hydrological effects of climate change, groundwater withdrawal, and land use in a small korean...

HYDROLOGICAL PROCESSESHydrol. Process. 21, 3046–3056 (2007)Published online 30 January 2007 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/hyp.6513

Hydrological effects of climate change, groundwaterwithdrawal, and land use in a small Korean watershed

Kil Seong Lee and Eun-Sung Chung*Department of Civil, Urban and Geosystem Engineering, Seoul National University, Sillim-Dong, Gwanak-gu, Seoul 151-742, South Korea

Abstract:The effects of variability in climate and watershed (groundwater withdrawal and land use) on dry-weather streamflows wereinvestigated using SWAT (Soil and Water Assessment Tool). The equation to predict the total runoff (TR) using climate datawas derived from simulation results for 30 years by multiple regression analysis. These may be used to estimate effects ofvarious climate variations (precipitation during the dry period, precipitation during the previous wet period, solar radiation,and maximum temperature). For example, if daily average maximum temperature increases by 3 °C, TR during the dry periodwill decrease by 27Ð9%. Similarly, groundwater withdrawals strongly affect streamflow during the dry period. However, landuse changes (increasing urbanization) within the forested watershed do not appear to significantly affect TR during the dryperiod. Finally, a combined equation was derived that describes the relationships between the TR during the dry period andthe climate, groundwater withdrawal and urban area proportion in a small monsoon watershed. This equation will be effectiveto predict the water availability during the dry periods in the future since it is closely related to changes of temperature,precipitation, solar radiation, urban area ratio, and groundwater withdrawal quantity. Copyright 2007 John Wiley & Sons,Ltd.

KEY WORDS climate change; groundwater withdrawal; land use; total runoff; dry period; input sensitivity analysis; SWAT

Received 29 January 2006; Accepted 12 July 2006

INTRODUCTION

Dry-weather streamflow is closely related to the rise andfall of groundwater tables, which depends on climatechange, groundwater withdrawal and land use. Since the1980s, dry-weather streamflow has reduced or depletedrapidly owing to limited precipitation during the dryperiod, injudicious groundwater withdrawal for agricul-tural and industrial uses, and excessive urbanization. Inturn, decreased dry-weather streamflow often results inecologic and environmental disasters such as decreasednumber of species and population sizes, water qual-ity deterioration, interference with navigable waterways,etc. Therefore, analysis of the influences on total runoff(TR) during the dry period (October–May; 8 months)and simulation of its variability is very important forthe watershed-level planning and management of waterresources, especially in the monsoon climate areas.

This study investigated the following questions:

ž How sensitive is TR during the dry period to climatechanges, groundwater withdrawal, and land use?

ž What are the most important factors that influence dry-weather runoff?

ž How will climate change affect TR during the dryperiod?

ž What is the formula to easily estimate water availabilityduring the dry period in the future?

* Correspondence to: Eun-Sung Chung, Department of Civil, Urban, andGeosystem Engineering, Seoul National University, Seoul 151-742, SouthKorea. E-mail: [email protected]

ž What are effective alternatives to secure the minimumin-stream flows required during the dry period?

The sensitivity of streamflow to climate data is relatedto research on climate change. Chiew et al. (1995), sim-ulated the impacts of climate (temperature and precip-itation) change on runoff and soil moisture in Aus-tralian catchments. Singh and Bengtsson (2004) inves-tigated hydrological sensitivity to climate changes bysimulating three temperature (T C 1 °C, T C 2 °C, T C3 °C) and four rainfall (P � 10%, P � 5%, P C 5% andP C 10%) scenarios. Albek et al. (2004) showed that anannual mean temperature increase of 3 °C owing to cli-mate change will decrease watershed outflow by 21%using Hydrological Simulation Program—FORTRAN([HSPF]; Bicknell et al., 2001) since the existence ofdeep-rooted vegetation covering the whole of the water-shed is observed to cause a decrease in the total streamoutflow. There have been many other researches tostudy the sensitivity of streamflow to the climate change(McCabe and Ayers, 1989; Lettenmaier and Gan, 1990;Nash and Gleick, 1991; Arnell, 1992; Burn, 1994; Duell,1994; Kirshen and Fennessey, 1995; Singh and Kumar,1997; Vogel et al., 1997).

