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Xinchen Gu, Guang Yang, Xinlin He*, Li Zhao*, Xiaolong Li, Pengfei Li, Bing Liu, Yongli Gao,Lianqing Xue, and Aihua Long
Hydrological process simulation in Manas RiverBasin using CMADS
https://doi.org/10.1515/geo-2020-0127received April 25, 2020; accepted August 24, 2020
Abstract: The inability to conduct hydrological simula-tions in areas that lack historical meteorological data is animportant factor limiting the development of watershedmodels, understanding of watershed water resources, andultimate development of effective sustainability policies.This study focuses on the Manas River Basin (MRB),which is a high-altitude area with no meteorologicalstations and is located on the northern slope of the
Tianshan Mountains, northern China. The hydrologicalprocesses were simulated using the China MeteorologicalAssimilation Driving Datasets for the SWAT model(CMADS) using the Soil and Water Assessment Tool(SWAT) model. Simulated runoff was corrected usingcalibration/uncertainty and sensitivity program for theSWAT. Through parameter sensitivity analysis, parametercalibration, and verification, the Nash–Sutcliffe efficiency(NSE), adjusted R-square (Radj
2 ), and percentage bias(PBIAS) were selected for evaluation. The results werecompared with statistics obtained from Kenswat Hydro-logical Station, where the monthly runoff simulationefficiency was =NSE 0.64, =R 0.69adj
2 , and =PBIAS–0.9, and the daily runoff simulation efficiency was
=NSE 0.75, Radj2 = 0.75, PBIAS = −1.5. These results
indicate that by employing CMADS data, hydrologicalprocesses within the MRB can be adequately simulated.This finding is significant, as CMADS provide continuoustemporal, detailed, and high-spatial-resolution meteorolo-gical data that can be used to build a hydrological modelwith adequate accuracy in areas that lack historicalmeteorological data.
Keywords: ungauged basin, simulated meteorologicaldata, hydrologic model, Tianshan area, cold and Aridregion
1 Introduction
It is difficult to make accurate and reasonable simula-tions of water resources and hydrological processes inareas that lack meteorological stations, such as cold dryareas in alpine regions where there are large fluctuationsin runoff and where the complex topography and largefluctuations make it difficult to construct or maintainstations [1]. However, existing studies have shownthat when meteorological data are lacking, the hydro-logical processes in models are greatly affected [2,3].The MRB is located in the arid area with a unique
Xiaolong Li, Pengfei Li, Bing Liu: College of Water and ArchitecturalEngineering, Shihezi University/Xinjiang Production andConstruction Group Key Laboratory of Modern Water-SavingIrrigation, Shihezi, 832000, ChinaXinchen Gu: College of Water and Architectural Engineering, ShiheziUniversity/Xinjiang Production and Construction Group KeyLaboratory of Modern Water-Saving Irrigation, Shihezi, 832000,China, e-mail: [email protected] Yang: College of Water and Architectural Engineering,Shihezi University/Xinjiang Production and Construction Group KeyLaboratory of Modern Water-Saving Irrigation, Shihezi, 832000,China, e-mail: [email protected]
* Corresponding author: Xinlin He, College of Water andArchitectural Engineering, Shihezi University/Xinjiang Productionand Construction Group Key Laboratory of Modern Water-SavingIrrigation, Shihezi, 832000, China, e-mail: [email protected]* Corresponding author: Li Zhao, College of Water and ArchitecturalEngineering, Shihezi University/Xinjiang Production andConstruction Group Key Laboratory of Modern Water-SavingIrrigation, Shihezi, 832000, China, e-mail: [email protected]
Yongli Gao: Department of Geological Sciences, Center for WaterResearch, University of Texas at San Antonio, Texas, TX, 78249,United StatesLianqing Xue: College of Hydrology and Water Resources, HohaiUniversity, Nanjing, 210098, China; College of Water andArchitectural Engineering, Shihezi University/Xinjiang Productionand Construction Group Key Laboratory of Modern Water-SavingIrrigation, Shihezi, 832000, ChinaAihua Long: College of Water and Architectural Engineering, ShiheziUniversity/Xinjiang Production and Construction Group KeyLaboratory of Modern Water-Saving Irrigation, Shihezi, 832000,China; China Institute of Water Resources and HydropowerResearch, Beijing, 100038, China
Open Geosciences 2020; 12: 946–957
Open Access. © 2020 Xinchen Gu et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0International License.
mountain–oasis–desert ecosystem. As the largest oasisfarming area in Xinjiang and the fourth largest irrigatedagricultural area in China, its ecological problems havealways been the focus of scientists [4–11].
