The China Meteorological Assimilation Driving Datasets for the SWAT Model ( CMADS ) Application in China : A Case Study in Heihe River Basin

Large-scale hydrological modeling in China is challenging given the sparse meteorological stations and large uncertainties associated with atmospheric forcing data.Here we introduce the development and use of the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) in the Heihe River Basin(HRB) for improving hydrologic modeling, by leveraging the datasets from the China Meteorological Administration Land Data Assimilation System (CLDAS)(including climate data from nearly 40000 area encryption stations, 2700 national automatic weather stations, FengYun (FY) 2 satellite and radar stations). CMADS uses the Space Time Multiscale Analysis System (STMAS) to fuse data based on ECWMF ambient field and ensure data accuracy. In addition, compared with CLDAS, CMADS includes relative humidity and climate data of varied resolutions to drive hydrological models such as the Soil and Water Assessment Tool (SWAT) model. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 3 February 2017 doi:10.20944/preprints201612.0091.v2

Although the weather patterns and assimilation systems used by these reanalysis datasets are different from each other, the common point is that the patterns and assimilation systems are business mature numerical forecasting models.For example, NCEP-NCAR (NCEP1) uses business numerical model GSM (T62) and SSI assimilation system on January, 1995; NCEP-DOE (NECP2) uses basically the same model and assimilation system with NCEP1, but does some improvements;ERA-40 uses integration forecasting system IFS (T159) by ECMWF and adopts improved 3DVar technology to assimilate data (ERA-40 did not refresh any more after August 2002); ERA-Interim uses ECMWF integration forecasting system IFS (T255) and 4DVAR assimilation system, which is a continuous product of ERA-40.Compared with ERA-40, ERA-Interim not only improves on horizontal resolution (T159->T255), but also adopts more advanced 4DVar technology.JRA-25 uses T106 global spectrum mode (JMA 2002), whose assimilation system is developed based on 3DVar technology.In addition, NCEP builds real-time updated CFSR global reanalysis dataset.Different from the past, CFSR adopts global high resolution atmosphere-ocean-land-ice coupled system (whose atmosphere mode is GFS, ocean mode is MOM4, land mode is Noah).Meanwhile the assimilation system of CFSR is also discrete (whose atmosphere mode is GIS-3DVAR, ocean-ice mode is GODAS, and land mode is GLDAS).NASA also builds MERRA global reanalysis data, which uses GEOS-5 ADAS assimilation system based on 3DVar GIS technology (whose horizontal resolution is 38km (T382)) and improves a lot in the field of water cycle simulation.Furthermore, JMA-55 reanalysis dataset adopts TL319L60 (about 60km) on December, 2009, 4DVar assimilation system (T106 inner model) and offline SiB land model (using 3-hour atmospheric forcing data).
Many scientists carried out reliability analysis on the above reanalysis data and obtained a lot of useful conclusions.However, different reanalysis data has its own advantages and disadvantages, and any kind of data does not have the same performance in different areas and time periods.For example, Zhao Tianbao et al(Zhao et al.,2006)compared and analyzed ERA-40 and NCEP-2 and found that the confidence level of ERA-40 was higher than NCEP-2.While Huang Gang(Huang et al.,2006)studiedChina sounding data and pointed out that before 1970s when researching East-Asia climate inter decadal variation, ERA-40 was better; after 1970s, on the description of troposphere geopotential height and temperature in Inner Mongolia and North China, NCEP/MCAR was better than ERA-40.Compared with the two reanalysis plans, the 6-hour global precipitation distribution and quantity produced by JRA-25 and JCDAS are the best in time and space.But due to the low resolution of JRA-25, it is not suitable for mesoscale analysis (Simmons et al., 2000).
Najafi et al (Najafi et al.,2012) used CSFR dataset to drive the soil moisture model (SAC-SMA) and analyzed runoff in Donghe River basin with water supply from snow fall and melting.Fuka et al (Fuka et al., 2014) used precipitation and temperature date from the CFSR dataset (http://cfs.ncep.noaa.gov/cfsr/) to drive SWAT model and found that the SWAT simulations driven by CSFR were better than that with TWS.
Smith et al (Smith et al., 2013) compared the water balance relations of ERA-Interim、 CSFR、MERRA between land surface and atmosphere and concluded that the above datasets all could reflect seasonal changes of water balance well.Lavers et al (Lavers et al.,2012) used ERA-Interim, CFSR, NCEP-NCAR and MERRA to study the relation between winter flood and large-scale climate, demonstrating that all these data could reflect a consistent relationship between the two.Quadro et al (Quadro et al., 2013) found that CSFR performed better in simulating South America water balance compared with NCEP Reanalysis II (NCEP-2) and MERRA.Besides, Wei et al (Wei et al., 2013) simulated three cyclones going through Taiwan Strait by using CFSR and TRMM.Although the CFSR dataset is widely used, we found that this datasethaslarge uncertaintiesin precipitation frequency and intensity although large-scale precipitation climatology is captured well (Higgins et al., 2010, Silva et al., 2011).Precipitation is one of the most important factors in the processes of generating runoff.However, due to the lack of reliable observations, the usability and accuracy of CFSR dataset in China are not satisfactory.
Because of the coarse resolution, global climate modes (GCMs) are unable to be completely applied in regional climate pattern, which is also stated in the IPCC fourth report (Gerald et al., 2007).Studies show that GCMs cannot be directly applied in the assessment of regional-scale future hydrological changes (Wood et al., 2004).Given the regional climate modes have higher spatial-temporal resolutions than GCMs,  Heihe River Basin (E98°34′-101°09′N37°43′-39°06′) is the second largest inland river of China, originating from Qilian Mountain in the South and flowing out of the mountain at Ying Luoxia hydrological station.The Heihe River Basin has a higher latitude in the south than in the north, higher in the west than in the east.This basin is characterized by scare precipitation, adequate sunshine and large diurnal temperature range.The total catchment area is 9973km 2 with an average elevation between 1980.629m and 4029.827m(Figure 1).Heihe River Basin has an average annual precipitation of 300mm-700mm and an average annual temperature between -3℃ and 7℃.The mountain area, whose altitude is above 4500m, is covered with ice and snow and the altitude of snow line increases from east to west.Due to the large amount of precipitation and glacier in Qilian Mountain as well as its mountainous underlying surface and good vegetation distribution, Qilian Mountain area is the upstream area of the whole Heihe River Basin.The multi annual average runoff at Ying Luoxia station is 1.58 billion m 3 .However, the annual runoff changes less in Heihe River Basin, whose maximum and minimum annual runoff are usually smaller than 3.There is large intra-seasonal variability with May and June accounting for 12%-25% of the annual runoff and July and September for 50%-55% of the annual runoff.The financial revenue mainly depends on animal husbandry.This area has abundant water resources and developed irrigation facilities.

