Selective water release from the deeper pools of reservoirs for energy generation alters the temperature of downstream rivers. Thermal destabilization of downstream rivers can be detrimental to riverine ecosystem by potentially disturbing the growth stages of various aquatic species. To predict this impact of planned hydropower dams worldwide, we developed, tested and implemented a framework called ‘FUture Temperatures Using River hISTory’ (FUTURIST). The framework used historical records of in-situ river temperatures from 107 dams in the U.S. to train an artificial neural network (ANN) model to predict temperature change between upstream and downstream rivers. The model was then independently validated over multiple existing hydropower dams in Southeast Asia. Application of the model over 216 planned dam sites afforded the prediction of their likely thermal impacts. Results predicted a consistent shift toward lower temperatures during summers and higher temperatures during winters. During Jun-Aug, 80% of the selected planned sites are likely to cool downstream rivers out of which 15% are expected to reduce temperatures by more than 6˚C. Reservoirs that experience strong thermal stratification tend to cool severely during warm seasons. Over the months of Dec-Feb, a relatively consistent pattern of moderate warming was observed with a likely temperature change varying between 1.0 to 4.5˚C. Such impacts, homogenized over time, raise concerns for the ecological biodiversity and native species. The presented outlook to future thermal pollution will help design sustainable hydropower expansion plans so that the upcoming dams do not face and cause the same problems identified with the existing ones.

The Surface Water Ocean Topography (SWOT) mission will launch in 2021 to provide the first global inventory of terrestrial surface water. Although SWOT is primarily a research mission with key science objectives in both the oceanography and hydrology domains, SWOT data is expected to have application potential to address many societal needs. To identify SWOT applications, prepare for the use of SWOT data, and quantify SWOT impacts prior to launch, realistic proxy SWOT observations with representative measurement errors are required. This paper provides a step-by-step description of two methods for deriving proxy SWOT water surface elevations (WSE) from an Observing System Simulation Experiment (OSSE) using the Weather Research and Forecasting hydrological extension package (WRF-Hydro). The first, a basic method, provides a simple and efficient way to sample WRF-Hydro output according to the SWOT orbit and add random white noise to simulate measurement error, similar to many previous approaches. An alternate method using the Centre National d’Etudes Spatiales (CNES) Large-scale SWOT Hydrology Simulator accounts for additional sources of measurement error and produces output in formats comparable to that expected from official SWOT products. The basic method is ideal for river hydrology applications in which a full representation of SWOT measurement errors and spatial resolution are unnecessary, whereas the CNES simulator approach is better-suited for more rigorous scientific studies that require a comprehensive error budget.

The hydropower fleet built in the Upper Mekong River, or Lancang, currently consists of eleven mainstream dams that can control about 55% of the annual flow to Northern Thailand and Laos. The operations of this fleet have become a source of controversy between China and downstream countries, with these dams often considered the culprit for droughts and other externalities. Assessing their actual impact is a challenging task because of the chronic lack of data on reservoir storage and operations. To overcome this challenge, we focus on the ten largest reservoirs and leverage satellite observations to infer 13-year time series of monthly storage variations. Specifically, we use area-storage curves (derived from a Digital Elevation Model) and time series of water surface area, which we estimate from Landsat images through a novel algorithm that removes the effects of clouds and other disturbances. We also use satellite radar altimetry data (Jason) to validate the results obtained from satellite imagery. Our results describe the evolution of the hydropower system and highlight the pivotal role played by Xiaowan and Nuozhadu reservoirs, which make up to ~85% of the total system’s storage in the Lancang River Basin. We show that these two reservoirs were filled in only two years, and that their operations did not change in response to the drought that occurred in the region in 2019-2020. Deciphering these operating strategies could help enrich existing monitoring tools and hydrological models, thereby supporting riparian countries in the design of more cooperative water-energy policies.

North-east region of Bangladesh is comprised of hilly transboundary rivers in the upstream and complex river network along with floodplain in the downstream. In this study, a satellite observation and numerical modeling based near real-time flash flood forecasting framework is developed for the north-east part of Bangladesh. For nowcast information, Global Precipitation Measurement Mission (GPM) developed latest near real-time IMERG precipitation product is chosen. To produce improved quality precipitation forecasts, Global Forecasting System (GFS) based precipitation predictions are corrected by applying climatology based bias adjustment technique. Flow from upstream river channels into the complex river network are calculated by using the nowcast and forecast precipitation products forced into Soil and Water Assessment Tool hydrological Model. The inflow discharges from the hydrological model then used in HecRAS hydrodynamic model to produce forecasted water height/discharge, flood depth and inundation in prominent locations in the study region. Both of the hydrological and hydrodynamic models are simulated sequentially in sub-daily scale to produce 120 hours lead time forecasts. SWAT Model was calibrated and validated in six inflow stations using rated discharge. HecRAS Model was calibrated and validated using measured water level at different locations.

Dam construction is on the rise in developing nations. Monitoring of these dams is necessary to understand downstream hydrologic impacts and for better planning and management of water resources. Satellite observations and advancements in information technology now present a unique opportunity to overcome the traditional limitations of reservoir monitoring. In this study, a global reservoir monitoring framework is developed as an online tool for near-realtime monitoring and impact analysis of existing and planned reservoirs based on publicly available and global satellite observations. The framework uses a mass balance approach to monitor 1598 reservoirs in South America, Africa, and South-East Asia region. The framework simulated streamflow was validated in 18 river basins and the storage change was validated against in-situ data of 77 reservoirs. The framework was able to capture reservoir state realistically for more than 75% of the reservoirs. The tool can now be used to study existing and planned reservoirs for hydrologic impact and operating pattern for short and long term decision making and policy analysis.