Groundwater management models that include sur-face water flows have been proposed for a variety ofapplications (Male and Mueller, 1992; Reichard, 1995).Many of these researchers used simulation models thatincorporated a surface water body as a boundary con-dition on the groundwater model, so flows within the

Copyright 2007 John Wiley & Sons, Ltd.

HYDROLOGICAL EFFECT ANALYSES USING SWAT 3047

surface water body were not explicitly modeled. Bar-low et al. (2003) developed the conjunctive models thatcouple numerical simulation with linear optimization toevaluate trade-offs between groundwater withdrawal andstreamflow depletions and showed that it is possible toreduce current rate of streamflow depletion by as muchas 35% during the summer, but such reductions wouldresult in smaller increases in groundwater withdrawals.Ahlfeld (2004) improved understanding of the charac-ter of the functional relationship between withdrawaland streamflow, considering a groundwater managementmodel that combines optimization methods with coupledstream/aquifer simulation models. Chuanji et al. (2004)compared two methods, an artificial neural work modeland a time series analysis model, and estimated the vari-ation in spring discharge, considering precipitation andgroundwater withdrawal.

Some researches have investigated relationships bet-ween storm runoff volume and urbanization. Brown(1988) found a positive correlation between storm runoffvolume and the amount of impervious cover associ-ated with different land use practices. Kang et al. (1998)through simulation modelling, showed that peak dis-charge increased and lag time to peak flow rate decreasedas a result of increasing urbanization. Brun and Band(2000) derived a logistic equation describing the rela-tionship between runoff ratio and baseflow as a functionof percent impervious cover and percent soil saturationusing HSPF. Chung et al. (2005) showed runoff charac-teristics (monthly runoff quantity and non-point pollutionloadings) owing to the land use changes using PC StormWater Management Model ([PCSWMM]; James et al.,2003) and Pollutant LOADings ([PLOAD]; Edwards andMiller, 2001). Furthermore, Schade and Shuster (2005)

showed the preliminary study about relative impactsbetween land use and climate change for a mixed landuse study area using SWMM 5Ð0 and Claessens et al.(2006) assessed the effects of historical changes in bothland use and climate on the water budget of a rapidlyurbanizing watershed.

Until now, there has been no research to simultane-ously investigate the effects of climate change, ground-water withdrawal, and land use and have been few studiesto address the effects of land use and climate changesat the local scale, even though three factors affect therunoff. Therefore, our goal is to assess the effects of cli-mate change, groundwater withdrawal and land use onTR during the dry period using a physically distributedsimulation model. Equations describing those relation-ships are derived by multiple regression analysis, andfinally a combined equation including those relationshipsis also proposed.

STUDY AREA

The study watershed, located in Gyeonggi Province,Korea, has an area of 13Ð42 km2 and contributes to theHan River through the Anyangcheon (Figure 1). There isa reservoir, Ojeon, which has a capacity of approximately57 000 m3. The outlet of the study watershed is thebridge of Otumulgyo, where water surface elevationshave been recorded at 15-min intervals since April 2004(the water-level gauge is also visible in Figure 1). Afterconsidering geographic information, storm sewers andstream networks, the watershed was carefully divided intofive sub-watersheds (Figure 1) since Tripathi et al. (2006)showed that watershed subdivision has a significant effecton the water balance components.

Figure 1. Description of study area

Copyright 2007 John Wiley & Sons, Ltd. Hydrol. Process. 21, 3046–3056 (2007)DOI: 10.1002/hyp

3048 K. S. LEE AND E.-S. CHUNG

Like other East Asian river systems, the study water-shed is dominated by the monsoon climate cycle, andshows strong seasonality. A total annual precipitation of67Ð1% occurs during the flood season (June, July, August,and September) while 13Ð2% falls during the busy farm-ing season (April and May). Only 17Ð7% occurs duringthe remaining 6 months of the year. Therefore, the totaldry-weather runoff during 8 months (October–May) is139Ð3 mm which is just 20Ð0% of TR (701Ð6 mm) in theyear 2004 (annual precipitation: 1271Ð4 mm) (Lee, 2005).The average annual precipitation is about 1260 mm andthe average temperature is about 11Ð6 °C.

