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Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Deep pre-storm storage critical for flood forecasting in Europe

Author
item MASSARI, C. - National Research Council - Italy
item TRAMBLAY, Y. - University Of Montpellier
item Crow, Wade
item GRUENDEMANN, G. - Ihe Delft Institute For Water Education
item CAMICI, S. - National Research Council - Italy
item BROCCA, L. - National Research Council - Italy
item MODANESI, S. - National Research Council - Italy
item MARRA, F. - National Research Council - Italy

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/1/2023
Publication Date: 9/12/2023
Citation: Massari, C., Tramblay, Y., Crow, W.T., Gruendemann, G., Camici, S., Brocca, L., Modanesi, S., Marra, F. 2023. Deep pre-storm storage critical for flood forecasting in Europe. Journal of Hydrology. 625, Part B: Article e130012. https://doi.org/10.1016/j.jhydrol.2023.130012.
DOI: https://doi.org/10.1016/j.jhydrol.2023.130012

Interpretive Summary: In an average year, flood losses in the United States top 30 million dollars. Due to climate change and increased use of flood-prone areas, this cost is expected to rise sharply in coming decades. Knowing the wetness state of a hydrologic catchment prior to intense rainfall is critical for predicting what fraction of rainfall will run off into river channels, and thus contribute to flooding, versus infiltrate into the soil. However, there are currently multiple competing ways to describe pre-storm wetness within a catchment - and it is unclear which of these ways is more informative for flood forecasting. Using a unique, long-term dataset, this study evaluated the predictive ability of different descriptions of pre-storm wetness conditions. It was found that deep (0 to 1-m) soil moisture and water-table depth provide a better description of pre-storm conditions than surface (0 to 5-cm) soil moisture and are therefore more effective variables for forecasting potentially dangerous floods. These results have important implications for operational flood forecasting agencies and water resource management authorities as they work to reduce the future impact of flooding disasters.

Technical Abstract: Flood and extreme precipitation occurrences in Europe do not always correspond. This complicates flood forecasting as incoming extreme precipitation does not necessarily cause extreme floods, which instead may emerge from mild precipitation. Catchment pre-storm conditions are among the primary causes of this discrepancy, and their adequate monitoring is critical for flood forecasting. The event runoff coefficient can be used to quantify pre-storm conditions; however, since it is only known after a flood has occurred, other proxies are needed from an operational point of view. Here, we show that deep pre-storm soil water storage states, like root-zone soil moisture and total water storage anomalies, contain important information regarding pre-storm catchment conditions. In particular, they outperform classical indices, such as antecedent precipitation or surface soil moisture, in the prediction of the event runoff coefficient - especially over cold/humid climates. Our results highlight the importance of deep pre-storm storage for flood forecasting in Europe.