hydrometeorological variability in the korean han river basin and its sub-watersheds during...

ORIGINAL PAPER

Hydrometeorological variability in the Korean Han River Basinand its sub-watersheds during different El Nino phases

Sun-Kwon Yoon Jong-Suk Kim Joo-Heon Lee

Young-Il Moon

Published online: 16 January 2013

Springer-Verlag Berlin Heidelberg 2013

Abstract This study investigated the characteristic changes

in precipitation and runoff that occur in the Korean Han

River Basin and its sub-basins in association with the cold-

tongue (CT) and warm-pool (WP) El Nino phases during

spring and summer. During the WP El Nino years, rainfall

in spring and its coefficient of variation were higher than

long-term normal precipitation. During the CT El Nino

years, summers tended to be drier than in climatologically

normal years, although the variability in precipitation

during the summer was relatively lower. The data for

runoff showed wetter springs compared to long-term nor-

mal years during both types of El Nino events and signif-

icant changes in runoff during summer under CT El Nino

conditions. During the WP El Nino years, increased runoff

was seen for 95.8 % of all basins and this increase was

statistically significant for 58.3 % of these basins, but

variability in runoff was small. Overall, the findings con-

firm that water resources in the Han River Basin during the

spring and summer are sensitive to CT/WP El Nino events.

Thus, for basins such as these, where seasonal variability

and the uncertainty of hydrologic data are high, investi-

gation of the relationship between climatic factors and

hydrologic parameters is necessary to maintain the stability

of the water supply system and to allow prediction for

water resources.

Keywords Seasonal rainfall Runoff CT El Nino WP El Nino Han River South Korea

1 Introduction

The seasonal characteristics of hydrometeorological vari-

ability are closely related to global climate phenomena and

climate changes (Kim et al. 2008; Grimm 2011). Therefore,

investigating the correlation between climatic factors and

hydrologic data (such as precipitation and streamflow) is

very important for the accurate prediction and management

of water resources (Horel and Wallace 1981; Pizarro and

Lall 2002; Kim et al. 2011, 2012c). However, regional

hydrologic variability has a complex relationship with cli-

mate, including effects across the hydrologic cycle rather

than being limited to independent phenomena. Changes in

global climate systems have significant consequences for

water resources, which are closely associated with weather

in the short term and climate phenomena in the long term.

Thus, understanding the changes in sea surface tempera-

tures (hereinafter referred to as SST) in the tropical

Pacific Ocean is very important because this can provide

useful information regarding how present and future

hydrological runoff may differ among different regions.

This information is also valuable for predicting the types of

natural disasters that may be caused locally and to take

preparatory measures to face these disasters. However,

despite the many studies (Wang et al. 2000; Wu et al. 2003;

Jin et al. 2005; Weng et al. 2007; Lee and Byun 2009;

Feng et al. 2010; Kim et al. 2012a) that have invest-

igated the relationship between climatic factors and

S.-K. Yoon

Climate Research Department, APEC Climate Center,

Busan 612-020, Republic of Korea

J.-S. Kim (&) Y.-I. MoonDepartment of Civil Engineering, The University of Seoul,

Seoul 130-743, Republic of Korea

e-mail: [email protected]

J.-H. Lee

Department of Civil Engineering, Joongbu Univeristy,

Chung-nam 312-702, Republic of Korea

123

Stoch Environ Res Risk Assess (2013) 27:14651477

DOI 10.1007/s00477-012-0683-9

hydrometeorological variables, these variables are still not

sufficiently understood for investigation of the changes in

SST in East Asian regions. In particular, abnormal SST

distributions, known as the El-Nino/Southern Oscillation

(hereinafter referred to as ENSO) and the nonlinear

behavior of climate systems can significantly influence

hydrologic characteristics (Piechota and Dracup 1996;

Piechota et al. 1998; She and Xia 2012). In fact, the ENSO

is known to be a major factor affecting atmospheric circu-

lation patterns, and hydrologic environmental changes have

a close relationship with seasonal anomalies in precipitation

and runoff (Schonher and Nicholson 1989; Gershunov et al.

1999; McPhaden et al. 2006; Kwon et al. 2009; Wang et al.

2011). For example, Weng et al. (2007) and Feng et al.

(2010) showed that El Nino phenomena cause significant

changes in the hydrometeorolgical cycle across many

regions of the world.

Today, the effects of global warming and climate

change must also be taken into consideration, as these have

been shown to alter the typical patterns of large-scale SST

rises and dips. This issue is of particular interest to coun-

tries close to the Pacific region (Weng et al. 2007; Kao and

Yu 2009; Yeh et al. 2009; Na et al. 2011). Specifically,

since the late 1970s, a clear change has been seen in the

frequency and intensity, as well as the location of El Nino

(Weng et al. 2007; Chang et al. 2008; Kao and Yu 2009),

and increases in atmospheric CO2 could have further

aggravated these El Nino changes. Indeed, Kug et al.