The watershed, which contained approximately 41 000residents in 2004, consisted of 68Ð4% forest, 17Ð1%urban, and 13Ð3% agricultural areas in 2000. Since 1975,the urbanized area of the study watershed increasedby 14Ð1% (Figure 2) and the population also increasedrapidly. As a consequence, groundwater withdrawal ratesrapidly increased during the same period. The quantityof groundwater withdrawal in the study watershed is70Ð1–84Ð5 mm year�1, which is about 7Ð5% of annualaverage precipitation. Compared to 26Ð2 mm year�1 ofgroundwater withdrawal in the Anyangcheon watershed,it is very large. The watershed slope is 1/60–1/30 andthe average elevation is about 171Ð5 EL.m. Comparedto 1/80–1/3, 470 and 82Ð8 EL m of the Anyangcheonwatershed, the study area is high and steep relatively. Thesoil consists of 56Ð9% sand, 26Ð3% silt, and 16Ð7% clay.

METHODS

SWAT model description

The hydrologic model used is the Soil and WaterAssessment Tool ([SWAT]; Arnold et al., 1998). SWATwas developed to predict the impact of land managementpractices on water, sediment, and agricultural chemical

yields in large and complex watersheds with varying soilsand land use and management conditions over long peri-ods of time. To satisfy this objective, the model is basedon the physical landscape, uses readily available inputs,is computationally efficient, and operates continuously ona daily time step. Major model components may include:climate cycles, hydrology, soil temperatures, plant growthrates, nutrients, pesticides, land management practices,stream routing, and pond/reservoir routing (Eckhardt andArnold, 2001). Spatially, the model divides a watershedinto sub-watersheds, or sub-basins, based on topographicinformation. The sub-watersheds could be further classi-fied into smaller spatial modelling units known as Hydro-logic Response Units (HRUs) depending on the hetero-geneity of land uses and soil types within the sub-basins.At the scale of an HRU, watershed variables—such assoil types, properties, land use—and related managementfeatures, climate, and topographic parameters are consid-ered homogeneous(Arnold and Fohrer, 2005). The modelhas been validated for several watersheds (Arnold et al.,1999; Santhi et al., 2001; Kim et al., 2003). Some impor-tant components of the SWAT model for this study areas follows.

Potential evaporation may be estimated by one ofthree methods: Hargreaves et al., 1985); Priestley–Taylor(1972); and Penman–Monteith (Monteith, 1965). TheHargreaves method uses only daily maximum and mini-mum temperatures to derive daily potential evaporation,while the others use more physical parameters for esti-mation, which are often difficult to obtain at a catchmentscale. It has been shown that the Hargreaves method isclose to the calculations of Penman–Monteith (Sun andCornish, 2005). Therefore, the Hargreaves method wasused in this study.

Surface runoff is computed using a modified Soil Con-servation Service (SCS) curve number method based

Figure 2. Land use changes of the study watershed



on moisture content (Soil Conservation Service, 1972).Although such a modification can be more accurate inidentifying the soil water moisture condition, it neverthe-less, made the runoff estimation dependent on soil profileinformation, such as the soil layer classification, and inparticular, the soil profile depth. Compared to the origi-nal SCS method, which describes moisture condition as afunction of antecedent rainfall, the modified curve num-ber method may cause calibration problems relating tosoil structure, profile depth and plants grown. For runoffand recharge estimation, the rooting depths of plants andthe growth season are the primary drivers governing thesoil water processes.

Soils are divided into layers and water balance is per-formed in each soil layer according to saturated conduc-tivity and soil water content of the soil layers. Whenrainfall occurs, surface runoff is estimated first, and therest of the rainfall enters into the soil profile for redis-tribution. Subsurface runoff is estimated using a simpli-fied slope storage concept where water flow through soilis estimated by using the saturated conductivity of thesoil layer and the slope length. Vertical water movementgoes through the soil layers when the soil water con-tent exceeds the field capacity of the layers. It entersinto an unsaturated vadose zone when it passes throughthe lowest soil layer, which becomes recharge to shal-low groundwater. Recharge to shallow groundwater maydischarge to the catchment outlet as groundwater. SWATallows the recharge to ‘revap’ from the shallow ground-water through the unsaturated vadose zone by capillaryactivity to meet the needs of evapotranspiration when thesoil profile is dry. This means the recharge to the shallowgroundwater does not necessarily all become groundwaterand discharge; rather it can be redrawn upwards by soilwater potential during the dry periods. This is a distinctfeature of SWAT, as many other models regard rechargebeyond the root zone as drainage lost permanently (Sunand Cornish, 2005).

