Impact of climatic change on hydrological balance and water resource ...

Modelling and Management of Sustainable Basin-scale Water Resource Systems (Proceedings of a Boulder Symposium, July 1995). IAHS Publ. no. 231, 1995. 141

Impact of climatic change on hydrological balance and water resource systems in the Dongjiang Basin, China

GUO SHENG LIAN Department of Hydrology and Water Environment, Wuhan University of Hydraulic and Electric Engineering, Wuhan 430072, China

Abstract A regional hydrological conceptual model and various water resources system models are developed for assessing the effects of climate change or its variability in the Dongjiang Basin in China. Based on the GCM outputs and assumed climatic scenarios, the sensitivity of hydrological and water resource systems variables to global warming is studied. It is found that the proposed models are capable of producing both the magnitude and timing of runoff and soil moisture conditions for modelling sustainable water resources development. Results of the case study indicates that runoff is more sensitive to variation in precipitation than to increase in temperature. The frequency of occurrence of floods and peak discharge is expected to increase by about 25 % for the wet/hot scenario, and the hydropower generation is expect to decrease by about 40% for the dry/hot scenario. The presently planned water resources systems may not satisfy the increasing water demand in the years 20002030.

INTRODUCTION Urbanization and industrialization, linked with rapid population growth, are expected to continue and to accelerate. Urban domestic and industrial consumers are using a large proportion of available water resources and, at the same time, degrading the quality of these resources with their waste. In recent years, concern over global climatic change caused by growing atmospheric concentration of carbon dioxide and other trace gases has increased. Climate change or its variability is expected to alter the timing and magnitude of runoff and soil moisture, etc. As a result it has important implications for the existing hydrological balance of water resources and for future water resources planning and management (Waggoner, 1990; IPCC, 1993). Urgent action is therefore required to improve the effectiveness of the use of water resources for their contribution to human well-being. The recent advent of climate simulations using physically based general circulation models (GCMs) show a significant global warming as a result of the doubling in C0 2 concentrations in the atmosphere by the year 2030. It is anticipated that annual temperatures will increase by about 1-4°C. Theoretically, climate simulations from the GCMs could be used directly as input variables for hydrological models, which in turn could be used to evaluate the impact of climate change on hydrological and water resources. However, only monthly to seasonal averages of GCM output variables have

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been verified. These models act on spatial scales of thousands of kilometres, which further complicates the issue by the incompatibility of temporal and spatial scales, since hydrological processes must account for variations at small scales, both of time and space. Therefore a variety of impact assessment techniques or tools must be developed and tested. Based on GCM outputs and hypothetical scenarios (combining temperature increases of 1, 2 and 4°C, with precipitation changes of -20, -10, 0, 10 and 20%), the sensitivity of hydrological and water resource system variables to global warming are investigated in the Dongjiang Basin. Future water demand and water supply are also studied in the basin.

BASIN AND DATA DESCRIPTION The Dongjiang Basin lies in a subtropical zone which has a warm and humid climate. It is one of the main tributaries of Pearl River in southern China. The basin area is 35 340 km2 and the mean annual rainfall and runoff are about 1750 mm and 29.6 billion m3, respectively. Front-type and typhoon-type rainfall are two important phenomena in the basin with 80 % of annual rainfall and runoff occurring in the wet season (from April to September). A number of water resources projects, including irrigation networks, hydropower generation, flood protection and water supply schemes, have been built in the basin in the past 40 years. The total reservoir storage capacity is about 20 billion m3 and it generates 1.5 billion kW h of electricity annually. There are about 7 million people living in the basin and it serves 3.73 x 106 mu (1 mu = 0.0667 ha) of irrigation land. The basin also supplies 50 m3 s"1 of high quality water for Hong Kong, Shenzhen and Guangzhou cities. As a result of the fast growth of the economy and urbanization in the region, water demand has increased dramatically in the last decade. For example, the population in Shenzhen city (one of the special economic zones in China) has increased from 30 000 in 1980 to 3 million in 1993. The city suffered serious economic setback due to a shortage of water supply during the 1991 dry season. In order to assess the impact of climate change or its variability on hydrological balance, ten sub-basins from the Dongjiang Basin are selected and shown in Fig. 1. The characteristics of these sub-basins and some sample statistics are listed in Table 1.

