application of gis and remote sensing for flood hazard and
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Construction Technology and Management Thesis
2021-05-08
Application of GIS and remote sensing
for flood hazard and risk assessment in
tana basin,denbiya flood plain;(a case
study of megech river)
Alemzewid, Abiyie Belay
http://ir.bdu.edu.et/handle/123456789/12466
Downloaded from DSpace Repository, DSpace Institution's institutional repository
I
© 2021 ALEMZEWID ABIYIE BELAY
All righte are resieved
II
DECLARATION
This is to certify that the thesis entitled, “Application of GIS and RS for flood hazard and risk as-
assessment in Tana basin, Denbiya flood plain: (a case study of Megech River).” submitted in
partial fulfillment of the requirements for the Degree of Master of Science in Hydraulic
Engineering under Civil and Water Resource Engineering, Bahir Dar Institute of Technology, in a
record of original work carried out by me and has never been submitted to this or any other insti-
tution to get any other degree or certificates. The assistance and help I received during the course
of this investigation have been duly acknowledge.
ALEMZEWID ABIYIE BELAY 4/15/2012 GC
Name of student Signature Date
III
IV
ACKNOLOEDGMENTS
First, thanks to the Almighty God for granting me His limitless care, love and blessings all along the way.
I am grateful to express my deepest gratitude to my advisor Dr. Mamaru Ayalew for his unreserved assis-
tance, constructive and timely comments at all stages of my work and for supplying me relevant materials
to carry out the research.
I should strongly appreciate his patience full guidance in many discussions we made on various problems I
faced during the course of the work.
I would like to acknowledge the Ethiopian Meteorological Services Agency and Amhara design and su-
pervision water work enterprise for providing the required data and information to fulfill this research
work.
Finally, I would like to express my warm feeling of appreciation and tank to my families and friends who
are always encouraging and rendering me the necessary services and are taking care of some of my re-
sponsibilities.
V
ABSTRACT
Flood is a natural disaster. However, human activities in many circumstances change flood Be-
havior. Activities in the catchment such as land clearing for agriculture may increase the
Magnitude of flood, which increases the damage to the properties and life. Denbiya flood plain is
one of the most severally flood affected areas in North Ethiopia in tana basin by Megech river.
The impact of floods had been increase due to a number of factors, with rising sea levels and in-
creased development on flood plain. The high magnitude of water that enters in to Megech River
overflows this river. For a number of reasons the most frequent choice should be protection from
the flooding by revitalizing the people to the safe ground, but there is also a need for a broader
and comprehensive program for managing flood hazard in the study area. Flood protection has
been helpful and must continue. Side by side, other preventive tools like effective land use plan-
ning, creation of a computerized GIS database for the flood prone areas and a detailed flood risk
assessment and mapping are required to minimize the harmful effects of flood hazard.
This study accomplished to use analytical hierarch process, Arc GIS 10.1, HEC Geo-RAS 10.1
and HEC-RAS.5.0.7. The flood causative factors developed in the Arc GIS, Analytical hierarch
process weighted, and overplayed in the principle of pair wise comparison and Multi criteria
evaluation technique in order to arrive at flood hazard, and flood risk map and HEC-RAS model
to develop flood inundation map. A catchment of Land use/land cover, soil and drainage factor,
annual maximum daily rainfall and temperature (1988-2019) and annual maximum daily gauge in
river flow data (1980-2019).
Flood frequency analysis was done by using Gumball‟s method return period 2, 10, 50 and 100-
and the result were 173.86m3/s, 264.2m
3/s, 323.9m
3/s, 455.5m
3/s and 511.14m
3/s to obtain respec-
tively. AHP weight overlay analysis and from the flood Inundation analysis resulted to shown
that 259.6 km2 areas to high and very high flood hazard and 25.21km
2 areas to high and very high
flood risk.
In general, 48,625 people may be affect by this flood result at Dembiya flood plain areas. The
overall study indicates that flood hazard, and risk assessment and flood inundation will be aggra-
vated in Denbiya flood plains, which needs flood management strategy, coupled with water
resource planning in the region to the available risk areas. Finally, for further study it need to
consider detail flood damage cost analysis and mitigation measurement.
Key words: - AHP weight, ArcGIS, Denbiya, flood hazard and risk, HEC-RAS and Inundation.
VI
Table of Contents
ACKNOLOEDGMENTS ............................................................................................................. IV
ABSTRACT .................................................................................................................................... V
LIST OF ABBREVIATIONS ...................................................................................................... IX
LIST OF FIGURE ........................................................................................................................ XI
LIST OF TABLE ....................................................................................................................... XIII
1. Introduction ................................................................................................................................. 1
1.1. Background ........................................................................................................................... 1
1.2. Statement of the problem ................................................................................................... 2
1.3. Objective of the study ......................................................................................................... 3
1.3.1. General objective ........................................................................................................... 3
1.3.2. Specific objectives .......................................................................................................... 3
1.4. Research questions .............................................................................................................. 3
1.5. Significance of study ........................................................................................................... 3
1.6 Thesis Outline ....................................................................................................................... 3
2. LITERATURE REVIEW .......................................................................................................... 4
2.1 Historical background flood risk in the world ................................................................. 4
2.2. Historical background of flood risk in Ethiopia ............................................................. 4
2.3. GIS and Remote Sensing for flood hazard and risk assessment .................................. 5
2.3.1 GIS analysis ..................................................................................................................... 5
2.3.2. Remote Sensing .............................................................................................................. 5
2.4. Approaches of flood hazard and risk assessment ........................................................... 6
2.5. Multi Criteria Spatial Decision Support Systems ........................................................... 7
2.6. Multi-Criteria decision making in GIS ............................................................................ 8
2.7. HEC-RAS and Model process ............................................................................................. 8
2.7.1 HEC-RAS ........................................................................................................................ 8
2.7.2 Model process.................................................................................................................. 9
2.8 Previous Study ..................................................................................................................... 10
3 Methodology .............................................................................................................................. 11
3.1 Description of Study Area ................................................................................................. 11
3.1.1 Location ......................................................................................................................... 11
VII
3.1.2 Topography ................................................................................................................... 12
3.1.3 Soil Type ........................................................................................................................ 12
3.1.5 Land use/Land cover .................................................................................................... 12
3.1.6 Climate........................................................................................................................... 14
3.1.7 Hydrology ...................................................................................................................... 14
3.2 General Research Method ................................................................................................ 16
3.2.1 Data Collection.............................................................................................................. 18
3.2.2 Catchment delineation ................................................................................................. 20
3.2.3 Hydro-meteorological data analysis ........................................................................... 21
3.2.3.1 Missing data analysis ................................................................................................. 21
3.2.3.2 Data quality analysis ................................................................................................. 21
4. Flood analysis ........................................................................................................................... 27
4.1 Flood frequency analysis .................................................................................................... 27
4.2 Flood factor Analysis .......................................................................................................... 28
4.2.1 Soil Factor reclassification ........................................................................................... 28
4.2.2 Slope Factor reclassification ........................................................................................ 29
4.2.3 Elevation reclassification ............................................................................................. 30
4.2.4 Land use/Land cover Reclassification ........................................................................ 30
4.2.5 Drainage Density Reclassification ............................................................................... 31
4.2.6 Rainfall Reclassification ............................................................................................... 32
4.2.7 Population Density Reclassification ............................................................................ 32
4.2.8 Model Input Parameters .............................................................................................. 35
4.2.9 HEC-RAS Stability Analysis ....................................................................................... 41
4.3 Flood Inundation Analysis .................................................................................................. 42
4.3.1 Flood flow analysis ....................................................................................................... 42
4.3.2 Pre HEC-RAS process ................................................................................................. 42
4.3.3 Geometrical data exporting to HEC-RAS .................................................................. 45
4.3.4 Entering Flow data and boundary condition ............................................................. 46
4.3.5 Pre HEC-RAS output ................................................................................................... 47
4.3.6 Exporting HEC-RAS results and post-HEC-RAS process ....................................... 49
4.3.7 Flood plain delineation ................................................................................................. 50
VIII
4.3.8 Post HEC-RAS mapper ............................................................................................... 50
4.4 Flood hazard analysis .......................................................................................................... 51
4.5 Flood risk analysis ............................................................................................................... 53
5. Result and discussion ................................................................................................................ 55
5.1 Flood Inundation ................................................................................................................. 55
5.1.1 Detailed output tables for selected cross sections ...................................................... 55
5.1.2 Detailed output velocity distribution at selected cross sections. ............................... 59
5.1.3 Flood hazard and risk level to classification .............................................................. 60
5.1.4 Flood Inundation Map ................................................................................................. 60
5.2 Flood hazard map ................................................................................................................ 64
5.3 Flood risk map ..................................................................................................................... 64
6. Conclusion and Recommendation ........................................................................................... 66
6.1 Conclusion ............................................................................................................................ 66
6.2 Recommendation ................................................................................................................. 67
Reference ....................................................................................................................................... 68
APPENDIX ..................................................................................................................................... 70
Appendix 1:- Model output view at Megech river Cross sections. ............................................. 70
Appendix 2: Gonder Rainfall data Quality analysis .................................................................... 71
Appendix 3: Ambageorgis Rainfall data Quality analysis ........................................................ 73
Appendix 4: Shembekit Rainfall data Quality analysis............................................................... 75
Appendix 5: Makisegnit Rainfall data Quality analysis .............................................................. 77
Appendix 6: Double Mass Curve for Consistency to use Annual Rainfall .............................. 79
Appendix 7: Mean Monthly Maximum and Minimum Temperatur ........................................ 80
Appendix 8:- Denbiya flood plain of population in reclassification. .......................................... 80
Appendix 9: Detail surveyor data collection at Denbiya Flood plain Megech River. ................ 81
Appendix 10: Estimated Maning‟s n value for channel and over banks .................................. 91
Appendix 11: Data Quality Test for Megech flow Discharge. ................................................. 92
IX
LIST OF ABBREVIATIONS
AHP Analytical Hierarch Process
A.S.L Above Sea Level
AVHRR Advance Very High resolution of radiometer
BM Bench Mark
BC Boundary Condition
CI Consistency Index
CR Consistency Ratio
DDN Drainage Density Network
DEM Digital Elevation Model
DFP Denbiya Flood Plain
DMC Double Mass Curve
DPPA Disaster Preparedness and Prevention Agency
DSS Decision Support System
DTM Digital Terrain Model
EMA Ethiopia meteorological
FHM Flood Hazard Map
FRM Flood Risk Map
GIS Geographic Information System
GPS Global Positioning System
HEC-Geo RAS Hydraulic Engineering Center Geometrical River Analysis System
HEC-RAS Hydraulic Engineering Center for River Analysis System
IDW Invers Distance Weight
LRB Left River Bank
LRE Left River Edge
LULC Land use and Land Cover
MCDM Multi Criteria Decision making
MCE Multi Criteria Evaluation
MODIS Moderate Resolution image spectrum
MoWIE Ministry of Water, Irrigation and Energy
NDVI Normalization difference Vegetation Index
X
NMA National Meteorological Agency
RC River Center
RF Rainfall
RI Random Index
RRE Right River Edge
RS Remote sensing
SDSS Spatial Decision Support System
SDV Standard division variation
SRTM Shuttle Radar Topography Mission
UNEP United Nation Environmental Program
UTM Universal Transverse Mercator
Tmax Temperature Maximum
Tmin Temperature Minimum
TIN Triangular Inverse Network
WLC Weighted Linear Combination
XI
LIST OF FIGURE
Figure 1; Location of study area ..................................................................................................... 11
Figure 2: Percentage of Land use/ land cover coverage in Megech catchment .............................. 13
Figure 3: Drainage, Soil, and Land use and land cover map of Megech watershed ...................... 13
Figure 4: Mean monthly precipitation of Megech watershed from (1988-2019) ........................... 15
Figure 5: Mean monthly maximum and minimum temperature of Megech watershed ................. 15
Figure 6: Average monthly discharge of Megech River (1980-2019) ......................................... 15
Figure 7: General Method of work flow chart ................................................................................ 17
Figure 8 : Photo taken in top map at Dembiya flood plain during filed surveying. ....................... 18
Figure 9 : Photo taken in River X-cross at Dembiya flood plain during filed surveying. .............. 18
Figure 10: Megech Catchment Delineation by train preprocess. .................................................... 20
Figure 11: Double Mass Curve analyses of test constancy Megech Catchment rainfall stations... 23
Figure 12: Homogeneity test of Megech catchment rainfall station. .............................................. 26
Figure 13: Areal rainfall coverage by IDW for Megech watershed ............................................... 26
Figure 14: Reclassification of Factors at Megech River Catchment .............................................. 33
Figure 15: Reclassification of Factors at Megech River Catchment .............................................. 34
Figure 16: Megech River cross sections sample image at Megech River cross section. ................ 39
Figure 17 : A digitized Megech River, TIN, Cross section, River, bank and geometry layers ...... 44
Figure 18: A cross section views in HEC-RAS geometric window imported from GIS. .............. 45
Figure 19: Detail manning and other model input data a boundary condition. .............................. 46
Figure 20: Cross section view at River station 30751.74 for profile (50-year rp) surface water. .. 47
Figure 21: Cross section view at river station 36650.39 for profile (100-year) distributions. ....... 47
Figure 22: Cross section view at river station 200.765 for profile (100-year) distribution ............ 48
Figure 23: General profile plot of the reach for 100-year storm..................................................... 48
Figure 24: 3D perspective view of the flood plain and the channel in HEC-RAS (100-year storm)
......................................................................................................................................................... 49
Figure 25: Bounding polygon for the water surface elevating and TIN generation ....................... 49
Figure 26: Water surface at TIN generated from bounding polygon.............................................. 50
Figure 27: Critical cross sections for simulated velocity distribution variations. ........................... 59
Figure 28: Critical cross sections for simulated velocity distribution variations. ........................... 59
Figure 29: Flood level (velocity vs depth) flood map at Denbiya Flood Plain. .............................. 61
XII
Figure 30: Flood velocity distribution of flood map at Denbiya Flood Plain. ................................ 62
Figure 31: Flood depth distribution of flood Map at Denbiya Flood Plain. ................................... 63
Figure 32: Flood hazard map of Megech River catchment........................................................... 65
Figure 33: Flood risk map Denbiya flood plain .............................................................................. 65
XIII
LIST OF TABLE
Table 1-Major-Soil-Types Group, texture and the Area coverage. ................................................ 12
Table-2: Land use/ Land cover. ...................................................................................................... 12
Table 3: Mean monthly maximum and minimum temperature of Megech watershed (1988-2019)
......................................................................................................................................................... 14
Table 4: Name and Location of Rainfall stations Megech watershed ............................................ 19
Table 5: Data type, duration, and purpose and source data. ........................................................... 19
Table 6: The Test of higher and lower the record flow data. .......................................................... 24
Table 7: Index of Megech River selecte the fittest distribution method ......................................... 27
Table 8: The flood analysis forecast in different return period ...................................................... 28
Table 9:- Soil group, and texture and the respective hydrologic soil group. .................................. 29
Table 10: Reclassification slope (Rate, Degree and Present) of Megech catchment ...................... 29
Table 11: Land use/ land cover reclassification. ............................................................................. 30
Table 12: Catchment area of drainage density reclassification. ..................................................... 31
Table 13: Catchment area of annual rainfall reclassification. ......................................................... 32
Table 14: Population Density reclassification. ............................................................................... 32
Table 15:- Megech River Geometry and their purpose. ................................................................. 35
Table 16: Types of channel Recommended of Manning's value. ................................................... 36
Table 17: Cowan Manning‟s roughness computation sheet ........................................................... 38
Table 18: Types and Places of Boundary Conditions ..................................................................... 40
Table 19: Parameters of flood hazard by Analytical Hierarchy Process module derivation. ......... 52
Table 20: Normalized flood hazard parameters by Analytical Hierarchy Process Module Result. 52
Table 21: Random index (RI) used to compute consistency ratios (CR) ....................................... 53
Table 22: Parameters of flood Risk: Analytical Hierarchy Process ................................................ 53
Table 23: Normalized flood hazard parameters of AHP module result. ........................................ 54
Table 24: Detaile output tables for piping simulation for selected cross sections (40433.11) ....... 55
Table 25: Detaile output tables for piping simulation for selected cross sections (40056.04) ....... 56
Table 26: Detaile output tables for piping simulation for selected cross sections (31727.22) ....... 56
Table 27: Detaile output tables for piping simulation for selected cross sections (21567.56). ...... 57
Table 28: Detaile output tables for piping simulation for selected cross sections (15341.9) ......... 57
Table 29: Detaile output tables for piping simulation for selected cross sections (10955.06) ....... 58
XIV
Table 30 Detaile output tables for piping simulation for selected cross sections (4936.394) ....... 58
Table 31: Flood hazard areas coverage based on depth vs velocity result. .................................... 64
Table 32: Flood risk areas coverage based on depth vs velocity result. ......................................... 64
1
1. Introduction
1.1. Background
Flood is the main natural hazard in the world, in terms of loss of life and monetary damage. It is
an inevitable natural phenomenon occurring from time to time in all rivers and natural drainage
systems, which not only damages the lives, natural resources and environment, but also causes the
loss of economy and health. The impact of floods had been increase due to a number of factors,
with rising sea levels and increased development on flood plain. Recurring flood losses have
handicapped the economic development of both developed and developing countries UNEP
(2002).
Flood risk is a big challenge in the world and in Ethiopia; Topographically Ethiopia is both a
highland/mountainous and lowland country. Especially during the rainy season (June-September),
the major perennial rivers as well as their numerous tributaries forming the country‟s drainage
systems carry their peak discharges; this discharges experiences two types of floods: flash floods
and river floods. Flash floods are the ones formed from excess rains falling on upstream water-
sheds and gush downstream with massive concentration, speed and force. Often, it is sudden and
appears unnoticed Disaster Preparedness and Prevention Agency (DPPA, 2006).
Therefore, such floods often result in a considerable toll; and the damage becomes especially pro-
nounced and devastating when it passes across or along human settlements and infrastructure
concentration. Therefore, to prevent this problem to need flood risk analysis; flood risk analyses
are essential tools to support territory organization, land-uses policies, flood management pro-
jects, recovery budget and insurance rates determination. Several types of analysis used with
different purposes and to evaluation of potential flood; damage is an important type of analysis
that is gaining importance over time. These analyses allow us to quantify the risk, taking into ac-
count different criteria and approaches according to the objectives of the evaluation (DPPA,
2006).
This research aimed to explore the evaluation of potential flood extent, its uncertainties associated
to the datasets, flood hazard, and estimate flood risks at Megech River in Denbiya flood plain.
The evaluation of potential flood damage becomes an essential tool used to quantify the economic
risk, and support portfolio organization, cost-benefit and multi-criteria analyses. Several methods
used to evaluate potential flood extent. The deterministic approaches are the most frequently used
with this purpose.
2
1.2. Statement of the problem
Flood is probably the most devastating, widespread and frequent natural hazard of the world. This
problem is more acute in highland areas like Ethiopia under strong environmental degradation due
to population pressure. According to UNEP (2002), the major environmental disasters in Africa
are recurrent droughts and floods.
Their socio-economic and ecological impacts are devastating to African countries, because most
of them do not have real time forecasting technology or resources for post-disaster rehabilitation.
Although flood events are not new to Ethiopia, the country, in its current main rainy season, had
been threat by unprecedented flooding of abnormal magnitude.
Apparently, for the large part, due to torrential or heavy rains falling for long days on the up-
stream highlands. The rains have caused most rivers to swell and overflow or breach their
courses, submerging the surrounding „flat' fields or floodplains. It is evident that the problem of
river flooding in Ethiopia is getting more and more acute due to human intervention in the fragile
highland areas at an ever-increasing scale.
The regulation of flood hazard areas coupled with enactment and enforcement of flood hazard
zoning could prevent damage of life and property from flooding in short term as well as in long
term. Flood management and control are necessary not only floods impose a curse on the society,
but also the optimal exploitation of the land and proper management and control of water re-
sources of vital importance for bring prosperity in the predominantly agricultural based economy
of this highly populated. This cannot become technically feasible without effective flood hazard
and flood risk maps.
Flood risk mapping is the vital component in flood mitigation measures and land use planning.
This thesis research attempts to synthesize the relevant database in a spatial framework to evolve
a flood hazard map and flood hazard map of Megech River at Denbiya flood plain.
Basic aim of this Research is to identify the area chronically suffering from flooding and create a
flood risk maps based on topographical, hydrological and meteorological data. The study has also
focused on the identification of factors controlling flood hazard and risk in the study area.
3
1.3. Objective of the study
1.3.1. General objective
The general objective of this study to assess flood risk in Denbiya flood plain by using multi crite-
ria evaluation (MCE) technique with Arc GIS and HEC-RAS Model.
1.3.2. Specific objectives
To develop area inundation map of Megech River with a particular return period flood fre-
quency analysis.
To develop flood hazard Map at Megech Catchment.
To develop flood risk map at Denbiya flood plain.
1.4. Research questions
What looks like the flood level in different return period?
Which part the Megech River affected by flood hazard?
What is the magnitude of flood risk in Denbiya flood plain?
1.5. Significance of study
The main significance of this thesis is to contribute for the understanding of the flood risk assess-
ment to provid reliable information for managing water resources in a sustained manner to be
vital. It also provides available information for researchers, planners, decision makers, stake-
holders and governments for integrated water resource management activities.
As a result of study the flood hazard and risk assessment on Denbiya flood plain to provide
information on the risk of future flood damage on crops, property, and control flooding
during important farming periods by applying mitigation measures.
1.6 Thesis Outline
This thesis is organized in to Six chapter where Chapter one present about general backgrounds of
the study, statement of the problem that leads to a needs for study, objectives and significances of
the study. Chapter two describes the reviewed literature related to this study and previous work in
Tana sub-basin. Chapter three deals with the general methodology including description of the
study area general work flow, data collection, data quality test, and analysis future hydro-
metrological variables. The forth chapter contain about flood analysis. Chapter five includes the
result and discussion of the study that of the flood inundation, Flood hazard and Risk map. The
last Chapter is the section for conclusions and recommendations of the study had presented.
