HSAF data assimilation under model and predictive uncertainties
Project: "H-SAF Data assimilation under model and predictive uncertainties"
Partner:
- University of Siegen, Chair of Water Management Risk Assessment and Climate Impact Research, Prof. P. Reggiani
Sponsor:
- Ferderal Institute of Hydrology (bfg)
Duration:
- 01.01.2019 - 31.12.2021
Short description:
The Federal Institute of hydrology deals within the framework of the EUMETSAT project "satellite application facility and water management on support to operational hydrology" (H-SAF) with the validation of satellite-based rainfall, snow and soil moisture data on the basis of observations in combination with hydrological models. The task of the HSAF project is to demonstrate the potential of satellite-based products for operational hydrological tasks. The methods for data assimilation and the implementation and analysis of predictive experiments will be rolled out, refined, compared and supplemented on a large scale and made available for operational applications. In order to fulfil these tasks, the use of so-called data assimilation procedures is necessary. These are statistical and numerical methods of data analysis that allow the direct or indirect input of observational data into hydrological modelling, taking into account their uncertainties. An essential basis is the appropriate integration of the drive data of the hydrological models, the hydrological system states or the water flows effective in the hydrological system into operational assimilation processes and optimization processes. Here, a selection of assimilation methods in connection with the H-SAF satellite products and other data sources will be tested and compared on a large spatial scale. As part of the model creation, hydrological models are created for the catchment areas of the rivers Main and Lahn and used in combination with the assimilation methods outlined above. The model structure, spatial resolution and model parametrization will be varied with the aim of empowering the BFG and other organizations to generate improved operational hydrological predictions in the river basin and to Publish.