Satellite inspired hydrology in an uncertain future: a H SAF and HEPEX workshop
Session
Conveners
Session 5: Novel hydrological data sources and assimilation techniques
- Renaud Hostache (Luxembourg Institute of Science and Technology)
- Filipe Aires (LERMA / Observatoire de Paris / CNRS)
Data assimilation (DA) allows for updating state variables in a model to represent reality more accurately than the initial (open loop) simulation. In hydrology, DA is often a pre-requisite for forecasting. Neural networks (NN) can learn almost any nonlinear relationship between inputs and outputs. Here, we hypothesize that NN could learn the relationship between the simulated streamflow...
A retrieval methodology based on Neural Networks was proposed (Aires et al. 2005) to retrieve and assimilate Soil Moisture (SM) from satellite observations. It is based on the fact that no radiative transfer model was satisfactory enough for a physically-based algorithm. An innovative aspect is to train the NN using modelled SMs. The resulting retrievals are not a reproduction of the model:...
Recent studies have demonstrated the potential of assimilating probabilistic inundation maps derived from Synthetic Aperture Radar (SAR) imagery for improved flood forecasts. However, high resolution SAR acquisition can only provide partial coverage of large catchments. Consequently, information on the impacts of location, timing, and frequency of inundation extent assimilation on flood...
Analyzing and forecasting the variability of snow is essential for runoff prediction especially in mountainous regions where the optimal operation of reservoirs is important. Remote sensing information has been extensively developed over the last decay with enhanced snow data sets at high resolutions. The implementation of satellite facilities in operational runoff forecasting systems by means...
Fully coupled modelling of atmospheric and hydrological processes is of growing interest among the hydrometeorology community. Understanding multi-time scale processes within a land-atmosphere modelling system is of importance for improving forecast between medium-range and seasonal forecasts. Improving forecast capabilities between sub-seasonal to seasonal forecasting, can help shape the...