Perspectives in using a remotely sensed dryness index in distributed hydrological models at the river‐basin scale

Abstract

Abstract:In a previous study a spatially distributed hydrological model, based on the MIKE SHE code, was constructed and validated for the 375 000 km 2 Senegal River basin in West Africa. The model was constructed using spatial data on topography, soil types and vegetation characteristics together with time-series of precipitation from 112 stations in the basin. The model was calibrated and validated based on river discharge data from nine stations in the basin for 11 years. Calibration and validation results suggested that the spatial resolution of the input data in parts of the area was not sufficient for a satisfactory evaluation of the modelling performance. The study further examined the spatial patterns in the model input and output, and it was found that particularly the spatial resolution of the precipitation input had a major impact on the model response.In an attempt to improve the model performance, this study examines a remotely sensed dryness index for its relationship to simulated soil moisture and evaporation for six days in the wet season 1990. The index is derived from observations of surface temperature and vegetation index as measured by the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor. The correlation results between the index and the simulation results are of mixed quality. A sensitivity analysis, conducted on both estimates, reveals significant uncertainties in both. The study suggests that the remotely sensed dryness index with its current use of NOAA AVHRR data does not offer information that leads to a better calibration or validation of the simulation model in a spatial sense. The method potentially may become more suitable with the use of the upcoming high-resolution temporal Meteosat Second Generation data.

Estimation of regional evapotranspiration over the North China Plain using geostationary satellite data

“…Sandholt et al, 2002;Grimes and Diop, 2003;Loukas et al, 2005;Immerzeel et al, 2009) or in combination with hydrological modeling (e.g. Cruise and Miller, 1993;Houser et al, 1998;Mauser and Schadlich, 1998;Andersen et al, 2002;Biftu and Gan, 2004;Boegh et al, 2004;Stisen et al, 2008a).…”

Assessing spatial variability of soil water content through thermal inertia and NDVI

“…The methodology for the determination of TVDI for multitemporal applications is reported in . In the same work and in a subsequent one (Andersen et al, 2002) 17 TVDI has been tested by comparison with MIKE-SHE model outputs. TVDI well represents soil moisture spatial variability; on the other hand it seems suffering in reproducing temporal dynamics.…”

Physically-based modeling of topographic effects on spatial evapotranspiration and soil moisture patterns through radiation and wind

“…Xu and Singh (2000) provided an extensive review of the temperature and radiation based method. Some empirical methods have been developed to utilize the remotely sensed LST and vegetation index data to calculate ETA, such as triangle method (Price, 1990), B-method (Carlson et al, 1995), temperature/vegetation dryness index (TVDI) (Andersen et al, 2002). A good overview of the remote-sensing based techniques can be found in Verstraeten et al (2008).…”