The modeling of the watershed hydrological modelwithout historical meteorological data is the focus ofscholars [12–15]. The SWAT model is a semi-distributedhydrological model based on physical mechanisms. It hasbeen successfully applied to watershed research in differentcountries and regions and is one of the most widely usedhydrological models for evaluating water resources indifferent types of basins globally [16–21]. For example,Pulighe et al. [22] used the SWAT model to predict therunoff and nutrient load of a Mediterranean semiarid smallwatershed, and the evaluation results from the model werefound to be consistent with observed values. Pradhan et al.[23] applied the SWAT model and an artificial neuralnetwork to basins in tropical, subtropical, arid, andsemiarid regions in Asia and showed that the SWAT modeland the artificial neural network model performed betterwhen used in low- and high-flow simulations instead ofaverage traffic, respectively. Mosbahi and Benabdallah [24]evaluated soil erosion land management in a semiaridwatershed in Tunis using the SWAT model, and the SWATmodel successfully reproduced the correlation betweenwater flow, simulated runoff, and sediment yield. Inaddition, Mengistu et al. [12] used a physical similarityregionalization method to allocate, calibrate, and verify,using the SWAT model in a catchment area where observeddata were limited, and accordingly proposed a new methodfor estimating the water balance composition in a data-deficient area in South Africa.
While many studies have shown the benefits of using theSWAT model, research has also been conducted to improveits simulation accuracy, especially in the MRB. For example,Luo et al. [25] extended the single reservoir basic flowmethodin the SWAT model to the MRB in Tianshan, China, andthought that the delayed response of a basic flow was morereasonable. However, owing to a lack of basic flowmeasurement data, the study was unable to determine whichof the filter- or model-based methods provided the mostrepresentative evaluation results. Yang et al. [26] applied theSWAT model to assess the hydrological heterogeneity in aridand alpine basins in Northwest China; the results showed theSWAT model to be a robust and highly accurate tool forcharacterizing flow processes in alpine regions. The hydro-logic heterogeneity in high-altitude areas was also deter-mined to be sensitive to energy, whereas that in low-altitudeareas was sensitive to drought stress. In addition, Meng et al.[13] drove a SWAT model using CMADS and found that theSWAT model provided satisfactory results through parameter
calibration in areas that had a high glacier supply rate.Furthermore, Zhang [14] used a SWAT model driven bymodel resolution imaging spectrometer and tropicalrainfall measuring mission remote sensing meteorolo-gical data and predicted that the runoff from the ManasRiver would continue to increase under future climatescenarios.
The main water source of the Manas River is perennialglacial meltwater and seasonal snowmelt water, and thereare no traditional meteorological stations within the basin.Although the studies mentioned earlier have attempted toextend different methods and data to the MRB and establisha watershed model, the research content has been relativelysingle and no substantive solution to the lack of hydro-meteorological data in the MRB has been found yet. Forexample, the physical similarity regionalization method[10,12] is a common method in the areas that are lacking indata. This method cannot meet the needs of more refinedhydrological models to enable the development andassociated management of this river basin [8,9]. Therefore,a more reasonable hydrological process simulation isrequired to provide scientific and theoretical support forpolicymakers. Due to the unique situation within the MRBand the lack of existing hydrological and meteorologicaldata, it is currently not possible to analyze the waterresources and implement relevant water policies. To fullyunderstand the hydrological processes with the MRB, it isnecessary to use a distributed hydrological model withexisting meteorological and reanalysis data as the drivingforces to simulate the hydrological processes within theMRB. In this respect, the SWAT model was employed anddriven by CMADS meteorological data in this study, andland use data, soil data, and digital elevation were alsoemployed. The applicability of using CMADS to compensatefor the scarcity of meteorological data was then discussedfrom the perspective of the significance of differenthydrological parameters.
2 Materials and methods
2.1 Study area
The MRB is located in the foothills of the northernTianshan Mountains in China (Figure 1) and coversan area spanning 5.05 × 103 km2. The MRB has acontinental arid climate: the annual average tempera-ture is approximately 5.9°C (maximum and minimumtemperature extremes are 40.0°C and −32.0°C,
CMADS + SWAT: hydrological model of Manas River Basin 947
respectively), and the amount of yearly precipitationand evaporation are 110–300 mm and 1,500–2,000 mm,respectively [14]. The highest and lowest elevations ofthis basin are 5,138 m above mean sea level (AMSL) and840 m, respectively. Perennial glacier cover exists at3,600 m AMSL and higher, and perennial glacialmeltwater and seasonal snowmelt water provide mostof the water for the Manas River and the MRB. A largeamount of perennial glacial meltwater and seasonalsnowmelt water is produced in the summer, which is arelatively stable water resource within the basin, andlow amounts of summer precipitation over a shortduration occur in the Piedmont area, thus supplement-ing the water resource. Kenswat Hydrometric Station(43°58′E, 85°57′N, 840m) is located at the pour pointwhere the Manas River flows out of the mountain, andthis is also the pour point of the entire basin. As shown inFigure 1, the Manas River originates from the TianshanMountains; it has an average annual flow of 12.9 × 108m3
and total water resources of 25.73 × 108m3. With respectto hydrogeological conditions, the fault and fold geolo-gical structural belts, which have the same east-west axisas the Tianshan Mountains, are distributed in the MRB,and they form a large anticline structure composed ofTertiary strata that block the groundwater connectionbetween the basin and the downstream plain area. Whenriver runoff passes through the anticline structure andenters the downstream alluvial plain, the surface waterand the underground water have a mutual transportrelationship.
