Spatial Modeling of Precipitation Based on Data-Driven Warping of Gaussian Processes.

Modeling and forecasting spatiotemporal patterns of precipitation is crucial for managing water resources and mitigating water-related hazards. Globally valid spatiotemporal models of precipitation are not available. This is due to the intermittent nature, non-Gaussian distribution, and complex geographical dependence of precipitation processes. Herein we propose a data-driven model of precipitation amount which employs a novel, data-driven (non-parametric) implementation of warped Gaussian processes. We investigate the proposed warped Gaussian process regression (wGPR) using (i) a synthetic test function contaminated with non-Gaussian noise and (ii) a reanalysis dataset of monthly precipitation from the Mediterranean island of Crete. Cross-validation analysis is used to establish the advantages of non-parametric warping for the interpolation of incomplete data. We conclude that wGPR equipped with the proposed data-driven warping provides enhanced flexibility and-at least for the cases studied- improved predictive accuracy for non-Gaussian data.

Geostatistical analysis of precipitation in the island of Crete (Greece) based on a sparse monitoring network.

Based on the predictions of General Circulation Models, significant reduction of precipitation in Mediterranean areas is a possible scenario. Hence, better understanding of the spatial and temporal precipitation patterns is necessary in order to quantify desertification risks and design suitable mitigation measures. This study uses monthly precipitation measurements from a sparse network of 54 monitoring stations on the Mediterranean island of Crete (Greece). The study period extends from 1948 to 2012. The data reveal strong correlations between the western and eastern parts of the island. However, the average annual precipitation in the West is about 450 mm higher than that in the East. We construct a spatial model of average annual precipitation in Crete. The model involves a topographic trend and residuals with anisotropic spatial correlations which are fitted with a recently developed variogram function. We use regression kriging to generate annual precipitation maps and to identify locations of high estimation uncertainty. To our knowledge, this is the most detailed spatial analysis of precipitation in Crete to date. We present the analysis in detail for the year 1971. The trend accounts for ≈ 74% of the total variance. The highest precipitation estimate is 2331 mm in the West and 1781 mm in the East. The highest relative estimation uncertainty (≈ 20%) is observed along the southeastern coastline of the island, where the lowest values of annual precipitation are observed. This region includes one of the major agricultural areas of the island. The same overall patterns are found for other years in the study. Finally, we find no statistical evidence for a decrease in the global (over the entire island) annual precipitation during the study period.