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Probabilistic Agro‐Hydrology: A Stochastic Framework for Irrigation Risk Assessment and Water Management
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Probabilistic Agro‐Hydrology: A Stochastic Framework for Irrigation Risk Assessment and Water Management
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Journal Article
Probabilistic Agro‐Hydrology: A Stochastic Framework for Irrigation Risk Assessment and Water Management
2026
Overview
Irrigation plays a critical role in stabilizing agricultural productivity under increasing climatic variability. However, the intensification of droughts and extreme weather events is revealing the vulnerability of irrigation systems, particularly due to mismatches between peak water needs and natural water availability. This study introduces a probabilistic framework that integrates distributed agro‐hydrological modeling with stochastically generated climate inputs to assess irrigation water needs (blue water, BW) and its associated risks. The framework is applied to eight climatically diverse, high‐productivity regions in Italy, highlighting the near absence of interannual memory, quantified through cross‐correlation analyses, and a steep increase in return period associated with small increases in BW. This behavior reflects the concentration of extreme irrigation needs during rare combinations of prolonged dry spells and peak crop water requirements (CWRs). Systemic challenges emerge in Northern Italy, which exhibits lower mean BW but higher interannual variability, driven by water‐intensive crops in highly variable precipitation regimes. Conversely, Southern Italy shows higher but more stable BW patterns, associated with chronic water scarcity, drought‐adapted cropping systems, and long‐standing reliance on irrigation. The results underscore the need to incorporate irrigation risk into water management strategies—similar to flood risk planning—and provide actionable insights for designing resilient irrigation infrastructure. Moving beyond deterministic simulations, the proposed approach enables the estimation of irrigation return periods, supports probabilistic forecasting, and uncovers key interdependencies among hydrological and agronomic variables. This approach provides a robust foundation for sustainable agricultural planning under uncertainty and climate change.
