Decadal Rainfall Distribution Shift in Northern Coastal Java: Histogram-Based Clustering Using Observations and CHIRPS 10.32526/ennrj/24/20250315
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Abstract
Coastal lowlands are highly vulnerable to seasonal changes and rainfall variability, which affect the risk of floods and droughts. Therefore, this study aimed to analyze decadal changes in the monthly rainfall distribution along the northern coast of Central Java (Pantura), using data from 51 stations of the Meteorology, Climatology, and Geophysics Agency (BMKG) and the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) v2.0. A smoothed histogram was used to calculate the probability density of rainfall for each decade. Subsequently, the distribution between decades was compared using 6 metrics to assess distribution displacement, divergence, overlap, and shift in peak, centroid, and width. The level of uncertainty was measured using the bootstrap method at confidence levels of 5%, 10%, and 25%. The six metrics were normalized and combined into a single composite index called the Total Normalized Index (TNI). K-means clustering was then applied to map changes according to the same rainfall patterns. Analysis of both sources showed a gradient pattern from west to east. Rainfall was concentrated in the west, with moderate changes in structure in the central area, and a more dispersed rainfall pattern with high variability in the east, particularly from 2011 to 2020. CHIRPS data recorded the regional trends but tended to cloud out local fluctuations. This study contributed to new investigations since the implementation of histogram-based rainfall distribution analysis into a composite index. The application of this method as part of a spatial clustering model was proposed to map alterations in rainfall distribution each decade. The method also helped in evaluating coastal climate risk and adaptation planning.
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