2.2 CMADS data
Owing to the lack of meteorological stations andavailable historical meteorological data in the studyarea, this study used the CMADS dataset version 1.1 asthe source of detailed and continuous high- spatial-resolution meteorological data. CMADS is a publicdataset that was developed by Prof. Xianyong Mengfrom the China Agricultural University (CAU). CMADSintegrates local analysis and prediction system/space–-time multiscale analysis system technology and adoptsmany technologies and scientific methods including datanesting, resampling model projection, and bilinearinterpolation [11]. CMADS series datasets can be usedto drive various hydrological models such as SWAT,variable infiltration capacity, and the storm watermanagement model. The datasets also allow the usersto easily extract various meteorological elements andconduct a detailed climate analysis. The data sources ofthe CMADS series include nearly 40,000 regionalautomatic stations within 2,421 national automatic andcommercial assessment centers in China [15]. Thisensures the wide applicability of CMADS data in Chinaand greatly improves the accuracy of data. The spatialresolution of the CMADS data grid is 0.25° × 0.25°, andthe data relate to the years spanning from 2008 to 2016.CMADS data were obtained over a nine-year period from1 January 2008 to 31 December 2016 and they includemany watersheds throughout East Asia [15,27–29].Studies have also used CMADS data and the Penman–
Figure 1: Manas River Basin is located in the middle of the Eurasian continent and on the north slope of Tianshan Mountain. The elevationdifference of the basin is large.
948 Xinchen Gu et al.
Monteith method to calculate potential evapotranspira-tion (PET) across China, and a reliable performance hasbeen noted [29,30].
Precipitation data were stitched using the climateprediction center morphing technique (CMORPH) globalprecipitation products and the National MeteorologicalInformation Center’s data of China (which is based onCMORPH integrated precipitation products): the lattercontains daily precipitation records observed at 2,400national meteorological stations and CMORPH satellite’sinversion precipitation products. The inversion algo-rithm for incoming solar radiation at the ground surfaceemploys the discrete longitudinal method by Stamenset al. [31] to calculate radiation transmission [32]. TheSWAT was then employed to automatically read datarelating to five meteorological elements (rainfall, tem-perature, relative humidity, solar radiation, and windspeed data) obtained from 12 grid nodes (Table 1).
2.3 SWAT model
The SWAT watershed area is discretized from a given digitalelevation model (DEM) to several sub-basins. The hydro-logical response unit (HRU) of each sub-basin has similarland use, soil type, and slope classification, which is thesmallest spatial unit of the model. The watershed HRU is aregion that has relatively single and uniform underlyingsurface characteristics and has similar hydrological char-acteristics. To further reveal this combination differencewithin a basin and reflect the spatial heterogeneity of theunderlying surface of the basin, it is necessary to divideeach sub-basin into several HRUs, each of which has only asingle land use type, soil type, and class of terrain slope.The model is then used to simulate hydrological processes
such as evapotranspiration, filtration, surface runoff,groundwater runoff, and sediment erosion in each HRU.The runoff from each HRU first flows into the main channelof each sub-basin and then flows from one sub-basin toanother until it finally reaches the pour point.
The water balance equation can be expressed asfollows:
∑= + ( − − − − )
=
R Q E w QSW SW ,i
t
t 01
day surf a seep gw (1)
where SWt is the final soil moisture content (mm); SW0 isthe initial soil moisture content (mm); t is the simulationtime (days); Rday is the daily precipitation (mm water);Qsurf is the daily surface runoff (mm water); Ea is thedaily evapotranspiration (mm); wseep is the amount ofwater entering the aeration zone from the soil profile ona given day (mm); and Qgw is the return flow on a givendate (mm). SW0 is the initial soil moisture contentobtained from soil moisture data provided by harmo-nized world soil database (HWSD).
