An Integrated Model for Simulating Regional Water Resources Based on Total Evapotranspiration Control Approach

Hindawi Publishing Corporation Advances in Meteorology Volume 2014, Article ID 345671, 10 pages http://dx.doi.org/10.1155/2014/345671

Research Article An Integrated Model for Simulating Regional Water Resources Based on Total Evapotranspiration Control Approach

Jianhua Wang, Xuefeng Sang, Zhengli Zhai, Yang Liu, and Zuhao Zhou

State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, 100038, China

Correspondence should be addressed to Xuefeng Sang; [email protected]

Received 29 January 2014; Revised 3 June 2014; Accepted 24 June 2014; Published 14 July 2014

Academic Editor: Dawei Han

Copyright © 2014 Jianhua Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Total evapotranspiration and water consumption (ET) control is considered an efficient method for water management. In this study, we developed a water allocation and simulation (WAS) model, which can simulate the water cycle and output different ET values for natural and artificial water use, such as crop evapotranspiration, grass evapotranspiration, forest evapotranspiration, living water consumption, and industry water consumption. In the calibration and validation periods, a “piece-by-piece” approach was used to evaluate the model from runoff to ET data, including the remote sensing ET data and regional measured ET data, which differ from the data from the traditional hydrology method. We applied the model to Tianjin City, China. The Nash-Sutcliffe 2 efficiency (Ens) of the runoff simulation was 0.82, and its regression coefficient 𝑅 was 0.92. The Nash-Sutcliffe Efficiency (Ens) of 2 regional total ET simulation was 0.93, and its regression coefficient 𝑅 was 0.98. These results demonstrate that ET of irrigation lands is the dominant part, which accounts for 53% of the total ET. The latter is also a priority in ET control for water management.

1. Introduction HSPF model [14], and VIC model [15], have been developed andwidelyusedforwaterplanningandmanagement. Water resources are becoming increasingly interconnected Nonetheless, a pragmatic model should include not only with social, economic, environmental, and political issues at natural factors such as plants, soil, and climate but also national, regional, and even international levels [1]. A hydro- water use in society and in the economy [16]. The above- logical cycle study usually takes into account precipitation, mentioned specialized hydrological models used for water surface runoff, rivers, and groundwater, but it should now management have limitations in regions affected by human also incorporate four components of the human water cycle: activities. It is known that the remote sensing (RS) technique uptake of water, transportation of water, use of water, and can objectively quantify different types of evapotranspiration drainage and regress. Thus, recent hydrological cycles have (ET) as a function of time [17]. In this work, we developed the characteristics of both a natural process and a human the water allocation and simulation (WAS) model to simulate intervention [2]. different types of natural ET and various types of artificial In general, hydrological models can be classified into water consumption; a “piece-by-piece” approach was utilised lumped models and distributed models, which are also called to evaluate the model from runoff to ET. data-driven models and mechanistic models [3]. Lumped models, such as linear time series models, nonlinear time 2. The WAS Model series analysis [4–6],andregressionmodels[7], can be used for various purposes, such as forecasting and modelling The WAS model can simulate dually natural-artificial water rainfall runoff and estimation of missing hydrological data. In cycles influenced by both nature and humans. It consists of past decades, distributed hydrological models, for example, two interoperative computational modules: SWAT and the the SWAT model [8], TOPMODEL [9], MODFLOW [10], artificial water optimized allocation module (AWOM), which FEFLOW [11], MIKE-SHE model [12], SVM model [13], was developed in this study. In particular, AWOM, which 2 Advances in Meteorology

Table 1: Generalized elements of the system of water resources in AWOM.

Elements Class The representative system Supply Reservoir, diversion works, well project, lakes, and marshes Water use City residents, rural residents, industry, agriculture, urban ecology, lake ecology, and wetland ecology Points Convergence Catchment zones and outflow, such as an ocean or a lake Water control Key section of a river or channel River Natural rivers Lines Channel Artificial channels