PROPOSED WATER BALANCE MODEL The use of conceptual models to simulate hydrological processes in a basin is a wellknown practice. Many models have been developed and are available in the literature. A systematic presentation of the most significant model selection criteria for the assessment of hydrological impact of climate change has been reported by Gleick (1988). A water balance model satisfies the criteria. An earlier example of the development of such models is given by Thornthwaite (1948). Such models have also been summarized and discussed by Alley (1984). They have been widely used in a variety of hydrological problems and a great deal of experience has been gained of how to efficiently apply such models. Water balance models have proved themselves to be both flexible and understandable. They incorporate

Impact of climatic change on water resources in the Dongjiang Basin

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1 2 3 4 5 6 7 8 9 10

Longtang Shuibei zhangfeng Taoxi Fengshuba Suntian Yuecheng Jiuzhou Nantang Boluo

Fig. 1 Dongjiang Basin and sub-basins used in the study.

a soil moisture accounting procedure, a snowmelt process and a procedure for the estimation of évapotranspiration. They use readily available hydrometeorological data and soil and vegetation characteristics. In this study a water balance model is developed to operate on a monthly basis because the GCM output, at present, can only be obtained on seasonal or monthly temperature and precipitation data. For a better understanding of model structure, some intermediate parts are described as follows: (a) Potential évapotranspiration (EPT) and snowmelt (SNM): monthly temperature, precipitation and other climatic data or pan evaporation data are input to a procedure which calculates potential évapotranspiration, EPT. It distinguishes precipitation, P, between rain, RA, and/or snow, SN, and calculates snowmelt, SNM. (b) Soil moisture storage (5): The procedure assumes SMAX is the maximum soil

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moisture storage. It is a basin average of the moisture holding capacity of the upper soil zone and an upper limit for S,, where S, is the soil moisture of the zth month; its value depends on precipitation P, and évapotranspiration PET,. If P, > PET,, then S, = St_i + P, — EPTj. If P, < PETh then water is drawn from the soil moisture accumulated in the previous month. The relationship between loss of soil moisture to potential évapotranspiration is described by: dS _ -(PETrP,) dt SMAX The solution of this equation is: S, = SM exp [-(PET, - P,)/SM AX].

(1)

(c) Surface runoff (QS) and ground water storage (G): When precipitation for a month exceeds the potential évapotranspiration and soil moisture storage attains its maximum capacity SMAX, then the total amount of excess water is equal to (S, - SMAX). Some of the excess water contributes to surface runoff, QS, = C(S, - SMAX), while the remaining quantity, WS, = (1 - C)(S, - SMAX), will in part flow eventually to the river as interflow, K{WSh and in part percolate to groundwater storage, Gt, which in turn contributes to baseflow with a lag of one month. The groundwater storage is thus calculated by: G, = G,„ + (1 - Kl)WSl - K2GiA for WS, > 0 G, = G,._, - K2GiA for WS, < 0

(2)

(d) Simulated runoff (QE): The total estimated monthly runoff QEt of month i is the sum of surface runoff QSt, interflow K\WSt and base flow K2Gt u i.e. QE, = QS, + K{WS, + K2G,A.

Table 1 Characteristics and model efficiency at Dongjiang Basin. Basin

Area (km 2 )

Baseline sample

Calibration Nc

Verification R\%)

Nv

R\%)

RE(%)

Longtang

684

1977-1988

108

88.99

36

91.98

-1.98

Shuibei

987

1980-1988

84

90.26

24

87.26

-0.77

Lizhangfeng

1400

1977-1988

108

89.47

36

89.62

0.60

Taoxi

1306

1977-1988

108

89.36

36

92.39

0.67

Fengshuba

5151

1977-1988

108

91.64

36

88.64

0.28

Suntian

1357

1967-1988

204

93.44

60

87.47

-0.50

Yuecheng

531

1966-1988

204

93.31

72

90.05

4.43

Jiuzhou

385

1960-1988

252

81.17

96

80.23

-1.26

Nantang

1080

1966-1988

204

90.39

72

89.63

-0.04

25 325

1967-1988

204

70.39

60

81.33

0.23

Boluo


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The water balance model has six parameters, i.e. SF, SMAX, C, K, Kx and K2, where SFand K are the snowmelt factor and the ratio of pan data to potential évapotranspiration, respectively. Model output variables are the estimated runoff QEt and soil moisture St. The model is calibrated on ten sub-basins in which the recorded data are divided in the calibration (Nc) and the verification (Nv) periods. Two criteria are used for the model evaluation. The efficiency criterion proposed by Nash & Sutcliffe (1970), analagously to the coefficient of determination in linear regression, may be expressed as: R2 = (F0 - F,)/F0 X 100%, where: F0 = UQrQ)2

F , = UQErQf

1=1

(3)

