A remote sensing driven distributed hydrological model of the Senegal River basin: a remote sensing-driven model

TY - JOUR

T1 - A remote sensing driven distributed hydrological model of the Senegal River basin

T2 - a remote sensing-driven model

AU - Stisen, Simon

AU - Jensen, Karsten Høgh

AU - Sandholt, Inge

AU - Grimes, David I.

PY - 2008

Y1 - 2008

N2 - Distributed hydrological models require extensive data amounts for driving the models and for parameterization of the land surface and subsurface. This study investigates the potential of applying remote sensing (RS) based input data in a hydrological model for the 350,000 km2 Senegal River basin in West Africa. By utilizing remote sensing data to estimate precipitation, potential evapotranspiration (PET) and leaf area index (LAI) the model was driven entirely by remote sensing based data and independent of traditional meteorological data. The remote sensing retrievals were based on data from the geostationary METEOSAT-7 and the polar orbiting advanced very high resolution radiometer (AVHRR) sensors using well documented techniques.The distributed hydrological model MIKE SHE was calibrated and validated against observed discharge for six individual subcatchments during the period 1998-2005. The model generally performed well for both root mean square error (RMSE), water balance error (WBE) and correlation coefficient (R2). For comparison a model based on standard meteorological driving variables was developed for a single subcatchment. The two models based on remote sensing and conventional data, respectively, exhibited similar model performances. Simulated actual evapotranspiration (AET) was compared to measurements at point scale and good agreement was obtained both on an event basis and seasonally. Although the spatial model simulations cannot be evaluated quantitatively a comparison between spatial outputs of AET from both model setups was carried out. This revealed substantial differences in the spatial patterns of AET for the examined subcatchment, in spite of similar values of predicted discharge and average AET. The potential for driving large scale hydrological models using remote sensing data was clearly demonstrated and further emphasized by the presence of long time records and near real time accessibility of the satellite data sources.

AB - Distributed hydrological models require extensive data amounts for driving the models and for parameterization of the land surface and subsurface. This study investigates the potential of applying remote sensing (RS) based input data in a hydrological model for the 350,000 km2 Senegal River basin in West Africa. By utilizing remote sensing data to estimate precipitation, potential evapotranspiration (PET) and leaf area index (LAI) the model was driven entirely by remote sensing based data and independent of traditional meteorological data. The remote sensing retrievals were based on data from the geostationary METEOSAT-7 and the polar orbiting advanced very high resolution radiometer (AVHRR) sensors using well documented techniques.The distributed hydrological model MIKE SHE was calibrated and validated against observed discharge for six individual subcatchments during the period 1998-2005. The model generally performed well for both root mean square error (RMSE), water balance error (WBE) and correlation coefficient (R2). For comparison a model based on standard meteorological driving variables was developed for a single subcatchment. The two models based on remote sensing and conventional data, respectively, exhibited similar model performances. Simulated actual evapotranspiration (AET) was compared to measurements at point scale and good agreement was obtained both on an event basis and seasonally. Although the spatial model simulations cannot be evaluated quantitatively a comparison between spatial outputs of AET from both model setups was carried out. This revealed substantial differences in the spatial patterns of AET for the examined subcatchment, in spite of similar values of predicted discharge and average AET. The potential for driving large scale hydrological models using remote sensing data was clearly demonstrated and further emphasized by the presence of long time records and near real time accessibility of the satellite data sources.

KW - Faculty of Humanities

KW - distributed hydrological modeling

KW - AVHRR

KW - METEOSAT

KW - remote sensing

KW - large scale

U2 - 10.1016/j.jhydrol.2008.03.006

DO - 10.1016/j.jhydrol.2008.03.006

M3 - Journal article

SN - 0022-1694

VL - 354

SP - 131

EP - 148

JO - Journal of Hydrology

JF - Journal of Hydrology

IS - 1-4

ER -