Using evapotranspiration estimates from Landsat TM data to analyse uncertainties of a spatially distributed hydrological model (PRMS)

Michael Vohland; Marion Stellmes

doi:10.1117/12.800271

2 October 2008 Using evapotranspiration estimates from Landsat TM data to analyse uncertainties of a spatially distributed hydrological model (PRMS)

Michael Vohland, Marion Stellmes

Author Affiliations +

Proceedings Volume 7104, Remote Sensing for Agriculture, Ecosystems, and Hydrology X; 710413 (2008) https://doi.org/10.1117/12.800271
Event: SPIE Remote Sensing, 2008, Cardiff, Wales, United Kingdom

Abstract

In the field of hydrological modelling, there is mostly a lack of spatially distributed data that may allow a detailed analysis of simulation results. This study was to demonstrate that remote sensing can partly fill this gap, as combining reflective and thermal data allows the retrieval of estimates for evapotranspiration (ET). Two Landsat-5 TM scenes were analysed, and the results were afterwards compared to the daily output of the Precipitation Runoff Modeling System, a conceptual model based on Hydrologic Response Units and designed for meso- to macroscale applications. For the study site, the mesoscale Ruwer basin located in the low mountain range of Rhineland-Palatinate (Germany), an overall good agreement of ET estimates retrieved by both approaches was found. At one date, some mismatches indicated clear inconsistencies in the model structure and parameterisation scheme. Based on these findings, a modified soil module was implemented to allow for a more realistic specification of land use dependant parameters. After this, PRMS provided ET estimates now very similar to those from Landsat TM, and the RMSE was reduced from 1.30 to 0.86 mm. These results indicate, that the representation of the hydrological cycle by hydrological modelling may be improved by the integration of appropriate remote sensing data.

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Michael Vohland and Marion Stellmes "Using evapotranspiration estimates from Landsat TM data to analyse uncertainties of a spatially distributed hydrological model (PRMS)", Proc. SPIE 7104, Remote Sensing for Agriculture, Ecosystems, and Hydrology X, 710413 (2 October 2008); https://doi.org/10.1117/12.800271

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KEYWORDS

Data modeling

Earth observing sensors

Landsat

Modeling

Vegetation

Remote sensing

Heat flux

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