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<p>Rainy-season integrity is fundamental to hydrological cycles and disaster risk management, yet climate change increasingly amplifies agricultural vulnerability, flood and landslide frequency, and socioeconomic fragility. Transcending the constraints of traditional univariate metrics, this research presents a diagnostic framework synthesizing composite indicators with hybrid regionalization. Implemented across the Huaihe Basin, this technique employs multivariate cluster analysis to divide the region into two precipitation-homogeneous zones (labeled as Region A and Region B, respectively) and further subdivides the rainy season into six distinct subtypes, each associated with different levels of hydrometeorological hazard potential. Using Accumulated Local Effects (ALE) analysis, we quantify the differentiated impacts of preceding-winter sea surface temperature (SST) anomalies, including ENSO, PDO, IOD, and IOBW. Results indicate that the dominant SST drivers differ across rainy-season types and characteristics. A structured SST–atmosphere–rainy-season framework further reveals that these oceanic signals are transmitted through distinct atmospheric pathways involving upper-level jet adjustments, mid-level geopotential height anomalies, vertical motion reorganization, and moisture convergence shifts over key Indo-Pacific sectors. By influence ranking under nonlinear coupling, the framework identifies physically interpretable precursor sets suitable for seasonal forecasting. These subtype-specific processes underscore the framework’s effectiveness in unraveling ocean-forced hydroclimatic variability and in enhancing regional predictability of rainfall-related hazards.</p>

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Diagnosing rainy-season heterogeneity in Huaihe Basin: a generalizable clustering and teleconnection-based analytical approach

  • 期刊论文

  • 发表日期：

    2026年5月12日

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  • Kanglong Liu,
  • Qing Cao,
  • Xiao Huang,
  • Yao Yang,
  • Wanjing Luo,
  • Weifang Wang

摘要

Rainy-season integrity is fundamental to hydrological cycles and disaster risk management, yet climate change increasingly amplifies agricultural vulnerability, flood and landslide frequency, and socioeconomic fragility. Transcending the constraints of traditional univariate metrics, this research presents a diagnostic framework synthesizing composite indicators with hybrid regionalization. Implemented across the Huaihe Basin, this technique employs multivariate cluster analysis to divide the region into two precipitation-homogeneous zones (labeled as Region A and Region B, respectively) and further subdivides the rainy season into six distinct subtypes, each associated with different levels of hydrometeorological hazard potential. Using Accumulated Local Effects (ALE) analysis, we quantify the differentiated impacts of preceding-winter sea surface temperature (SST) anomalies, including ENSO, PDO, IOD, and IOBW. Results indicate that the dominant SST drivers differ across rainy-season types and characteristics. A structured SST–atmosphere–rainy-season framework further reveals that these oceanic signals are transmitted through distinct atmospheric pathways involving upper-level jet adjustments, mid-level geopotential height anomalies, vertical motion reorganization, and moisture convergence shifts over key Indo-Pacific sectors. By influence ranking under nonlinear coupling, the framework identifies physically interpretable precursor sets suitable for seasonal forecasting. These subtype-specific processes underscore the framework’s effectiveness in unraveling ocean-forced hydroclimatic variability and in enhancing regional predictability of rainfall-related hazards.