
Proceedings Paper
Using evapotranspiration estimates from Landsat TM data to analyse uncertainties of a spatially distributed hydrological model (PRMS)Format | Member Price | Non-Member Price |
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Paper 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.
Paper Details
Date Published: 2 October 2008
PDF: 10 pages
Proc. SPIE 7104, Remote Sensing for Agriculture, Ecosystems, and Hydrology X, 710413 (2 October 2008); doi: 10.1117/12.800271
Published in SPIE Proceedings Vol. 7104:
Remote Sensing for Agriculture, Ecosystems, and Hydrology X
Christopher M. U. Neale; Manfred Owe; Guido D'Urso, Editor(s)
PDF: 10 pages
Proc. SPIE 7104, Remote Sensing for Agriculture, Ecosystems, and Hydrology X, 710413 (2 October 2008); doi: 10.1117/12.800271
Show Author Affiliations
Michael Vohland, Univ. Trier (Germany)
Marion Stellmes, Univ. Trier (Germany)
Published in SPIE Proceedings Vol. 7104:
Remote Sensing for Agriculture, Ecosystems, and Hydrology X
Christopher M. U. Neale; Manfred Owe; Guido D'Urso, Editor(s)
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