Using the hierarchical modeling approach to derive spatial distribution of precipitation and temperature datasets. A case study for the area of Sicily (Italy).

Abstract

The interest for spatial interpolating climatic variables available by means of point measurements, as precipitation and temperature, arises from different needs, ranging from their usage for hydrological models to the reconstruction of climatic atlas of spatially distributed data. In some areas the spatial distribution of these variables can be related to the extremely variable morphology of the area. While simple deterministic interpolation methods usually produce just the spatial distribution of the variable of interest, implicitly relying on the spatial autocorrelation and manually tuning a few parameters, more complex statistical models, are able to derive the uncertainty associated with the estimate and model its different components, like those related to the measurement error and the parameters selection. With reference to the area of Sicily (Italy), mean annual and monthly precipitation and temperature data have been modeled using a hierarchical bayesian spatial model considering both the univariate approach and the multivariate approach with the elevation data, provided by a Digital Elevation Model, as secondary information source. Comparison with traditional geostatistical methods is reported. Highlights about the insights provided by the hierarchical models are commented, in particular with reference to the uncertainty associated with the estimates and with the measurements.