Scholarlink

<p>Flood risk to agriculture is strongly influenced by the timing of inundation relative to crop development stages, making flood seasonality a critical but often overlooked component in damage estimation. This study introduces a generalizable regionalization framework that combines hydrological clustering and machine learning to incorporate seasonal flood probability into agricultural risk assessment. The approach involves identifying clusters of gauged catchments with similar patterns of intra-annual flood occurrence and using supervised classification to extrapolate these seasonal regimes to ungauged catchments based on their physical attributes. The resulting spatially distributed maps of monthly flood probability can be then integrated with a flood damage model to calculate expected annual losses and support risk estimates across entire river districts. The proposed framework, applied in this study to the Po River District (Italy) for illustrative purposes, is scalable and adaptable to different regions, contributing to more robust and context-sensitive adaptation planning in agriculture. Results highlight the importance of accounting for flood seasonality in cost-benefit analyses within agricultural contexts, as neglecting intra-annual variability can lead to overestimated damage projections and suboptimal mitigation strategies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An integrated regionalization framework for incorporating flood seasonality into agricultural flood risk assessments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月8日

查看全文
  • Anna Rita Scorzini,
  • Charlie Dayane Paz Idarraga,
  • Daniela Molinari

摘要

Flood risk to agriculture is strongly influenced by the timing of inundation relative to crop development stages, making flood seasonality a critical but often overlooked component in damage estimation. This study introduces a generalizable regionalization framework that combines hydrological clustering and machine learning to incorporate seasonal flood probability into agricultural risk assessment. The approach involves identifying clusters of gauged catchments with similar patterns of intra-annual flood occurrence and using supervised classification to extrapolate these seasonal regimes to ungauged catchments based on their physical attributes. The resulting spatially distributed maps of monthly flood probability can be then integrated with a flood damage model to calculate expected annual losses and support risk estimates across entire river districts. The proposed framework, applied in this study to the Po River District (Italy) for illustrative purposes, is scalable and adaptable to different regions, contributing to more robust and context-sensitive adaptation planning in agriculture. Results highlight the importance of accounting for flood seasonality in cost-benefit analyses within agricultural contexts, as neglecting intra-annual variability can lead to overestimated damage projections and suboptimal mitigation strategies.