(2009), based on the transition mechanism of observed SST

data from the Nino3 and Nino4 regions, identified a new

type of El Nino event that is different from the conven-

tional cold-tongue (hereinafter referred to as CT) event.

This new type of event, called a warm-pool (hereinafter

referred to as WP) El Nino event is characterized by

abnormal increases in SST that occur only in the central

Pacific region (Ashok et al. 2007; Ashok and Yamagata

2009; Feng et al. 2010; Pradhan et al. 2011). In addition,

Ren and Jin (2011) further classified El Nino events into

CT and WP on the basis of the SST data provided by Kug

et al. (2009) and proposed a simple transformation to better

depict the WP El Nino that is almost completely inde-

pendent from the CT El Nino. Although several studies

(Wang et al. 2000; Wu et al. 2003; Jin et al. 2005; Lee and

Byun 2009) have been conducted to investigate if a sys-

tematic relationship between climate variability and SST

around the Korean peninsula and East Asia exists, few

studies (Weng et al. 2007; Feng et al. 2010; Kim et al.

2012a) have explored the consequences of this new El

Nino event on the characteristic changes in precipitation

and runoff in the East Asia region. In addition, little

attention has been given to spatial variations in precipita-

tion and runoff focused on the Korean peninsula during the

new type of El Nino.

The aim of the present study was therefore to use the

CT/WP El Nino categorization results of Ren and Jin

(2011) to determine their relationships with precipitation

and runoff in the Korean Han River Basin and its sub-

basins. Specifically this study has been conducted to ana-

lyze the effect of different types of El Nino on seasonal

variability and extreme events at the local scale in Korea.

We expect that this diagnostic study could lead a better

understanding of characteristic changes in precipitation and

runoff within a climatological context. Moreover, it may

provide a first step in developing seasonal hydrologic

estimates conditioned upon large-scale climate state for

stable water supply and flood risk management in a

changing climate.

In this paper, the geographical study area and data are

presented in Sect. 2, the methods are described in Sect. 3,

and the analysis results are discussed in Sect. 4. Section 5

presents the discussion and conclusions.

2 Study area and data

2.1 Study area

The Han River Basin and its sub-basins are located in the

central part of the Korean peninsula and are delineated by

the latitudes 3630038550N and longitudes 126240129020E (Fig. 1). The river is 481.7 km in length and thebasins cover a total area of 26,356 km2, which constitutes

23 % of the land area of South Korea. The average basin

slope is 19 %, with an average elevation of 406 m above

sea level (WAMIS 2012), and most of the eastern part of

the basin region is composed of mountains and valleys with

steep slopes (Chang 2008; Kim et al. 2011, 2012b).

The annual average precipitation in the Han River Basin

from the periods 19662007 was 1,253.3 mm (Fig. 2;

Table 1). The spatial distribution of the annual average

precipitation in the basin is relatively even, although high

annual precipitation occurred for some sub-basins in the

central and western parts (Fig. 2a). Precipitation also

exhibits strong seasonal variability in the Han River Basin.

The lowest rainfall month is December (21 mm) and the

highest (307 mm) is July. Precipitation during spring is

167225 mm, and the average spring precipitation for the

23 sub-basins is 200.5 mm, which accounts for 16.0 % of

the average annual precipitation. The spatial variation of

spring precipitation is smaller to the other seasons

(Fig. 2b). During summer, however, average precipitation

is 760.2 mm. This constitutes 60.6 % of the average annual

precipitation for the entire basin region, reflecting the

concentration of precipitation during summer (Kim and

Jain 2011; Kim et al. 2012c). The average summer pre-

cipitation appears to be relatively high in the northwest and

1466 Stoch Environ Res Risk Assess (2013) 27:14651477

123

central regions including Seoul, which is adjacent to the

West Sea (Yellow Sea) (Fig. 2c).

Runoff from the Han River Basin during spring is

10.817.7 % of the total annual runoff, with a large pro-

portion coming from some of the sub-basins in the eastern

and central parts of the basin region. The average seasonal

fractional runoff from the 24 sub-basins is 14.4 %

(Fig. 3a), and the coefficients of variation (CV; the ratio of

standard deviation to mean) for spring runoff in each sub-

basin, compared to the average CV for the total region for

19712000, varies from a maximum of 0.83 to a minimum

of 0.44, with an average of 0.64. This suggests that large

variations in runoff occur during the spring (Fig. 3c). The

northwest region dominated the summer runoff, accounting

for 47.267.4 % of the total, and the average seasonal

fractional runoff from sub-basins was estimated at 60.2 %

(Fig. 3b). In addition, the CV for the summer runoff from

each sub-basin, when compared to the normal average of

the CV value for the same 30 years, varied from a maxi-

mum of 0.33 to a minimum of 0.19, with an average 0.23

(Fig. 3d). Thus, runoff is rather evenly distributed across

the 24 sub-basins, confirming that variation in runoff dur-

ing the summer is particularly high. The spatial distribution

of summer runoff revealed a somewhat large variation in

sub-basins located in the southern parts.