Runoff Total ET 2.2. Development of AWOM. Allowing for natural and artificial “dualistic” characteristics of a hydrological cycle, AWOM Runoff ValidationRS ET Validation Artificial ET Validation that is based on the ET control theory has been utilised in this Coupling work to simulate the artificial water cycle. SWAT AWOM 2.2.1. Description of the Water System Methodology. Awater Simulating Simulating system methodology for the regional water resources is Natural water cycle Artificial water cycle used here to describe an actual water system in AWOM, which includes five components (Figure 3): (a) multiple water sources, such as surface water, ground water, transfer Figure 1: The structure of the WAS model (RS ET is ET data retrieval water, recycled water, and desalination and brackish water; with remote sensing). (b) multiple storage facilities, such as reservoirs, diversion works, well projects, lakes, and marshes; (c) multiple transfer systems, such as natural rivers, artificial channels, and the can implement optimization of regional water utilization by pipe network; (d) multiple users, including city residents, regional ET control, is the core for regional water resource rural residents, industry, agriculture, ecology, and shipping; management. SWAT provides basal data to AWOM. Syn- and (e) multiple drainage systems, including rivers, rainwater chronously, the SWAT and AWOM are the key to ET control channels, and sewage channels. of a region because regional ET includes not only natural ET such as ET of soil, grass, crops, and water (which can be 2.2.2. Generalized Methodology of the Water System. Because obtained from SWAT and verified with data from RS) but of the complexity of the real-world system of water resources, also artificial ET-like consumptive use (ET) by residential we need to abstract the main characteristics and processes of a buildings and by the industry, which can be provided by real water resource system according to an actual situation in AWOM. Total regional ET can be verified using the water the region. According to the similarity principle, the regional balancemethodandsimulatedusingtheWASmodel,which system of water resources is usually generalized as point is shown in Figure 1. Figure 1 shows the general architecture elements and line elements to describe generalized water and state information flows of the WAS model, and Figure 2 resources (Table 1). illustrates the overall cyclic flow of this model. 2.2.3. The Delineation Method for Subzones. Astudyareamay 2.1. Development of the SWAT Model. SWAT was initially be divided into water allocation units (WAUs). WAUs are developed by the United States Department of Agriculture portions of a subbasin that possess unique subwatersheds for comprehensive modelling of the impact of management or attributes of administrative divisions. The delineation practices on water yield, sediment yield, and crop growth in process requires a digital map of subwatersheds and a map large complex watersheds [8].Therearesomedisadvantages of administrative divisions in the shape (PolyLine) format. of SWAT. One example is that its artificial water simulation WAUs should be delineated via superimposition of the two is limited and the scenarios are set passively; therefore, it above-mentioned maps in ArcGIS software (Figure 4). cannot actively resolve conflicting water allocation options using data from social, economic, environmental, and water 2.2.4. The Relationship between Data from AWOM and SWAT. resources. In our study, some functions of SWAT were The holistic approach can objectively and clearly demonstrate developed to incorporate the impact of human activities as the amount of allocation and drainage of water and show follows.(1)Weimprovedtheagriculturemodulebyadding where it comes from and where it goes. In particular, AWOM an irrigation function that can locate more water sources is suitable for optimisation of the profit from water resources for irrigation water within the same time frame. (2) We according to water availability per capita and a higher value developed the consumptive water use module, which may of use considering regional ET control, groundwater exploita- be adapted to spatial and temporal variability of water use tion control, among other factors that can improve develop- in different years; these data have been published previously ment of water resources and of the regional economy. In fact, [18]. some input data for AWOM, such as runoff, groundwater, and Advances in Meteorology 3

SWAT Precipitation

Pervious surface Impervious surface Outside water Outside Evaporation

Irrigation Infiltration Runoff River Pond/reservoir River Lateral flow Transpiration

Evapotranspiration Supply

Soil storage Drainage

Sewage Supply Supply Recycled water

Evaporation Seepage Supply ET ET Return flow Return Supply ET ET Shallow aquifer Living Industry Agr. Ecology

Supply Taking Supply Supply Deep aquifer Rain Seawater Saltish water Other

AWOM

Figure 2: The flow chart of the WAS model.

Atmosphere system

Evapotranspiration Water consumption

Runoff Reservoir City residents Rural residents Groundwater Diversion works Natural river Rivers Industry Transfer water Well project Rainwater channel Artificial channel Agriculture Desalination Supply works Sewage channel Urban ecology Rainwater Lake Pipe network Lake ecology Recycled water Wastewater Marsh Wetland ecology treatment engineering Shipping

Water source Water storage Water transmission Water use Drainage

Figure3:DescriptionofthewatersysteminAWOM. water of sluice facilities, are as important as the output data millionin2011.TianjinCityislocatedinthenorthernpartof on water resources demanded by users. In this study, water China and is the last city downstream of the Hai River flowing sourcedataareprovidedasoutputofSWAT.Homoplastically, into the Bo Sea. output variables of AWOM for a study region (which reflect the artificial consumptive water process) can be spatially and 3.1. Materials temporally assimilated into SWAT and used for calculations. 3.1.1. Hydrological Data. TheinputoftheWASmodel involves a large amount of data, which include the following: 3. Practical Application of the Model (1) the DEM of Tianjin is SRTM 30 m digital elevation data (downloaded from http://srtm.csi.cgiar.org/); (2) data on a The WAS model described above was applied to Tianjin City, regional river system on the scale of 1 : 250,000, obtained 2 which has an area of 11,920 km and had a population of 13.6 from the Water Resource Department of Tianjin (Figure 5); 4 Advances in Meteorology

Sub-watersheds +

Water allocation units

Administrative divisions N Figure 4: Delineation of water allocation units.

