Pritam Das

The Surface Water and Ocean Topography (SWOT) satellite, launched in December 2022, represents a significant advancement in the remote sensing of global water bodies, providing simultaneous measurements of water surface elevation and extent in all-weather conditions. This study evaluates SWOT’s capability to estimate reservoir storage dynamics in comparison to pre-SWOT methods. SWOT demonstrates high accuracy in measuring water surface elevation, achieving a median R^2 close to 1 and root mean square errors (RMSE) nearly an order of magnitude lower compared to earlier non-SWOT approaches. SWOT offers substantial improvement over single-sensor and multi-sensor methods, due to spatial averaging of distributed elevation measurements, which was further validated by similar measurements of the ICESat-2 satellite. The key limiting factor in estimating storage from elevation measuring sensors was found to be the accuracy of Area-Elevation-Volume (AEV) curve. Furthermore, preliminary applications of machine learning to integrate SWOT with non-SWOT datasets show promise, although constrained by limited data availability of SWOT as of late 2024.

River regulation in mountainous and high-precipitation regions with hydropower dams often struggles to find the right balance between hydropower generation while ensuring flood protection for downstream inhabitants. The goal of hydropower generation is to keep reservoirs at the maximum pool as often as possible while for flood control, it is to maintain sufficient cushion in available storage to absorb an incoming flood wave. Using weather forecasts to proactively manage reservoir operations for such conflicting goals is now a well-known solution. However, this challenge of applying forecast-informed reservoir operations is magnified in developing regions where there is a paucity of ground data to track reservoir dynamics. In this study, we explore the utility of using publicly available precipitation forecasts from the Global Ensemble Forecasting System (GEFS) with a fully satellite-based reservoir tracking framework called reservoir assessment tool (RAT) to understand the potential of forecast-informed operations in highly mountainous and high-precipitation regions that are mostly ungauged. We apply our investigation to the case of damaging floods that took place in 2018 in the Southern Indian state of Kerala where river regulation is carried out with a fleet of hydropower dams. Our results show that the precipitation forecast from GEFS has sufficient skill, if focused on trends and bias adjustment, to predict reservoir inflow peaks up to a week ahead of time where the trend for the timing of the peak and rate of rise match well. Using our satellite-based RAT framework, we explore the range of actionable scenarios for dam operators that could potentially minimize downstream flood risk with this forecast-informed reservoir operations scheme.

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. On average, ResORR improved regulated 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 software 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.

In light of the rapidly increasing regulation of rivers due to planned and constructed reservoirs, monitoring reservoir operations has become very crucial. The Reservoir Assessment Tool (RAT) framework was developed to monitor reservoir operations globally, using hydrological modeling and satellite observations. With feedback from stakeholders, improvements in the RAT framework are demonstrated in this study using the Mekong River Basin as an example. A novel multi-sensor reservoir area mapping technique was developed using comple mentary strengths of optical and SAR sensors at a 1 –5 day temporal resolution, allowing the quantification of sub-weekly reservoir operations. Additionally, the skill of radar altimeters in the RAT framework was tested using the Jason-3 altimeter. Using in-situ data from three Thai reservoirs in the Mekong Basin, consistent improvements were observed as compared to the original RAT framework. A functionality for visualizing forecasted outflow was also added using historically inferred reservoir operations or stakeholder-driven target storage expectations.