Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.
ITS1.7/HS12.1 | Data fusion in hydrological modeling: improving hydrological forecasts incorporating remote sensing precipitation and soil moisture estimates
EDI
Data fusion in hydrological modeling: improving hydrological forecasts incorporating remote sensing precipitation and soil moisture estimates
Convener: Ali Torabi Haghighi | Co-conveners: Stavros StathopoulosECSECS, Alexandra Gemitzi, Miroslaw Zimnoch
Hydrological modeling plays a crucial role in understanding and predicting the behavior of water systems, which is important for water resource management, flood forecasting, and environmental planning. However, the accuracy of these models heavily relies on accurate input data, which can be challenging to obtain, especially in regions with limited ground-based observations. This is where remote sensing technology comes into play. By harnessing data from remote sensing platforms, researchers can provide spatially and temporally comprehensive information on precipitation and soil moisture, filling critical gaps in traditional observation networks. Data fusion in hydrological modeling involves combining remote sensing-derived data with ground-based measurements to create a more complete picture of the hydrological cycle. This integration is achieved through a synergy of advanced techniques such as data assimilation, machine learning algorithms and statistical methods.

This session welcomes, but is not limited to, contributions on:
• Novel data assimilation methods that effectively incorporate remote sensing precipitation and soil moisture estimates into hydrological models
• Applications of machine learning algorithms for fusing remote sensing data with ground-based observations to improve hydrological predictions
• Methodologies for quantifying and propagating uncertainties associated with remote sensing precipitation and soil moisture estimates through hydrological models
• Methodologies for downscaling remote sensing data to finer spatial and temporal resolutions, making them compatible with hydrological models that require higher detail for accurate predictions
• Techniques for validating and verifying hydrological models that incorporate remote sensing data
• Emerging trends in data fusion in hydrological modeling