Learning Resources
The SEED-FD learning resources offer insights into the project’s innovative hydrological modelling and forecasting methods. New resources and information will be added regularly, so revisit this page to stay updated.
SEED-FD IN A NUTSHELL
The SEED-FD flyer provides information about the project, including its objectives, methods, phases, expected outcomes, and contact information. Download it for a quick overview of the SEED-FD project.
GloFAS
The Global Flood Awareness System (GloFAS), part of the Copernicus Emergency Management Service (CEMS), provides valuable flood forecasts for global river basins. The SEED-FD project aims to enhance GloFAS by integrating advanced hydrological data, increasing accuracy, and expanding its flood forecasting products to strengthen global disaster preparedness.
LISFLOOD
LISFLOOD is a hydrological model, which simulates floods and droughts. It powers the CEMS Early Warning Systems like GloFAS, providing real-time forecasts globally. SEED-FD further develops LISFLOOD by integrating data and innovative modelling techniques, ensuring more accurate predictions for better disaster management. Interested developers and hydrological scientists are invited to contribute.
Publications
Prudhomme, C., Zsótér, E., Matthews, G., Melet, A., Grimaldi, S., Zuo, H., Hansford, E., Harrigan, S., Mazzetti, C., De Boisseson, E., Salamon, P., & Garric, G. (2024). Global hydrological reanalyses: The value of river discharge information for world‐wide downstream applications – The example of the Global Flood Awareness System GloFAS. Meteorological Applications, 31(2), e2192. https://doi.org/10.1002/met.2192
Prudhomme, C., Barker, L. J., Cammalleri, C., Harrigan, S., Ionita, M., & Vogt, J. (2024). Drought early warning systems: Monitoring and forecasting. In Hydrological Drought (S. 595–635). Elsevier. https://doi.org/10.1016/B978-0-12-819082-1.00002-3
Dasgupta, A., Arnal, L., Emerton, R., Harrigan, S., Matthews, G., Muhammad, A., O’Regan, K., Pérez‐Ciria, T., Valdez, E., Van Osnabrugge, B., Werner, M., Buontempo, C., Cloke, H., Pappenberger, F., Pechlivanidis, I. G., Prudhomme, C., Ramos, M., & Salamon, P. (2023). Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop. Journal of Flood Risk Management, e12880. https://doi.org/10.1111/jfr3.12880
Harrigan, S., Zsoter, E., Cloke, H., Salamon, P., & Prudhomme, C. (2023). Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System. Hydrology and Earth System Sciences, 27(1), 1–19. https://doi.org/10.5194/hess-27-1-2023
Matthews, G., Barnard, C., Cloke, H., Dance, S. L., Jurlina, T., Mazzetti, C., & Prudhomme, C. (2022). Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System. Hydrology and Earth System Sciences, 26(11), 2939–2968. https://doi.org/10.5194/hess-26-2939-2022
Brocca, L., Barbetta, S., Camici, S., Ciabatta, L., Dari, J., Filippucci, P., Massari, C., Modanesi, S., Tarpanelli, A., Bonaccorsi, B., Mosaffa, H., Wagner, W., Vreugdenhil, M., Quast, R., Alfieri, L., Gabellani, S., Avanzi, F., Rains, D., Miralles, D. G., … Fernandez, D. (2024). A Digital Twin of the terrestrial water cycle: A glimpse into the future through high-resolution Earth observations. Frontiers in Science, 1, 1190191. https://doi.org/10.3389/fsci.2023.1190191
Brocca, L., Gaona, J., Bavera, D., Fioravanti, G., Puca, S., Ciabatta, L., Filippucci, P., Mosaffa, H., Esposito, G., Roberto, N., Dari, J., Vreugdenhil, M., & Wagner, W. (2024). Exploring the actual spatial resolution of 1 km satellite soil moisture products. Science of The Total Environment, 945, 174087. https://doi.org/10.1016/j.scitotenv.2024.174087
Filippucci, P., Brocca, L., Quast, R., Ciabatta, L., Saltalippi, C., Wagner, W., & Tarpanelli, A. (2022). High-resolution (1 km) satellite rainfall estimation from SM2RAIN applied to Sentinel-1: Po River basin as a case study. Hydrology and Earth System Sciences, 26(9), 2481–2497. https://doi.org/10.5194/hess-26-2481-2022
Filippucci, P., Brocca, L., Bonafoni, S., Saltalippi, C., Wagner, W., & Tarpanelli, A. (2022). Sentinel-2 high-resolution data for river discharge monitoring. Remote Sensing of Environment, 281, 113255. https://doi.org/10.1016/j.rse.2022.113255
Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., & Wagner, W. (2019). SM2RAIN–ASCAT (2007–2018): Global daily satellite rainfall data from ASCAT soil moisture observations. Earth System Science Data, 11(4), 1583–1601. https://doi.org/10.5194/essd-11-1583-2019
Cammalleri, C., McCormick, N., Spinoni, J., & Nielsen-Gammon, J. W. (2024). An analysis of the lagged relationship between anomalies of precipitation and soil moisture and its potential role in agricultural drought early warning. Journal of Applied Meteorology and Climatology, 63(2), 339–350. https://doi.org/10.1175/JAMC-D-23-0077.1
Cammalleri, C., De Michele, C., & Toreti, A. (2024). Exploring the joint probability of precipitation and soil moisture over Europe using copulas. Hydrology and Earth System Sciences, 28(1), 103–115. https://doi.org/10.5194/hess-28-103-2024
Cammalleri, C., Acosta Navarro, J. C., Bavera, D., Diaz, V., Di Ciollo, C., Maetens, W., Magni, D., Masante, D., Spinoni, J., & Toreti, A. (2023). An event-oriented database of meteorological droughts in Europe based on spatio-temporal clustering. Scientific Reports, 13(1), 3145. https://doi.org/10.1038/s41598-023-30153-6
Cammalleri, C., & Toreti, A. (2023). A generalized density-based algorithm for the spatiotemporal tracking of drought events. Journal of Hydrometeorology, 24(3), 537–548. https://doi.org/10.1175/JHM-D-22-0115.1
Cammalleri, C., McCormick, N., & Toreti, A. (2022). Analysis of the relationship between yield in cereals and remotely sensed fAPAR in the framework of monitoring drought impacts in Europe. Natural Hazards and Earth System Sciences, 22(11), 3737–3750. https://doi.org/10.5194/nhess-22-3737-2022