Terrestrial water storage (TWS) is a critical variable in the global hydrological cycle. The TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) allow us to better understand water exchanges between the atmosphere, land surface, sea, and glaciers. However, missing historical (pre-2002) GRACE data limit their further application. In this study, we developed a random forest (RF) model to reconstruct the monthly terrestrial water storage anomaly (TWSA) time series using Global Land Data Assimilation System (GLDAS) and Climatic Research Unit (CRU) data for the Lancang-Mekong River basin. The results show that the RF-built TWSA time series agrees well with the GRACE TWSA time series for 2003–2014, showing that correlation coefficients (R) of 0.97 and 0.90 at the basin and grid scales, respectively, which demonstrates the reliability of the RF model. Furthermore, this method is used to reconstruct the historical TWSA time series for 1980–2002. Moreover, the discharge can be obtained by subtracting the evapotranspiration (ET) and RF-built terrestrial water storage change (TWSC) from the precipitation. The comparison between the discharge calculated from the water balance method and the observed discharge showed significant consistency, with a correlation coefficient of 0.89 for 2003–2014 but a slightly lower correlation coefficient (0.86) for 1980–2002. The methods and findings in this study can provide an effective means of reconstructing the TWSA and discharge time series in basins with sparse hydrological data.

The delay caused by the troposphere is one of the major sources of errors limiting the accuracy of InSAR measurements. The tropospheric correction of InSAR measurements is important. The Weather Research and Forecasting (WRF) Model is a state-of-the-art mesoscale numerical weather prediction system designed for atmospheric research applications. It can be applied to InSAR tropospheric correction. Its parameters can be altered according to the requirements of the given application. WRF is usually initialized based on 3 h- or 6 h temporal resolution data in InSAR tropospheric correction studies, a lower temporal resolution compared to ERA5 data. A lower time resolution means a longer integration time for WRF to simulate from the initial time to the target time. Initialization with a higher resolution can shorten the integration time of the simulation theoretically and improve its accuracy. However, an evaluation of the effectiveness of ERA5_WRF for InSAR tropospheric correction is lacking. To evaluate the efficiency of WRF tropospheric correction, we used Reanalysis v5 (ERA5) from the European Centre for Medium-Range Weather Forecasts (ECMWF) for initialization to drive the WRF (ERA5_WRF) for efficient applications in InSAR. Three methods based on global atmospheric models—FNL_WRF (tropospheric correction method based on WRF driven by NCEP FNL), Generic Atmospheric Correction Online Service for InSAR (GACOS), and ERA5—were used to evaluate the corrective effects of ERA5_WRF. The reliability of ERA5_WRF in different scenarios with large tropospheric delay was evaluated from the spatial and temporal perspectives by considering seasonal, topographic, and climatic factors. Its applications in the local space showed that ERA5_WRF could adequately correct tropospheric delay. Benefits include its high-quality data sources and the simulation of WRF, and its application in different seasons had proven superior to other methods in terms of the corrective effects of elevation-related and spatially related delays in summer. By analyzing the data sources and downscaling methods of correction methods and weather conditions of cases, ERA5_WRF had superior performance under the condition of large content and hourly variation of tropospheric delay. Furthermore, WRF showed the potential for tropospheric correction when other higher-quality data appear in the future.