Calibration and validation

The ability of a rainfall-runoff model to predict stream-flow typically depends on the accurate calibration ofmodel parameters. Parameter estimation can be con-ducted either manually or in an automatic fashion. Inmanual calibration, essential model parameters would beadjusted by trial-and-error methods until model simula-tions satisfactorily match the measured data (Refsgaard,1997; Santhi et al., 2001; Albek et al., 2004). The objec-tive function for the calibration is the efficiency of amodel as follows (Nash and Sutcliffe, 1970):

max R2 D F20 � F2

F20

�1�

F20 D

n∑

iD1

�Mob � Qob,i�2 �2�

F2 Dn∑

iD1

�Qsim,i � Qob,i�2 �3�

where n is the number of values, i is the order of day,Qob,i is the observed value of ith day, Mob is the averageof observed values, and Qsim,i is the simulated value of ithday. F2

0 describes initial variation of observed values andF2 is the index of disagreement between the observed andthe simulated value. The model becomes more efficientas R2 approaches 1.

Sensitivity analysis

A system can be described by three functions: the inputfunction, the output function, and the system response, ortransfer function. The transfer function is the componentof the system that transforms the input function into theoutput function. It could consist of all components of acontinuous simulation model.

Sensitivity analyses can be used to measure the effectof parametric variations on the output. Such analyses ofmodels can be used to measure the effect of parametricvariations on the output. Such analyses focus on theoutput and response functions. Using the followingequation, parametric sensitivity can be mathematicallyexpressed as

Spi D ∂Q

∂PiD f�Pi C Pi; Pjjj 6Di� � f�P1, P2, . . . , Pn�

Pi�4�

where Q represents the output function and Pi is theparameter of the system response function under con-sideration.

Unfortunately, the general concept of sensitivity hasbeen overshadowed by parametric sensitivity. As hydro-logic models have become more complex, derivation ofparametric sensitivity estimates has become increasinglydifficult and often impossible to compute. However, thegeneral definition can be changed to another form bysimply considering the input and output functions. Specif-ically, component sensitivity can be used to measure theeffect of varying input function I on the output functionQ (McCuen, 2003).

Sc D ∂Q

∂ID Q

I�5�

Therefore, input sensitivity can be defined as the rateof change in one output factor with respect to change ininput factors such as climate, groundwater withdrawal, orland use.

MODEL FORMULATION

Input data

Application of SWAT to a watershed requires water-shed physical data (topographic, soil, and land use) andclimate data for the basin. In addition, streamflow dataare required for the calibration. For the study area, a1 : 25 000 digital elevation model (DEM) and land usemaps of the years of 1975, 1980, 1985, 1990, 1995,and 2000 were obtained from the National Geographic



Information Institute (NGII) of the Ministry of Construc-tion and Transportation. A 1 : 25 000 soil map from theNational Institution of Agricultural Science (NIAS) ofTechnology from the Rural Development Administrationwas also obtained. Daily historical data (1973–2004) ofprecipitation, maximum temperature, minimum tempera-ture, average wind speed, average humidity, and averagesolar radiation were obtained from the Suwon stationof Korea Meteorological Administration (KMA). Suwonis not too far from the study watershed. Real ground-water withdrawal values were obtained from the KoreaWater Resources Corporation (1995–2004). Finally, dailystreamflow volumes obtained from the real-time water-level station (Otumulgyo) where the rating curve wasdeveloped by Lee (2005) were used for calibration andvalidation.

SWAT formulation

DEM resolution is a very important factor for accu-rately simulating streamflow with distributed hydrolog-ical models. Chaubey et al. (2005) showed that DEMresolution affects the watershed delineation, stream net-work and sub-basin classification in the SWAT model. Adecrease in DEM resolution resulted in decreased stream-flow. Furthermore, Cho et al. (2003) suggested that asuitable DEM pixel size for analysis is from 25 to 50 m.Therefore, a DEM with a resolution of 30 m was used inthis study.