2.2 Data

The hydrometeorological data used in this research were

precipitation and runoff data collected from 1966 to 2007

by the Water Management Information System (WAMIS

2012). The average precipitation data was estimated for

24 sub-basins using the Thiessen polygon method on data

from 43 precipitation gauge stations in the Han River

Basin (Kim and Jain 2011). Runoff data available for the

24 sub-basins were calculated using the widely applied

Precipitation-Runoff Modeling System (PRMS), a physi-

cally based deterministic and distributed-parameter rain-

fall-runoff model developed by the US Geological Survey

(Leavesley et al. 1983; Dressler et al. 2006; Bae et al.

2008; Chang and Jung 2010). These daily runoff data,

which are an assessment of the national watershed

research project in Korea (Kim et al. 2012b; WAMIS

2012), are used for analyzing the long-term effects of

hydrological changes at the regional scale and for setting

up mid-to-long-term water resources planning and man-

agement. For daily runoff computations, the PRMS model

requires daily precipitation and minimum and maximum

air temperatures. Bae et al. (2008) also provides a more

detailed description of PRMS model calibration and

regionalization of hydrologic responses over the Korean

peninsula. Furthermore, it is important to note that the

precipitation dataset for the sub-basin (Kumgangsan Dam)

located in North Korea is not available on the WAMIS

system. Therefore, in this study, we used precipitation

information for 23 sub-basins excluding this sub-basin,

but we recommend that analysis results of the runoff data

for the Kumgangsan Dam basin presented here be used

for reference purposes as it may have a large degree of

uncertainty.

Fig. 1 Location of the studyarea. The Han River Basin

(126240E129020E,36300N38550N) is locatedin the center of the Korean

peninsula. The red line indicates

the demilitarized zone (DMZ)

between North and South

Korea. More information on the

sub-basins is available in

Table 1. (Color figure online)

Stoch Environ Res Risk Assess (2013) 27:14651477 1467

123

The monthly ENSO data used in this research were

obtained from the National Oceanic and Atmospheric

Administration (NOAA 2012). Among the data collection

sites, observation data from Nino3 (5S5N, 15090W)and Nino4 (5S5N, 160E150W) sites are known to

have strong correlation with El Nino (Trenberth 1997). The

SST data provided by the Hadley Center, called HadISST,

were used to investigate the correlation between hydrome-

teorological parameters (seasonal precipitation and runoff,

annual maximum flow, 7-day lowflow) and large-scale

NA

13001337mm1250130012001250115012001100115010811100

(a) Annual total precipitation

NA 220225mm210220200210190200180190170180167170

(b) Spring total precipitation (MarchMay)

NA 825852mm800825775800750775725750700725670700

(c) Summer total precipitation (JuneAugust)

Fig. 2 Annual total precipitation and seasonal mean precipitationover the Mid-watershed in the Han River basin. a represents annualtotal precipitation, b and c are seasonal precipitation during the spring

(March to May) season and the summer (June to August) season,

respectively. NA indicates that data are not available

Table 1 Sub-basin information for the Han River Basin, South Korea

ID Han River Basin Catchment

area (km2)

Annual

precipitation

(mm)

Fractional flow

(spring/annual)

(%)

Fractional flow

(summer/annual)

(%)

Sub-basins Name

1001 Upstream of Namhan River 2,447.9 1,240 17.3 47.2

1002 Pyeongchang River 1,773.4 1,294 15.5 56.2

1003 Chungju Dam 2,483.8 1,210 16.2 54.1

1004 Dal Stream 1,614.4 1,174 15.0 54.4

1005 Downstream of Chungju Dam 524.4 1,202 15.8 59.0

1006 Seom River 1,491.0 1,298 11.5 60.7

1007 Downstream of Namhan River 2,072.7 1,297 12.6 61.4

1008 Kumgangsan Dam 2,983.0 NA 12.9 61.7

1009 Pyeongwha Dam 351.3 1,081 14.2 57.3

1010 Chuncheon Dam 1,587.4 1,186 16.4 62.5

1011 Inbook Stream 931.3 1,149 16.5 51.6

1012 Soyang River 1,852.0 1,251 17.7 55.1

1013 Euiam Dam 721.7 1,308 15.4 64.6

1014 Hongcheon River 1,566.0 1,302 16.3 63.0

1015 Cheongpyeong Dam 760.6 1,337 15.5 65.6

1016 Kyeongan Stream 561.1 1,266 16.1 62.5

1017 Paldang Dam 43.9 1,191 14.6 67.4

1018 Han River in Seoul 1,537.2 1,291 14.1 63.4

1019 Han River in Goyang 826.3 1,259 13.4 63.1

1020 Gomitan Stream 2,195.2 1,287 12.6 62.4

1021 Upstream of Imjin River 2,072.7 1,290 11.0 63.4

1022 Hantan River 2,452.2 1,292 11.4 62.9

1023 Downstream of Imjin River 1,419.2 1,316 10.8 64.4

1024 Downstream of Hantan River 146.4 1,306 11.5 60.7

Average 1,358.0 1,253.4 14.4 60.2

The hydrometeorological data (19662007) used here were obtained from the Water Management Information System (WAMIS)

NA represents that data is not available


123

atmospheric circulation patterns. This dataset, which is

available on the Internet (URL: http://www.cpc.ncep.noaa.

gov/data/indices), has a spatial resolution of 1 9 1 and isupdated on a monthly basis (Rayner et al. 2003).

