Paper
19 October 2012 Comparing results of a remote sensing driven interception-infiltration model for regional to global applications with ECMWF data
M. Tum, E. Borg
Author Affiliations +
Abstract
We present results of a remote sensing based modelling approach to simulate the 1D water transport in the vadose zone of unsaturated soils on a daily basis, which can be used for regional to global applications. To calculate the hydraulic conductivity our model is driven by van Genuchten parameters, which we calculated for Bavaria (South-East-Germany), which we choose as area of investigation, using the ISRIC-WISE Harmonized Global Soil Profile Dataset Ver. 3.1 and the Rosetta programme. Soil depth and layering of up to six layers were defined independently for each soil. Interception by vegetation is also considered by using Leaf Area Index (LAI) time series from SPOT-VEGETATION. Precipitation is based on daily time series from the European Centre for Medium-Range Weather Forecasts (ECMWF). The model was applied to the Biosphere Energy Transfer Hydrology (BETHY/DLR) vegetation model, driven at the German Aerospace Center (DLR), to discuss the possibility of regionalization of a global model concept, regarding the soil water budged. Furthermore we compare our results with ECMWF data and discuss the results for the state of Bavaria. We found a good agreement for the general characteristics of our results with this dataset, especially for soils which are close to the standard characteristics of the ECMWF. Disagreements were found for shallow soils and soils under stagnant moisture, which are not considered in the ECMWF modelling scheme, but are distinguished in our approach.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
M. Tum and E. Borg "Comparing results of a remote sensing driven interception-infiltration model for regional to global applications with ECMWF data", Proc. SPIE 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV, 853102 (19 October 2012); https://doi.org/10.1117/12.974553
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KEYWORDS
Data modeling

Soil science

Remote sensing

Vegetation

Modeling

Hydrology

Meteorology

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