Soil available water capacity interpolation and spatial uncertainty modelling at multiple geographical extents

Assessment of the spatial distribution of environmental variables and of the associated uncertainty is a key issue in environmental modelling. The water content of soil plays an important role in many ecological and hydrological processes for land suitability evaluation. In this study we present a flexible procedure to interpolate soil-related variables that uses covariates to estimate the spatial trend of the variables and quantifies the uncertainty dealing with non-linear relationships. The procedure further extends approaches based on generalized additive models. The use of Gaussian simulations of the error allows the assessment of spatial uncertainty. The method was applied to available soil water capacity for three different nested extents: national, regional, and catchment. The models fitted have different significant covariates and different estimated values according to the region considered. The results suggest that the estimates from the model fitted at the appropriate extent are the most accurate. Taking into account the uncertainty of the trend, the results provided a realistic estimation of the variability and they are spatially consistent with the geomorphological patterns. Estimating the variability with the proposed procedure is useful for further environmental and land use modelling and it can be integrated with uncertainty from other variables, such as those derived from climate models.