3 Methodology

The effect of different phases of ENSO events on the

spring and summer precipitation and runoff characteristics

was analyzed by adopting the classification criteria pro-

posed by Ren and Jin (2011) for hydrologic variables from

24 sub-basins. The traditional Nino3 and Nino4, which

were recently used to identify the two types of ENSO

events (Kug et al. 2009; Yeh et al. 2009), are highly cor-

related, which means that these indices are not optimal for

differentiating the two types of El Nino (i.e., WP and CT).

To overcome this, we used two new indices (NCT and NWP)

from a simple nonlinear transformation proposed by Ren

and Jin (2011) to distinguish a CT El Nino from a WP El

Nino. The equation is defined as follows:

NCT N3 aN4NWP N4 aN3

a 2=5;N3N4 [ 00; otherwise:

n1

Here, N3 and N4 indicate indices for Nino3 and Nino4,

respectively, and NCT and NWP are new indices based on a

simple transformation from Nino3 and Nino4 indices. A

comparison of Nino3 and Nino4 anomalies using a scatter

plot revealed a correlation of 0.77 between the two events.

However, a comparison between CT El Nino (NCT) and WP

El Nino (NWP) anomalies yielded a correlation of only 0.19.

We assessed the independence between Nino3 and Nino4

data using CT El Nino and WP El Nino indices that

showed a smaller relative correlation (Fig. 4). In addition,

we performed a correlation analysis using time series data

for Nino3 and CT El Nino indices (NCT) for the period

19502011, which afforded a correlation coefficient (q) of0.98 and standard deviation (r) of 0.6964. However,Nino4 shows a relatively weak correlation with NWPindex (q = 0.86, r = 0.40) compared to the results ofNino3 and NCT. To further examine local hydrologic

responses within the context of a seasonal and episodic

event, the five strongest events of CT El Nino type (1972/

1973, 1982/1983, 1986/1987, 1991/1992 and 1997/1998)

were identified using the NCT index. WP El Nino years

(1968/1969, 1990/1991, 1994/1995, 2002/2003 and

2004/2005) were selected for the five strongest events

using the Nwp index during the period 19502011. For

those five CT/WP El Nino years, we also examined the

evolution patterns of the sea surface temperature (SST)

over the western Pacific Oceans.

The variability in precipitation and runoff was analyzed

based on multiple flow metrics such as seasonal volume,

annual maximum flow (AMF), annual lowflow using the

coefficients of variation (CV), and percentage changes for

CT against WP for the five selected El Nino events. CV is

defined as the ratio of the standard deviation to the mean,

which shows the extent of variability in relation to the

mean. The percentage changes are compared to a standard

30-year climatology (19712000), AMF is defined as the

annual maximum flow, and lowflow was calculated from

the streamflow showing the lowest 7-day average flow

occurring each year.

(a) Seasonal fractional flow (Spring)

17.017.7%16.017.015.016.014.015.013.014.012.013.011.012.010.811.0

(c) Coefficient of variation (Spring)

0.800.830.750.800.700.750.650.700.600.650.550.600.500.550.440.50

(b) Seasonal fractional flow (Summer)

65.067.4%62.565.060.062.557.560.055.057.552.555.050.052.547.250.0

0.300.330.280.300.250.280.230.250.200.230.190.20

(d) Coefficient of variation (Summer)

Fig. 3 Percentage changes ofseasonal fractional flows and the

coefficients of variation during

a and c the spring season(March to May) and b and d thesummer season (June to August)

in the Han River Basin, Korea.

a and b The seasonal fractionalflows. c and d The coefficientsof variation (CV: standard

deviation/absolute mean)


123

The empirical probability density function (PDF) was

also shown for the five El Nino events using the nonpara-

metric Kernel approach, which is widely used in theoretical

and applied statistics (Bowman and Azzalini 1997, 2007).

The Kernel density plot shows the optimal distribution for

the actual values of the sample without any assumptions of a

parent distribution. The main objective of using the Kernel

approach is to identify the sensitivity of changes in seasonal

precipitation and runoff anomalies (departures from the

19712000 normals) within the context of the CT/WP El

Nino events. In addition, composite analysis (CA), known as

the composite sampling technique, was applied to analyze

the effect of the different El Nino types on the precipitation

and runoff in the Han River Basin. A significance test using

the Monte Carlo re-sampling technique (Ripley 1987;

Becker et al. 1988) was performed for each seasonal variable

with a 90 % confidence interval.

