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Spatial functional data analysis for regionalizing precipitation seasonality and intensity in a sparsely monitored region: Unveiling the spatio‐temporal dependencies of precipitation in Ecuador

The identification of area‐wise homogeneous precipitation regions helps to unveil similar precipitation patterns and amounts, where similar atmospheric processes at diverse temporal scales are likely to occur. However, although scientifically and socially relevant, the regionalization of precipitation is challenging, specially in areas of complex orography and with sparse monitoring. This limits our understanding of complex spatio‐temporal dependencies and hinders any information‐based resource management decision‐making. Gridded satellite precipitation products are useful in this context, even though they contain bias errors. Spatial functional data analysis (sFDA) is a novel technique that considers time as well as space dependencies by means of spatial autocorrelation and complete time functions, one for each spatial point. Therefore, the aim of this study is to evaluate sFDA as a tool to regionalize seasonality and intensity precipitation patterns, having Ecuador as a case study. The Tropical Rainfall Measuring Mission (TRMM 3B43) satellite precipitation is used to create an exhaustive spatial delineation. To the best of our knowledge, this is the first time that a sFDA regionalization approach is performed on gridded satellite precipitation. The complex orography and heat‐driven atmospheric processes in Ecuador's latitude make it a highly non‐trivial case to test the aforementioned technique. As a result, five relevant regions of precipitation seasonality were spatially delineated and temporally characterized. Three of them were zonally oriented, and two meridional‐wise in the coast. In addition, 20 relevant intensity regions across Ecuador were identified specially in regions with sparse monitoring. The regions were related to regional climate processes. However, limitations were found in regions with important orographic precipitation and locally variability patterns, probably due to the shortcomings of TRMM precipitation quantification. After the successful application of hierarchical regionalization using sFDA in a tropical region with sparse monitoring, it is reasonable to conclude that sFDA is a robust method to detect compact and meaningful homogeneous areas.