E!115410 Snow Water Equivalent forecasting service for the hydropower sector

In snow-rich mountain regions, hydropower companies play a crucial role in the management of water resources, relying on snowmelt water to power their facilities. The purpose of the SnoWE project is to model the snow water equivalent (SWE) and predict water inflow using advanced technologies, including a mobile Ground-Penetrating Radar (GPR) radar (Mitta) and satellite monitoring (ExoLabs). By combining data from these sources in numerical weather models (Ubimet), we have worked to improve snow forecasting and water inflow prediction with the aim of optimizing hydropower operations and reducing flood risk. Methods and Technologies: The project has involved several key components to achieve its goals. 1. Mobile GPR Radar: We have developed a mobile GPR radar that has been continuously improved over multiple winter seasons. This radar provides in-situ radargrams of the snow area, and the new multi-channel GPR radar enhances the detail of data collection. This has resulted in more accurate measurements of snow depth and SWE. 2. Processing of GPR Data (TORP): Our proprietary GPR data processing tool, known as TORP (Think Outside Radar Processing), has been significantly enhanced. TORP automates the processing of radar data, removes noise, and integrates additional data such as GPS data for faster delivery of processed data to customers. 3. Satellite Monitoring and Weather Models (ExoLabs and Ubimet): Satellite observations of snow cover are integrated into numerical weather models to provide reliable forecasts for snow balance and water inflow. 4. Logger System (DingoLogger): We are working on developing a new logger system that provides a simpler user experience, allows the operator to control data collection through a web application, and enhances the efficiency of data collection. Results and Future Plans: The results of the project have laid the foundation for our services. We use the knowledge and technology from the project to improve existing services and develop new offerings. For example, we are utilizing the technology from RR600 to develop a multi-channel radar, and we are working on implementing a cloud-based solution to enhance user-friendliness and data quality. The SnoWE project has demonstrated significant advancements in improving snow forecasts and water inflow calculations, positively impacting the efficiency of the hydropower industry and reducing flood risk in snow-rich mountain areas. The project's success promises future opportunities for even more accurate and reliable services for water resource management in such regions.

Prosjektets resultater har ikke blitt målt konkret i estimert deviasjon av SWE. Men basert på foreløpige målinger og tall estimerer vi en deviasjon på ca. 15%. Dette er en betydelig forbedring og har en stor innvirkning på estimert SWE i kroner og øre. I tillegg effektiviserer prosjektet innsamlingsmetodene, noe som bidrar til innhenting av mye mer data over samme tid. Innsamlingsmetodene forenkler også arbeidet til den utøvende part, både når det gjelder den fysiske innhentingen, samt beregninger av SWE. Det er fortsatt potensiale til å effektivisere metodene enda mer, samt minske deviasjonen, noe som vi vil jobbe videre med fremover.

Hydropower operation and planning requires streamflow forecasts at both short (typically, hours to days) and long ranges (a season ahead), which serves a variety of decisions from production planning to flood protection. In snow-prone mountain areas, snowmelt runoff poses a significant challenge given the uncertainties related to the timing and volume of snowmelt water. Quantifying this water resource is challenging due to the highly variable snowpack in the complex mountain terrain. This uncertainty leads to inefficient water management, which causes significant losses for the hydropower industry. Furthermore, floods regularly exceed reservoirs to capacities resulting in water spills and severe downstream damage (1). Furthermore, global warming causes shifts in precipitation patters with earlier snowmelt onsets (2). These changes further increase the inaccuracies of hydrological models that rely on historical snowmelt patterns. The main objective of this SnoWE project is to significantly improve streamflow forecasts based on highly accurate and spatially explicit snowmelt information. The foundation for these improved snow forecasts builds a sophisticated physical snow model. This model in turn is driven by numerical weather forecasts, multi-satellite observations and in-situ measurement on the snowpack, such as snow depth and SWE. All these input data are provided complementary by the consortium partners. By improving streamflow forecasts, we optimize the efficiency of renewable hydropower, mitigate climate change effects, and reduce flood risks. To achieve this key objective, the main components of this proposed SnoWE project comprise: • Near real-time snow cover extent based on multi-satellite imagery • In-situ measurements on snow depth and SWE using our own radar sensor (see Annex for more details) • Numerical weather forecasts to drive the snow balance model • API to provide automated access to the snow forecast products