4
2. LITERATURE REVIEW
2.1 Historical background flood risk in the world
Flood risk was mainly associated with hazard occurrence probability. However, flood hazard is a
concept in constant evolution. Nowadays, the flood risk considered as the combination of the haz-
ardous phenomenon of flooding and a vulnerable system susceptible to suffer loss. As inferred by
White (1945), “Floods are acts of god, but flood losses are largely acts of man”. The flood risk
brings different aspects together, e.g. natural, human, social, economic and environmental. In
some cases, floods can generate benefits. However, extreme events always caused several prob-
lems to our societies. The flood risk is nowadays the more damaging natural hazard in the world.
2.2. Historical background of flood risk in Ethiopia
Flood risk assessment of the flood prone areas in Ethiopia is not an easy task. The catastrophes
occurred during this last decade and their impacts on the society revealed that we are not yet pre-
pared to deal with this problem. Damage caused by floods on human health is the most adverse
consequence of flooding, e.g. psychological problems, injuries and loss of human life. However,
the loss of goods and disruption of activities as well as environmental issues also gained the atten-
tion of experts all over the world. Moreover, the absence of stream flow data and the secrecy
about survey reports of some major rivers, classified as “International Rivers”, effectively block
any thorough study of the topic. However, there are some studies, particularly done by the
(DPPA) and by some other organizations and individuals, on flood risk in Ethiopia. In the past,
there have been floods, which have taken both human lives and destroyed properties. Although
flood events are not new to Ethiopia, the country, in 2006 main rainy season, has threatened by
unprecedented flooding of abnormal magnitude and damage.
Apparently, this is, for the large part, due to torrential or heavy rains falling for long days on the
upstream highlands. The rains have caused most rivers to swell and overflow or breach their
courses, submerging the surrounding „flat' fields or floodplains, which are mostly located in the
outlying pastoralist regions of the country. Because of the extended and widespread heavy rainfall
as of the beginning of 2006 and 2011 main rainy season, many areas have already experienced
devastating damage. The downstream of Megech River to known the flood prone areas in Dembi-
ya flood plain.
5
2.3. GIS and Remote Sensing for flood hazard and risk assessment
2.3.1 GIS analysis
Nowadays GIS is emerging as a powerful tool for the assessment of risk and management of Nat-
ural Hazards. Due to these techniques, natural hazard mapping can be prepared now to delineate
flood prone areas on the map. Such kind of maps will help the civil authorities for quick assess-
ment of potential impact of a natural hazard and initiation of appropriate measures for reducing
the impact. Moreover, GIS provides a broad range of tool for determining areas affected by floods
or forecasting areas likely to be flooded due to high discharge of the river.
When spatial data used in an information system, one tends to speak of a spatial information sys-
tem. Spatial data has a physical dimension and geographic location. With the help of sequential
images of certain area, we can find out the behavior of the flood routing and damages. Spatial da-
ta stored in the digital database of the GIS, such as a digital elevation model (DEM), used to
predict the future flood events. The reason to choose raster for base map was to show clearly the
situation and location. Vector and raster are both primary data types used in GIS and both of them
have spatial referencing systems.
2.3.2. Remote Sensing
The United Nations, Conference on the Exploration and Peaceful uses of outer space, defined the
term Remote Sensing as “The sensing of the Earth's surface from space by making use of the
properties of electromagnetic waves emitted, reflected or diffracted by the sensed objects, for the
purpose of improving natural resources management, land use and the protection of the environ-
ment”. Remote Sensing can be define as the science and technology by which the characteristics
of objects of interest can be identified, measured or analyzed through the information obtained by
a device that records reflected, emitted, or diffracted electromagnetic energy without direct con-
tact. Remote-sensing systems, mainly the once deployed on satellites, provide a continuous and
consistent view of Earth transmitting the information to ground station facilitating the ability to
monitor Land surface and the atmosphere surrounding the earth. In much of remote sensing, the
process involves an interaction between incident radiation and the targets of interest. Remotely
sensing images from satellites and aircrafts are often the only source that can provide this infor-
mation for large areas at acceptable costs Sluiter, R. (2005).
Application of Remotely acquired DEM for determining watershed Geometry
6
Remote sensing data used to obtain almost any information that was typically obtain from maps
or aerial photography. The availability of relatively high-resolution digital elevation data has re-
placed the classical method of watershed delineation. Making use of GIS system with the help
remotely acquired DEM (Digital elevation Model) data, it has become simple to obtain physical
characteristic of a watershed such as catchments boundaries, drainage lines and slope P. Sarma,
(2006). GIS based Modules and utilities to be develop to automate these processes of deriving
catchments boundary and drainage line processing. The level of accuracy has been validating with
manual delineation of catchment and the results have provided high accuracy. In this study also
satellite derived products of The Shuttle Radar Topography Mission (SRTM) obtained elevation
data have been utilized to derive the catchment geometry and drainage pattern of Lake Tana basin
and the sub catchments within the basin. The use of remote sensing techniques and satellite-
derived data has grown a wide popularity in hydrologic analysis. Remote sensing techniques pro-
vide a means of monitoring and measuring hydrological state variables over large areas. In
addition, it has become a powerful tool in its advantage of capturing spatial as well as temporal
distribution of the variables within practical accuracy.
Application of satellite image for flood plain delineation
Information obtain through remote sensing systems have appreciably been applied in determining
area extent of flooded zone. Many researchers have made use of different remote sensing products
for flood plain delineation Normalized Difference Vegetation Index (NDVI) products to use for
flood delineation with the assumption that water has very low reflectance in the near infrared por-
tion of the spectra. Multi spectral imageries NDVI maps were use along with a DEM for
delineation flood. Landsat derived NDVI images have also been incorporate in flooded area anal-
ysis as demonstrate. It has made estimation of flooded area in Bahir-el Jebel basin using Moderate
Resolution Imaging Spectrometer (MODIS) Terra derived images by employing unsupervised
classification techniques Kiefer (2004).
2.4. Approaches of flood hazard and risk assessment
Flooding occurs when the amount of water reaching the drainage network exceeds the amount of
water, which could be contain by the drainage channels and overflows out onto the floodplain.
Several factors influence whether or not a flood occur :(the total amount of rainfall falling over
the catchment; the geographical spread and concentration of rainfall over the catchment, i.e. the
spatial variation; rainfall intensity and duration, i.e. the temporal variation; antecedent catchment
7
and weather conditions; ground cover; and the capacity of the drainage system to contain the wa-
ter. Widespread flooding and/or non-flash flooding (lasting for more than 24 hours), occurs
following rainfall of high intensity or long duration over the whole or a large proportion of the
catchment. Runoff is typically low in areas where the percentage of vegetation cover is high, as
vegetated areas allow high infiltration until the earth is saturated. Where the ground pre-saturated,
such as following a long wet period, medium rainfall events can cause flooding as runoff begins
almost immediately Malczewski, J. (1999).
Flood levels in urban areas quickly rise where the percentage of impermeable surfaces on the
floodplain, such as buildings, roads and car parks, is high. On sloping concrete and bitumen sur-
faces, for example, runoff is immediate. The flood hazard can be assessing by two major
approaches: (1) The statistical or hydrological and (2) Geomorphological. Alexander (1993) stat-
ed that the hydrological approach comprises methods of calculating or analyzing mainly,
variables like discharge, recurrence intervals, flood hydrographs, water yield from the drainage
basin and hydraulic geometry.
2.5. Multi Criteria Spatial Decision Support Systems
A decision is a choice between alternatives. The alternatives may represent different courses of
action, different hypotheses about the character of a feature, different classifications, and so on.
Broadly speaking a Decision Support System (DSS) is simply a computer system that helps you
make a decision. Decision makers historically have indicated that inaccessibility of required geo-
graphic data and difficulties in synthesizing various recommendations are primary obstacles to
spatial problem solving. Studies have shown that the quality of decisions (i.e., the ability to pro-
duce meaningful solutions) can be improve if these obstacles are lessened or removed through an
integrated systems approach, such as a spatial decision support system (SDSS), particular and im-
portant types of DSS. SDSS refers to those support systems that combine the use of GIS
technology with software packages for selection of alternatives of location for different activities.
In addition, multi-criteria decision making (MCDM) and a wide range of related methodologies
offer a variety of techniques and practices to uncover and integrate decision makers‟ preferences
in order to solve “real-world” GIS-based planning and management problems. However, because
of conceptual difficulties (i.e., dynamic preference structures and large decision alternative and
evaluation criteria sets) involved in formulating and solving spatial decision problems, research-
ers have developed multi-criteria-spatial decision support systems (MC-SDSS) P. Sarma, (2006).
8
2.6. Multi-Criteria decision making in GIS
Spatial Multi-criteria decision problems typically involve a set of geographically defined alterna-
tives (events) from which a choice of one or more alternatives is made with respect to a given set
of evaluation criteria (Malczewski, 1996). To meet a specific objective, it is frequently the case
that several criteria to be evaluate. Such a procedure was calling Multi-Criteria Evaluation. A cri-
terion is some basis for a decision that can be measure and evaluate. It is the evidence upon which
an individual can be assign to a decision set. Criteria can be of two kinds: factors and constraints.
A factor is a criterion that enhances or detracts from the suitability of a specific alternative for the
activity under consideration.
The weight of each parameter is defined following the Analytical Hierarchy Process (AHP)
(Saaty, 1990a, b). AHP is a structured technique used for analyzing complex problems, where a
large number of interrelated objectives or criteria is involved. The weights of these criteria are
defined after they were rank according to their relative importance. Thus, once all criteria were
sort in a hierarchical manner, a pairwise comparison matrix for each criterion creates to enable a
significance comparison.
The relative significance between the criteria was evaluate from one to nine indicating less im-
portant to much more important criteria, respectively. It is worth noting that pairwise
comparisons and variable hierarchization in AHP result from a Delphi consensus already used in
other indexed approaches (Aller et al., 1987), which is subjective (Pacheco and Fernandes, 2013).
However, weighting by AHP is widely used in many applications (Valle Junior et al., 2014;
Oikonomidis etc al.2015) and recommend using for regional studies (Ayalew and Yamagishi,
2005).
2.7. HEC-RAS and Model process
2.7.1 HEC-RAS
Hydrologic Engineering Center River Analysis System (HEC-RAS) is an integrated system
of software designed for interactive use in multi-tasking, multi-user network environment.
The system is comprised of a graphical user interface (GUI), separate hydraulic analysis
components, data storage and management capabilities, graphics and reporting facilities. HEC-
RAS model has performed the following analysis:Steady flow water surface profile computa-
tion, Unsteady flow simulation, Moveable boundary sediment transport computations and Water
quality analysis. A key element is that all four components use a common geometric data
9
representation and common geometric and hydraulic computations routines. HEC-RAS have the
ability to present hydraulic properties computed during a flow simulation (Davis, 2002).
In February 2016, RAS model with ability to perform 2D hydrodynamic unsteady flow
routing using St. Venant equation or Diffusion wave equation was introduced (Kumar,
2017). HEC-RAS is taking into consideration hydraulic effects of bridges, culverts, weirs, and
other structures in the river and floodplain on water surface calculations (USACE, 2016).
2.7.2 Model process
By using HEC- Geo-RAS, the geo-database design supports analysis of spatial data for hydraulic
modeling floodplain mapping. It uses Arc-GIS desktop to develop spatial data input for HEC-
RAS models from digital terrain models and other datasets.
Both HEC-Geo RAS and Arc GIS extension, is used as the interface between HEC-RAS and GIS
for pre-processing and post-processing of the data in GIS. The availability of floodplain survey
data for the new and the old alignment of the river, the pre and post processing using the HEC-
Geo RAS is not complicated. The geometric data of the flood plain and River is obtained from the
digital elevation model (DEM) for the points where the plain showing less number of cross-
sections (Davis, California, April 2000). Water surface profiles, along the river reach under study,
for floods of various return periods were compute with sub critical flow simulation.
These profiles were export to GIS and water surface Triangular Irregular Network (TIN) was
generate. An intersection of the terrain TIN and water surface TIN results in flood map After the
model results are calculated in HEC- RAS, then the output of HEC-RAS exported to Geo-RAS
for post processing and then for floodplain depth and extent mapping. This helps for floodplain
managers and emergency management personnel may use the resulting contingencies to protect
against the loss of life and property damage.
HEC-RAS uses a number of input parameters for hydraulic analysis of the stream channel geome-
try and water flow. The three reach lengths represent the average flow path through each segment
of the cross-section pair. As such, the three reach lengths between adjacent cross-sections may
differ in magnitude due to bends in the stream.), manning‟s roughness coefficients (may vary hor-
izontally or vertically) and Channel contraction and expansion coefficients (Y.WANG, 2005).
10
2.8 Previous Study
As my literature review previous stud‟s shows that related to flood in upper Blue Nile at tana sub-
basin: Flood Inundation Area Mapping under Climate Change Scenario in South East of Lake
Tana, Upper Blue Nile Basin, Ethiopia (Temeselew Yeshitila, April 29, 2018). Flood Hazard As-
sessment Using Multi-criteria Evaluation Approach in Dembiya Woreda, Amhara Region, Ethio-
Ethiopia [(Dessie Tegegne, Ebrahim Esa and Assayew Nebere, December 2018). This study to use
only GIS Tool and four flood factors, which says that, this sentence “However, in this study, the effect
of land use and drainage density on flood magnitude is excluded due to the lack of input data for
the study area”. Not‟s only this did not to use HEC RAS model so there methodology different
from me]. Studies on climate change on Gumara river basin conducted by Melke and Abegaz
(2017). The Watershed characteristics on River Flow for the tana basin, Case study Ribb and
Gummara Catchments (Ephrem Alemu 2011). Estimate peak flood and produce flood inundation
map using several flood prediction tools. The study applied HEC-HMS for flood frequency mod-
eling, HEC-RAS hydrodynamic modeling to prepare flood mapping at Rib River (Zelalem
Ayalew 2011). Flood Hazard and Risk Assessment in Fogera Woreda using GIS & Remote Sens-
ing (Woubet Gashaw.2007). Remote sensing based assessment of water resource potential for
Lake Tana basin (Yohannes Daniel 2007).
The uniqueness of this study other than that of the previous one tried to at Denbiya Flood Plain to
assess flood hazard and Risk map by used special evaluate technics which was multi criteria eval-
uation technics to use GIS tool and HEC-RAS model. Therefore, it was necessary to addresses the
flooding problem area to assessment and prepare flood hazard, and risk map and flood inundation
map for the decision maker.
11
3 Methodology
3.1 Description of Study Area
3.1.1 Location
The study area is located in Amhara Regional State of Ethiopia in North Gonder Zone Denbiya
flood plain (in case of Megech River) 745km from Addis Ababa and 180km to North of Bahir
Dar. The geographic location of the study area which is located at the latitude and longitude of
12°16′18″N and 12°35′40″N latitude to 37°08′16″E and 37°35′35″E longitude. The flood plain
located in Megech watershed which is part of the upper part of Blue Nile basin specifically in the
in the North of Lake Tana (the largest freshwater reserve in Ethiopia). The Megech River origi-
nates near the North Mountains which is approximation altitude range between 1670 and
3000m.a.s.l and to passes through Denbiya flood plain and joins/drains/ in to Lake Tana. The total
watershed of Megech is 798.18km2 at the lake inlet of which 478 km2 is gauged this means 60%
gauged. Flood Palin Area where found the downstream part of Megech watershed. Thirteen
kebeles from Denbiya woreda are the common area affected by Megech river flood.
Figure 1; Location of study area
12
3.1.2 Topography
The altitude of the catchment ranging from 1670m a.s.l to 3000m a.s.l and the flood plain ranging
from1670m a.s.l to 2021m a.s.l. The lower reach of the Megech River has a length of 78,710.57m
passing through the Denbiya flood plain with a slope of 29% the river at this stretch has a very
flat slope tending to change its course with rising of its bed with silt.
3.1.3 Soil Type
The major soil types of Megech watershed Eutric-cambisols, chromic vertisols, Orthic luvisols,
Eutric-nitosols, Lithosols, Chromic-luvisols, Rock surface and Eutric fluvisols. The low land or
flood plain area is dominated by chromic-vertisols and the upper part of the watershed covered by
other soil type and shown at figure 3 (A)
Table 1-Major-Soil-Types Group, texture and the Area coverage.
No SOIL_TYPE MAJOR_SOIL Area %Weight
1 Eutric Cambisols Cambisols 301.35 37.754
2 Chromic Vertisols Vertisols 292.76 36.678
3 Orthic Luvisols Luvisols 86.16 10.794
4 Eutric Nitosols Nitosols 59.82 7.495
5 Lithosols Lithosols 31.99 4.008
6 rock surface Rock Surface 23.65 2.962
8 Eutric Fluvisols Fluvisols 2.45 0.307
Source ;-( Amhara Design and Supervision works Enterprise)
3.1.5 Land use/Land cover
There are seven type of major land use/cover in Megech watershed namely Built up Area,
Cultivated Land ,Forest Land, Grass Land ,Marsh Land, Shrub and Bush Land Water Body. From
those land use land cover type 84% of watershed covered by cultivated land and grass land. The
majority of cultivated land covered by crops are Teff, maize and dagusa. Shown at figure 3(B).
Table-2: Land use/ Land cover.
No Major land use/land cover Area(km2) % Weight
1 Cultivated Land 448.91 56.242
2 Grass / bar / Land 214.26 26.844
3 Shrub and Bush Land 82.44 10.329
4 Built Up Area 29.08 3.644
5 Marsh Land 13.83 1.732
6 Water Body 9.63 1.207
7 Forest Land 0.02 0.003
13
Figure 2: Percentage of Land use/ land cover coverage in Megech catchment
Figure 3: Drainage, Soil, and Land use and land cover map of Megech watershed
Cultivated
Land Grass Land
Shrub and
Bush Land
Built Up
Area
Marsh Land Water Body
Forest Land
A B
14
3.1.6 Climate
The climate nature of the study area divided into two rainy and dray season. The seasonal rainfall
has a Unimodal distribution with peak in July. The study area is characterized by one main rainy
season between June and September, in which 70% to 90 % of the average annual rainfall occurs
locally known as kiremt. The second is the dry period, which extends between October to April
and locally known as Bega. The catchment is characterized by a rainfall which amounts from
1021.7mm to 1133.6mm per year. The mean annual rainfall and mean a maximum & minimum
temperature for the period of (1988-2019) is different from station to station 1021.7mm (22.77c°
& 9.84c°) at Ambageorgis, 1133.6mm at Shembekit, 1106.1mm mm (27.00c° & 13.67c°) at
Gonder & 1072.3mm (28.54c° &13.15c°) at Makisegnit and shown at (figure 4 and 5)
Table 3: Mean monthly maximum and minimum temperature of Megech watershed (1988-2019)
No Month Mean of maximum temperature (C
°)
Tmax
mean
Mean of minimum temperature
(C°) Tmin.
mean Ambageorgis Gonder Maksegnit
Am-
bageorgis Gonder Maksegnit
1 Jan 23.69 27.81 28.76 26.76 9.19 11.72 11.37 10.76
2 Feb 25.03 28.46 29.70 27.73 10.06 13.08 12.46 11.87
3 Mar 25.14 29.98 31.50 28.87 11.21 15.03 13.96 13.40
4 Apr 25.42 30.06 31.25 28.91 12.18 15.94 14.63 14.25
5 Maye 24.54 28.77 30.86 28.05 12.12 15.87 14.63 14.21
6 Jun 22.15 25.67 27.62 25.15 10.75 14.41 13.85 13.01
7 Jul 19.21 22.97 24.32 22.17 9.62 13.79 13.75 12.39
8 Aug 19.52 23.14 25.18 22.62 9.63 13.64 13.49 12.25
9 Sep 21.66 25.28 26.83 24.59 9.63 13.14 13.13 11.97
10 Octo 21.78 26.78 28.63 25.73 9.06 12.92 12.99 11.66
11 Nov 22.11 27.54 29.58 26.41 7.59 12.46 12.20 10.75
12 Dec 23.01 27.56 28.24 26.27 7.06 11.97 11.33 10.12
3.1.7 Hydrology
The hydrological station of Megech River is located in the geographic location Latitude of 12.48°
N and Longitude 37.45° E near Azezo town 462km2 measure. Hence highest river discharge is
measured during main rainy season of the year, 71.3% mean flow occurred from July to
September Minimum mean flow occurred in February month which is 2.33m3/s & maximum
mean flow occurred in august month which is 41.16 m3/s. The figure 7 shows that the river flows
was variable flow because the minimum and max mean flow highly different magnitude to show
at (figure 6).
15
Figure 4: Mean monthly precipitation of Megech watershed from (1988-2019)
Figure 5: Mean monthly maximum and minimum temperature of Megech watershed
Figure 6: Average monthly discharge of Megech River (1980-2019)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
Jan Feb Mar Apr may Jun Jul Agu Sep Oct Nov Dec
Mea
n R
ain
fall
(mm
)
Month
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
Jan Feb Mar Apr Maye Jun Jul Aug Sep Octo Nov Dec
Mea
n T
emp
retu
re((
c°)
Month
Maxcemum Temprature
Minumem Tempratur
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
Jan Feb Mar Apr May June July Aug Sep Octo Nove Dece
Mea
n f
low
m3
/s
Month
16
3.2 General Research Method
The general methodology includes collecting the metrological data from NMA, and hydrological
data from MoWIE, topographic data from field surveying. The data quality was checked by using
data quality tests.
