Enhancing Research-to-Operations in Hydrological Forecasting: Innovations across Scales and Horizons
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| Format: | Recurso digital |
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Zenodo
2025
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| author | Pechlivanidis, Ilias Yiheng, Du James, Bennett Marie-Amélie, Boucher Annie Y. Y., Chang Louise, Crochemore Antara, Dasgupta Giuliano, Di Baldassarre Jürg, Luterbacher Florian, Pappenberger Maria-Helena, Ramos Louise, Slate Stefan, Uhlenbrook Fredrik, Wetterhall Andrew, Wood Waldo, Lavado-Casimiro Kei, Yoshimura Ruben, Imhoff Peter J., van Oevelen Carolina, Cantone Céline, Cattoën Rafael, Pimentel Micha, Werner |
| author_facet | Pechlivanidis, Ilias Yiheng, Du James, Bennett Marie-Amélie, Boucher Annie Y. Y., Chang Louise, Crochemore Antara, Dasgupta Giuliano, Di Baldassarre Jürg, Luterbacher Florian, Pappenberger Maria-Helena, Ramos Louise, Slate Stefan, Uhlenbrook Fredrik, Wetterhall Andrew, Wood Waldo, Lavado-Casimiro Kei, Yoshimura Ruben, Imhoff Peter J., van Oevelen Carolina, Cantone Céline, Cattoën Rafael, Pimentel Micha, Werner |
| contents | <h2>Abstract</h2> <p>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_1175_BAMS-D-24-0322_1 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Enhancing Research-to-Operations in Hydrological Forecasting: Innovations across Scales and Horizons Pechlivanidis, Ilias Yiheng, Du James, Bennett Marie-Amélie, Boucher Annie Y. Y., Chang Louise, Crochemore Antara, Dasgupta Giuliano, Di Baldassarre Jürg, Luterbacher Florian, Pappenberger Maria-Helena, Ramos Louise, Slate Stefan, Uhlenbrook Fredrik, Wetterhall Andrew, Wood Waldo, Lavado-Casimiro Kei, Yoshimura Ruben, Imhoff Peter J., van Oevelen Carolina, Cantone Céline, Cattoën Rafael, Pimentel Micha, Werner <h2>Abstract</h2> <p>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.</p> |
| title | Enhancing Research-to-Operations in Hydrological Forecasting: Innovations across Scales and Horizons |
| url | https://doi.org/10.1175/BAMS-D-24-0322.1 |