Assessing the soil texture-specific sensitivity of simulated soil moisture to projected climate change by SVAT modelling

Abstract

Climate change is assumed to have a regionally specific impact on the soil moisture regime. The impact of climate change on soil moisture can be expected to depend on the soil texture. Because soil moisture observations are not available operationally, models can be used to predict these changes. In this study, a soil vegetation atmosphere transfer scheme (SVAT) was applied to virtual soil columns to assess the soil texture-specific sensitivity of simulated soil moisture to projected climate change. For each of the 31 soil texture classes of the German soil texture classification, long-term simulations were carried out based on the observed and scenario-based climate data representing five different climate regions in Germany. The simulation results indicate that soil moisture regimes considerably differ from region to region and among different soil texture classes. Different soil texture classes showed different sensitivities of soil moisture with respect to the projected climate change. While the differences in soil moisture between the current conditions and the climate scenarios were largest for silt soils, they were smallest for clay soils for both continental and humid climates. Sand and loam soils behaved intermediately, showing a moderate sensitivity. The results also showed that the soil texture-specific sensitivity of soil moisture to climate change was largest for soils that were not affected by groundwater (no capillary rise). Differences between soil texture classes decreased with an increasing influence of groundwater. In contrast, increasing vegetation density, rooting depths and transpiration demand induced the sensitivity of soil moisture to climate change, except in continental climates. This study indicates that validated, physically based soil hydrological models serve as suitable tools to assess the response of soil moisture to changing climate conditions. Based on virtual soil columns, modelling experiments systematically reveal soil texture-dependent sensitivities that cannot readily be identified in real world studies due to the limited availability and accessibility of the wide spectrum of different soil textures.