Combining Landsat observations with hydrological modelling for improved surface water monitoring of small lakes
| Autor: | Pierre-Olivier Malaterre, Gilles Belaud, Patrick Le Goulven, Roger Calvez, Andrew Ogilvie, Mark Mulligan, Sylvain Massuel |
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| Přispěvatelé: | Institut de Recherche pour le Développement (IRD), King‘s College London, Gestion de l'Eau, Acteurs, Usages (UMR G-EAU), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
WATER HARVESTING
Hydrological modelling 0208 environmental biotechnology Rainfall-runoff model 02 engineering and technology KALMAN FILTERS Water balance Data assimilation DATA ASSIMILATION ENSEMBLE KALMAN FILTER Water harvesting Satellite imagery TUNISIE RAINFALL-RUNOFF MODEL Water Science and Technology FLOODS 15. Life on land Remote sensing 6. Clean water 020801 environmental engineering Ensemble Kalman Filter HYDROLOGICAL MODELING 13. Climate action Temporal resolution Climatology WATER BALANCE [SDE]Environmental Sciences ENVIRONMENTAL MONITORING Environmental science Ensemble Kalman filter Surface runoff Surface water |
| Zdroj: | Journal of Hydrology Journal of Hydrology, 2018, 566, pp.109-121. ⟨10.1016/j.jhydrol.2018.08.076⟩ Journal of Hydrology, Elsevier, 2018, 566, pp.109-121. ⟨10.1016/j.jhydrol.2018.08.076⟩ |
| ISSN: | 0022-1694 |
| DOI: | 10.1016/j.jhydrol.2018.08.076⟩ |
| Popis: | [Departement_IRSTEA]Eaux [TR1_IRSTEA]GEUSI [ADD1_IRSTEA]Gestion intégrée de la ressource et des infrastructures; International audience; Small reservoirs represent a critical water supply to millions of farmers across semi-arid regions, but their hydrological modelling suffers from data scarcity and highly variable and localised rainfall intensities. Increased availability of satellite imagery provide substantial opportunities but the monitoring of surface water resources is constrained by the small size and rapid flood declines in small reservoirs. To overcome remote sensing and hydrological modelling difficulties, the benefits of combining field data, numerical modelling and satellite observations to monitor small reservoirs were investigated. Building on substantial field data, coupled daily rainfall-runoff and water balance models were developed for 7 small reservoirs (1'10 ha) in semi arid Tunisia over 1999'2014. Surface water observations from MNDWI classifications on 546 Landsat TM, ETM+ and OLI sensors were used to update model outputs through an Ensemble (n = 100) Kalman Filter over the 15 year period. The Ensemble Kalman Filter, providing near-real time corrections, reduced runoff errors by modulating incorrectly modelled rainfall events, while compensating for Landsat's limited temporal resolution and correcting classification outliers. Validated against long term hydrometric field data, daily volume root mean square errors (RMSE) decreased by 54% to 31 200 m3 across 7 lakes compared to the initial model forecast. The method reproduced the amplitude and timing of major floods and their decline phases, providing a valuable approach to improve hydrological monitoring (NSE increase from 0.64 up to 0.94) of flood dynamics in small water bodies. In the smallest and data-scarce lakes, higher temporal and spatial resolution time series are essential to improve monitoring accuracy. |
| Databáze: | OpenAIRE |
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