Kriging has been successfully used in environmental, meteorological, and hydrological studies to generate spatial maps based on partial data. The spatial patterns of precipitation are calculated by means of stochastic spatial interpolation methods known as Kriging. Furthermore, despite significant progress over the last 30 years, the modeling of interactions between spatial and temporal correlations is an open research topic. If the temporal dimension is taken into account, modeling is further complicated due to the strong seasonal variability and intermittence of precipitation. The total amount of precipitation received by an area over a specific time window is often modeled by means of parametric, non-Gaussian, probability distributions. In the Mediterranean region, the two large water bodies (the Atlantic Ocean and the Mediterranean Sea), as well as the major European mountain ranges are considered the main causes of extreme precipitation. Īccurate spatial models of precipitation are difficult to formulate, due to the variability and intermittent nature of precipitation across different temporal and spatial scales. Especially in such regions the interplay of climate change and changes in land use is crucial for water resource availability. Certain areas, including the Mediterranean basin, have been characterized as “climate change hot spots”. The impact of climate change on societies worldwide has renewed interest in quantitative methodologies that can estimate spatiotemporal climate and weather patterns. Heavy precipitation events are expected to increase in several Mediterranean countries leading to increased flooding risks. In addition, there is evidence that the number of precipitation events has decreased while the intensity per event has increased. In the Mediterranean region, on the other hand, it is expected that summer precipitation will decrease, thus increasing the risk of drought and aridification. In certain areas, both the frequency and intensity of heavy precipitation events have increased. It is thus important to better understand and forecast the spatial and temporal patterns of precipitation since these patterns affect the hydrological cycle and are crucial for the sustainability of human life on the planet.Įxpert estimates-included in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change-indicate an increase in the global averaged precipitation since 1950. One of the main concerns is their impact on the availability of water resources. Climate and land-use changes affect ecosystems and human societies globally. Such adverse impacts have been anticipated by scientists. At the same time, prolonged droughts plagued several parts of the world. In 2021, extreme rainfall hit the Henan Province of China in July, Western Europe suffered severe flooding in mid-July, while extreme rainfall and flooding also affected the northern Amazon basin in South America and several parts of Africa. 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.Ĭlimate change combined with changes in land use is causing increased frequencies of drought and flooding events in many parts of the world. Cross-validation analysis is used to establish the advantages of non-parametric warping for the interpolation of incomplete data. 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. Herein we propose a data-driven model of precipitation amount which employs a novel, data-driven (non-parametric) implementation of warped Gaussian processes. This is due to the intermittent nature, non-Gaussian distribution, and complex geographical dependence of precipitation processes. Globally valid spatiotemporal models of precipitation are not available. Modeling and forecasting spatiotemporal patterns of precipitation is crucial for managing water resources and mitigating water-related hazards.
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