Analyzing Space–Time Coherence in Precipitation Seasonality across Different European Climates

Abstract

Seasonality is a fundamental feature of environmental systems which critically depend on the climate annual cycle. The regularity of the precipitation regime, in particular, is a basic factor to sustain equilibrium conditions. An incomplete or biased understanding of precipitation seasonality, in terms of temporal and spatial properties, could severely limit our ability to respond to climate risk, especially in areas with limited water resources or fragile ecosystems. Here, we analyze precipitation data from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) at 0.050 resolution to study the spatial features of the precipitation seasonality across different climate zones in Central-Southern Europe during the period 1981–2018. A cluster analysis of the average annual precipitation cycle shows that seasonality under the current climate can be synthesized in the form of a progressive deformation process of the annual cycle, which starts from the northernmost areas with maximum values in summer and ends in the south, where maximum values are recorded in winter. Our analysis is useful to detect local season-dependent changes, enhancing our understanding of the geography of climate change. As an example of application to this issue, we discuss the seasonality analysis in a simulated scenario based on IPCC projections.

Exploring the Use of Sentinel-2 Data to Monitor Heterogeneous Effects of Contextual Drought and Heatwaves on Mediterranean Forests

“…The use of Sentinel-2 for a continuous monitoring which accounts for local damage heterogeneity was evaluated, beyond the mere identification of affected areas which is usually investigated in the current literature. Such a continuous monitoring is fundamental to follow the health status of Mediterranean forests, which are among the most vulnerable to climate risk, not only because of the expected increase of extreme events but also for possible changes in the rainfall regimes [75] that can alter the ecosystem phenological cycles. Our analyses focused on in situ and remote data separately to make independent assessments of the forest status.…”

Modeling Spatio-Temporal Divergence in Land Vulnerability to Desertification with Local Regressions

“…Central Italy is positioned in-between these regions and alternates between areas with thriving activities and economically disadvantaged zones (basically, rural and mountain areas). These pronounced differences generate a diverse range of ecosystems responses to natural and anthropogenic disturbance, sometimes exacerbated by the effects of climate change (e.g., irregular precipitation regimes, heatwaves, late frost events, and flooding [64][65][66]). Thanks to such factors, Italy is particularly suitable for testing the impact of these geographical gradients in the spatial distribution of vulnerable areas to land degradation [67][68][69].…”

Deriving tropical cyclone associated flood hazard information using clustered GPM-IMERG rainfall signatures: a case study in Dominica

“…K-means clustering in RStudio outputs a list of statistics helpful in determining the optimal number of clusters (K) and evaluating the quality of the partition. For example, the within-cluster sum of squares (WSS), the total sum of squares (TSS), and the between-cluster sum of squares (BSS) are computed using equations 1, 2, and 3, respectively (Soetewey 2020; Lanfredi et al 2020). Where is the overall mean of observations, and any given observation is assigned to a given cluster for which it has the least squared distance from the cluster centroid .…”