Based on dynamic storage models and the gradientof the terrain, the soil hydraulic conductivity (SOL_K),spatiotemporal changes in soil moisture, and character-istics of soil internal flow can be calculated using theSWAT. However, it is also important to consider lateralflow with respect to collecting soil water in the areaswith high surface water conductivity. In this respect,Sloan [33] combined the SWAT with a groundwater flowmovement storage model to simultaneously calculateseepage. Shallow aquifers collect groundwater into themain channel of the sub-basin, and surface runoff isthus from rainfall that remains after plant closure andsoil infiltration. The Green–Ampt infiltration method andthe soil conservation service (SCS) curve method can beused to estimate surface runoff [34]. In addition, thepeak runoff rate reflects the erosion force of rainstormsand can be used to predict the amount of sediment loss.
Evapotranspiration includes canopy evaporationand soil evaporation, and many methods can be usedto simulate PET, including the Priestley–Taylor method[35], the Penman–Montes method, and the Hargreavesmethod [36], all of which are included in SWAT.
Based on the study of evapotranspiration in arid andsemiarid areas, Hargreaves and Samani’s formula [36] canbe used to calculate PET based on temperature and solarradiation. As this method provides good calculationresults, the United Nations Food and Agriculture Organi-zation (FAO) recommends using it when meteorologicaldata are lacking. In this study, therefore, the Hargreavesmethod [36] is used to simulate PET as follows:
Table 1: CMADS meteorological grid node information
Grid nodes Latitude Longitude Altitude (m)
174-102 43.28 85.28 3,570174-103 43.28 85.53 3,522174-104 43.28 85.78 3,233174-105 43.28 86.03 3,273175-101 43.53 85.03 4,351175-102 43.53 85.28 3,938175-103 43.53 85.53 3,304175-104 43.53 85.78 2,424175-105 43.53 86.03 4,014176-104 43.78 85.78 1,494176-105 43.78 86.03 2,559177-105 44.03 86.03 945
CMADS + SWAT: hydrological model of Manas River Basin 949
=
+
+ −−
R T T T TET 0.00232
17.8x nx n0 Har a (2)
where−
ET0 Har is PET calculated using Hargreaves’method (mm/day); Tx is the daily maximum temperature(°C); Tn is the minimum temperature (°C); and Ra is thesolar radiation at the top of the atmosphere (MJ/(m2 day)) and can be calculated using the latitude andatmospheric top radiation table provided by the FAO.
2.4 CMADS + SWAT
The DEM data for the basin were provided by the geospatialdata cloud platform of the computer network informationcenter of the Chinese Academy of Sciences (http://www.gscloud.cn). DEM is ASTER GDEM v2 released by theMinistry of Economy, the Trade and Industry of Japan, andthe National Aeronautics and Space Administration of theUnited States in 2015, with a resolution of 30m. ArcMap wasused to fill the depressions relating to DEM data to reduceerrors, and to calculate the DEM and generate theboundaries, river network, and slope data for the basinand sub-basins. Google’s satellite image data provided bythe ArcGIS Online basemap were employed (Figure 1).
The soil data with a resolution of 1 km employedwere based on the HWSD version 1.1 provided by thescientific data center for cold and dry areas (http://westdc.westgis.ac.cn). Land use data with a resolution of30m for the summer of 2015 were obtained from theNational Geoscience Data Sharing Centre (http://www.geodata.cn), which uses Landsat-8 remote sensingimages with a small amount of cloud and rich surface
feature information that was obtained through remotesensing interpretations.
In this study, in accordance with the DEM, the SWATprogram automatically divided the MRB into 21 sub-basins. In each of these sub-basins, the runoff flows intothe pour point and then into the next sub-basin. Therunoff ultimately flows into sub-basin 1, which is theriver pour point, and the Kenswat Hydrological Station isalso located in sub-basin 1. The 12 CMADS meteorolo-gical grid nodes located around the MRB are shown inFigure 2(b). The SWAT model used the data from these 12meteorological grid nodes (Table 1).
The land use, soil type, and slope-gradient distribu-tion within the Manas watershed are shown in Figure 3.The soil type distribution is shown in Figure 3(a) and itspercentage distribution is as follows: gelic leptosol soil(47.61%), glacier (21.54%), mollic leptosol soil (18.17%),haplic greyzem soil (4.83%), luvic kastanozem soil(4.32%), haplic chernozem soil (2.60%), haplic kasta-nozem soil (0.64%), and calcaric fluvisol soil (0.30%).The percentage distributions of the land use types shownin Figure 3(b) are as follows: grassland (48.49%),impervious surface (26.20%), ice on glacier (20.29%),forest (4.97%), cropland (0.04%), and bareland (0.01%).Furthermore, the percentage slope-gradient distributionrelating to Figure 3(c) is as follows: 0–15° (7.65%),15–25° (9.12%), 25–75° (63.19%), and ≥75° (9.12%).