Material and methodology
This study used the SWAT model to illustrate the added value of the CMADS data with Heihe River Basin as the study region.The observed stream flows from three hydrological stations in this area were obtained from Heihe River Basin Authority.
Then three simulations were conducted with SWAT model driven by CMADS, CFSR and TWS, respectively.Finally, the simulated results were compared with the observations.

Digital elevation model (DEM)
The spatial input data of SWAT model includes DEM data, river network data and land use data.DEM data used in this study is the SRTM -(90m) DEM, which is archived from CGIAR-CSI SRTM 90 database(http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp),(Jarvis et al.,2008).
To ensure the consistency of model, this study set spatial resolution of DEM, soil and land use as 1km, and set projection coordinates as Beijing_1954_GK_Zone_17N.

Hydrological verification data
This study used daily stream flow observations of ZhaMashenke, Qilian Mountain and Ying Luoxia hydrological stations.The details of each station are shown in Table 1.

2.3Atmospheric forcing input data
The study chooses three kinds of datasets as the atmospheric forcing data of SWAT model (shown in Table 2) and Heihe River Basin has four national basic meteorological observation stations (i.e.Tuo Le (T1), Ye Niugou (T3), Qilian (T4) and Zhang Ye (T2)).The observation stations can be regarded as the most authoritative results in space.In order to assess the accuracy of CFSR and CMADS in the basin, we will analyze the interpolation results of CFSR and CMADS in location T1-T4 of TWS in the following pages.For TWS, The emphasis of this research is to obtain daily average air pressure, average wind speed, average temperature, average relative humidity, daily maximum/minimum temperature, daily precipitation and sunshine duration.The missing values of the observations are filled by the SWAT  (Neitsch et al., 2009) and then adopts the centroid method to interpolate station elements (Andersson et al.,2012).