This research work makes use of a GIS and remote sensing catchment of Megech River Northern
part of Tana basin. The method steep is as follows: data collection from institutions such as
Ministry of water resource, National Meteorological Agency and etc, after collecting the
necessary data for the research to differentiate causes of the flooding problems in Megech River
to identify, maps of flood risk zonation and riverbank hazard of the study, Area to be made based
on photo-interpretation and fieldwork observations, The scenario for the privies flood event to
create based on fieldwork data (interviews and field observations) and its return period to be
obtained from gauge flow data, A combined analysis of flood causes location of critical points
across the Catchment.
Used to HEC_RAS 5.0.7 develop Flood Inundation map and the flood hazard analysis is compute
using multi criteria evaluation (MCE). To run MCE, the to be select flood parameters are include
as soil type, elevation, slope, drainage density, land use land cover and rain fall are develop and
weight overlay for flood hazard. Then weight overlay technique will be compute in Arc GIS 10.1
to use generate flood hazard and risk map. Considering the degree of loss to be total for the study
area, the vulnerability is assumed to be one. Finally to generate flood risk map of the catchment,
elements at risk layer (population density and land use wetland) and the flood hazard map
overlaid use weight overlay analysis technique in Arc GIS 10.1 environment and shown figure 8.
17
Figure 7: General Method of work flow chart
Flow data
Gumball EV I
GIS
Ele
vat
ions
re-c
lass
Soil
re-
clas
ses
LU
/LC
re-
clas
ses
Dra
inag
e re
-cla
sses
Slo
pe
re-c
lass
es
RF
re-
clas
ses
Check D Index
Flo
od
Fact
or
an
aly
sis
Flo
od f
requen
cy a
nal
ysi
s
Popula
tio
ns
re-c
lass
Flood hazard map
Boundary condition
If yes
Flo
od
haza
rd a
naly
sis
Flood Factor
Flo
od
ris
k a
naly
sis
DEM & Filed Surveying
Manning value
Flood Inundation map
HEC-Geo RAS
Post HEC_RAS process
River X-cross If no
Pre
HE
C_R
AS
Pro
cess
Elevation
Peak flow
Element of
risk LU/LC &
population
Data collection
AHP weight overly
Flood Risk map
AHP Weight overlay
18
3.2.1 Data Collection
To accomplish the thesis work effectively, data collection and analysis was the backbone step of
the task. For this study two types of data are collected from different sources.
Primary data collection
A primary data had been collecting from the site by using total Station, hand GPS, and Google
Earth, which includes river cross- sections and top map data (to show appendix 10). The follow-
ing photos had taken during primary data collection.
Figure 8 : Photo taken in top map at Dembiya flood plain during filed surveying.
Figure 9 : Photo taken in River X-cross at Dembiya flood plain during filed surveying.
RRE RC
RRB
Megech River cross section at flood plain Area at Adisge in February 2020
Megech River in flood plain detail surveyor top map data February, 2020
19
Secondary data collection
Secondary data collected from different organizations and literatures. A hydrological data collect-
ed from Ministry of Water, Irrigation and Energy (MoWIE) at hydrology department and
metrological data collected from Ethiopia Meteorological Agency (EMA) in Amhara Regional
branch and DEM (30m) to gate SRTM I high resolutions from December 30, 2010 to February
30, 2011 GC. The land use/land cover, soil type and geology of the studied area to be taken from
Amhara design and supervision work enterprise and Abay basin development office.
Meteorological data
The required of Meteorological data collected different Metrological station from the 32 years of
daily rainfall data from (1988-2019).
Table 4: Name and Location of Rainfall stations Megech watershed
No Nam of station Location in (°)
Altitude(m) Period of record Position of Station Longitude Latitude
1 Abageorgis 37.62 12.77 2964 1988-2019 Near a catchment
2 Shembekit 37.5 12.67 2463 1988-2019 On catchment
3 Gonder 37.43 12.52 1986 1988-2019 On catchment
4 Makisegnit 37.481 12.36 1972 1988-2019 Near a catchment
(Source: EMA/NMA, Bahir dar branch)
Hydrological data
The available hydrological data collected from the Ministry of Water Irrigation & electricity
(MoWIE) Ethiopia from (1980-2019) at Megech river gauge station near to Gonder-Azezo town.
The following table-5 to shows the type, source and purpose of the collected data.
Table 5: Data type, duration, and purpose and source data.
No Data types Data Availability/ Du-
ration/
Data Types Purpose Data Source
1 Metrological data 32 Trend analysis/Annual & dai-
ly Max/ IDW
EMA/NMA
B/dar branch
2 Hydrological data
(gauge)
40 Flood Analysis MoWIE
3 Topographic map
she. File
Ethio-DEM-30 Delineation of watershed SRTM
4 River Cross sec-
tion
February 18-30/2020 Inundated Flood mapping Field surveying and
DEM
5 DFP Population SCS 2007 Flood Risk map Denbiya Woreda
20
3.2.2 Catchment delineation
In this step a terrain model used an input to derive eight additional datasets that collectively
describe the drainage pattern of the watershed and allows for stream and sub-basin delineation
by used Arc GIS 10.1 and Arc Hydro tool. Using the terrain data as an input, terrain
preprocessing is a series of steps to drive the drainage network.
The steps consist of computing DEM reconditioning, fill sinks, flow direction, flow
accumulation, stream definition, stream segmentation, catchment grid delineation, catchment
polygon processing, drainage line processing, and delineation of catchment processing.
Figure 10: Megech Catchment Delineation by train preprocess.
21
3.2.3 Hydro-meteorological data analysis
Engineering studies of water resources development and management depend heavily on
hydrological data. These data sets are the key inputs for hydrological modeling However; the
selection of representative meteorological gauging stations depends on the data availability
(including existence of enough periods of record and distance from the area of interest). The data
should be no jumping, stationary, consistent, and homogeneous when they are used, to simulate a
hydrological system.
3.2.3.1 Missing data analysis
The main problem in hydrologic analysis is that these data are not found fully. The use of a
rainfall data series with missing values may critically influence the statistical power and accuracy
of a study. This problem some reason behind the shortage of these data is either the
malfunctioning of the instrument or may be due to the absence of an observer to make the visit of
the gauge. These gaps in rainfall and temperature record data can be filled by several methods.
Diverse techniques was proposed and adopted in filling missing data: Arithmetic mean (less than
10%), Normal ratio Method ( more than 10%), Regression and distance power method are the
most commonly used methods for estimation of missing rainfall & temperature data sets.
Depending on the simplicity and length of missing data and the missing precipitation of all the
station is less than 10%, Arithmetic mean method used to fill the missing precipitation data(Shaw,
1988). When the average annual precipitation at each of the adjacent stations differs from the av-
erage at the missing data station by less than 10%, the following formula used to estimate the
missing daily data.
By this case to used Arithmetic mean method to calculate missing data.
The General formula is Px=
------------------------------------------Eq. (3.1)
Px = missing precipitation record.
N = number of neighboring stations.
P1, P2 +….and Pn are the precipitation records at the neighboring stations.
3.2.3.2 Data quality analysis
In hydrology when the data used for frequency analyses and to simulate hydrological model the
data should be Independency and stationary, consistent and Outliers (Dahmen and Delft, 1989).
To determine whether the data meets these criteria, the hydrologist needs a simple but efficient
22
screening procedure of statistical variability. The quality of the data in this study to be analyze the
following procedures.
Independency and Stationary Test: the Independency and stationary of the time series was
measured by using statistical properties (mean, standard deviation, variance and higher order
moment).These statistical properties of the hydrological time series not affected by the choice of
time origin, to independency and stationary. The time series data was checked based on stability
of the variance by using F-test and the stability of mean by using T-test under 5% confidence
interval two tail Wald-Wolfowitz (1943)(W-W). Detail result show Appendix (2, 3, 4, 5 and 6).
R=∑ ------------------------------------------------------------------------Eq. (3.2)
Mean ® = (
) ----------------------------------------------------------------------------------Eq. (3.3)
Var®=
-------------------------------------Eq. (3.4)
U=
---------------------------------------------------------------------------------------- Eq. (3.5)
When U is standard normal variant
Consistency test: Significance change occurs in metrological station may affect the time series
data of the station and this change may give inconsistency of the data. when the catchment rainfall
at rain gages is inconsistent over a period of time and adjustment of the measured data is
necessary to provide a consistent record. A consistent record is one where the characteristics of
the record have not changed with time. Inconsistency may result from: change in gauge location,
exposure, instrumentation, or an observational procedure is not real and on time. To overcome the
problem in consistency a technique most widely applied called double mass curve is used.
Double-Mass Curve (DMC) analysis is a graphical method for identifying or adjusting
inconsistencies in a station record by comparing its time trend with those of other stations nearby
(Shaw,1988). The adjustment is done by applying a correction factor (N‟/ N) = K.
Px‟ = Px*
---------------------------------------------------------------------------------------Eq.(3.6)
Where: Px‟ = corrected ppt. at station X
Px = original recorded ppt. at station X
N‟ = corrected slope of the double mass curve
N = original slope of the double mass curve
The graphs almost have straight slopes show that at (figure-11). Due to this the data that collected
From the stations are consistent.
23
Figure 11: Double Mass Curve analyses of test constancy Megech Catchment rainfall stations.
Testing for Outliers
Outliers are data points that depart significantly from the trend of the remaining data. The
retention or deletion of these outliers can significantly affect the magnitude of statistical
parameters computed from the data, especially for small samples. Procedures for treating outliers
require judgment involving both mathematical and hydrologic considerations. According to the
Water Resources Council (1981), if the station skew is greater than +0.4, tests for high outliers
are considered first; if the station skew is less than -0.4, tests for low outliers are considered first.
Where the station skew is between ±0.4, tests for both high and low outliers should be applied
before eliminating any outliers from the data set. The following frequency equation can use to
detect high outliers:
YH = Y + KnSy ----------------------------------------------------------------------------------------Eq.(3.7)
where YH is the high outlier threshold in log units and Kn was as given in for sample size n=40.
If the logarithms of the values in a sample are greater than in the above equation, then they are
considered high outliers.
A similar equation can use to detect low outliers: YL = Y-Kn*Sy where YL ------------Eq. (3.8) is
the low outlier threshold in log units. Flood peaks considered low outliers are deleted from the
record and a conditional probability adjustment described by the Water Resources Council (1981)
can be applied. Based on this information this research the skewness was less than -4 so to
conceder to lower outliers 1.67.
R² = 0.9989
R² = 0.9992 R² = 0.998 R² = 0.998
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
0
10
,00
0
20
,00
0
30
,00
0
40
,00
0
50
,00
0
60
,00
0
70
,00
0
80
,00
0
90
,00
0
10
0,0
00
11
0,0
00
12
0,0
00
13
0,0
00
14
0,0
00
15
0,0
00
An
nu
al c
um
ula
tive
RF
each
sta
tio
n
(mm
)
Comulative All annual Rf station
Double-Mass-corve
24
Table 6: The Test of higher and lower the record flow data.
Maximum daily flow(m3/s)
Year Xi Yn=logXi Year Xi Yn=logXi
1980 199.297 2.300 2003 214.650 2.332
1981 317.967 2.502 2004 263.668 2.421
1982 47.25 1.67 2005 238.387 2.377
1983 142.389 2.153 2006 186.040 2.270
1984 160.708 2.206 2007 169.665 2.230
1985 186.201 2.270 2008 214.650 2.332
1986 143.544 2.157 2009 188.152 2.275
1987 57.739 1.761 2010 274.220 2.438
1988 72.487 1.860 2011 407.660 2.610
1989 47.642 1.678 2012 355.347 2.551
1990 65.939 1.819 2013 290.527 2.463
1991 135.775 2.133 2014 194.577 2.289
1992 96.695 1.985 2015 205.580 2.313
1993 130.124 2.114 2016 180.440 2.256
1994 136.884 2.136 2017 223.962 2.350
1995 274.802 2.439 2018 156.088 2.193
1996 89.792 1.953 2019 188.600 2.276
1997 100.050 2.000 sum 7270.513 88.235
1998 190.848 2.281 mean 187.97 2.206
1999 242.301 2.384 STDEV.S 90.92 0.233
2000 83.946 1.924 Skewness 0.48 -0.72
2001 261.069 2.417 minimum 47.25 1.646
2002 137.881 2.140 Maximum 407.660 2.610
Checking for outliers YH and YL
Based on N =40 data for table Kn is 2.682
Y minimum=Ymean-Kn*YnSTDV=1.58, then value of X minimum= 10^Yminimum
=10^1.58=38.011m3/s is less than 47.25 m
3/s. So lower outlier ok
Y maximum=Ymean+Kn*YnSTDV=2.832, then the value of X maximum=10^Ymaximum
=10^2.832=678.94m3/s is also greater than 407.66m
3/s. So the higher outlier ok
Test for absence of Trend
By using Spearman‟s Rank-Correlation Method we can test the absence of trend.
Rsp= 1-6* ∑
= 1-6*
=0.45-------------------------------------Eq. (3.9)
Where: Rsp = Spearman‟s rank-correlation, D = difference,
i= chronological order, n= toal number of data
25
Di = KXi – Kyi-------------------------------------------------------------------------Eq. (3.10)
Kxi = rank of the variable x, which is the chronological order number of the observations Kyi is
the chronological order number of an observation in the original series. For the null hypothesis,
Ho,:Rsp= O (there is no trend), against the alternate hypothesis, H1,: Rsp < > O (there is a trend),
with the test statistic:
Tt = Rsp*
=3.2 ----------------------------------------------------------------------Eq. (3.11)
The null hypothesis accepted if Tt is not contained in the critical region. In other words, the time
series has no trend, if: t (v, 0.05%) < Tt < t (v, 99.95%), where v = n-2 degree of freedom. From t-
distribution table t-critical = 3.57 for a degree of freedom V= -3.57 < 3.2 <3 .57 ok! Therefore,
null hypothesis is accepted. The overall estimation of this test to attach annexes.
Homogeneity Test
Homogeneity is an important issue to detect the variability of the data. In general when the data is
homogeneous, it means that the measurements of the data are taken at a time with the same
instruments and environments. However, it is a hard task when dealing with rainfall data because
it is always caused by changes in measurement techniques, observational procedures,
environment characteristics, structures, and location of the stations. In this study, homogeneity
test has been applied at 4 meteorological stations in Megech catchment area from the period 1988
– 2019.
To check homogeneity of the selected stations in the watershed the non-dimensional rainfall
records plotted to compare the stations with each other. The following equation was used.
Pi =
*100 --------------------------------------------------------------------------------------Eq. (3. 12)
Where: Pi is non - dimensional value of precipitation for the month in station pi, Piave is over
year‟s averaged monthly precipitation for the station pi and Pav is over year‟s averaged yearly
precipitation of the station pi.
26
Figure 12: Homogeneity test of Megech catchment rainfall station.
Areal rainfall distribution
The areal rainfall Distribution required as input for Weight Overlay analysis computed by
different methods. from these methods; Rational method (in small area), Arithmetic mean method
(bounded area), Isohyet method (subjective and limited data), Inverse Distance Weight method
and Thiessen polygon method (to considered rectangle weight each station). However, IDW
method best option to compare others gives weight to station data in proportion to the space
between the stations. Daily rainfall data from (1988-2019) for four metrological station around
the catchment is prepared. The point covered by each station in the watershed created by IDW
tools in Arc-GIS environment.
Figure 13: Areal rainfall coverage by IDW for Megech watershed
-
100
200
300
400
1 2 3 4 5 6 7 8 9 10 11 12
Pi V
alu
e
Month
Homogeneity Test AmbaGiorgis
Gondar
Shembekit
27
4. Flood analysis
4.1 Flood frequency analysis
Flood frequency analysis was one of the important studies of river hydrology. It was essential to
interpret the past record of flood events in order to evaluate future possibilities of such
occurrences. The estimation of the frequencies of flood was essential for the quantitative
assessment of the flood problem. The knowledge of magnitude and probable frequency of such
recurrence is also required for proper design and location of hydraulic structures and for other
allied studies.
The gauge data which are random variable follow the law of statistical distribution. After a
detailed study of the distribution of the random variables and its parameters such as standard
deviation, skew ness etc. and applying probability theory, one can reasonably predict the
probability of occurrence of any major flood events in terms of discharge or water level for a
specified return period (Chow, 1953). Flood frequency analysis done in this study by selecting
annual maximum gauge discharge at Megech river from gauge discharge recorded data for 40
years (1980-2019) the Station point at (11°50ʹ0ʺ N and 37°38ʹ 0ʺ E ) used different distribution
methods to compared and selected the fittest distribution method. Based on this, Gumball‟s EV I
was the best fit and show at appendix-12 & Table-7.
Table 7: Index of Megech River selecte the fittest distribution method
Parameters Normal Gumball EV I Log Normal Log person Type III smallest D-index
D-index 2.02 1.92 3.42 1.96 1.92
The fittest distribution method has the smallest D-index value, hence Gumball’s EV I is the fittest
Gumball’s EV I Method: It defined a flood as the largest of the 365 & 366 daily flows and the
annul series of flood flows constitute a series of largest values of flows. This study attempt to find
out water discharge at different return period using the Gumball's EVI equation:
XT = Xmean + k * SDV ----------------------------------------Eq. (4.1)
Where, XT = Value of variant with a return period „T‟
Xmean = Mean of the variant (187.97 m3/s)
SDV = Standard deviation of the sample (90.92 m3/s)
k = Frequency factor expressed as k = (YT – Yn) / Sn----Eq. (4.2)
Yn and Sn depend on number year or n value
Yn=0.54 and Sn=1.14
28
YT =Reduced variant expressed by
(YT) = -ln [ln {T/ (T-1)}] -------------------------------Eq. (4.3)
T =Return period
Table 8: The flood analysis forecast in different return period
Gumbel EV I method
no Return period(year) T/(T-1) Yt KT Xt(m3/s)
1 2 2.000 0.37 -0.16 173.86
2 5 1.250 1.50 0.84 264.16
3 10 1.111 2.25 1.50 323.94
4 50 1.020 3.90 2.94 455.52
5 100 1.010 4.60 3.55 511.14
4.2 Flood factor Analysis
The major causes of floods include intensity, duration and spatial distribution of rainfall on
catchments; sedimentation on river channels and overflow of water from the riverbanks; steep
slopes, deforestation and poor soil infiltration capacity; failure of hydrologic structures and sud-
den release of waters from dams; and landslides. These factors influence the magnitude, run-off
or velocity of the flood and increase the risk of flood damage.
The excessive and torrential rainfall, steep slope with low-lying plains along the major rivers, de-
forested catchment, etc. it is causing both flash-and river-floods. The flash-floods damages are not
well recorded and documented compared to river floods damage. Flood parameter/factor/ particu-
larly in Megech catchment were identified to slope, soil type, elevation, land use type, drainage
density, rainfall and population are listed in order of importance of Standardized values of each
parameters in the reclassification process were listed in below.
4.2.1 Soil Factor reclassification
Soil types defined as the rate and extent of water movement in the soil, including movement
across the surface as well as downward through the soil. Other factors include texture, structure,
and physical condition of surface and subsoil layers. Major soil types in Megech catchment in-
clude Eutric-cambisols, Chromic vertisols, Orthic- luvisols, Eutric-nitosols, Lithosols, Chromic-
luvisols, Rock surface and Eutric-fluvisols.
From soil types chromic-vertisols are churning, heavy clay soils with a high proportion of swell-
ing clays. These soils form deep wide cracks from the surface downward when they dry out,
29
which happens in most years. Vertisols, which dominate the downstream of the catchment, are
imperfectly drained to poorly drained, deep, very dark grey to greyish brown, mottled firm clay
soils so that are given the highest scale in the flood hazard rating.
These soil types converted to raster format and finally reclassified based on their water infiltration
capacity and show at figure 15 (A).
Table 9:- Soil group, and texture and the respective hydrologic soil group.
No Major Soil group Soil texture Drainage condition Hydrological
soil group
Flood level
1 Eutric Vertisols Clay poorly drained D Very high(1)
2 Lithic Leptosols Clay Loam moderately deep to deep C High(2)
3 Eutric Fluvisols Silty clay Moderately well drained B- Moderate(3)
4 Luvisols/ Nitisols Silty clay well drained B Very low(4)
5 Eutric-Lithosol Sandy loam Excessively drained A Low(5)
(Source; Amhara design, supervision and work enterprise)
4.2.2 Slope Factor reclassification
Water flows from higher to lower slope, so it influences the amount of surface runoff and infiltra-
tion. A flatter slope has higher probability of an area to inundate by flood. The slope of the
Catchment derived from 30 m interval contour that digitized from 1:250,000 Topographic Map.
This contour converted to 3D shape file using 3D Analyst and the Convert feature to 3D module
by interpolating contour using an attribute as a source. Further TIN created to use 3D Analyst in
Create TIN from Feature (3D shape). Slope feature class converted to raster using Conversion
Tool in To Raster/ Feature to Raster module. The slope raster layer reclassified in five sub group
using standard classification schemes namely Quartiles. This classification scheme divides the
range of attribute values into equal-sized sub ranges, allowing you to specify the number of inter-
vals 50 while Arc Map determines where the breaks should be. Finally, the slope was
reclassifying in to continuous scale in order of flood hazard rating and shown at figure 15 (B).
Table 10: Reclassification slope (Rate, Degree and Present) of Megech catchment
No Rate Angel(degree) % of parameters Slope class Flood level
1 less than 0.54 less than 5 less than 5 very genteel slope Very high(1)
2 0.55-1.13 5 to 11 5 to11 Genteel slope High (2)
3 1.14-1.83 12 to 18 12 to 18 Moderate Moderate(3)
4 1.89-2.72 19 to 27 19 to 27 Steepest Low(4)
5 >2.72 >27 more than 28 Very steepest Very low(5)
30
4.2.3 Elevation reclassification
All the processes for the development of the elevation Parameters are as explained in the slope
Parameter development. The raster layer reclassified in to a common scale according to their in-
fluence to flood hazard, which shown at figure 15 ©.