The elevation differs considerably within the area,and the soil types and vegetation coverage thus alsoshow significant vertical zone differentiation. Waterabsorption capacity thus differs in accordance with thesoil properties. For example, the particle sizes of thevarious soil types differ, and permeability thus differs,
Figure 2: (a) The SWAT program divided the MRB into 21 sub-basins and (b) employed data from 12 CMADS meteorological grid nodeswithin the area. The CMADS meteorological grid in the figure almost perfectly covers the whole basin.
950 Xinchen Gu et al.
which affects the amount of water absorbed andsubsequent runoff. Furthermore, the different vegetationtypes and their litter layers have varying degrees ofprecipitation interception and can thus enhance surfacerunoff and prolong infiltration time. Vegetation withhigher coverage reduces the depth of surface runoff andincreases soil and underground runoff. The interactionbetween these factors means that the confluence ofprecipitation and ice/snowmelt water is complex and isthus an important hydrological process.
In summary, the SWAT model of the MRB wasultimately constructed using sub-basin divisions, im-ported data from CMADS meteorological grid nodes, landuse data, soil type data, slope division data, HRUgeneration, a time scale, and a preheating period setting.
2.5 Model calibration and validation
In this study, the sequential uncertainty fitting algorithm inSWAT-CUP was used to estimate the unknown parametersiteratively through a sequential fitting process. Thisprogram also considers the uncertainty of the model input,model structure, input parameters, and observation data.During the calibration process, this program uses theglobal sensitivity analysis method, considers the balancephenomenon in the calibration process, and uses the t-stat(Student’s t-test) and p-value method to evaluate
sensitivity. These different parameter settings generateacceptable runoff curves. In this process, the higher thesensitivity of the parameters, the greater the absolute valueof the t-stat and the closer the p-value is to zero [37].
Based on the comprehensive evaluation method,Moriasi proposed four quantitative statistical indicators[38]: the NSE, PBIAS, relative root-mean-square error(RRMSE), and the Radj
2 . This study used three common
indicators, the NSE, PBIAS, and Radj2 , to quantify the
model performance, as these three performance indexesmatch the simulation value of the quantitative numericalmodel with the measured value. The NSE is a statisticalindicator that quantifies the relative size of the residualvariance [39], and the PBIAS shows whether theaverage trend of the simulation data is greater or lessthan that of the corresponding observation data [40],which is different from the variance used for observa-tion data.
The NSE, Radj2 , and PBIAS are calculated as follows:
=
∑ ( − ) − ∑ ( − )
∑ ( − )
= =
=
o o s oo o
NSE¯
¯in
i in
i i
in
i
12
12
12 (3)
=
∑ ( − )( − )
∑ ( − ) ∑ ( − )
=
= =
Ro o s s
o o s s
¯ ¯
¯ ¯in
i i
in
i in
i
2 1
12
12
2
(4)
= − ( − )⋅
−
− −
R R nn k
1 1 11adj
2 2 (5)
Figure 3: Map of the HRUs showing the different (a) soil types, (b) land use types, and (c) slope gradients within the MRB. Different soiltypes, land use, and slope gradient make up different HRUs.
CMADS + SWAT: hydrological model of Manas River Basin 951
= ×
∑ ( − )
∑
=
o so
PBIAS 100 in
i i
i
1 (6)
where R2 is the R-square; Radj2 is the adjusted R-square;
NSE is the Nash–Sutcliffe efficiency coefficient; PBIASis the deviation of the data being evaluated (expressedas a percentage); oi is the measured runoff (m3/s);si is the simulated runoff (m3/s); o is the mean measuredrunoff during the simulation period (m3/s); s is themean simulated runoff during the simulation period(m3/s); n is the number of flow measurements in theanalysis; and k is the number of independentvariables.
The closer the NSE and Radj2 values are to 1, the better
the SWAT performance. Furthermore, when PBIAS isclose to zero, the simulation is more accurate. Moriasiproposed that when NSE ≥ 0.5 and PBIAS is within ±25%, the model is considered to provide a satisfactorysimulation. This study used the criteria developed byMoriasi (Table 2) to conduct the evaluation.
3 Analysis of results
In accordance with the topography and river network,ArcSWAT was used to divide the study area into 21 sub-basins. To obtain high-resolution land use, soil properties,and slope, these sub-basins were further divided into 136HRUs. The SWAT model divided each sub-basin into severalHRUs and then selected the HRUs as the hydrological unitto be simulated. On this basis, monthly and daily-scalesimulations were conducted using the SWAT model todetermine the model’s capabilities and the model wascalibrated according to the runoff observed at the KenswatHydrometric Station. The results of the sensitivity analysisdetermined 15 parameters that could be used to correct themodel, and accuracy requirements were achieved in thevalidation period. The final runoff simulation results were
NSE = 0.64, Radj2 = 0.69, and PBIAS = −0.9 (monthly); and
NSE = 0.75, Radj2 = 0.75, and PBIAS = −1.5 (daily).
