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<p>This study employs a Multi-Model Ensemble (MME) approach to assess future drought risks in Pakistan and their agricultural impacts. The literature has relied on coarse-resolution climate data and conventional methods without incorporating Shared Socioeconomic Pathways (SSPs), limiting local relevance. We address this gap by developing an Artificial Neural Network (ANN)–based MME using Coupled Model Intercomparison Project Phase 6 (CMIP6) analysis with SSPs to generate high-resolution drought projections. These projections cover the historical period (2000–2023), near future (2025–2050), mid-future (2051–2075), and far future (2076–2100). Results indicate a precipitation decline from an average of 500–600&#xa0;mm/year historically to 35.57&#xa0;mm/year by the far future under SSP1-2.6, alongside a Potential Evapotranspiration (PET) increase from 0.39&#xa0;mm/year to 0.44&#xa0;mm/year across scenarios, heightening aridity risks. Standardized Precipitation Evapotranspiration Index (SPEI) projections show drought frequency rising, with historical October–December drought occurrence at 86.69% increasing to 99.56% in April–June under SSP5-8.5 by 2100, and mean SPEI values dropping from 0.12 to as low as -0.56 in key periods. Spatial analysis reveals that southern and eastern regions are facing severe drought, with up to 60% of agricultural areas affected during the growing season (November–April). The study highlights significant agricultural impacts, with maize and sugarcane yields exhibiting strong negative correlation (-0.79 and -0.78, respectively) with SPEI-3 during critical growing months (November to January), indicating heightened vulnerability. Under SSP5-8.5, drought severity peaks after 2050, threatening food security. These findings offer actionable guidance for optimizing irrigation, water allocation, and crop calendars to reduce drought impacts on Pakistan’s major crops.</p> Graphical Abstract <p></p>

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CMIP6-Driven Projections of Drought Severity and Their Implications for the Agricultural Sector in Pakistan

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月29日

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  • Fahad Shah,
  • Md. Omar Sarif,
  • Ayyoob Sharifi

摘要

This study employs a Multi-Model Ensemble (MME) approach to assess future drought risks in Pakistan and their agricultural impacts. The literature has relied on coarse-resolution climate data and conventional methods without incorporating Shared Socioeconomic Pathways (SSPs), limiting local relevance. We address this gap by developing an Artificial Neural Network (ANN)–based MME using Coupled Model Intercomparison Project Phase 6 (CMIP6) analysis with SSPs to generate high-resolution drought projections. These projections cover the historical period (2000–2023), near future (2025–2050), mid-future (2051–2075), and far future (2076–2100). Results indicate a precipitation decline from an average of 500–600 mm/year historically to 35.57 mm/year by the far future under SSP1-2.6, alongside a Potential Evapotranspiration (PET) increase from 0.39 mm/year to 0.44 mm/year across scenarios, heightening aridity risks. Standardized Precipitation Evapotranspiration Index (SPEI) projections show drought frequency rising, with historical October–December drought occurrence at 86.69% increasing to 99.56% in April–June under SSP5-8.5 by 2100, and mean SPEI values dropping from 0.12 to as low as -0.56 in key periods. Spatial analysis reveals that southern and eastern regions are facing severe drought, with up to 60% of agricultural areas affected during the growing season (November–April). The study highlights significant agricultural impacts, with maize and sugarcane yields exhibiting strong negative correlation (-0.79 and -0.78, respectively) with SPEI-3 during critical growing months (November to January), indicating heightened vulnerability. Under SSP5-8.5, drought severity peaks after 2050, threatening food security. These findings offer actionable guidance for optimizing irrigation, water allocation, and crop calendars to reduce drought impacts on Pakistan’s major crops.

Graphical Abstract