4 Results

4.1 Characteristics of the SST patterns during CT/WP

El Ninos

Figure 5 shows the composite anomalies of the mean value of

SST from December to February, when El Ninos develop. In

the case of CT El Nino years, positive anomalies occurred in

the central and eastern Pacific areas, and negative anomalies

occurred throughout the western Pacific region (Fig. 5a). As

a CT El Nino evolved, the positive SST pattern extended

towards latitude, and the negative SST pattern extended

towards the east. The warm SST anomaly pattern reached its

maximum amplitude in the fall and winter, whereas it

disappeared in the summer decaying phase, and was replaced

by a SST anomaly pattern with a relatively low temperature

in the eastern Pacific region. However, the WP El Nino

evolution pattern showed a definite difference when com-

pared to the CT El Nino (Fig. 5b). In the WP El Nino years,

small positive SST anomalies first appeared in the western

Pacific region in the spring, and these patterns moved towards

the central Pacific direction. In the same period of time, a

negative anomaly was observed in the eastern and western

Pacific regions. A warm SST anomaly pattern was located in

the central Pacific Ocean, and this anomaly grew stronger and

extended to the east during the fall. In the wintertime, a

positive SST anomaly pattern strengthened, but as spring

arrived, the WP El Nino weakened and disappeared.

The main characteristics of the CT El Nino and the WP

El Nino can be summarized as follows: Firstly, in the CT

El Nino years, a warm SST anomaly pattern is located

mostly in the eastern Pacific region, while the WP El Nino

is in the central Pacific region. Secondly, the SST anomaly

pattern appears stronger in the CT El Nino years than in the

WP El Nino years. Thirdly, in the WP El Nino period, the

SST anomaly pattern tends to decline much more rapidly

than in the years of CT El Nino. Previously, we looked at

the evolving pattern of the CT/WP El Ninos, but the

present study attempted to analyze how hydrological flow

occurs due to different aspects that depend on changes in

the CT/WP El Ninos, rather than investigating the physical

mechanism of the two patterns [for a more detailed

explanation of CT/WP El Nino patterns, refer to Kug et al.

(2009) and Feng et al. (2010)]. These different SST

anomaly patterns may affect air circulation, thereby giving

rise to changes in seasonal precipitation and runoff in the

Pacific Rim countries.

(a) Correlation between N3 and N4 (b) Correlation between NCT and NWP

Fig. 4 Scatter plots a for N3 and N4 indices, and b for NCT and NWP indices. CORs denote correlations between two indices in each panel


123

The spatial distributions of spring and summer precipi-

tation and runoff were remarkably different during the two

different types of El Nino events in the Han River Basin

(Fig. 6). During spring (Fig. 6a), the empirical PDF

showed a higher peak and lower variation for CT El Nino

events. This indicates that seasonal precipitation and runoff

are not sensitive to CT El Nino events. In contrast, the low

peak and large variation in the empirical PDF for precipi-

tation and runoff during WP El Nino events indicate that it

is becoming increasingly difficult to properly manage

water resources systems properly in this region. However,

in the figure shown (Fig. 6b), relatively lower variation was

illustrated for the WP El Nino periods compared to the CT

periods for the summer season.

In the following sections, we describe a finer-scale

analysis carried out to gain a better understanding of the

potential impacts of CT/WP El Ninos on variability in

seasonal precipitation and runoff over the sub-watersheds

in the Han River Basin.

4.2 Seasonal precipitation and runoff during CT/WP El

Ninos

The statistical estimates for CT/WP El Nino years are

summarized in Tables 2 and 3. Figure 7 shows an overall

increased pattern for the percentile average of spring pre-

cipitation in the range of 6.926.1 % for the 23 sub-basins,

with an average of 17.80 % during WP El Nino periods. As

is shown in Table 3, increases in spring precipitation in the

Han River Basin during WP El Nino events can be

expected, but the variation of spring precipitation is high,

so that a decreasing rainfall pattern can be found in some

sub-basins during the WP El Nino years. However, sig-

nificant increases in spring precipitation were observed in

only 2 sub-basins. It was found that precipitation was rel-

atively low for the summer season during the CT El Nino

years, but quite high for almost all sub-basins during the

WP El Nino years (Fig. 8). The summer precipitation

anomalies during WP El Nino years were -3.5 to 38.4 %,

(a) CT El Nio years

(b) WP El Nio years

Fig. 5 Composite sea surface temperature anomalies (SSTA) based on a standard 30-year climatology (19712000) for peak stages of CT andWP El Ninos during DecemberFebruary. a SSTA in the CT El Nino years. b SSTA in the WP El Nino years. (Color figure online)


123

with an average of 21.8 %, indicating a strong increasing

pattern. For the WP El Nino years, 10 out of 23 sub-basins

showed a significant increase in summer precipitation

mostly occurring in a central-southern region of the Han

River Basin.