3.1 Sensitivity analysis
A sensitivity analysis was conducted to determine theparameters that had a strong influence on the snowmeltwater runoff simulation. The sensitivity analysis wasconducted by incorporating the Latin-Hypercube–Onefactor-At-a-Time (LH-OAT) method into SWAT-CUP. Aparameter sensitivity analysis was conducted forcommon parameters; the parameters with high modelsensitivity were selected for parameter adjustment, and15 parameters were finally selected (Table 3).
Table 3 lists the sensitivity of the parametersconsidered in the final calibration results. The precipita-tion {2010001–2016365} parameter had the highestsensitivity and was adjusted to 1.90 times the rainfallin 2010–2016. The optimal value of the snowmelt basetemperature (SMTMP) was 6.71, which indicated thatsnowmelt began at 6.71°C. Furthermore, the optimalvalue of the snowfall base temperature (SFTMP) was6.34, which indicated that the rainfall began to changeto snowfall at 6.34°C. This table also lists otherparameters, such as hydraulic conductivity (SOL_K),the SCS runoff curve coefficient (CN2), slope (HRU_SLP),and the soil effective water content (SOL_AWC).
The 15 hydrological parameters used are all within areasonable range, and some are further discussed laterto explore their practical significance.
3.2 Model calibration and validation
The runoff data from Kenswat Hydrological Station from2008 to 2016 were used to preheat, calibrate, and verifythe use of the SWAT model within the MRB. In thisstudy, the warm-up time was set to two years(2008–2009) to initialize the SWAT model. The above-mentioned 15 parameters and parameter values (Table 3)were determined by the calibration of surface runoff datafrom 2010 to 2013, and the evaluation standard met therequirements. The 2014–2016 surface runoff data werethen used for the verification, and the 15 parametervalues determined during calibration were entered intothe SWAT model to evaluate similarities and differencesbetween simulated runoff values and measured runoffvalues. In general, the runoff simulated by the SWATmodel was similar to that of measured runoff, and theflood occurrence times of simulated and measured runoffdata were in good agreement.
In the monthly scale simulation (Figure 4), the peaksummer flows in 2010, 2012, and 2014 were higher than
Table 2: Classification of performance grades for statisticalindicators: NSE, PBIAS, and Radj
2
Performance grades NSE PBIAS
Very good 1.00 ≥ NSE ≥ 0.75 |PBIAS| < 10Good 0.75 > NSE ≥ 0.65 15 ≥ |PBIAS| > 10Satisfactory 0.65 > NSE ≥ 0.5 25 ≥ |PBIAS| > 15Unsatisfactory NSE < 0.50 |PBIAS| > 25
952 Xinchen Gu et al.
the measured runoff; the peak summer flows in 2011 and2013 were lower than the measured runoff; and the peaksummer flows in 2015 and 2016 were in better agreementwith observed values. The simulation results weregenerally good throughout 2010–2016 (with the excep-tion of 2015), although the winter flow in other years waslower than measured values.
The results of the daily-scale simulation (Figure 5)show that the peak summer flows in 2010, 2012, 2013,and 2016 were similar to the actual measurements, butpeak summer flows in 2011, 2014, and 2015 were quitedifferent from the actual measurements (and that of 2014was relatively large). The winter flows in 2010 and 2014were relatively low, while those of other years wererelatively high.
The calibration results for the monthly scale simula-tion (2010–2013) (Table 4) were NSE = 0.64, Radj
2 = 0.69,and PBIAS = 0.90, and the validation results (2014–2016)were NSE = 0.82, Radj
2 = 0.83, and PBIAS = −3.80. Thecalibration results for the daily-scale simulation(2010–2013) were NSE = 0.75, Radj
2 = 0.75, and PBIAS =−1.50, and the validation results (2014–2016) were NSE =0.66, Radj
2 = 0.67, and PBIAS = −12.60.The performance criteria for the monthly and daily-
scale simulations shown in Table 2 reveal very goodcalibration and verification simulation results for threeperformance parameters, NSE, Radj
2 , and PBIAS. Themodel’s simulation values are consistent with themeasured values; therefore, the accuracy of the modelsatisfies the requirements after parameter calibration.
