Simulation effect evaluation of single-outlet and multi-outlet calibration of Soil and Water Assessment Tool model driven by Climate Forecast System Reanalysis data and ground-based meteorological station data – a case study in a Yellow River source

It is the research hotspot in the field of hydrology to apply the climate model and its downscaling data into hydrological simulations, and it is very important to evaluate the accuracy of its data. In this study, the accuracy of Climate Forecast System Reanalysis (CFSR) was evaluated from two perspectives: statistical evaluation and hydrological evaluation. In the hydrological evaluation, the applicability of CFSR in the Soil and Water Assessment Tool (SWAT) model of the Yellow River source area was studied. The results show that CFSR temperature data at the source of the Yellow River is consistent with the measured temperature data, and CFSR precipitation data overestimates precipitation. In the Yellow River source runoff simulation, the SWAT model driven by CFSR can obtain satisfactory simulation results. It does not reduce the simulation accuracy at the total outlet of the basin under the multi-outlet calibrationmethod. It also considers the spatial differences of hydrological characteristics of each sub-basin and improve the simulation accuracy of the sub-basin simulation.


INTRODUCTION
The global climate system has been undergoing significant changes since the 1900s, and the increasingly intense human activities affect the process of hydrological cycles to varying degrees (Yang et al. ). Therefore, the hydrological cycle and the vulnerability of water resources against the backdrop of climate changes have become a focus of hydrological research. Hydrological modeling, which is based on mathematical principles and hydrometeorological data to simulate the complex hydrological cycles in nature, is a necessary and important method for hydrological research (Ouermi et al. ). Building a hydrological model driven by groundbased meteorological station (GMS) can provide a strong support for simulation of regional water cycles. High-precision and high-quality meteorological data can reduce the uncertainty of the model, thereby increasing the accuracy of simulation and prediction results (Barnett et al. ).
Conventional methods of hydrological research use the meteorological data collected from GMS to drive the hydrological model to simulate hydrological cycles and perform flood forecasting (Arnold et al. ; Golmohammadi et al.). However, limited by financial investment and monitoring technology, GMS is rare and unevenly distributed. In remote areas and inaccessible rivers, the lack of GMS leads to challenges in collecting effective meteorological data and subsequent hydrological modelling. To solve this problem, scholars all over the world have used the output data of the climate models and their downscaling data in large-scale hydrological simulations and achieved good results (Smith & Kummerow ). However, due to the low resolution of climate models and reanalysis data, large deviations occur when these data are applied to simulation of regional climate change with complex underlying surface features (Liu et al. ). Therefore, in hydrological response research, it is important to study the application effect and sensitivity of different meteorological reanalysis data and climate model products in the Soil and Water Assessment Tool (SWAT) model. At present, the China Meteorological Assimilation Driving Datasets (CMADS) for the SWAT model established by Meng Xianyong is widely used in China (Meng & Wang ). The application of this data set in several basins in China shows that its simulation results are better than those achieved by the conventional method, which is based on data from GMS.
Nowadays, the CMADS has become a mature data set for hydrological studies. On the other hand, the Climate Forecast System Reanalysis (CFSR) data set, developed by the National Centers for Environmental Information (NCEI) of the U.S., has shown in recent years that its simulation effect is as good or better than that of data from GMS. However, some scholars point out that this data set fails to deliver good simulation results in certain regions, especially the tropical basins. They suggest that CFSR data be used in regions where conventional GMS are available.
The SWAT model is a semi-distributed hydrological model developed by the U.S. Department of Agriculture.
It is mainly used to simulate and evaluate the hydrological situation and water quality changes of basins under various management measures and climate changes. To judge whether the SWAT model is applicable to a basin, it is necessary to calibrate the parameters of the model first.
At present, the SWAT model parameter calibration methods mainly include single-station calibration and multi-station calibration. The method of single-station calibration is to set a total basin outlet, and the parameters are assumed the same throughout the whole basin (Thavhana et al. ). This method ignores the uniqueness of subbasins, and thus the calibrated parameters cannot describe the characteristics of the corresponding sub-basin. On the other hand, the multi-station calibration method can show the spatial differences among sub-basins and improve the simulation accuracy of each sub-basin without impairing the simulation accuracy at the whole basin (Yu et al.

).
To this end, this paper takes the source of the Yellow River as the research area and evaluates the CFSR data set from two aspects: statistical evaluation and hydrological evaluation. In hydrological evaluation, CFSR data is used to drive the SWAT model. To reasonably evaluate product characteristics and reduce the impact of hydrological model calibration methods on product performance, runoff is simulated under singleoutlet and multiple-outlet scenarios, and the simulation accuracy is evaluated.

