Water data science

Greenhouse gas (GHG) emission from tropical large hydropower reservoirs (LHRs) is the highest among all climatic zones due to the combinatory effect of elevated content of flooded organic matter and high temperatures. Traditional methods for GHG emission estimation involve extensive fieldwork, topographic surveys, hydrological analyses, and environmental assessments with high-end instrument requirements. In a country like India, where the hydropower sector is mushrooming rapidly, implementing these techniques on such a large scale is challenging. Alternatively, cloud-based tools like Google Earth Engine (GEE), G-res, and Earth Observation (EO) data related to biophysical and climatic conditions with in-situ reservoir water levels provide an opportunity to quantify GHG emissions from LHRs efficiently. In the present study, Maithon, one of the oldest LHRs in India, situated in a tropical climatic zone, has been studied by integrating site-specific parameters to estimate GHG emissions. The results from this study, which show that at the mean operating level (146.31 m) of the reservoir, net GHG emission is 1,024 - 1,271 gCO2e/m2/yr (with a 95% confidence interval), are of significant importance. This study highlights the GHG emissions varying greatly between the full reservoir level (786 gCO2e/m2/yr) and near the dead storage level (3,855 gCO2e/m2/yr), indicating the role of reservoir operating level in mitigating GHG emissions to achieve global goals like net zero emissions. There has been limited work globally using the G-res tool, and this is the first comprehensive study of initial GHG emission estimation of a tropical reservoir using G-res and GEE incorporating updated high-resolution land use land cover and Sentinel-1 images.

The brief identifies critical gaps in water-related data and skills necessary for the effective implementation of anticipatory water management actions in response to climate change in the Dolo Ado and Bokolmayo districts, Ethiopia. It evaluates current community and local government practices and recommends improvements in data collection, capacity building, and integrated water management to enhance resilience against droughts and floods. Key findings highlight the need for better data monitoring, early warning systems, and infrastructure investment to mitigate water-related hazards and improve local adaptive capacities.

This quot;Operational SWAT+ Limpopo River Basin Seasonal Forecasting Systemquot; report outlines the development and implementation of an automated hydrological forecasting system using the Soil and Water Assessment Tool Plus (SWAT+). This system leverages publicly available global datasets and open-source modeling tools integrated within a custom developed automated system to predict seasonal water availability in the Limpopo River Basin (LRB). Key components include integrating the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data.) and ECMWF (European Centre for Medium-Range Weather Forecasts) precipitation data, comprehensive database management, and real-time monitoring scripts. The system provides accurate and timely water availability forecasts within the LRB to support operational decision making. Future directions focus on improving model calibration, incorporating additional weather variables, better representation of large reservoirs and irrigated areas, applying database optimization procedures, and transitioning to a Docker-based deployment on Amazon Web Services (AWS) for improved scalability and reliability. This SWAT+ operational seasonal forecasting system for the LRB marks a significant step towards bridging a key knowledge gap in the basin to support better decision making on multiple water uses and users including provision for environmental flows. This seasonal forecasting system as a part of the larger river basin Digital Twin is designed to influence effective water resource management in the Southern African region.

As part of the CGIAR Initiative on Digital Innovation, GroundTruth, in partnership with the International Water Management Institute (IWMI), investigated the use of Unmanned Aerial Vehicles (UAVs) to aid in performing bathymetric and water quality surveys. A key outcome of the research and development within this collaboration has been the design and construction of a low-cost survey system comprising a survey vessel equipped with multiple sensors that collect water quality and bathymetric data and can be connected to a UAV. Traditional point-sampling methods limit comprehensive assessments of river health over large spatial areas. This challenge was addressed by developing a drone-towed survey vessel for spatially extensive water quality and depth data collection. The aim of developing the survey vessel was to improve the data collection process which could be useful to further develop the linkages between river flow regimes and water quality parameters. The vessel that was built was equipped with temperature sensors to capture thermal variations across a section of the river, a Total Dissolved Solids (TDS) sensor to monitor total dissolved solids, a Sound Navigation and Ranging (SoNAR) device to map variations in the riverbed profile, a turbidity sensor to provide a measure of water clarity and a Global Positioning System (GPS) module for precise geotagging of all sensor readings. The system was tested at the Lions River in KwaZulu-Natal, South Africa, and the drone-tethered data collection vessel enabled a safe and efficient survey of a river segment. The system was used to collect high-resolution data related to water quality along a reach of the river. The depth data acquired from the vessel was integrated with Light Detection and Ranging (LiDAR) data to generate detailed bathymetric maps for the site. This highlights the potential for expansion in terms of data collection capabilities compared to traditional point sampling methods.