A regional monthly precipitation simulation model based on an L-moment smoothed statistical regionalization approach

Using rain-gauge station records for the statistical characterization and simulation modeling of spatio-temporal precipitation field involves many issues and simplistic assumptions. One major issue is related to dealing with uncertainty at-site sample statistical inference, because of the limited length of records. Regional frequency analysis uses the idea of substituting space for time in order to reduce uncertainty. It assumes equal shapes of the precipitation statistical distributions in a region. However, this assumption limits the area of the analyzed region where this assumption is valid. The extension is dependent on terrain complexity. This work presents a new approach for the statistical regionalization of a large precipitation field, replacing the shape constancy assumption for the hypothesis of smooth spatial variation. The approach accounts for every uncertainty on site information, using an L-moment method for inference analysis. Additionally, the orographic effect is introduced in the regionalization, which substantially improves the interpolation performance and estimation of areal precipitation. The approach is used for modeling the monthly precipitation field in the Júcar River Basin Authority Demarcation (Spain), incorporating its stochastic structure, and spatial dependency coming from a geostatistical analysis. Issues related to the estimation of regional precipitation, and mean areal precipitation are discussed in the exposition.