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
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