W E

Lin River N S Li River

Swat land use class Jiyun River FRSD SWRN

Zhou River FRSE UINS Chaobai River Huanxiang River FRST URHD Qinglongwan LTLD URLD Beiyun River ORCD WATR RICE WETL

Yongding River RNGB WETN RNGE WWHT Jinzhong River Hai River Figure 6: Land use in Tianjin.

Nanyun River

Ziya River Duliu River aquifer, obtained from the Water Resource Department of Tianjin.

New Ziya River Ju River North Drainage 3.1.2. Artificial Data. The artificial data comprises five components: (1) the volume of water demanded by a city and River industry;(2)thedataonthescheduleofirrigation,cropplant- Country ing, fertilization, and other parameters related to farming Figure 5: Rivers in Tianjin. management; (3) the volume of waste water drained by the city and industry; (4) the water supply source of each zone; and(5)therelationshipofsupplyanddrainageamongvarious supply systems and demand zones. All of the above data were (3) daily weather data, including information on precipita- obtained from Tianjin water resource departments. tion, temperature, wind speed, solar radiation, and relative humidity, supplied by the National Meteorological Site of 3.2. Development of the Model. The boundaries of the model China; (4) regional river inflow data obtained from the follow the borders of the Tianjin district and some natural Water Resource Department of Tianjin; (5) soil types and features, such as rivers, creeks, and coastline. As for the land types of land use (Figures 6 and 7)providedbytheWater surface, the research area was divided into 325 subbasins and Resource Department of Tianjin; (6) infiltration recharge 1,414 hydrologic response units in the SWAT model based data from rainfall, rivers, reservoirs, wetlands, and irrigation on DEM, soil type, and land use of Tianjin (Figure 8). The and data on geological parameters for the groundwater water resource system of the Tianjin basin was generalized Advances in Meteorology 5

Table 2: The delineated subzones of Tianjin in AWOM.

Unit MAJX PAJX PABD PANH PAHG PAWQ PABC PBBC PBXQ PBCQ PBJN PBDL PBTG PBDG PBJH name North Water three resources North four plain areas Daqing River plain area mountain name areas District Central Jixian Jixian Baodi Ninghe Hangu Wuqing Beichen Beichen Xiqing Jinnan Dongli Tanggu Dagang Jinghai name urban

1 2 8 3 4 5 11 10

14 6 17 16 15 13 19 12 9 7 20 18 21 24 23 22 25 27 26 30 28 32 40 29 31 3633 35 38 44 37 414234 39 45 43 46 48 50 47 59 100 49 51 52 58 5354 60 6664 77 76 62 6568 61 71 75 86 56 74 57 67 78 88 63 73 84 90 91 89 69 79 72 80 81 85 87106 108 97 104 92 93 82 128 96 129 105 83 114 98 110 109 101 70 107 99 102 118 111 103 115 116 125 112 94 95 126 121119 117 120 124 113 138 139 123 122 150 132 127 130 140 144 131 136 133 143 147 145 135 142 137 141 152 149 146 134 155 159 148 167 151 168 164 162 153 154 158157 161 165 163 170 169 175 172 160 166 178 181 180 174 187 173 192 194 176 186 188 190 195 171 193 182183 179 191 198 177 211 184 196 199 185 202 206208 197 203207 212 214213 204 209 218 200 201 210 N 217 205 221 220 223 219 229 227 225236 222 248 216 240 215 224 232 226 230237 239 244 231 249 233 W E 238 242 246 241 234 254 252 256 259 263 247 258 235 251 243 257 262 264 228 266 255 267 253 270 265 268 272 260 261 283 279 245 S 276 271 273 278 269 274 282 290 292 288 281284 250 297 291 286 280 295 294293 289 275 298 285 277 302 309 300 305 296 287 307 299 303 304 311 301 308 315 312 310 306 317 Soil class 316 313 314 320 318 319 322 BHYT SLRT 323 321 324 325 CHT ST CLRT SZCT NZCT YHCT Figure 8: Delineation of watersheds in the Tianjin basin. RLRT YLRT RZCT YSCT SCT 4. Results and Discussion Figure 7: Soils in Tianjin. A“piece-by-piece”approachwasusedheretoevaluatethe WAS model from runoff to ET. The idea behind the piece- by-piece approach is to provide the model with rational and to describe the four processes of the Tianjin artificial water available results step by step. The WAS model, like other large cycle (Figure 9). The artificial zones were delineated as15 models, is subdivided into several pieces and then solved subzones in AWOM using the method that superimposes the step by step with one piece solved at each step. At each watershed and the district. step, the solution of the current partial model begins with The name of a subzone is an abbreviation, composed of a solution found in the previous step, and the solution from four letters; the first two letters represent the regionalisation the current step is saved for the next step. At the final step, of water resources and the last two letters represent district the model contains all pieces and the whole model is then regionalization. Table 2 shows descriptions of subzones. calibrated. Verification of the WAS model is subdivided into 6 Advances in Meteorology