An analysis of runoff anomalies during spring showed

some decreases in 3 sub-basins (1006: Seom River, 1016:

Kyeongan Stream, 1017: Paldang Dam) during CT El Nino

periods, while statistically significant changes in spring

runoff were seen in 4 sub-basins (1010: Chuncheon Dam,

1011: Inbook Stream, 1013: Euiam Dam and 1020: Gom-

itan Stream) with an average increase of 47.64 % (Fig. 9).

However, summer runoff data showed a dry tendency, with

high variation during CT El Nino periods. Runoff increases

in summer were also found for most sub-basins during WP

El Nino periods, with statistically significant results for 14

sub-basins (58.8 % of the total area), but with less runoff

variation (Fig. 10). The summer runoff anomalies were

relatively large, with a coefficient of variation of 0.69

during the CT El Nino years, and with high variations,

particularly in the southern parts of the Han River Basin.

4.3 Episodic extreme events during CT/WP El Ninos

Figure 11 shows the change in percentile obtained by

comparing local distributions of annual maximum flow

(AMF) and annual lowflow occurring in the CT/WP El

Nino years. During the CT/WP El Nino years, the AMF

ranged from -61.8 to 53.2 % and showed a variety of local

changes. In the CT El Nino years (Fig. 11a), 71.5 % of the

Han River Basin appeared lower than the long-term nor-

mal, except for some parts of the western Han River Basin.

In six sub-basins, a statistically significant (p \ 0.10)decreasing pattern was noted in 28.6 % of the overall

basins associated with the Han River Basin. In contrast, in

the WP El Nino years (Fig. 11b), increasing patterns

occurred in the mid- and upper-streams in the Han River.

However, the Chungpyung Dam basin was the only basin

to show statically significant results, and the southern

region of the Han River showed a greater decrease than

was seen in long-term normal years. The changes in the

lowflow rate during CT El Nino years (Fig. 11c) were

the reverse of those of the AMF presented in Fig. 11a. The

positive pattern was identified in 23 sub-basins, and stati-

cally significant increasing values were present in 65.6 %

of the total area of the Han River Basin. However, in the

period of the WP El Nino (Fig. 11d), a general weakening

tendency was seen, except for in 4 sub-basins. A statisti-

cally significant decreasing patter occurred in 20.0 % of

total area of the Han River Basin.

5 Discussion and conclusion

During the WP El Nino years, a warm SST anomaly pattern

was located mostly in the central Pacific region, while CT

El Nino was located in the eastern Pacific region.

In the CT El Nino period, the SST anomaly pattern

tended to decline much more steadily than in the WP El

Nino years. These characteristics of the SST patterns are

consistent with the results from Kug et al. (2009) and Feng

et al. (2010).

The seasonal variability in precipitation and runoff over

the Korean Han River Basin for the CT/WP El Nino years

(a) Spring (MarchMay) (b) Summer (JuneAugust)

Percentage of seasonal composite anomaly to the 19712000 normal

Prob

abilit

y de

nsity

func

tion

300% 200% 100% 0 100% 200% 300%

0.000

0.005

0.010

0.015

0.020

0.025

0.030 Precipitation (CT)Precipitation (WP)Streamflow (CT)Streamflow (WP)

Percentage of seasonal composite anomaly to the 19712000 normal

Prob

abilit

y de

nsity

func

tion

300% 200% 100% 0 100% 200% 300%

0.000

0.005

0.010

0.015

0.020

0.025

0.030 Precipitation (CT)Precipitation (WP)Streamflow (CT)Streamflow (WP)

Fig. 6 Empirical probability density function for the seasonalprecipitation and the seasonal runoff by percentage of seasonal

composite anomalies during CT El Nino and WP El Nino over the

spring and summer season in the Han River Basin, Korea. The solid

blue line indicates the distribution of the seasonal precipitation in the

phase of the CT El Nino years and the solid red line indicates the

distribution in the phase of the WP El Nino years. The dotted blue line

indicates the distribution of the seasonal runoff in the phase of the CT

El Nino years and the dotted red line indicates this distribution in the

phase of the WP El Nino years. (Color figure online)


123

can be summarized as follows: Firstly, during the WP El

Nino years, precipitation in spring and its CV were higher

than long-term normal precipitation. Secondly, in the

CT El Nino years, summers tended to be relatively

drier than in climatologically normal years, although the

variability in precipitation during the summer was lower.

Lastly, significant increases in summer precipitation and

runoff were seen in the WP El Nino years. These increases

mostly occurred in a central-southern region of the Han

River Basin. However, summer runoff data showed a rel-

atively dry tendency, with high variation during CT El

Nino periods.

