCN (Durre
et al., 2010), namely spatial corroboration check, spatial outlier check was run.
On detecting spatial outlier, a spatial unit needs to be determined as a base of
selecting neighbor gauges. It is not rational to use study area entirely as a spatial
unit since the study area is very large, 47 130.15 Km2
(www.jatimprov.go.id.) with
complex topography. Therefore, spatial correlation among rain gauges was
examined first as a function of distance.
Figure 5-12 shows the plot of correlation of daily rainfall data for any
distance developed using 126 rain stations. This is a number of stations which are
90 % complete excluding those of located in Madura Island. 1038 observation
data of each station were involved from which the daily rainfall data for all gauges
are exist (missing values are not allowed). At significance level of 5 %, the critical
value for 1038 pairs sample, based on table of product-moment correlation, is
0.061 (see Appendix 2). It means that the correlation among variables is
significant if coefficient is higher than 0.061. The maximum distance in respect of
critical value is around 40 kilometers.
Figure 5-12. Spatial correlation function of daily rainfall over study area (Source: data
processing)
54
The correlation decreases strongly over a distance of 0 to 30 kilometers.
The correlation value seems to be constant for distances larger than 30 km.
Between 0 - 30 km, the correlation coefficient varies from 0.14 up to 0.9 which is
higher than critical value meaning that rain gauges separated by 30 kilometers
have relatively similar daily rainfall data.
The outlier detection for the current study was run based on distance of 25
kilometers. Smaller distance will reduce the number of neighbors while the larger
distance will probably not be adequate since their data are not really correlated
well. Given the distance of 25 kilometers, the minimum neighbors for a tested
gauge are 5 neighbors, while the maximum neighbors are 41 neighbors (see
appendix 3). An outlier threshold was calculated based on daily data using gauges
within 25 km.
A script written under Matlab language was developed to run outliers
check automatically. The below scheme was used in calculating threshold as
described in session 4.2.2.
Day Tested Neighbor -1 ………… Neighbor -n
Threshold**
Q3 + ( 3 * IQR )
1 Value-1 Value-2 ………… Value-n Threshold-1
2 Value-1 Value-2 ………… Value-n Threshold -2
3 Value-1 Value-2 ………… Value-n Threshold -3
. . . ……….. . .
. . . ………... . .
. . . ………... . .
n Value-1 Value-2 ………… Value-n Threshold -n
**Threshold was calculated following study of Gonzalez-Roucoet.al (2001). IQR = inter-quartile
range (see Chapter 4).
Only non-zero daily rainfall at neighboring stations are considered for the
threshold calculation as using zero daily rainfall could produce zero thresholds.
Consequently, if threshold is zero, non-zero data of tested gauge will be removed
since they trespass the threshold of zero. Also zero values would lower the
55
threshold, so outliers could erroneously be detected as incorrect values. Instead of
deleting them, the detected outliers were replaced then by threshold value to keep
information of extreme (Barnett and Lewis 1994, Eischeid et al. 1995, cited in
Gonzalez-Rouco et al., 2001).
The outliers detected per gauge are 18 observations (0.21 %) in average
ranging from 0 to100 observations (0 - 1.32 %). The detail number of outliers is
shown in Appendix 3 while summary for each district is shown in the Table 5-2.
The percentage of outliers was calculated relative to number of observations
excluding missing values.
The example of substituting detected outlier by threshold is shown in
Figure 5-13. Station Pakisan (ID: 39, District of Bondowoso) was tested using 8
neighbors found within 25 km. The yellow cell, red cell and green cell represent
tested value, calculated threshold and corrected value, respectively. The tested
value was trespassed so that it falls in the statistical range (IQR) but the corrected
value still keeps information of extreme.
Figure 5-14 and 5-15 demonstrate the success of test on detecting outlier in
series of Station Gombal (ID: 239, Appendix 3), District of Madiun. The original
value of May 11, 2010 is 689 mm. Since this value is not spatially coherent with
its neighbors, the value was detected as an outlier and was replaced then by
threshold, 123 mm.
On the other hand, the observation of December 26, 2007 i.e. 230 mm is
still kept as correct value even though it seems too high, because the value falls in
the range of data from neighbors (Figure 5-15). The heavy rainfall event occurred
on December 26, 2007 is triggering event for flood disaster over East Java in the
end of 2007 (see table 1-1, chapter 1).
56
Table 5-2. Summary of spatial outliers check in respect of district. Bold character highlights the
minimum and maximum value (Source: data processing).
CODE DISTRICT NEIGHBORS OUTLIERS (%)
Min Max Min Max
1 BANYUWANGI 7 15 0.02 0.64
2 SITUBONDO 7 22 0.02 0.49
3 BONDOWOSO 8 23 0.11 1.10
4 JEMBER 6 18 0.05 0.41
5 PROBOLINGGO 12 19 0.01 0.77
6 LUMAJANG 5 24 0.04 0.49
7 PASURUAN 7 32 0.04 0.74
8 MALANG 7 18 0.03 0.49
9 BLITAR 13 28 0.09 1.03
10 KEDIRI 22 30 0.07 0.36
11 TULUNGAGUNG 12 22 0.08 0.92
12 TRENGGALEK 9 22 0.10 1.32
13 PACITAN 5 20 0.00 0.58
14 PONOROGO 16 30 0.10 0.43
15 MAGETAN 22 40 0.12 0.63
16 MADIUN 17 34 0.16 0.64
17 NGAWI 7 32 0.05 0.63
18 BOJONEGORO 8 27 0.08 0.52
19 TUBAN 9 28 0.04 0.40
20 LAMONGAN 14 23 0.05 0.40
21 GRESIK 12 23 0.06 0.22
22 SURABAYA 17 26 0.10 0.30
23 SIDOARJO 28 38 0.05 0.38
24 MOJOKERTO 28 41 0.05 0.72
25 JOMBANG 28 37 0.13 0.40
26 NGANJUK 19 32 0.03 0.62
27 BANGKALAN 6 16 0.02 0.25
28 SAMPANG 5 11 0.00 0.08
29 PAMEKASAN 8 13 0.01 0.19
30 SUMENEP 7 13 0.00 0.26
57
Figure 5-13. An example of replacing outliers by statistical threshold. The ID of neighbors refers
to Appendix 3 (Source: data processing).
Figure 5-14. Scatter plot of original series of Station Gombal, District of Madiun. Daily time step
starts from 1/1/1981 to 12/31/2010 (10,957 observations).
58
Figure 5-15. Scatter plot of series of Station Gombal, District of Madiun after being corrected.
The outliers were substituted while natural extreme events were kept.
5.4. Missing Value Check
Using 10 % as maximum tolerance which is described in chapter of
method (session 4.2.2.), the missing value test removes 67 % of 427 tested
gauges. In total, 140 rain stations have sufficient data to be analyzed for
homogeneity and extreme value statistics. The spatial distribution of those gauges
is shown by Figure 5-17.
Summarizing the result, missing values of 427 gauges are 21.13 % in
average, ranging from 0 – 93.33 %. Minimum and maximum percentages of MVs
per district are displayed in the Table 5-3. In this stage, six station are categorized
as best quality since their series are 100 % complete namely Ajung, Karang
Kedawung, Ledok Ombo, Sukowono (District of Jember), Bangil (District of
Pasuruan) and Pangkatrejo (District of Lamongan).
In contrast, there are 10 districts from which the series with at least 90 %
complete are not found. These districts were figured as blank areas in Figure 5-17.
The missing values were not filled since the estimated values could produce the
bias on trend assessment. Thus the missing values were still kept in series as “no
value”.
59
Table 5-3. Summary of MVs check with regard to district. Bold style refers to districts without
complete series (Source: data processing).
ID DISTRICT MISSING VALUE (%) SELECTED GAUGE
Min Max
1 BANYUWANGI 0.3 30.3 5
2 SITUBONDO 30.8 41.9 0
3 BONDOWOSO 23.3 28.6 0
4 JEMBER 0.0 37.5 10
5 PROBOLINGGO 0.6 36.4 4
6 LUMAJANG 23.9 34.4 0
7 PASURUAN 0.0 34.7 12
8 MALANG 0.3 37.0 16
9 BLITAR 30.3 48.6 0
10 KEDIRI 20.3 46.7 0
11 TULUNGAGUNG 30.0 37.1 0
12 TRENGGALEK 30.0 36.9 0
13 PACITAN 13.6 93.3 0
14 PONOROGO 0.5 79.4 10
15 MAGETAN 3.3 49.0 4
16 MADIUN 0.6 30.0 6
17 NGAWI 4.7 64.5 5
18 BOJONEGORO 0.3 27.2 9
19 TUBAN 0.5 60.0 18
20 LAMONGAN 0.0 14.8 9
21 GRESIK 1.9 13.3 6
22 SURABAYA 1.1 10.5 7
23 SIDOARJO 2.2 35.4 7
24 MOJOKERTO 1.7 30.6 3
25 JOMBANG 1.1 33.8 2
26 NGANJUK 28.0 45.0 0
27 BANGKALAN 36.1 55.9 0
28 SAMPANG 8.9 38.0 2
29 PAMEKASAN 6.4 55.5 1
30 SUMENEP 1.9 63.6 4
60
The analysis of data of those 10 Districts showed that the missing values were
dominantly found in the year prior 1990s as shown in Figure 5-16. The percentage
of missing values (counted as average of 10 districts) within 1981 – 1989 is more
than 60 % per year while after 1990, the percentage varies from 3 – 19 % per year.
The distribution of percentage of missing value in these districts might be a reason
why the number of duplication cases found prior to 1990s is low (see Figure 5-9).
Figure 5-16. The average of missing values (in %) derived from 10 districts where there is no 90%
complete series (Source: data processing).
61
Figure 5-17. Spatial distribution of selected gauges based on missing value criteria. Green refers to series which is 100 % complete, yellow to series which is at
least 90 % complete. Label of rain gauges refers to Appendix 4 (Source: data processing).
62
5.5. Homogeneity Test
Using xlstat software package (an Add-Ins package for Ms Excel, available
at www.xlstat.com/), the homogeneity assessment was applied to the remaining
140 rain gauges. The software provides four type of homogeneity test i.e. the
Pettitt test, the SNHT test, the Buishand test and the von Neumann test (see
session 4.2.3) as shown in a cropped GUI of Figure 5-18. The test output was
displayed as a descriptive statistic and chart.
Instead of using critical value for each test to define result, the software
calculates p-value and compares it to value of selected significance level. The p-
value is commonly used in statistics test representing the risk if we reject the null
hypothesis while it is true. If the p-value is lower than significance level that we
select before, the null hypothesis should be rejected. For the current study,
significance level selected for all test is 5 %.
Figure 5-18. An interface of tool of homogeneity test on xlstat Add-Ins package (Source: data
processing)
63
The examples of homogeneity test in a series are shown in Figure 5-19 and
Figure 5-20. In Figure 5-19, the Pettitt test and the Buishand test detected a shift
in series of Wagir Station, District of Malang. However, the location the detected
shift is different between those two tests. The Pettitt test detected a break at 1997
while the Buishand test detected a break at 2004. The difference of detected shift
is caused the difference of their sensitivity on detecting a shift as elaborated in
session 4.2.3.
Figure 5-19. A shift in series of Wagir rain station, District of Malang detected by Pettitt test (left
panel, shift at 1997) and Buishand test (right panel, shift at 2004) (Source: data processing)
Similar case, Figure 5-20 shows the difference of detected location of a
break between SNHT test and Buishand test when they were applied to test series
of Dam Sembah station, District of Jember. SNHT test detected a shift at 1984
while Buishand test detected a shift at 1988.
The test revealed that 84 series are categorized as homogeneous series and
are classified as “useful” series. The spatial distribution of useful series is
displayed in Figure 5-21. Compared to Figure 5-17, the suspect series was found
for almost all districts except District of Ngawi, Probolinggo and Sumenep. In
contrast, the test has excluded all stations of District Jombang, Mojokerto and
Sampang since they all were identified as suspect series.
0
50
100
150
200
250
1980 1990 2000 2010
rain
fall
(mm
)
time
PETTITT TEST
µ1 = 85.125 µ2 = 113
0
50
100
150
200
250
1980 1990 2000 2010
rain
fall
(mm
)
time
BUISHAND TEST
µ1 = 87.545 µ2 = 132
64
The other 56 series are classified as “suspect” with one or two detected
breaks per series. In total, 56, 29, 11, 4 and 12 series are the series where one,
two, three and four tests reject the null hypothesis, respectively. In respect of
individual absolute test, the Pettitt test detected a break in 22 series, the SNHT test
identified a break in 35 series, the Buishand test recognized a break in 21 series
and the von Neumann test found out a break in 33 series.
Figure 5-20. A break in series of Dam Sembah rain station, District of Jember detected by SNHT
test (left panel, shift at 1984) and Buishand test (right panel, shift at 1988) (Source: data
processing)
The result also proves that using hybrid method which comprises some
homogeneity tests is more powerful on finding out inhomogeneities since an
absolute test only could not detect always a break in series. The result is detailed
in Appendix 5. Finally, those 84 rainfall series (green dots, Figure 5-21) are ready
for trend analysis because they have no suspected artificial shift. The map of those
high quality rainfall data and each corresponding district is presented in Figure 5-
22.
60
70
80
90
100
110
120
130
140
150
160
1980 1990 2000 2010
rain
fall
(mm
)
time
SNHT TEST
µ1 = 134 µ2 = 95.917
60
70
80
90
100
110
120
130
140
150
160
1980 1990 2000 2010
rain
fall
(mm
)
time
BUISHAND TEST
µ1 = 120.250 µ2 = 93.800
65
Figure 5-21. Spatial distribution of useful series (green dots, 84) and suspect series (yellow series, 56). Code of rain station and result of absolute test for each
gauge refer to Appendix 5 (Source: data processing)
66
Figure 5-22. Spatial distribution of high quality rainfall data passing all quality control procedures overlaid by boundaries and names of district (Source: data
processing)
67
VI. RESULT
6.1. Spatial Characteristic of Extreme Rainfall Events
6.1.1. Fix Threshold
There were at least 22 hydro-meteorological disaster events causing
casualties occurred in the last ten years. The maximum daily rainfall observed per
district related to those disaster ranges from 66 up to 350 mm as displayed in
Figure 6-1. In general, concluding from box plot displayed in the Figure 6-1, the
daily rainfall depth mainly fall in the range of 150 – 250 mm with 200 mm as a
mean.
Figure 6-1. Histogram and box plot of rainfall correspond to disaster. Events ID refer to Table 6-1
and Figure 6-2. Red line indicates fix threshold (Source: data processing).
It was seen from Figure 6-1 that daily rainfall being more than 100 mm is
potential to generate serious problem to environment. Thus, a fix threshold of 100
mm as designed by BMKG is operable because it links well to hydro-
meteorological hazard. It does not mean that every rainfall event exceeding those
thresholds will always trigger disaster. However, in context of disaster
preparation, 100 mm could be designed as fix threshold for occurring hydro-
meteorological hazard.
68
Table 6-1. List of disasters occurred in the last ten year over East Java Province (Source: BNPB, Gov of East Java Prov., BMKG)
ID DISASTER DISTRICT SUB-DISTRICT YYYY MM DD RAINFALL
(mm) RAIN STATION CASUALTIES INJURED
1 FLOOD SITUBONDO Situbondo, Asembagus, Panarukan,
Mlandingan, Suboh, Besuki and
Sumbermalang
2002 2 5 243 Baderan 21 -
2 FLOOD BONDOWOSO Tenggarang, Wonosari, Tapen, Prajekan,
Pakem and Wringin
2002 2 5 210 Wringin 17 -
3 FLOOD MALANG Tirtoyudo, Sumbermanjing, Dampit,
Donomulyo, Ampelgading, gedangan and
Pagak
2003 11 22 312 Clumprit 3 18
4 FLOOD AND LANDSLIDE MOJOKERTO Pacet, Pungging, Mojosari, Mojoanyar,
Puri, Sooko, Jatirejo, Magersari dan
Prajurit Kulon
2004 2 3 225 Pacet 4 1
5 FLOOD BLITAR Kademangan, Sutojayan 2004 12 3 350 Lodoyo 14 -
6 LANDSLIDE TRENGGALEK Munjungan 2005 12 11 255 trenggalek 1 -
7 FLOOD AND LANDSLIDE JEMBER Panti, Sukarambi, Rambipuji 2006 1 1 178 Klatakan 92 68
8 FLOOD AND LANDSLIDE MALANG Bareng 2006 1 24 120 rejoagung 1 4
9 FLOOD AND LANDSLIDE TRENGGALEK Kampak, Gandusari 2006 4 19 165 Bagong 18 517
10 FLOOD AND LANDSLIDE PACITAN Pacitan 2007 12 26 231 Bandar 2 -
11 FLOOD AND LANDSLIDE PONOROGO Ponorogo 2007 12 26 263 Ponorogo 4 -
12 LANDSLIDE TRENGGALEK Trenggalek, Bendungan, Tugu, Karangan,
Pule
2007 12 26 234 Pule 2 -
13 FLOOD MALANG Bantur, Kepanjen, tirtoyudo, Gedangan 2007 12 26 225 dampit 4 2
14 FLOOD AND LANDSLIDE NGAWI Kwadungan, Ngawi, Pangkur, pitu,
karanganyar, Mantingan
2007 12 26 246 Kedung Urung 15 -
15 FLOOD BONDOWOSO Prajekan, Kelabang, Tapen 2008 2 8 275 Talep 1 76
16 FLOOD SITUBONDO Paowan, Campoan, Sumber kolak, Kolakan 2008 2 8 254 Baderan 12 -
17 LANDSLIDE MAGETAN Kartoharjo, Karangmojo 2008 3 10 178 Barat_PU 1 1
18 LANDSLIDE TULUNGAGUNG Sendang 2009 11 21 109 Kalidawir 2 1
19 LANDSLIDE BLITAR Wlingi 2010 4 28 153 Wlingi 2 -
20 FLOOD AND LANDSLIDE TRENGGALEK Pogalan, gandusari, kampak, Bendungan,
Durenan, Munjungan
2010 5 5 66 Bendungan 8 1
21 LANDSLIDE TULUNGAGUNG Pagerwojo 2010 10 30 124 Sumberpandan 3 -
22 FLOOD MADIUN Kebonsari 2010 12 6 120 Gombal 1 -
69
Figure 6-2. The affected area due to hydro-meteorological disaster causing loss of life within last 10 years mapped in sub-district unit (Source: data
processing).
70
6.1.2. Site Specific Threshold
There are four indices representing the characteristic of rainfall intensity at
given site i.e. rainfall depth at 90th
percentile (RI90p), rainfall depth for 1-year
return period (R1yr), rainfall depth for 5-year return period (R5yr) and rainfall
depth for 25-year return period (R25yr). Series of 30 years data per station was
examined as single series to define each index. Thus, one only obtains single
value per station for each index so that the trend assessment was not applied for
these site specific thresholds.
Threshold for 1-year return period (R1yr) at each station was calculated by
sorting daily rainfall depth from the largest to the smallest. The value of threshold
was taken from rank of 30th
of the sorted series. Meanwhile threshold value for 5-
year return period (R5yr) and 25-year return period (R25yr) were calculated using
statistic toolbox under Matlab based on generalized extreme value distribution.
Annual maxima series of each station was set first and was tested using GEV
function to generate the parameter of shape (k), scale (σ), and location (μ) of series.
Those three parameters were used then to calculate return level, a value of rainfall
for given return period.
A box plot for all site specific thresholds is shown in Figure 6-3. Each box
encloses the middle of 50 % of data in which the median and mean value is
displayed as a line and red plus symbol “+” within each box, respectively. The
upper and lower ends of the box are upper and lower quartile (Q3 and Q1). The
lines extending from the top and bottom of each box denote the minimum and
maximum values based on quartile criteria (1.5 times the inter-quartile distance,
Q3 – Q1). Blue dots are the minimum and maximum values based on threshold
distribution.
The calculation revealed that threshold values based on 90th
percentile
(RI90p) varies from 48 mm up to 81 mm. The minimum value was observed at
Station Lamongan (ID: 68) and Bluto (ID: 82) while the maximum value of
threshold was found at Station Pasewaran (ID: 3). Threshold values for 1-year
return period vary from 70 – 119 mm. Station Galis (ID: 80) recorded rainfall
71
amount of 70 mm as minimum value. Meanwhile Station Pasewaran (ID: 3)
observed rainfall amount of 119 mm as maximum value for 1-year return period.
Threshold values for 5-year recurrence interval range from 84 mm recorded at
Station Bluto (ID: 82) up to 154 mm observed at Station Pasewaran (ID: 3). For
25-year recurrence interval, threshold values diverge from 103 mm which was
found at Station Lojejer (ID: 9) up to 238 mm which was identified at Station
Gondanglegi (ID: 27). Station Bluto recorded the highest value for almost all site
specific thresholds. The tested stations counted an average of 59, 89, 115 1nd 151
mm for rainfall amount at 90th
percentile, 1-year, 5-year and 25-year return period
correspondingly.
Figure 6-3. Box plot of threshold based on 90th
percentile, 1-yr, 5-yr and 25-yr return period
(Source: data processing)
Spatial distribution of threshold values based on 90th
percentile (RI90p) is
shown in Figure 6-4. In the northern coast including District of Tuban and
Lamongan, the values are dominated by threshold being less than 60 mm (blue
boxes). The cluster of threshold less than 60 mm was also found in District of
72
Ponorogo located in the valley of Mount Lawu and Mount Wilis and in District of
Jember, placed in the southern slope of Mount Argopuro and Mount Raung. Only
two stations were observed with threshold more than 75 mm (red boxes) i.e.
Station Pasewaran, Banyuwangi (ID: 3) which is loacated in the eastern slope of
Mount Ijen and Station Babat, Lamongan (ID: 64) which is located in north coast.
However, Station Babat is not spatially coherent with its neighbors.
Figure 6-5 depicts spatial distribution of threshold for 1-year return period
(R1yr). The values less than 80 mm were identified in small part of northern coast,
in the valley of Mount Lawu and the southern coast of Madura Island.
Nevertheless, the cluster is not really clear. In the northeastern coast including
District of Gresik, Surabaya, Sidoarjo, Pasuruan and Probolinggo and in the
mountainous areas, threshold are dominated by values more than 80 mm.
The pattern of threshold for 5-year return period (R5yr) is relatively similar
with that for 1-year return period (see Figure 6-6). In the high land and
northeastern coast, a group of threshold values more than 105 mm was generally
observed. In contrast the value less than 105 mm tends to be randomly distributed.
Threshold for 25-year return period (R25yr) is displayed in Figure 6-7. The
threshold which is less than 150 mm were seen in some places including northern
coast, northeastern coast even in the high land such as District of Jember and
small part of District of Ponorogo. The threshold more than 200 mm was found
randomly both in low land and high land.
For those site specific thresholds which are representative of daily
intensities, the general spatial pattern can be recognized. In the northern coast, the
daily intensity for given recurrence interval are commonly low. The mainland of
Madura and the valley of Mount Lawu and Wilis also recorded the low intensity.