No Elevation range Area(km2) %Weight Level of scale
1 1,672 - 1,919 422.25 52.97 very high(1)
2 1,920 - 2,141 202.81 25.44 High(2)
3 2,142 - 2,363 92.36 11.59 Moderate(3)
4 2,364 - 2,591 58.67 7.36 Low(4)
5 2,592 - 2,966 21.10 2.65 very Low(5)
4.2.4 Land use/Land cover Reclassification
Land use/land cover was influence sing infiltration rate, the interrelationship between surface and
ground water as well as debris flow. Thus, while forest and lush vegetation favor infiltration. Ur-
ban and pasture areas support the overland flow of water, which taken from Amhara design,
supervision and work enterprise 2011. These are important to consider back flow of Lake Tana
and areas that covered by floodwater. Generally, this raster data layer shows the different types of
land use/land cover across the Megech Catchment for the flood hazard analysis and Denbiya
flood plain for the flood risk analysis.
The data layer was already in raster format; this raster format was further reclassifying into a
common scale in order of their rainwater abstraction capacities. In addition, new values re-
assigned in order of flood hazard rating for hazard analysis and flood risk rating for flood risk
analysis and shown at figure 15 (D).
Table 11: Land use/ land cover reclassification.
No Major LU/LC Area % Weight Level Scale
1 Water Body 8.634 1.083 very high(1)
2 Built up area 29.084 3.648 High(2)
3 Grass Land 214.262 26.877 Moderate(3)
4 Cultivated Land 448.909 56.312 Low(4)
5 Forest ,bush and marsh Land 96.290 12.079 very Low(5)
(Source; Amhara design, supervision and work enterprise at 2011 GC)
31
4.2.5 Drainage Density Reclassification
Megech River digitized from 1:25000 Topographic Maps and using the spatial analyst, line densi-
ty module and drainage density to calculate (Eq. 4.4). Line density module calculates a magnitude
per unit area from polyline features that fall within a radius around each cell. When deriving
drainage density for a catchment area, both perennial and intermittent rivers/tributaries need to
take into consideration. If only perennial streams are included, the drainage density value for
catchments with only intermittent streams would be equal to zero. In the case of a flood event,
when both perennial and intermittent streams are active, its values would be unrealistic.
∑
------------------------------------------------------------------Eq. (4.4)
Where: L-Length of the waterway (km), N-Number of waterways, F-contributing drainage area
(km2). Higher drainage density values indicate lower infiltration rates and higher surface flow ve-
locity. Hydrogeological and geomorphological systems often have heterogeneous characteristics
that vary with scale from microstructures to continents. Drainage network patterns are no excep-
tion, and, consequently, nor is drainage density. The factors that influence drainage basin
characteristics vary according to the scale of the input data (e.g., river network maps, digital ele-
vation maps). According to Gregory and Walling, the usefulness of drainage density as a model
input parameter limit by the method used to derive the drainage network, as well as the map and
its scale that represents the catchment river network. This layer reclassified in five-sub group us-
ing standard classification schemes namely equal interval. In addition, to specify the number of
intervals while Arc map determines where the breaks should be. Moreover, new values (common
scale) reassigned in order of flood hazard rating in the assumption that the lesser the drainage
network in a given area the higher the flood hazard and shown at figure 16 (A).
Table 12: Catchment area of drainage density reclassification.
No Area(km2) Drainage density/km/sq.km/ % Weight Range of scale Flood level
1 11.097 7.0930 1.39 >2.00 very high(1)
2 39.681 1.9836 4.98 1.19-1.984 High(2)
3 66.368 1.1860 8.33 0.312-1.186 Moderate(3)
4 252.827 0.3113 31.72 0.185-0.311 Low(4)
5 427.207 0.1842 53.59 <0.1842 very Low(5)
32
4.2.6 Rainfall Reclassification
The IDW technique in the geo statistical analyst extension in ArcGIS 10.1 used to convert the
point rainfall to areal rainfall. This areal rainfall reclassified in to common scale. In addition, a
higher rainfall amount to have higher contribution of flood hazard and shown at Figure 16(B).
Table 13: Catchment area of annual rainfall reclassification.
No Annual RF(mm) Area(km2) % weight Level of scale
1 >1116 64.796 8.13 Very High(1)
2 1102-1115 301.928 37.87 High (2)
3 1101-1091 231.112 28.99 Moderate(3)
4 1090-1081 111.864 14.03 Low(4)
5 <1080 87.479 10.97 Very low(5)
4.2.7 Population Density Reclassification
Population reclassification to develop from Denbiya woreda map and According to Ethiopian
Statistical agency 2007 EC the number of population around Dembiya flood plain. Then gross
population a density to calculate the number of person per square kilometers. Right after updat-
ing, population shape file converted to raster layer using Conversion Tools/Feature to Raster.
Then the data layer reclassified into fiver sub-factors, which are classified. Finally, new values re-
assigned in order of increasing number of population that was more susceptible to more detail
scale and flood hazard index sub-factor classification based on parameter to description and
shown table 11 and figure 16 (C).
Table 14: Population Density reclassification.
No Name Area(km2) Population Pop density Range scale
1 Achera 14.42 10,777 748 >400
Very high
(1) 2 Debirzuria Adisge tsion 22.20 14,690 662
3 Arebia Diba(Robet town) 25.46 9,865 387
226-399
High (2)
4 Tana Weyina 13.73 4,910 358
5 Seraba Dablo 13.05 4,620 354
6 Wekerako Dalko 23.65 5,225 221
201-225
Moderate
(3) 7 Guramba Mikaeln 20.25 4,220 208
8 Jerjer Abanov 6.74 1,390 206
9 Hamsafej wana 1.68 298 177 175-200
Low(4)
10 Guramba Batana 19.30 3,405 176
11 Jangua Mariamn 3.11 480 154
<174
Very
Low(5) 12 Sufan Karamerew Gubia 45.87 6,920 151
13 Gebebachilona salij 12.19 1,825 150
33
Figure 14: Reclassification of Factors at Megech River Catchment C D
A B
34
Figure 15: Reclassification of Factors at Megech River Catchment
A B
C
35
4.2.8 Model Input Parameters
For this thesis, most HEC-RAS input data processed out of the model and some were processing
inside the model. It used a number of input parameters for hydraulic analysis of the stream
channel geometry and water flow.
Geometrical data: these data used to define the shape of the stream and related hydraulic
structures with their physical characteristics. For this study area, the river cross section data were
digitize in Arc-GIS 10.1 with compatible HEC-Geo-RAS software and were been imported into
HEC-RAS. This parameter was use to establish a series of cross-sections along the stream. In
each cross-section, the locations of the stream banks were identify and used to divide into
segments of left floodway (overbank), main channel, and right floodway. HEC-RAS was need
several input data such as elevation, River cross-section, Left and right bank, stream centerline
and right flood way of adjacent cross-sections (the three reach lengths represent the average flow
path through each segment of the cross-section pair as such, the three reach lengths between
adjacent cross-sections may differ in magnitude due to bends in the stream).
Table 15:- Megech River Geometry and their purpose.
No RAS Layer Purpose Used
1 Stream Center-
line
to identify the connectivity of the river network and assign river stations
to computation points
2 Bank Lines to identify the main channel from over bank areas
3 Flow Path Cen-
terlines
to identify the center mass of flow in the main channel and over banks to
compute the downstream reach lengths between cross sections
4 Cut Lines to extract elevation transects from the DEM at specified locations
Manning Roughness: Estimation of Manning's roughness coefficient (or Manning's n) is very
important to simulate open channel flows. As an empirical parameter, the roughness coefficient
actually includes the components of surface friction resistance, wave resistance and resistance due
to flow unsteadiness (Y.WANG, 2005). The roughness coefficient (n) in natural channels is
difficult to determine in field. Various factors affecting the values of roughness coefficients were
presented by (Chow, 1959): Surface roughness, Vegetation, Channel irregularities, Channel
alignment, Scour and deposition, Obstructions, Size and shape of the channel, Stage and
discharge, Seasonal changes, temperature and Suspended material & bed load etc.
For this study the Author was unable to access any official Manning roughness values for the
modeling reaches, therefore had to use arbitrarily but, judicially chosen Manning roughness
values based on (Chow, 1959). HEC-RAS User's Manual suggested to use a compiled “n” values
36
for stream and floodplains found in open channel hydraulics, (Chow, 1959). a number of methods
exist for estimating the roughness coefficient.
The first method is using books that provide series of pictures of stream channel with a
recommended Manning's value for any natural channel by comparing with actual field observed
river properties a value of Manning's roughness most similar one can be obtained. U.S Geological
Survey Manning's Roughness estimated values However; this method was developed considering
other country rivers and natural condition that may have a different topographic, soil type, river
characteristics, as well as weather condition and many other factors as compare to our country
Ethiopia. For this reason, it should not advisable to apply directly for our Country Rivers and
floodplains.
The second method was using table that recommended by different authors that give typical or
average values of n for different channel conditions, a value of n can be obtained by comparing
the roughness characteristics of the observed channel using the table13, the one that appears most
similar with actual river channel properties would select as shown in table 14.
Table 16: Types of channel Recommended of Manning's value.
No Types of channel and Description
Manning‟s Roughness (n) Values
Minimum Normal Maximum
1 Natural Streams(Main channels)
1.1 Clean ,straight ,full ,no rifts or deep pools 0.025 0.03 0.033
1.2 Same as above ,but more stones and weeds 0.03 0.035 0.04
1.3 Clean ,winding ,some pools and shoals 0.033 0.04 0.045
1.4 Same as above ,but some weeds and stones 0.035 0.045 0.05
1.5
Same as above ,lower stage ,more ineffective slopes and
sections 0.04 0.048 0.055
1.6 Same as “d” but more stones 0.045 0.05 0.06
1.7 Sluggish reaches ,weedy ,deep pools 0.05 0.07 0.08
1.8
Very weedy reaches ,deep pools floodways with heavy
stands of timber and brush 0.07 0.1 0.15
2 Flood plains(Pasture no brush)
2.1 Short grass 0.025 0.03 0.05
2.2 High grass 0.03 0.035 0.05
2.3 Cultivated areas no crop 0.02 0.03 0.04
2.4 Mature row crops 0.025 0.035 0.045
2.5 Mature field crops 0.03 0.04 0.05
3.1 Brush
3.1 Scattered brush ,heavy weeds 0.035 0.05 0.07
3.2 Light brush and trees ,in winter 0.035 0.05 0.06
37
3.3 Light brush and trees ,in summer 0.04 0.06 0.08
3.4 Medium to dense brush, in winter 0.045 0.07 0.11
3.5 Medium to dense brush, in summer 0.07 0.1 0.16
4 Trees
4.1 Cleared land with tree stumps ,no sprouts 0.03 0.04 0.05
4.2 Same as above ,but heavy sprouts 0.05 0.06 0.08
4.3
Heavy stands of timber ,few down trees ,little under
growth ,flow below branches 0.08 0.1 0.12
4.4 Same as above , but with flow into branches 0.1 0.12 0.16
4.5 Dense willows, ,straight ,straight 0.11 0.15 0.2
5
Mountain streams ,no vegetation in channel banks usual-
ly steep & submerged
5.1 Bottom : gravels ,cobbles ,and few boulders 0.03 0.04 0.05
5.2 Bottom : cobbles with large boulders 0.04 0.05 0.07
Source; (Chow, 1959)
In our country Ethiopia for many engineering designs as well as different studies, the picture
comparison and tabular look up methods have been used frequently because of their
simplicity. However, it was defecate to identify actual n value.
A third method which is developed by Cowan (1956), involves the selection of a base value
of n and correcting the base value for each of the following five factors according to table 13.
1. The natural bed material of the channel
2. for channel cross section surfaces, degree of regularity
3. for cross section, character of variations in size and shape
4. for the presence and characteristics of obstructions in the channel
5. the effect of vegetation on flow condition
6. the degree of channel meandering
7. Then final the Menning‟s n will be n= (n1+n2+n3+n4+n5)*m6-----------------------Eq.(4.4)
Where:
n1= a straight, uniform, smooth channel in natural materials
n2 = a correction factor for the effect of surface irregularities
n3 = a value for variations in shape and size of the channel cross section
n4 = a value for obstruction
n5 = a value for vegetation and flow conditions
m6 = a correction factor for meandering of the channel
38
Table 17: Cowan Manning‟s roughness computation sheet
Variable Alternatives
Recommended
Value Variable Alternatives
Recommended
Value
Basic(n1 )
Earth 0.02 Negligible 0
Rock 0.025
Obstructions(n4 )
Minor 0.01- 0.015
Fine gravel 0.024 Appreciable 0.02 – 0.03
Coarse gravel 0.028 Severe 0.04 – 0.05
Smooth 0
Vegetation(n5 )
Low 0.005 – 0.01
Irregularity(n2 )
Minor 0.005 Medium 0.01 – 0.02
Moderate 0.01 High 0.025 – 0.05
Severe 0.02 Very high 0.05 – 0.1
Gradual 0
Meandering(m6)
Minor 0
Cross section(n3)
Occasional 0.005 Appreciable 0.15
Alternating 0.01 – 0.015 Sever 0.3
Source Cowan (1956)
From the above three listed methods, the third method selected for this study due to its simplicity
to compute Manning's value from equation 4.4
During the field visit, the stream had characterized by Earth/fine gravel/ bed; sever meandering,
moderate irregularity, alternating change in shape and size, minor obstruction, and
low/medium/ vegetation cover. The following figure 1 7 has more clarified the river bank
covers and river bed forms.
39
Figure 16: Megech River cross sections sample image at Megech River cross section.
For most cross sections, the river had almost uniform bed materials and bank land covers.
Therefore; it was possible to take uniform Manning value for all cross sections in the channel
and for both right and left banks. Due to this the Author had used Manning roughness “n‟‟
values of 0.022 for main channel and 0.023 for both left and right flood plains. For more detail
see Aappendix 11.
Grass and vegetation at Megech River Feb
2020
Megech River Adisge kebele February 2020
Megech River at upper Ribet town in August 2019 Megech River at adisge kebele in August 2019
At river sand, grass & small vegetation
Sand gravel & Grass
40
Boundary conditions: Boundary conditions used to define the influence of the external
environment on a given specific study area. Upstream and downstream boundary conditions in
unsteady flow applied for this study. There were several different types of boundary conditions
available for unsteady dam break analysis. As per US Army Corps of Engineers, HEC-RAS
User's Manual the main boundary conditions and the possible place to use them given in the
following table.
Table 18: Types and Places of Boundary Conditions
Type of Boundary Conditions Appropriate (possible) place to use BC
Flow Upstream & downstream BC(mostly used as upstream BC)
Normal depth Upstream and downstream BC
Manning rophness upstream and downstream BC
For this work, flow hydrograph that forecasted by using Gumball EV I and normal depth
that was average river bed drop which was equal to 0.005 & 0.004 for upstream and
downstream boundary condition respectively.
Contraction and expansion coefficients: the Author had used the default value of 0.1 for
contraction and 0.3 for expansion at each cross section as enable the HEC-RAS to
calculate the loss at that cross section.
Hydrological flow: Mathematically it defines the relationship of Qt the flow at any time t, to an
initial value.
𝑄𝑡 = 𝑄𝑜 kt ---------------------------------------------------------------------------------------Eq-(4.5)
Where: Qt is the flow at time t, Qo is initial base flow at time zero, and K is an exponential decay
constant. This indicates that K is defined as the ratio of base flow at time t to the base flow one
day earlier.
The required parameters for recession method are:
41
4.2.9 HEC-RAS Stability Analysis
After flow boundary conditions and manning's roughness coefficients were set, HEC RAS
simulates of flow through the channel and flood plain. Simulation date and time duration set by the
modeler. The flow rout that processed in three stages. The first task analyzed the geometry data set
followed by unsteady flow simulation.
In flow simulation HEC-RAS used Saint Venant equation, i.e. to describe the spatial and temporal
variation of the flow variables. The governing continuity and momentum equations derived by
Barkau (1996) are bases for flow solution within the HEC-RAS model.
+
+
= 0 => Continuity equation----------------------------------------------------------Eq. (4.6)
+𝑉
+ 𝑔
= g (So-Sf) => Momentum equation-----------------------------------------------Eq. (4.7)
where: y is flow depth, t is time , A is flow area, T is top width, V is flow velocity, x is length, g is
acceleration due to gravity, So is channel bottom slope, and Sf is friction slope.
Cross section spacing: cross sectional cut lines should be created to capture the entire extent of
flooding anticipated by the peak flow.
As in any hydraulic modeling study, cross sections must be laid out to accurately the channel and
floodplain geometry. There must be enough cross sections to describe contractions and expansions
of the channel, changes in bed slope, changes in roughness, and significant change in discharge.
Cross sections also need to be added immediately upstream and downstream of; tributary inflow
locations, dams and other inline structures. Cross sections spaced to far apart will cause additional
numerical diffusion of the flood wave, due to the derivatives with respect to distance being
averaged for long distance, which derived equations by Dr.Danny Fread (Fread, 1993) and
(Samuels, 1989)P.G. Samuels.
For Peak flood studies, Samuel‟s may be too strict, in that it requires much tighter cross section
spacing than needed, but this equation used to rough estimate the maximum cross section spacing.
∆X
--------------------------------------------------------------------------------Eq. (4.8)
Where:
∆X = the cross section spacing distance (m)
D = the average main channel bank full depth (m)
So = the bed slope (m/m)
Therefore; for this thesis Samuels's equation was used to roughly estimate the maximum cross
42
section spacing. Megech River has average bed slope, 0.004, and average main channel bank full
depth, 6.56feet.Hence the maximum cross section spacing of the channel was 75m and the
minimum spacing that was determined based on the river meandering behavior and the value was
set 25m.
Computational time step: in the development of any flow model, stability and numerical
accuracy can be improved by selecting a time step that satisfies Courant condition. The best way
to estimate a computational time step for HEC-RAS is to use the Courant Condition.
Computational time step calculated by dividing the change in cross section (∆X) to the flood
wave speed (V) which was normally greater than average velocity.
According to an encyclopedia of scientific essays the speed of river varies from 0.02 to 5.5ms- for
computational time step stability and accuracy had been achieved by selecting a time step that
satisfies the courant condition (Cr≤1).
Cr =
≤ 1 =
= 0.7 < 1 ---------------------------------------------------------------(Eq-3.9)
Theta: Theta was a weighing factor and its value theoretically can vary from 0.5 to 1 and a
default value of 1 was adopted for simulation. Making the tolerance larger can reduce the stability
problem and making them smaller can cause the program to go the maximum number of iterations
every time. For new version i.e. 5.0.7 a default value of 0.006 is used and it applied for this study.
4.3 Flood Inundation Analysis
4.3.1 Flood flow analysis
By used different analysis methods, the calculated gauge data of Megech River for 2, 10, 50 and
100-year return period Megech river flood forecast are 173.86.m3/s, 264.16m3/s, 323.94m3/s,
455.52m3/s and 511.14m3/s respectively. The good fit test comparing computed values with ob-
served values was carrying out to find the best-fit method. Gumball's method was finding to the
best fit for Megech Rivers. It result value lower than others which was (1.92). Therefore, inunda-
tion area mapping was done use the Gumball's method beset flood result.
4.3.2 Pre HEC-RAS process
One of the functions of the HEC-RAS program is to determine surface elevations at any
point of interest. The data needed to perform these computations are separated into geometric
data and steady flow data (boundary conditions). The input data for HEC-RAS is imported from
ArcGIS which is discussed below.
The goal of this part was to develop the basic spatial data required to generate the
43
HEC-RAS Geometry Import File. The process required the Generation of a digital terrain
model (in this paper TIN generated from field data and the DEM of the study area), Definition
of base 2D spatial features and Generation of 3D spatial data and HEC-RAS Geometry Import
File. With the DTM/TIN generated earlier, the next step is 2D spatial feature definition.
2D Spatial Features Definition
With the digital terrain representation (TIN) created, the next step is to extract the geometric
information required by HEC-RAS. This step started with the delineation of a series of 2D
spatial features corresponding to the stream centerlines, the left and right bank lines, the flow
paths, and the cross sections along the streams. The contour lines may be helpful in this regard if
the resolution of the TIN is poor.
Cross section geometry
Boundary geometry for the analysis of flow in natural streams is specified in terms of ground
surface profiles (cross sections) and the measured distances between them. Cross sections should
be perpendicular to the anticipated flow lines and extend across the entire flood plain (these cross
sections may be curved or bent). For this research paper it is made to extend to about a total of
43km with 500m at each stretch of the floodplain.
Cross sections are required at locations where changes occur in discharge, slope, shape or
roughness; at locations where levees begin or end and at bridges or control structures such as
weirs. Each cross section is identified by a Reach and River Station label. The cross section is
described by entering the station and elevations (x-y data) from left to right, with respect to
looking in the downstream direction. The cross section for this research work is extracted both
from field data and its counterpart digitized DEM/TIN. The study area TIN is made from the
field survey data and the DEM of the area. During the extraction, the field data is assumed to
represent the channel geometry than the DEM.
The above five features extracted from prepared TIN of the study area. The TIN generated from
field survey of the area and DEM. Accordingly actual field data made to represent channel cross
section where as the DEM to the flood plain. During digitizing shape files of the river or contour
can be used to Show figure 18 at (a & b)
The layers are; stream center line, flow path center line, flow path lines (left and right) and bank
lines to show figure 18 at (d) Preprocessing by HEC-Geo RAS in Arc GIS is the first step in the
extraction processes.
44
Figure 17 : A digitized Megech River, TIN, Cross section, River, bank and geometry layers
(D)
A (B)
45
4.3.3 Geometrical data exporting to HEC-RAS
It is very important to edit and geo-reference all necessary layers in GIS. Although the HEC-
RAS has an editing interface for the exported value, the GIS is a better way to reduce the error
during post HEC-RAS process (flood mapping and delineation). There are different options to
leave or export RAS layers depending on their use and necessity.