Table 2 Percentage anomalies in the Han River basins during CT/WP El Ninos

ID CT El Nino years WP El Nino years

Spring (MarchMay) Summer (JuneAugust) Spring (MarchMay) Summer (JuneAugust)

Precipitation Runoff Precipitation Runoff Precipitation Runoff Precipitation Runoff

1001 -8.70 17.31 -2.00 -3.12 19.80 38.14 9.80 23.93

1002 -3.80 5.17 3.10 1.49 17.00 44.49 21.30 42.72

1003 2.30 25.72 0.00 16.31 16.20 49.72 16.10 25.15

1004 7.50 16.63 8.30 16.36 14.00 34.15 26.40 55.49

1005 7.60 6.94 3.90 4.54 25.00 39.21 24.10 45.53

1006 1.40 -0.46 5.20 4.51 17.70 67.17 23.90 53.79

1007 3.20 4.60 6.80 11.03 21.20 52.53 21.80 39.84

1008 NA 35.48 NA 15.02 NA 15.02 NA 40.23

1009 -3.70 28.63 -0.60 -10.12 24.30 49.78 38.40 66.64

1010 4.50 40.33 2.00 -1.28 20.60 28.18 29.20 44.41

1011 1.70 47.50 1.00 0.38 26.10 41.40 33.40 54.90

1012 -2.10 24.04 1.60 -1.42 12.20 22.44 23.40 35.96

1013 14.90 50.77 10.00 10.06 10.90 22.00 20.50 26.14

1014 0.40 22.59 0.50 -1.60 21.00 41.17 25.10 38.33

1015 0.30 26.14 14.40 18.21 12.20 16.50 23.00 33.61

1016 -6.50 -0.24 6.50 7.50 17.60 40.34 30.30 45.78

1017 -14.80 -7.19 18.00 23.94 6.90 -7.70 36.50 55.51

1018 5.80 16.06 15.00 22.61 12.10 16.78 14.70 18.01

1019 2.90 10.16 7.30 8.39 20.00 43.89 19.50 30.74

1020 20.20 51.97 -3.80 -10.40 21.70 40.79 19.50 33.82

1021 18.10 41.46 -2.20 -7.86 20.70 31.84 17.20 27.66

1022 14.50 29.07 5.00 2.73 20.60 28.94 23.00 34.21

1023 8.00 14.02 12.70 15.96 15.00 40.90 6.70 7.76

1024 6.10 17.28 15.80 21.21 16.70 41.05 -3.50 -6.87

NA represents that data is not available

Statistical estimates shown in boldface are statistically significant at the 10 % level

Table 3 Summary statistics for seasonal variability in the Han River Basin

El Nino type Statistics Spring (MarchMay) Summer (JuneAugust)

Precipitation Runoff Precipitation Runoff

CT El Nino Average change (%) 3.47 21.83 5.59 6.85

Significant stations (p \ 0.10) 0/23 4/23 0/24 0/24CV 0.20 0.30 0.44 0.69

WP El Nino Average change (%) 17.80 36.51 21.75 36.39

Significant stations (p \ 0.10) 2/23 8/23 10/24 14/24CV 0.54 0.89 0.24 0.30

CV the ratio of standard deviation to mean


123

Yeh et al. (2009) reported an increase in the frequency

of WP El Nino events during recent years, in association

with global warming, and stated that the projected global

warming scenarios indicate that these events will become

more frequent. They suggested that this new type of event

is currently affecting the Korean peninsula as well as other

East Asian regions. However, current observational studies

are highly dependent on the size of the available sample;

hence, it remains to be verified whether the recent trend

toward the WP type of El Nino is a sign of climate change

in the region. During the WP El Nino years, a large-scale

ascending motion was observed over the central Pacific and

a sinking motion was noted over the western and eastern

Pacific region associated with the Walker circulation.

However, during the CT El Nino years, an anomalous

rising motion appears over the eastern pacific and a

descending motion over the western Pacific region (Feng

et al. 2010). Furthermore, the location and intensity of the

anomalous western north Pacific (WNP) cyclone or anti-

cyclone forced by different SST warming associated with

NA NA

(a) CT El Nio years (Spring Rainfall) (b) WP El Nio years (Spring Rainfall)

25% ~ 26.1%20 ~ 2515 ~ 2010 ~ 15 5 ~ 10 0 ~ 55 ~ 010 ~ 514.8 ~ 10

NA NA

(c) CT El Nio (Coefficient of Variation) (d) WP El Nio (Coefficient of Variation)

0.70 ~ 0.780.60 ~ 0.700.50 ~ 0.600.40 ~ 0.500.30 ~ 0.400.20 ~ 0.300.10 ~ 0.200.08 ~ 0.10

Fig. 7 Percentage changes ofspring precipitation and

coefficients of variation in

composite anomalies

(departures from the 19712000

normals) during a and c CT ElNino years and b and d WP ElNino years in the Han River

Basin, Korea. The effects of

both phases of ENSO are shown

with different color schemes

(increases in blues and

decreases in reds). The hatched

polygon indicates statistically

significance in seasonal

precipitation (March to May) at

the 10 % confidence level. NA

indicates that data are not

available. (Color figure online)

NA NA

(a) CT El Nio years (Summer Rainfall) (b) WP El Nio years (Summer Rainfall)