Meanwhile, the northeastern coast which is close to Straits of Madura and the
mountainous areas recorded relatively high daily intensity. Surprisingly, the
highest threshold was not always seen in the high land.
73
Figure 6-4. Spatial distribution of threshold based on 90
th percentile, RI90p. Blue refers to low threshold, green to moderate and red to high threshold (Source:
data processing).
74
Figure 6-5. As Figure 6-4 but for rainfall with 1-year return period, R1yr (Source: data processing)
75
Figure 6-6. As Figure 6-4 but for rainfall with 5-year return period, R5yr (Source: data processing)
76
Figure 6-7. As Figure 6-4 but for rainfall with 25-year return period, 25yr (Source: data processing)
77
6.1.3. Climatological Mean of Annual Indices
Different with site specific threshold, the annual index was calculated
based on annual block meaning that there is one value per year. Within the study
period, we will have maximum 30 values per station for each index in case of
station with complete data. In the certain year where the data is not complete, the
value of index was not calculated. The indices was calculated using RCLIMDEX
software, the R language GUI recommended by WMO for extreme climate.
To characterize spatial pattern of extreme indices, each annual index was
averaged to obtain climatological mean of each index per station. Both frequency
and intensity indicators were assessed and mapped. Rainfall total values (RTOT)
of last 30 years, for instance, were averaged to acquire the annual mean of rainfall
total per site. Figure 6-8 up to Figure 6-15 depict the annual mean of all indices.
Annual frequency of daily rainfall exceeding 20 mm (R20mm) ranges from
18 up to 55 day per year. The stations with low frequency being less than 28 day
per year (red dot in Figure 6-8) are mainly found in coastal area except District of
Surabaya and Sidoarjo. In those districts, frequency of these events is classified as
moderate even though the districts are very close to coast. In the southwestern
study area, the cluster of low frequency was also found located in the valley
between Mount Lawu and Mount Wilis. In the eastern part of Madura Island, the
pattern is dominated by low frequency. The moderate frequency (yellow dots)
ranging from 28 – 42 events per year is mainly distributed in the area whose
elevation is less than 600 meters. Meanwhile, the high frequency of those events
(blue dots) was seen in the mountainous area. Almost all stations which are close
to summit of mountain experienced high frequency (more than 42 events per
year).
Figure 6-9 shows spatial distribution of annual frequency of daily rainfall
exceeding 50 mm (R50mm). An average of 8 days was observed over the study
area, with the lowest frequency (red dots) identified in coastal area and a valley
between Mount Lawu and Mount Wilis. This pattern is spatially coherent with
that of daily rainfall exceeding 20 mm. However, the highest frequency was only
78
seen in the northeastern slope of Mount Arjuno and Mount Ijen. The spatial
distribution of daily rainfall exceeding 90th
percentile (R90p) is similar with that
of two indices explained before (see Figure 6-10). For those three frequency
indicators, the high frequency has never been found in low land. They are only
distributed in the high land. The topography and geographical effects noticeably
influence the distribution of frequency of extreme events.
In term of wet spell (CWD), it can be concluded that almost all areas in
East Java Province experienced the same average of maximum wet spell of
between 5 – 10 days per year. As shown in Figure 6-11, only few places which are
mainly located in the mountainous area recorded more than 10 days of wet spell.
Even one station only was found recording long wet spell which is more than 15
days per year in average i.e. Station Jawi (ID: 18) located in the eastern slope of
Mount Arjuno.
The climatological means of maximum daily rainfall (RX1d) over study
area range from 68 up to 130 mm as shown in Figure 6-12. The lowest values of
average between 68 – 90 mm were observed randomly in the low land and high
land. In general, most of stations identified as low class in the previous indices
also categorized as low class for index of RX1d. The highest values of this index
which are more than 120 mm were seen in the mountainous area.
Figure 6-13 shows spatial pattern of climatological mean of highest 5-day
rainfall total (RX5d). The values vary from 132 up to 290 mm. The highest class
being more than 225 mm was identified not only in the mountainous area but also
in small part of coastal area i.e. District of Surabaya (Station 73) and southern part
of Malang (Station 23). The dominant long term average for this index ranges
from 150 – 225 mm which is distributed randomly over the study area. Only few
places, around 8 % of total stations were found with climatological mean less than
150 mm. The pattern of index of RX1d and RX5d, was found spatially coherent
actually since the highest value identified in District of Surabaya and Malang are
only less higher than limit of class. Meanwhile the limit is 225 mm, their value is
235 mm and 231 mm for Station 73 and 23 respectively. It is clear that the way to
classify the values could mislead in interpretation process.
79
The spatial variation of annual total (RTOT) calculated using last 30 years
data diverge from less than 1000 mm per year (Station Alas Buluh, ID: 1) up to
more than 3000 mm per year (Station Prigen, ID: 22). Figure 6-14 presents map of
annual rainfall over study area. The wettest areas with annual rainfall more than
2400 mm were observed in the mountainous area, near to the summit. Whereas
the driest areas in which rainfall total is not more than 1500 mm per year were
identified mainly in the low land, near to coast. The valley between Mount Lawu
and Mount Wilis was also recognized as dry area. The annual mean of daily
rainfall intensity (SDII) is displayed in Figure 6-15. The values range from 14 up
to 27 mm/day. The distribution of lowest and largest value is similar with that of
other indices.
Overall, the pattern of climatological mean of indices can be drawn. All
extreme indices, both frequency and intensity indicators behave similarly and
form a specific pattern in respect of topographic and geographic feature. The
lowest values of indices were predominantly found in coastal area while the
largest values were identified mostly in the mountainous area. Thus, we can say
that the rainfall events over East Java Province are mostly categorized as
orographic rainfall.
80
Figure 6-8. The climatological mean of number of daily rainfall event exceeding 20 mm, R20mm. Red corresponds to low class, blue to high class. (Source:
data processing).
81
Figure 6-9. As Figure 6-8 but for daily rainfall event exceeding 50 mm, R50mm (Source: data processing)
82
Figure 6-10. As Figure 6-8 but for daily rainfall event exceeding 90
th percentile, R90p (Source: data processing)
83
Figure 6-11. As Figure 6-8 but for maximum wet spell, CWD (Source: data processing)
84
Figure 6-12. As Figure 6-8 but for maximum daily rainfall event, RX1d (Source: data processing)
85
Figure 6-13. As Figure 6-8 but for maximum 5-day rainfall total, RX5d (Source: data processing)
86
Figure 6-14. As Figure 6-8 but for annual rainfall, RTOT (Source: data processing)
87
Figure 6-15. As Figure 6-8 but for daily rainfall intensity, SDII (Source: data processing)
88
6.1.4. Topography Effect
The effect of topography on the extreme indices was also explored by
plotting them in the X-Y graph. Drawing the fitted equation in a scatter plot of
value of climatological mean versus elevation of station allows us to assess the
level of relation. The elevation of stations ranges from 2 up to 1788 meters. Using
this original value, the scatter plot will looks like concentrated points since the
elevation of stations is mostly less than 500 meters. To obtain clear pattern, the
elevation has been converted using logarithmic transformation. Thus the elevation
value will seem in short range rather than that of the original value.
Appendix 6 presents the scatter plot of climatological mean of indices for
any elevation. Frequency of daily rainfall event exceeding 20 mm (R20mm) was
found having clear linier relationship with elevation by correlation coefficient (r)
of 0.5. The determinant factor (R2) of fitted line equation is 0.224 (see Appendix
6.1). This value tells that using given linier equation, topography factor can only
describe 22% of index variation. The remaining variation is influenced by other
factors.
The influence of elevation to index R50mm and R90p was observed being
weaker than that to index R20mm as shown in Appendix 6-2 and Appendix 6-3
respectively. For index R50mm, the linier equation fitted to the scatter plot
produce determinant factor of 0.088 whereas that for index R90p is 0.196. The
correlation coefficient for those two indices in their relation to elevation is 0.3 and
0.4, respectively. Compared to index R20mm, the influence of elevation to the
frequency of rainfall with higher intensity such as R50mm and R90p is less
noticeable. Overall, using 20 mm, 50 mm and 90th
percentile as a threshold, we
can expect to find more frequently rainfall event in the higher location.
Figure 6-16 shows a scatter plot of index CWD which represents maximum
wet spell given for any elevation value. The control of elevation on wet spell was
identified relatively strong. The correlation between those two variables produced
correlation coefficient of 0.6. The linier equation can fit well enough to the scatter
point by capturing about 34 % of index variation.
89
Figure 6-16. The scatter plot of index CWD versus log of elevation (Source: data processing)
The elevation was also found influencing the index RX1d and index RX5d.
The correlation test between the index RX1d and index RX5d versus elevation
gives correlation coefficient of 0.2 and 0.4 respectively. The scatter plot for those
two indices is shown in Appendix 6-4 and 6-5. The pattern of points which do not
diverge closely to the equation line confirms that the relation is weak particularly
for index RX1d.
The relation of elevation to annual rainfall (index RTOT) is displayed in
Appendix 6-6 while that for daily rainfall intensity (index SDII) is presented in
Appendix 6-7. The correlation coefficient between index SDII and elevation is 0.2
proving that the control of elevation is weak. On the other hand, influence of
elevation on annual rainfall total (index RTOT) is strong enough confirmed by
correlation coefficient of 0.5.
Overall, elevation was found influencing relatively significant to the index
R20mm, CWD and RTOT. The correlation coefficient for those three indices is at
least 0.5. For the other indices, the control of elevation is relatively weak. The
most significant relation was identified for maximum wet spell, CWD, and the
lesser significant was seen for daily rainfall intensity, SDII.
90
6.2. Temporal Trend of Extreme Rainfall Events
Temporal trend of indices was assessed using MAKESEN version 1.0, an
Excel template developed by Finnish Meteorological Institute. The software
provides Mann-Kendal test to check the monotonic trend of tested series and
Theil-Sen slope estimator to estimate magnitude of trend slope. The significance
level () was selected at 0.1, 1, 5 and 10 %.
6.2.1. Result of the Assessment
Trend assessment was aimed to identify possible temporal change of
frequency and intensity of extreme events. Appendix 7 presents a complete output
of trend assessment for all indices using MAKESEN software. The fourth column
“Z” is a statistic value (see session 4.5.) from which the sign of trend can be
determined. The absolute value of Z is compared to the standard normal
cumulative distribution to define if there is a trend or not at the selected level α of
significance. A positive (negative) value of Z indicates an upward (downward)
trend. The fifth column, “sig”, is the smallest significance level () at which the
trend is identified. The symbol of “ *** ”, “ ** “, “ * ” and “ + ” indicates 0.1 %, 1
%, 5 % and 10 % level of significance, respectively, while the blank cell means
significance level is greater than 10 % (not significant). The sixth column, m, is
the estimated slope representing the magnitude of variable change per year e.g.
events/year for frequency indicators and mm/year for intensity indicators.
However, for data analysis (and also in mapping spatial pattern), the magnitude of
trend is expressed in decade (per 10 years), thus the value of “m” calculated
originally in unit of per year was multiplied by 10. The seventh column, “b”, is
the constant of b in linier equation of 𝑦 = (𝑚 ∗ 𝑥) + 𝑏.
6.2.1.1. Frequency Indicators
The number of daily rainfall event exceeding 20 mm (moderate rainfall
event, R20mm) was assessed first. The result revealed that not-significant
temporal change is the dominant trend over the study area. From total of 84 tested
91
stations, 53 stations were identified decreasing of which 8 stations were locally
significant. Meanwhile, 29 stations were observed increasing where 6 series were
locally significant.
Figure 6-17 displays examples of result of individual trend assessment.
The frequency of daily rainfall events exceeding 20 mm is increasing significantly
at station Sukowono and is decreasing significantly at station Wirolegi, Jember.
The slope estimated using Theil-Sen estimator is also presented in the graph.
Figure 6-17. Significant temporal change detected from the assessment of R20mm presented in
scatter plot at Station Sukowono, left panel and Wirolegi, right panel (Source: Data processing).
The next assessed index is R50mm, the number of daily rainfall event
exceeding 50 mm (heavy rainfall event). The result revealed that 46 of tested
gauges show negative trend where 11 are statistically significant. 37 stations show
positive trend where 7 of them are statistically significant. 1 rain stations only
were identified as series without trend. Overall the dominant identified temporal
change is not-significant decreasing trend, similar with index R20mm. The detail
trend assessment for this extreme index is presented in Appendix 7. Figure 6-18
shows the example of detected trend at station Bangil, Pasuruan (left panel) and at
Karangkedawuh, Jember (right panel). The magnitude of trend is presented in a
robust linier equation. Even though the scatter dots are not fitting well to the linier
line, the obvious trend can still be recognized from the graphs.
92
Figure 6-18. Scatter plot of index R50mm at Station Bangil-left and Karangkedawuh-right
(Source: data processing).
The trend assessment of daily rainfall events going beyond 90th
percentile
(RI90p) for each station showed that 45 rain stations were identified as decreasing
series, 36 rain stations were identified as increasing series and 3 stations were
recognized as series without trend. Among 45 decreasing series, 12 stations are
decreasing significantly while number for significantly increasing trend is 9
stations. The example of significant positive and negative trend detected from the
assessment for index of R90p are shown in Figure 6-19 (Station Dampit, Malang,
left panel) and (Station Bululawang, Malang, right panel).
The series with not significant downdraft trend were found as dominant result
when assessing maximum length of wet spell which is expressed by CWD,
consecutive wet day. The number for no-trend series is 3 stations. 14 rain stations
were identified as significant downdraft series and 8 rain stations as significant
updraft series. The total stations showing downdraft trend both significant and not
significant is 49 stations and that for upward trend is 32 stations.
93
Figure 6-19. Scatter plot of index R90p at Station Dampit, left and Bululawang, right. Both two
stations are located in Malang District (Source: data processing).
Figure 6-20 presents the assessment result at Station Maelang, Banyuwangi
for case of significant positive trend and at Station Bantur, Malang for case of
significant negative trend. Finally, Table 6-2 presents number of station for any
trend categories summarized for all frequency indicators. In general, not
significant trend (both for increasing or decreasing trend) is dominant temporal
change found from the assessment.
Figure 6-20. The temporal change in index of consecutive wet day, CWD detected at Station
Maelang- Banyuwangi, left and Bantur- Malang, right (Source: data processing).
94
Table 6-2. Summary of trend assessment for all frequency indicators presented as number of
station. Sig. = significant (Source: data processing).
INDEX DECREASE INCREASE NO TREND
Not Sig. Sig. Total (%) Not Sig. Sig. Total (%)
R20mm 45 8 63 23 6 35 2
R50mm 35 11 55 30 7 44 1
R90p 33 12 54 27 9 43 3
CWD 35 14 58 24 8 38 3
6.2.1.2. Intensity Indicators
Maximum daily rainfall (RX1d), maximum cumulative rainfall amount of 5
consecutive rain days (RX5d), annual rainfall total (RTOT) and simple daily
intensity index (SDII) were selected to indicate intensity of extreme events. The
assessment of these indicators is aimed to find possible temporal change of
intensity of extreme events.
The assessment of maximum daily rainfall (RX1d) revealed that 42 stations
show decreasing trend from which 8 of them are significant and 40 stations show
increasing trend from which 10 of them are significant. 2 stations only were found
as series without trend. Not significant decreasing trend is dominant trend found
from the test. Figure 6-21 presents the example of assessment result for District of
Sumenep drawn in scatter plot. The maximum daily rainfall is increasing
significantly at Station Robaru and is decreasing significantly at Station
Kebonagung. The linier equation added in each chart gives magnitude of trend.
Similar with maximum daily rainfall, the dominant trend identified for
maximum cumulative rainfall depth of 5 consecutive rain days (RX5d) is not-
significant negative trend. In general, 7 stations were recognized as significant
decreasing series and 6 stations as significant increasing series. Total decreasing
series both significant and not significant is 44 stations and that for increasing
series is 38 series. Only 2 stations were identified as no trend series. Figure 6-22
below gives example of temporal change of RX5d, maximum cumulative rainfall
depth of 5 consecutive rain days. A significant positive trend was detected at
95
Station Poncokusumo, Malang while significant negative trend was identified at
Station Ngilo, Ponorogo.
Figure 6-21. Scatter plot of maximum daily rainfall as time series for Station Robaru, left and
Kebonagung, right (Source: data processing).
Figure 6-22. Maximum cumulative rainfall depth of 5 consecutive rain days presented in scatter
plot at Station Poncokusumo, left and Ngilo, right (Source: data processing).
The next intensity indicator is annual total. Trend assessment for this index
showed that 12 (5) stations are decreasing (increasing) significantly. 3 stations
were found as no-trend series. Total decreasing series both significant and not
significant is 54 stations while total increasing series is 27 stations. The example
of detected trend on annual rainfall is shown in Figure 6-23. The significant
updraft trend was detected at Station Simo, Surabaya while significant downdraft
trend was found at Station Babat, Lamongan. The gradient of linier line represents
the magnitude of temporal change.
96
The assessment of simple daily intensity index (SDII, annual total divided by
number of rain days) expressing the daily intensity stated 1 station only was found
without trend. 15 stations were found with significant positive trend. The number
of significant negative trend is also 15 stations. In total, both for significant and
not significant, the number of positive trend is 46 stations and that for negative
trend is 37 stations. The temporal change of daily rainfall intensity detected from
the test is presented in Figure 6-24, as an example. The daily rainfall intensity was
found increasing significantly at Station Mantingan, Ngawi and was detected
decreasing significantly at Station Cawak, Bojonegoro.
Figure 6-23. Temporal change of annual rainfall at Station Simo, left and Babat, right (Source:
data processing).
Figure 6-24. Temporal change of daily rainfall intensity expressed as SDII at Station Mantingan,
left and Cawak, right (Source: data processing).
97
The summary of assessment for all intensity indicators, RX1d, RX5d,
RTOT and SDII is displayed in Table 6-3. Similar with frequency indicator, the
not-significant trend is dominant finding for all indices of intensity indicator.
Table 6-3. Summary of trend assessment for all intensity indicators given in the number of station.
Sig. = significant (Source: data processing).
INDEX DECREASE INCREASE NO TREND
Not Sig. Sig. Total (%) Not Sig. Sig. Total (%)
RX1d 34 8 50 30 10 48 2
RX5d 37 7 52 32 6 45 2
RTOT 42 12 64 22 5 32 3
SDII 22 15 44 31 15 55 1
6.2.2. Spatial Pattern of Detected Trend
The aim of spatial pattern analysis of detected trend is to identify the
region where the trend coherent is. In this session, method of point pattern
analysis was applied again by plotting station in a point map. The magnitude of
significant trend is displayed in proportional symbol while negative and positive
trend are distinguished by color.
6.2.2.1. Frequency Indicator
The spatial pattern of detected trend for index R20mm is shown in Figure
6-25. A cluster of decreasing trend (red legend) was seen in the north-west part of
study area particularly in the north coast up to the low land located approximately
70 km from the coast. This area is known as the lower part of bengawan solo
watershed. In the southwestern study area, a cluster of decreasing trend was also
identified, located in the slope of Mount Lawu and Wilis which is including into
madiun sub-watershed. In those two areas, 7 rain stations were even detected
decreasing significantly distributed in District of Ponorogo, Ngawi, Bojonegoro,
Tuban and Gresik. The few small and isolated areas with not-significant
decreasing trend were also found in all other districts.
98
In contrast, the small areas showing positive trend was mainly seen in the
District of Surabaya, Sidoarjo, Pasuruan, Malang and Jember. There is at least one
station with significant positive trend in those districts. Generally, the west part of
East Java Province is dominated by decreasing trend while in the east part the
decreasing and increasing trend take balanced proportion.
Figure 6-26 and 6-34 shows spatial pattern of trend of index R50mm and
R90p. Those two indices showed relatively similar in spatial pattern of trend. The
location of clustered negative trend is generally same with index of R20mm,
except for the area close to north coast which is dominated by positive trend for
the last two indices. For index R50mm, 11 stations showing significant negative
trend are distributed in District of Ngawi, Bojonegoro, Tuban, Lamongan,
Malang, Jember and Sumenep while significant positive trend was observed in
District of Tuban, Surabaya, Jember and Pasuruan. For index R90p, significant
negative trend was identified in that of R50mm plus District of Madiun, Gresik
and Surabaya. The significant positive trend was found in District of Tuban,
Surabaya, Jember, Pasuruan and Malang.
The index of CWD, on the other hand have different spatial pattern as
shown in Figure 6-28. Small area with clustered of negative trend was observed in
the north coast and northeast coast of Java including District of Sidoarjo,
Pasuruan and Probolinggo. In Sidoarjo and Probolinggo, the negative trend was
identified consistent for all stations. The cluster of positive trend was seen in
District of Lamongan while in other places, the stations with positive trend are
distributed randomly.
As shown in Figure 6-25, Figure 6-26, Figure 6-27 and Figure 6-28, the
negative and positive trend of frequency indicator are generally distributed
randomly. Some cluster cases were found but not clear for other places. Indeed,
some districts (as mentioned before) show consistent temporal change but the
trend of individual station per district is generally not consistent.
99
Figure 6-25. Spatial pattern of detected trend for daily rainfall events exceeding 20 mm. Blue corresponds to wetter conditions, red to dryer conditions. Box
indicates not-significant trend. The magnitude of trend is given in “events/decade” (Source: Data processing).
100
Figure 6-26. As Figure 6-25 but for daily rainfall events exceeding 50 mm (Source: Data processing)
101
Figure 6-27. As Figure 6-25 but for daily rainfall events exceeding 90
th percentile (Source: Data processing)
102
Figure 6-28. As Figure 6-25 but for consecutive wet day, CWD (Source: Data processing)
103
6.2.2.2. Intensity Indicator
Spatial pattern of all intensity indicators are shown in Figure 6-29, Figure
6-30, Figure 6-31 and Figure 6-32. As shown in Figure 6-29, in north coast, from
District of Tuban up to District of Surabaya, trend of maximum daily rainfall,
RX1d, was dominated by positive trend. The isolated positive trend was also seen
in the southern part of District of Malang. In the other places, there is no
recognized cluster of positive trend. Cluster of negative trend was observed in
District of Bojonegoro, Lamongan and Jember. An isolated few small areas with
negative trend were also identified in eastern part of Madura Island and the
northern part of Malang District. In district of Lamongan, Pasuruan, Jember and
Sumenep, both of significant negative and positive trend were found.
For index RX5d, the pattern is generally similar except that the domination
of positive trend in the north coast was not seen as displayed in Figure 6-30. In
this area, negative trend was observed as dominant trend. The significant positive
trend was observed in the middle part of East Java Province including District of
Surabaya, Pasuruan and Malang while significant negative trend was identified in
District of Bojonegoro, Ponorogo and Sumenep.
The annual rainfall was observed decreasing in most of western part of
East Java Province (see Figure 6-31). However, the number of stations showing
not-significant trend is much larger than that of significant trend. Almost in all
districts in this region, significant decreasing trend was observed including
District of Ponorogo, Ngawi, Bojonegoro, Tuban and Lamongan. In contrast,
there was no significant increasing trend seen in this region. In the eastern part of
East Java Province, increasing and decreasing trend was distributed randomly.
Even, in Pasuruan District, the significant increasing and decreasing trend was
observed being close to each other.