Introduction of CFSR dataset
The CFSR dataset is provided by American National Environmental Forecasting Center (Saha et al.,2010), which is an global reanalysis dataset with high resolution covering between 98°34′-101°09′E and 37°43′-39°06′N (atmospheric horizontal resolution is T382, about 38km, 64 floors in vertical).There are 15 interpolating points (CF1 -CF15) in the study region (the distribution of interpolating points is shown in Figure 4).The space resolution is 0.313°*0.313°and the temporal resolution is daily time step from 1 st Jan, 2008 to 31th Dec, 2013, including precipitation, maximum/minimum temperature, wind speed, relative humidity and solar radiation.
The official website of SWAT model also recommends using CFSR dataset to drive and build model globally.However, the effectiveness of driving SWAT model by CFSR dataset has not been verified systematically in China.

Introduction of CMADS dataset
CMADS is a new dataset developed by this study, which is based on CLDAS data assimilation technology.CLDAS assimilation system fuses multi-source data such as satellite observation, land surface observation and numerical products (Meng et al., 2017, Shi et al., 2008, Shi et al., 2011, Zhang et al., 2013).This study built CMADS dataset (temporal resolution: day by day; spatial resolution: 1/3°; time scale: 2008-2013) by using data loop nesting, resampling and bilinear interpolation methods.
The dataset was formatted to be consistent with SWAT model requirement.In order to verify the applicability of CMADS in China, we used bilinear interpolation method to compare CMADS dataset with elements from national Thirdly, the bias distribution of CMADS relative humidity in China (Figure 2e) is between -2% and -6%, while in northwestern, southwestern and northern China, the bias is mainly negative (between 1% and -4%).The distribution of root-mean-square error of relative humidity in China is displayed in Figure 3f, showing that the error is around 3%~9% in most areas of China, while some stations in Xinjiang area, the   Through the above analysis we find that there are few meteorological stations in western China, which can not satisfy large-scale hydrological simulation.Compared to TWS, CMADS and CFSR exhibit obvious advantages.This study used 11 and 15 meteorological stations from CMADS and CFSR dataset respectively, while only 4 TWS (T1-T4) were available in this basin.Notably, we found that there were missing values in each station, with the missing ratio up to 3.395%, 8.762%, 4.654% and 7.448% in Tuo Le(T1), Zhang Ye(T2), Ye Niugou(T3), Qilian(T4), respectively.

CMADS evaluation in China
However, there is no missing value of CMADS and CFSR dataset driven by SWAT model, which is the advantage of assimilation dataset compared to TWS.  3.  Negative bias shows the value underestimated TWS observations, and the positive bias shows the value over-estimated observations.
The SWAT model was driven with the above datasets (TWS, CMADS and CFSR) to further investigate the hydrological performances of CMADS.

Introduction of SWAT model
The SWAT model is a semi-distributed model, which can simulate basin-scale hydrology, sediment and non-point source pollution (Neitsch et al., 2009).Different from other hydrological models, SWAT model separates one basin into several HRUs and set areas with the same land use, soil category and gradient as one independent HRU.SWAT model has been widely used throughout the world since publication (Zhang et al., 2013).

3SWAT model settings
The study area is divided into 24 sub-basins based on DEM.Then SWAT model divides each sub-basin into several HRUs.In SWAT model, water balance of each HRU is calculated based on surface runoff, interflow, base flow, infiltration, river period is from 2011 to 2013.

Sensitivity analysis
The study used SWAT-CUP software developed by EWAGE (Abbaspour et al., 2007b) to analyze and calibrate parameters of three modes.SUFI-2 algorithm (Abbaspour et al., 2004, Abbaspour et al., 2007a) was chosen to run SWAT-CUP Software (Abbaspour, 2011), including model calibration, validation, sensitivity analysis and uncertainty analysis.The algorithm involves all kinds of uncertainties, such as parameters, conceptual models, input and so on, in order to reach a 95% prediction uncertainty (95PPU) for the majority of measured data.The 95PPU is calculated at the 2.5% and 97.5% levels of the cumulative distribution of an output variable obtained through Latin hypercube sampling.Sensitivity analysis was used to analyze which parameter or which kind of parameters was most sensitive.In this study, we analyzed parameters related to runoff (26 parameters in total).After that we obtained the rank of sensitive parameters driven by three kinds of meteorological data as shown in Table 4.

