Sensitivity of a soil-vegetation-atmosphere-transfer scheme to input data resolution and data classification

This paper presents modelling of the effects of input data resolution and classification of a regionally applied soil-vegetation-atmosphere-transfer (SVAT) scheme. Most SVAT schemes were developed at local scales but often are applied at regional scales to simulate regional water balances and to predict effects of environmental changes on catchment hydrology. Applying models at different scales requires investigating sensitivity to the available input data. In this study, investigated input data include soil maps, vegetation classifications, topographic information and weather data of varying temporal and spatial resolutions. Target quantities are simulated water fluxes such as evapotranspiration rates, groundwater recharge and runoff generation rates. Model sensitivity is estimated with respect to water balances and water flows, focusing on different time periods (months, years). The soil vegetation atmosphere transfer scheme SIMULAT is applied to two different catchments representing different environments where data sets of varying data quality and resolution are available. Results show that, on an annual time scale, SIMULAT is most sensitive to aggregation of soil information and mis-classification in vegetation data. On the monthly time scale, SIMULAT is also very sensitive to disaggregation of precipitation data. The sensitivity to spatial distribution of land-use data and spatio-temporal resolution of weather data is low. Based on the investigations, a ranking of the sensitivity of the model to resolution and classification of different input data sets is proposed. Minimum requirements concerning data resolution for regional scale SVAT applications are derived.