Trend of daily rainfall intensity (SDII) was presented in Figure 6-32. In
low land close to north coast (District of Bojonegoro and Lamongan) and in
district of Sumenep, cluster of downdraft trend was recognized, similar with that
of previous intensity index. In Bojonegoro and Sumenep, the significant
104
decreasing trend is dominant. Meanwhile, cluster of updraft trend was found in
District of Tuban, Surabaya, Sidoarjo and Malang. The interesting changing was
observed in District of Tuban and Ngawi. They show contrast trend between index
of rainfall total and index of daily rainfall intensity. In those two districts, some
stations were found with decreasing trend for annual total but increasing trend for
daily intensity. This probably related to the significant decrease on the number of
rainy days. Overall, District of Bojonegoro, Lamongan and Sumenep were
observed showing consistent decreasing trend for all intensity indicators.
105
Figure 6-29. Spatial distribution of trend of maximum daily rainfall, RX1d (Source: data processing)
106
Figure 6-30. Spatial distribution of trend of maximum cumulative rainfall of 5 consecutive rain days, RX5d (Source: data processing)
107
Figure 6-31. Spatial distribution of trend of annual total, RTOT (Source: data processing)
108
Figure 6-32. Spatial distribution of trend of daily rainfall intensity, SDII (Source: data processing)
109
6.3. Severity Analysis
The climatological mean of index R50mm and index R90p was used to represent
the frequency of extreme rainfall events from which the severity index was
generated. The trend of extreme events was not considered on severity analysis
since the detected trend for a district is generally not consistent. The index of
selected stations was weighted using thiessen polygon to calculate district index.
The stations involved for calculating district index were not only those located in
related district but also stations located in radius of 25 kilometers from the
boundary. Using this technique, the minimum selected stations for a district are 3
stations (for Pamekasan) and the maximum are 15 stations (for Tuban). The
districts without rain station were not analyzed (see Figure 6-33).
District index was classified into three classes using equal interval technique.
Class 1 refers to low frequency, class 2 to moderate frequency and class 3
corresponds to high frequency. Class of index R50mm and class of index R90p
was overlaid to generate severity index following severity matrix as shown in
Table 6-5.
Table 6-5. The matrix used for generating severity index. Green cell refer to less severe, yellow to
moderate and red to more severe (Source: data processing).
R90p
R5
0m
m
1 2 3
1 2 3 4
2 3 4 5
3 4 5 6
The analysis produced severity map for extreme rainfall events given for
district level (see Figure 6-34). The districts where rainfall events categorized
110
being less severe includes Pamekasan and Sumenep, both are located in Madura
Island. The districts in which rainfall events classified being more severe are
Ponorogo, Madiun, Ngawi (located in the slope of Mount Lawu and Wilis),
Bojonegoro (located in the low land), Malang, Pasuruan (located in the slope of
Mount Semeru and Arjuno), Surabaya, Sidoarjo (located in the coastal area) and
Jember which is located in the slope of Mount Argopuro. In the Districts of
Magetan, Tuban, Lamongan, Gresik, Probolinggo, and Banyuwangi, extreme
rainfall events were classified as moderate class. 13 districts could not be assessed
since there is no sufficient rainfall data.
111
Figure 6-33. Thiessen polygons created for regional analysis (Source: data processing)
112
Figure 6-34. The severity map for extreme rainfall events given per district level (Source: data processing)
113
VII. DISCUSSION
The analysis of daily rainfall observations within period of 1981 – 2010
over East Java Province shows that the data quality is relatively poor with regard
to quality control procedures proposed in the study. This set of strict criteria was
designed because trend assessment is critically dependent on the data quality. In
average, around 5% of data are not original, but they are duplicates of other data.
Less than 1 % of data per station, in average were detected as spatial outliers
meaning that spatial consistency of data is quite good. Missing observation is the
main problem. The missing value test removed most of stations from the list.
There were only 33 % of stations categorized as complete series with at least 90 %
data are available. Data homogeneity is also major problem. Finally, only 84 daily
rainfall series from the total of 461 series (18 % approximately) survived all
selection criteria. The proposed quality control procedures were sufficient to clean
rainfall series particularly spatial outlier check which successfully identifies some
not-spatially coherent observations.
The link has been detected between fix threshold and site specific threshold
of extreme rainfall events used in the study. Comparing fix and site specific
threshold, shows that a fix threshold of 100 mm developed by BMKG probably
related to an event with 5 year return period. For this return period, the daily
rainfall amount is around 115 mm in average over study area. Thus, the fix
threshold of 100 mm is reasonable to describe frequency of extreme which is
commonly rare.
The climatological situation of extreme indices over study area indicates
that there are three regions which always recorded low value both for frequency
and intensity indicators. Those three regions are north coast, Madura Island and
southwest region of study area. Meanwhile, the high value was commonly
observed in the mountainous areas. This situation confirms that orographic
process is dominant system generating rainfall events over East Java Province.
This also explains why the coastal area recorded low value of extreme indices.
114
The southwest region of study area, which is located between Mount Lawu and
Mount Wilis, also experienced lowest frequency and intensity of extreme rainfall.
This might be influenced by high land stretching out in the south of the valley.
This high land is potential to block the moist air blowing from Indian Ocean so
that no more convective cloud formed in the valley. The exploration of
climatological mean of extreme indices also found a clear effect of topography
except for index R50mm, RX1d and SDII.
Trend assessment of extreme rainfall events using both frequency
indicators and intensity indicators result in some interesting note. Using join
distribution technique, the trend assessment of rainfall exceeding 20 mm and 50
mm could be explored to identify where actually the temporal change comes from.
Table 7-1 displays contingency table of those two indices.
Right-top box corresponds to series which were detected decreasing in
R20mm yet they were detected increasing in R50mm. It means that in the future,
the frequency of moderate rain (20 – 50 mm) tends to be low while the frequency
of heavy rainfall (> 50 mm) tends to be high leading the high risk for occurring
disaster generated by extreme rainfall. Those thirteen stations are Pasewaran,
Ledokombo (Jember), Jembrung (Pasuruan), Kalipare (Malang), Lembeyan
(Magetan), Tretes (Bojonegoro), Mundri, Kejuron, Widang (Tuban),
Karangbinangun, Blawi, Pangkatrejo (Lamongan) and Panokawan (Sidoarjo).
Left-bottom box on the other hand, represent stations which were found as
increasing series in R20mm but they were identified as decreasing series in
R50mm. Thus, in the future one could expect high frequency of moderate rainfall
and low frequency of heavy rainfall. Those six stations are Renes (Jember),
Poncokusumo (Malang), Lamongan (Lamongan), Wonorejo-Rungkut (Surabaya),
Prambon and Sidoarjo (Sidoarjo).
115
Table 7-1. Contingency table showing inter-index relation (R20mm and R50mm) given in the
number of gauge. NSD = not sig. decrease, NSI = not sig. increase and NT = no trend (Source:
data processing).
Rainfall > 50 mm
NSD SD NSI SI NT Total
Ra
infa
ll >
20
mm
NSD
26 6 11 1 1 45
SD
2 5 1
8
NSI
5
16 2
23
SI
1
1 4
6
NT
1
1
2
Total
35 11 30 7 1 84
Summarizing from trend assessment for individual station given in session
6.2.1., the trend assessment also successfully detected the stations whose trend is
significant and consistent for at least three extreme indices (see also Appendix 7
which present detail result for individual station for each index). This consistent
trend was observed both for significant positive trend and significant negative
trend.
Station Glundengan (Jember, ID: 6), Sukowono (Jember, ID: 11), Bangil
(Pasuruan, ID: 17), Prigen (Pasuruan, ID: 22), Dampit (Malang, ID: 26),
Poncokusumo (Malang, ID: 32), Kebonharjo (Tuban, ID: 55), Tuban (Tuban, ID:
62) and Station Simo (Surabaya, ID: 73) were detected increasing significantly
and consistently. The detail of extreme indices for which the trend assessment
shows significant increasing trend, is shown in Table 7-2.
116
Table 7-2. List of stations which are consistently increasing. Sig. = Significant positive (Source:
data processing)
ID GAUGE R20mm R50mm R90p CWD RX1d RX5d SDII RTOT
6 GLUNDENGAN Sig. Sig. Sig. - - - - Sig.
11 SUKOWONO Sig. Sig. - - - - Sig. Sig.
17 BANGIL Sig. Sig. Sig. - Sig. Sig. Sig. Sig.
22 PRIGEN - - Sig. Sig. Sig. Sig. - Sig.
26 DAMPIT - - Sig. - Sig. - Sig. -
32 PONCOKUSUMO Sig. - - - - Sig. Sig. -
55 KEBONHARJO - Sig. Sig. - - - Sig. -
62 TUBAN Sig. - Sig. - - - Sig. -
73 SIMO Sig. Sig. Sig. - - Sig. Sig. Sig.
For consistently significant decreasing trend, the assessment identified 12
rain stations e.g. Station Karang Kedawuh (Jember, ID: 7), Wirolegi (Jember, ID:
12), Bululawang (Malang, ID: 25), Ngilo (Ponorogo, ID: 36), Tretes (Ngawi, ID:
46), Cawak (Bojonegoro, ID: 48), Leran (Bojonegoro, ID: 50), Sendang (Tuban,
ID: 59), Babat (Lamongan, ID: 64), Benjeng (Gresik, ID: 71), Bluto (Sumenep,
ID: 82) and Station Kebonagung (Sumenep, ID: 83). Table 7-3 shows extreme
indices where the assessment detected consistently significant decreasing trend for
those stations.
Table 7-3. List of stations which are consistently decreasing. Sig. = Significant negative (Source:
data processing)
ID GAUGE R20mm R50mm R90p CWD RX1d RX5d SDII RTOT
7 KRNG KEDAWUH - Sig. Sig. - Sig. - Sig. Sig.
12 WIROLEGI Sig. Sig. Sig. - - - Sig. Sig.
25 BULULAWANG - Sig. Sig. - - - Sig. -
36 NGILO Sig. - - - - Sig. Sig. Sig.
46 TRETES Sig. Sig. - - - - - Sig.
48 CAWAK - - - - - Sig. Sig. Sig.
50 LERAN Sig. Sig. Sig. - Sig. Sig. Sig. Sig.
59 SENDANG Sig. Sig. Sig. Sig. - - Sig. Sig.
64 BABAT Sig. Sig. Sig. - Sig. Sig. Sig. Sig.
71 BENJENG Sig. - Sig. - - - - Sig.
82 BLUTO - Sig. Sig. - Sig. Sig. Sig. -
83 KEBONAGUNG - Sig. Sig. - Sig. - Sig. -
117
Spatial distribution of the stations showing consistent trend is presented in
Figure 7-1. The consistent negative trend was observed in District of Ponorogo,
Ngawi, Bojonegoro, Gresik and Sumenep. There was no significant positive trend
found in those districts. This condition could lead serious problem in the future
related to probability of drought. In district of Surabaya and Pasuruan the
significant positive trend was found for at least 5 indices which potentially lead to
high risk of hydro-meteorological disaster. For Surabaya, flooding will probably
become serious hazard since it is the populous urban area. Whereas for Pasuruan,
the consistently significant positive trend probably lead to high frequency of
landslide since Pasuruan is located in the slope of Mount Arjuno. In Tuban,
Malang and Jember, the consistent positive and negative trend was found together.
In general, both for frequency and intensity indicators, the not-significant
trend is dominant temporal change. Only few number of station showing
significant trend. This finding is coherent with the assessment of extreme rainfall
events done by Suhaila et al, (2010) for Peninsular Malaysia. They found that
only few stations show significant trend while the dominant is not-significant
trend. Taking place in South East Asia region, these two studies confirms that
trend of extreme rainfall events in this region is not really clear. The current study
also agreed with study of Manton et al, (2001) when assessing trend in Asia
Pacific Region. They found that the extreme rainfall indices showed less spatial
consistency over the region.
The districts found with more severe extreme rainfall events should get
priority concerning to minimize the risk. Some of those areas are located in the
slope area which topographically is high risk for landslide event. For District of
Surabaya and Pasuruan, this condition could become serious since the trend was
detected significantly increasing. Relating the severity map to the area affected by
hydro-meteorological disasters (Figure 6-2), the good link was found for District
of Ngawi, Madiun, Malang and Jember. Classified as places of more severe
extreme rainfall events, those areas experienced serious disasters during last ten
years.
118
Figure 7-1. Spatial distribution of stations showing consistently significant trend. The magnitude of legend refers to number of indices where significant trend
were detected. Red corresponds to significant negative trend, blue to significant positive trend.
119
VIII. CONCLUSION AND RECOMMENDATION
8.1. Conclusion
Some remarkable finding could be concluded from the study. Among other
aspects, missing observations and homogeneity are the major problems on the
quality of daily rainfall observations over study area where the criteria removed
most of tested stations. Only 18 % of available series (84 out of 461 rain stations)
were finally categorized as high quality data. The presented evidence confirms
that the quality control procedures applied in the study were adequate to filter the
daily rainfall data.
Evaluated using historical data of disaster, a fix threshold designed by
BMKG is reasonable to describe extreme events. For East Java Province, in
average, a fix threshold of 100 mm represents the daily rainfall amount with 5
year return period. The threshold corresponding to the local characteristic could
be defined by using percentile threshold and also by extreme value distribution.
The threshold of 90th
percentile is effective to describe extreme events since it
represents the top 10% of rainfall record within 1981 – 2010. The frequency of
rainfall exceeding 90th
percentile is commonly lesser than frequency of rainfall
exceeding 50 mm. Meanwhile the threshold for any return period defined using
generalized extreme value distribution (GEV) is effective to describe very rare
events. Over study area, the daily rainfall amount for return period of 1-year, 5-
year and 25-year is around 90 mm, 115 mm and 150 mm in average, respectively.
The spatial pattern of extreme rainfall events over East Java Province both
for frequency indicator and intensity indicator, is generally characterized by low
value in the coastal area and in the valley and high value in the mountainous area.
The north coast (District of Tuban, Lamongan and Gresik) and Madura Island
recorded low frequency and low intensity while the south part of study area which
is dominated by mountainous area observed high frequency and intensity of
extreme rainfall events (including District of Malang, Pasuruan, Probolinggo and
Jember). The effect of topography could be identified but not really clear for some
120
indices. In the high elevation, the frequency and intensity of extreme rainfall
events is typically high. The control of topography was found being most
significant for maximum length of wet spell and being lesser significant for daily
rainfall intensity.
In general, the trend of extreme rainfall events detected from the study is
dominated by not-significant trend. However, some places with consistently
significant trend could be recognized. In the west part of province, the
consistently significant negative trend was observed in District of Ponorogo,
Ngawi, Bojonegoro and Gresik. This was also found in District of Sumenep, in
Madura Island, the east part of province. The consistently significant positive
trend, on the other hand, was seen in District of Surabaya and Pasuruan. In
District of Tuban, Malang and Jember, the consistently significant positive trend
was identified together with consistently significant negative trend while for other
districts, consistent trend was not found. Thus for province level, the trend is not
really clear.
The frequency of rainfall exceeding 50 mm and exceeding 90th
percentile
calculated as climatological mean, could be used to quantify the severity of
extreme rainfall events using technique of regional analysis. Those two extreme
indices represent fix and site specific threshold, respectively. Weighted by
thiessen polygons, the application of the method successfully identified the most
severe extreme rainfall events which occur over District of Ponorogo, Madiun,
Ngawi, Bojonegoro, Malang, Pasuruan, Surabaya, Sidoarjo and Jember.
8.2. Recommendation
Based on limitation of the study, the following recommendations were
formulated for better extreme rainfall assessment.
1. Since the data period is crucial, using longer records of rainfall data will
produce more actual trend. This will be hard task for BMKG to compile long
record series since rainfall data in Indonesia are fragmented.
2. The quality is the main problem on processing rainfall data in Indonesia.
There should be a single standard procedure applied for all series including
121
gross error check, adjustment to missing value and temporal-spatial
consistency check as well. The low quality data have limited the study to
assess rainfall trend over East Java Province entirely.
3. The assessment should be extended to other provinces to get comprehensive
review of trend of extreme rainfall events across the country.
4. Local Government of Ponorogo, Ngawi, Bojonegoro and Gresik should be
aware to the possibility of drought in the future due to significant decreasing
trend not only for extreme rainfall but also for moderate rainfall and annual
total as well. Whereas Local Government of Surabaya and Pasuruan should
be aware to the increasing of possibility of hydro-meteorological hazard due
to significant increasing trend of extreme rainfall events detected there.
122
IX. REFERENCES
Aldrian E and RD Susanto. 2003. Identification of Three Dominant Rainfall
Regions within Indonesia and Their Relationship to Sea Surface
Temperature. International Journal of Climatology. 23. Pages: 1435 –
1452. DOI: 10.1002/joc.950.
Aldrian E, LD Gates and FH Widodo. 2007. Seasonal Variability of Indonesian
Rainfall in ECHAM4 Simulations and in the Re-analyses: The Role of
ENSO. Journal of Theoretical and Applied Climatology. 87. Pages: 41 –
59. DOI: 10.1007/s00704-006-0218-8.
Aldrian E and YS Djamil. 2007. Spatio-temporal Climatic Change of rainfall in
East Java Indonesia. International Journal of Climatology. DOI:
10.1002/joc.1543.
Atsamon L. L Sangchan and T. Sriburi. 2009. Assessment of Extreme Weather
Events along The Coastal Areas of Thailand. Proceeding of 21th
conference on climate variability and change. Washington.
Barrett EC and DW Martin. 1981. The use of Satellite data in Rainfall
Monitoring. Academic Press Inc. London.
BMKG – Badan Meteorologi Klimatologi dan Geofisika. 2010. The Condition of
Weather and Climate Extreme, 2010 – 2011: a Press Release. Jakarta.
Published in indonesian (bahasa indonesia). Available on
http://data.bmkg.go.id/share/Dokumen/press%20release%20kondisi%20c
uaca%20ekstrim%20dan%20iklim%20tahun%202010-2011.pdf.
Bodini A and QA Cossu. 2010. Vulnerability Assessment of Central-East Sardinia
(Italy) To Extreme Rainfall Events. Journal of Natural Hazards and Earth
System Sciences. 10. Pages: 61 – 72.
Buishand TA. 1982. Some Methods for Testing the Homogeneity of Rainfall
Records. Journal of Hydrology. 58. Pages: 11 – 27.
Carvalho LMV, C Jones and B Liebmann. 2002. Extreme Precipitation Events in
Southeastern South America and Large-Scale Convective Patterns in the
South Atlantic Convergence Zone. Journal of Climate. 15. Pages: 2377 –
2394.
Coldwell R. 2002. Extreme Value Theory and Applications to Flood Probability
Calculations. Material of lecture. Available on:
http://www.mcs.sdsmt.edu/rwjohnso/html/Extreme_theory.doc
Coles SG and RSJ Sparks. 2001. Chapter 5: Extreme Value Methods for Modeling
Historical Series of Large Volcanic Magnitudes. Available on:
http://www.cas.usf.edu/~cconnor/geolsoc/html/chapter5.pdf
Coles S. 2001. An Introduction to Statistical Modeling of Extreme Values.
Springer series in statistics. London.
123
Durre I, MJ Menne, BE Gleason, TG Houston and RS Vose. 2010.
Comprehensive Automated Quality Assurance of Daily Surface
Observations. Journal of Applied Meteorology and Climatology. 49.
Pages: 1615 – 1633. DOI: 10.1175/2010JAMC2375.1
Easterling DR, JL Evans, PY Groisman, TR Karl, KE Kunkel and P Ambenje.
2000. Observed Variability and Trends in Extreme Climate Events: A
Brief Review. Bulletin of the American Meteorological Society. Vol 81.
No 3. Pages: 417 – 425.
Fu G, NR Viney, SP Charles and J Liu. 2010. Long-Term Temporal Variation of
Extreme Rainfall Events in Australia: 1910-2006. Journal of
Hydrometeorology. 11. Pages: 950 – 965.
Frei C and C Schar. 2001. Detection Probability of Trends in Rare Events: Theory
and Application to Heavy Precipitation in the Alpine Region. Journal of
Climate. 14. Pages: 1568 – 1584.
Gilli M and E Kellezi. 2003. An Application of Extreme Value Theory for
Measuring Risk. Preprint submitted to Elsevier Science. Available on:
http://www.globalriskguard.com/resources/market/evt.pdf
Gonzalez-Rouco JF, JL Jimenez, V Quesada and F Valero. 2001. Quality Control
and Homogeneity of Precipitation Data in the Southwest of Europe.
Journal of Climate. 14. Pages: 967 – 978.
Goswami P and KV Ramesh. 2007. Extreme Rainfall Events: Vulnerability
Analysis for Disaster Management and Observation System Design.
Research report. CSIR.
Haberlandt U. 2007. Geostatistical Interpolation of Hourly Precipitation from
Rain Gauges and Radar for a Large-Scale Extreme Rainfall Event. Journal
of Hydrology. 332. Pages: 144 – 157.
Hernandez, ARP. RC Balling Jr and LR Barbar-Matinez. 2009. Comparative
Analysis of Indices of Extreme Rainfall Events: Variation and Trend from
Southern Mexico. Journal de Atmosfera. 2. Pages: 219 – 228.
Hersfield DM. 1973. On the Probability of Extreme Rainfall Events. Bulletin
American Meteorological Society. Vol 54. 10. Pages: 1013 – 1018.
Hidayat F, HM Sungguh and Harianto. 2008. Impact of climate change on floods
in Bengawan Solo and Brantas River Basins, Indonesia. Paper presented at
11th River Symposium, Brisbane, Australia.
Hipel, KW and AI Mc Leod. 1994. Time Series Modeling for Water Resource and
Environmental System. Elsevier Science Pub Co. Amsterdam.
HydroGeoLogic, Inc. 2005. Appendix D: Mann Kendall Analysis. Annual
groundwater Monitoring Report.
124
Jones C, DE Waliser, KM Lau and W Stern. 2004. Global Occurrences of
Extreme Precipitation and Madden-Julian Oscillation: Observation and
Predictability. Journal of Climate. 17. Pages: 4575 – 4589.
Klein Tank AMG and GP Konnen. 2003. Trends in Indices of Daily Temperature
and Precipitation Extremes in Europe, 1946 – 1999. Journal of Climate.
16. Pages: 3665 – 3680.
Kunkel KE, K Andsager and DR Easterling, 1999. Long-term trends in extreme
precipitation events over the conterminous United States and Canada.
Journal of Climate. 12. Pages: 2515 – 2527.
Kunkel KE, DR Easterling, K Redmond and K Hubbard. 2003. Temporal
Variations of Extreme Precipitation Events in the United States: 1895–
2000. Geophysical Research Letters. Vol. 30. No. 17. 1900. DOI:
10.1029/2003GL018052
Linsley Jr RK, MA Kohler and JLH Paulhus. 1982. Hydrology for Engineers.
McGraw-Hill Book Company. USA.
Mailhot A, S Duchesne, D Caya and G Talbot. 2007. Assessment of Future
Change in Intensity–Duration–Frequency (IDF) Curves for Southern
Quebec using the Canadian Regional Climate Model (CRCM). Journal of
Hydrology. 347. Pages: 197 – 210.