Model calibration
The study chose the first 14 sensitive parameters for calibration according to significant parameters and simulated conditions (Abbaspour, et al., 2015) between 2009 and 2010 and verified the model performance from 2011 to 2013 driven by different datasets.After being calibrated at the monthly scale, we carried out parameter calibration with daily data and verified daily runoff.During this process, we firstly considered the ratio between annual evaporation and runoff, and then ensured a reasonable level of simulated total evaporation, precipitation and runoff.
Besides, when calibrating three hydrological stations (Ying Luoxia, ZhaMashenke and Qilian Mountain), we calibrated Qilian Mountain station at first, then ZhaMashenke station and finally Ying Luoxia station.This is because compared with the other two stations, Ying Luoxia station locates in the downstream, and then the accurate calibration of upstream parameters can be a good foundation for downstream calibration.
It is found that there is difference between the best parameters of three modes.

3.3Model assessment
The study usestwo evaluation index: Nash-Sutcliffe Efficiency (NSE) and determination efficiency (R 2 ) (Nash et al., 1970), which are both widely used to assess model performance.
Nash-Sutcliffe Efficiency is a normal statistic equation, which reflects fitting degree between observed data and simulated results (Schaefli et al., 2007).NSE can be calculated with equation ( 1): Where Q is runoff variable, m Q and s Q represent runoff observed value and simulated value respectively and m Q is runoff average observed value.NSE ranges from -∞ to 1.When NSE equals 1, it denotes that observed data fits well with simulated data.When NSE is between 0.1 and 1, indicating simulation results can be accepted.When NSE is smaller than 0, we deem that simulation result is bad.
Determination efficiency: it reflects the correlation degree between measured variables.R 2 can be calculated by equation ( 2 Where m Q and s Q represent runoff observed value and simulated value respectively, i is the ith simulated or observed value. Some studies choose R2＞0.5andNSE＞0.5 as the satisfactory criterion of SWAT model (Santhi et al.,2001), while others think that NSE＞0.4 can also be the satisfactory criterion (Ahmad et al.,2011).This study adoptes evaluation criterion by Moriasi et al (Moriasi et al., 2007).Namely, during model calibration period, if monthly-scale simulation result NSE≥ 0.65 or daily-scale result NSE≥ 0. 5, then the results can be acceptable (Santhi et al., 2001).(Moriasi et al.,2007) and Santhi (Santhi et al.,2001), it is found that at the monthly-scale, CMADS+SWAT mode and TWS+SWAT mode both achieved satisfactory performance at three stations (shown in Table 5).At the monthly-scale (Figure 7, Figure 8 and Figure 9), the simulation results of CMADS+SWAT mode (Figure 8A) were better than the results of TWS+SWAT mode at ZhaMashenke station (Figure 8B).Due to no meteorological stations at ZhaMashenke, CMADS dataset had greater advantages than TWS dataset.However, the monthly simulation results of CMADS+SWAT mode were slightly over-estimated compared with TWS+SWAT mode at No.2 sub-basin (Ying Luoxia), which might be caused by more precipitation (Figure 16C) of CMADS+SWAT mode (May-Oct each year) than TWS+SWAT mode.The over-estimation came from centroid interpolation method and elevation with secondary adjustment of SWAT model itself and meteorological In addition, we found that the simulation effects of CFSR+SWAT model at three stations were unsatisfactory.Runoff was overestimated compared with observed data with the largest NSE efficiency coefficient being 0.49 (Figure 7C, 8C, 9C).
Furthermore, runoff overestimation existed during the increasing runoff period from October to August next year at all three sub-basins.In September each year, simulation results of CFSR+SWAT mode were underestimated.Because the distribution of precipitation within the year was overestimated, the base flow was also overestimated each year (Figure 7C, 8C, 9C).This was because CFSR data was not corrected against observed meteorological stations in China, then precipitation was overestimated.Although runoff was simulated well after model parameter calibration, SFSR+SWAT mode tendedto have overestimated evaporation (Figure 5), which might also be related to the underestimation of maximum temperature (Figure 6).Due to the over estimation of CFSR precipitation, evaporation exceeded local annual evaporation greatly when calibrating CFSR+SWAT mode (Figure 14).After monthly-scale calibration at three sub-basins (Figure 7 to Figure 9), we introduced the optimum parameters into SWAT model to continue calibrating and adjusting three modes at daily scale.Results indicated that similar to monthly simulation, both CMADS+SWAT mode and TWS+SWAT mode performed well at daily scale (Table 5, Figure 10, Figure 11 and Figure 12).Runoff simulation results of the above two modes exhibiteda good consistency in the daily hydrological maps of three stations.However, the simulated peak value of TWS+SWAT mode were underestimated both at Qilian Mountain station (Figure 10B) and ZhaMashenke station (Figure 11B), while the peak was slightly overestimated at Ying Luoxia station.
The daily simulated results of CMADS+SWAT mode at Qilian Mountain (NS= 0.58, R 2 = 0.66) could be accepted.Meanwhile, models showed satisfactory performance at Ying Luoxia (NS= 0.77, R 2 = 0.80) and ZhaMashenke (NS=0.75,R 2 =0.78).At mode were higher than observed results and had larger amplitude.However, the simulation results were better than TWS+SWAT mode at other periods.Furthermore, we also found that in terms of peak simulation, the accuracy of CMADS+SWAT mode at Qilian Mountain and ZhaMashenke was higher than that of TWS+SWAT mode and CFSR+SWAT mode.Besides, the simulation of CMADS+SWAT mode agreed better with observed data than the other two modes, especially at Qilian Mountain and ZhaMashenke control stations.All of these indicated that compared with CMADS data, traditional meteorological stations could not capture spatial heterogeneity based on limited stations, which limited its application in simulating basin water balance.
By comparing monthly-scale simulation results with daily-scale simulation results of SWAT model driven by three kinds of datasets (TWS, CSFR and CMADS),we found that CMADS+SWAT mode could simulate historical process of Heihe River Basin runoff well, while CFSR that has been used widely around the world performed bad. Figure 5, Figure 6 and Table 3 gave some verification.