Longmenkou Lin Shuipingkou Yuqiao res. Sha River River (1) MAJX Lin River Qianmao

Xiaodingfu (2) PAJX Ju River Zhou River Zhou Huanxiang Shanhe Jiuwang Zhuang Xiaohe River Ganshui (3) PABD Tumenlou Jiyun Qinglongwan Dahuangpu res. (4) PANH Muchang Juntun Chaobai (5) PAHG Beijing Beiyun Erwang res. Dongqilihai res. River River (6) PAWQ River River Guan (7) PABC

Qujiadian Dongtai Ningchegu Yongding Jiyunzha (8) PBBC River Huanggang res. Beitang res. Xihe (10) PBCQ (12) PBDL (13) PBTG Hai River Bo (9) PBXQ Hongni Sea (11) PBJN River Duliu Nanyun Jinhong River Beidagong res. Tuanbowa res. River (15) PBJH River Jinhong Machang Jiuxian (14) PBDG Ziya River Outside water from yellow river Zhouguan

Login River Supply line Res. of river Transfer water Large res. Mid res. Drainage Sealine Planning res. Hydrologic station Subzone Figure 9: Water resource network of Tianjin. the following three parts: the runoff, the ET measured using hydrological cycle was strongly affected by human activities, water balance analysis, and the ET obtained by means of RS. and the spatial-temporal distribution of artificial water use in the model fails to reflect a real situation, although the SWAT 4.1. Verification of the Model on Runoff. The surface runoff module was developed to account for human activities. (2) in the Tianjin basin was calibrated with daily observed The schedule of irrigation was also an influential factor streamflow data from 1985 to 1999 from four hydrological because irrigation represented the dominant water use (70% stations in Tianjin: on the Jiyun River, on the Chaobai River, on the Hai River, and on the Duliujian River. Comparison of of all off-stream uses), and the irrigation data necessitated the simulated and observed daily stream flow involved model consideration of precipitation and soil moisture. Overall, the 2 efficiency (Ens) [19] and a regression coefficient (𝑅 ). Surface predictions of runoff are relatively acceptable. runoff was calibrated until average measured and simulated 2 surfacerunoffhadmonthly𝑅 > 0.6 and Ens > 0.5. The 4.2. Verification of the Model on ET Measured Using Water results of the calibration are shown in Figure 10. Balance. RegionaltotalETincludesnaturalETandartificial The Nash-Sutcliffe efficiency (Ens) of the model was 0.82 ET. The ET measured using water balance is calculated from on average, with the maximum 0.89 and minimum 0.78; inflow and rainfall data and outflow data in Tianjin from 1980 2 its regression coefficient 𝑅 was 0.92 on average, with the to 2004; these measurements were made by Tianjin water maximum 0.94 and minimum 0.91 (Table 3). resource departments (Figure 11). Several factors may contribute to the discrepancies In this work, ET of various underlying surface features between the simulation and the observed data. (1) The was simulated and analysed using the distributed hydrology Advances in Meteorology 7 /s) 3 /s) 80 3 80.0 70 70.0 60 60.0 50 50.0 40 40.0 30 30.0 20 20.0 10 10.0 0 0.0 Flow volume of year (m year of volume Flow Flow volume of year (m year of volume Flow 2000 2001 2002 2003 2004 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1997 1998 1995 1996 1999 2000 2001 2002 2003 2004 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1997 1998 1995 1996 1999 Time Time Simulated Simulated Observed Observed (a) (b) /s) /s) 3 3 50.0 80.0 70.0 40.0 60.0 30.0 50.0 40.0 20.0 30.0 10.0 20.0 10.0 0.0 0.0 Flow volume of year (m year of volume Flow Flow volume of year (m year of volume Flow 2000 2001 2002 2003 2004 1985 2000 2001 2002 2003 2004 1986 1987 1988 1989 1985 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 1986 1987 1988 1989 1990 1991 1992 1993 1994 1997 1998 1995 1996 1999 Time Time Simulated Simulated Observed Observed (c) (d) Figure 10: Calibration of the model. Comparison between simulated and observed streamflow at the four stations: Jiyun River (a), Chaobai River (b), Hai River (c), and Duliu River (d).