Manton MJ, PM Della-Marta, MR Haylock, KJ Hennessy, N Nicholls, LE
Chambers, DA Collins, D Daw, A Finet, D Gunawan, K Inape, H Isobe,
TS Kestin, P Lefale, CH Leyu, T Lwin, L Maitrepierre, N Ouprasitwong,
CM Page, J Pahalad, N Plummer, MJ Salinger, R Suppiah, VL Tran, B
Trewin, I Tibig and D Yee. 2001. Trends in Extreme Daily Rainfall and
Temperature in Southeast Asia and the South Pacific: 1961 – 1999.
International Journal of Climatology. DOI: 10.1002:joc.610.
Mason SJ, PR Waylen, GM Mimmack, B Rajaratnam and M Harrison. 1999.
Changes in Extreme Rainfall Events in South Africa. Journal of Climate
Change. 41. Pages: 249 – 257.
Meijerink AMJ, HAM de Brouwer, CM Mannaerts and CR Valenzuela. 1994.
Introduction to the use of geographic Information Systems for Practical
Hydrology. ITC. The Netherland.
Monkhouse FJ. 1959. Principles of Physical Geography. University of London
Press Ltd. London.
Murphy B and PL Jackson. 1997. Extreme Value Analysis: Return Periods of
Severe Wind Events in the Central Interior of British Columbia. Report
prepare for McGregor Model Forest Association. Canada.
Naveau P, M Nogaj, C Ammann, P Yiou, D Cooley and V Jomelli. 2005.
Statistical Methods for the Analysis of Climate Extremes. Journal of
Comptes Rendus Geoscience. 337. Pages: 1013 – 1022. DOI:
10.1016/j.crte.2005.04.015
125
Ngongondo C, C Xu, L Gottschalk and B Alemaw. 2011. Evaluation of Spatial
and Temporal Characteristics of Rainfall in Malawi: a Case of Data
Scarce Region. Journal of Theory and Applied Climatology. DOI:
10.1007/s00704-011-0413-0.
Orjubin G and MF Wong. 2008. Use of Generalized Extreme Value Distribution
to Model the Maximums of the Field inside a Reverberation Chamber.
Proceedings of the XXIXth
URSI General Assembly, International Union
of Radio Science. Chicago.
Petterssen S. 1958. Introduction to Meteorology. McGraw-Hill Book Company,
Inc. Second Edition. New York. USA.
Peterson TC, DR Easterling, TR Karla, P Groisman, N Nicholls, N Plummer, S
Torok, I Auer, R Boehm, D Gullett, L Vincent, R Heino, H Tuomenvirta,
O Mestreg, TS Szentimrey, J Salinger, EJ Førland, I Hanssen-Bauer, H
Alexandersson, P Jones and D Parker. 1998. Homogeneity Adjustments of
in situ Atmospheric Climate Data: A Review. International Journal of
Climatology. 18. Pages: 1493 – 1517.
Rosenzweig C, G Casassa, DJ Karoly, A Imeson, C Liu, A Menzel, S Rawlins,
TL Root, B Seguin, and P Tryjanowski. 2007. Assessment of Observed
Changes and Responses in Natural and Managed Systems. Climate
Change 2007: Impacts, Adaptation and Vulnerability. Contribution of
Working Group II to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, [ML. Parry, OF Canziani,
JP Palutikof, PJ van der Linden and CE Hanson, (Eds)]. Cambridge
University Press. Cambridge. UK. 79 - 131. Pages: 110.
Stepanek P. 2003. Homogenization of Air Temperature Series in the Czech
Republic during a Period of Instrumental Measurements. Paper presented
for fourth seminar for homogenization and quality control in
climatological databases. Budapest, Hungaria.
Subyani AM. 2004. Geostatistical Study of Annual and Seasonal Mean Rainfall
Pattern in Southwest Saudi Arabia. Journal of Hydrological Science. 49.
Pages: 803 – 817.
Suppiah R and KJ Hennessy. 1998. Trends in Total Rainfall, Heavy Rain Events
and Number of Dry Days in Australia, 1910 – 1990. International Journal
of. Climatology. 10. Pages: 1141 – 1164.
Suhaila J, SM Deni, WZ Wan Zin and AA Jemain. 2010. Spatial Patterns and
Trends of Daily Rainfall Regime in Peninsular Malaysia during the
Southwest and Northeast Monsoons: 1975 – 2004. Journal of
Meteorological and Atmospheric Physic. 110. Pages: 1 – 18. DOI:
10.1007/s00703-010-0108-6.
126
Trenberth KE, PD Jones, P Ambenje, R Bojariu, D Easterling, A Klein Tank, D
Parker, F Rahimzadeh, JA Renwick, M Rusticucci, B Soden and P Zhai,
2007: Observations: Surface and Atmospheric Climate Change. In:
Climate Change 2007: The Physical Science Basis. Contribution of
Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change [Solomon S, D Qin, M
Manning, Z Chen, M Marquis, KB Averyt, M Tignor and HL Miller
(eds.)]. Cambridge University Press, Cambridge, United Kingdom and
New York. New York. USA. Pages: 299.
Vejen F, C Jacobsson, U Fredriksson, M Moe, L Andresen, E Hellsten, P
Rissanen, T Palsdóttir and T Arason. 2002. Quality Control of
Meteorological Observations: Automatic Methods Used in the Nordic
Countries. Climate Report. Norwegian Meteorological Institute.
Wijngaard JB, AMG Klein Tank and GP Konnen. 2003. Homogeneity of 20th
Century European Daily Temperature and Precipitation Series.
International Journal of Climatology. 23. Pages: 679 – 692. DOI:
10.1002/joc.906
WMO - World Meteorological Organization. 1988. Analyzing Long Time Series of
Hydrological Data with Respect to Climate Variability. WCAP – No. 3.
WMO - TD No. 224.
WMO - World Meteorological Organization. 2009. Guidelines on Analysis of
Extremes in a Changing Climate in Support of Informed Decisions for
Adaptation. WCDMP - No. 72. WMO – TD No. 1500.
Yue S and C Wang. 2004. The Mann-Kendall Test Modified by Effective Sample
Size to Detect Trend in Serially Correlated Hydrological Series. Journal of
Water Resources Management. 18. Pages: 201 – 218
Zang X, WD Hogg and E Mekis. 2001. Spatial and Temporal Characteristics of
Heavy Precipitation Events over Canada. Journal of Climate. 14. Pages:
1923 – 1936.
The official web of Statistics Indnesia, BPS. www.bps.go.id.
The official web of National Agency for Disaster Management, Republic of
Indonesia, BNPB. www.bnpb.go.id.
The official web of government of East Java Province, Republic of Indonesia.
www.jatimprov.go.id
http://pubs.usgs.gov/sir/2005/5275/downloads/. Accessed on 6/14/2011.
http://cccma.seos.uvic.ca/ETCCDI. Accessed on 6/3/2011.
http://www.idfcurve.org. Accessed on 6/29/2011.
127
APPENDICES
Appendix 1. Critical values for absolute tests used for the study.
a. Critical values for statistic of XE, Pettitt test, for any number of sample (n) at
significance level of 1 and 5 % (Source: Wijngaard et al. 2003).
n 20 30 40 50 70 100
1% 71 133 208 293 488 841 5% 57 107 167 235 393 677
b. Critical values for statistic of T0, SNHT test, for any number of sample (n) at
significance level of 1 % (Jaruˇskov´a, 1994) and 5 % (Alexandersson and
Moberg, 1997, cited in Wijngaard et al. 2003).
n 20 30 40 50 70 100
1% 9.56 10.45 11.01 11.38 11.89 12.32 5% 6.95 7.65 8.1 8.45 8.8 9.15
c. Critical values for statistic of 𝑅 𝑛 , Buishand range test, as a function of
number of sample (n) at significance level of 1 and 5 % (Source: Buishand,
1982).
n 20 30 40 50 70 100
1% 1.6 1.7 1.74 1.78 1.81 1.86
5% 1.43 1.5 1.53 1.55 1.59 1.62
d. Critical values for statistic of N, von Neumann test for any number of sample
(n) at significance level of 1 and 5 %. For n ≤ 50 these values are taken from
Owen (1962); for n = 70 and n = 100 the critical values are based on the
asymptotic normal distribution of N (Buishand, 1981, cited in Wijngaard et
al. 2003).
n 20 30 40 50 70 100
1% 1.04 1.2 1.29 1.36 1.45 1.54 5% 1.3 1.42 1.49 1.54 1.61 1.67
128
Appendix 2. Table of critical values for the product-moment correlation
coefficient (Source: data processing, generated in Ms Excel).
LEVEL OF SIGNIFICANCE
0.05 0.025 0.01 0.005 one -tail
N df 0.1 0.05 0.02 0.01 two -tail
3 1 0.988 0.997 1.000 1.000
4 2 0.900 0.950 0.980 0.990
5 3 0.805 0.878 0.934 0.959
6 4 0.729 0.811 0.882 0.917
7 5 0.669 0.754 0.833 0.875
8 6 0.621 0.707 0.789 0.834
9 7 0.582 0.666 0.750 0.798
10 8 0.549 0.632 0.715 0.765
11 9 0.521 0.602 0.685 0.735
12 10 0.497 0.576 0.658 0.708
13 11 0.476 0.553 0.634 0.684
14 12 0.458 0.532 0.612 0.661
15 13 0.441 0.514 0.592 0.641
25 23 0.337 0.396 0.462 0.505
82 80 0.183 0.217 0.257 0.283
100 98 0.165 0.197 0.232 0.256
200 198 0.117 0.139 0.164 0.182
300 298 0.095 0.113 0.134 0.149
400 398 0.082 0.098 0.116 0.129
500 498 0.074 0.088 0.104 0.115
600 598 0.067 0.080 0.095 0.105
700 698 0.062 0.074 0.088 0.097
800 798 0.058 0.069 0.082 0.091
900 898 0.055 0.065 0.078 0.086
1000 998 0.052 0.062 0.074 0.081
1038 1036 0.051 0.061 0.072 0.080
129
Appendix 3. Detail number of outliers found per gauge. Since missing value of
three gauges are too many, only 427 out of 430 are involved in outliers check
(Source: data processing).
CODE LAT LONG GAUGE DISTRICT NEIGHBORS OUTLIERS (%)
1 -8.01 114.36 ALASBULUH BANYUWANGI 13 0.06
2 -7.93 114.38 BAJULMATI BANYUWANGI 7 0.09
3 -8.28 114.33 DADAPAN BANYUWANGI 11 0.10
4 -8.22 114.29 GLAGAH BANYUWANGI 13 0.07
5 -8.28 114.31 KABAT BANYUWANGI 11 0.04
6 -8.11 114.30 KALIKLATHAK BANYUWANGI 15 0.30
7 -8.14 114.22 KAWAH IJEN BANYUWANGI 15 0.02
8 -8.19 114.27 LICIN BANYUWANGI 13 0.17
9 -8.18 114.26 LIJENJAMBU BANYUWANGI 14 0.56
10 -7.92 114.28 MAELANG BANYUWANGI 8 0.09
11 -8.26 114.30 MCN PTH TAMBONG BANYUWANGI 11 0.11
12 -8.18 114.27 PAKEM BANYUWANGI 14 0.67
13 -7.96 114.24 PASEWARAN BANYUWANGI 11 0.39
14 -8.35 114.28 ROGOJAMPI BANYUWANGI 9 0.36
15 -8.08 114.35 SELOGIRI BANYUWANGI 15 0.33
16 -8.01 114.30 SIDOMULYO BANYUWANGI 13 0.16
17 -8.19 114.24 UNGUP BANYUWANGI 13 0.03
18 -7.82 113.69 BADERAN SITUBONDO 16 0.53
19 -7.74 113.73 BUDUAN SITUBONDO 17 0.09
20 -7.76 113.64 DAM CURAH SURI SITUBONDO 15 0.20
21 -7.75 113.72 DAM DAWUHAN SITUBONDO 17 0.04
22 -7.86 114.21 DAM LIWUNG SITUBONDO 8 0.05
23 -7.76 113.67 JATIBANTENG SITUBONDO 13 0.02
24 -7.73 113.92 KENDIT SITUBONDO 15 0.20
25 -7.79 113.79 MLANDINGAN SITUBONDO 22 0.11
26 -7.74 113.69 NOGOSROMO SITUBONDO 14 0.09
27 -7.69 113.95 PG WRINGINANOM SITUBONDO 11 0.14
28 -7.72 114.00 SITUBONDO SITUBONDO 9 0.08
29 -7.72 114.08 WONOKOYO SITUBONDO 7 0.10
30 -7.77 113.65 WRINGINANOM SITUBONDO 14 0.03
31 -7.94 113.77 ANCAR BONDOWOSO 21 0.22
32 -7.87 113.72 BLIMBING BONDOWOSO 17 1.11
33 -7.91 113.80 BONDOWOSO BONDOWOSO 23 0.14
34 -7.78 114.05 CERME BONDOWOSO 9 0.10
35 -7.88 114.00 GLENDENGAN BONDOWOSO 16 0.13
36 -7.95 113.83 GRUJUKAN BONDOWOSO 19 0.13
37 -7.85 113.82 KLABANG BONDOWOSO 23 0.24
38 -8.02 113.76 MAESAN BONDOWOSO 20 0.30
39 -8.01 113.01 PAKISAN BONDOWOSO 8 0.13
40 -7.97 113.94 PINANGPAHIT BONDOWOSO 16 0.29
130
CODE LAT LONG GAUGE DISTRICT NEIGHBORS OUTLIERS (%)
41 -7.89 113.78 SELOLEMBU BONDOWOSO 23 0.26
42 -8.01 114.02 SUMBERGADING BONDOWOSO 9 0.21
43 -7.77 113.95 TALEP BONDOWOSO 16 0.13
44 -7.87 113.90 WONOSARI BONDOWOSO 22 0.37
45 -7.80 113.76 WRINGIN BONDOWOSO 19 0.36
46 -8.11 113.84 AJUNG JEMBER 15 0.29
47 -8.17 113.65 DAM SEMBAH JEMBER 10 0.32
48 -8.30 113.59 GLUNDENGAN JEMBER 10 0.05
49 -8.25 113.57 GUMELAR TIMUR JEMBER 11 0.16
50 -8.23 113.80 KARANG KEDAWUH JEMBER 11 0.18
51 -8.26 113.37 KENCONG JEMBER 13 0.15
52 -8.11 113.91 LEDOKOMBO JEMBER 11 0.26
53 -8.39 113.54 LOJEJER JEMBER 6 0.06
54 -8.35 113.44 MENAMPU JEMBER 6 0.06
55 -8.24 113.36 PLANDINGAN JEMBER 12 0.07
56 -8.21 113.67 RENES JEMBER 11 0.23
57 -8.22 113.77 SEPUTIH JEMBER 11 0.20
58 -8.05 113.83 SUKOWONO JEMBER 18 0.25
59 -8.06 113.93 SUMBERJAMBE JEMBER 14 0.41
60 -8.19 113.45 TANGGUL JEMBER 14 0.22
61 -8.18 113.72 WIROLEGI JEMBER 10 0.13
62 -7.79 113.34 ADIBOYO PROBOLINGGO 15 0.07
63 -7.74 113.14 BAYEMAN PROBOLINGGO 13 0.13
64 -7.86 113.12 BOTOGARDU PROBOLINGGO 16 0.53
65 -7.77 113.25 DRINGU PROBOLINGGO 16 0.11
66 -7.80 113.38 JATIAMPUH PROBOLINGGO 13 0.09
67 -7.78 113.20 KADEMANGAN PROBOLINGGO 17 0.12
68 -7.79 113.42 KATIMOHO PROBOLINGGO 13 0.07
69 -7.80 113.42 KREJENGAN PROBOLINGGO 12 0.10
70 -7.86 113.24 LECES PROBOLINGGO 19 0.81
71 -7.83 113.07 LUMBANG PROBOLINGGO 14 0.31
72 -7.81 113.17 MUNENG PROBOLINGGO 16 0.10
73 -7.77 113.38 PAJARAKAN PROBOLINGGO 12 0.01
74 -7.84 113.15 PATALAN PROBOLINGGO 16 0.54
75 -7.75 113.22 PROBOLINGGO PROBOLINGGO 16 0.05
76 -8.09 113.30 BANYU PUTIH KDL LUMAJANG 16 0.09
77 -8.08 112.98 BESUKSAT LUMAJANG 5 0.09
78 -8.14 113.26 BLUKON LUMAJANG 16 0.29
79 -8.02 113.13 GUCIALIT LUMAJANG 18 0.25
80 -8.15 113.12 JOKARTO LUMAJANG 10 0.15
81 -8.09 113.36 KALIBOTO LUMAJANG 12 0.40
82 -8.02 113.34 KALIPENGGUNG LUMAJANG 14 0.53
83 -8.26 113.20 MALEMAN LUMAJANG 9 0.06
84 -7.97 113.28 RANUKLAKAH LUMAJANG 22 0.28
131
CODE LAT LONG GAUGE DISTRICT NEIGHBORS OUTLIERS (%)
85 -7.98 113.29 RANUPAKIS LUMAJANG 21 0.26
86 -7.93 113.26 RANUYOSO LUMAJANG 24 0.40
87 -8.10 113.32 ROJOPOLO LUMAJANG 16 0.17
88 -8.19 113.11 SEMENU LUMAJANG 10 0.20
89 -8.08 113.23 SUKODONO LUMAJANG 17 0.21
90 -8.15 113.27 TEKUNG LUMAJANG 16 0.14
91 -7.62 112.77 BANGIL PASURUAN 22 0.20
92 -7.64 112.69 BARENG PASURUAN 25 0.08
93 -7.62 112.76 BEKACAK PASURUAN 24 0.10
94 -7.59 112.70 GEMPOL PASURUAN 31 0.18
95 -7.70 112.67 JAWI PASURUAN 21 0.35
96 -7.59 112.68 JEMBRUNG PASURUAN 32 0.23
97 -7.68 112.93 KAWISREJO PASURUAN 7 0.05
98 -7.71 112.96 KEDAWUNG PASURUAN 9 0.04
99 -7.62 112.70 KEPULUNGAN PASURUAN 28 0.10
100 -7.64 112.88 PEGI PASURUAN 16 0.26
101 -7.72 112.64 PRIGEN PASURUAN 21 0.74
102 -7.62 112.71 RANDUPITU PASURUAN 25 0.12
103 -7.72 113.01 RANUGRATI PASURUAN 10 0.14
104 -7.66 112.67 WINONGAN PASURUAN 23 0.22
105 -8.30 112.56 BANTUR MALANG 7 0.21
106 -8.10 112.60 BLAMBANGAN MALANG 16 0.21
107 -8.07 112.64 BULULAWANG MALANG 13 0.12
108 -8.20 112.73 DAMPIT MALANG 8 0.22
109 -8.16 112.61 GONDANG LEGI MALANG 14 0.25
110 -8.20 112.45 KALIPARE MALANG 16 0.18
111 -7.98 112.65 KDKANDANG MALANG 14 0.41
112 -8.13 112.56 KEPANJEN MALANG 17 0.48
113 -7.86 112.51 NGAGLIK MALANG 13 0.06
114 -8.08 112.53 NGAJUM MALANG 18 0.23
115 -7.85 112.53 NGUJUNG MALANG 11 0.03
116 -8.03 112.76 PONCOKUSUMO MALANG 12 0.23
