TY - JOUR
T1 - Enhancing Research-to-Operations in Hydrological Forecasting
T2 - Innovations across Scales and Horizons
AU - Pechlivanidis, Ilias G.
AU - Du, Yiheng
AU - Bennett, James
AU - Boucher, Marie Amélie
AU - Chang, Annie Y.Y.
AU - Crochemore, Louise
AU - Dasgupta, Antara
AU - Baldassarre, Giuliano Di
AU - Luterbacher, Jürg
AU - Pappenberger, Florian
AU - Ramos, Maria Helena
AU - Slater, Louise
AU - Uhlenbrook, Stefan
AU - Wetterhall, Fredrik
AU - Wood, Andrew
AU - Lavado-Casimiro, Waldo
AU - Yoshimura, Kei
AU - Imhoff, Ruben
AU - van Oevelen, Peter J.
AU - Cantone, Carolina
AU - Cattoën, Céline
AU - Pimentel, Rafael
AU - Werner, Micha
N1 - Publisher Copyright:
© 2025 American Meteorological Society.
PY - 2025/5
Y1 - 2025/5
N2 - Over the past 20 years, the Hydrological Ensemble Prediction Experiment (HEPEX) international community of practice has advanced the science and practice of hydrological ensemble prediction and its application in impact- and risk-based decision-making, fostering innovations through cutting-edge techniques and data that enhance water-related sectors. Here, we present insights from those 20 years on the key priorities for (co)creating broadly applicable hydrological forecasting systems that add value across spatial scales and time horizons. We highlight the advancement of hydrological forecasting chains through rigorous data management that incorporates diverse, high-quality data sources, data assimilation techniques, and the application of artificial intelligence (AI) to improve predictive accuracy. HEPEX has played a critical role in enhancing the reliability of water resources and water-related risk management globally by standardizing ensemble forecasting. This effort complements HEPEX’s broader initiative to strengthen research to operations, making innovative forecasting solutions both practical and accessible. Additionally, efforts have been made toward supporting the United Nations Early Warnings for All initiative through developing robust and reliable early warning systems by means of global training, education and capacity development, and the sharing of technology. Finally, we note that the integration of advanced science, user-centric methods, and global collaboration can provide a solid framework for improving the prediction and management of hydrological extremes, aligning forecasting systems with the dynamic needs of water resource and risk management in a changing climate. To effectively meet future demands, it is crucial to accelerate the integration of innovative science within operational frameworks, fostering adaptable and resilient hydrological forecasting systems globally.
AB - Over the past 20 years, the Hydrological Ensemble Prediction Experiment (HEPEX) international community of practice has advanced the science and practice of hydrological ensemble prediction and its application in impact- and risk-based decision-making, fostering innovations through cutting-edge techniques and data that enhance water-related sectors. Here, we present insights from those 20 years on the key priorities for (co)creating broadly applicable hydrological forecasting systems that add value across spatial scales and time horizons. We highlight the advancement of hydrological forecasting chains through rigorous data management that incorporates diverse, high-quality data sources, data assimilation techniques, and the application of artificial intelligence (AI) to improve predictive accuracy. HEPEX has played a critical role in enhancing the reliability of water resources and water-related risk management globally by standardizing ensemble forecasting. This effort complements HEPEX’s broader initiative to strengthen research to operations, making innovative forecasting solutions both practical and accessible. Additionally, efforts have been made toward supporting the United Nations Early Warnings for All initiative through developing robust and reliable early warning systems by means of global training, education and capacity development, and the sharing of technology. Finally, we note that the integration of advanced science, user-centric methods, and global collaboration can provide a solid framework for improving the prediction and management of hydrological extremes, aligning forecasting systems with the dynamic needs of water resource and risk management in a changing climate. To effectively meet future demands, it is crucial to accelerate the integration of innovative science within operational frameworks, fostering adaptable and resilient hydrological forecasting systems globally.
KW - Communications/ decision making
KW - Emergency preparedness
KW - Ensembles
KW - Forecasting
KW - Hydrology
KW - Water resources
UR - https://www.scopus.com/pages/publications/105005493152
U2 - 10.1175/BAMS-D-24-0322.1
DO - 10.1175/BAMS-D-24-0322.1
M3 - Article
AN - SCOPUS:105005493152
SN - 0003-0007
VL - 106
SP - E894-E919
JO - Bulletin of the American Meteorological Society
JF - Bulletin of the American Meteorological Society
IS - 5
ER -