CryoBrain interaction with Norwegian cryosphere expert

   TwitCount

To promote knowledge sharing on cryosphere research regarding the Hindu Kush Himalaya (HKH), the International Centre for Integrated Mountain Development’s (ICIMOD) Cryosphere Initiative regularly organizes CryoBrain events, welcoming members of international scientific communities to discuss their work and share their findings with ICIMOD’s team of glaciologists and cryosphere researchers.

Kjetil Melvold, researcher at the Norwegian Water Resources and Energy Directorate (NVE), presented his ongoing research on sub-grid snow distribution (i.e., a part of the grid covered by snow) and methods of parameterizing this in large-scale snow models. Melvold was visiting ICIMOD as one of the major contributors to the Snow Accumulation and Melt Process (SnowAMP) project, a collaboration between ICIMOD and NVE that aims to increase scientific knowledge on snow accumulation and melt processes in the Himalayas.

Kjetil Melvold, researcher at the Norwegian Water Resources and Energy Directorate (NVE), presents his ongoing research on sub-grid snow distribution (Photo: Chimi Seldon/ICIMOD)

Snowmelt is an important source of water in Norway as well as in the HKH. Factors such as snow depth, terrains, and solar radiation are studied to establish the extent of their influence on the melt process. The land surface of the research area is often presented as a grid of flat, uniform cells in remote-sensing and modelling products. These products can indicate, for example, whether the cell is snow free or covered by snow. The reality, however, is much more complex than that. 

Melvold highlighted the importance of sub-grid snow distribution for large-scale hydrological models: “Sub-grid snow distribution is needed, for example, to more accurately validate modelled snow depth with observations and remote-sensing products. It is also important for obtaining accurate snow melt discharge from the snowpack during snowmelt season.”  

Research works such as Melvold’s provide evidence-based knowledge for implementing effective water resource management and predicting future water availability.