The bank stations which made fit with the cross section points in GIS may not match when
exported to HEC-RAS. In this case manual edition should be applied. The exported cross section
may not also be readable by the HEC-RAS. The problem may emerge from the unit system
between the HEC-RAS and that used in GIS. The GIS unit system must be re-projected
according to the RAS unit. Since most GIS inputs such as DEM, TIN and field cross sections are
in metric unit it must be projected to the same unit. In the figure 19 below the last two cross
sections bank points are away from the channel bank lines, so that they require manual edition
Figure 18: A cross section views in HEC-RAS geometric window imported from GIS.
The geometric data window edits not only the river sections but also structures associated with
the river system. This structures may be; bridges/culverts, deck/roadway, weir structures, levees,
dykes etc. These structures are also digitized and geo-referenced in GIS and exported to HEC-
RAS for further editing.
46
4.3.4 Entering Flow data and boundary condition
The discharge forecast values for different return periods can be entered either manually or
connecting to the desired location on excel by exporting to HEC-RAS. The roughness
coefficients (Manning‟s coefficient) and boundary conditions were added to the model manually.
The model was run for subcritical flow regime conditions and unsteady flow water surface
profile computations. The iterative solution of the energy equation, using the standard step
method, solved the steady flow, while Manning‟s equation and contraction/expansion
coefficients determined head losses.
Before applying the computation process the model must be set up for boundary condition. There
are various methods of boundary condition used. The method used in this paper is the critical
depth at the downstream end of the reach. The model calculates the depth from the given
elevation data and discharge. The water surface can also be used if the accurate and up-to-date
value is available.
Finally the plan must established for each model simulation. The plan had a user specified
description and application.
Figure 19: Detail manning and other model input data a boundary condition.
47
4.3.5 Pre HEC-RAS output
HEC-RAS requires flow and topographic data as channel and flood plain cross section. Given
this two sets of data in appropriate form the resulting model efficiency is high. The model given
the food plain water surface profile in 2D view.
Figure 20: Cross section view at River station 30751.74 for profile (50-year rp) surface water.
Figure 21: Cross section view at river station 36650.39 for profile (100-year) distributions.
48
Figure 22: Cross section view at river station 200.765 for profile (100-year) distribution
In the output cross section view, most of the river channel was seeing as trapezoidal and V-
shaped.
Figure 23: General profile plot of the reach for 100-year storm
Other application of HEC-RAS was providing the 2D river water profile to ArcGIS to display
the flood plain in 3D. The flood plain was mapping and finally delineated output, which used the
RAS output in the form of the river profile.
49
Figure 24: 3D perspective view of the flood plain and the channel in HEC-RAS (100-year storm)
4.3.6 Exporting HEC-RAS results and post-HEC-RAS process
Once HEC-RAS computed values completed with no errors, the next step is exporting the output
to RAS Mapper for post HEC-RAS processing. Post HEC-RAS process which used HEC-RAS
output for floodplain inundation mapping and delineation.
Figure 25: Bounding polygon for the water surface elevating and TIN generation
50
4.3.7 Flood plain delineation
Areas inundated by flooding occur wherever the elevation of the flood water exceeds that of the
land. To delineate these areas, we have created surface models of the flood water and land
surface, and then compare the elevation.
HEC-RAS represent a flood plain to compute water surface elevation at each cross-section.
During the data import step, these elevation to brought into ArcGIS, along with the distance from
the stream centerline to the left and right flood plain boundaries. Hence, two things are known
about the flood plain at each cross-section: water surface elevation and width on each side of the
centerline.
4.3.8 Post HEC-RAS mapper
With the bounding polygon created (figure 26), water surface TIN created from the given
profiles and underlying DTM/TIN. The water surface TIN consequently given rise to flood plain
delineation.
Figure 26: Water surface at TIN generated from bounding polygon.
51
4.4 Flood hazard analysis
Multi-Criteria Evaluation(MCE) technique was used to assess flood hazard of the Catchment
using GIS. MCE was a procedure which needs several criteria to be evaluated to meet a specific
objective. Applications can be extended to assess flood hazard zone in other areas. Its flexibility
along with the provided validation by the sensitivity analysis facilitates this. Obviously,
parameters can be added or removed according to local study area of river hydrogeological,
hydrological, and morphological characteristics and also adjust a rank based on local study area.
The comparison of the flood hazard maps obtained with the FHIS indicate that FHIS index is
more reliable according to the historical flood records and manages to describe better high- and
very high-risk areas. Therefore, the FHIS could be applied in other regions to estimate the flood
hazard areas. However, validation and reliability tests to require a consistency ratio must bean
checked. When Consistency ratio less than 10% to accept if not to change their score until CR is
below 10% or less than 0.1. The parameters should be adapted in the specific characteristics of
the applied region of flood factor method (Nerantzis Kazakis, 2015 and Dr. Paul Samuels, 2019,
Dr. Pratik Dash, 2020).The standardized raster layers weighted to use Eigen vector that important
to show the importance of each factor as compared to other in the contribution of flood hazard.
Accordingly, the weight of each parameter is defined following the Analytical Hierarchy Process
(AHP) (Saaty, 1990a,b). It is a structured technique used for analyzing complex problems, where
a large number of interrelated objectives or criteria are involved. Thus, once all criteria are sorted
in a hierarchical manner, a pairwise comparison matrix for each criterion is created to enable a
significance comparison. The relative significance between the criteria to evaluate from 1 to 6
indicating less important to much more important criteria, respectively. It was worth noting that
pairwise comparisons and variable hierarchization in AHP result from a Delphi consensus already
used in other indexed approaches (Aller et al., 1987), which is subjective (Pacheco and
Fernandes, 2013). However, weighting by AHP is widely used in many applications (Valle Junior
et al., 2014; Oikonomidis et al.2015) and is recommended to be used for regional studies (Ayalew
and Yamagishi, 2005 and Wubet Gashaw, 2007). The proposed methodology suggests a pairwise
comparison, using a 6 × 6 matrix, where diagonal elements are equal to 1. In Table 16 the criteria
of the factors method are sorted in a hierarchical manner, for the studied basin. The first Row of
the Table illustrates the importance of Drainage in regard to the other parameters which are
placed in the columns. For example, Drainage was significantly more important therefore
52
assigned the value one, More details Analytical Hierarchy Process is applied can be found in
Saaty (1990a). Soil was assigned the Second importance parameters but more permeability can be
of critical importance for the runoff and the occurrence of flood, especially in smaller basins with
sparse vegetation (e.g. due to deforestation. Land Use and land cover had been consider to the
Third most important parameter in alignment with relevant studies. since to this research
examines smaller basins containing urban areas and grass land, land cover has a higher influence
in flood occurrence compared to large forest). Elevation had been considering to the Fourth most
important parameter in alignment with relevant studies. Rainfall intensity considered as the fifth
more important parameters. In areas with diverse terrain, like the studied area, Rainfall intensity is
also indirectly associated to elevation. The last was considered to Slope, It was some how
considered in the Elevation parameter.
Table 19: Parameters of flood hazard by Analytical Hierarchy Process module derivation.
No Factor Drainage Soil Elevation LU RF Slope
1 Drainage 1 2 2 3 3 5
2 Soil 1/2 1 1 2 2 3
3 Elevation 1/2 1 1 2 2 2
4 LU 1/3 1/2 1/2 1 2 2
5 RF 1/3 1/2 1/2 1/2 1 2
6 Slope 1/5 1/3 1/2 1/2 1/2 1
Table 20: Normalized flood hazard parameters by Analytical Hierarchy Process Module Result.
Factor Drainage Soil Elevation LU RF Slope Weight FW FW/W
Drainage 0.349 0.375 0.364 0.333 0.286 0.333 0.340 2.077 6.108
Soil 0.174 0.188 0.182 0.222 0.190 0.200 0.193 1.181 6.128
Elevation 0.174 0.188 0.182 0.222 0.190 0.133 0.182 1.116 6.143
LU 0.116 0.094 0.091 0.111 0.190 0.133 0.123 0.749 6.109
RF 0.116 0.094 0.091 0.056 0.095 0.133 0.098 0.590 6.054
Slope 0.070 0.063 0.091 0.056 0.048 0.067 0.066 0.399 6.086
Consistency check
The creation of the eigenvector matrix of the AHP module, its consistency needs to evaluate the
parameters hypothesis accept or not checked by consistency evaluated the following index.
……………………………………………………………………………….Eq. (4.5)
CR consistency Ratio
CI consistency index
53
RI Random index which depend on flood factor to taken from (table-17)
……………………………………………………………Eq. (4.6)
n , number of flood factor criteria
λ mean max, eigenvector maximum mean value which is λ max=6.105
Table 21: Random index (RI) used to compute consistency ratios (CR)
n 1 2 3 4 5 6 7 8 9 10
RI 0 0 0.58 0.9 1.12 1.24 1.32 1.35 1.45 1.56
So to calculate
Therefore Value of Consistency Ratio
The value CR has less than 0.1 the hypotheses is accepted.
The Weight module fixed with the pair wise comparison matrix file of the factors in a Pair wise
comparison Point continuous scale.
4.5 Flood risk analysis
Flood risk analysis used to study as changes likely to occur in environment characteristics
that may result due to flood for finding out the impact of flood on land use, a land use
map have to be created in the software(AHP module). The created flood map and land use map
are overlay in the Arc GIS and AHP module to obtain the flood risk map. Areas had
water level or flood level standard policy to pair wise comparison matrix with the flood hazard,
population and LU/LC. Then after flood risk = from flood hazard to overlay weight analysis with
population and LU&LC.
Table 22: Parameters of flood Risk: Analytical Hierarchy Process
No Factor Flood hazard LU/LC Population
1 Flood Hazard 1 1 3
2 LU/LC 1 1 2
3 Population 1/3 1/2 1
54
Table 23: Normalized flood hazard parameters of AHP module result.
No Factor Flood hazard LU/LC Population Weight FW FW/W
1 Flood hazard 0.429 0.400 0.500 0.443 1.340 3.025
2 LU/LC 0.429 0.400 0.333 0.387 1.170 3.020
3 Population 0.143 0.200 0.167 0.170 0.511 3.009
λ mean maximum 3.02
Consistency check
The creation of the eigenvector matrix of the AHP module, its consistency needs to evaluate the
parameters hypothesis accept or not. The required level of consistency had evaluated using the
following index. To checked the consistency an AHP Module of pair comparison of flood risk
parameters and used to Eq. (4.5) and Eq. (4.6).
λ max, eigenvector maximum mean value which is λ max=3.053
Random index (RI) used to from (Table-18) when number of factors Three, so RI to taken 0.58
Then to calculate
Therefore Value of Consistency Ratio
The value of CR is less than 0.1, so the hypotheses are accepted.
The Weight module fixed with the pair wise comparison matrix file of the factors in a Pair wise
comparison Point continuous scale
55
5. Result and discussion
5.1 Flood Inundation
5.1.1 Detailed output tables for selected cross sections
The detailed output table shows discharge amount, reach length, flow area, top width, average
velocity, hydraulic depth, wetted perimeter, shear, stream power, cumulative volume, cumulative
storage area for channel, left and right over banks.
Table 24: Detaile output tables for piping simulation for selected cross sections (40433.11)
From table 24 flow area, flow, top width, wetted perimeter, cumulative volume, and cumulative
storage area values in the left bank are greater than the values in the right bank. This result
indicates that the area on the left side is more affected by the catastrophic flood. The reason is the
area on the leftside has flat slope than the right side.
56
Table 25: Detaile output tables for piping simulation for selected cross sections (40056.04)
From Table 25 flow area, flow, top width, average velocity, hydraulic depth, wetted perimeter,
shear, cumulative volume, and cumulative storage area values in the left bank are greater than the
values in the right bank. This result indicates that the area on the left side is more affected by the
catastrophic flood. The reason is the area on the left side has flat slope than the right side.
Table 26: Detaile output tables for piping simulation for selected cross sections (31727.22)
From table 26 flow area, flow, top width, average velocity, hydraulic depth, wetted perimeter,
shear, stream power values in the right bank are greater than the values in the left bank. This
result indicates that the area on the right side is more affected by the catastrophic flood. The
reason is the area on the right side has flat slope than the left side.
57
Table 27: Detaile output tables for piping simulation for selected cross sections (21567.56).
From table 27 flow area, flow, average velocity, hydraulic depth, wetted perimeter, cumulative
volume, and cumulative storage area values in the left bank are greater than the values in the right
bank. This result indicates that the area on the left side is more affected by the catastrophic flood.
The reason is the area on the left side has flat slope than the right side.
Table 28: Detaile output tables for piping simulation for selected cross sections (15341.9)
From table 28 flow area, flow, top width, average velocity, hydraulic depth, wetted perimeter,
cumulative volume, and cumulative storage area values in the left bank are greater than the values
in the right bank. This result indicates that the area on the left side is more affected by the
catastrophic flood. The reason is the area on the left side has flat slope than the right side.
58
Table 29: Detaile output tables for piping simulation for selected cross sections (10955.06)
From table 29 flow area, flow, top width, average velocity, hydraulic depth, wetted perimeter,
shear, cumulative volume, and cumulative storage area values in the left bank are greater than the
values in the right bank. This result indicates that the area on the left side is more affected by the
catastrophic flood. The reason is the area on the left side has flat slope than the right side.
Table 30 Detaile output tables for piping simulation for selected cross sections (4936.394)
From table 30 flow area, flow, average velocity, hydraulic depth, shear, stream power, cumulative
volume, and cumulative storage area values in the right bank are greater than the values in the left
bank. This result indicates that the area on the right side is more affected by the catastrophic
flood. The reason is the area on the right side has flat slope than the left side.
59
5.1.2 Detailed output velocity distribution at selected cross sections.
The following figure(27 & 28) to showed water surface elevations and velocity including the
variation of flow by painting/shading different color at selected cross sections. At starting station
up to end station(reach) the velocity varies from 0.02m/s up to 6m/s.
Figure 27: Critical cross sections for simulated velocity distribution variations.
From cross section (36650.39) around the 20km far from the gage station the result of average
velocity varies between 0.4m/s and 1m/s. in the center of channel average velocity is 1m/s , the
right side is average velocity is 0.8m/s and the left side is average velocity is 0.4m/s . This result
indicates that the area on the right side more affected by the catastrophic flood.
Figure 28: Critical cross sections for simulated velocity distribution variations.
From cross section (200.7648) around the 40km far from the gage station the result of average
velocity varies between 1m/s and 1.1m/s . in the center of channel average velocity is 1m/s , the
left and the right side average velocity are 1.1m/s . This result indicates that the area on the left
and right side affected by the catastrophic flood are equal.
60
5.1.3 Flood hazard and risk level to classification
According to the downstream hazard classification guidelines were classified according to Garvey
and Lansdowne, the flood danger charts have been developed to ascertain the potential hazard that
various combinations of flow depth and velocity pose to persons in passenger vehicles, houses,
and those who are unprotected. Flood map rsult determain depth, velocit and areas, Which use to
clasifay of flood hazard and risk zone. For a well-managed river system, the allowable discharge
(Qa) for every river section must be analyzed and pre-determined for river management. In this
study, a flooding hazard level was defined as Idv = Qm/Qa to assess the level of downstream
flooding hazard caused by a overtop flood. For a relatively low value of Idv, the chance of
flooding may be low. For quick assessment of downstream inundation, we propose the use of the
Idv index for the quick assessment of downstream flooding hazard. This flood hazard and risk type
zone at Megech River flood area had classified by the flood danger level relationships, which
were divid into five zones of defined according to the range of Idv as follows: very low (level I)
for Idv= 0.0–1.525, low (level II) for Idv = 1.526–3.465, moderate (level III) for Idv = 3.466-6.099,
high (level IV) for Idv = 6.1–12.06, and very high (level V) for Idv=>12.07. This classification to
refer technical flood risk management of guideline flood hazard (7-3).
5.1.4 Flood Inundation Map
Flood map rsult determain depth, velocit and areas, Which use to classify of flood hazard and risk
zone. According to the exported inundation map, from 43km up to the end of the cross section the
catastrophic flood attacks many lives and properties. Adasgie Kebele, Robit Town, Anchera and
Tana Woyina kebele are critically endangered areas affected by flood.
The following figure(29-31) shows the velocity and depth along the downstream of the gauge
station.
61
Figure 29: Flood level (velocity vs depth) flood map at Denbiya Flood Plain.
62
Figure 30: Flood velocity distribution of flood map at Denbiya Flood Plain.
63
Figure 31: Flood depth distribution of flood Map at Denbiya Flood Plain.
64
5.2 Flood hazard map
The flood hazard maps (figure-32) below shows that 22.27, 237.35, 141.33, 390.89, 6.34 square
kilometer of Megech Catchments were subjected respectively to very high, high, moderate, low
and very low flood hazards (this classification based on flood hazard and risk management guide-
line and Flood depth vs velocity). A Megech catchment 33% of area fall under high and very high
flood hazard. All of these areas lie in the downstream part of Megech Rivers where they join to
Lake Tana.
Table 31: Flood hazard areas coverage based on depth vs velocity result.
No Scale(Level) Area(km2) % Weight
1 Very high 22.27 2.8
2 High 237.35 29.7
3 Moderate 141.33 17.7
4 Low 390.89 49.0
5 Very Low 6.34 0.8
5.3 Flood risk map
Flood risk mapping and assessment was done for Denbiya flood plain kebele by taking population
and land use/land cover elements that are at risk combined with the degree of flood hazards of the
Woreda. According to the flood risk map (figure-33), it was estimated that 16.3, 9.0, 37.5, 131.3
and 36.4 square kilometer areas of Denbiya flood plain were subjected respectively to very high,
high, moderate, low, and very low flood risk (Table 32). Around 11% of Denbiya flood plain to
shows high and very high flood risk coverage.
Table 32: Flood risk areas coverage based on depth vs velocity result.
No Scale(Level) Area(km2) % Weight
1 Very high 16.26 7.06
2 High 8.95 3.88
3 Moderate 37.50 16.28
4 Low 131.29 56.99
5 Very Low 36.39 15.79
65
Figure 32: Flood hazard map of Megech River catchment
Figure 33: Flood risk map Denbiya flood plain
B
66
6. Conclusion and Recommendation
6.1 Conclusion
The basic idea of flood hazard and risk assessment and mapping as undertaken in this study is to regulate
land use by flood plain zoning in order to restrict the damages. In the light of above discussion, it can be
said that flood risk mapping, being an important non-structural flood management technique, will go long
way in reducing flood damages in areas frequented by flood. The flood hazard and risk map to generation
was a good approach to deduce a sound decision for a fourth-coming flood disaster, provided the required
data were standardizing to a common scale in personal geo database.
For this study, Megech River assessing of flood hazard, risk and inundation flood analysis done
by using MCE techniques and HEC-RAS models. The input data for a watershed characteristics
elevation and slope, rainfall, LU/LC, drainage and soil data of the study area and maximum flood
analysis, which used as an upstream boundary condition for HEC-RAS model.
The peak flow analysis used for flood simulation as inflow boundary conditions were 173.86m3/s,
264.16m3/s, 323.94m
3/s, 455052m
3/s and 511.14 m
3/s
- for 2, 5, 10, 50 and 100 year for respec-
tively, which used as an upstream boundary condition for HEC-RAS model.
The flood profiles for different flood intensities with different return periods plotted for entire
length of river reach, depth, velocity, magnitude and many other hydraulic parameters for each
cross section.
The assessment of flood hazard, risk and flood inundation analysis result show, that 259.6 square
km areas to high and very high flood hazard and 25.21 square km areas to high and very high
flood risk. Based on this result 48,625 people may be affected by this flood, which includes De-
bir Zuriya, Adisge, Robit Town, Anchera and Tana Woyina kebeles were critically endangered
and third of fourth areas affected by High flood.
In general, assess the flood hazard, risk and inundation map in Megech River to affect very high
flood inundation area of Denbiya flood plain. Moreover, the combination of the two results would be
a step forward for flood management and mitigation strategies to minimize its adverse effects by better
planning based on result.
67
6.2 Recommendation
This flood hazard, risk and flood level assessment of a catchment that used by the pertinent deci-
sion makers to act upon the current land use policy for reducing vulnerability to flood disaster in
Denbiya flood plain.
The responsible bodies of the flood plain as well as the Region should incorporate the flood haz-
ard and flood risk assessment studies in their development strategies.
Watershed management practices in the uplands of the catchment are crucial in alleviating future
flood disasters in the study area.
To adopting an appropriate land use/land cover and drainage network planning in flood plain area,
which can play very important role to reduce the adverse effects of flood.
The population in these areas to the safe ground would be better to reduce the frequently recurring
flood risk.
Disaster related research activities should be undertaken flood damage cost analysis and flood risk
mitigation needed.
Finally, these flood inundation maps should be share a local Emergency Management Agencies to
develop evacuation plans and Denbiya woreda administrative with responsible authorities should
create awareness based on this study about the disasters that the communities will faced during
the flood.
68
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70
APPENDIX
Appendix 1:- Model output view at Megech river Cross sections.