Effective calibration of distributed models like SWATbegins by developing a proper mechanism for reducingthe number of parameters to be calibrated. Therefore,sensitivity analyses of certain parameters to TR and peak

flow were conducted. The selected sensitive parametersof which ranges are over 1% to TR and peak flow areSOL AWC (the available water capacity of the soil layermm mm�1), GW DELAY (the groundwater delay timewhich is essentially the lag between the time that waterexits the soil profile and enters the shallow aquifer) andCN2 (the initial SCS runoff curve number for moisturecondition II). Using these three parameters, SWAT wascalibrated through a trial-and-error method. The splitsample method was used in this process. The results ofcalibration (1 November 2004 ¾12.31) and validation(1 January 2005 ¾3Ð31) are in Figures 3 and 4 andTable I.

RESULTS

Climate data

Sensitivity. Since climate data is an important factorfor estimating runoff, the daily runoff simulations dur-ing the dry period were simulated over a 31-year period(1974–2004) by SWAT using land use data and ground-water withdrawal quantity in the year 2000. The results

Table I. Results of calibration and validation

Category Beforecalibration

Aftercalibration

Validation

RMSE (CMS) 0Ð021 0Ð014 0Ð008RMAE 0Ð220 0Ð148 0Ð364R2 0Ð65 0Ð85 0Ð87Efficiency 0Ð53 0Ð79 0Ð84

11/1/04 11/21/04 12/11/04 12/31/04Date

0

0.1

0.2

0.3

Flo

wra

te (

m3 /

s)

25201510

50

Pre

cipi

tatio

n(m

m)

ObservedBefore calibrationAfter calibration

Figure 3. Comparison of pre-calibrated, calibrated and measured values for daily streamflow



Figure 4. Results of validation

for 1974 were used for the warming and the results for theremaining 30 years were analysed. Correlations betweendry-period TR and climate data are shown in Table II.Since dry-period precipitation and daily average solarradiation have high correlation coefficients to TR, sen-sitivity analyses of those were conducted respectively,assuming all other conditions were the same as in 2000.The used input scenarios are the real data from 1975 to2004. Results are shown in Figures 5 and 6. The regres-sion equations describing two relationships are shown inEquations (6) and (7).

y D 46Ð05 exp�0Ð003x1� �R2 : 0Ð601� �6�

y D 118Ð29 exp��0Ð012x2� �R2 : 0Ð519� �7�

where y is TR during the dry period, x1 is the totalprecipitation during the dry period (mm), and x2 is thedaily average solar radiation (MJ m2 day�1).

Estimation of TR. Using simulation results of 30 years,the equation to estimate TR through climate variables

Table II. Correlation coefficients to the TR

Climatic variables Correlation coefficient

Precipitation during the wet period 0Ð0618Daily average maximum temperature �0Ð0742Relative humidity 0Ð1363Daily average solar radiation �0Ð4188Daily average wind speed 0Ð0107Precipitation during the dry period 0Ð6570

y 46.05e0.003x

R2 = 0.601

400

300

200

100

0100 200 300 400 500 600 700

Precipitation (mm)

Tot

al r

unof

f (m

m)

0

Figure 5. Sensitivity to precipitation during the dry period

can be derived by multiple regression analysis. Sincethere are high correlations among climatic variables,variable selection for a multiple linear regression shouldbe conducted by the direct search on t method (Danieland Wood, 1980) to find an equation explaining therelationship between climate data and dry-period TR.The following regression equation among all the possiblealternatives has the smallest Cp values (the standardizedtotal mean square error; Mallows, 1973).

y D 1957 C 3Ð930 x1 � 0Ð1669x1x3 C 0Ð0136 x3 x4

� 538Ð3 ln�x1x2x4� C 468Ð9 ln�x2x3x4� �R2 : 0Ð766� �8�



Figure 6. Sensitivity to daily average solar radiation

0

0

100

200

400

Pre

dict

ed v

alue

s (m

m)

300

100

Simulated values (mm)

200 300 400

Figure 7. Comparison between the simulated and the predicted values(Equation (8))

where x3 is the daily average maximum temperature (°C),and x4 is the total precipitation during the wet period(mm). The assumptions of this equation are that thewatershed conditions such as land use and groundwaterwithdrawal quantity are the same as in 2000. Thesimulated values from SWAT and the predicted valuesfrom Equation (7) are compared in Figure 7 (the line’sslope is 1).