Differences caused by water balance
Water balance analysis is an important tool for evaluating water resources in the world.It helps us to understand quality differences of different forcing data (Zhang et al., 2012, Silva et al., 2011).After analyzing water balance components in three sub-basins of Heihe River Basin by using three modes we found that the overestimated CFSR precipitation as inputs of SWAT model leaded to larger evaporation and higher estimation of water balance than the other two kinds of datasets (Figure 14).respectively, while only 25.5% of precipitation of CFSR+SWAT mode was partitioned to runoff.After comparison we found that the proportion of side flow, subsurface flow and lateral seepage flow in the runoff generation period were higher for CFSR+SWAT mode than for CMADS+SWAT mode and TWS+SWAT mode.The proportion of side flow, subsurface flow and lateral seepage flow in total runoff generation for CFSR+SWAT mode was 44.2%, 39.9% and 44.17%, respectively.
Results also indicated that CFSR+SWAT mode with overestimated precipitation produced smaller soil moisture compared with TWS+SWAT and CMADS+SWAT mode.This might be related to large evaporation of CFSR+SWAT mode.On the contrary, the actual evapotranspiration of CFSR+SWAT mode was much larger than the other two modes (annual average evapotranspiration of CFSR+SWAT mode was 498.27mm, while for CMADS+SWAT and TWS+SWAT mode, the annual average evapotranspiration were 245.18mm and 253.09mm respectively).However, statistics showed that annual average evapotranspiration in Heihe River mountain area and main stream area is around 279.3~294.1mm(Yin et al., 2013).In order to fit water balance of CFSR+SWAT mode with observed runoff, it caused overestimated precipitation of CFSR+SWAT mode resulting in increasing evaporation, and then caused soil moisture to be lower.In conclusion, although water balance of CFSR+SWAT mode is similar to the other two modes, poor performance of evaporation and precipitation decrease the qualityof CFSR in Heihe River basin greatly.To quantitatively investigate how SWAT model built-in evaluation module influences precipitation distribution, we analyzed precipitation of three sub-basins with or without evaluation module (Figure 16).Where "-E" represents precipitation after evaluation adjustment of SWAT model and "-NE" represents precipitation without evaluation adjustment.We found that there existed some consistent relations between precipitation distribution (Figure 16) and previous water balance (Figure 14).
Precipitation of CFSR dataset at three natural sub-basins exceeded TWS dataset and CMADS dataset.Precipitation of CFSR dataset at three sub-basins were 526.42mm、 1012.982mm and 1053.66mmrespectively, which were much larger than local multi-annual average precipitation (459.7mm)(Yin et al.,2013).From Figure 12 we found that compared with TWS, precipitation peak value of CFSR and CMADS was more concentrated, especially in Qilian Mountain basin (Figure 16a).After evaluation module was applied in SWAT model, there was a certain increaseof precipitation, which gradually increased with close to July.Besides, precipitation of CMADS+SWAT mode in Ying Luoxia between May and September was about 39.7% higher than that of TWS+SWAT mode (Figure 16C).It caused bigger overestimated monthly runoff of CMADS+SWAT mode at Ying Luoxia sub-basin than TWS+SWAT mode.However, R 2 reached 0.8 in daily runoff simulation of CMADS+SWAT mode, which exceeded that of TWS+SWAT mode (Table 5).It is also found that if weather stations are far away from hydrological stations or the area lacks of weather stations, CMADS+SWAT mode would achieve better results.Furthermore, Figure 15B showed that precipitation of CMADS+SWAT mode was smaller than TWS+SWAT mode between April and June, August and October; while fitting results of simulated peak value and base flow of CMADS+SWAT mode in ZhaMashenke sub-basin (Figure 8a and Figure 11a) were better than TWS+SWAT mode (Figure 8b, Figure 11b, Table 5 and Figure 13b).
Simulation results of CMADS+SWAT mode and TWS+SWAT mode were both satisfactory in Qilian Mountain sub-basin.

