# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## 1 Introduction  
Terrestrial water storage (TWS) is the sum of all forms of water storage over land surfaces and plays a vital role in the global and regional hydrological cycle [1, 2, 3]. It reflects all types of water stored on continents, including surface water, soil water, and groundwater [4]. TWS change (TWSC) has significant impacts on terrestrial ecosystems, human beings, and even the sea level [5]. Monitoring the spatial and temporal variabilities in TWSC is of great importance for effective water resources management and sustainable utilization [6]. The launch of the Gravity Recovery and Climate Experiment (GRACE) satellites has provided a new method for terrestrial hydrology research, which can efficiently improve the monitoring result of the changes in the water cycle at a large scale [7]. Numerous studies have shown that GRACE satellites play a key role in the large-scale monitoring of TWSC or TWS anomalies (TWSA) [8, 9, 10]. However, the applications of GRACE satellites are limited by the low spatial resolution that cannot be directly used in small-scale research [11, 12, 13]. To use GRACE data at small basins, some studies have derived TWS at small basin scales by downscaling GRACE data [14, 15, 16]. There are two typical types of methods to downscale GRACE data: statistical downscaling and dynamic downscaling. Scholars have usedstatistical downscaling to establish observed spatial and temporal relationships between GRACE data and inputs (dependent variables such as precipitation, soil moisture, runoff, ET, and so on) using coarse-scale datasets and have applied the established relationships and fine-scale input data to produce the TWS data at small scales [17; 18; 19]. The major limitation of the statistical downscaling method comes from the assumption of stationarity between the coarse-scale and fine-scale dynamics and from the uncertainty and probability associated with this assumption [20]. In terms of dynamic downscaling methods, many scholars have assimilated GRACE data into the land surface model to obtain high-resolution TWS data [14; 21; 22]. However, the construction of the dynamic downscaling model is based on defined physical relationships, is physically based but computationally expensive, and is strongly dependent on boundary conditions, and the calculation is relatively complicated; the theoretical model often causes large errors [20]. In recent years, the application of hydrological models has been greatly expanded, which can be further used to estimate small-scale TWSC. In addition to runoff simulations, hydrological models are also used to simulate evapotranspiration (ET), soil moisture, and other water balance components. The hydrological models are effective tools for linking the water balance of the basin with runoff and other hydrological processes. The hydrological models can be divided into empirical models, conceptual models, and physically based models. Empirical models only obtain information from existing data without considering the characteristics and processes of the hydrological system, so these models are only effective within the boundary. The conceptual model uses a semi-empirical equation, and a large amount of in situ data are required for calibration. Calibration involves curve fitting, which makes interpretation difficult, so the effects of land-use change cannot be predicted with great confidence. While the physically based model can overcome many of the shortcomings of the other two models due to the use of parameters with a physical interpretation, it can provide a large amount of information even outside the boundaries and can be applied to a variety of situations [23]. Several studies have used remote sensing and in situ data based on water balance equations, combined with ET and runoff output from various hydrological models, to estimate TWSC at grid scales [24; 25; 26]. However, as [PERSON] et al. [24] mentioned, this method may be suitable for the regions where in situ data are lacking or unavailable.