APRIL

The proposed research aims at a temporally disaggregate long term quantitative prediction of monthly streamflow from mountain catchments in Europe, and at setting up a system which can then be used operationally.
To this end, it is planned to fully exploit the potentials of data on the accumulation of water in mountain catchments, notably in the form of snowpack and glaciers from EO, along with products derived from remotely sensed images on the state of vegetation and soil moisture in catchments, weather and climatic variables, and qualitative predictors of wet and dry periods, available virtually everywhere in the world. 
The hypothesis that we want to test in the proposed research is that quantitative drought predictions can be achieved with a lead time of some months through statistical machine learning techniques.
The basic idea behind the approach proposed here is that, using information on a climatic signal, it is possible to predict the catchment state and precipitation, using an a posteriori probability distribution and assuming for simplicity, and without loss of generality, that catchment state is represented by snow cover only.
The project has a strong relevance for the Istitute strategy and objectives. It further contributes to the development of retrieval methodologies and use of remotely sensed derived parameters (as snow cover area) as input to determine other related variables (water availability).