This equation can be used to approximate the effectof climatic changes on TR during the dry period. Thecoupled ocean–atmosphere climate model (EuropeanCommunity Hamburg Model (ECHAM) C Large-ScaleGeostrophic (LSG) ocean model) demonstrated good sim-ulation of the characteristic features of the Asian summermonsoon cycle as well as the broad circulation featuresover the Indian subcontinent (Lal et al., 1992). Singhand Kumar (1997) adopted these scenarios to repre-sent estimates of changes in climatic variables (IPCC,

1992; Lal et al., 1992). Changes in temperature wereT C 1 °C, T C 2 °C, and T C 3 °C, and those for rainfallwere P � 10%, P � 5%, P C 5%, and P C 10%. Identi-cal changes were used in this study, and changes in dailysolar radiation (S � 15%, S � 10%, S � 5%, S C 5%,S C 10%, and S C 15%) were added. The precipitationwas divided into two seasons (wet and dry period).

Changes in dry-period TR for various climatic sce-narios are shown in Table III. Dry-period streamflowdecreases as temperature and solar radiation increase andprecipitation during both wet and dry periods decreases.If daily maximum temperatures increase by 3 °C, TR willdecrease by 27Ð9%. The second sensitive variable is pre-cipitation during the dry period, the third is solar radiationand the last is precipitation during the previous floodperiod.

Increased concentration of greenhouse gases is expec-ted to alter the radiative balance of the atmosphere, caus-ing increases in temperature and changes in precipitationpatterns and other climatic variables (IPCC, 1992). There-fore, it is necessary to investigate the effect of variation indaily maximum temperature and precipitation. The resultis shown in Table IV. Since temperature changes exertgreater influence on dry-period TR than the precipitationchanges, TR during the dry period will decrease rapidly ifclimate change happens. If daily maximum temperaturesincrease by 3 °C, TR will decrease by 23Ð2% though theprecipitation during the dry period will increase to 115%of the present amount. Table IV shows that increases inprecipitation produce greater increases in TR at lowertemperatures than at higher temperatures and increasesin TR get larger as the temperature increases. This isowing to the increased evaporation and evapotranspira-tion, that results from higher temperatures. This was alsoshown in Vogel et al. (1997).

Watershed data

Groundwater withdrawal. Excessive groundwater with-drawals for consumptive use lower the groundwatertable and thus greatly decrease dry-period runoff. Theevaluation and simulation of dry-period runoff incor-porating groundwater withdrawal was also conductedby SWAT. It is assumed that withdrawn groundwateris consumptive water use and so removes groundwa-ter from the watershed. The groundwater withdrawalof the study watershed is also consumptive since usedwater flows into the wastewater treatment, which isout of the watershed. Variations in runoff values dur-ing the dry period owing to groundwater withdrawalare shown in Figure 8. The effect of groundwater with-drawal on TR during the dry period is quite critical.The first 1000 m3 day�1 of withdrawal removes about928 m3 day�1 of streamflow. The second 1000 m3 day�1

gets rid of about 741 m3 day�1 of streamflow. Asthe quantity of groundwater withdrawal increases, thedecreased quantity of runoff becomes small. Therefore,the relationship between TR and groundwater withdrawal



Table III. Changes of TR during the dry period for the various climatic scenarios

Precipitation during the previous wet period

Scenarios (mm) 15% " 10% " 5% " Average 5% # 10% # 15% #1009Ð6 965Ð7 921Ð8 877Ð9 834Ð0 790Ð1 746Ð2

TR (mm) 188Ð2 184Ð3 180Ð4 176Ð8 173Ð3 170Ð0 166Ð9Ratioa 6Ð49% 4Ð25% 2Ð08% 0Ð00% �1Ð98% �3Ð85% �5Ð60%

Maximum temperature

Scenarios (mm) 1% # 0.5% # Average 0.5% " 1% " 2% " 3% "10Ð80 11Ð30 11Ð8 12Ð30 12Ð80 13Ð80 14Ð80

TR (mm) 187Ð0 182Ð4 176Ð8 170Ð3 163Ð1 146Ð5 127Ð4Ratioa 5Ð80% 3Ð17% 0Ð00% �3Ð65% �7Ð74% �17Ð13% �27Ð93%

Solar radiation

Scenarios (MJ m2 day�1) 15% # 10% # 5% # Average 5% " 10% " 15% "12Ð12 12Ð83 13Ð54 14Ð3 14Ð97 15Ð68 16Ð39


Precipitation during the dry period

Scenarios (mm) 15% " 10% " 5% " Average 5% # 10% # 15% #440Ð4 421Ð3 402Ð1 383 363Ð8 344Ð7 325Ð5


a With respect to no-change scenario.