Table 3: Correlations at the four observed stations during calibration and validation periods.

Calibration period (1985–1999) Validation period (2000–2004) ID a b c d abcd Average Average Station Jinyun Chaobai Hai Duliu Jinyun Chaobai Hai Duliu Nash 0.82 0.89 0.78 0.82 0.82 0.61 0.78 0.65 0.73 0.69 2 𝑅 0.91 0.94 0.92 0.91 0.92 0.67 0.82 0.75 0.78 0.76 2 Nash: Nash-Sutcliffe efficiency (Ens); 𝑅 : regression coefficient.

800 were obtained in the analysis of water balance in Tianjin. 750 The Nash-Sutcliffe efficiency (Ens) of the model was 0.67, 2 700 and its regression coefficient 𝑅 was 0.83. From 1980 to 2004 650 (Figure 12), 64% of years deviated by less than 5% and 88% (mm) 600 of years deviated by less than 8%. The average total ET of 550 25 years simulated by the WAS model was 654 mm, which 500 equalled the measured ET. 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Year 4.3. Verification of the Model on ET Obtained Using RS. Currently, the margin of error of ET obtained using RS can ET measured by water balance ET simulated by WAS be larger than 15 percentage points, but the relationship of ET with various types of land use is accurate on a spatial scale Figure 11: Comparison of total ET between simulated and measured in the same period. Therefore, we compared the ET results data. (average value from 2001 to 2004) pertaining to various types of land use between the WAS model and RS in order to model of SWAT, and artificial ET was simulated and calcu- validate the results for the model for various types of land use. lated in the artificial consumptive water module of AWOM. We found that the ET simulated by the WAS model was Figures 5 and 6 show comparisons of integrated regional close to the ET data retrieved using the RS technique on ET between the simulation and the measured data, which different lands, as shown in Figure 13. The Nash-Sutcliffe 8 Advances in Meteorology

20.0 15.0 10.0 5.0 (%) 0.0 −5.0 −10.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 Year Figure 12: The deviation rate of simulated ET compared to measured ET.

1000

800

600

(mm) 400

200

0 Irrigated Paddy Forest Grass Wetland Water land field land land ET by simulation ET by RS Figure 13: Comparison between simulated ET from the model and observed ET from RS in various lands.

900 750 600 450 (mm) 300 150 0 Water Paddy Dry land land Marshland Shrub land Beach land Forest land grassland lands grassland High coverage coverage High Urban land use land Urban Open woodland Rural residential Rural Other woodland Moderate coverage Moderate Other construction Land use Figure 14: Average ET of lands used for various purposes in Tianjin from 1980 to 2004. efficiency (Ens) of the model was 0.93, and its regression construction lands showed low ET: 326 mm, 236 mm, and 2 coefficient 𝑅 was 0.98. The ET from water, wetlands, and 233 mm, respectively. paddy lands was higher than that from the irrigated land, Figure 15 shows composition and distribution of inte- forest, and grass land; these results were consistent with the grated ET in Tianjin. It demonstrates that ET from irrigation actual context and regional features. lands is the dominant part that accounts for 53% of the total ET; the second highest ET was from ecology, with 23% 5. Analysis of Regional Total ET from town ecology and 20% from other ecology. Accordingly, thesequenceofthefeasibilityofETcontrolfromeasyto The average ET of various types of land use in Tianjin from difficult is as follows: agriculture, ecology in town, industry, 1981 to 2004, calculated by the WAS model, is shown in and residential use. Figure 14.ThewaterETis991mmonaverageandwasthe JudgingbytheresultsofanalysisofETfromvarious highest.Marshlandandbeachlandarethesecondandthe types of land use and industries, the industries showing lower third:724mmand701mm,respectively.ETofpaddyfields water use efficiency require some improvements. Control and dry lands is also high, 602 mm and 530 mm, respectively. of the regional integrated ET can be attained, and regional Then follow grassland and forest land, from 482 mm to authorities may finally implement the goals of water use 501 mm. The rural residential land, urban land, and other efficiency and reap the resulting benefits. Advances in Meteorology 9

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