117 -7.92 112.59 SENGKALING MALANG 14 0.17
118 -7.98 112.61 SUKUN MALANG 14 0.43
119 -8.06 112.63 TAJIAN MALANG 13 0.23
120 -7.83 112.51 TINJOMOYO MALANG 12 0.20
121 -7.98 112.75 TUMPANG MALANG 10 0.19
122 -8.00 112.58 WAGIR MALANG 16 0.41
123 -7.99 112.35 BANTARAN BLITAR 21 1.01
124 -8.07 112.17 BENDOGERIT BLITAR 24 0.27
125 -8.12 112.26 BENDOSEWU BLITAR 21 0.17
126 -8.22 112.37 BIROWO BLITAR 13 0.14
127 -8.09 112.37 DOKO BLITAR 19 0.35
128 -8.05 112.02 GANDEKAN BLITAR 25 0.11
132
CODE LAT LONG GAUGE DISTRICT NEIGHBORS OUTLIERS (%)
129 -8.04 112.31 GANDUSARI BLITAR 23 0.52
130 -8.04 112.21 GARUM BLITAR 26 0.15
131 -8.23 112.26 JUDEG BLITAR 14 0.20
132 -7.99 112.22 KALIBADAK BLITAR 27 0.67
133 -8.14 112.38 KALIMANIS BLITAR 18 0.20
134 -8.14 112.28 KAULON BLITAR 17 0.09
135 -8.17 112.13 KLAMPOK BLITAR 15 0.25
136 -8.16 112.22 LODOYO BLITAR 19 0.20
137 -8.00 112.03 MANGUNAN BLITAR 28 0.13
138 -7.98 112.10 SLEMANAN BLITAR 25 0.25
139 -8.04 112.07 SRENGAT BLITAR 28 0.09
140 -8.04 112.14 SUMBER RINGIN BLITAR 27 0.13
141 -8.10 112.29 TALUN BLITAR 21 0.12
142 -8.07 112.34 WLINGI BLITAR 19 0.22
143 -7.78 112.27 DAMARWULAN KEDIRI 25 0.07
144 -7.84 112.16 DERMO KEDIRI 27 0.24
145 -7.80 112.09 GURAH KEDIRI 28 0.33
146 -7.87 112.15 KALASAN KEDIRI 27 0.15
147 -7.81 111.92 KANYORAN KEDIRI 22 0.17
148 -7.81 112.00 KEDIRI KEDIRI 23 0.17
149 -7.75 112.23 KENCONG KEDIRI 29 0.14
150 -7.93 111.97 KRAS KEDIRI 24 0.28
151 -7.93 112.11 KUTUKAN KEDIRI 27 0.07
152 -7.72 112.06 MINGGIRAN KEDIRI 29 0.21
153 -7.91 112.18 ONGGOBOYO KEDIRI 30 0.16
154 -7.93 112.15 PANDANTOYO KEDIRI 28 0.12
155 -7.69 112.08 PAPAR KEDIRI 30 0.14
156 -7.76 112.19 PARE KEDIRI 28 0.23
157 -7.76 112.31 PENGAJARAN KEDIRI 24 0.34
158 -7.85 112.09 SIDOMULYO KEDIRI 28 0.20
159 -7.94 112.25 SUMBERLUMBU KEDIRI 28 0.13
160 -7.74 112.21 SUROWONO KEDIRI 30 0.13
161 -7.74 112.14 TANGKILAN KEDIRI 29 0.15
162 -7.91 112.12 WATES KEDIRI 29 0.25
163 -8.16 111.89 BANDUNG TULUNGAGUNG 13 0.14
164 -8.10 111.89 BOYOLANGU TULUNGAGUNG 15 0.10
165 -8.19 111.94 KALIDAWIR TULUNGAGUNG 12 0.16
166 -8.02 111.86 MOJOPANGGUNG TULUNGAGUNG 19 0.32
167 -8.00 111.92 NGANTRU TULUNGAGUNG 22 0.12
168 -7.97 111.85 NGANTUP TULUNGAGUNG 19 0.73
169 -8.04 111.85 PAINGAN TULUNGAGUNG 18 0.16
170 -7.91 111.82 SUMBERPANDAN TULUNGAGUNG 22 0.92
171 -8.05 111.91 TULUNGAGUNG TULUNGAGUNG 17 0.08
172 -8.20 111.85 TUMPAKMERGO TULUNGAGUNG 12 0.59
133
CODE LAT LONG GAUGE DISTRICT NEIGHBORS OUTLIERS (%)
173 -8.04 111.71 BAGONG TRENGGALEK 18 0.36
174 -7.93 111.69 BENDUNGAN TRENGGALEK 22 1.32
175 -8.09 111.65 JABUNG TRENGGALEK 12 0.14
176 -8.16 111.65 KAMPAK TRENGGALEK 9 0.16
177 -8.22 111.44 PANGGUL TRENGGALEK 9 0.27
178 -8.12 111.54 PULE TRENGGALEK 14 0.42
179 -8.12 111.72 WIDORO TRENGGALEK 14 0.10
180 -8.10 111.16 ARJOSARI PACITAN 12 0.07
181 -7.97 111.30 BANDAR PACITAN 16 0.61
182 -8.08 110.96 DONOREJO PACITAN 5 0.00
183 -8.20 111.17 KEBONAGUNG PACITAN 10 0.37
184 -8.06 111.14 KERTI PACITAN 11 0.20
185 -7.99 111.19 NAWANGAN PACITAN 12 0.07
186 -8.18 111.37 NGADIROJO PACITAN 12 0.09
187 -8.18 111.09 PACITAN PACITAN 8 0.00
188 -8.17 111.03 PRINGKUKU PACITAN 6 0.12
189 -8.11 111.01 PUNONG PACITAN 7 0.07
190 -8.21 111.39 SUDIMORO PACITAN 10 0.09
191 -7.99 111.36 TAHUNAN PACITAN 20 0.31
192 -8.04 111.32 TEGALOMBO PACITAN 17 0.22
193 -8.14 111.31 TULAKAN PACITAN 15 0.24
194 -7.84 111.50 BABADAN PONOROGO 30 0.10
195 -7.87 111.34 BADEGAN PONOROGO 24 0.26
196 -7.94 111.46 BALUNG PONOROGO 23 0.14
197 -7.82 111.55 BOLLU PONOROGO 28 0.18
198 -7.82 111.62 KESUGIHAN PONOROGO 22 0.32
199 -7.81 111.63 NGEBEL PONOROGO 24 0.40
200 -8.02 111.40 NGILO PONOROGO 18 0.19
201 -8.07 111.47 NGRAYUN PONOROGO 16 0.27
202 -7.79 111.31 POHIJO PONOROGO 25 0.18
203 -7.86 111.48 PONOROGO PONOROGO 30 0.26
204 -7.84 111.76 PUDAK PONOROGO 24 0.46
205 -7.86 111.60 PULUNG PONOROGO 23 0.22
206 -7.97 111.59 SAWOO PONOROGO 24 0.17
207 -8.05 111.45 SLAHUNG PONOROGO 19 0.21
208 -7.89 111.66 SOKO PONOROGO 23 0.31
209 -7.85 111.39 SUMOROTO PONOROGO 27 0.14
210 -7.89 111.39 SUNGKUR PONOROGO 24 0.16
211 -7.80 111.66 TALUN PONOROGO 23 0.44
212 -7.58 111.46 BARAT MAGETAN 37 0.19
213 -7.64 111.44 BENDO MAGETAN 37 0.49
214 -7.67 111.42 BOGEM MAGETAN 35 0.25
215 -7.64 111.46 GONDANG KERIK MAGETAN 36 0.24
216 -7.73 111.50 JATI MAGETAN 40 0.23
134
CODE LAT LONG GAUGE DISTRICT NEIGHBORS OUTLIERS (%)
217 -7.66 111.32 JEJERUK MAGETAN 29 0.15
218 -7.57 111.41 JUNGKE MAGETAN 35 0.15
219 -7.52 111.48 KARANGMOJO MAGETAN 31 0.29
220 -7.70 111.41 KAWEDANAN MAGETAN 34 0.12
221 -7.75 111.39 LEMBEYAN MAGETAN 30 0.21
222 -7.67 111.28 NITIKAN MAGETAN 28 0.19
223 -7.73 111.32 PARANG MAGETAN 29 0.22
224 -7.74 111.27 PONCOL MAGETAN 22 0.30
225 -7.57 111.43 PURWODADI MAGETAN 34 0.17
226 -7.66 111.24 SARANGAN MAGETAN 24 0.53
227 -7.66 111.28 SLAGRENG MAGETAN 28 0.17
228 -7.63 111.30 SUMBERDODOL MAGETAN 30 0.63
229 -7.60 111.38 TAJI MAGETAN 32 0.36
230 -7.60 111.42 TINAP MAGETAN 36 0.20
231 -7.56 111.59 BALEREJO MADIUN 29 0.19
232 -7.56 111.67 CARUBAN MADIUN 22 0.33
233 -7.78 111.68 CATUR MADIUN 24 0.17
234 -7.63 111.63 CAU MADIUN 32 0.16
235 -7.59 111.65 DAWUHAN MADIUN 29 0.19
236 -7.70 111.66 DUNGUS MADIUN 28 0.26
237 -7.62 111.76 GEMARANG MADIUN 23 0.19
238 -7.73 111.26 GIRINGAN MADIUN 23 0.32
239 -7.78 111.54 GOMBAL MADIUN 34 0.26
240 -7.79 111.73 KANDANGAN MADIUN 24 0.24
241 -7.75 111.71 KARE MADIUN 27 0.25
242 -7.66 111.54 KERTOBANYON MADIUN 34 0.21
243 -7.49 111.70 NOTOPURO MADIUN 17 0.28
244 -7.56 111.75 SARADAN MADIUN 19 0.64
245 -7.73 111.57 SARENG MADIUN 34 0.19
246 -7.51 111.25 BABADAN NGAWI 26 0.21
247 -7.52 111.41 GUYUNG NGAWI 32 0.36
248 -7.56 111.25 JOGOROGO NGAWI 28 0.65
249 -7.38 111.57 KD BENDO NGAWI 15 0.26
250 -7.43 111.33 KD GALAR NGAWI 21 0.24
251 -7.48 111.16 KD URUNG NGAWI 12 0.40
252 -7.56 111.32 KENDAL NGAWI 31 0.55
253 -7.46 111.64 KR JATI NGAWI 17 0.33
254 -7.40 111.14 MANTINGAN NGAWI 7 0.13
255 -7.40 111.44 MARDIASRI NGAWI 20 0.09
256 -7.42 111.39 NGALE NGAWI 23 0.11
257 -7.51 111.23 NGRAMBE NGAWI 22 0.06
258 -7.40 111.52 PADAS NGAWI 19 0.21
259 -7.45 111.42 PARON NGAWI 26 0.15
260 -7.43 111.58 SAMBIROTO NGAWI 19 0.09
135
CODE LAT LONG GAUGE DISTRICT NEIGHBORS OUTLIERS (%)
261 -7.36 111.40 SOKONGADIRJO NGAWI 17 0.25
262 -7.48 111.18 TRETES NGAWI 14 0.19
263 -7.38 111.26 WALIKUKUN NGAWI 13 0.30
264 -7.22 111.96 BALEN BOJONEGORO 19 0.19
265 -7.14 112.12 BAURENO BOJONEGORO 22 0.18
266 -7.17 111.90 BOJONEGORO BOJONEGORO 23 0.22
267 -7.24 112.11 CAWAK BOJONEGORO 22 0.22
268 -7.27 111.85 DANDER BOJONEGORO 13 0.10
269 -7.28 111.88 JATIBLIMBING BOJONEGORO 16 0.17
270 -7.10 112.02 KANOR BOJONEGORO 27 0.12
271 -7.21 111.93 KAPAS BOJONEGORO 21 0.23
272 -7.27 111.54 KARANGNONGKO BOJONEGORO 8 0.32
273 -7.37 112.05 KEDUNG ADEM BOJONEGORO 24 0.31
274 -7.22 112.09 KERJO BOJONEGORO 23 0.08
275 -7.28 111.91 KLEPEK BOJONEGORO 18 0.15
276 -7.18 111.82 LERAN BOJONEGORO 20 0.52
277 -7.25 112.05 MEKURIS BOJONEGORO 25 0.14
278 -7.33 111.95 SUGIHAN BOJONEGORO 22 0.19
279 -7.43 111.83 SUKUN BOJONEGORO 17 0.15
280 -7.18 112.00 SUMBEREJO BOJONEGORO 26 0.09
281 -7.38 111.89 TRETES BOJONEGORO 21 0.19
282 -6.99 111.73 BANGILAN TUBAN 14 0.04
283 -6.84 111.84 BELIKANGET TUBAN 14 0.15
284 -6.97 111.81 JOJOGAN TUBAN 19 0.30
285 -6.89 111.63 KEBONHARJO TUBAN 9 0.42
286 -6.99 111.70 KEJURON TUBAN 14 0.10
287 -6.99 112.08 KEPET TUBAN 16 0.18
288 -6.91 111.91 KEREK TUBAN 19 0.28
289 -7.06 112.13 KLOTOK TUBAN 21 0.21
290 -6.99 111.78 LAJU TUBAN 17 0.18
291 -7.05 112.01 MAIBIT TUBAN 23 0.13
292 -6.95 111.90 MONTONG TUBAN 20 0.25
293 -6.96 111.71 MUNDRI TUBAN 13 0.11
294 -7.02 111.83 NGABONGAN TUBAN 20 0.38
295 -7.05 112.01 RENGEL TUBAN 23 0.17
296 -6.89 111.71 SENDANG TUBAN 12 0.12
297 -6.87 111.80 SIMO TUBAN 14 0.25
298 -7.11 111.96 SOKO TUBAN 28 0.20
299 -7.01 111.75 SOKOMEDALEM TUBAN 16 0.13
300 -6.99 111.92 SUMURGUNG TUBAN 23 0.09
301 -6.95 111.97 TEGALREJO TUBAN 17 0.30
302 -6.93 112.06 TUBAN TUBAN 14 0.14
303 -7.06 112.17 WIDANG TUBAN 21 0.09
304 -7.11 112.17 BABAT LAMONGAN 20 0.41
136
CODE LAT LONG GAUGE DISTRICT NEIGHBORS OUTLIERS (%)
305 -7.05 112.45 BLAWI LAMONGAN 14 0.08
306 -7.28 112.13 BLULUK LAMONGAN 23 0.37
307 -7.02 112.24 JABUNG LAMONGAN 15 0.30
308 -7.02 112.51 KARANGBINANGUN LAMONGAN 16 0.08
309 -7.20 112.21 KEDUNGPRING LAMONGAN 18 0.11
310 -7.18 112.35 KEMBANG BAHU LAMONGAN 17 0.22
311 -7.03 112.50 KURO LAMONGAN 16 0.05
312 -7.12 112.43 LAMONGAN LAMONGAN 15 0.17
313 -7.01 112.28 PANGKATREJO LAMONGAN 17 0.10
314 -7.22 112.21 PRIJETAN LAMONGAN 19 0.23
315 -7.10 112.32 SUKODADI LAMONGAN 17 0.17
316 -7.19 112.40 TAKERAN LAMONGAN 14 0.22
317 -7.18 112.58 BENJENG GRESIK 19 0.17
318 -7.22 112.57 CERME GRESIK 23 0.22
319 -7.19 112.52 DUDUKSAMPEAN GRESIK 16 0.16
320 -7.18 112.65 GRESIK GRESIK 19 0.11
321 -6.98 112.42 LOWAYU GRESIK 12 0.24
322 -6.99 112.45 MENTARAS GRESIK 12 0.07
323 -7.07 112.62 TAMBAKOMBO GRESIK 15 0.06
324 -7.26 112.76 GUBENG SURABAYA 20 0.10
325 -7.35 112.69 GUNUNGSARI SURABAYA 26 0.20
326 -7.23 112.64 KANDANGAN SURABAYA 23 0.21
327 -7.33 112.74 KEBONAGUNG SURABAYA 24 0.17
328 -7.30 112.78 KEPUTIH SURABAYA 20 0.13
329 -7.29 112.81 LARANGAN SURABAYA 17 0.20
330 -7.26 112.68 SIMO SURABAYA 25 0.30
331 -7.33 112.79 WONOREJO-RUNGKUT SURABAYA 18 0.12
332 -7.42 112.54 BAKALAN SIDOARJO 34 0.19
333 -7.43 112.69 KARANGNONGKO SIDOARJO 31 0.20
334 -7.44 112.50 KEMLATEN SIDOARJO 28 0.21
335 -7.42 112.68 KETAWANG SIDOARJO 33 0.04
336 -7.37 112.71 KETEGEN SIDOARJO 28 0.30
337 -7.45 112.66 KETINTANG SIDOARJO 38 0.26
338 -7.51 112.74 KLUDAN SIDOARJO 31 0.16
339 -7.42 112.58 KRIAN SIDOARJO 33 0.12
340 -7.41 112.61 PANOKAWAN SIDOARJO 35 0.13
341 -7.55 112.71 PORONG SIDOARJO 32 0.21
342 -7.48 112.57 PRAMBON SIDOARJO 38 0.22
343 -7.51 112.75 PUTAT SIDOARJO 29 0.19
344 -7.47 112.73 SIDOARJO SIDOARJO 30 0.40
345 -7.41 112.72 SRUNI SIDOARJO 29 0.32
346 -7.46 112.58 WATUTULIS SIDOARJO 38 0.10
347 -7.60 112.43 CAKARAYAM MOJOKERTO 30 0.18
348 -7.45 112.40 GEDEG MOJOKERTO 31 0.42
137
CODE LAT LONG GAUGE DISTRICT NEIGHBORS OUTLIERS (%)
349 -7.55 112.41 KASIHAN MOJOKERTO 31 0.09
350 -7.55 112.49 KETANGI MOJOKERTO 38 0.21
351 -7.57 112.47 KLEGEN MOJOKERTO 36 0.27
352 -7.53 112.56 MOJOSARI MOJOKERTO 41 0.22
353 -7.66 112.54 PACET MOJOKERTO 31 0.60
354 -7.60 112.53 PANDAN MOJOKERTO 34 0.39
355 -7.54 112.38 PANDANSILI MOJOKERTO 32 0.05
356 -7.49 112.45 PASINAN MOJOKERTO 30 0.11
357 -7.48 112.49 PUDAKSARI MOJOKERTO 32 0.27
358 -7.50 112.41 SAMBIROTO MOJOKERTO 33 0.25
359 -7.54 112.46 TANGUNAN MOJOKERTO 32 0.13
360 -7.67 112.60 TRAWAS MOJOKERTO 28 0.72
361 -7.56 112.38 TROWULAN MOJOKERTO 32 0.13
362 -7.63 112.24 BLIMBING JOMBANG 34 0.40
363 -7.62 112.25 CUKIR JOMBANG 37 0.12
364 -7.55 112.23 JOMBANG JOMBANG 35 0.19
365 -7.40 112.22 KABUH JOMBANG 28 0.24
366 -7.50 112.26 KEDUNG JOMBANG 29 0.17
367 -7.55 112.28 KEPLAKSARI JOMBANG 34 0.12
368 -7.57 112.36 MOJOAGUNG JOMBANG 35 0.13
369 -7.63 112.30 MOJOWARNO JOMBANG 30 0.20
370 -7.59 112.18 PERAK JOMBANG 33 0.24
371 -7.55 112.30 PETERONGAN JOMBANG 33 0.13
372 -7.47 112.23 PLOSO JOMBANG 28 0.17
373 -7.60 112.32 SELOREJO JOMBANG 34 0.14
374 -7.60 112.28 SUMBERPENGANTEN JOMBANG 35 0.13
375 -7.44 112.29 TAPEN JOMBANG 32 0.17
376 -7.40 112.21 WADUK MANGUNAN JOMBANG 28 0.16
377 -7.67 111.94 BANARAN NGANJUK 20 0.16
378 -7.50 112.07 BANGLE NGANJUK 27 0.05
379 -7.66 112.06 DINGIN NGANJUK 28 0.25
380 -7.51 111.94 KEDUNG PINGIT NGANJUK 20 0.15
381 -7.60 112.13 KERTOSONO NGANJUK 32 0.33
382 -7.53 112.09 LENGKONG NGANJUK 27 0.05
383 -7.49 112.09 LOGAWE NGANJUK 28 0.03
384 -7.48 111.95 MATOKAN NGANJUK 19 0.12
385 -7.59 111.90 NGANJUK NGANJUK 22 0.14
386 -7.59 111.84 NGUDIKAN NGANJUK 19 0.12
387 -7.71 112.04 PRAMBON NGANJUK 27 0.17
388 -7.71 111.78 SAWAHAN NGANJUK 21 0.63
389 -7.49 112.10 SUMBER PADAS NGANJUK 26 0.05
390 -7.61 111.84 SUMBER SOKO NGANJUK 20 0.10
391 -7.51 112.01 TRETES NGANJUK 23 0.29
392 -6.96 112.83 AROSBAYA BANGKALAN 6 0.12
138
CODE LAT LONG GAUGE DISTRICT NEIGHBORS OUTLIERS (%)
393 -7.05 112.72 BANGKALAN BANGKALAN 13 0.17
394 -7.13 113.09 BLEGA BANGKALAN 7 0.19
395 -6.95 113.05 DUPOK KOKOP BANGKALAN 6 0.02
396 -7.11 112.97 GALIS BKLN BANGKALAN 6 0.02
397 -7.02 112.93 GEGER CAMPOR BANGKALAN 8 0.04
398 -7.05 113.09 KONANG BANGKALAN 9 0.08
399 -7.10 112.71 SOCAH BANGKALAN 16 0.25
400 -7.09 112.81 TRAGAH BANGKALAN 12 0.23
401 -6.91 113.17 BANYUATES SAMPANG 5 0.00
402 -7.09 113.27 KEDUNGDUNG SAMPANG 11 0.04
403 -6.90 113.30 KETAPANG SAMPANG 6 0.01
404 -7.11 113.33 OMBEN SAMPANG 11 0.08
405 -7.01 113.31 ROBATAL SAMPANG 11 0.01
406 -7.14 113.28 SAMPANG SAMPANG 10 0.07
407 -6.92 113.46 SOKOBANAH SAMPANG 7 0.04
408 -7.15 113.21 TORJUN SAMPANG 6 0.01
409 -7.17 113.56 GALIS PAMEKASAN 9 0.02
410 -7.14 113.46 KLAMPAR PAMEKASAN 13 0.19
411 -7.13 113.56 LARANGAN PAMEKASAN 12 0.01
412 -7.04 113.56 PAKONG PAMEKASAN 13 0.15
413 -7.04 113.42 PALENGAAN PAMEKASAN 12 0.15
414 -7.16 113.45 SAMIRAN PAMEKASAN 11 0.18
415 -7.22 113.47 TLANAKAN PAMEKASAN 8 0.04
416 -6.90 113.73 AMBUNTEN SUMENEP 11 0.14
417 -6.88 113.92 BATUPUTIH SUMENEP 7 0.00
418 -7.12 113.82 BLUTO SUMENEP 8 0.03
419 -7.05 113.77 DAM JEPUN SUMENEP 13 0.12
420 -6.89 113.86 DASUK SUMENEP 8 0.09
421 -7.05 113.71 GANDING SUMENEP 13 0.18
422 -7.08 113.67 GULUK SUMENEP 13 0.08
423 -7.05 113.90 KALIANGET SUMENEP 10 0.08
424 -7.01 113.85 KEBONAGUNG SUMENEP 11 0.11
425 -7.11 113.69 PERENDUAN SUMENEP 13 0.05
426 -6.97 113.74 ROBARU SUMENEP 12 0.26
427 -7.02 113.87 SUMENEP SUMENEP 11 0.00
139
Appendix 4. List of rain gauges passing missing value test.
