*2.3. Data*

An overview of the datasets can be found in Table 2. This table lists relevant information about the datasets used in this study, such as the TWSC, precipitation, runoff, ET, spatial resolution, and corresponding links of data access. A detailed description of these data is provided below.


**Table 2.** Summary of the datasets used in this study.

#### 2.3.1. GRACE–Derived TWSC Data

We used the Center for Space Research (CSR) GRACE RL05 Mascons data to estimate the TWSC. The mascon solutions are global and can be better applied to hydrology, oceanography, and the cryosphere without any post–processing and without applying any empirical scaling factors [41]. The data can be downloaded from http://www2.csr.utexas.edu/grace. The Mascons data is represented at a 0.5-degree lon-lat grid and is estimated with the same standards as the CSR RL05 spherical harmonics solutions using GRACE Level-1 observations. C20 coefficients were replaced, degree-1 coefficients (Geocenter) and glacial isostatic adjustment (GIA) corrections were applied. More details about the CSR GRACE RL05 Mascons (CSR-M) can be found in Save et al. [41]. With the development of post-processing GRACE satellite data, several GRACE solutions can be used for hydrology applications. However, different solutions would lead to different TWSC estimates.

To evaluate the impact of TWSC from different GRACE solutions on the estimate of ET, we also take JPL Mascons [42], CSR GRCTellus Land data [43], and CSR RL05 spherical harmonics solutions with the DDK4 filter applied (CSR-DDK4) [44] as a comparison. All of these above solutions are processed with the same C20 coefficients replaced, the same degree-1 coefficients, and GIA corrections.

The processing of JPL Mascons is based on external information provided by near-global geophysical models to constrain the solution. JPL Mascons use the coarse 3-degree spherical cap Mascons, and they are downscaled to 0.5◦ × 0.5◦ using downscaling factors (dsf) calculated from Community Land Model (CLM ver. 4.0) [42], the grid values of JPL Mascons are multiplied by downscaling factors (JPL-M.dsf). The CSR and JPL mascon solutions can be used directly without leakage corrections. CSR GRCTellus Land data is developed by Landerer and Swenson [43] from CSR data, and scaling factors are provided to account for the signal loss during processing related to truncation to degree and order 60 and application of a 300 km Gaussian smoothing filter. The grid values of CSR GRCTellus Land are multiplied by scaling factors (CSRT-GSH.sf). The DDK filter is proposed by Kusche et al. [44], and the DDK4 filter shows a good performance in the application of the upper Yellow River [45]. The process of CSR-DDK4 is similar to CSR GRCTellus Land data while replacing 300 km Gaussian smoothing filter and destriping filter with the DDK4 filter. There is no leakage correction applied in CSR-DDK4 in this study, as with in Yi et al. [45], and in fact, the results for CSR-DDK4 are at the same level with the other solutions.

#### 2.3.2. In Situ Precipitation and Runoff Data

Gridded precipitation data was obtained from the China Meteorological Data Service Center (CMDC, hereafter, PCMDC). The gridded precipitation data was generated by a thin plate spline spatial interpolation of precipitation observations from 2472 weather stations. It has a monthly temporal resolution and 0.5◦ × 0.5◦ spatial resolution over all of China [46]. This data is validated by cross-validation and error analysis with gauge-based precipitation, indicating good quality. This precipitation data has been used in several studies [13,22,47]. The monthly runoff datasets are from eleven gauge stations recorded in RSBC (hereafter, QRSBC) (http://www.mwr.gov.cn/sj/tjgb/ zghlnsgb/) [35], which integrates all runoff considering the upstream of the corresponding catchment. The runoff measurements are all from well-gauged rivers in China.

#### 2.3.3. Land Evapotranspiration Products

We use two kinds of land evapotranspiration products for comparison, which included GLDAS ET and GLEAM ET (Table 2). Two versions of GLDAS LSM data are used for inter-comparison in this paper, i.e., GLDAS version 1 (GLDAS-1) and GLDAS version 2.1 (GLDAS-2.1) [9]. ET outputs from both GLDAS versions are driven by Noah LSM [48]. GLDAS-1 datasets cover the time period from 1979 to the present. GLDAS-2.1 datasets cover the period from 2000 to the present. Their temporal resolutions used here are monthly. More information and details about the GLDAS-1 and some improvements and changes about the GLDAS-2.1 are available at https://ldas.gsfc.nasa.gov/gldas/. The ET outputs from GLDAS-1 and GLDAS-2.1 are expressed as ETGLDAS–1 and ETGLDAS–2.1.

We use GLEAM v3.2a ET products (hereafter, ETGLEAM), which were published jointly by Vrije Universiteit Amsterdam, Netherlands and Ghent University, Belgium [6]. The data has a spatial resolution of 0.25◦ × 0.25◦ and daily temporal resolution. We sum them to the monthly results in this study. GLEAM uses a set of algorithms to separately estimate the different components (transpiration, bare-soil evaporation, interception loss, open-water evaporation, and sublimation) of land ET. The Priestley and Taylor equation was used in GLEAM to calculate potential evaporation based on observations of surface net radiation and near-surface air temperature. The rationale of GLEAM is to maximize the recovery of information on evaporation contained in current satellite observations of climatic and environmental variables [6].

#### 2.3.4. Precipitation Forcing Data and Modeled Runoff Data

The precipitation forcing data from the GLDAS-1, GLDAS-2.1 (hereafter, PGLDAS–1 and PGLDAS–2.1), and the Multi-Source Weighted-Ensemble Precipitation (MSWEP, precipitation forcing data of GLEAM, hereafter, PMSWEP) datasets [49] are used to explain the difference of ET results. They are also computed as regional averages. As runoff is another critical variable in the water balance equation, we also calculate the mean runoff outputs of the nine exorheic catchments from GLDAS-1 and GLDAS-2.1 Noah LSM (hereafter, QGLDAS–1 and QGLDAS–2.1) and compare the results with those for in situ runoff.
