The water cycle is one of the most important of the earth’s cycles, and it plays a crucial role in biosphere changes. Water balance elements in a basin are affected by natural and human factors, such as the types of land use, soil properties [1
], geological conditions, glacier [2
] and human economic activity [3
]. It is necessary to study the contribution to the water budget by different hydrological elements in a basin for the purpose of land use management, water resources management, and hydrological process analysis. Because the contribution to the water budget by different hydrological elements is hard to measure in the field, it is more practical to estimate the water cycle components of a watershed using a hydrological model [5
The Soil and Water Assessment Tool (SWAT) model is an important tool in the development of water management strategies [6
]. At the beginning of SWAT model establishment, it is difficult to calculate the water cycle components, especially groundwater [7
]. Sophocleous et al. [8
] simulated combined surface-water, ground-water, and stream-aquifer interactions using a comprehensive SWATMOD basin model, which was based on the Modular Three-Dimensional Finite-Difference Ground-Water Flow Model (MODFLOW). Because the SWAT model was established using the characteristics of a North American river basin, the accuracy of the model can be compromised in other areas. For example, the SWAT99.2 version could not satisfactorily calculate the runoff in low mountain regions of Germany. To address these shortcomings, Eckhardt et al. [9
] developed the SWAT Giessen (SWAT-G) version for simulating the runoff in catchments with predominantly steep slopes, shallow soils, and consolidated rock aquifers. In addition, Easton et al. [11
] established a Soil and Water Assessment Tool-Variable Source Area (SWAT-VSA) model for predicting runoff by modifying the curve number and available water content in variable source areas.
Although the SWAT hydrological model has been widely used for nutrient transport and hydrological modeling, the model is difficult to apply in areas where meteorological data are scarce, such as glacial and deserts areas [12
]. Therefore, meteorological data are urgently needed for runoff simulation and prediction in non-data basins [13
]. The CMADS was developed by Dr. Xianyong Meng from the China Institute of Water Resources and Hydropower Research (IWHR). The data range is from 2008 to 2016. It covers the entire East Asian region [14
]. Some studies considered that CMADS+SWAT have better results for runoff simulation [15
]. Meng et al. [17
] evaluated the water cycle in an area without meteorological data using the CMADS meteorological data. They obtained satisfactory results through parameter calibration in areas with a high glacial recharge rate. Meng et al. also used three different datasets to simulate runoff in the Heihe Basin, and the results showed that the simulation accuracy of the CMADS was higher than other datasets [18
]. The uncertainty analysis based on CMADS data has also been investigated [19
]. In recent years, SWAT has been successfully applied in the study of hydrological elements in various watersheds. For example, the SWAT model was applied to study changes in the water budget caused by climate change [20
]. The SWAT model was used to study hydrological elements in ice- and snow-covered mountainous area [24
]. The SWAT model has also been used to study the main hydrological elements in agricultural areas [27
Although the CMADS data have been applied worldwide since its release in 2016, the application of CMADS in abundant rainfall areas in southern China is lacking [30
]. Further investigations of the applicability of the CMADS in the SWAT model are needed to better understand and evaluate the accuracy and efficacy of the dataset. The Lijiang River is an important water system in the Pearl River Basin, and the CMADS data have not been verified in this basin. To address this knowledge gap, the present study applied the SWAT model to explore the applicability of the CMADS in this basin. The Sequential Uncertainty Fitting (SUFI-2) algorithm was used for parameter sensitivity and uncertainty analysis at a daily scale. Pair-wise correlation between parameters and the uncertainties associated with equifinality in model parameter estimation was also investigated. The simulation results were used to investigate the water budget and its elements in the basin. The study also investigated the spatial variation and temporal variation of the water budget elements. In addition, the correlation between hydrological elements and precipitation were investigated.
The present study used CMADS data and the SWAT model to successfully generate daily and monthly scale runoff simulations for the Lijiang River Basin. The analysis of pair-wise correlations between the parameters shows that the redundancy was small in the parameterization. Both the R-factor and P-factor reached ideal values in the calibration and validation periods, which indicated that there was low uncertainty in the simulation results and model parameters.
Using the model’s output, the average annual contribution to the water budget by hydrological elements was analyzed. From 2009–2016, the average annual value of surface runoff, ET, lateral flow, and shallow groundwater in the Lijiang River Basin were 518.36 mm, 750.60 mm, 129.21 mm, 555.34 mm, respectively. The spatial distribution of surface runoff and groundwater discharge was related to precipitation. The highest ET values were obtained in the west of the basin, where agriculture is prevalent. The water budget of groundwater discharge, lateral flow, and surface runoff reached the highest values in the flood year, and reached the lowest values in the driest year. The high correlation between these elements and precipitation was reflected in the regression analysis. ET remained stable during the calibration and validation period. The results for the hydrological elements could provide valuable reference information for water resources management in the Lijiang River Basin.