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<p>Dry-season rainfall patterns are a critical determinant of drought severity in monsoonal regions of Indonesia. These patterns are closely associated with climate anomalies, particularly the El Niño–Southern Oscillation (ENSO) with localized responses. However, the spatial heterogeneity of dry-season rainfall remains poorly understood. We applied a comprehensive framework combining principal component analysis (PCA), k-means++ clustering, and local spatial autocorrelation to delineate coherent dry-season rainfall regions in East Java. The analysis uses principal component scores derived from bias-corrected Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) rainfall for the period 1982–2020. The results identify nine distinct rainfall clusters which reflect north–south contrast between southern mountainous region and northern lowland. Southern clusters acts as rainfall hot spots which receiving higher dry-season rainfall. However, northern lowland clusters function as cold spots which received less rainfall. Regression analyses indicates rainfall declines of approximately 50–60&#xa0;mm per 1&#xa0;°C Nino 3.4 sea surface temperature anomalies in southern mountains, while northern coastal zones exhibit relatively smaller responses. This spatial heterogeneity reflects the combined influence of large-scale circulation, local sea surface temperature variability, and orographic amplification. The resulting regionalization provides a basis for localized drought preparedness and water resources planning. Further research should prioritize the southern mountainous region to better resolve complex physical mechanisms and reduce uncertainty in rainfall estimates.</p>

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Comprehensive regionalization of dry season rainfall for enhanced drought risk management in monsoonal region of Indonesia

  • 期刊论文

  • 发表日期：

    2026年1月22日

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  • Heri Mulyanti,
  • I. Istadi,
  • Rahmat Gernowo

摘要

Dry-season rainfall patterns are a critical determinant of drought severity in monsoonal regions of Indonesia. These patterns are closely associated with climate anomalies, particularly the El Niño–Southern Oscillation (ENSO) with localized responses. However, the spatial heterogeneity of dry-season rainfall remains poorly understood. We applied a comprehensive framework combining principal component analysis (PCA), k-means++ clustering, and local spatial autocorrelation to delineate coherent dry-season rainfall regions in East Java. The analysis uses principal component scores derived from bias-corrected Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) rainfall for the period 1982–2020. The results identify nine distinct rainfall clusters which reflect north–south contrast between southern mountainous region and northern lowland. Southern clusters acts as rainfall hot spots which receiving higher dry-season rainfall. However, northern lowland clusters function as cold spots which received less rainfall. Regression analyses indicates rainfall declines of approximately 50–60 mm per 1 °C Nino 3.4 sea surface temperature anomalies in southern mountains, while northern coastal zones exhibit relatively smaller responses. This spatial heterogeneity reflects the combined influence of large-scale circulation, local sea surface temperature variability, and orographic amplification. The resulting regionalization provides a basis for localized drought preparedness and water resources planning. Further research should prioritize the southern mountainous region to better resolve complex physical mechanisms and reduce uncertainty in rainfall estimates.