SnowInflow: Optimized inflow forecast for the snowmelt period

Norwegian hydropower plays a crucial role in the Nordic energy system, balancing variable renewable energy production in Northern Europe. Snowmelt contributes approximately to half of the inflow to Norwegian hydropower plants. However, optimally managing the reservoirs during the critical snowmelt period remains highly challenging due to substantial errors in snow amount estimates and predicted inflows. The main objective of SnowInflow is to reduce errors in inflow forecasts during the snowmelt period. To achieve this goal, we will capitalize on advancements in snow monitoring, modeling, and data assimilation techniques. Today, accurate remote sensing techniques, such as airborne laser scanning, exist for measuring snow depths over vast areas. Also, physics-based snow models have become useful operationally, and can in combination with data assimilation reduce errors in inflow forecasts. The main research challenges of this project include (a) determining the optimal combination of ground and remote sensing observations for enhanced inflow forecasts, (b) developing physics-based snow models optimal for Nordic snow and climate conditions as well as for use in hydropower inflow forecasting tools, (c) creating automated data assimilation techniques for accurate incorporation of inflow and snow data, and (d) assessing the financial benefits of inflow forecast using different snow measurements, modeling, and data assimilation methods. The SnowInflow project will greatly improve hydropower scheduling, resulting in numerous benefits, such as higher financial gains, better utilization of existing installed hydropower infrastructure and reduced risk of flood damages. The project will contribute to the transition towards a low-emission society by improving the efficiency of the Norwegian hydropower system, that can balance wind and solar production, which is expected to dominate energy generation in West-Central Europe in the mid of this century.

Norwegian hydropower plays a crucial role in the Nordic energy system, and snowmelt contributes approximately to half of the inflow to the powerplants. However, optimally managing the reservoirs during the critical snowmelt period remains highly challenging due to large errors in snow amount estimates and predicted inflows. These errors mainly stem from limited spatial coverage of ground measurements of snow and forecasting models depicting processes overly simplistic. In this project, Statkraft will collaborate with snow scientists at the WSL Institute for Snow and Avalanche Research SLF in Switzerland and data assimilation experts at NORCE in Bergen for improving inflow forecasts. The main objective of SnowInflow is to reduce errors in inflow forecasts during the snowmelt period. To achieve this goal, we will capitalize on advancements in snow monitoring, modelling, and data assimilation techniques. Today, accurate remote sensing techniques, such as airborne laser scanning, exist for measuring snow depths over vast areas. Moreover, energy-balance snow models that depict the physical processes driving snow accumulation and melt have become useful operationally. In combination with data assimilation, these developments currently hold the highest potential for reducing errors in inflow forecasts. The main research challenges of this project include (a) determining the optimal combination of ground and remote sensing observations of snow for enhanced inflow forecasts, (b) developing a physics-based snow model adapted to Norwegian conditions for use in a hydropower inflow forecasting tool, (c) integrating automated data assimilation techniques for accurate incorporation of inflow and snow data without violating the water balance over time, and (d) assessing the financial benefits of inflow forecasts using the newly developed methods. The improved inflow forecasts will lead to more efficient hydropower scheduling, higher financial gains and reduced risk for flooding.