The water in Earth’s rivers propagates as waves through space and time across hydrographic networks. A detailed understanding of river dynamics globally is essential for achieving the accurate knowledge of surface water storage and fluxes to support water resources management and water-related disaster forecasting and mitigation. Global in situ information on river flows are crucial to support such an investigation but remain difficult to obtain at adequate spatiotemporal scales, if they even exist. Many expectations are placed on remote sensing techniques as key contributors. Despite a rapid expansion of satellite capabilities, however, it remains unclear what temporal revisit, spatial coverage, footprint size, spatial resolution, observation accuracy, latency time, and variables of interest from satellites are best suited to capture the space-time propagation of water in rivers. Additionally, the ability of numerical models to compensate for data sparsity through model-data fusion remains elusive. We review recent efforts to identify the type of remote sensing observations that could enhance understanding and representation of river dynamics. Key priorities include: (a) resolving narrow water bodies (finer than 50-100 m), (b) further analysis of signal accuracy versus hydrologic variability and relevant technologies (optical/SAR imagery, altimetry, microwave radiometry), (c) achieving 1-3 days observation intervals, (d) leveraging data assimilation and multi-satellite approaches using existing constellations, and (e) new variable measurement for accurate water flux and discharge estimates. We recommend a hydrology-focused, multi-mission observing system comprising: (1) a cutting-edge single or dual-satellite mission for advanced surface water measurements, and (2) a constellation of cost-effective satellites targeting dynamic processes.

Storage and release of surface water by reservoirs can alter the natural streamflow pattern of rivers with negative impacts on the environment. Such reservoir-driven river regulation is poorly understood at a global scale due to a lack of publicly available in-situ data on reservoir operations. However, with rapid advancements in satellite remote sensing-based tracking of reservoir state, this gap in data availability can be bridged. In this study, we modeled regulated flow of rivers using only satellite-observed reservoir state and hydrological modeling forced also with satellite precipitation data. We propose a globally scalable algorithm, ResORR (Reservoir Operations driven River Regulation), to predict regulated river flow and tested it over the heavily regulated basin of the Cumberland River in the US. ResORR was found able to model regulated river flow due to upstream reservoir operations of the Cumberland River. Over a mountainous basin dominated by high rainfall, ResORR was effective in capturing extreme flooding modified by upstream hydropower dam operations. ResORR successfully captured the peak of the regulated river flow altered by hydropower dam and flood control operations during the devastating floods of 2018 in the South Indian state of Kerala. On average, ResORR improved regulation river flow simulation by more than 50% across all performance metrics when compared to a hydrologic model without a regulation module. ResORR is a timely algorithm for understanding human regulation of surface water as satellite-estimated reservoir state is expected to improve globally with the recently launched Surface Water and Ocean Topography (SWOT) mission.

Suspended sediment concentration, flux, and river discharge are essential indicators of river ecosystem health and reflect watershed-scale processes. Monitoring these variables is labor-intensive, leading to sparse and geographically biased observations and the development of models to fill in the observational gaps. These models generally use either climatological data or satellite images to estimate one of these variables. In this work, we present a novel deep learning model that can leverage multiple data sources with different temporal characteristics to produce continuous daily estimates of suspended sediment concentration (SSC), suspended sediment flux (SSF), and discharge. The model first encodes daily hydrological data from the ERA5-Land reanalysis using a Long Short-Term Memory network and water color data from Landsat satellites using a Multi-Layer Perceptron network, then merge these encoded data sources using a cross-attention decoder. We train and test the model on a large dataset of in-situ observations from 630 river sites over 43 years in the contiguous United States, covering a wide range of watersheds and conditions. We produce SSC, SSF, and discharge predictions with respective relative errors of 54\%, 73\%, and 28\%, and relative bias of -15\%, -19\%, and -3\%. We use our model to create a dataset of continuous daily SSC, SSF, and discharge for all large rivers in the contiguous United States. This new model architecture provides a valuable tool for monitoring river systems, addressing limitations of single-source models and offering a framework applicable to other Earth systems monitoring problems where integrating diverse data streams may be useful.

The Lower Mekong is facing an increasing impact of droughts and at the regional level, the Mekong River Commission (MRC) is mandated to work with government agencies on creating and distributing flood, drought, water resource governance and use to improve policy and practice. The MRC is striving to provide regional, locally calibrated and downscaled information on drought forecasts and real-time monitoring through a portal. The Regional Drought and Crop Yield Information System (RDCYIS) is built on regionally and locally calibrated Regional Hydrologic Extreme Assessment System (RHEAS) framework that integrates the Variable Infiltration Capacity (VIC) and Decision Support System for Agro-technology Transfer (DSSAT) models, allowing both nowcast and forecast of drought. This model is co-developed by NASA Jet Propulsion Laboratory (JPL) and the SERVIR-Mekong teams. In this work, we outline how the MRC Drought Team’s requirements were met via RHEAS. Driven with earth observation data, the main aim of this service is to improve present regional and national drought monitoring and forecasting services to Lower Mekong countries for their water allocation and drought mitigation information needs. We provide an overview of the model calibration and validation methodology, and we find reasonable reliability of the soil moisture model results with the satellite based observations from the SMAP and SMOS retrievals. Through this support to MRC in integrating new drought assessment, monitoring and warning methodologies using RHEAS, more data and analyses will be available to support them to develop improved advice on drought early warning to the National Mekong Committees across the Mekong countries. MRC’s assistance is envisaged to enable comprehensive, accurate and useful warnings for the decision-makers at local and provincial level to take effective action. Ultimately this service is expected to assist farmers to make preemptive decisions about their water use, cropping and planting patterns and market decisions which should reduce crop loss and support livelihoods from farming, including from appropriate compensation to farmers from the governments, wherever this is in effect.