@article{fdi:010074463,
  title = {{C}ombining {L}andsat observations with hydrological modelling for improved surface water monitoring of small lakes},
  author = {{O}gilvie, {A}ndrew and {B}elaud, {G}. and {M}assuel, {S}ylvain and {M}ulligan, {M}. and {L}e {G}oulven, {P}atrick and {M}alaterre, {P}. {O}. and {C}alvez, {R}oger},
  editor = {},
  language = {{ENG}},
  abstract = {{S}mall 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. {I}ncreased 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. {T}o overcome remote sensing and hydrological modelling difficulties, the benefits of combining field data, numerical modelling and satellite observations to monitor small reservoirs were investigated. {B}uilding on substantial field data, coupled daily rainfall-runoff and water balance models were developed for 7 small reservoirs (1-10 ha) in semi arid {T}unisia over 1999-2014. {S}urface water observations from {MNDWI} classifications on 546 {L}andsat {TM}, {ETM} + and {OLI} sensors were used to update model outputs through an {E}nsemble (n = 100) {K}alman {F}ilter over the 15 year period. {T}he {E}nsemble {K}alman {F}ilter, providing near-real time corrections, reduced runoff errors by modulating incorrectly modelled rainfall events, while compensating for {L}andsat's limited temporal resolution and correcting classification outliers. {V}alidated 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. {T}he 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. {I}n the smallest and data-scarce lakes, higher temporal and spatial resolution time series are essential to improve monitoring accuracy.},
  keywords = {{R}emote sensing ; {W}ater balance ; {R}ainfall-runoff model ; {D}ata assimilation ; {E}nsemble {K}alman {F}ilter ; {W}ater harvesting ; {TUNISIE} ; {ZONE} {SEMIARIDE}},
  booktitle = {},
  journal = {{J}ournal of {H}ydrology},
  volume = {566},
  numero = {},
  pages = {109--121},
  ISSN = {0022-1694},
  year = {2018},
  DOI = {10.1016/j.jhydrol.2018.08.076},
  URL = {https://www.documentation.ird.fr/hor/fdi:010074463},
}