/=i

in which F{ is the sum of squares of differences between the observed Qi and the estimated QEt values. F0 is the initial variance, Q is the mean of Qt during the calibration period, and n is the number of data points. R2 is the proportionate reduction of the initial variance by means of the water balance model. Another criterion is mean relative error (RE), i.e.: RE = ( E 2 £ r E Ô , - ) / i ; ô , - x i 0 0 % 1=1

r=l

(4)

1=1

The proposed water balance model has been calibrated and verified for the ten subbasins. The calibration and verification periods and the results are given in Table 1. It can be seen that, on an average, the value ofR2 reaches 84.45 % in the calibration period and 86.44% in the verification period. The relative error RE is less than 2% for all the sub-basins. REGIONAL HYDROLOGICAL EFFECTS The water balance model was used to simulate monthly runoff and soil moisture for these sub-basins under different climatic conditions. It has been shown that the potential evaporation will increase by about 5 % per degree Celsius increase in temperature, based on the average value of modified Penman, Morton, Budyko and Thornthwaite models (Guo, 1994). The magnitude of changes in runoff and soil moisture induced by the hypothetical scenarios and GCM outputs are calculated. Due to the similarity of these subbasins, only the results of the Lizhangfeng sub-basin are discussed in detail in this paper. A temperature increase of 1 °C, combined with ±20% change in expected precipitation, is likely to result in runoff and soil moisture changes of from -42.29% to 38.47% and from -13.63% to 5.42%, respectively in the dry season; and from -34.93% to 31.24% and -4.39% to 1.24%, respectively in the flood season. Figure 2 plots the variation of mean monthly runoff and soil moisture within a year for the given climatic scenario at the sub-basin. THE IMPACT ON FLOOD FREQUENCY AND PEAK DISCHARGE In the Dongjiang Basin, 80% of rainfall and runoff occur in the flood season. There is

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month month Fig. 2 The variation of (a) monthly runoff and (b) monthly soil moisture within a year (temperature increase of 2°C with variation of precipitation, simulated, H h àP ,h r*r\m An = ... + 1iftff An = - 110n%c/., AT} =— - 2 0on% and •dP 0 % , . . . âP àP GCM). 20!

a relatively good relationship between monthly average runoff and peak flood discharge. Based on this relationship and the proposed water balance model, a method to assess flood probability or frequency under changed climatic conditions is developed (Guo, 1994). From the hydrological yearbook, monthly mean runoff Qm and peak discharge Qp for the month under investigation are obtained. Qm is classified into several classes with a given class width. The conditional probability Pk of occurrence of a peak flood exceeding Qc in a month with average runoff Qm in class k, is then defined by: Pk(Qp > Qc Q,n ek)

P(Qm ek) X P(Qp > Qc

Qm «*)

(5)

in which Qc is the critical discharge. The probability of a peak flood exceeding Qc can be calculated by adding the joint probabilities for all Qm classes k from equation (5): P(Qp^Qc)

= lPk(QP *=i

^QcQmek)

(6)

where L the is total number of classes of Qm. For a series of discharges calculated by the water balance model for a given climate scenario, the probability of a peak flood exceeding Qc in each month is calculated by replacing in equation (5) the present Qm and Qp series by the model outputs under climate change conditions. The impact of climate change on flood probability or frequency can be quantitatively estimated by comparing the results before and after climatic change. The use of the above flood frequency assessment model is complicated by the estimation of conditional probability P{Qp > Qc \ Qm ek) for each discharge class in equation (5). The nonparametric method is suggested for this purpose and EV1 kernel is chosen. The smoothing factor of the kernel function is estimated by a modified maximum likelihood criterion from the given sample (Guo, 1991, 1993). The impact of climate change on a two-year flood frequency (or probability = 0.5 in frequency curve) is calculated for baseline and different climate scenarios. The criterion of relative change value (RCV) is used: RCV = Œ

-X,baseline'

baseline, x l 0 0 %

• ) / *

&

(6)


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in which X represents flood frequency or peak discharge. Table 2 summarizes the results of impact of climate change on two-yearfloodfrequency at Boluo sub-basin. It is shown that temperature increases of 2°C and rainfall changes between +10% and 20% will increase the occurrence of a two-year flood frequency from 19.1 % to 47.4%. The ratio RA(i) of annual maximum flood and annual maximum monthly flow (water balance model output at present climate condition) are calculated. If it is assumed that the ratio of RA(i) is unchanged before and after climate change, then the annual maximum flood series Q'(i) of given climatic scenarios are computed based on the water balance model output and RA(i). For the purpose of visual comparison, the simulated