In Norway, operational snow cover area monitoring is performed based on satelitte data and is a useful tool in hydropower production planning, snow monitoring and flood prediction systems. Hydrological precipitation-runoff models have been in operational use for more than 20 years for flood warning, planning, design and operation of hydropower systems in addition to impact assessments. The models have important variables, which potentially can be derived from satellite data, such as snow water equivalent , snow cover area , snow wetness and albedo. However, these systems are currently not able to fully utilise remote sensing data as input to hydrological models.
The goal of the SnowMan project is to improve methodology for remote sensing of snow parameters and the use of snow parameters in hydrological models in order to achieve better water management practices related to snow. This should result in better flood prediction, management of rivers and dams, and hydropower production planning.
We will improve the understanding of how satellite-measured microwaves and light interact with snow cover in mountainous areas and, thereby, develop new and improved algorithms for snow parameter retrieval. The main parameters are snow water equivalent, snow cover area, snow wetness, albedo and snow surface temperature.
A multisensor and multitemporal approach is also applied in order to utilise the complementary sensor characteristics of optical and radar data. The methods will in particular be suitable for RADARSAT, Terra-1's MODIS, MISR and ASTER sensor, ENVISAT's ASAR, MERIS and AATSR. New methods for inclusion of the remote-sensing derived snow parameters in a lumped and distributed hydrological model will be developed. The updated models will be verified and demonstrate in real time in a semi-operational environment using both a lumped and a distributed hydrological model.
To improve methodology for remote sensing of snow parameters and the use of snow parameters in hydrological models in order to achieve better water management practices related to snow. This should result in better flood prediction, management of rivers and dams, and hydropower production planning.
- Improve the understanding of microwave interaction with snow cover in mountainous areas and develop synthetic aperture radar based algorithms for snow water equivalent of dry snow, snow cover area of wet snow and snow wetness particular suitable for ENVISAT ASAR and RADARSAT.
- Develop optical methods for accurate snow-cover-fraction estimation at the sub-pixel level, albedo, snow wetness and, if possible, accurate free water estimation all particularly suitable for Terra-1’s MODIS, MISR and ASTER sensors and ENVISAT’s MERIS and AATSR.
- Develop multisensor (optical + radar) algorithms for deriving information on the snow cover area, snow wetness and free water prediction
- Develop and improve methods for inclusion of the remote-sensing derived snow parameters snow cover area, snow water equivalent, snow wetness and albedo in a lumped (HBV) and distributed (ECOMAG) hydrological model
- Verify and demonstrate in real time the new methods in a semi-operational environment