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<p>Drought is a widespread natural disaster with severe consequences on water resources, ecosystems, and agriculture worldwide. In Iran—marked by arid to semi-arid climates, rapid warming, and chronic water scarcity—its impacts are particularly acute, often termed “water bankruptcy”. Both large-scale ocean–atmosphere teleconnections and nearby seas influence Iran’s drought variability, yet their combined roles remain underexplored. This study integrates 13 well-established global indices (AMO, AO, EAWR, ENSO, IOD, NAO, NPI, PDO, PNA, PW, SAM, SCAND, and TPI)— together with newly derived sea surface temperature (SST) and sea level pressure (SLP) indices from seven surrounding seas (Caspian, Red, Black, Mediterranean, Arabian, Persian Gulf, and Bay of Bengal). Predictor series (1957–2019) were partitioned into training-validation (1957–2017; 80/20 split) and prediction (2018–2019). The framework modeled both Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) across 18 homogeneous regions. MLP models explained 55–97% (77% on average) of SPI and 55–92% (81% on average) of SPEI variance during 2018–2019, closely matching observations. Variable-importance analysis showed SPI is most influenced by regional SST anomalies—especially in the Red Sea, Mediterranean, and Persian Gulf —together with Pacific indices (particularly ENSO), while SPEI is more sensitive to Red Sea SST and Persian Gulf SLP as well as AMO, PNA, and AO. Results demonstrate that combining global teleconnections with regional marine indicators substantially enhances drought prediction skill. The framework is transferable to other drought-prone regions and offers physical insights to support adaptation planning and improve the climate model representation of regional hydroclimate under future change.</p>

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Integrated global to regional atmosphere predictors for drought modeling in Iran

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • Faezeh Mirzaei,
  • Abolfazl Rezaei,
  • Mahdi Vasighi

摘要

Drought is a widespread natural disaster with severe consequences on water resources, ecosystems, and agriculture worldwide. In Iran—marked by arid to semi-arid climates, rapid warming, and chronic water scarcity—its impacts are particularly acute, often termed “water bankruptcy”. Both large-scale ocean–atmosphere teleconnections and nearby seas influence Iran’s drought variability, yet their combined roles remain underexplored. This study integrates 13 well-established global indices (AMO, AO, EAWR, ENSO, IOD, NAO, NPI, PDO, PNA, PW, SAM, SCAND, and TPI)— together with newly derived sea surface temperature (SST) and sea level pressure (SLP) indices from seven surrounding seas (Caspian, Red, Black, Mediterranean, Arabian, Persian Gulf, and Bay of Bengal). Predictor series (1957–2019) were partitioned into training-validation (1957–2017; 80/20 split) and prediction (2018–2019). The framework modeled both Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) across 18 homogeneous regions. MLP models explained 55–97% (77% on average) of SPI and 55–92% (81% on average) of SPEI variance during 2018–2019, closely matching observations. Variable-importance analysis showed SPI is most influenced by regional SST anomalies—especially in the Red Sea, Mediterranean, and Persian Gulf —together with Pacific indices (particularly ENSO), while SPEI is more sensitive to Red Sea SST and Persian Gulf SLP as well as AMO, PNA, and AO. Results demonstrate that combining global teleconnections with regional marine indicators substantially enhances drought prediction skill. The framework is transferable to other drought-prone regions and offers physical insights to support adaptation planning and improve the climate model representation of regional hydroclimate under future change.