Combining Landsat observations with hydrological modelling for improved surface water monitoring of small lakes

JOURNAL OF HYDROLOGY

卷:566

Article

Ogilvie, Andrew^1,2  Belaud, Gilles¹  Massuel, Sylvain¹  Mulligan, Mark²  Le Goulven, Patrick¹  Malaterre, Pierre-Olivier¹  Calvez, Roger¹ 

[1] Univ Montpellier, Montpellier SupAgro, IRSTEA, G EAU,AgroParisTech,Cirad,IRD, Montpellier, France

[2] Kings Coll London, Dept Geog, London WC2R 2LS, England

关键词: Remote sensing;    Water balance;    Rainfall-runoff model;    Data assimilation;    Ensemble Kalman Filter;    Water harvesting;   

DOI  :  10.1016/j.jhydrol.2018.08.076

来源: Elsevier

【 摘 要 】

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 31200 m(3) 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.

【 授权许可】

Free