CODE LAT LONG GAUGE DISTRICT MVs (%)
1 -8.01 114.36 ALASBULUH BANYUWANGI 3.90
2 -7.93 114.38 BAJULMATI BANYUWANGI 1.41
3 -7.92 114.28 MAELANG BANYUWANGI 1.13
4 -7.96 114.24 PASEWARAN BANYUWANGI 0.84
5 -8.01 114.30 SIDOMULYO BANYUWANGI 0.28
6 -8.11 113.84 AJUNG JEMBER 0.00
7 -8.17 113.65 DAM SEMBAH JEMBER 3.36
8 -8.30 113.59 GLUNDENGAN JEMBER 7.23
9 -8.23 113.80 KARANG KEDAWUH JEMBER 0.00
10 -8.11 113.91 LEDOKOMBO JEMBER 0.00
11 -8.39 113.54 LOJEJER JEMBER 7.22
12 -8.21 113.67 RENES JEMBER 6.29
13 -8.05 113.83 SUKOWONO JEMBER 0.00
14 -8.06 113.93 SUMBERJAMBE JEMBER 2.99
15 -8.18 113.72 WIROLEGI JEMBER 6.89
16 -7.79 113.34 ADIBOYO PROBOLINGGO 0.57
17 -7.74 113.14 BAYEMAN PROBOLINGGO 1.13
18 -7.86 113.12 BOTOGARDU PROBOLINGGO 0.85
19 -7.83 113.07 LUMBANG PROBOLINGGO 1.12
20 -7.62 112.77 BANGIL PASURUAN 0.00
21 -7.62 112.76 BEKACAK PASURUAN 0.27
22 -7.59 112.70 GEMPOL PASURUAN 3.34
23 -7.70 112.67 JAWI PASURUAN 0.81
24 -7.59 112.68 JEMBRUNG PASURUAN 3.50
25 -7.68 112.93 KAWISREJO PASURUAN 3.61
26 -7.71 112.96 KEDAWUNG PASURUAN 4.73
27 -7.62 112.70 KEPULUNGAN PASURUAN 0.27
28 -7.64 112.88 PEGI PASURUAN 1.10
29 -7.72 112.64 PRIGEN PASURUAN 2.76
30 -7.62 112.71 RANDUPITU PASURUAN 0.27
31 -7.72 113.01 RANUGRATI PASURUAN 6.36
32 -8.30 112.56 BANTUR MALANG 4.41
33 -8.10 112.60 BLAMBANGAN MALANG 7.87
34 -8.07 112.64 BULULAWANG MALANG 1.54
35 -8.20 112.73 DAMPIT MALANG 0.56
36 -8.16 112.61 GONDANG LEGI MALANG 3.74
37 -8.20 112.45 KALIPARE MALANG 6.03
38 -7.98 112.65 KDKANDANG MALANG 9.28
39 -8.13 112.56 KEPANJEN MALANG 6.37
40 -7.86 112.51 NGAGLIK MALANG 0.27
41 -8.08 112.53 NGAJUM MALANG 3.61
42 -8.03 112.76 PONCOKUSUMO MALANG 0.27
43 -7.92 112.59 SENGKALING MALANG 0.55
44 -7.98 112.61 SUKUN MALANG 9.99
45 -8.06 112.63 TAJIAN MALANG 7.31
46 -7.98 112.75 TUMPANG MALANG 0.27
47 -8.00 112.58 WAGIR MALANG 0.53
140
CODE LAT LONG GAUGE DISTRICT MVs (%)
48 -7.84 111.50 BABADAN PONOROGO 4.14
49 -7.94 111.46 BALUNG PONOROGO 5.25
50 -7.82 111.55 BOLLU PONOROGO 5.03
51 -8.02 111.40 NGILO PONOROGO 6.70
52 -7.84 111.76 PUDAK PONOROGO 4.19
53 -7.86 111.60 PULUNG PONOROGO 7.24
54 -7.97 111.59 SAWOO PONOROGO 9.48
55 -7.89 111.66 SOKO PONOROGO 0.54
56 -7.85 111.39 SUMOROTO PONOROGO 4.19
57 -7.80 111.66 TALUN PONOROGO 4.47
58 -7.75 111.39 LEMBEYAN MAGETAN 3.34
59 -7.67 111.28 NITIKAN MAGETAN 3.61
60 -7.66 111.24 SARANGAN MAGETAN 4.74
61 -7.60 111.42 TINAP MAGETAN 7.51
62 -7.63 111.63 CAU MADIUN 3.37
63 -7.70 111.66 DUNGUS MADIUN 7.24
64 -7.73 111.26 GIRINGAN MADIUN 6.99
65 -7.78 111.54 GOMBAL MADIUN 4.73
66 -7.49 111.70 NOTOPURO MADIUN 4.80
67 -7.56 111.75 SARADAN MADIUN 0.57
68 -7.40 111.14 MANTINGAN NGAWI 5.00
69 -7.42 111.39 NGALE NGAWI 5.16
70 -7.51 111.23 NGRAMBE NGAWI 4.74
71 -7.48 111.18 TRETES NGAWI 7.94
72 -7.38 111.26 WALIKUKUN NGAWI 7.48
73 -7.14 112.12 BAURENO BOJONEGORO 2.80
74 -7.24 112.11 CAWAK BOJONEGORO 0.28
75 -7.27 111.85 DANDER BOJONEGORO 0.55
76 -7.37 112.05 KEDUNG ADEM BOJONEGORO 0.57
77 -7.28 111.91 KLEPEK BOJONEGORO 0.56
78 -7.18 111.82 LERAN BOJONEGORO 0.83
79 -7.25 112.05 MEKURIS BOJONEGORO 0.55
80 -7.18 112.00 SUMBEREJO BOJONEGORO 1.10
81 -7.38 111.89 TRETES BOJONEGORO 2.81
82 -6.99 111.73 BANGILAN TUBAN 4.16
83 -6.84 111.84 BELIKANGET TUBAN 0.55
84 -6.97 111.81 JOJOGAN TUBAN 1.10
85 -6.89 111.63 KEBONHARJO TUBAN 0.83
86 -6.99 111.70 KEJURON TUBAN 0.59
87 -7.05 112.01 MAIBIT TUBAN 2.78
88 -6.95 111.90 MONTONG TUBAN 4.71
89 -6.96 111.71 MUNDRI TUBAN 4.48
90 -7.02 111.83 NGABONGAN TUBAN 1.10
91 -7.05 112.01 RENGEL TUBAN 1.67
92 -6.89 111.71 SENDANG TUBAN 1.11
93 -6.87 111.80 SIMO TUBAN 0.56
94 -7.11 111.96 SOKO TUBAN 0.83
95 -7.01 111.75 SOKOMEDALEM TUBAN 2.36
96 -6.99 111.92 SUMURGUNG TUBAN 1.10
141
CODE LAT LONG GAUGE DISTRICT MVs (%)
97 -6.95 111.97 TEGALREJO TUBAN 1.11
98 -6.93 112.06 TUBAN TUBAN 0.55
99 -7.06 112.17 WIDANG TUBAN 1.11
100 -7.11 112.17 BABAT LAMONGAN 4.17
101 -7.05 112.45 BLAWI LAMONGAN 4.44
102 -7.28 112.13 BLULUK LAMONGAN 6.14
103 -7.02 112.51 KARANGBINANGUN LAMONGAN 0.54
104 -7.20 112.21 KEDUNGPRING LAMONGAN 1.38
105 -7.18 112.35 KEMBANG BAHU LAMONGAN 0.27
106 -7.12 112.43 LAMONGAN LAMONGAN 4.16
107 -7.01 112.28 PANGKATREJO LAMONGAN 0.00
108 -7.10 112.32 SUKODADI LAMONGAN 0.56
109 -7.18 112.58 BENJENG GRESIK 2.77
110 -7.22 112.57 CERME GRESIK 2.21
111 -7.18 112.65 GRESIK GRESIK 2.49
112 -6.98 112.42 LOWAYU GRESIK 3.04
113 -6.99 112.45 MENTARAS GRESIK 2.21
114 -7.07 112.62 TAMBAKOMBO GRESIK 1.93
115 -7.26 112.76 GUBENG SURABAYA 1.94
116 -7.23 112.64 KANDANGAN SURABAYA 1.64
117 -7.33 112.74 KEBONAGUNG SURABAYA 1.90
118 -7.30 112.78 KEPUTIH SURABAYA 8.60
119 -7.29 112.81 LARANGAN SURABAYA 2.76
120 -7.26 112.68 SIMO SURABAYA 4.44
121 -7.33 112.79 WONOREJO-RUNGKUT SURABAYA 1.10
122 -7.42 112.58 KRIAN SIDOARJO 2.50
123 -7.41 112.61 PANOKAWAN SIDOARJO 4.05
124 -7.55 112.71 PORONG SIDOARJO 2.77
125 -7.48 112.57 PRAMBON SIDOARJO 2.20
126 -7.51 112.75 PUTAT SIDOARJO 7.52
127 -7.47 112.73 SIDOARJO SIDOARJO 3.61
128 -7.41 112.72 SRUNI SIDOARJO 3.06
129 -7.54 112.46 TANGUNAN MOJOKERTO 1.66
130 -7.67 112.60 TRAWAS MOJOKERTO 1.94
131 -7.56 112.38 TROWULAN MOJOKERTO 1.66
132 -7.55 112.23 JOMBANG JOMBANG 8.70
133 -7.44 112.29 TAPEN JOMBANG 1.10
134 -6.90 113.30 KETAPANG SAMPANG 9.72
135 -7.14 113.28 SAMPANG SAMPANG 8.87
136 -7.17 113.56 GALIS PAMEKASAN 6.36
137 -6.90 113.73 AMBUNTEN SUMENEP 6.62
138 -7.12 113.82 BLUTO SUMENEP 7.78
139 -7.01 113.85 KEBONAGUNG SUMENEP 1.93
140 -6.97 113.74 ROBARU SUMENEP 3.60
142
Appendix 5. The detail result of homogeneity test. H = homogeneous, B = break,
year indicates the time break occurs.
CODE LAT LONG GAUGE PETIT SNHT BUISHAND NEUMAN CLASS
1 -8.01 114.36 ALASBULUH H H H H USEFUL
2 -7.93 114.38 BAJULMATI H H H B SUSPECT
3 -7.92 114.28 MAELANG H H H H USEFUL
4 -7.96 114.24 PASEWARAN H H H H USEFUL
5 -8.01 114.30 SIDOMULYO H H H H USEFUL
6 -8.11 113.84 AJUNG H H H H USEFUL
7 -8.17 113.65 DAM SEMBAH H B / 1984 B / 1988 H SUSPECT
8 -8.30 113.59 GLUNDENGAN H H H H USEFUL
9 -8.23 113.80 KARANG KEDAWUH H H H H USEFUL
10 -8.11 113.91 LEDOKOMBO H H H H USEFUL
11 -8.39 113.54 LOJEJER H H H H USEFUL
12 -8.21 113.67 RENES H H H H USEFUL
13 -8.05 113.83 SUKOWONO H H H H USEFUL
14 -8.06 113.93 SUMBERJAMBE H H H B SUSPECT
15 -8.18 113.72 WIROLEGI H H H H USEFUL
16 -7.79 113.34 ADIBOYO H H H H USEFUL
17 -7.74 113.14 BAYEMAN H H H H USEFUL
18 -7.86 113.12 BOTOGARDU H H H H USEFUL
19 -7.83 113.07 LUMBANG H H H H USEFUL
20 -7.62 112.77 BANGIL H H H H USEFUL
21 -7.62 112.76 BEKACAK H H H B SUSPECT
22 -7.59 112.70 GEMPOL B / 2001 H H B SUSPECT
23 -7.70 112.67 JAWI H H H H USEFUL
24 -7.59 112.68 JEMBRUNG H H H H USEFUL
25 -7.68 112.93 KAWISREJO H B / 2009 H B SUSPECT
26 -7.71 112.96 KEDAWUNG B / 1992 H H H SUSPECT
27 -7.62 112.70 KEPULUNGAN H H H H USEFUL
28 -7.64 112.88 PEGI H H H H USEFUL
29 -7.72 112.64 PRIGEN H H H H USEFUL
30 -7.62 112.71 RANDUPITU H B / 2009 H H SUSPECT
31 -7.72 113.01 RANUGRATI B / 1987 B / 1986 B / 1987 B SUSPECT
32 -8.30 112.56 BANTUR H H H H USEFUL
33 -8.10 112.60 BLAMBANGAN H H H H USEFUL
34 -8.07 112.64 BULULAWANG H H H H USEFUL
35 -8.20 112.73 DAMPIT H H H H USEFUL
36 -8.16 112.61 GONDANG LEGI H H H H USEFUL
37 -8.20 112.45 KALIPARE H H H H USEFUL
38 -7.98 112.65 KDKANDANG H H H H USEFUL
39 -8.13 112.56 KEPANJEN H H H H USEFUL
40 -7.86 112.51 NGAGLIK H B / 2009 H H SUSPECT
41 -8.08 112.53 NGAJUM H H H H USEFUL
42 -8.03 112.76 PONCOKUSUMO H H H H USEFUL
43 -7.92 112.59 SENGKALING H B / 2009 H B SUSPECT
44 -7.98 112.61 SUKUN H B / 2009 H H SUSPECT
45 -8.06 112.63 TAJIAN H B / 2009 H H SUSPECT
46 -7.98 112.75 TUMPANG H B / 2009 H H SUSPECT
143
CODE LAT LONG GAUGE PETIT SNHT BUISHAND NEUMAN CLASS
47 -8.00 112.58 WAGIR B / 1997 H B / 2004 H SUSPECT
48 -7.84 111.50 BABADAN H H H H USEFUL
49 -7.94 111.46 BALUNG H H H H USEFUL
50 -7.82 111.55 BOLLU H H H H USEFUL
51 -8.02 111.40 NGILO H H H H USEFUL
52 -7.84 111.76 PUDAK H H H H USEFUL
53 -7.86 111.60 PULUNG H H H H USEFUL
54 -7.97 111.59 SAWOO B / 1997 H B / 1997 B SUSPECT
55 -7.89 111.66 SOKO H B / 2009 H H SUSPECT
56 -7.85 111.39 SUMOROTO H H H H USEFUL
57 -7.80 111.66 TALUN H B / 2009 H H SUSPECT
58 -7.75 111.39 LEMBEYAN H H H H USEFUL
59 -7.67 111.28 NITIKAN B / 1999 H B / 1999 H SUSPECT
60 -7.66 111.24 SARANGAN B / 1996 B / 1996 B / 1996 H SUSPECT
61 -7.60 111.42 TINAP H H H B SUSPECT
62 -7.63 111.63 CAU H H H H USEFUL
63 -7.70 111.66 DUNGUS H H H H USEFUL
64 -7.73 111.26 GIRINGAN H B / 2009 H B SUSPECT
65 -7.78 111.54 GOMBAL H H H B SUSPECT
66 -7.49 111.70 NOTOPURO B / 1996 B / 1996 B / 1996 B SUSPECT
67 -7.56 111.75 SARADAN B / 1995 B / 1995 B / 1995 B SUSPECT
68 -7.40 111.14 MANTINGAN H H H H USEFUL
69 -7.42 111.39 NGALE H H H H USEFUL
70 -7.51 111.23 NGRAMBE H H H H USEFUL
71 -7.48 111.18 TRETES H H H H USEFUL
72 -7.38 111.26 WALIKUKUN H H H H USEFUL
73 -7.14 112.12 BAURENO H H H B SUSPECT
74 -7.24 112.11 CAWAK H H H H USEFUL
75 -7.27 111.85 DANDER H H H B SUSPECT
76 -7.37 112.05 KEDUNG ADEM H H H H USEFUL
77 -7.28 111.91 KLEPEK B / 1994 B / 2009 B / 1994 B SUSPECT
78 -7.18 111.82 LERAN H H H H USEFUL
79 -7.25 112.05 MEKURIS H B / 2009 H H SUSPECT
80 -7.18 112.00 SUMBEREJO H H H H USEFUL
81 -7.38 111.89 TRETES H H H H USEFUL
82 -6.99 111.73 BANGILAN H H H H USEFUL
83 -6.84 111.84 BELIKANGET B / 2001 B / 2001 B / 2001 B SUSPECT
84 -6.97 111.81 JOJOGAN H H H H USEFUL
85 -6.89 111.63 KEBONHARJO H H H H USEFUL
86 -6.99 111.70 KEJURON H H H H USEFUL
87 -7.05 112.01 MAIBIT H B / 2009 H H SUSPECT
88 -6.95 111.90 MONTONG B / 1995 B / 1995 B / 1995 H SUSPECT
89 -6.96 111.71 MUNDRI H H H H USEFUL
90 -7.02 111.83 NGABONGAN H H H H USEFUL
91 -7.05 112.01 RENGEL B / 1989 H B / 1986 B SUSPECT
92 -6.89 111.71 SENDANG H H H H USEFUL
93 -6.87 111.80 SIMO H H H H USEFUL
94 -7.11 111.96 SOKO B / 1989 B / 1986 B / 1989 B SUSPECT
95 -7.01 111.75 SOKOMEDALEM H H H H USEFUL
144
CODE LAT LONG GAUGE PETIT SNHT BUISHAND NEUMAN CLASS
96 -6.99 111.92 SUMURGUNG H H H B SUSPECT
97 -6.95 111.97 TEGALREJO H B / 1984 H H SUSPECT
98 -6.93 112.06 TUBAN H H H H USEFUL
99 -7.06 112.17 WIDANG H H H H USEFUL
100 -7.11 112.17 BABAT H H H H USEFUL
101 -7.05 112.45 BLAWI H H H H USEFUL
102 -7.28 112.13 BLULUK H H H B SUSPECT
103 -7.02 112.51 KARANGBINANGUN H H H H USEFUL
104 -7.20 112.21 KEDUNGPRING H H H H USEFUL
105 -7.18 112.35 KEMBANG BAHU H H H B SUSPECT
106 -7.12 112.43 LAMONGAN H H H H USEFUL
107 -7.01 112.28 PANGKATREJO H H H H USEFUL
108 -7.10 112.32 SUKODADI H H H H USEFUL
109 -7.18 112.58 BENJENG H H H H USEFUL
110 -7.22 112.57 CERME B / 1997 B / 1997 B / 1997 B SUSPECT
111 -7.18 112.65 GRESIK H H H B SUSPECT
112 -6.98 112.42 LOWAYU H H H H USEFUL
113 -6.99 112.45 MENTARAS H B / 2009 H B SUSPECT
114 -7.07 112.62 TAMBAKOMBO H B / 2009 H H SUSPECT
115 -7.26 112.76 GUBENG H B / 2009 H H SUSPECT
116 -7.23 112.64 KANDANGAN H B / 2009 H H SUSPECT
117 -7.33 112.74 KEBONAGUNG H B / 2009 H H SUSPECT
118 -7.30 112.78 KEPUTIH H B / 2007 H B SUSPECT
119 -7.29 112.81 LARANGAN H B / 2007 H H SUSPECT
120 -7.26 112.68 SIMO H H H H USEFUL
121 -7.33 112.79 WONOREJO-
RUNGKUT H H H H USEFUL
122 -7.42 112.58 KRIAN H H H H USEFUL
123 -7.41 112.61 PANOKAWAN H H H H USEFUL
124 -7.55 112.71 PORONG H H H B SUSPECT
125 -7.48 112.57 PRAMBON H H H H USEFUL
126 -7.51 112.75 PUTAT B / 1989 B / 1989 B / 1989 B SUSPECT
127 -7.47 112.73 SIDOARJO H H H H USEFUL
128 -7.41 112.72 SRUNI H H H H USEFUL
129 -7.54 112.46 TANGUNAN B / 2001 B / 2001 B / 2001 B SUSPECT
130 -7.67 112.60 TRAWAS B / 1990 B / 1990 B / 1990 B SUSPECT
131 -7.56 112.38 TROWULAN H H H B SUSPECT
132 -7.55 112.23 JOMBANG B / 1998 H B / 1998 H SUSPECT
133 -7.44 112.29 TAPEN B / 1996 B / 1996 B / 1996 B SUSPECT
134 -6.90 113.30 KETAPANG B / 1994 B / 1993 B / 1994 B SUSPECT
135 -7.14 113.28 SAMPANG B / 1991 H B / 1997 H SUSPECT
136 -7.17 113.56 GALIS H H H H USEFUL
137 -6.90 113.73 AMBUNTEN H H H H USEFUL
138 -7.12 113.82 BLUTO H H H H USEFUL
139 -7.01 113.85 KEBONAGUNG H H H H USEFUL
140 -6.97 113.74 ROBARU H H H H USEFUL
145
Appendix 6.1. The scatter plot of index R20mm versus log of elevation (Source:
data processing)
Appendix 6.2. The scatter plot of index R50mm versus log of elevation (Source:
data processing)
146
Appendix 6.3. The scatter plot of index R90p versus log of elevation (Source:
data processing)
Appendix 6.4. The scatter plot of index RX1d versus log of elevation (Source:
data processing)
147
Appendix 6.5. The scatter plot of index RX5d versus log of elevation (Source:
data processing)
Appendix 6.6. The scatter plot of index RTOT versus log of elevation (Source:
data processing)
148
Appendix 6.7. The scatter plot of index SDII versus log of elevation (Source:
data processing)
149
Appendix 7-1. The output of trend assessment for daily rainfall event exceeding 20 mm (R20mm) presented per gauge. Bold characters
indicate maximum and minimum magnitude (Source: data processing).
CODE GAUGE DISTRICT Z Sig m b CODE GAUGE DISTRICT Z Sig m b
1 ALASBULUH BANYUWANGI 0.4602
0.0526 16.3684 43 MANTINGAN NGAWI 0.1636
0.0513 35.5895 2 MAELANG BANYUWANGI -0.5757
-0.0833 29.5833 44 NGALE NGAWI -0.9886
-0.1500 34.7000
3 PASEWARAN BANYUWANGI -0.3552
-0.0714 41.9286 45 NGRAMBE NGAWI -0.2340
-0.0635 35.0635 4 SIDOMULYO BANYUWANGI 0.2256
0.0513 21.7171 46 TRETES NGAWI -2.3806 * -0.6111 44.1389
5 AJUNG JEMBER 0.5022
0.0588 40.5294 47 WALIKUKUN NGAWI 1.3346
0.2679 34.1964 6 GLUNDENGAN JEMBER 2.0308 * 0.4167 17.5417 48 CAWAK BOJONEGORO -1.3721
-0.2500 34.7500
7 KARANG KEDAWUH JEMBER -1.4676
-0.3750 38.8125 49 KEDUNG ADEM BOJONEGORO -0.0753
0.0000 33.0000 8 LEDOKOMBO JEMBER -0.6083
-0.1111 39.3889 50 LERAN BOJONEGORO -2.8405 ** -0.6667 43.3333
9 LOJEJER JEMBER 1.1529
0.2000 17.8000 51 SUMBEREJO BOJONEGORO -0.5641
-0.1667 31.5000 10 RENES JEMBER 0.2732
0.0972 38.7708 52 TRETES BOJONEGORO -1.1031
-0.2361 41.4861
11 SUKOWONO JEMBER 2.7374 ** 0.5000 29.0000 53 BANGILAN TUBAN 0.7303
0.1500 24.9000 12 WIROLEGI JEMBER -2.4727 * -0.5500 41.0500 54 JOJOGAN TUBAN -0.8854
-0.2222 33.7778
13 ADIBOYO PROBOLINGGO -0.2072
0.0000 20.0000 55 KEBONHARJO TUBAN 1.2955
0.2222 22.6667 14 BAYEMAN PROBOLINGGO 0.5442
0.0556 18.9444 56 KEJURON TUBAN -1.6684 + -0.2087 28.7087
15 BOTOGARDU PROBOLINGGO -1.3259
-0.4000 50.5000 57 MUNDRI TUBAN -0.3290
-0.0217 28.5217 16 LUMBANG PROBOLINGGO -0.1784
0.0000 34.5000 58 NGABONGAN TUBAN 0.0000
0.0000 32.0000
17 BANGIL PASURUAN 1.6806 + 0.3529 27.2353 59 SENDANG TUBAN -1.7477 + -0.3333 35.0000 18 JAWI PASURUAN 0.3551
0.0625 43.7500 60 SIMO TUBAN -1.6057
-0.3038 26.8423
19 JEMBRUNG PASURUAN -1.2114
-0.2500 36.7500 61 SOKOMEDALEM TUBAN 0.0000
0.0000 26.0000 20 KEPULUNGAN PASURUAN -1.3742
-0.3431 39.5784 62 TUBAN TUBAN 1.8820 + 0.3153 18.1392
21 PEGI PASURUAN -1.1715
-0.1667 21.3333 63 WIDANG TUBAN -0.5523
-0.1364 29.6818 22 PRIGEN PASURUAN 1.3802
0.3828 47.3205 64 BABAT LAMONGAN -2.3402 * -0.7143 48.2143
23 BANTUR MALANG 0.1045
0.0000 29.0000 65 BLAWI LAMONGAN -0.9140
-0.2042 30.4083 24 BLAMBANGAN MALANG -0.4677
-0.1307 41.6136 66 KARANGBINANGUN LAMONGAN -0.9533
-0.1250 27.8125
25 BULULAWANG MALANG -0.1548
0.0000 37.5000 67 KEDUNGPRING LAMONGAN -0.9194
-0.1364 34.7727 26 DAMPIT MALANG 0.8525
0.2566 28.7533 68 LAMONGAN LAMONGAN 1.0448
0.2500 24.0000
27 GONDANG LEGI MALANG 0.6142
0.1111 36.3889 69 PANGKATREJO LAMONGAN -1.1829
-0.1765 28.0588 28 KALIPARE MALANG -0.7150
-0.1111 29.0000 70 SUKODADI LAMONGAN -0.9317
-0.1213 28.1838
29 KDKANDANG MALANG -0.2486
-0.0812 35.1234 71 BENJENG GRESIK -2.0643 * -0.3186 34.4779 30 KEPANJEN MALANG 0.9828
0.3229 35.2500 72 LOWAYU GRESIK 0.4479
0.0961 25.4039
31 NGAJUM MALANG -0.1780
-0.0572 36.9575 73 SIMO SURABAYA 3.1566 ** 0.7399 23.8607 32 PONCOKUSUMO MALANG 1.6543 + 0.4667 37.7333 74 WONOREJO-RUNGKUT SURABAYA 1.0902
0.2071 32.0500
33 BABADAN PONOROGO -1.3791
-0.2667 31.0000 75 KRIAN SIDOARJO -0.7267
-0.1962 35.7077 34 BALUNG PONOROGO -0.2759
0.0000 25.0000 76 PANOKAWAN SIDOARJO -0.4502
-0.0714 36.0000
35 BOLLU PONOROGO 0.1494
0.0000 28.0000 77 PRAMBON SIDOARJO 0.2877
0.0385 33.6923 36 NGILO PONOROGO -2.2514 * -0.4286 34.4286 78 SIDOARJO SIDOARJO 1.1928
0.2222 30.3333
37 PUDAK PONOROGO -0.2867
-0.1429 47.9286 79 SRUNI SIDOARJO -0.1743
0.0000 36.5000 38 PULUNG PONOROGO -0.0441
-0.0400 34.2600 80 GALIS PAMEKASAN -0.1407
0.0000 20.0000
39 SUMOROTO PONOROGO -1.4441
-0.2222 24.4444 81 AMBUNTEN SUMENEP -1.5758
-0.3452 28.6667 40 LEMBEYAN MAGETAN -1.1865
-0.1667 28.6667 82 BLUTO SUMENEP -1.2876
-0.2601 25.5419
41 CAU MADIUN -0.9409
-0.1429 36.7143 83 KEBONAGUNG SUMENEP -1.0345
-0.2143 28.7857 42 DUNGUS MADIUN -1.5032
-0.4000 39.8000 84 ROBARU SUMENEP 1.0050
0.2174 25.5652
150
Appendix 7-2. The output of trend assessment for daily rainfall event exceeding 50 mm (R50mm) presented per gauge. Bold characters
indicate maximum and minimum magnitude (Source: data processing).
