The scarcity of groundwater storage change data at the global scale hinders our ability to monitor groundwater resources effectively. In this study, we assimilate a state‐of‐the‐art terrestrial water storage (TWS) product derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into NASA's Catchment land surface model (CLSM) at the global scale, with the goal of generating groundwater storage time series that are useful for drought monitoring and other applications. Evaluation using in situ data from nearly 4,000 wells shows that GRACE data assimilation improves the simulation of groundwater, with estimation errors reduced by 36% and 10% and correlation improved by 16% and 22% at the regional and point scales, respectively. The biggest improvements are observed in regions with large interannual variability in precipitation, where simulated groundwater responds too strongly to changes in atmospheric forcing. The positive impacts of GRACE data assimilation are further demonstrated using observed low flow data. CLSM and GRACE data assimilation performance is also examined across different permeability categories. The evaluation reveals that GRACE data assimilation fails to compensate for the lack of a groundwater withdrawal scheme in CLSM when it comes to simulating realistic groundwater variations in regions with intensive groundwater abstraction. CLSM simulated groundwater correlates strongly with 12‐month precipitation anomalies in low and mid‐latitude areas. A groundwater drought indicator based on GRACE data assimilation generally agrees with other regional‐scale drought indicators, with discrepancies mainly in their estimated drought severity.

Extreme droughts have caused significant socioeconomic and environmental damage worldwide. In Brazil, ineffective energy development and water management policies have magnified the impacts of recent severe droughts, which include massive agricultural losses, water supply restrictions, and energy rationing. Spaceborne remote sensing data advance our understanding of the spatiotemporal variability of large-scale droughts and enhance the detection and monitoring of extreme water-related events. In this study, data derived from the Gravity Recovery and Climate Experiment (GRACE) mission are used to detect and quantify an extended major drought over eastern Brazil and provide estimates of impacted areas and region-specific water deficits. Two structural breakpoint detection methods were applied to time series of GRACE-based terrestrial water storage anomalies (TWSA), determining when two abrupt changes occurred. One, in particular, defines the beginning of the current drought. Using TWSA, a water loss rate of −6.1 cm yr−1 over southeastern Brazil was detected from 2012 to 2015. Based on analysis of Global Land Data Assimilation System (GLDAS) outputs, the extreme drought is mostly related to lower-than-usual precipitation rates, resulting in high soil moisture depletion and lower-than-usual rates of evapotranspiration. A reduction of 20%–23% of precipitation over an extended period of 3 years is enough to raise serious water scarcity conditions in the country. Correlations between monthly time series of both grid-based TWSA and ground-based water storage measurements at 16 reservoirs located within southeastern Brazil varied from 0.42 to 0.82. Differences are mainly explained by reservoir sizes and proximity to the drought nucleus.

Recent advances in global flow routing schemes have shown the importance of using high-resolution topography for representing floodplain inundation dynamics more reliably. This study presents and evaluates the Hydrological Modeling and Analysis Platform (HyMAP), which is a global flow routing scheme specifically designed to bridge the gap between current state-of-the-art global flow routing schemes by combining their main features and introducing new features to better capture floodplain dynamics. The ultimate goals of HyMAP are to provide the scientific community with a novel scheme suited to the assimilation of satellite altimetry data for global water discharge forecasts and a model that can be potentially coupled with atmospheric models. In this first model evaluation, HyMAP is coupled with the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface model in order to simulate the surface water dynamics in the Amazon basin. The model is evaluated over the 1986–2006 period against an unprecedented source of information, including in situ and satellite-based datasets of water discharge and level, flow velocity, and floodplain extent. Results show that the model can satisfactorily simulate the large-scale features of the water surface dynamics of the Amazon River basin. Among all stream gauges considered, 23% have Nash–Sutcliffe coefficients (NS) higher than 0.50 and 68% above zero. About 28% of the stations have volume errors lower than 15%. Simulated discharges at Óbidos had NS = 0.89. Time series of simulated floodplains at the basin scale agrees well with satellite-based estimates, with a relative error of 7% and correlation of 0.89. These results indicate nonnegligible improvements in comparison to previous studies for the same region.

In the last two decades, rainfall estimates provided by the Tropical Rainfall Measurement Mission (TRMM) have proven applicable in hydrological studies. The Global Precipitation Measurement (GPM) mission, which provides the new generation of rainfall estimates, is now considered a global successor to TRMM. The usefulness of GPM data in hydrological applications, however, has not yet been evaluated over the Andean and Amazonian regions. This study uses GPM data provided by the Integrated Multi-satellite Retrievals (IMERG) (product/final run) as input to a distributed hydrological model for the Amazon Basin of Peru and Ecuador for a 16-month period (from March 2014 to June 2015) when all datasets are available. TRMM products (TMPA V7 and TMPA RT datasets) and a gridded precipitation dataset processed from observed rainfall are used for comparison. The results indicate that precipitation data derived from GPM-IMERG correspond more closely to TMPA V7 than TMPA RT datasets, but both GPM-IMERG and TMPA V7 precipitation data tend to overestimate, compared to observed rainfall (by 11.1 and 15.7 %, respectively). In general, GPM-IMERG, TMPA V7 and TMPA RT correlate with observed rainfall, with a similar number of rain events correctly detected ( ∼ 20 %). Statistical analysis of modeled streamflows indicates that GPM-IMERG is as useful as TMPA V7 or TMPA RT datasets in southern regions (Ucayali Basin). GPM-IMERG, TMPA V7 and TMPA RT do not properly simulate streamflows in northern regions (Marañón and Napo basins), probably because of the lack of adequate rainfall estimates in northern Peru and the Ecuadorian Amazon.