71
Appendix 2: Gonder Rainfall data Quality analysis
Xi Yt Xi Yt
No Year Max Rf log(Xi) No Year Max Rf log(Xi)
1 1988 51.3 1.710 20 2007 73.000 1.863
2 1989 47.9 1.680 21 2008 51.200 1.709
3 1990 63 1.799 22 2009 46.000 1.663
4 1991 52.8 1.723 23 2010 52.000 1.716
5 1992 36.8 1.566 24 2011 88.100 1.945
6 1993 54.3 1.735 25 2012 52.800 1.723
7 1994 40.1 1.603 26 2013 41.300 1.616
8 1995 30.9 1.490 27 2014 37.500 1.574
9 1996 35.6 1.551 28 2015 48.000 1.681
10 1997 72.3 1.859 29 2016 50.300 1.702
11 1998 40.7 1.610 30 2017 65.800 1.818
12 1999 48 1.681 31 2018 43.200 1.635
13 2000 58.3 1.766 32 2019 70.000 1.845
14 2001 41 1.613 mean 52.759 1.710
15 2002 60.2 1.780 STDV 13.210 0.106
16 2003 69.4 1.841 SKEWS 0.702 0.152
17 2004 51.1 1.708 minimum 30.900 1.490
18 2005 44.9 1.652 Maximum 88.100 1.945
19 2006 70.5 1.848 N 32
1) Checking for outliers
Kn for talken to depened on number of year (N) 2.591 from table
Yminimum=Ymean-Kn*YnSTDV 1.434 OK
Ymaximum=Ymean+Kn*YnSTDV 1.985 OK
Xminimum=10^Yminimum 27.168 OK
Xmaximum=10^Ymaximum 96.625 OK
Xmin and Xmax is bounded by Ymin and Ymax so outliers are ok
2) Percent of error P=STDV/(mean*sqrt(N))*100<10% 4.426134
The valu of percent of error is 4.43%< 10% it is ok
3) Independency and stationary or Trend test by Wald-Wolfowitz (1943) (W-W)
S.N RF data(xi) lag by1( Xi+1) xi(xi+1) xi2 xi3 xi4
1 88.10 73.00 6,431.30 7,761.61 683,797.84 60,242,589.79
2 73.00 72.30 5,277.90 5,329.00 389,017.00 28,398,241.00
3 72.30 70.50 5,097.15 5,227.29 377,933.07 27,324,560.74
4 70.50 70.00 4,935.00 4,970.25 350,402.63 24,703,385.06
5 70.00 69.40 4,858.00 4,900.00 343,000.00 24,010,000.00
6 69.40 65.80 4,566.52 4,816.36 334,255.38 23,197,323.65
7 65.80 63.00 4,145.40 4,329.64 284,890.31 18,745,782.53
72
8 63.00 60.20 3,792.60 3,969.00 250,047.00 15,752,961.00
9 60.20 58.30 3,509.66 3,624.04 218,167.21 13,133,665.92
10 58.30 54.30 3,165.69 3,398.89 198,155.29 11,552,453.23
11 54.30 52.80 2,867.04 2,948.49 160,103.01 8,693,593.28
12 52.80 52.80 2,787.84 2,787.84 147,197.95 7,772,051.87
13 52.80 52.00 2,745.60 2,787.84 147,197.95 7,772,051.87
14 52.00 51.30 2,667.60 2,704.00 140,608.00 7,311,616.00
15 51.30 51.20 2,626.56 2,631.69 135,005.70 6,925,792.26
16 51.20 51.10 2,616.32 2,621.44 134,217.73 6,871,947.67
17 51.10 50.30 2,570.33 2,611.21 133,432.83 6,818,417.66
18 50.30 48.00 2,414.40 2,530.09 127,263.53 6,401,355.41
19 48.00 48.00 2,304.00 2,304.00 110,592.00 5,308,416.00
20 48.00 47.90 2,299.20 2,304.00 110,592.00 5,308,416.00
21 47.90 46.00 2,203.40 2,294.41 109,902.24 5,264,317.25
22 46.00 44.90 2,065.40 2,116.00 97,336.00 4,477,456.00
23 44.90 43.20 1,939.68 2,016.01 90,518.85 4,064,296.32
24 43.20 41.30 1,784.16 1,866.24 80,621.57 3,482,851.74
25 41.30 41.00 1,693.30 1,705.69 70,445.00 2,909,378.38
26 41.00 40.70 1,668.70 1,681.00 68,921.00 2,825,761.00
27 40.70 40.10 1,632.07 1,656.49 67,419.14 2,743,959.12
28 40.10 37.50 1,503.75 1,608.01 64,481.20 2,585,696.16
29 37.50 36.80 1,380.00 1,406.25 52,734.38 1,977,539.06
30 36.80 35.60 1,310.08 1,354.24 49,836.03 1,833,965.98
31 35.60 30.90 1,100.04 1,267.36 45,118.02 1,606,201.37
32 30.90 - 954.81 29,503.63 911,662.14
Sum
1,688.30 89,958.69 94,483.1
9 5,602,713.47 350,927,705.45
S1 R S2 S3 S4
R (bar)= 88,899.15 STDV 3232.735
var R = 10,450,578.12 u 0.328
At 5% significance level for two tail
alpha=5%/2 0.025 NORMSINV 1.96
then,=1-5%/2= 0.975 Values range from -1.96 to 1.96
Then, u lies within the range, accept the hypothesis
In this case -1.96<0.328<1.96 The data is independent and stationary no trend
73
Appendix 3: Ambageorgis Rainfall data Quality analysis
Xi Yi Xi Yi
No year Max RF (mm) Log(Xi) No year Max RF (mm) Log(Xi)
1 1988 30.00 1.48 20 2007 80.00 1.90
2 1989 27.20 1.43 21 2008 48.70 1.69
3 1990 25.50 1.41 22 2009 35.70 1.55
4 1991 27.30 1.44 23 2010 30.20 1.48
5 1992 83.70 1.92 24 2011 50.20 1.70
6 1993 46.00 1.66 25 2012 25.80 1.41
7 1994 30.70 1.49 26 2013 36.50 1.56
8 1995 98.00 1.99 27 2014 47.80 1.68
9 1996 33.60 1.53 28 2015 34.70 1.54
10 1997 43.10 1.63 29 2016 45.50 1.66
11 1998 50.30 1.70 30 2017 58.20 1.76
12 1999 39.50 1.60 31 2018 76.80 1.89
13 2000 58.50 1.77 32 2019 48.00 1.68
14 2001 36.20 1.56 mean 45.48 1.63
15 2002 44.20 1.65 STVD 17.66 0.15
16 2003 45.60 1.66 Skewness 1.44 0.64
17 2004 33.10 1.52 minimum 25.50 1.41
18 2005 41.80 1.62 maximum 98.00 1.99
19 2006 43.00 1.63 n 32
1) Checking for outliers
Kn for given N 2.59
Y minimum=Y mean-Kn*YnSTDV 1.24
Y maximum=Y mean +Kn*YnSTDV 2.02
X minimum=10^Yminimum 17.42 Ok
X maximum=10^Ymaximum 104.90 Ok
2) percent of error(P) STDV/(mean*sqrt(N))*100<10% 6.865 Ok
74
3) Independency and stationary or Trend test by Wald-Wolfowitz (1943) (W-W)
S.N RF data(xi) ( Xi+1) xi(xi+1) xi2 xi3 xi4
1 98.80 95.80 9,465.04 9,761.44 964,430.27 95,285,710.87
2 95.80 80.00 7,664.00 9,177.64 879,217.91 84,229,075.97
3 80.00 76.00 6,080.00 6,400.00 512,000.00 40,960,000.00
4 76.00 75.80 5,760.80 5,776.00 438,976.00 33,362,176.00
5 75.80 70.40 5,336.32 5,745.64 435,519.51 33,012,379.01
6 70.40 70.30 4,949.12 4,956.16 348,913.66 24,563,521.95
7 70.30 69.90 4,913.97 4,942.09 347,428.93 24,424,253.57
8 69.90 68.30 4,774.17 4,886.01 341,532.10 23,873,093.72
9 68.30 68.10 4,651.23 4,664.89 318,611.99 21,761,198.71
10 68.10 65.40 4,453.74 4,637.61 315,821.24 21,507,426.51
11 65.40 65.10 4,257.54 4,277.16 279,726.26 18,294,097.67
12 65.10 64.40 4,192.44 4,238.01 275,894.45 17,960,728.76
13 64.40 61.70 3,973.48 4,147.36 267,089.98 17,200,594.97
14 61.70 60.70 3,745.19 3,806.89 234,885.11 14,492,411.47
15 60.70 56.70 3,441.69 3,684.49 223,648.54 13,575,466.56
16 56.70 55.80 3,163.86 3,214.89 182,284.26 10,335,517.71
17 55.80 53.50 2,985.30 3,113.64 173,741.11 9,694,754.05
18 53.50 53.20 2,846.20 2,862.25 153,130.38 8,192,475.06
19 53.20 52.20 2,777.04 2,830.24 150,568.77 8,010,258.46
20 52.20 50.00 2,610.00 2,724.84 142,236.65 7,424,753.03
21 50.00 47.20 2,360.00 2,500.00 125,000.00 6,250,000.00
22 47.20 46.90 2,213.68 2,227.84 105,154.05 4,963,271.07
23 46.90 42.50 1,993.25 2,199.61 103,161.71 4,838,284.15
24 42.50 41.30 1,755.25 1,806.25 76,765.63 3,262,539.06
25 41.30 40.20 1,660.26 1,705.69 70,445.00 2,909,378.38
26 40.20 40.10 1,612.02 1,616.04 64,964.81 2,611,585.28
27 40.10 39.20 1,571.92 1,608.01 64,481.20 2,585,696.16
28 39.20 39.00 1,528.80 1,536.64 60,236.29 2,361,262.49
29 39.00 38.50 1,501.50 1,521.00 59,319.00 2,313,441.00
30 38.50 38.20 1,470.70 1,482.25 57,066.63 2,197,065.06
31 38.20 34.90 1,333.18 1,459.24 55,742.97 2,129,381.38
32 34.90 - 1,218.01 42,508.55 1,483,548.36
Sum 1,860.10 111,041.69 116,727.83 7,870,502.95 566,065,346.43
S1 R S2 S3 S4
R (bar)= 107,846.59 STDV 4,100.88
var R= 16,817,201.71 u 0.779
At 5% significance level for two tail
alpha=5%/2 0.025 NORMSINV 1.96
Then, =1-5%/2= 0.975 Values range from -1.96 to 1.96
Then,u lies within the range,accept the hypothesis
In this case -1.96<0.779<1.96 The data is independent and stationary no trend
75
Appendix 4: Shembekit Rainfall data Quality analysis
Xi Yt Xi Yt
No Year Max Rf log(Xi) No Year Max Rf log(Xi)
1 1988 70.40 1.848 20 2007 53.20 1.726
2 1989 38.20 1.582 21 2008 42.50 1.628
3 1990 95.80 1.981 22 2009 80.00 1.903
4 1991 34.90 1.543 23 2010 64.40 1.809
5 1992 65.10 1.814 24 2011 68.10 1.833
6 1993 40.20 1.604 25 2012 69.90 1.844
7 1994 68.30 1.834 26 2013 61.70 1.790
8 1995 98.80 1.995 27 2014 39.30 1.594
9 1996 65.40 1.816 28 2015 75.80 1.880
10 1997 40.10 1.603 29 2016 60.70 1.783
11 1998 76.00 1.881 30 2017 46.90 1.671
12 1999 70.30 1.847 31 2018 55.80 1.747
13 2000 41.30 1.616 32 2019 39.00 1.591
14 2001 56.70 1.754 mean 58.13 1.748
15 2002 38.50 1.585 STDV 16.66 0.123
16 2003 52.20 1.718 SKEWS 0.63 0.107
17 2004 53.50 1.728 Minimum 34.90 1.543
18 2005 50.00 1.699 Maximum 98.80 1.995
19 2006 47.20 1.674 Number of Year 32
1) Checking for outliers
Kn for given N 2.6
Y minimum=Ymean-Kn*YnSTDV 1.428
Y maximum=Ymean+Kn*YnSTDV 2.067
X minimum=10^Yminimum 26.818 Ok
X maximum=10^Ymaximum 116.591 Ok
Xmin and Xmax is bounded by Ymin and Ymax so outliers are ok
2) Percent of error P= STDV/(mean*sqrt(N))*100<10% 5.065 Ok
76
3) Independency and stationary or Trend test by Wald-Wolfowitz (1943) (W-W)
S.N RF data(xi) lag by1( Xi+1) xi*(xi+1) (xi)^2 (xi)^3 (xi)^4
1 83.7 80.0 6,696.00 7,005.69 586,376.25 49,079,692.38
2 80.0 76.8 6,144.00 6,400.00 512,000.00 40,960,000.00
3 76.8 58.5 4,492.80 5,898.24 452,984.83 34,789,235.10
4 58.5 58.2 3,404.70 3,422.25 200,201.63 11,711,795.06
5 58.2 50.3 2,927.46 3,387.24 197,137.37 11,473,394.82
6 50.3 50.2 2,525.06 2,530.09 127,263.53 6,401,355.41
7 50.2 48.7 2,444.74 2,520.04 126,506.01 6,350,601.60
8 48.7 48.0 2,337.60 2,371.69 115,501.30 5,624,913.46
9 48.0 47.8 2,294.40 2,304.00 110,592.00 5,308,416.00
10 47.8 46.0 2,198.80 2,284.84 109,215.35 5,220,493.83
11 46.0 45.6 2,097.60 2,116.00 97,336.00 4,477,456.00
12 45.6 45.5 2,074.80 2,079.36 94,818.82 4,323,738.01
13 45.5 44.2 2,011.10 2,070.25 94,196.38 4,285,935.06
14 44.2 43.1 1,905.02 1,953.64 86,350.89 3,816,709.25
15 43.1 43.0 1,853.30 1,857.61 80,062.99 3,450,714.91
16 43.0 41.8 1,797.40 1,849.00 79,507.00 3,418,801.00
17 41.8 39.5 1,651.10 1,747.24 73,034.63 3,052,847.62
18 39.5 36.5 1,441.75 1,560.25 61,629.88 2,434,380.06
19 36.5 36.4 1,328.60 1,332.25 48,627.13 1,774,890.06
20 36.4 36.2 1,317.68 1,324.96 48,228.54 1,755,519.00
21 36.2 35.7 1,292.34 1,310.44 47,437.93 1,717,252.99
22 35.7 34.7 1,238.79 1,274.49 45,499.29 1,624,324.76
23 34.7 33.6 1,165.92 1,204.09 41,781.92 1,449,832.73
24 33.6 33.1 1,112.16 1,128.96 37,933.06 1,274,550.68
25 33.1 30.7 1,016.17 1,095.61 36,264.69 1,200,361.27
26 30.7 30.2 927.14 942.49 28,934.44 888,287.40
27 30.2 30.0 906.00 912.04 27,543.61 831,816.96
28 30.0 27.3 819.00 900.00 27,000.00 810,000.00
29 27.3 27.2 742.56 745.29 20,346.42 555,457.18
30 27.2 25.8 701.76 739.84 20,123.65 547,363.23
31 25.8 25.5 657.90 665.64 17,173.51 443,076.61
32 25.5
- 650.25 16,581.38 422,825.06
Sum 1,393.80
63,523.65 67,583.78 3,668,190.41 221,476,037.50
S1 R S2 S3 S4
R (bar)= 60,471.32 STDV= 1,789.39
var R= 3,201,900.20 u= 1.706
At 5% significance level for two tail
alpha=5%/2 0.025 NORMSINV 1.96
Then, nkey =1-5%/2 0.975 Values range from -1.96 to 1.96
Then, u lies within the range, accept the hypothesis In this case 1.96<1.706<1.96 No trend
77
Appendix 5: Makisegnit Rainfall data Quality analysis
Xi Yt Xi Yt
No Year Max Rf log(Xi) No Year Max Rf log(Xi)
1 1988 37.60 1.58 20 2007 50.80 1.71
2 1989 29.40 1.47 21 2008 67.10 1.83
3 1990 28.50 1.45 22 2009 46.50 1.67
4 1991 28.40 1.45 23 2010 68.00 1.83
5 1992 29.60 1.47 24 2011 67.00 1.83
6 1993 28.60 1.46 25 2012 54.00 1.73
7 1994 32.00 1.51 26 2013 32.30 1.51
8 1995 70.00 1.85 27 2014 33.70 1.53
9 1996 57.20 1.76 28 2015 42.00 1.62
10 1997 72.40 1.86 29 2016 44.80 1.65
11 1998 76.30 1.88 30 2017 76.60 1.88
12 1999 83.40 1.92 31 2018 63.80 1.80
13 2000 65.90 1.82 32 2019 71.90 1.86
14 2001 78.20 1.89 mean 52.32 1.69
15 2002 42.90 1.63 STDV 18.68 0.16
16 2003 27.80 1.44 SKEWS 0.12 -0.19
17 2004 45.70 1.66 Minimum 27.80 1.44
18 2005 42.20 1.63 Maximum 83.40 1.92
19 2006 79.60 1.90 Number of Year 32
1) Checking for outliers
Kn for given N 2.591
Yminimum=Ymean-Kn*YnSTDV 1.265
Ymaximum=Ymean+Kn*YnSTDV 2.115
Xminimum=10^Yminimum 18.408
Xmaximum=10^Ymaximum 130.187
Xmin and Xmax is bounded by Ymin and Ymax so outliers are ok
2) Percent of error P= STDV/(mean*sqrt(N))*100<10% 6.31%
78
3) Independency and stationary or Trend test by Wald-Wolfowitz (1943) (W-W)
S.N RF data(xi) lag by1( Xi+1) xi(xi+1) xi2 xi3 xi4
1 83.4 79.6 6,638.6 6,955.6 580,093.7 48,379,814.9
2 79.6 78.2 6,224.7 6,336.2 504,358.3 40,146,923.5
3 78.2 76.6 5,990.1 6,115.2 478,211.8 37,396,160.3
4 76.6 76.3 5,844.6 5,867.6 449,455.1 34,428,260.4
5 76.3 72.4 5,524.1 5,821.7 444,194.9 33,892,074.5
6 72.4 71.9 5,205.6 5,241.8 379,503.4 27,476,047.9
7 71.9 70.0 5,033.0 5,169.6 371,695.0 26,724,867.6
8 70.0 68.0 4,760.0 4,900.0 343,000.0 24,010,000.0
9 68.0 67.1 4,562.8 4,624.0 314,432.0 21,381,376.0
10 67.1 67.0 4,495.7 4,502.4 302,111.7 20,271,695.8
11 67.0 65.9 4,415.3 4,489.0 300,763.0 20,151,121.0
12 65.9 63.8 4,204.4 4,342.8 286,191.2 18,859,998.7
13 63.8 57.2 3,649.4 4,070.4 259,694.1 16,568,481.8
14 57.2 54.0 3,088.8 3,271.8 187,149.2 10,704,937.0
15 54.0 50.8 2,743.2 2,916.0 157,464.0 8,503,056.0
16 50.8 46.5 2,362.2 2,580.6 131,096.5 6,659,702.8
17 46.5 45.7 2,125.1 2,162.3 100,544.6 4,675,325.1
18 45.7 44.8 2,047.4 2,088.5 95,444.0 4,361,790.5
19 44.8 42.9 1,921.9 2,007.0 89,915.4 4,028,209.6
20 42.9 42.2 1,810.4 1,840.4 78,953.6 3,387,109.0
21 42.2 42.0 1,772.4 1,780.8 75,151.4 3,171,391.1
22 42.0 37.6 1,579.2 1,764.0 74,088.0 3,111,696.0
23 37.6 33.7 1,267.1 1,413.8 53,157.4 1,998,717.3
24 33.7 32.3 1,088.5 1,135.7 38,272.8 1,289,791.8
25 32.3 32.0 1,033.6 1,043.3 33,698.3 1,088,454.0
26 32.0 29.6 947.2 1,024.0 32,768.0 1,048,576.0
27 29.6 29.4 870.2 876.2 25,934.3 767,656.3
28 29.4 28.6 840.8 864.4 25,412.2 747,118.2
29 28.6 28.5 815.1 818.0 23,393.7 669,058.6
30 28.5 28.4 809.4 812.3 23,149.1 659,750.1
31 28.4 27.8 789.5 806.6 22,906.3 650,539.0
32 27.8
- 772.8 21,485.0 597,281.7
Sum 1,674.20
94,460.4 98,414.6 6,303,688.0 427,806,982.3
S1
R S2 S3 S4
R(bar)= 87,242.94 STDV 3,742.07
var R= 14,003,055.21 u 1.929
At 5% significance level for two tail
alpha=5%/2 0.025 NORMSINV 1.960
Then,=1-5%/2= 0.975 Values range from -1.96 to 1.96
Then, u lies within the range, accept the hypothesis In this case -1.96<1.93<1.96 No trend
79
Appendix 6: Double Mass Curve for Consistency to use Annual Rainfall
Year Abagiorgis
Shembekit
Gonder Maksegnit
All sta sum.Rf
All sta cum. Rf
Abagiorgis
Shembekit
Gonder Maksegnit
1988 983 1,018 1,092 944 4,037 4,037 983 1,018 1,092 944
1989 542 1,068 1,050 820 3,480 7,517 1,525 2,086 2,142 1,764
1990 963 973 844 692 3,471 10,987 2,488 3,059 2,985 2,455
1991 945 1,090 1,035 933 4,002 14,989 3,433 4,148 4,020 3,388
1992 1,008 1,500 920 641 4,069 19,059 4,441 5,648 4,941 4,029
1993 830 969 1,131 723 3,653 22,712 5,270 6,617 6,072 4,752
1994 921 1,252 990 651 3,814 26,526 6,191 7,870 7,062 5,403
1995 912 1,199 978 681 3,770 30,296 7,103 9,068 8,040 6,084
1996 1,014 1,137 1,195 1,476 4,822 35,119 8,118 10,205 9,235 7,561
1997 1,033 1,142 1,132 1,510 4,816 39,935 9,151 11,347 10,367 9,070
1998 930 1,142 1,034 1,361 4,466 44,401 10,080 12,489 11,401 10,431
1999 1,077 1,271 1,169 1,609 5,125 49,526 11,157 13,760 12,570 12,040
2000 801 1,052 1,111 1,332 4,296 53,822 11,958 14,811 13,681 13,372
2001 1,127 1,140 1,201 1,678 5,146 58,969 13,085 15,951 14,882 15,050
2002 877 1,020 1,018 788 3,704 62,673 13,962 16,971 15,900 15,838
2003 1,327 1,045 1,074 1,085 4,531 67,203 15,290 18,017 16,974 16,923
2004 908 1,052 1,168 942 4,070 71,273 16,198 19,068 18,142 17,865
2005 1,116 1,057 1,054 920 4,146 75,419 17,314 20,125 19,195 18,784
2006 1,082 1,056 1,232 1,387 4,757 80,175 18,396 21,181 20,427 20,171
2007 1,308 1,069 1,259 1,418 5,055 85,230 19,704 22,251 21,686 21,590
2008 1,088 1,125 1,221 1,201 4,634 89,864 20,792 23,375 22,906 22,790
2009 703 1,368 1,006 750 3,828 93,693 21,496 24,744 23,912 23,541
2010 1,070 1,050 1,082 1,190 4,392 98,085 22,566 25,793 24,995 24,730
2011 1,683 1,294 1,029 974 4,980 103,065 24,249 27,087 26,024 25,704
2012 950 1,169 1,137 919 4,175 107,239 25,199 28,256 27,161 26,623
2013 1,338 1,104 967 907 4,315 111,555 26,537 29,360 28,128 27,530
2014 1,045 1,129 1,189 947 4,310 115,865 27,581 30,489 29,317 28,477
2015 928 1,133 1,046 841 3,947 119,812 28,509 31,622 30,363 29,318
2016 1,268 1,261 1,113 1,048 4,688 124,500 29,777 32,883 31,475 30,365
2017 864 1,019 1,397 1,231 4,510 129,010 30,640 33,902 32,872 31,596
2018 1,112 1,174 1,187 1,401 4,874 133,883 31,752 35,076 34,059 32,997
2019 918 1,200 1,337 1,318 4,773 138,656 32,670 36,276 35,395 34,314
80
Appendix 7: Mean Monthly Maximum and Minimum Temperatur
Mean Tmax Mean Mean Tmin Mean
No Mounth Amba giorgis Gonder
Makseg-nit Tmax Amba giorgis Gonder Maksegnit Tmin
1 Jan 23.69 27.81 28.76 26.76 9.19 11.72 11.37 10.76
2 Feb 25.03 28.46 29.70 27.73 10.06 13.08 12.46 11.87
3 Mar 25.14 29.98 31.50 28.87 11.21 15.03 13.96 13.40
4 Apr 25.42 30.06 31.25 28.91 12.18 15.94 14.63 14.25
5 Maye 24.54 28.77 30.86 28.05 12.12 15.87 14.63 14.21
6 Jun 22.15 25.67 27.62 25.15 10.75 14.41 13.85 13.01
7 Jul 19.21 22.97 24.32 22.17 9.62 13.79 13.75 12.39
8 Aug 19.52 23.14 25.18 22.62 9.63 13.64 13.49 12.25
9 Sep 21.66 25.28 26.83 24.59 9.63 13.14 13.13 11.97
10 Octo 21.78 26.78 28.63 25.73 9.06 12.92 12.99 11.66
11 Nov 22.11 27.54 29.58 26.41 7.59 12.46 12.20 10.75
12 Dec 23.01 27.56 28.24 26.27 7.06 11.97 11.33 10.12
Appendix 8:- Denbiya flood plain of population in reclassification.
