The Cost of Bad Timing: How Phenology and Frequency Determine Agricultural Flood Risk
不適切なタイミングのコスト:フェノロジーと頻度が農業洪水リスクを決定する方法 (AI 翻訳)
S. Jalilov, Robert Maltsbarger, H. Gedikoglu
🤖 gxceed AI 要約
日本語
本研究は、作物の成長段階に依存する洪水被害を評価する新しい枠組みを提案。中西部の事例から、頻度の高い小規模洪水が、成長期と重なる確率が高いため、経済的損失の約45%を占めることを示した。従来のリスクモデルを覆す知見。
English
This study develops a capital valuation framework for agricultural flood risk, integrating phenology and hydrologic frequency. Using a U.S. Midwest case, it shows that high-probability, low-severity floods account for ~45% of expected annual damage due to timing with sensitive growth stages, challenging conventional risk models.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では農業洪水リスクの評価手法として、フェノロジーを考慮した被害関数の導入はまだ一般的でない。本手法は、気候変動適応策や農業保険設計の高度化に示唆を与える。
In the global GX context
This paper offers a methodological advance in agricultural flood risk valuation that is globally relevant for climate adaptation, crop insurance, and water resource management, particularly under increasing climate volatility.
👥 読者別の含意
🔬研究者:A novel framework for integrating phenology into flood risk assessment, relevant for agricultural economics and climate adaptation research.
🏢実務担当者:Crop insurers and agricultural risk managers can use the findings to refine pricing and mitigation strategies based on flood timing.
🏛政策担当者:Policy insights for prioritizing flood mitigation investments that address high-frequency events rather than only extreme ones.
📄 Abstract(原文)
Accurate valuation of flood risk is fundamental to efficient resource allocation, insurance pricing, and public investment in agriculture. Standard economic models, which often link asset damage directly to hazard magnitude, fail to capture the unique vulnerability of agricultural capital—where the value of standing crops is contingent upon phenological stage. This study develops a capital valuation framework to deconstruct the economic burden of flooding on cropland, demonstrating that the timing of a flood is a primary determinant of financial loss, often outweighing the role of physical flood magnitude. We model agricultural flood risk as expected annual damage (EAD) to crop capital, integrating hydrologic frequency analysis for a U.S. Midwest County with a phenologically-explicit damage function. This function disaggregates risk into two components: a Flood Hazard Index (FHI), quantifying flood intensity and duration across return periods, and a Flood Susceptibility Index (FSI), representing the time-sensitive depreciation rate of crop capital at different growth stages. Probability-weighted losses are summed across all flood scenarios to derive total EAD. Results reveal that the distribution of losses is heavily skewed toward high-probability, low-severity events. The 2-year and 25-year floods collectively account for approximately 45% of total EAD, despite extreme (≥ 100-year) events generating substantially larger per-event losses. Frequent floods impose the highest economic cost not because of peak discharge, but because their high likelihood of coinciding with phenologically sensitive periods is compounded by longer inundation durations—a “double liability” where occurrence probability and capital impairment duration are simultaneously maximized. Conversely, low-probability, high-severity events tend to occur outside the growing season, leaving lower-value capital exposed. These findings invert conventional risk models and carry significant implications for crop insurance design, flood mitigation investment, and agricultural capital management under climate volatility.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1007/s11269-026-04801-1first seen 2026-06-29 08:51:59
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録(無料・3キーワードまで)。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。