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<div class="csl-entry">Gabellani, S., Alfieri, L., Avanzi, F., Delogu, F., Bruno, G., Campo, L., Libertino, A., Brocca, L., Massari, C., Tarpanelli, A., Camici, S., Miralles, D., Rains, D., Vreugdenhil, M., Quast, R., & Wagner, W. (2022, May 27). <i>Innovative high resolution satellite Earth observations improve distribute hydrological modeling in northern Italy</i> [Conference Presentation]. ESA Living Planet Symposium 2022, Bonn, Germany.</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/115842
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dc.description.abstract
Satellite Earth observations are an accurate and reliable data source for use in atmospheric and environmental science. Their increasing spatial and temporal resolution, as well as the seamless availability over ungauged regions, make them appealing for hydrological modeling. This work shows the results of the ESA DTE Hydrology project as a contribution to the development of a Digital Twin Earth focused on the water cycle and hydrological processes. It aims to highlight the potential of high-resolution satellite products in describing the water cycle and monitoring hydrological extremes and water resources. Through various dedicated experiments, we test the influence of five innovative high-resolution satellite-derived datasets on the performance of the distributed hydrological model Continuum set up for the entire Po River Basin, in northern Italy. The distributed hydrological model is forced, by high resolution satellite precipitation and evaporation, while high resolution satellite-derived soil moisture and snow water equivalent are ingested through a data-assimilation scheme. Further, satellite-based estimates of precipitation, evaporation and river discharge are used for hydrological model calibration and results compared with those based on conventional data. Despite the high density of conventional measurements and the strong human influence in the focus region, all satellite products are found to have strong potential in operational hydrology, with skillful estimates of river discharge throughout the model domain. Satellite based evaporation and snow water equivalent marginally improve (by 2% and 4%) the mean Kling-Gupta efficiency (KGE) at 27 river gauges, compared to a baseline simulation (KGE_mean=0.51) forced by high quality conventional data. Precipitation has the largest impact on model performance. Further, we take substantial steps towards a distributed hydrological model fully relying on satellite data as dynamic input, by calibrating and subsequently running the model using satellite precipitation and evaporation as forcing, and satellite-based estimates of river discharge as benchmark data to calibrate the model.
en
dc.language.iso
en
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dc.subject
high resolution satellite Earth observation
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dc.subject
hydrological modeling
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dc.title
Innovative high resolution satellite Earth observations improve distribute hydrological modeling in northern Italy