A modern scientific approach tends more and more to smooth the difference between environmental systems monitoring and modeling. The necessity, on one side, and the advantages, on the other, of combining model results and observations of state variables and fluxes from different observation sources impose an approach that unifies data and models in describing environmental dynamical systems. In this framework data fusion and data assimilation techniques offer to researchers integration tools among different sources of information and models that allow a better description on environmental variables and are efficient diagnostic tools for complex systems. CIMA Foundation is carrying on different research threads using data fusion and data assimilation techniques.

Rainfall Fields Optimal Estimates
Precipitation represents a fundamental variable to be estimated for the prediction and simulation of flood events. For hydrological applications and especially for the ones regarding civil protection requirements exist regarding coverage, spatial-temporal resolution, accuracy. More...

Snow Water Equivalent Estimates
Snow plays a fundamental role in mountain hydrology, hence better estimates of Snow Water Equivalent (SWE) are important for the water resources management and flood forecast. More...

Data Assimilation in Hydrologic models
With regards to data assimilation in the hydrologic models developed by CIMA Foundation researchers, efforts are put towards utilizing classical hydrologic observations (e.g., discharge) and satellite observations (e.g., LST), both in the calibration phase and in the state variable updating. More...