35% ~ 38.4%30 ~ 3525 ~ 3020 ~ 2515 ~ 2010 ~ 15 5 ~ 10 0 ~ 53.8 ~ 0

NA NA


0.55 ~ 0.590.50 ~ 0.550.45 ~ 0.500.40 ~ 0.450.35 ~ 0.400.30 ~ 0.350.25 ~ 0.300.20 ~ 0.250.12 ~ 0.20

Fig. 8 Percentage changes ofsummer precipitation and

coefficients of variation in

composite anomalies

(departures from the 19712000

normals) during a and c CT ElNino years and b and d WP ElNino years in the Han River

Basin, Korea. The effects of

both phases of ENSO are shown

with different color schemes

(increases in blues and



significance in seasonal

precipitation (June to August) at

the 10 % confidence level. NA




123

the CT/WP El Nino could have potential implications for

the regional moisture transport in many countries bordering

the Pacific rim (Wang et al. 2000; Weng et al. 2007; Lee

et al. 2009; Feng et al. 2010).

The structure and function of stream and riparian eco-

systems is closely associated with the natural hydrologic

regime. Therefore, the effects of episodic, seasonal, or

long-term climate changes on the hydrologic regime must

be clarified in order to gain a better understanding of the

interaction between human and environmental systems. In

this study, we aimed to relate the sensitivity of relevant

flow metrics to climate variability and extremes in the

Korean Han River Basin. We found that water resources

during the spring and summer in the Han River Basin are

very sensitive to WP El Nino events, but not as sensitive to

CT El Nino events. However, it should be noted that this

study is based on relatively short observations and limited

conditions.

Our results indicate that studies on the efficient predic-

tion and management of water resources are urgently

(a) CT El Nio years (Spring Streamflow) (b) WP El Nio years (Spring Streamflow)

60% ~ 67.2%50 ~ 6040 ~ 5030 ~ 4020 ~ 3010 ~ 20 0 ~ 107.7 ~ 0


1.40 ~ 1.561.20 ~ 1.401.00 ~ 1.200.80 ~ 1.000.60 ~ 0.800.40 ~ 0.600.20 ~ 0.400.12 ~ 0.20

Fig. 9 Percentage changes ofspring runoff and coefficients of

variation in composite

anomalies (departures from the

19712000 normals) during

a and c CT El Nino years andb and d WP El Nino years in theHan River Basin, Korea. The

effects of both phases of ENSO

are shown with different color

schemes (increases in blues and



significance in seasonal runoff

(March to May) at the 10 %

confidence level. Note NA



(a) CT El Nio years (Summer Streamflow) (b) WP El Nio years (Summer Streamflow)

35% ~ 38.4%30 ~ 3525 ~ 3020 ~ 2515 ~ 2010 ~ 15 5 ~ 10 0 ~ 53.8 ~ 0

(c) CT El Nio (Coefficient of Variation) (d) WP El Nio (Coefficient of Variation)0.90 ~ 1.040.80 ~ 0.900.70 ~ 0.800.60 ~ 0.700.50 ~ 0.600.40 ~ 0.500.30 ~ 0.400.20 ~ 0.300.16 ~ 0.20

Fig. 10 Percentage changes ofsummer runoff and coefficients

of variation in composite

anomalies (departures from the

19712000 normals) during

a and c CT El Nino years andb and d WP El Nino years in theHan River Basin, Korea. The

effects of both phases of ENSO

are shown with different color

schemes (increases in blues and



significance in seasonal runoff

(June to August) at the 10 %

confidence level. Note NA




123

required. This includes the study of natural disasters such

as drought and flood in basins like the Han River Basin,

where seasonal variations and uncertainties are high.

Investigation of the correlations between climatic factors

and hydrologic variables are also required to establish

stable water supplies and realistically adaptable strategies

for resilience against extreme events in a changing climate.

Further examination and understanding of the synergistic

impacts of large-scale atmospheric circulations and the

evolution of the emerging El Nino conditions on local

climate and hydrology will require additional studies using

physical mechanism-based numerical simulations to reduce

the uncertainties.

Acknowledgments This CRI work was supported by the NationalResearch Foundation of Korea (NRF) grant funded by the Korea

government (2012R1A1A2005304) and also funded by The Korea

Meteorological Administration Research and Development Program

under grant CATER 2012-3100 (Development of Drought Outlook &

Response Techniques on Korea and East Asia Region). The authors

thank the two anonymous reviewers for their comments and valuable

suggestions.

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Hydrometeorological variability in the Korean Han River Basin and its sub-watersheds during different El Nio phasesAbstractIntroductionStudy area and dataStudy areaData

MethodologyResultsCharacteristics of the SST patterns during CT/WP El NiosSeasonal precipitation and runoff during CT/WP El NiosEpisodic extreme events during CT/WP El Nios

Discussion and conclusionAcknowledgmentsReferences

hydrometeorological variability in the korean han river basin and its sub-watersheds during...

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