4 Discussion
The MRB is a small watershed located on the northernslope of the Tianshan Mountains. In the past 40 years,owing to vigorous developments in water-saving irriga-tion, the artificial oasis area in the MRB has expanded by129.56% [8], and the social and economic effects relatingto these oases have actively promoted development inthis area [9]. The demand for water resources in the MRBhas thus simultaneously increased [41]. Althoughhuman activities mainly affect the plain area below theKenswat Hydrometric Station in the MRB, the generationand concentration of runoff in the mountain area aremainly affected by climate change.
It is necessary to simulate and analyze waterresources within the basin to enable their control andassociated sustainable regional development. However,owing to the characteristics of the MRB, existingTa
ble3:
Sen
sitivity
andop
timal
parameter
values
Parameters
Des
cription
Sen
sitivity
priority
t-stat
p-value
Optim
alvalue
R_Precipitation{20
10001–
2016
365}.pcp
Daily
rainfallfrom
2010
to20
16(m
m)
1−2
7.59
1.5×10
−124
1.90
V_S
MTM
P.bs
nSno
wmeltba
setempe
rature
(°C)
215.13
1.2×10
−46
6.71
V_S
MFM
X.bs
nMax
imum
meltrate
forsn
owdu
ring
year
(occurson
summer
solstice)(
mm/°Cda
y)3
−14.71
2.0×10
−44
1.09
R_SOL_K.sol
Saturated
hydrau
licco
nduc
tivity
(mm/h
)4
−12.57
1.0×10
−33
−0.46
V_S
FTMP.bs
nSno
wfalltempe
rature
(°C)
57.17
1.4×10
−12
6.34
R_SLS
UBBSN.hru
Average
slop
eleng
th(m
)6
4.99
7.2×10
−7
0.39
R_CN2.mgt
SCSruno
ffcu
rvenu
mbe
rformoistureco
nditionII
7−4
.62
4.4×10
−6
−0.45
R_HRU
_SLP
.hru
Average
slop
estee
pnes
s(m
/m)
8−3
.53
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CMADS + SWAT: hydrological model of Manas River Basin 953
hydrological and meteorological data are scarce andthus cannot be used in model simulations of water
resources. Therefore, in this study, CMADS data wereapplied to the SWAT model to explore the hydrologicalprocesses occurring within the basin and to compensatefor the missing hydrological and meteorological data.
The MRB was divided into 21 sub-basins and 136 HRUsusing the SWAT model. The basin was modeled usingArcMap-ArcSWAT 2012 and adjusted using SWAT-CUP. Mostof the parameters used in this process were found to fallwithin a reasonable range. In addition, the optimal value ofthe SMTMP was found to be 6.71, and the optimal valueof the SFTMP was 6.34; this indicates that the temperatureof snowmelt water in the MRB was about 6°C, which isconsistent with previous studies that have concluded that
2010/01 2011/01 2012/01 2013/01 2014/01 2015/01 2016/01
0
50
100
150
200
250
Validation
2014
Mon
thly
ave
rage
runo
ff (m
3 /s)
Monthly
Observed Simulated
Calibration
Figure 4: Observed (at Kenswat hydrometric station) and calibrated monthly average runoff between 2010 and 2016 showing highconsistency between the results. The runoff was almost perfectly reproduced in 2015–2016.
0
100
200
300
400
500
2016/01/012015/01/012014/01/012013/01/012012/01/012011/01/012010/01/01Daily
2014
Dai
ly a
vera
ge ru
noff
(m3 /s
)
Observed Simulated
noitadilaVnoitarbilaC
Figure 5: Measured runoff values (at Kenswat Hydrometric Station) and simulated runoff values on a daily scale showing high consistencybetween the results, especially the runoff in summer.
Table 4: Values obtained during calibration and verification ofevaluation indexes
Statisticalindicators
Calibration(2010–2013)
Validation (2014–2016)
Daily Monthly Daily Monthly
NSE 0.75 0.64 0.66 0.82
Radj2 0.75 0.69 0.67 0.83
PBIAS −1.50 0.90 −12.60 −3.80
954 Xinchen Gu et al.
snowfall events in the Tianshan Mountain area and itssurrounding areas basically occur between −35 and 5°C [42].
