Intercomparison of Earth Observation products for hyper-resolution hydrological modelling over Europe
[EN] The increasing frequency and severity of hydrological extremes demand the development of early warning systems and effective adaptation and mitigation strategies. Such systems and strategies require spatially detailed hydrological predictions, mostly provided by hydrological models. However, cu...
| Autores: | , , , , , , , , , , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| 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:10251/231251 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/231251 |
| Access Level: | acceso abierto |
| Palabra clave: | Earth Observations Hydrological modelling Precipitation Snow cover area Surface soil moisture Evapotranspiration |
| Sumario: | [EN] The increasing frequency and severity of hydrological extremes demand the development of early warning systems and effective adaptation and mitigation strategies. Such systems and strategies require spatially detailed hydrological predictions, mostly provided by hydrological models. However, current state-of-the-art hydrological predictions remain limited in their spatial resolution. A proposed solution is the integration of high-resolution (<1 km) Earth observation (EO) products in hydrological modelling in order to reach hyper-resolution (approximately 1 km(2)). Nonetheless, proper use of these data in hydrological modelling requires a comprehensive characterization of their uncertainties. Here, we evaluate the performance of high-resolution EO products of four hydrological variables (7 precipitation products, 5 snow cover area products, 6 surface soil moisture products, and 6 actual evapotranspiration products) against observational references. Two merged EO precipitation products at 1 km resolution (merged IMERG-SM2A and merged ERA5-IMERG-SM2A) reached correlation coefficients >0.5 with the benchmark reference over most areas and are recommended for hyper-resolution hydrological modelling over Europe. The MODIS (resolution of 250 m) and Sentinel-2/Landsat-8 (resolution of 20/30 m) snow cover products showed the highest classification accuracy and were selected as the best choice for the use of snow cover area products in hyper-resolution hydrological modelling. For surface soil moisture, the NSIDC SMAP product at 1 km resolution yielded correlation coefficients >0.6 at most stations and is recommended for hyper-resolution hydrological modelling. Finally, evapotranspiration products showed similar performances at the selected flux sites (correlations coefficients > 0.8). While the MODIS-Terra/Aqua evapotranspiration products (MOD16A2/MYD16A2) offer higher spatial resolution (500 m), making them potentially advantageous for hyper-resolution hydrological modelling, their temporal resolution is coarser (8-day intervals). In contrast, products like ETMonitor (1 km), ALEXI, and HOLAPS (5 km) provide daily estimates, albeit at lower spatial resolution. The assimilation of the proposed high-resolution products in models individually or in combination could lead us to hyper-resolution hydrological modelling. Still, developing integration workflows is required to overcome difficulties related to scale mismatches and data-gaps. |
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