CODE GAUGE DISTRICT Z Sig m b CODE GAUGE DISTRICT Z Sig m b
1 ALASBULUH BANYUWANGI 0.2108
0.0000 4.0000 43 MANTINGAN NGAWI 0.5244
0.0000 9.0000 2 MAELANG BANYUWANGI -0.4002
0.0000 6.0000 44 NGALE NGAWI -2.4151 * -0.1319 10.1101
3 PASEWARAN BANYUWANGI 1.4657
0.1852 11.4074 45 NGRAMBE NGAWI -2.4785 * -0.2354 9.1200 4 SIDOMULYO BANYUWANGI 0.3592
0.0000 6.0000 46 TRETES NGAWI -1.8893 + -0.1667 11.5000
5 AJUNG JEMBER 1.0599
0.1000 7.2000 47 WALIKUKUN NGAWI 1.5541
0.1366 8.9068 6 GLUNDENGAN JEMBER 1.7113 + 0.1667 1.9167 48 CAWAK BOJONEGORO -1.1332
-0.1181 10.5972
7 KARANG KEDAWUH JEMBER -2.9457 ** -0.2222 10.6667 49 KEDUNG ADEM BOJONEGORO -1.2852
-0.1091 10.6548 8 LEDOKOMBO JEMBER 0.4309
0.0000 8.0000 50 LERAN BOJONEGORO -3.4990 *** -0.5000 18.5000
9 LOJEJER JEMBER 1.6072
0.1000 1.7000 51 SUMBEREJO BOJONEGORO -0.9030
-0.0714 7.6429 10 RENES JEMBER -0.1505
0.0000 9.0000 52 TRETES BOJONEGORO 0.5667
0.0000 8.0000
11 SUKOWONO JEMBER 1.7543 + 0.1111 5.5556 53 BANGILAN TUBAN 2.4083 * 0.1154 3.5769 12 WIROLEGI JEMBER -1.9397 + -0.1333 8.7000 54 JOJOGAN TUBAN -0.1780
0.0000 7.0000
13 ADIBOYO PROBOLINGGO -0.3029
0.0000 5.0000 55 KEBONHARJO TUBAN 2.1271 * 0.1429 3.0000 14 BAYEMAN PROBOLINGGO 0.1471
0.0000 5.0000 56 KEJURON TUBAN 0.1002
0.0000 5.0000
15 BOTOGARDU PROBOLINGGO -0.0994
0.0000 10.5000 57 MUNDRI TUBAN 2.1691 * 0.1250 4.2500 16 LUMBANG PROBOLINGGO -0.9348
-0.0774 11.3929 58 NGABONGAN TUBAN 0.8230
0.0588 6.0588
17 BANGIL PASURUAN 3.1186 ** 0.3704 4.7963 59 SENDANG TUBAN -2.5160 * -0.1667 8.3333 18 JAWI PASURUAN 1.5990
0.1500 12.5500 60 SIMO TUBAN -0.9816
-0.0488 5.2446
19 JEMBRUNG PASURUAN 0.0210
0.0000 11.0000 61 SOKOMEDALEM TUBAN -0.5352
0.0000 5.0000 20 KEPULUNGAN PASURUAN -1.0555
-0.1156 10.6178 62 TUBAN TUBAN 1.6040
0.0871 3.4337
21 PEGI PASURUAN -0.9154
0.0000 5.0000 63 WIDANG TUBAN 0.4221
0.0000 5.0000 22 PRIGEN PASURUAN 1.5225
0.2808 12.4384 64 BABAT LAMONGAN -1.9954 * -0.5000 18.5000
23 BANTUR MALANG 0.8592
0.0833 5.6667 65 BLAWI LAMONGAN 0.2819
0.0000 7.0000 24 BLAMBANGAN MALANG -0.7744
-0.0769 10.6923 66 KARANGBINANGUN LAMONGAN 0.3812
0.0000 5.0000
25 BULULAWANG MALANG -2.2685 * -0.1667 11.0833 67 KEDUNGPRING LAMONGAN -0.3994
0.0000 8.0000 26 DAMPIT MALANG 1.5712
0.1667 6.0833 68 LAMONGAN LAMONGAN -0.0212
0.0000 4.0000
27 GONDANG LEGI MALANG 0.1202
0.0000 9.0000 69 PANGKATREJO LAMONGAN 0.6143
0.0000 5.0000 28 KALIPARE MALANG 0.1075
0.0000 6.0000 70 SUKODADI LAMONGAN -0.9410
-0.0541 6.0139
29 KDKANDANG MALANG -0.5993
-0.0690 9.2774 71 BENJENG GRESIK -1.1323
-0.0500 7.1500 30 KEPANJEN MALANG 0.1644
0.0000 9.0000 72 LOWAYU GRESIK 0.4250
0.0000 5.0000
31 NGAJUM MALANG -0.3832
0.0000 8.5000 73 SIMO SURABAYA 2.7014 ** 0.2829 5.3029 32 PONCOKUSUMO MALANG -0.6297
0.0000 6.0000 74 WONOREJO-RUNGKUT SURABAYA -0.3988
0.0000 9.0000
33 BABADAN PONOROGO -0.1056
0.0000 5.0000 75 KRIAN SIDOARJO -1.0343
-0.1053 9.3158 34 BALUNG PONOROGO 0.0000
0.0000 4.5000 76 PANOKAWAN SIDOARJO 0.1601
0.0000 10.0000
35 BOLLU PONOROGO 0.3020
0.0000 5.0000 77 PRAMBON SIDOARJO -0.0667
0.0000 10.0000 36 NGILO PONOROGO -0.9886
-0.0556 6.3333 78 SIDOARJO SIDOARJO -0.3995
0.0000 9.0000
37 PUDAK PONOROGO -0.3764
-0.0400 10.7000 79 SRUNI SIDOARJO -0.6761
-0.0513 11.2059 38 PULUNG PONOROGO -0.3126
0.0000 6.5000 80 GALIS PAMEKASAN -1.1741
-0.1000 4.9000
39 SUMOROTO PONOROGO -0.6896
-0.0385 5.0769 81 AMBUNTEN SUMENEP -0.5191
-0.0208 4.5208 40 LEMBEYAN MAGETAN 0.6801
0.0541 4.8099 82 BLUTO SUMENEP -2.9080 ** -0.1429 4.5714
41 CAU MADIUN -1.1206
-0.0667 9.7333 83 KEBONAGUNG SUMENEP -2.2690 * -0.1500 7.9000 42 DUNGUS MADIUN -1.6336
-0.2000 10.8000 84 ROBARU SUMENEP 1.4376
0.1000 4.8000
151
Appendix 7-3. The output of trend assessment for daily rainfall event exceeding 90th
percentile (RI90p) presented per gauge. Bold characters
indicate maximum and minimum magnitude (Source: data processing).
CODE GAUGE DISTRICT Z Sig m b CODE GAUGE DISTRICT Z Sig m b
1 ALASBULUH BANYUWANGI -0.2762
0.0000 2.0000 43 MANTINGAN NGAWI 0.0954
0.0000 5.0000 2 MAELANG BANYUWANGI -0.0226
0.0000 3.0000 44 NGALE NGAWI -1.2674
-0.0625 6.5000
3 PASEWARAN BANYUWANGI -0.2940
0.0000 4.0000 45 NGRAMBE NGAWI -2.2129 * -0.2124 8.5226 4 SIDOMULYO BANYUWANGI 0.5007
0.0000 2.0000 46 TRETES NGAWI -0.9639
-0.1000 6.7500
5 AJUNG JEMBER 0.1803
0.0000 6.0000 47 WALIKUKUN NGAWI 1.0837
0.0755 3.0940 6 GLUNDENGAN JEMBER 1.7113 + 0.1667 1.9167 48 CAWAK BOJONEGORO -1.2946
-0.0714 4.3571
7 KARANG KEDAWUH JEMBER -2.3778 * -0.1250 6.3750 49 KEDUNG ADEM BOJONEGORO -0.8565
-0.0185 5.2222 8 LEDOKOMBO JEMBER -0.0360
0.0000 5.0000 50 LERAN BOJONEGORO -4.2293 *** -0.3077 8.9231
9 LOJEJER JEMBER 1.2305
0.0625 1.8750 51 SUMBEREJO BOJONEGORO -0.6969
0.0000 5.0000 10 RENES JEMBER -0.3508
0.0000 5.5000 52 TRETES BOJONEGORO -1.2136
-0.0501 5.2506
11 SUKOWONO JEMBER 1.2788
0.0714 4.4643 53 BANGILAN TUBAN 2.3149 * 0.1429 2.8571 12 WIROLEGI JEMBER -2.2704 * -0.2000 9.1000 54 JOJOGAN TUBAN 0.3824
0.0000 4.0000
13 ADIBOYO PROBOLINGGO 0.2655
0.0000 3.0000 55 KEBONHARJO TUBAN 2.0886 * 0.0769 1.2308 14 BAYEMAN PROBOLINGGO 0.3446
0.0000 3.0000 56 KEJURON TUBAN 0.3413
0.0000 4.5000
15 BOTOGARDU PROBOLINGGO 1.1400
0.0670 2.0603 57 MUNDRI TUBAN 2.1691 * 0.1250 4.2500 16 LUMBANG PROBOLINGGO -0.8606
-0.0501 5.4273 58 NGABONGAN TUBAN 0.9636
0.0400 3.9600
17 BANGIL PASURUAN 3.7432 *** 0.2105 1.8947 59 SENDANG TUBAN -1.7163 + -0.1111 6.0556 18 JAWI PASURUAN 1.4719
0.1111 4.4444 60 SIMO TUBAN -1.2988
-0.0714 4.7143
19 JEMBRUNG PASURUAN -0.4894
0.0000 5.0000 61 SOKOMEDALEM TUBAN -0.4287
0.0000 3.0000 20 KEPULUNGAN PASURUAN -0.9505
-0.0471 5.2357 62 TUBAN TUBAN 2.2450 * 0.1000 1.5500
21 PEGI PASURUAN -0.9498
-0.0714 4.8571 63 WIDANG TUBAN 0.5125
0.0000 5.0000 22 PRIGEN PASURUAN 1.9333 + 0.1429 4.4286 64 BABAT LAMONGAN -2.6236 ** -0.2857 8.3571 23 BANTUR MALANG 0.8761
0.0000 3.0000 65 BLAWI LAMONGAN 0.6363
0.0426 3.7871
24 BLAMBANGAN MALANG -0.1177
0.0000 5.0000 66 KARANGBINANGUN LAMONGAN 0.6021
0.0000 4.0000 25 BULULAWANG MALANG -2.6786 ** -0.1429 7.3571 67 KEDUNGPRING LAMONGAN -0.9860
-0.0769 6.3077
26 DAMPIT MALANG 2.2262 * 0.1667 2.5000 68 LAMONGAN LAMONGAN 0.2760
0.0000 5.0000 27 GONDANG LEGI MALANG -0.4021
0.0000 5.0000 69 PANGKATREJO LAMONGAN 1.1550
0.0000 4.0000
28 KALIPARE MALANG 0.0000
0.0000 5.0000 70 SUKODADI LAMONGAN -0.6798
0.0000 5.0000 29 KDKANDANG MALANG -1.1554
-0.1250 7.1875 71 BENJENG GRESIK -2.1479 * -0.0785 4.2454
30 KEPANJEN MALANG 0.5665
0.0000 4.0000 72 LOWAYU GRESIK 0.2792
0.0000 2.5000 31 NGAJUM MALANG -0.2003
0.0000 5.5000 73 SIMO SURABAYA 2.3527 * 0.1863 1.2546
32 PONCOKUSUMO MALANG -0.6839
0.0000 7.0000 74 WONOREJO-RUNGKUT SURABAYA -2.0133 * -0.0909 6.3182 33 BABADAN PONOROGO -0.1496
0.0000 4.0000 75 KRIAN SIDOARJO -1.3198
-0.0976 6.2929
34 BALUNG PONOROGO 0.0000
0.0000 4.5000 76 PANOKAWAN SIDOARJO 0.0000
0.0000 5.0000 35 BOLLU PONOROGO 0.7769
0.0541 4.0804 77 PRAMBON SIDOARJO 0.9763
0.0833 2.8750
36 NGILO PONOROGO -0.5070
0.0000 4.0000 78 SIDOARJO SIDOARJO 0.1607
0.0000 4.0000 37 PUDAK PONOROGO -0.1780
0.0000 7.0000 79 SRUNI SIDOARJO 0.8382
0.0000 4.0000
38 PULUNG PONOROGO -0.3567
0.0000 5.0000 80 GALIS PAMEKASAN -0.5735
0.0000 3.0000 39 SUMOROTO PONOROGO -0.3348
0.0000 4.0000 81 AMBUNTEN SUMENEP -0.6735
-0.0371 3.4451
40 LEMBEYAN MAGETAN 1.1402
0.0541 2.9722 82 BLUTO SUMENEP -2.9236 ** -0.1752 5.2263 41 CAU MADIUN -0.4066
0.0000 5.0000 83 KEBONAGUNG SUMENEP -2.5818 ** -0.1304 5.4783
42 DUNGUS MADIUN -1.6608 + -0.1111 6.4444 84 ROBARU SUMENEP 1.0733
0.0714 2.0000
152
Appendix 7-4. The output of trend assessment for maximum length of consecutive wet day (CWD) presented per gauge. Bold characters
indicate maximum and minimum magnitude (Source: data processing).
CODE GAUGE DISTRICT Z Sig m b CODE GAUGE DISTRICT Z Sig m b
1 ALASBULUH BANYUWANGI -0.5085
0.0000 6.0000 43 MANTINGAN NGAWI -0.6838
-0.0392 9.5100 2 MAELANG BANYUWANGI 2.6931 ** 0.2667 5.2667 44 NGALE NGAWI -0.1427
0.0000 9.0000
3 PASEWARAN BANYUWANGI 1.0083
0.0909 11.5455 45 NGRAMBE NGAWI 0.8705
0.0690 11.4119 4 SIDOMULYO BANYUWANGI 0.1342
0.0000 7.0000 46 TRETES NGAWI 0.0284
0.0000 10.5000
5 AJUNG JEMBER -0.3981
0.0000 11.5000 47 WALIKUKUN NGAWI -0.4017
0.0000 8.0000 6 GLUNDENGAN JEMBER -1.6120
-0.0769 10.1923 48 CAWAK BOJONEGORO 0.8935
0.0000 6.0000
7 KARANG KEDAWUH JEMBER 2.2352 * 0.1667 6.3333 49 KEDUNG ADEM BOJONEGORO -0.5906
0.0000 7.0000 8 LEDOKOMBO JEMBER -0.1264
0.0000 9.0000 50 LERAN BOJONEGORO 0.3619
0.0000 7.0000
9 LOJEJER JEMBER -0.6790
0.0000 6.0000 51 SUMBEREJO BOJONEGORO -0.5074
0.0000 7.0000 10 RENES JEMBER 0.3247
0.0000 10.0000 52 TRETES BOJONEGORO -0.4043
0.0000 8.0000
11 SUKOWONO JEMBER 1.5500
0.1333 10.0667 53 BANGILAN TUBAN -0.3560
0.0000 6.0000 12 WIROLEGI JEMBER 0.1557
0.0000 10.0000 54 JOJOGAN TUBAN -1.8093 + -0.0833 8.8750
13 ADIBOYO PROBOLINGGO -1.6501 + -0.0625 8.3750 55 KEBONHARJO TUBAN 1.5130
0.0625 5.2500 14 BAYEMAN PROBOLINGGO -0.2125
0.0000 6.0000 56 KEJURON TUBAN -0.9099
0.0000 7.0000
15 BOTOGARDU PROBOLINGGO -0.3968
-0.0477 11.9784 57 MUNDRI TUBAN -0.4804
0.0000 7.0000 16 LUMBANG PROBOLINGGO -1.1722
-0.1111 12.8889 58 NGABONGAN TUBAN -0.9584
0.0000 6.0000
17 BANGIL PASURUAN -2.1391 * -0.1250 10.2500 59 SENDANG TUBAN -1.7881 + -0.1000 8.0500 18 JAWI PASURUAN 1.4273
0.1429 11.1429 60 SIMO TUBAN -0.9045
0.0000 6.0000
19 JEMBRUNG PASURUAN -1.0313
-0.1053 12.1053 61 SOKOMEDALEM TUBAN -1.3277
-0.0455 6.4091 20 KEPULUNGAN PASURUAN -2.4695 * -0.2500 14.5000 62 TUBAN TUBAN -1.3213
-0.0667 6.5333
21 PEGI PASURUAN -0.6353
0.0000 8.0000 63 WIDANG TUBAN 0.3129
0.0000 7.0000 22 PRIGEN PASURUAN 1.9674 * 0.3846 16.0000 64 BABAT LAMONGAN 1.8844 + 0.0870 4.8043 23 BANTUR MALANG -2.3698 * -0.1111 7.2222 65 BLAWI LAMONGAN 0.6481
0.0000 7.0000
24 BLAMBANGAN MALANG 2.2179 * 0.2265 6.8846 66 KARANGBINANGUN LAMONGAN -2.0285 * -0.0513 6.9493 25 BULULAWANG MALANG 0.8464
0.0667 9.0667 67 KEDUNGPRING LAMONGAN -0.4252
0.0000 7.0000
26 DAMPIT MALANG -0.8373
-0.0477 11.1682 68 LAMONGAN LAMONGAN -2.0764 * -0.1000 8.7000 27 GONDANG LEGI MALANG -0.8170
-0.0646 8.9688 69 PANGKATREJO LAMONGAN 0.4526
0.0000 7.0000
28 KALIPARE MALANG 2.2031 * 0.1818 7.0909 70 SUKODADI LAMONGAN 0.6587
0.0000 8.0000 29 KDKANDANG MALANG -0.6045
0.0000 11.5000 71 BENJENG GRESIK 0.0000
0.0000 6.5000
30 KEPANJEN MALANG 0.5638
0.0690 10.2738 72 LOWAYU GRESIK 2.7114 ** 0.0785 4.5585 31 NGAJUM MALANG -0.8943
-0.0670 12.7746 73 SIMO SURABAYA 1.2777
0.0769 5.9615
32 PONCOKUSUMO MALANG 0.0944
0.0000 13.0000 74 WONOREJO-RUNGKUT SURABAYA -1.3820
-0.0667 8.9333 33 BABADAN PONOROGO -1.0594
-0.0455 8.2727 75 KRIAN SIDOARJO -2.0598 * -0.1111 11.2222
34 BALUNG PONOROGO -1.0028
-0.0646 9.7917 76 PANOKAWAN SIDOARJO -1.7095 + -0.1176 10.9412 35 BOLLU PONOROGO 1.1252
0.0955 9.2591 77 PRAMBON SIDOARJO -2.5885 ** -0.1154 9.3077
36 NGILO PONOROGO 0.0000
0.0000 8.0000 78 SIDOARJO SIDOARJO -1.6490 + -0.1429 11.2857 37 PUDAK PONOROGO 1.1078
0.1053 9.3684 79 SRUNI SIDOARJO -0.3266
0.0000 7.0000
38 PULUNG PONOROGO 1.7311 + 0.1333 10.1667 80 GALIS PAMEKASAN -0.2145
0.0000 6.0000 39 SUMOROTO PONOROGO 0.7167
0.0000 7.0000 81 AMBUNTEN SUMENEP -0.2764
0.0000 6.0000
40 LEMBEYAN MAGETAN -0.7971
-0.0403 6.9662 82 BLUTO SUMENEP 0.8470
0.0000 4.0000 41 CAU MADIUN -2.3165 * -0.1111 9.8889 83 KEBONAGUNG SUMENEP 1.6425
0.0769 4.4615
42 DUNGUS MADIUN -1.8377 + -0.1250 10.6250 84 ROBARU SUMENEP 0.0000
0.0000 6.0000
153
Appendix 7-5. The output of trend assessment for maximum daily rainfall (RX1d) presented per gauge. Bold characters refer to maximum and
minimum magnitude (Source: data processing).