OBJECTID Name of kebele Area Population Pop density sub-f scale
1 Achera 14.42 10,777 748
400-2400 1
2 Debirzuria Adisge tsion 22.20 14,690 662
3 Arebia Dibakesge 25.46 9,865 387
225-400 2 4 Awiha Abona Tana Weyina 13.73 4,910 358
5 Seraba Dablo 13.05 4,620 354
6 Wekerako Dalko 23.65 5,225 221
200-225 3 7 Guramba Mikaelna Jankura 20.25 4,220 208
8 Jerjer Abanov 6.74 1,390 206
9 Hamsafej wana 1.68 298 177
175-200 4
10 Guramba Batana Chanqua 19.30 3,405 176
11 Jangua Mariamna Jangua Abriham 3.11 480 154
150-175 5 12 Sufan Karamerew Gubia 45.87 6,920 151
13 Gebebachilona salij 12.19 1,825 150
81
Appendix 9: Detail surveyor data collection at Denbiya Flood plain Megech River.
No North East Elevation Deactivation
1 4323910.058 1364350.056 1792.963 BM 1
2 5323976.678 1364049.586 1792.349 BM 3
3 6323898.309 1364537.791 1791.056 T
4 7324015.628 1364575.601 1790.852 T
5 8323970.610 1364469.975 1790.920 T
6 9324093.234 1364511.499 1790.995 T
7 324015.839 1364408.465 1791.075 T
8 1323951.544 1364382.675 1791.291 T
9 2324015.829 1364408.447 1791.084 T
10 3324159.511 1364468.269 1790.706 T
11 4323871.665 1364465.274 1791.133 T
12 5323914.303 1364414.744 1791.076 T
13 6324060.988 1364340.390 1790.898 T
14 7324221.500 1364374.907 1790.835 T
15 8323994.765 1364339.726 1791.287 T
16 9324253.454 1364285.581 1791.028 T
17 324119.226 1364272.207 1790.742 T
18 1324032.178 1364269.595 1791.119 T
19 2324076.965 1364213.235 1791.034 T
20 3324145.182 1364224.035 1790.552 T
21 4324257.216 1364229.261 1790.839 T
22 5324108.823 1364151.842 1790.793 T
23 6324178.688 1364159.530 1790.366 T
24 7324285.369 1364183.950 1790.819 T
25 8324025.613 1364203.475 1791.266 LRB
26 9324017.552 1364204.710 1788.347 LRE
27 324002.745 1364264.254 1788.124 LRE
28 1324006.425 1364270.573 1791.048 LRB
29 2323978.999 1364329.615 1791.400 LRB
30 3323973.070 1364321.662 1788.157 LRE
31 4323936.950 1364369.441 1789.230 LRE
32 5323941.575 1364373.468 1790.948 LRB
33 6323903.679 1364393.693 1788.346 LRE
34 7323864.866 1364416.924 1788.642 LRE
35 8323826.944 1364439.975 1788.385 LRE
36 9323906.812 1364396.605 1790.449 LRB
37 323869.661 1364422.819 1790.782 LRB
82
38 1323830.585 1364448.169 1791.341 LRB
39 2323816.971 1364433.238 1787.965 RC
40 3323863.609 1364405.519 1787.770 RC
41 4323897.071 1364383.527 1787.803 RC
42 5323807.726 1364424.439 1788.115 RRE
43 6323862.509 1364396.562 1787.973 RRE
44 7323896.925 1364374.352 1787.886 RRE
45 8323808.679 1364417.278 1791.367 RRB
46 9323867.442 1364388.898 1790.188 RRB
47 323897.265 1364365.826 1791.170 RRB
48 1323913.302 1364353.181 1791.015 RRB
49 2323916.056 1364355.837 1788.060 RRE
50 3323920.171 1364359.811 1787.744 RC
51 4323931.821 1364371.043 1788.273 LRE
52 5323942.267 1364321.093 1790.844 RRB
53 6323948.298 1364323.134 1787.963 RRE
54 7323955.308 1364324.396 1787.790 RC
55 8323981.610 1364283.763 1787.557 RC
56 9323974.195 1364281.555 1788.204 RRE
57 323969.293 1364280.907 1790.571 RRB
58 1323937.929 1364376.925 1792.993 BM2
59 2323782.195 1364418.631 1791.070 T
60 3323720.395 1364354.357 1790.852 T
61 4323621.824 1364282.376 1790.899 T
62 5323857.547 1364352.090 1791.192 T
63 6323754.531 1364267.718 1790.297 T
64 7323637.203 1364164.049 1790.089 T
65 8323651.872 1364099.552 1789.982 T
66 9323762.883 1364204.982 1790.133 T
67 323896.370 1364331.122 1790.851 T
68 1323658.191 1364062.080 1790.316 T
69 2323767.085 1364161.364 1790.169 T
70 3323902.775 1364248.179 1790.600 T
71 4323912.968 1364166.985 1790.543 T
72 5323796.059 1364082.721 1789.951 T
73 6323682.335 1363988.454 1789.854 T
74 7323710.440 1363919.743 1789.575 T
75 8323808.281 1363998.189 1790.387 T
76 9323918.944 1364068.958 1790.300 T
77 323724.382 1363847.690 1789.448 T
83
78 1323827.854 1363915.109 1789.827 T
79 2323922.828 1363970.427 1790.119 T
80 3323909.468 1364348.191 1792.353 T
81 4323833.061 1363867.593 1790.131 T
82 5323931.937 1364310.874 1791.047 T
83 6323958.252 1364246.098 1790.838 T
84 7323950.155 1364190.034 1791.110 T
85 8323976.611 1364049.544 1792.359 BM3
86 9323910.028 1364350.089 1792.937 BM3
87 324003.259 1364205.853 1787.313 RC
88 1323996.479 1364198.312 1787.906 RRE
89 2323992.136 1364210.408 1789.581 RRB
90 3323979.419 1364146.592 1790.906 RRB
91 4323984.536 1364143.576 1787.695 RRE
92 5323991.914 1364138.499 1787.488 RC
93 6323946.033 1364072.074 1790.861 RRB
94 7323950.819 1364070.122 1787.583 RRE
95 8323959.994 1364064.778 1787.397 RC
96 9323956.076 1364013.525 1787.486 RC
97 323946.427 1364012.443 1787.749 RRE
98 1323937.302 1364009.477 1791.009 RRB
99 2323948.911 1363961.611 1790.639 RRB
100 3323956.940 1363963.326 1788.005 RRE
101 4323964.354 1363967.145 1787.358 RC
102 5323975.968 1364000.224 1790.758 LRB
103 6323970.073 1364007.799 1788.225 LRE
104 7323969.409 1364034.127 1788.756 LRE
105 8323973.385 1364034.455 1790.716 LRB
106 9323978.399 1364060.660 1791.166 LRB
107 323972.828 1364062.549 1788.323 LRE
108 1324009.936 1364118.421 1791.068 LRB
109 2324002.313 1364121.211 1788.246 LRE
110 3324018.676 1364170.342 1788.470 LRE
111 4324024.005 1364169.986 1791.209 LRB
112 5323985.819 1364171.128 1791.130 FM
113 6324025.245 1364191.122 1791.291 FM
114 7324031.614 1364054.223 1790.837 T
115 8324198.138 1364079.010 1790.258 T
116 9324345.799 1364099.491 1790.800 T
117 324394.829 1363979.827 1790.224 T
84
118 1324233.518 1363974.389 1790.273 T
119 2324054.278 1363938.458 1790.748 T
120 3324282.172 1363856.122 1790.160 T
121 4324439.683 1363901.813 1790.136 T
122 5324072.351 1363807.394 1790.676 T
123 6324323.305 1363736.672 1790.108 T
124 7324464.191 1363757.457 1789.869 T
125 8324132.603 1363703.591 1790.464 T
126 9324026.138 1363732.278 1792.378 BM4
127 323976.670 1364049.411 1792.329 BM3
128 1324234.360 1363403.990 1792.027 BM5
129 2323948.792 1363895.184 1790.137 T
130 3323982.113 1363802.563 1790.134 T
131 4324015.122 1363719.892 1790.109 T
132 5323899.266 1363722.055 1789.779 T
133 6324056.550 1363643.824 1790.114 T
134 7323848.793 1363710.525 1790.047 T
135 8323898.800 1363615.425 1789.808 T
136 9323848.147 1363655.175 1789.828 T
137 323911.973 1363518.221 1789.694 T
138 1323998.750 1363556.781 1789.978 T
139 2323841.043 1363555.933 1789.370 T
140 3323907.223 1363433.845 1789.372 T
141 4323837.364 1363523.619 1789.304 T
142 5323990.652 1363446.918 1789.792 T
143 6324011.110 1363420.198 1789.739 T
144 7324064.800 1363450.060 1789.685 T
145 8324112.814 1363477.870 1790.099 T
146 9324140.978 1363478.092 1790.287 T
147 324065.879 1363654.006 1790.220 T
148 1324113.841 1363616.651 1790.091 T
149 2324157.839 1363571.021 1790.211 T
150 3324216.346 1363573.455 1787.146 RC
151 4324206.359 1363571.305 1787.466 RRE
152 5324195.140 1363575.963 1790.547 RRB
153 6324137.577 1363610.964 1790.585 RRB
154 7324140.069 1363615.156 1787.365 RRE
155 8324145.674 1363624.061 1787.183 RC
156 9324072.964 1363693.994 1787.293 RC
157 324060.828 1363686.939 1787.894 RRE
85
158 1324057.757 1363683.509 1790.514 RRB
159 2324027.829 1363739.532 1790.681 RRB
160 3324031.479 1363743.408 1788.191 RRE
161 4324041.074 1363748.306 1787.311 RC
162 5324009.686 1363840.943 1787.049 RC
163 6324000.709 1363835.120 1788.215 RRE
164 7323984.786 1363845.664 1790.953 RRB
165 8323963.452 1363922.747 1789.983 RRB
166 9323964.965 1363939.146 1787.997 RRE
167 323973.685 1363940.458 1787.553 RC
168 1323992.172 1363926.539 1787.830 LRE
169 2323998.781 1363918.804 1789.960 LRB
170 3324023.563 1363824.527 1789.629 LRB
171 4324018.153 1363822.793 1787.639 LRE
172 5324045.943 1363755.706 1787.771 LRE
173 6324050.027 1363757.449 1790.275 LRB
174 7324067.116 1363715.929 1787.581 LBE
175 8324071.750 1363719.647 1790.424 LRB
176 9324108.288 1363676.208 1790.305 LRB
177 324101.721 1363669.654 1787.566 LRE
178 1324164.083 1363621.482 1787.425 LRE
179 2324153.479 1363643.638 1790.335 LRB
180 3324180.828 1363623.448 1790.299 LRB
181 4324122.694 1363655.902 1788.378 LRE
182 5324026.158 1363732.247 1792.322 BM4
183 6324070.035 1363220.850 1789.271 VIL
184 7324200.130 1363255.970 1789.480 VIL
185 8324117.613 1363250.675 1789.472 VIL
186 9324193.790 1363281.179 1789.386 VIL
187 324254.369 1363260.871 1789.546 VIL
188 1324314.334 1363223.812 1789.662 VIL
189 2324341.663 1363208.108 1789.542 VIL
190 3324264.094 1363331.560 1789.921 T
191 4324287.979 1363297.212 1789.612 T
192 5324310.321 1363267.868 1789.676 T
193 6324420.923 1363124.894 1790.510 RRB
194 7324423.160 1363127.916 1787.257 RRE
195 8324434.122 1363143.496 1786.903 RC
196 9324360.135 1363243.501 1787.011 RC
197 324351.574 1363238.496 1787.196 RRE
86
198 1324339.511 1363239.318 1790.920 RRB
199 2324307.806 1363297.992 1790.293 RRB
200 3324310.956 1363299.206 1787.200 RRE
201 4324317.152 1363303.324 1786.992 RC
202 5324260.914 1363376.704 1786.991 RC
203 6324255.113 1363374.761 1787.490 RRE
204 7324251.337 1363374.032 1790.346 RRB
205 8324250.922 1363419.518 1787.048 RC
206 9324240.231 1363417.196 1787.618 RRE
207 324234.161 1363415.377 1789.872 RRB
208 1324221.159 1363494.615 1790.178 RRB
209 2324228.332 1363494.520 1787.715 RRE
210 3324243.106 1363494.427 1787.119 RC
211 4324223.953 1363563.191 1787.139 RC
212 5324217.442 1363558.742 1787.370 RRE
213 6324205.949 1363556.854 1790.494 RRB
214 7324214.200 1363581.275 1787.188 RRE
215 8324221.541 1363588.898 1787.591 RRE
216 9324226.960 1363593.387 1790.582 LRB
217 324238.603 1363551.954 1787.368 LRE
218 1324242.605 1363553.761 1790.018 LRB
219 2324255.285 1363495.376 1790.410 LRB
220 3324249.704 1363494.907 1787.233 LRE
221 4324256.466 1363432.152 1787.256 LRE
222 5324261.286 1363431.196 1790.228 LRB
223 6324256.446 1363432.170 1787.261 LRE
224 7324285.808 1363369.589 1787.636 LRE
225 8324291.950 1363371.474 1789.869 LRB
226 9324341.647 1363309.144 1790.059 LRB
227 324336.085 1363304.427 1787.662 LRE
228 1324368.015 1363252.305 1787.250 LRE
229 2324372.293 1363254.458 1790.623 LRB
230 3324395.012 1363221.577 1790.651 LRB
231 4324389.170 1363219.960 1787.337 LRE
232 5324443.887 1363147.128 1787.947 LRE
233 6324450.479 1363148.993 1790.619 LRB
234 7324500.187 1363207.053 1790.098 VIL
235 8324518.744 1363300.810 1789.757 VIL
236 9324500.595 1363245.447 1790.006 VIL
237 324499.666 1363276.891 1789.861 VIL
87
238 1324541.147 1363353.911 1789.765 T
239 2324539.505 1363404.540 1789.830 T
240 3324483.108 1363328.521 1789.843 T
241 4324472.511 1363408.192 1790.075 T
242 5324541.141 1363448.186 1789.785 T
243 6324463.322 1363475.862 1789.941 T
244 7324526.979 1363515.514 1789.870 T
245 8324527.213 1363570.767 1789.959 T
246 9324471.919 1363529.493 1789.838 T
247 324596.858 1362969.327 1792.380 BM6
248 1324310.636 1363458.802 1790.329 T
249 2324341.113 1363407.679 1790.127 T
250 3324356.582 1363359.290 1790.263 T
251 4324394.353 1363303.617 1790.007 T
252 5324384.985 1363295.019 1790.042 T
253 6324412.918 1363259.058 1790.159 T
254 7324445.709 1363228.269 1790.072 T
255 8324444.320 1363195.206 1790.138 T
256 9324234.402 1363403.953 1792.028 BM5
257 324509.132 1363076.609 1786.923 RC
258 1324516.001 1363087.942 1787.413 RRE
259 2324520.465 1363094.259 1790.850 RRB
260 3324505.241 1363063.444 1787.137 LRE
261 4324501.594 1363058.363 1789.248 LRB
262 5324535.613 1363008.638 1789.083 RRB
263 6324548.756 1363008.808 1787.269 RRE
264 7324570.495 1363015.320 1786.779 RC
265 8324584.254 1363023.804 1790.050 LRB
266 9324577.802 1363022.235 1787.155 LRE
267 324581.943 1363072.916 1790.156 VIL
268 1324597.115 1363022.164 1790.908 VIL
269 2324599.221 1362979.466 1790.851 VIL
270 3324593.281 1362932.408 1790.234 VIL
271 4324575.483 1362970.181 1786.635 RC
272 5324588.701 1362973.457 1787.323 LRE
273 6324594.847 1362972.637 1790.880 LRB
274 7324554.544 1362969.851 1787.879 RRE
275 8324548.768 1362968.468 1790.254 RRB
276 9324548.066 1362890.958 1791.436 BM7
277 324531.648 1362938.893 1789.614 VIL
88
278 1324487.562 1363060.882 1789.947 VIL
279 2324502.414 1363018.432 1789.201 VIL
280 3324552.606 1362925.938 1790.292 RRB
281 4324557.161 1362924.208 1787.826 RRE
282 5324570.621 1362922.050 1786.697 RC
283 6324591.949 1362951.341 1791.318 LRB
284 7324580.191 1362922.937 1786.681 LRE
285 8324596.842 1362969.300 1792.382 BM6
286 9324686.200 1362740.678 1791.972 BM8
287 324544.604 1362854.037 1789.377 VIL
288 1324557.197 1362856.026 1789.622 RRB
289 2324564.052 1362857.104 1787.465 RRE
290 3324570.291 1362857.984 1786.538 RC
291 4324586.534 1362861.481 1786.914 LRE
292 5324593.608 1362860.702 1790.002 LRB
293 6324631.192 1362823.915 1789.572 LRB
294 7324631.098 1362816.866 1787.483 LRE
295 8324630.041 1362809.764 1786.541 RC
296 9324620.355 1362803.162 1787.324 RRE
297 324617.448 1362800.845 1788.647 RRB
298 1324655.846 1362772.140 1786.255 RC
299 2324664.023 1362773.826 1786.674 LRE
300 3324669.537 1362775.666 1789.566 LRB
301 4324675.225 1362737.627 1786.431 RC
302 5324674.687 1362748.679 1787.078 LRE
303 6324679.161 1362741.651 1789.849 LRB
304 7324548.192 1362890.822 1791.433 BM7
305 8324644.088 1362742.609 1789.830 RRB
306 9324650.756 1362742.657 1786.905 RRE
307 324695.029 1362712.060 1786.395 RC
308 1324689.949 1362702.080 1786.636 RRE
309 2324688.778 1362698.467 1789.272 RRB
310 3324699.792 1362724.929 1787.668 LRE
311 4324703.381 1362729.249 1789.673 LRB
312 5324783.267 1362698.028 1788.021 LRB
313 6324780.629 1362691.041 1786.480 LRE
314 7324778.150 1362683.924 1786.313 RC
315 8324773.748 1362678.334 1786.647 RRE
316 9324775.390 1362673.662 1789.414 RRB
317 324849.201 1362656.655 1789.687 LRB
89
318 1324846.548 1362654.136 1786.801 LRE
319 2324833.038 1362643.763 1786.112 RC
320 3324828.538 1362639.487 1786.552 RRE
321 4324894.982 1362589.753 1786.861 LRE
322 5324896.361 1362595.136 1789.550 LRB
323 6324910.870 1362435.370 1792.398 BM9
324 7324686.223 1362740.646 1791.938 BM8
325 8324870.554 1362583.316 1787.001 RRE
326 9324867.100 1362581.510 1789.022 RRB
327 324881.999 1362509.953 1789.211 RRB
328 1324889.024 1362511.742 1787.224 RRE
329 2324904.248 1362512.885 1786.201 RC
330 3324911.006 1362512.924 1786.649 LRE
331 4324916.293 1362515.750 1789.367 LRB
332 5324874.097 1362413.796 1789.794 RRB
333 6324880.447 1362418.584 1786.615 RRE
334 7324904.876 1362424.893 1789.775 LRB
335 8324900.354 1362421.133 1786.552 LRE
336 9324890.091 1362418.097 1786.224 RC
337 324926.767 1362242.560 1786.063 RC
338 1324931.563 1362249.154 1786.793 RRE
339 2324908.445 1362238.216 1786.339 RRE
340 3324895.150 1362234.206 1790.042 RRB
341 4324937.369 1362253.465 1790.274 LRB
342 5324935.904 1362212.243 1785.882 RC
343 6324920.846 1362207.813 1788.610 RRB
344 7324922.845 1362217.532 1786.206 RRE
345 8324826.018 1362228.769 1788.084 T
346 9325016.981 1362257.286 1788.728 T
347 325206.858 1362259.736 1788.332 T
348 1324813.062 1362299.310 1788.016 T
349 2325041.126 1362345.600 1788.790 T
350 3325182.073 1362326.092 1788.651 T
351 4324772.188 1362351.845 1788.186 T
352 5324819.932 1362392.205 1788.167 SCOOL
353 6324830.996 1362487.313 1788.478 SCOOL
354 7325043.795 1362446.172 1788.686 T
355 8325176.657 1362402.331 1788.358 T
356 9325028.949 1362532.274 1788.683 T
357 325131.389 1362475.775 1788.428 T
90
358 1325124.316 1362552.469 1788.325 T
359 2325012.838 1362610.997 1788.431 T
360 3325116.449 1362636.522 1788.232 T
361 4325012.748 1362611.061 1788.431 T
362 5324996.230 1362674.319 1788.596 T
363 6325137.536 1362714.266 1788.434 T
364 7324982.221 1362744.337 1788.790 T
365 8325153.210 1362843.409 1788.602 T
366 9325137.669 1362132.425 1791.975 BM10
367 324785.805 1362609.448 1788.712 T
368 1324789.536 1362552.428 1788.604 T
369 2324785.410 1362526.659 1788.500 SCOOL
370 3324911.005 1362435.202 1792.361 BM9
371 4325061.844 1362154.275 1788.796 LRB
372 5325059.600 1362145.851 1787.023 LRE
373 6325058.934 1362140.333 1786.688 RC
374 7325054.561 1362126.403 1786.316 RRE
375 8325055.135 1362122.174 1788.485 RRB
376 9325139.618 1362114.377 1787.200 LRE
377 325145.264 1362125.824 1789.804 LRB
378 1325138.498 1362105.355 1785.943 RC
379 2325132.706 1362090.649 1786.255 RRE
380 3325131.792 1362088.759 1787.850 RRB
381 4324878.971 1362097.399 1787.672 T
382 5325291.029 1362055.182 1787.529 LRB
383 6325286.645 1362049.264 1786.274 LRE
384 7325277.186 1362040.410 1785.698 RC
385 8325275.333 1362036.726 1786.400 RRE
386 9325268.841 1362029.015 1789.481 RRB
387 324933.192 1362022.437 1787.585 T
388 1324959.288 1361997.021 1787.628 T
389 2325257.340 1361969.624 1787.599 T
390 3325008.116 1361983.827 1787.571 T
391 4325221.785 1361939.184 1787.668 T
392 5325077.957 1361969.098 1787.527 T
393 6325192.728 1361931.482 1787.611 T
394 7325104.102 1362039.079 1787.623 T
395 8325183.179 1361995.817 1787.568 T
396 9325199.204 1362033.846 1787.562 T
397 325035.740 1362064.377 1787.746 T
91
398 1325229.401 1362136.198 1788.414 T
399 2325224.444 1362178.568 1788.218 T
400 3324976.121 1362112.841 1787.551 T
401 4324883.995 1362125.798 1787.723 T
402 5325166.987 1362207.756 1788.447 T
403 6325181.936 1362146.298 1786.930 T
404 7325116.902 1362232.589 1788.557 T
405 8325097.138 1362167.021 1788.430 T
406 9325064.669 1362238.351 1788.653 T
407 325054.241 1362188.245 1788.580 T
408 1325009.544 1362208.940 1788.683 T
409 2325013.902 1362243.570 1788.753 T
Appendix 10: Estimated Maning‟s n value for channel and over banks
No Variables Alternatives Recommended Value
1 Channel natural materials Fine gravel 0.024
2 Surface irregularities Moderate 0.01
3 the channel cross sections Alternating 0.015
4 Obstruction Minor 0.015
5 Vegetation low 0.01
Sum 0.074
6 Meandering of the channel Sever 0.3
Estimated manning’s n to channel 0.022
1 Channel natural materials earth 0.02
2 irregularities Moderate 0.01
3 channel cross sections Alternating 0.015
4 Obstruction Minor 0.01
5 Vegetation Medium 0.02
Sum 0.075
6 Meandering of the channel Sever 0.3
estimated manning‟s n to over banks 0.023
92
Appendix 11: Data Quality Test for Megech flow Discharge.