Table IV. Effect of variation in daily maximum temperature andprecipitation during the dry period on TR during the dry period

with respect to no-change scenario

Scenarios Maximum temperature

0Ð5° "(%)

1° "(%)

2° "(%)

3° "(%)

Precipitation duringthe dry period (%)

15 " 14Ð82 8Ð01 �6Ð80 �23Ð02

10 " 8Ð01 2Ð11 �10Ð89 �25Ð315 " 1Ð84 �3Ð16 �14Ð36 �26Ð97— �3Ð65 �7Ð74 �17Ð13 �27Ð935 # �8Ð37 �11Ð56 �19Ð14 �28Ð14

10 # �12Ð25 �14Ð54 �20Ð31 �27Ð4915 # �15Ð19 �16Ð57 �20Ð54 �25Ð91

is logarithmic. Therefore, dry-period TR can be estimatedby the following equation:

y0 D y � 23Ð51 ln�x5� C 146Ð7 �R2 : 0Ð994� �9�

where y0 is the transformed TR (mm) during thedry period, y is TR without groundwater withdrawal,and x5 is the quantity of groundwater withdrawn(½1000 m3 day�1).

Land use. In general, the first-order hydrologic impactsassociated with urbanization result from increasing imper-vious area (Schuler, 1994). Increased imperviousnesswithin a watershed reduces infiltration and groundwa-ter recharge, increases surface runoff volumes and rates,reduces soil moisture, and modifies the spatial distri-bution and magnitude of surface storage and fluxes ofwater and energy. Sensitivity to increasing urbanizationwas simulated for a 30-year period (1975–2004), using

0

1000

2000

3000

0 1000 2000 3000 4000 5000 6000

Groundwater withdrawa quantity (m3/day)

Dec

reas

ed to

tal r

unof

f(m

3/d

ay)

Figure 8. Sensitivity to groundwater withdrawal

six actual land use data in the years 1975, 1980, 1985,1990, 1995, and 2000. The simulated results of the 1975and 2000 land use conditions are shown in Figure 9 andTable V. TR during the dry period of the 2000 landuse condition decreased by just 6Ð5 mm, which is 1Ð7%of precipitation and 3Ð6% of TR of the dry period ofthe 1975 land use condition. However, the proportionof urbanized area changes from 3Ð0 to 17Ð1% over the30-year interval. Therefore, dry-period TR cannot be con-sidered to be particularly sensitive to changing land use(increasing urbanization) within this study area.

From multiple regression analysis of the simulatedresults, the equation to calculate the TR during the dry

Table V. Comparison of simulated results to land use in the years1975 and 2000

Land use 1975 2000 Difference

Urban area ratio 3Ð0% 17Ð1% 14Ð1%Average TR 184Ð0 mm 177Ð5 mm 6Ð5 mm



40019752000

350

300

250

Tot

al R

unof

f(m

m)

200

150

100

501975 1980 1985 1990

Year

1995 2000

Figure 9. Simulated results of land uses between 1975 and 2000

100 200 300 400Simulated values (mm)

0

100

200

300

400

Pre

dict

ed v

alue

s (m

m)

0

Figure 10. Comparison between the simulated and the predicted values(Equation (10))

period can be derived as follows:

y D 2949 C 1Ð185 x1 � 4Ð035x3 C 0Ð3396 x4

� 0Ð4225x6 � 228Ð8 ln�x1x2x4� �R2 : 0Ð708� �10�

where x1, x2, x3, and x4 are as in Equation (8) and x6 isthe urban area ratio (%). Simulated values from SWATand predicted values from Equation (10) are comparedin Figure 10 (the line’s slope is 1). Equation (10) has alower R2 value than Equation (8), but it may be moreuseful for estimating effects of not only the climatecondition but also land use change since it is certainthat changes in both climate and land use will happenin the future. If the urban area ratio increases by 30%,TR during the dry period will decrease by 5Ð5 mm, whichis 1Ð4% of precipitation and 3Ð1% of TR in the year 2000.

CONCLUSIONS

Watersheds, by nature, are dynamic systems; therefore,they are in a constant state of change (McCuen, 2003).