CODE GAUGE DISTRICT Z Sig m b CODE GAUGE DISTRICT Z Sig m b
1 ALASBULUH BANYUWANGI -1.003
-0.600 88.200 43 MANTINGAN NGAWI -0.351
-0.331 121.994 2 MAELANG BANYUWANGI -1.501
-0.941 114.735 44 NGALE NGAWI 0.070
0.000 105.000
3 PASEWARAN BANYUWANGI -3.379 *** -2.500 166.500 45 NGRAMBE NGAWI -1.309
-1.149 101.299 4 SIDOMULYO BANYUWANGI 1.089
0.935 72.646 46 TRETES NGAWI 0.085
0.071 97.964
5 AJUNG JEMBER 0.375
0.200 99.700 47 WALIKUKUN NGAWI -0.934
-0.391 108.332 6 GLUNDENGAN JEMBER -0.662
-0.417 90.542 48 CAWAK BOJONEGORO -1.628
-0.326 85.212
7 KARANG KEDAWUH JEMBER -1.842 + -0.619 101.143 49 KEDUNG ADEM BOJONEGORO 0.357
0.174 104.038 8 LEDOKOMBO JEMBER 2.129 * 0.643 83.179 50 LERAN BOJONEGORO -3.988 *** -2.455 134.273 9 LOJEJER JEMBER 1.942 + 0.846 65.923 51 SUMBEREJO BOJONEGORO -0.292
-0.345 100.103
10 RENES JEMBER -0.423
-0.121 94.895 52 TRETES BOJONEGORO -2.105 * -1.056 114.000 11 SUKOWONO JEMBER -0.447
-0.273 101.364 53 BANGILAN TUBAN 0.243
0.125 86.375
12 WIROLEGI JEMBER -0.419
-0.200 93.800 54 JOJOGAN TUBAN 1.147
0.455 89.318 13 ADIBOYO PROBOLINGGO -0.169
-0.075 96.057 55 KEBONHARJO TUBAN 0.000
0.000 85.000
14 BAYEMAN PROBOLINGGO 1.378
0.650 84.050 56 KEJURON TUBAN 0.732
0.310 79.019 15 BOTOGARDU PROBOLINGGO -0.139
-0.033 91.817 57 MUNDRI TUBAN 0.585
0.567 73.100
16 LUMBANG PROBOLINGGO 2.207 * 0.594 94.341 58 NGABONGAN TUBAN 0.994
0.200 88.900 17 BANGIL PASURUAN 2.110 * 0.714 96.857 59 SENDANG TUBAN -0.088
-0.100 92.950
18 JAWI PASURUAN 0.772
0.500 86.000 60 SIMO TUBAN 0.079
0.025 94.000 19 JEMBRUNG PASURUAN -1.691 + -0.667 108.667 61 SOKOMEDALEM TUBAN -0.767
-0.429 90.857
20 KEPULUNGAN PASURUAN -1.051
-0.385 114.231 62 TUBAN TUBAN -0.870
-0.333 87.833 21 PEGI PASURUAN -1.147
-0.852 100.889 63 WIDANG TUBAN -0.442
-0.154 87.385
22 PRIGEN PASURUAN 2.313 * 1.842 91.950 64 BABAT LAMONGAN -2.692 ** -1.267 126.367 23 BANTUR MALANG 1.691 + 1.375 78.750 65 BLAWI LAMONGAN -0.047
-0.033 96.967
24 BLAMBANGAN MALANG 0.070
0.000 80.000 66 KARANGBINANGUN LAMONGAN 0.652
0.345 85.310 25 BULULAWANG MALANG -0.551
-0.400 97.100 67 KEDUNGPRING LAMONGAN -0.563
-0.304 94.348
26 DAMPIT MALANG 1.661 + 1.500 78.500 68 LAMONGAN LAMONGAN 0.522
0.391 67.957 27 GONDANG LEGI MALANG -0.125
-0.143 97.429 69 PANGKATREJO LAMONGAN 2.111 * 0.583 78.500
28 KALIPARE MALANG 0.555
0.318 84.591 70 SUKODADI LAMONGAN -0.773
-0.215 86.153 29 KDKANDANG MALANG -0.714
-0.500 105.500 71 BENJENG GRESIK -1.068
-0.418 94.984
30 KEPANJEN MALANG 1.265
0.708 90.333 72 LOWAYU GRESIK 1.938 + 0.726 79.744 31 NGAJUM MALANG 1.504
0.555 87.118 73 SIMO SURABAYA 1.391
1.000 109.500
32 PONCOKUSUMO MALANG -0.038
0.000 78.000 74 WONOREJO-RUNGKUT SURABAYA 0.000
0.000 91.500 33 BABADAN PONOROGO 0.146
0.095 88.333 75 KRIAN SIDOARJO 0.515
0.146 98.399
34 BALUNG PONOROGO -0.471
-0.261 82.848 76 PANOKAWAN SIDOARJO 0.264
0.250 97.500 35 BOLLU PONOROGO -0.273
-0.155 93.131 77 PRAMBON SIDOARJO 1.303
0.625 86.750
36 NGILO PONOROGO -1.374
-0.818 100.818 78 SIDOARJO SIDOARJO -0.238
-0.067 97.467 37 PUDAK PONOROGO 0.199
0.095 86.905 79 SRUNI SIDOARJO 1.444
0.417 90.783
38 PULUNG PONOROGO 0.883
0.333 80.500 80 GALIS PAMEKASAN 0.560
0.429 78.000 39 SUMOROTO PONOROGO -0.927
-0.500 86.250 81 AMBUNTEN SUMENEP -0.423
-0.488 84.179
40 LEMBEYAN MAGETAN 1.445
0.931 72.621 82 BLUTO SUMENEP -2.620 ** -1.359 90.076 41 CAU MADIUN 0.807
0.540 99.063 83 KEBONAGUNG SUMENEP -2.886 ** -1.545 113.182
42 DUNGUS MADIUN -1.043
-0.600 108.000 84 ROBARU SUMENEP 2.143 * 1.533 86.867
154
Appendix 7-6. The output of trend assessment for maximum cumulative rainfall depth of 5 consecutive rain days (RX5d) presented per gauge.
Bold characters indicate maximum and minimum magnitude (Source: data processing).
CODE GAUGE DISTRICT Z Sig m b CODE GAUGE DISTRICT Z Sig m b
1 ALASBULUH BANYUWANGI 0.313
0.455 146.182 43 MANTINGAN NGAWI -0.234
-0.157 195.511 2 MAELANG BANYUWANGI -0.088
-0.087 213.565 44 NGALE NGAWI -0.023
-0.024 189.333
3 PASEWARAN BANYUWANGI -0.897
-1.000 320.000 45 NGRAMBE NGAWI -0.771
-0.980 182.740 4 SIDOMULYO BANYUWANGI 0.619
0.761 135.716 46 TRETES NGAWI 0.480
1.133 193.467
5 AJUNG JEMBER 1.106
1.333 188.333 47 WALIKUKUN NGAWI 1.286
1.261 189.727 6 GLUNDENGAN JEMBER 0.000
0.000 164.000 48 CAWAK BOJONEGORO -1.652 + -2.045 213.136
7 KARANG KEDAWUH JEMBER -0.161
-0.250 195.750 49 KEDUNG ADEM BOJONEGORO 0.225
0.230 184.783 8 LEDOKOMBO JEMBER 0.839
0.813 185.969 50 LERAN BOJONEGORO -3.190 ** -4.619 273.000
9 LOJEJER JEMBER 0.309
0.429 146.214 51 SUMBEREJO BOJONEGORO -0.021
-0.063 164.563 10 RENES JEMBER -0.794
-0.948 217.776 52 TRETES BOJONEGORO -0.701
-0.675 214.350
11 SUKOWONO JEMBER 0.946
0.727 194.818 53 BANGILAN TUBAN 0.485
0.529 143.588 12 WIROLEGI JEMBER -0.749
-0.909 202.136 54 JOJOGAN TUBAN -0.132
-0.091 179.864
13 ADIBOYO PROBOLINGGO 0.000
0.000 167.000 55 KEBONHARJO TUBAN 1.001
1.250 146.000 14 BAYEMAN PROBOLINGGO 2.107 * 2.200 134.200 56 KEJURON TUBAN -0.178
-0.160 143.962
15 BOTOGARDU PROBOLINGGO -0.909
-0.955 249.886 57 MUNDRI TUBAN 1.635
1.112 146.085 16 LUMBANG PROBOLINGGO 0.395
0.274 227.246 58 NGABONGAN TUBAN 0.838
0.700 159.550
17 BANGIL PASURUAN 2.499 * 2.667 169.333 59 SENDANG TUBAN -1.543
-1.950 204.750 18 JAWI PASURUAN 0.918
1.000 205.000 60 SIMO TUBAN -0.850
-0.904 150.273
19 JEMBRUNG PASURUAN -0.542
-0.563 217.125 61 SOKOMEDALEM TUBAN -0.291
-0.524 157.095 20 KEPULUNGAN PASURUAN -0.319
-0.408 210.267 62 TUBAN TUBAN 0.870
0.660 142.417
21 PEGI PASURUAN -1.293
-1.429 176.000 63 WIDANG TUBAN -0.882
-0.667 154.333 22 PRIGEN PASURUAN 2.336 * 3.375 213.625 64 BABAT LAMONGAN -2.823 ** -3.750 247.375 23 BANTUR MALANG 0.542
0.792 203.958 65 BLAWI LAMONGAN -0.281
-0.305 179.104
24 BLAMBANGAN MALANG 0.678
0.784 194.813 66 KARANGBINANGUN LAMONGAN -0.277
-0.200 143.200 25 BULULAWANG MALANG 0.662
0.667 182.500 67 KEDUNGPRING LAMONGAN -1.753 + -1.520 201.240
26 DAMPIT MALANG 0.731
0.872 194.008 68 LAMONGAN LAMONGAN -0.125
-0.115 149.038 27 GONDANG LEGI MALANG -0.104
-0.056 202.611 69 PANGKATREJO LAMONGAN -0.286
-0.235 161.412
28 KALIPARE MALANG 1.004
1.600 159.400 70 SUKODADI LAMONGAN -1.660 + -1.383 166.668 29 KDKANDANG MALANG -0.898
-1.875 235.125 71 BENJENG GRESIK -1.365
-1.140 186.862
30 KEPANJEN MALANG 2.454 * 3.988 166.000 72 LOWAYU GRESIK 0.993
0.692 145.154 31 NGAJUM MALANG 0.376
0.567 184.362 73 SIMO SURABAYA 2.680 ** 4.302 173.223
32 PONCOKUSUMO MALANG 2.815 ** 2.472 166.028 74 WONOREJO-RUNGKUT SURABAYA 0.573
0.550 186.425 33 BABADAN PONOROGO 0.021
0.000 149.000 75 KRIAN SIDOARJO -1.614
-0.883 209.667
34 BALUNG PONOROGO 0.075
0.000 146.000 76 PANOKAWAN SIDOARJO -1.004
-1.300 214.900 35 BOLLU PONOROGO -0.844
-0.895 171.376 77 PRAMBON SIDOARJO 0.860
1.000 170.500
36 NGILO PONOROGO -2.007 * -2.250 211.250 78 SIDOARJO SIDOARJO 0.634
1.167 180.000 37 PUDAK PONOROGO 0.309
0.400 191.600 79 SRUNI SIDOARJO -0.198
-0.236 222.660
38 PULUNG PONOROGO -0.463
-0.313 177.781 80 GALIS PAMEKASAN -0.280
-0.667 147.667 39 SUMOROTO PONOROGO -1.103
-1.150 156.125 81 AMBUNTEN SUMENEP -1.450
-2.737 187.840
40 LEMBEYAN MAGETAN 0.094
0.293 159.316 82 BLUTO SUMENEP -1.962 * -2.282 163.325 41 CAU MADIUN 1.013
0.765 186.285 83 KEBONAGUNG SUMENEP -1.190
-1.667 184.000
42 DUNGUS MADIUN -1.356
-1.889 213.778 84 ROBARU SUMENEP 1.479
1.773 153.500
155
Appendix 7-7. The output of trend assessment for annual total (RTOT) presented per gauge. Bold characters indicate maximum and minimum
magnitude (Source: data processing).
CODE GAUGE DISTRICT Z Sig m b CODE GAUGE DISTRICT Z Sig m b
1 ALASBULUH BANYUWANGI 0.000
-0.083 914.00 43 MANTINGAN NGAWI -0.304
-5.891 2121.93 2 MAELANG BANYUWANGI -0.132
-0.529 1472.24 44 NGALE NGAWI -1.822 + -17.527 2200.58
3 PASEWARAN BANYUWANGI -0.083
-1.091 2522.27 45 NGRAMBE NGAWI -1.098
-16.302 2228.02 4 SIDOMULYO BANYUWANGI 0.019
1.800 1158.40 46 TRETES NGAWI -2.143 * -34.467 2453.30
5 AJUNG JEMBER 0.000
0.105 2152.87 47 WALIKUKUN NGAWI 0.257
2.689 2026.47 6 GLUNDENGAN JEMBER 1.742 + 18.042 1104.65 48 CAWAK BOJONEGORO -1.744 + -16.239 1786.00 7 KARANG KEDAWUH JEMBER -1.659 + -16.250 2111.13 49 KEDUNG ADEM BOJONEGORO -0.619
-6.531 2049.81
8 LEDOKOMBO JEMBER -0.107
-3.040 1939.66 50 LERAN BOJONEGORO -3.586 *** -51.500 2739.50 9 LOJEJER JEMBER 1.367
11.000 931.50 51 SUMBEREJO BOJONEGORO -0.917
-10.176 1835.53
10 RENES JEMBER 0.273
3.206 2033.92 52 TRETES BOJONEGORO -0.584
-7.385 2103.63 11 SUKOWONO JEMBER 2.391 * 19.833 1717.17 53 BANGILAN TUBAN 0.772
7.333 1388.50
12 WIROLEGI JEMBER -2.292 * -25.529 2208.74 54 JOJOGAN TUBAN -1.168
-10.333 1881.17 13 ADIBOYO PROBOLINGGO -0.244
-3.040 1264.92 55 KEBONHARJO TUBAN 1.167
8.300 1279.90
14 BAYEMAN PROBOLINGGO 0.667
2.750 1043.00 56 KEJURON TUBAN -1.107
-6.270 1586.99 15 BOTOGARDU PROBOLINGGO -0.771
-11.271 2307.27 57 MUNDRI TUBAN -0.911
-10.279 1717.62
16 LUMBANG PROBOLINGGO -0.494
-4.000 1975.50 58 NGABONGAN TUBAN -0.044
-0.909 1682.00 17 BANGIL PASURUAN 2.391 * 21.909 1517.14 59 SENDANG TUBAN -1.984 * -18.650 1928.60 18 JAWI PASURUAN 0.959
11.722 2295.78 60 SIMO TUBAN -1.205
-14.569 1577.89
19 JEMBRUNG PASURUAN -1.042
-14.739 2105.96 61 SOKOMEDALEM TUBAN -0.898
-5.600 1489.60 20 KEPULUNGAN PASURUAN -1.857 + -22.529 2199.64 62 TUBAN TUBAN 0.869
8.158 1148.11
21 PEGI PASURUAN -1.063
-9.625 1233.50 63 WIDANG TUBAN -0.419
-3.067 1562.97 22 PRIGEN PASURUAN 2.265 * 31.024 2501.88 64 BABAT LAMONGAN -2.116 * -53.500 3020.00 23 BANTUR MALANG 0.792
12.231 1546.08 65 BLAWI LAMONGAN 0.023
0.067 1608.00
24 BLAMBANGAN MALANG -0.350
-4.667 2114.00 66 KARANGBINANGUN LAMONGAN -1.008
-7.829 1570.80 25 BULULAWANG MALANG -1.014
-13.500 2224.50 67 KEDUNGPRING LAMONGAN -0.542
-4.846 2005.31
26 DAMPIT MALANG 1.107
8.693 1694.76 68 LAMONGAN LAMONGAN 0.709
5.591 1446.82 27 GONDANG LEGI MALANG 0.415
2.571 1937.79 69 PANGKATREJO LAMONGAN -0.821
-4.222 1495.67
28 KALIPARE MALANG -0.211
-3.400 1700.20 70 SUKODADI LAMONGAN -0.672
-4.368 1506.31 29 KDKANDANG MALANG -0.819
-16.141 2257.25 71 BENJENG GRESIK -2.233 * -17.454 1881.53
30 KEPANJEN MALANG 1.051
15.227 1995.64 72 LOWAYU GRESIK 0.819
6.282 1277.51 31 NGAJUM MALANG -0.810
-4.847 2092.36 73 SIMO SURABAYA 3.845 *** 35.783 1535.80
32 PONCOKUSUMO MALANG 1.313
16.217 2096.23 74 WONOREJO-RUNGKUT SURABAYA -0.178
-4.250 2019.88 33 BABADAN PONOROGO -1.251
-11.111 1700.22 75 KRIAN SIDOARJO -1.425
-14.766 2097.08
34 BALUNG PONOROGO -1.538
-9.967 1547.12 76 PANOKAWAN SIDOARJO -0.845
-6.667 2025.33 35 BOLLU PONOROGO 0.000
-0.100 1608.00 77 PRAMBON SIDOARJO -0.220
-1.889 1953.39
36 NGILO PONOROGO -2.324 * -19.400 1843.60 78 SIDOARJO SIDOARJO 0.687
7.333 1766.00 37 PUDAK PONOROGO -0.485
-12.700 2817.75 79 SRUNI SIDOARJO 0.025
1.245 1885.75
38 PULUNG PONOROGO 0.044
1.333 1874.00 80 GALIS PAMEKASAN 0.140
1.692 1097.15 39 SUMOROTO PONOROGO -1.719 + -15.769 1522.50 81 AMBUNTEN SUMENEP -0.453
-7.497 1355.48
40 LEMBEYAN MAGETAN -1.107
-8.607 1557.64 82 BLUTO SUMENEP -0.631
-6.757 1128.06 41 CAU MADIUN -0.450
-3.158 2043.63 83 KEBONAGUNG SUMENEP -1.056
-9.643 1412.36
42 DUNGUS MADIUN -1.606
-21.045 2116.18 84 ROBARU SUMENEP 1.637
15.778 1263.44
156
Appendix 7-8. The output of trend assessment for simple daily intensity index (SDII) presented per gauge. Bold characters indicate maximum
and minimum magnitude (Source: data processing).
CODE GAUGE DISTRICT Z Sig m b CODE GAUGE DISTRICT Z Sig m b
1 ALASBULUH BANYUWANGI 1.106
0.083 18.633 43 MANTINGAN NGAWI 3.411 *** 0.380 14.920 2 MAELANG BANYUWANGI -0.485
-0.064 22.507 44 NGALE NGAWI -0.889
-0.035 16.892
3 PASEWARAN BANYUWANGI 0.083
0.010 25.540 45 NGRAMBE NGAWI 0.771
0.081 14.110 4 SIDOMULYO BANYUWANGI -0.075
-0.013 19.389 46 TRETES NGAWI -0.113
-0.025 17.838
5 AJUNG JEMBER 0.821
0.050 16.800 47 WALIKUKUN NGAWI 2.033 * 0.234 18.628 6 GLUNDENGAN JEMBER 1.544
0.120 13.080 48 CAWAK BOJONEGORO -3.060 ** -0.223 22.749
7 KARANG KEDAWUH JEMBER -2.375 * -0.260 23.510 49 KEDUNG ADEM BOJONEGORO -1.070
-0.069 22.185 8 LEDOKOMBO JEMBER 1.535
0.120 17.530 50 LERAN BOJONEGORO -3.629 *** -0.562 31.010
9 LOJEJER JEMBER 1.082
0.127 15.683 51 SUMBEREJO BOJONEGORO -1.690 + -0.095 18.486 10 RENES JEMBER -1.067
-0.119 21.059 52 TRETES BOJONEGORO 1.309
0.100 19.600
11 SUKOWONO JEMBER 1.661 + 0.092 15.663 53 BANGILAN TUBAN 1.501
0.059 14.615 12 WIROLEGI JEMBER -2.408 * -0.171 19.164 54 JOJOGAN TUBAN 0.463
0.043 16.771
13 ADIBOYO PROBOLINGGO 0.263
0.006 16.912 55 KEBONHARJO TUBAN 2.567 * 0.179 16.867 14 BAYEMAN PROBOLINGGO -0.083
-0.013 18.500 56 KEJURON TUBAN 0.554
0.031 15.121
15 BOTOGARDU PROBOLINGGO -0.948
-0.133 26.123 57 MUNDRI TUBAN 0.585
0.051 14.972 16 LUMBANG PROBOLINGGO -0.692
-0.057 22.139 58 NGABONGAN TUBAN 2.513 * 0.246 17.427
17 BANGIL PASURUAN 3.445 *** 0.370 16.287 59 SENDANG TUBAN -1.654 + -0.133 19.717 18 JAWI PASURUAN 0.522
0.023 21.100 60 SIMO TUBAN -0.593
-0.057 17.016
19 JEMBRUNG PASURUAN -1.126
-0.067 20.600 61 SOKOMEDALEM TUBAN 1.507
0.074 16.978 20 KEPULUNGAN PASURUAN -0.507
-0.042 19.768 62 TUBAN TUBAN 2.550 * 0.183 16.300
21 PEGI PASURUAN 0.250
0.033 13.767 63 WIDANG TUBAN -0.882
-0.100 19.750 22 PRIGEN PASURUAN -0.023
-0.002 21.123 64 BABAT LAMONGAN -3.020 ** -0.531 30.796
23 BANTUR MALANG 2.315 * 0.179 22.550 65 BLAWI LAMONGAN -1.379
-0.094 18.833 24 BLAMBANGAN MALANG 0.631
0.095 19.660 66 KARANGBINANGUN LAMONGAN -0.573
-0.032 17.025
25 BULULAWANG MALANG -1.897 + -0.109 21.064 67 KEDUNGPRING LAMONGAN -3.861 *** -0.217 22.067 26 DAMPIT MALANG 2.885 ** 0.254 14.886 68 LAMONGAN LAMONGAN 0.669
0.031 14.750
27 GONDANG LEGI MALANG 2.273 * 0.177 20.543 69 PANGKATREJO LAMONGAN 0.876
0.025 16.000 28 KALIPARE MALANG -2.034 * -0.126 18.805 70 SUKODADI LAMONGAN -2.296 * -0.133 18.618 29 KDKANDANG MALANG 0.571
0.054 16.858 71 BENJENG GRESIK -0.323
-0.024 21.249
30 KEPANJEN MALANG 0.093
0.011 19.000 72 LOWAYU GRESIK -1.193
-0.092 21.329 31 NGAJUM MALANG 1.917 + 0.108 16.210 73 SIMO SURABAYA 2.806 ** 0.363 22.760 32 PONCOKUSUMO MALANG 2.558 * 0.161 16.114 74 WONOREJO-RUNGKUT SURABAYA 2.845 ** 0.185 19.201 33 BABADAN PONOROGO 0.292
0.023 17.392 75 KRIAN SIDOARJO 0.000
-0.005 17.927
34 BALUNG PONOROGO 1.464
0.065 14.665 76 PANOKAWAN SIDOARJO 0.555
0.056 18.032 35 BOLLU PONOROGO 0.174
0.015 15.379 77 PRAMBON SIDOARJO 1.544
0.185 19.800
36 NGILO PONOROGO -2.246 * -0.215 22.400 78 SIDOARJO SIDOARJO 1.612
0.217 18.017 37 PUDAK PONOROGO 0.265
0.029 18.843 79 SRUNI SIDOARJO -0.099
0.000 27.600
38 PULUNG PONOROGO -1.236
-0.050 18.500 80 GALIS PAMEKASAN 0.175
0.033 17.800 39 SUMOROTO PONOROGO -1.147
-0.053 17.339 81 AMBUNTEN SUMENEP -1.963 * -0.311 24.300
40 LEMBEYAN MAGETAN 0.788
0.058 19.135 82 BLUTO SUMENEP -3.317 *** -0.358 22.916 41 CAU MADIUN 1.952 + 0.098 18.517 83 KEBONAGUNG SUMENEP -2.827 ** -0.313 25.738 42 DUNGUS MADIUN -0.898
-0.053 19.459 84 ROBARU SUMENEP 0.291
0.044 21.111
157