Simulation data can be based on data obtained froma wide range of data sources, such as meteorologicalobservation stations, to compensate for lacking histor-ical meteorological data within an area [11,15,43]. Whenestablishing the hydrological model, the spatial resolu-tion of the simulation dataset was high, and availableCMADS data were thus employed. Compared with theClimatic Research Unit (CRU) data and other widely useddatasets, CMADS data have a higher spatial resolutionand are more suitably applied in a small watershed[43–47]. For a watershed lacking meteorological data,the CMADS dataset can ensure accuracy on large spatialand temporal scales. It is common to employ availablemeteorological data from stations to calibrate a dataset,as these can smooth out the typicality and particularityof the watershed with respect to certain types ofmeteorological elements. Applying such data is neces-sary for smoothing any distortions within the dataset.However, there was no available historical meteorolo-gical data in the MRB due to the lack of stations. In thisstudy, there was a certain amount of distortion in therainfall data from CMADS when applied to the wa-tershed. The CMADS rainfall data applied to the MRBwere thus adjusted (1.9 times the original), and theresulting adjusted rainfall data able to drive the SWATmodel adequately and model the MRB were found. Thisresult shows that for areas that lack meteorological data,datasets such as CMADS and CRU can be used forhydrological analysis, simulation, and modeling. TheSWAT model adapted well to the MRB and a morereasonable hydrological process was obtained whenCMADS data were combined with the model. Withrespect to the simulation effect and scale, comparedwith the hydrological model [10,12] established by thephysical similarity regionalization method, CMADS datawere capable of meeting the needs of increased refine-ment and scale diversification.
After modifying the CMADS rainfall data used in thisstudy, an ideal hydrological simulation effect wasobtained. This indicates that CMADS rainfall data mayrequire modification prior to use in watershed hydro-logical simulation studies, as described in a previousstudy [13]. However, due to the different scale modelsimulations, certain ecological fallacies [48] may beintroduced; for example, the model quality index on amonthly scale was not synchronous with that on a dailyscale. In the future work, the aim will be to quantita-tively evaluate the accuracy of using CMADS meteor-ological data (not only rainfall data) in the MRB, to
provide enhanced results for use in water resourceanalysis and associated management. In general, theSWAT model based on CMADS data provided continuousand high-spatial-resolution meteorological data andenabled good simulation of the annual hydrologicalconditions of the MRB, which can be used to explain theimpact of climate change and human activities on waterresources in the basin. These data have good applic-ability within the MRB and can serve as a reference whenconducting a hydrological analysis of this basin inNorthwest China, which has few meteorological sta-tions [49].
5 Conclusions
This study used CMADS data and the SWAT model tosimulate and verify the runoff within the MRB. Withrespect to the uncertainty of the model simulation,monthly and daily runoff data were obtained fromKenswat Hydrological Station and calibrated and verifiedby conducting a parameter sensitivity analysis andparameter optimization calibration. A hydrologicalmodel of the MRB was thus established, and thefollowing conclusions were drawn:(1) Through the parameter sensitivity analysis and
parameter calibration verification of the temperatureindex method, the SWAT model was shown to havegood adaptability for use in the MRB. However, withrespect to the impact of climate change and humanfactors within the MRB, and the potentially large errorsin the summers of some years that occurred in themodel, it will be necessary to conduct further studies tocontinuously verify whether the CMADS data andSWAT model are suitable for use in this region.
(2) Runoff at Kenswat Hydrological Station in the MRBwas located and simulated using CMADS data andthe SWAT model. These results show that the SWATmodel driven by CMADS can well reproduce thehydrological process of the MRB on monthly anddaily scales.
(3) CMADS can provide continuous temporal and high-spatial-resolution meteorological data for simulatingthe water resources in the MRB, without the need formeteorological data. However, compared with tradi-tional weather stations, the weather data providedby CMADS require adjustment prior to modeling.
In summary, CMADS data and the SWAT model wereused to simulate the hydrology of the MRB, which has a
CMADS + SWAT: hydrological model of Manas River Basin 955
high glacier recharge rate. The results show that CMADSdata can serve as a reference for studying the use of theSWAT model in evaluating water resources in cold anddry areas that lack meteorological data.
Acknowledgments: This research was funded by theNational Natural Science Foundation of China (Grant No.U1803244); Key Technologies Research and DevelopmentProgram (Grant No. 2017YFC0404303); XinjiangProduction and Construction Corps (Grant Nos.2018CB023, CZ027204, 2018AB027, and 2018BC007);and Shihezi University (Grant Nos. CXRC201801 andRCZK2018C22). This work was also supported by theTalent Program of Xinjiang Production and ConstructionCorps and Key Laboratory of Modern Water-SavingIrrigation.
Author contributions: XG, GY, and LZ conceptualized theresearch; methodology was outlined by GY, LZ, and XG;PL and XL worked with the software; the validation wasdone by XG, GY, and XH; formal analysis was carried outby BL and XL; the study was investigated by BL; thenecessary resources were provided by LX; the datacuration was done by GY; the original draft was preparedby XG, GY, and XH; the visualization was created by LAand YG; XH, YG, and XG supervised the process; thewhole project was administrated by XH; and fundingwas managed by XH. All authors have read and agreedto the publication of this manuscript.,
Conflicts of interest: The authors declare no conflict ofinterest.
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