Relative elements analysis of CMADS driving SWAT model in Heihe River Basin
The  July and September.Comparing Figure 6 and Figure 16 we found that precipitation reached maximum between July and September in Heihe River Basin. Figure 17(a-f) also indicated that larger WYLD occurred more often in the middle and high altitude.
In addition, WYLD bias was large in different sub-basins, indicating that there were more WYLD in the high altitude than in the low/middle altitude (Figure 18).This might be caused by distribution of precipitation in the mountains as well as snowmelt in cold highland area.

Discussion and Conclusions
The study used CMADS, TWS and CFSR datasets to force the SWAT model and evaluated their performance for stream flow simulation in the Heihe River basin.It is found that CFSR overestimates precipitation, especially in summer, but underestimates mean annual precipitation.In addition, the CMADS data performes better than CFSR regarding both accuracy and spatial resolution, as CMADS introduces advanced assimilation technology and is bias corrected through China's national automatic observation stations.For TWS, it does not perform well in China especially in Western China where climate stations are sparse.
For a large river basin, quantitative analysis of water balance components is essential for supporting ecological and hydrological managements.TWS data often cannot satisfy current large-scale hydrological modeling needs in regions with sparse observations.Therefore, when there are scarce or even no weather stations in the basin, CMADS will be a valuable source to provide atmospheric forcing data for hydrological modeling exercises.Another advantage of CMADS compared with TWS is that it contains complete climate forcing data over a specific time period without missing values, which helps to save much time spent on data quality assurance.
Although we only demonstrate the value of CMADS for improving SWAT model, it can also be easily reformatted for other hydrological models.