RESEARCH AREA AND MATERIALS
Overview of the research area

Spatial data
The spatial data, digital elevation digital elevation model (DEM) data, land use and soil data, and DEM data required for the present study were obtained from ASTER GDEM

Hydrometeorological data
In hydrological models, meteorological data input is the key factor affecting the result of runoff simulation. GMS required for the SWAT model includes daily precipitation, temperature, relative humidity, wind speed, and solar radiation (Zhang et al. ). In this research, SWAT models were driven by two data sets: GMS data set and CFSR data set.
The GMS data set is derived from the China Meteorological Data Network. GMS data from nine GMS, including Xinghai, Dari, and Henan, which are located in the source area of the Yellow River, were selected as input into the SWAT model with a time span from 1997 to 2013. On the other hand, CFSR data, a third-generation analytical product developed by the NCEI of the US, is a global, highly variable, coupled atmospheric-ocean-land-sea surface-sea ice system designed to provide best estimates of these coupling states over the period. In the present study, data from 260 CFSR stations in the source area ranging from 95 30 0 -103 30″ E and 32 10 0 -36 05 0 N were used, with a spatial resolution of 38 km and a time span from 1997 to 2013.
where SW t represents the final soil water content (mm), SW 0 represents the initial soil water content, t is time (day), R i represents precipitation (mm), Q i represents the surface runoff (mm), ET i represents evaporation (mm), and W i and The correlation coefficient (R 2 ), the Nash-Sutcliffe model efficiency (NSE) coefficient and Percent Bias (PBIAS) were selected to evaluate the performance of the SWAT model in simulating runoffs. The specific calculation formula is as follows: where O i is the measured runoff sequence, S i is the simulated runoff sequence, Ōis the measured average runoff, S is the simulated average runoff, and n is the series length.
It is generally considered that if R 2 > 0.6, NSE > 0.5, and PBIAS is <25%, the performance of the model to simulate daily runoff is satisfactory. In monthly runoff simulation, if R 2 > 0.7, NSE > 0.55 and PBIAS is <25%, the performance of the monthly runoff model is considered to be satisfactory (Moriasi et al. ).

Evaluation on CFSR data
In order to fully quantify the accuracy of CFSR data, continuous statistical indexes were used to assess the accuracy of monthly temperature and precipitation of CFSR data set. The correlation coefficient (CC), mean error (ME), where G i is the temperature/precipitation data in CFSR; O i is the temperature/precipitation data in GMS; Ḡ is the average temperature/precipitation data in CFSR; Ōis the average temperature/precipitation data in GMS; and n is the data length.

Simulation method
Single-outlet simulation According to t-stat and P-value, the greater the absolute value of t-stat and the closer the P-value to 0, the more sensitive the parameters are to the model. In this study, 12 parameters with high sensitivity are selected for model calibration (Table 1).

Valuation of temperature and precipitation data
In order to assess the accuracy of CFSR temperature data in the source area of the Yellow River, GMS data were used to verify the accuracy of CFSR data. In this paper, hydrometeorological data from nine GMSs were used to drive the SWAT model. We chose four representative stations, and selected CFSR stations with similar coordinates based on their latitude and longitude for evaluation.
The CC, RMSE, ME, and MBE were used as evaluation indexes. The specific evaluation results are shown in Table 2. As for precipitation data, the CC values of the daily CFSR data at all four stations were more than 0.4, which indicates that a weak correlation between CFSR precipitation data and GMS precipitation data. The mean values of ME and MBE of CFSR data from four stations are <0, which indicates that CFSR data overestimated four meteorological precipitation data in the source area of the Yellow River.

Single-outlet simulation
According to the actual measured hydrological data sequence and in order to make all the hydrological pro- Using the combination of the SUFI-2 optimization algorithm  in SWAT-CUP2012 and manual calibration, the 12 main parameters in the model were calibrated. Figure 2 shows the runoff simulation results during the model calibration period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013). According to existing studies, the retention and regulation of the two lakes make the annual runoff distribution of the Yellow River, along with the source of the Yellow River, more uniform (Li et al. ). However, the study found that the runoff from the Huangheyan Hydrological Station only accounted for 19%, 6%, 5%, and 4% from Jimai, Maqu, Jungong, and Tangnaihai, respectively. Therefore, the influence from Zhaling Lake and Eling Lake on the runoff to the lower Yellow River gradually decreases.
In general, the SWAT model results constructed with GMS data are better than those using CFSR data in the source area of the Yellow River.

Multi-outlet simulation
In this paper, considering the geographical location of However, during the verification period, the multi-outlet calibration method is generally better than the single-outlet calibration method, and the simulation results fit the measured runoff process line to a higher degree. From the evaluation indicators of the model in Table 5, it can be seen that during the calibration period, both R 2 and NSE indicators are significantly reduced, especially at the Maqu Station, R 2 is reduced by 18%, and NSE is reduced by Tangnaihai Station increased by 5%. In general, the multioutlet simulation does not reduce the simulation accuracy of the basin total outlet, and it also enables the spatial differences of each sub-basin hydrological characteristics to improve the simulation accuracy of the sub-basin simulation.
As shown in Table 6   According to the data statistical evaluation, it is found that the CFSR temperature data at the source of the Yellow River is basically consistent with the measured temperature data, but the temperature is overestimated. CFSR precipitation data has a general linear relationship with GMS precipitation data, but the precipitation is also overestimated.
This paper used CFSR data and GMS data to drive the SWAT model, and compared model runoff simulation results at Jimai, Maqu, Jungong, and Tangnaihai Hydrological Stations. It found that both data set-driven models can achieve satisfactory simulation results. In general, the simulation effect of GMS is better than that of CFSR data.
The simulation result of the CFSR data set under the multi-outlet calibration was not as good as the single-outlet calibration method. However, during the verification period, the simulation result of the multi-outlet calibration method was worse than the single-outlet calibration method. In addition, the multi-outlet calibration method did not reduce the simulation accuracy of the total outlet of the basin, but it can consider the spatial differences of the hydrological characteristics of each sub-basin to improve the simulation accuracy of the sub-basin simulation.