How good are drought forecasts? Skill of multi-model seasonal forecast of meteorological droughts in a semi-arid Mediterranean basin
[EN] Droughts pose a significant challenge to water management, particularly in semi-arid regions with high water demand. In this context, drought indices have proven to be valuable tools for enhancing drought awareness and decision-making, as they provide critical information for water resource man...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/231135 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/231135 |
| Access Level: | acceso embargado |
| Palabra clave: | Meteorological drought indices Multi-model seasonal forecasting Semi-arid regions Mediterranean basin Forecast skill. 06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos 13.- Tomar medidas urgentes para combatir el cambio climático y sus efectos |
| Sumario: | [EN] Droughts pose a significant challenge to water management, particularly in semi-arid regions with high water demand. In this context, drought indices have proven to be valuable tools for enhancing drought awareness and decision-making, as they provide critical information for water resource management. However, their integration with seasonal forecasts remains underexplored. Most currently operational drought forecasting and early warning services either do not incorporate indices or are limited to using a small subset of them. In this study, we present a multi-model seasonal forecasting system for meteorological drought indices, integrating forecasts from four systems (ECMWF-SEAS5, Météo-France System8, DWD-GCFS2.1, and CMCC-SPSv3.5) available through the Copernicus Climate Change Service (C3S) with ERA5 reanalysis for post-processing with artificial intelligence. Evaluated over the 1995¿2014 hindcasts period. The system computes two widely used drought indices, SPI and SPEI, at multiple aggregation scales (6, 12, 18, and 24 months). Forecast skill is evaluated using the Continuous Ranked Probability Skill Score (CRPSS), shows high skill, with values around 90% at one lead month and remaining above 64% and 67% at three lead months for SPI-6 and SPEI-6, respectively. Longer aggregations retain useful skill up to five lead months. The methodology is applied to the Jucar River Basin (Spain), a representative semi-arid Mediterranean basin characterized by recurrent and severe droughts. Results highlight the potential of multi-model seasonal forecasts for supporting drought early warning and water management. An operational web-based implementation further demonstrates the system¿s applicability for decision-making, although the methodological framework is transferable to other drought-prone regions. |
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