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Evapotranspiration (ET) is an important approach for enabling water and energy exchange between the atmosphere and the land, and it has a very close relationship with terrestrial water resources and the ecological environment. Therefore, it is of great scientific to accurately quantify the spatiotemporal change in ET and its impact factors to understand the terrestrial water change pattern, maintaining water resource security and protecting the ecological environment. Our goal is to study the spatiotemporal characteristics of ET in the Indus River basin (IRB) and their driving factors. In our study, we first integrated the multi-source satellite gravimetry observations using the generalized three-cornered hat and least square methods to obtain the high-precision and continuous spatiotemporal evolution features of ET in the IRB from 2003 to 2021. Finally, we combined nine hydrometeorological and land cover type data to analyze the factors influencing ET. The results indicate that the algorithm used in our study can improve the ET accuracy by 40%. During the study period, ET shows a significant increasing trend (0.64 ± 0.73 mm/a), and the increasing rate presents spatial distribution characteristics of high variability in the northern areas and low variability in the southern areas of the study region. ET has a close relationship with precipitation, specific humidity, total canopy water storage, surface temperature and wind speed (with a correlation coefficients greater than 0.53 and variable importance of projection greater than 0.84). Among these factors, precipitation, specific humidity and surface temperature have significant correlations with ET (correlation coefficients greater than 0.85 and variable importance of projection greater than 1.42). And wind speed has a more significant positive effect on ET in the densely vegetated regions. The impacts of climate change on ET are significantly greater than those of land cover types, especially for similar land cover types. Ice and snow are significantly different to other land cover types. In this region, ET is only significantly correlated with precipitation, specific humidity and snow water equivalent (variable importance of projection greater than 0.81), and the impacts of precipitation and specific humidity on ET have been significantly weakened, while that of snow water equivalent is significantly enhanced. Our results contribute to furthering the understanding of the terrestrial water cycle in subtropical regions.

Floods have always threatened the survival and development of human beings. To reduce the adverse effects of floods, it is very important to understand the influencing factors of floods and their formation mechanisms. In our study, we integrated the Gravity Recovery and Climate Experiment and its Follow-On and Swarm solutions to estimate an uninterrupted 19-year flood potential index (FPI) time series, discussed the spatiotemporal distribution characteristics of the FPI and monitored major floods in the Volga River basin (VRB) from 2003 to 2021. Finally, we analyzed the relationship between the FPI and hydrometeorological factors to comprehend the flood formation mechanism. The results show that data fusion has reduced the uncertainty of terrestrial water storage change (TWSC), and the TWSC from the combined satellite gravity observations has a good consistency with that from the Global Land Data Assimilation System model (correlation coefficient = 0.92). During the study period, two major floods (June 2005 and May 2018) occurred in the VRB. The FPI has a significant seasonal change characteristic, and shows a high flood risk in spring and a low one in autumn. With regards to spatial distribution, the flood risk is increasing in the north (increasing rate = 0.1) and decreasing in the south (decreasing rate = 0.39). Snow water equivalent (SWE, correlation coefficient = 0.75) has a stronger correlation with the FPI than precipitation (PPT, correlation coefficient = 0.46), which is attributed to the recharge of SWE on water resources greater than that of PPT. The rising surface temperature (ST) speeds up snow melt, resulting in excessive groundwater and soil moisture, and the flood risk greatly increases at this time. The process lasts about three months. Therefore, except for PPT, ST is also a climatic factor leading to the floods in the VRB. Our study provides a reference for flood research in high-latitude regions.

The climate anomaly in the Amazon River basin (ARB) has a very important influence on global climate change and has always been the focus of scientists from all over the world. To fill the 11-month data gap between Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions, we fused the TWSC results from six GRACE solutions by using the generalized three-cornered hat and the least square method to improve the reliability of TWSC results, and then combined Swarm data to construct an uninterrupted long time series of a TWSC-based drought index (GRACE/Swarm-DSI). The drought index was used to detect and characterize the drought events in the ARB between 2003 and 2020. The results show that GRACE/Swarm-DSI has a strong correlation with Self-Calibrating Palmer Drought Severity Index (SCPDSI) (0.6345), Standardized Precipitation Evapotranspiration Index-3 (SPEI-3) (0.5411), SPEI-6 (0.6377) and SPEI-12 (0.6820), and the Nash–Sutcliffe efficiency between GRACE/Swarm-DSI and the above four drought indices are 0.3348, 0.2786, 0.4044 and 0.4627, respectively. Eleven drought events were identified in the ARB during the study period, and the 2005, 2010 and 2016 droughts are the most severe and the longest. The correlation between GRACE/Swarm-DSI and precipitation (PPT) (the correlation coefficient is 0.55 with a 2-month delay) is higher than that of evapotranspiration (ET) (the correlation coefficient is −0.18 with a 12-month delay). It explains that less PPT is the main cause of drought events in the ARB. The influence of PPT is greater in the plains than the one in the mountains and the response time of GRACE/Swarm-DSI to PPT is 1~2 months in most regions. Our results provide a certain reference for the hydrological application of the Swarm model in filling the gap between GRACE and GRACE-FO missions.