Fig. 1 .
Fig. 1.Distribution of meteorological stations and hydrological stations in the study area

Preprints
Fig. 2. Evaluation of CMADS dataset in China

Fig. 3 .Fig. 4 .
Fig. 3.The range of CMADS V1.0 dataset and the space position in this study

Fig. 5 .Fig. 6 .
Fig. 5.The cumulative average monthly (from year 2009 to 2013) rainfall of TWS, CMADS and CFSR at four sites (T1-T4) -scale and monthly-scale runoff simulation results of three kinds of modes at three sub-basins This study used three different modes (CMADS+SWAT mode, CFSR+SWAT mode and TWS+SWAT mode) to obtain monthly and daily runoff at three stations (Qilian Mountains, ZhaMashenke and Ying Luoxia).Based onthe model evaluation index by Moriasi Fig. 7.Simulation results of monthly average runoff of three different modes at Qilian Mountain control station (2009-2013)

Figure 14
Figure 14 indicated that precipitation distribution of CFSR in three sub-basins

Fig. 15 .
Fig. 15.Bias distribution of annual average precipitation of CMADS, CFSR and TWS dataset in different sub-basinsPrecipitation is an important factor controlling watershed runoff process.In order

Fig. 17 .Fig. 18 .
Fig. 17. Figure of space-time relationships between snowmelt and soil humidity of CMADS+SWAT mode Figure 17 (a-e) showed the spatial distribution of snowmelt in Heihe River Basin right hand showed the changing relations between snowmelt and WYLD in three basins (Qilian Mountain, ZhaMashenke and Ying Luoxia).Analysis indicated that WYLD of Heihe River Basin would reach the peak value between July and September.As shown in Figure18a-f, snowmelt contributed little to WYLD between

3 February 2017 doi:10.20944/preprints201612.0091.v2
Nevertheless, the CMADS dataset consists of two formats (i.e..dbfand .txt),which can be easily converted for use in other hydrological models.The first version of Preprints (www.preprints.org)| NOT PEER-REVIEWED | Posted: resolution ratio) as one of the forces of SWAT model.The spatial range of CMADS lies between 0N and 65N, 60E and 160E, consisting of 300*195 grid points.Totally, 58500 stations are used for analysis in East Asia area and each station includes daily average temperature, daily maximum/minimum temperature, daily accumulative precipitation, daily average solar radiation, daily average air pressure and daily average wind speed.
well.Firstly, the temperature bias ranges from-0.5K to 0.5K in China and in extremely specific stations occurs large errors such as -4K error in Qinghai-Tibet Plateau.The total root-mean-square error of CMADS land surface temperature in China ranges from 0.5K to2.0K in western China (especially in Xinjiang and Tibet), while in South and Southeast China the error is majorly smaller than 0.5K.Secondly, from figure2cwe can see that the bias of air pressure is smaller in east than in west, ranging from 0Hpa ta 5Hpa in eastern China, Yangtze River and Huai River area; while the bias is between 0Hpa and 17Hpa in Southwest and Northwest China.In most areas of China the root-mean-square error of air pressure is under 11Hpa (under 3Hpa in northwestern region, under 5Hpa in northeastern, northern and eastern China).
In conclusion, after evaluating the accuracy of CMADS data in China against national automatic stations, we can see that CMADS dataset match ES the observations well.In this study, SWAT model requires 11 interpolated stations (CM1-CM11) of CMADS V1.0 (resolution ration: 0.333°).The distribution of multi-annual total precipitation and maximum/minimum temperature of CMADS in Ying Luoxia River Basin is shown in Figure4.The study emphasizes on verifying the utility of CMADS dataset for driving hydrological model in China.Information of the three different meteorological forcing datasets is shown in Table2.

3 February 2017 doi:10.20944/preprints201612.0091.v2
(Wood et al.,2004)tself.Given daily input data, all three modes adopt Soil Conservation Service's curve (SCS) to calculate surface runoff and SCS curve, which is a non-linear function between precipitation and initial loss.Surface runoff is calculated in each HRU and finally routes into the main channel.Finally, we choose river storage method based on continuity equation to calculate main channel water.Based on Centriod interpolation principle, SWAT model can interpolate spatial discrete meteorological data at single point into the whole basin(Wood et al.,2004).This study chooses the simulation period as2008-2013, withyear 2008for model spin-up.Here, the calibration period is from 2009 to 2010, while the verification Preprints (www.preprints.org)| NOT PEER-REVIEWED | Posted:

Table 4
is the final value and sensitive ranking of model parameters.Analysis shows that parameter sensitivity rank has close relation with atmospheric forcing data, model itself and observed data.

scale runoff simulation results of three kinds of modes at three sub-basins in 5 years
After parameter calibration, the water yield (WYLD) of CFSR+SWAT mode