To check outliers and percent of error of flow data
No Year Xi Yt=logXi No Year Xi Yt=logXi
1 1980 199.3 2.30 21 2000 83.9 1.92
2 1981 318.0 2.50 22 2001 261.1 2.42
3 1982 47.25 1.65 23 2002 137.9 2.14
4 1983 142.4 2.15 24 2003 214.7 2.33
5 1984 160.7 2.21 25 2004 263.7 2.42
6 1985 186.2 2.27 26 2005 238.4 2.38
7 1986 143.5 2.16 27 2006 186.0 2.27
8 1987 57.7 1.76 28 2007 169.7 2.23
9 1988 72.5 1.86 29 2008 214.7 2.33
10 1989 47.6 1.68 30 2009 188.2 2.27
11 1990 65.9 1.82 31 2010 274.2 2.44
12 1991 135.8 2.13 32 2011 407.7 2.61
13 1992 96.7 1.99 33 2012 355.3 2.55
14 1993 130.1 2.11 34 2013 290.5 2.46
15 1994 136.9 2.14 35 2014 194.6 2.29
16 1995 274.8 2.44 36 2015 205.6 2.31
17 1996 89.8 1.95 37 2016 180.4 2.26
18 1997 100.1 2.00 38 2017 224.0 2.35
19 1998 190.8 2.28 39 2018 156.1 2.19
20 1999 242.3 2.38 40 2019 188.6 2.28
sum 7,267.5 88.20 N 40
mean 187.97 2.21 Kn for given N 2.7
STDEV.S 90.92 0.24 Yminimum=Ymean-Kn*YnSTDV 1.6
Skewness 0.47 -0.78 Ymaximum=Ymean+Kn*YnSTDV 2.8
minimum 47.25 1.65 Xminimum=10^Yminimum 37.5
Maximum 407.66 2.61 Xmaximum=10^Ymaximum 685.9
1) Checking for outliers Higher and lower was bounded so it ok
2) percent of error P=STDV/(mean*sqrt(N))*100<10% 7.3
3) Independency and stationary or Trend test by Wald-Wolfowitz (1943) (W-W)
S.N RF dat(xi) lag by1( Xi+1) xi(xi+1) xi2 xi3 xi4
1 407.7 355.3 144,875.0 166,219.3 67,767,604.5 27,628,852,368.1
2 355.3 318.0 112,988.6 126,271.5 44,870,195.3 15,944,489,290.1
3 318.0 290.5 92,378.0 101,103.0 32,147,421.8 10,221,819,255.7
4 290.5 274.8 79,837.4 84,405.9 24,522,203.9 7,124,362,323.9
5 274.8 274.2 75,356.2 75,516.1 20,751,986.1 5,702,687,280.3
6 274.2 263.7 72,303.0 75,196.6 20,620,414.0 5,654,529,914.9
7 263.7 261.1 68,835.5 69,520.8 18,330,414.0 4,833,143,610.4
8 261.1 242.3 63,257.3 68,157.0 17,793,685.8 4,645,379,751.6
93
9 242.3 238.4 57,761.4 58,709.8 14,225,437.1 3,446,837,633.7
10 238.4 224.0 53,389.6 56,828.4 13,547,142.7 3,229,462,701.3
11 224.0 214.7 48,073.4 50,159.0 11,233,704.9 2,515,923,018.2
12 214.7 214.7 46,074.6 46,074.6 9,889,917.7 2,122,870,838.5
13 214.7 205.6 44,127.7 46,074.6 9,889,917.7 2,122,870,838.5
14 205.6 199.3 40,971.5 42,263.1 8,688,455.6 1,786,172,698.4
15 199.3 194.6 38,778.6 39,719.3 7,915,936.2 1,577,622,332.5
16 194.6 190.8 37,134.6 37,860.2 7,366,725.9 1,433,395,420.1
17 190.8 188.6 35,993.9 36,423.0 6,951,248.9 1,326,631,949.9
18 188.6 188.2 35,485.5 35,570.0 6,708,494.5 1,265,222,054.4
19 188.2 186.2 35,034.1 35,401.2 6,660,801.9 1,253,243,198.7
20 186.2 186.0 34,640.8 34,670.8 6,455,739.9 1,202,065,232.5
21 186.0 180.4 33,569.1 34,610.9 6,439,008.4 1,197,913,125.1
22 180.4 169.7 30,614.4 32,558.6 5,874,872.6 1,060,062,017.2
23 169.7 160.7 27,266.5 28,786.2 4,884,012.7 828,646,014.3
24 160.7 156.1 25,084.6 25,827.1 4,150,615.4 667,037,093.5
25 156.1 143.5 22,405.5 24,363.5 3,802,844.3 593,578,365.6
26 143.5 142.4 20,439.1 20,604.9 2,957,706.9 424,561,077.2
27 142.4 137.9 19,632.7 20,274.6 2,886,883.9 411,060,513.0
28 137.9 136.9 18,873.7 19,011.2 2,621,279.2 361,424,590.9
29 136.9 135.8 18,585.4 18,737.2 2,564,826.9 351,083,767.7
30 135.8 130.1 17,667.6 18,434.9 2,502,991.8 339,843,717.6
31 130.1 100.1 13,018.9 16,932.3 2,203,292.8 286,701,272.1
32 100.1 96.7 9,674.3 10,010.0 1,001,500.8 100,200,150.1
33 96.7 89.8 8,682.4 9,349.9 904,090.8 87,421,060.6
34 89.8 83.9 7,537.7 8,062.6 723,957.3 65,005,571.4
35 83.9 72.5 6,085.0 7,046.9 591,561.7 49,659,235.3
36 72.5 65.9 4,779.7 5,254.4 380,873.2 27,608,353.3
37 65.9 57.7 3,807.3 4,348.0 286,699.6 18,904,684.2
38 57.7 47.6 2,750.8 3,333.8 192,489.8 11,114,169.9
39 47.6 41.2 1,962.9 2,269.8 108,135.9 5,151,811.2
40 47.25
- 1,697.4 69,934.5 2,881,302.6
Sum S1=7,267.5
1,509,735 1,597,658.2 401,485,026.7 111,927,439,604.5
S1
R S2 S3 S4
R (bar) 1,313,317.38 At 5% significance level for two tail
var R 1,662,916,769,806 alpha=5%/2 0.025 NORMSINV=1.96
STDV 1,289,541.30 then,kent=1-0.025 0.975 U=0.15
Values range from -1.96 to 1.96 then,u lies within the range,accept the hypothesis
3, Test of Trend analysis
Year Xi Xmean Ordered Xi (Xi-Xmean)2 Rank(Kxi) Rank(Kyi) Di=Kxi-Kyi Di*Di
1980 199.30 181.69 41.20 19,736.65 1 3 -2 4
1981 317.97 181.69 47.64 17,968.12 2 10 -8 64
94
1982 47.25 181.69 57.74 15,363.16 3 8 -5 25
1983 142.39 181.69 65.94 13,397.65 4 11 -7 49
1984 160.71 181.69 72.49 11,924.68 5 9 -4 16
1985 186.20 181.69 83.95 9,553.34 6 21 -15 225
1986 143.54 181.69 89.79 8,444.73 7 17 -10 100
1987 57.74 181.69 96.70 7,223.67 8 13 -5 25
1988 72.49 181.69 100.05 6,664.63 9 18 -9 81
1989 47.64 181.69 130.12 2,658.76 10 14 -4 16
1990 65.94 181.69 135.78 2,107.93 11 12 -1 1
1991 135.78 181.69 136.88 2,007.33 12 15 -3 9
1992 96.70 181.69 137.88 1,918.98 13 23 -10 100
1993 130.12 181.69 142.39 1,544.35 14 4 10 100
1994 136.88 181.69 143.54 1,454.90 15 7 8 64
1995 274.80 181.69 156.09 655.32 16 39 -23 529
1996 89.79 181.69 160.71 440.13 17 5 12 144
1997 100.05 181.69 169.67 144.53 18 28 -10 100
1998 190.85 181.69 180.44 1.56 19 37 -18 324
1999 242.30 181.69 186.04 18.95 20 27 -7 49
2000 83.95 181.69 186.20 20.37 21 6 15 225
2001 261.07 181.69 188.15 41.79 22 30 -8 64
2002 137.88 181.69 188.60 47.79 23 40 -17 289
2003 214.65 181.69 190.85 83.92 24 19 5 25
2004 263.67 181.69 194.58 166.15 25 35 -10 100
2005 238.39 181.69 199.30 310.11 26 1 25 625
2006 186.04 181.69 205.58 570.87 27 36 -9 81
2007 169.67 181.69 214.65 1,086.55 28 24 4 16
2008 214.65 181.69 214.65 1,086.55 29 29 0 0
2009 188.15 181.69 223.96 1,787.16 30 38 -8 64
2010 274.22 181.69 238.39 3,214.87 31 26 5 25
2011 407.66 181.7 242.30 3,674.03 32 20 12 144
2012 355.35 181.69 261.07 6,301.47 33 22 11 121
2013 290.53 181.69 263.67 6,720.85 34 25 9 81
2014 194.58 181.69 274.22 8,562.32 35 31 4 16
2015 205.58 181.69 274.80 8,670.37 36 16 20 400
2016 180.44 181.69 290.53 11,846.10 37 34 3 9
2017 223.96 181.69 317.97 18,572.18 38 2 36 1296
2018 156.09 181.69 355.35 30,157.73 39 33 6 36
2019 188.60 181.69 407.70 51,081.79 40 32 8 64
sum 7267.488 277,232.32 5706
mean 187.97 n 40 n*n-1 1599 DF or V 38
STDEV.S 90.92 n*n 1600 n*(n*n-1) 63960 tt 3.19
Tthe time series has no trend if: t(v,0.05%) < tt < t(v,99.95%), V degree of freedom t(38,0.05%) (3.57 <3.2< 3.57)
95
4) The fittest distribution method has the smallest D-index value
Normal GUMBLE EVI Lognormal Log person Type III
XI P W Z=KT XI' XI-
XI' YT KT XI' XI-XI'
ANT-
LOG XI'
XI-
XI' K KT
ANT-
LOG XI'
XI-
XI'
408 0.02 2.73 1.97 348 59.8 3.70 2.77 415 7.3 2.7 466.6 58.9 (0.13) 1.58 2.58 377 30.7
355 0.05 2.46 1.66 321 33.9 3.00 2.15 363 7.5 2.6 393.6 38.3 (0.13) 1.40 2.54 343 12.4
318 0.07 2.29 1.45 304 13.8 2.58 1.78 332 13.9 2.5 352.4 34.4 (0.13) 1.28 2.51 321 2.6
291 0.10 2.16 1.30 291 0.4 2.28 1.52 310 19.2 2.5 323.6 33.1 (0.13) 1.18 2.48 303 12.9
275 0.12 2.05 1.17 280 5.1 2.04 1.31 292 17.4 2.5 301.6 26.8 (0.13) 1.09 2.46 289 14.5
274 0.15 1.96 1.05 270 3.8 1.84 1.14 278 3.5 2.5 283.6 9.4 (0.13) 1.01 2.44 277 2.9
264 0.17 1.88 0.95 262 1.8 1.68 0.99 265 1.6 2.4 268.5 4.8 (0.13) 0.94 2.43 266 2.7
261 0.20 1.81 0.86 254 7.0 1.53 0.86 254 6.7 2.4 255.4 5.6 (0.13) 0.87 2.41 257 4.5
242 0.22 1.74 0.77 247 4.6 1.39 0.75 245 2.3 2.4 243.9 1.6 (0.13) 0.80 2.39 248 5.3
238 0.24 1.68 0.69 240 1.8 1.27 0.64 236 2.7 2.4 233.5 4.9 (0.13) 0.74 2.38 239 0.9
224 0.27 1.62 0.62 234 9.8 1.16 0.54 227 3.5 2.4 224.1 0.2 (0.13) 0.68 2.36 232 7.5
215 0.29 1.57 0.55 228 13.0 1.06 0.45 220 5.2 2.3 215.5 0.8 (0.13) 0.62 2.35 224 9.5
215 0.32 1.52 0.48 222 7.1 0.96 0.37 213 1.9 2.3 207.5 7.1 (0.13) 0.56 2.34 217 2.5
206 0.34 1.47 0.41 216 10.5 0.87 0.29 206 0.4 2.3 200.1 5.5 (0.13) 0.50 2.32 210 4.9
199 0.37 1.42 0.34 211 11.3 0.79 0.21 200 0.3 2.3 193.1 6.2 (0.13) 0.44 2.31 204 4.7
195 0.39 1.37 0.28 205 10.6 0.70 0.14 194 1.1 2.3 186.5 8.1 (0.13) 0.39 2.30 198 3.3
191 0.41 1.33 0.22 200 9.0 0.62 0.07 188 3.2 2.3 180.2 10.6 (0.13) 0.33 2.28 192 1.0
189 0.44 1.28 0.15 195 6.0 0.55 0.00 182 6.6 2.2 174.2 14.4 (0.13) 0.27 2.27 186 2.6
188 0.46 1.24 0.09 189 1.3 0.47 (0.06) 177 11.6 2.2 168.5 19.6 (0.13) 0.22 2.26 180 7.9
186 0.49 1.20 0.03 184 1.9 0.40 (0.12) 171 15.0 2.2 163.0 23.2 (0.13) 0.16 2.24 175 11.5
96
186 0.51 1.16 (0.03) 179 6.9 0.33 (0.19) 166 20.0 2.2 157.7 28.3 (0.13) 0.10 2.23 169 16.8
180 0.54 1.12 (0.09) 174 6.5 0.26 (0.25) 161 19.5 2.2 152.6 27.8 (0.13) 0.04 2.21 164 16.6
170 0.56 1.08 (0.15) 169 0.9 0.19 (0.31) 156 13.8 2.2 147.6 22.0 (0.13) (0.02) 2.20 158 11.2
161 0.59 1.03 (0.21) 164 2.9 0.13 (0.36) 151 9.8 2.2 142.8 17.9 (0.13) (0.08) 2.19 153 7.5
156 0.61 0.99 (0.28) 158 2.2 0.06 (0.42) 146 10.1 2.1 138.0 18.1 (0.13) (0.15) 2.17 148 8.2
144 0.63 0.95 (0.34) 153 9.4 (0.01) (0.48) 141 2.4 2.1 133.3 10.2 (0.13) (0.22) 2.15 143 0.9
142 0.66 0.91 (0.41) 147 5.1 (0.07) (0.54) 136 6.2 2.1 128.7 13.7 (0.13) (0.28) 2.14 137 5.0
138 0.68 0.87 (0.47) 142 4.0 (0.14) (0.60) 131 6.6 2.1 124.2 13.7 (0.13) (0.36) 2.12 132 5.7
137 0.71 0.83 (0.54) 136 0.8 (0.21) (0.66) 126 10.6 2.1 119.6 17.2 (0.13) (0.43) 2.10 127 10.0
136 0.73 0.79 (0.61) 130 5.6 (0.27) (0.72) 121 14.5 2.1 115.1 20.6 (0.13) (0.51) 2.08 122 14.2
130 0.76 0.75 (0.69) 124 6.2 (0.34) (0.78) 116 14.0 2.0 110.6 19.5 (0.13) (0.60) 2.06 116 14.0
100 0.78 0.70 (0.76) 117 17.3 (0.42) (0.84) 111 10.7 2.0 106.0 6.0 (0.13) (0.69) 2.04 111 10.5
97 0.80 0.66 (0.85) 110 13.7 (0.49) (0.91) 105 8.6 2.0 101.4 4.7 (0.13) (0.78) 2.02 105 8.2
90 0.83 0.61 (0.93) 103 13.2 (0.57) (0.98) 99 9.7 2.0 96.7 6.9 (0.13) (0.89) 2.00 99 9.2
84 0.85 0.56 (1.03) 95 11.0 (0.65) (1.05) 93 9.3 2.0 91.8 7.9 (0.13) (1.01) 1.97 93 8.9
72 0.88 0.51 (1.13) 86 13.6 (0.74) (1.13) 87 14.1 1.9 86.7 14.2 (0.13) (1.14) 1.94 86 13.9
66 0.90 0.45 (1.25) 76 10.1 (0.84) (1.22) 79 13.2 1.9 81.3 15.4 (0.13) (1.30) 1.90 79 13.5
58 0.93 0.39 (1.39) 64 6.5 (0.96) (1.32) 71 12.8 1.9 75.4 17.7 (0.13) (1.48) 1.86 72 14.1
48 0.95 0.32 (1.57) 50 2.1 (1.11) (1.44) 60 12.2 1.8 68.7 21.0 (0.13) (1.72) 1.80 63 15.5
47 0.98 0.22 (1.81) 29 11.8 (1.31) (1.63) 45 3.4 1.8 60.2 19.0 (0.13) (2.07) 1.72 52 11.0
187.9 Sum or mean 377 182 360
642 18.7 366
90.9 Sum/MEAN or SD 2.0 85.4 1.9 93.0 3.4 80.8 2.0
The fittest distribution method has the smallest D-index value, hence GUMBLE EVI is the fittest