The response of a watershed can change drastically owingto changes associated with human activities as wellas natural watershed changes. But recently, mankind’sboundless desires have changed the natural componentssuch as climate, groundwater level and land use.

In this study, sensitivities of climate and watersheddata to TR during the dry period in a small Koreanmonsoon climate region were studied using SWAT.Climatic variables include precipitation during the wetand the dry periods, daily average maximum temperature,daily average solar radiation, daily average wind speed,and daily average relative humidity. Watershed variablesinclude groundwater withdrawal and land use.

Climate data

Total precipitation during the dry period is the mostcorrelated factor to the dry-period TR. Daily solar radi-ation influx is the second-most correlated factor amongclimatic variables. Therefore, the sensitivities of thosefactors were conducted and two equations to explain therelationships between TR and precipitation during thedry period and solar radiation were presented respec-tively. Furthermore, an equation describing the relation-ship between dry-period TR and climate variables wasderived using multiple regression analysis. This equationwas used to estimate effects of climatic change (pre-cipitation, temperature and solar radiation). Streamflowduring the dry period decreases as temperature and solarradiation increase and precipitation during both the wetand dry periods decreases. If daily maximum tempera-tures increase by 3 °C, TR will decrease by 27Ð9%. Sincetemperature changes are even more influential than pre-cipitation during the dry period, TR during the dry periodwill decrease if climate change (temperature increase)happens.

Watershed data: groundwater withdrawal

The effect of groundwater withdrawal on TR during thedry periods is quite critical. The first 1000 m3 day�1 ofwithdrawal removes about 928 m3 day�1 of streamflow.The second 1000 m3 day�1 reduces about 741 m3 day�1

of streamflow. As the quantity of groundwater withdrawalincreases, the decreased quantity of runoff becomessmall. Therefore, the relationship between TR andgroundwater withdrawal is logarithmic.

Watershed data: land use

Sensitivity to land use was simulated for 30 years(1975–2004) using six actual land use data in the years1975, 1980, 1985, 1990, 1995, and 2000. TR during thedry period of 2000 land use conditions decreased by just6Ð5 mm, which is 1Ð7% of precipitation and 3Ð6% ofTR in 1975 land use conditions, while the urban areaproportion changed from 3Ð0 to 17Ð1%. Changes in landuse (increased urbanization) do not significantly affectthe dry-period runoff in this mountainous watershedsince the baseflow in the study area was not enoughbefore urbanization. Therefore, the primary alternative for



securing the minimum in-stream flow requirement duringthe dry period is to regulate groundwater withdrawalrather than alter land use patterns in this watershed.

Finally, a combined equation describing the relation-ship between the TR during the dry period and the cli-mate, groundwater withdrawal and urban area ratio at theOtumulgyo (bridge) was derived as follows:

y D 3096 C 1Ð185 x1 � 4Ð035x3 C 0Ð3396 x4

� 23Ð51 ln�x5� � 0Ð4225x6 � 228Ð8 ln�x1x2x4� �11�

This result may be interpreted systematically. Runoff (y)is the output of the system, the precipitation during thedry period (x1) is input, the daily average solar radiation(x2) and the daily average maximum temperature (x3)is loss of system to the atmosphere, the precipitationduring the wet period (x4) is the initial condition, thegroundwater withdrawal (x5) is loss out of the system tothe ground, and the urban area ratio (x6) is the systemcharacteristics.

The proposed equation will be useful in predictingwater availability during the dry periods in the futuresince it is dependent upon changes of temperature, precip-itation, solar radiation, urban area ratio, and groundwaterwithdrawal quantity.

This study sets out to estimate the effects of climatechange, groundwater withdrawal, and land use on TRduring the dry period for a small monsoon Koreanwatershed, which has an area of 13Ð42 km2. It mayhelp watershed managers and planners determine whetherchanging conditions could dry a stream, and forecastwhich year may be most strongly influenced.

Other researches can be established that additional fac-tors such as channel slope may influence dry runoff val-ues. Therefore, additional inquiries should be conductedin order to refine the general equation derived in thisinquiry.

ACKNOWLEDGEMENTS

This research was supported by a grant (1-7-2) fromSustainable Water Resources Research Center of 21stCentury Frontier Research Program (Ministry of Scienceand Technology of the Korean Government), throughEngineering Research Institute of Seoul National Uni-versity and Brain Korea 21st (Ministry of Education ofthe Korean Government).

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