The compounding effects of agricultural expansion and snow drought on lake urmia's drying crisis

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dc.authorid0000-0002-5870-5789
dc.authorid0009-0007-5647-6402
dc.authorid0000-0002-2310-6778
dc.contributor.authorShahbazi, Afshin
dc.contributor.authorAydin, Yusuf
dc.contributor.authorSemiz, Guluzar Duygu
dc.contributor.authorTorun, Elifnaz
dc.contributor.authorVaheddoost, Babak
dc.contributor.authorBeirami, Neda
dc.contributor.authorAghakouchak, Amir
dc.date.accessioned2026-02-08T15:15:30Z
dc.date.available2026-02-08T15:15:30Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractLake Urmia, one of the world's largest hypersaline lakes, has experienced severe drought in recent decades. This study investigates the combined impacts of agricultural expansion and climate variability on river inflows from 1985 to 2020. A hybrid framework incorporating statistical models and Convolutional Neural Networks was employed to estimate river discharge and disentangle the effects of hydroclimatic and anthropogenic drivers. Results indicate a persistent snow drought beginning in the late 1990s, concurrent with exceeding fourfold increase in irrigated lands. Scenario-based analysis, restoring key parameters to pre-1999 levels revealed that reverting agricultural water use was the dominant factor driving changes in river inflows, accounting for approximately 66% (95% CI: 56%-76%) of the total impact. In contrast, restoring precipitation and evaporation contributed 25% (95% CI: 18%-33%) and 9% (95% CI: 7%-12%), respectively, while restoring both simultaneously explained 34% (95% CI: 26%-43%) of the change. These results underscore the primary role of agricultural water demand amplified by declining snowpack and climatic shifts in altering basin hydrology. The findings highlight the urgent need for integrated water resource management, with a focus on climate adaptation, snowpack monitoring, and sustainable agricultural practices to address ongoing environmental degradation and ensure long-term water security.
dc.description.sponsorshipNational Aeronautics and Space Administration (NASA)
dc.description.sponsorshipThe authors gratefully acknowledge the National Aeronautics and Space Administration (NASA) and the European Centre for Medium-Range Weather Forecasts (ECMWF) for providing access to satellite and reanalysis data utilized in this study. We also extend our sincere appreciation to the National Oceanic and Atmospheric Administration (NOAA) for providing precipitation data through the PERSIANN CDR system. Special thanks are due to the Iran Meteorological Organization for supplying valuable meteorological and ground-observed snow data, which played a critical role in the validation and analysis phases of this research.
dc.identifier.doi10.1038/s41598-025-21735-7
dc.identifier.issn2045-2322
dc.identifier.issue1
dc.identifier.pmid41173890
dc.identifier.scopus2-s2.0-105020652877
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1038/s41598-025-21735-7
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5806
dc.identifier.volume15
dc.identifier.wosWOS:001606982600014
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherNature Portfolio
dc.relation.ispartofScientific Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectLake urmia
dc.subjectSnow dynamics
dc.subjectAgricultural water use
dc.subjectRiver inflows
dc.subjectMultiple linear regression
dc.subjectResponse surface model
dc.subjectConvolutional neural network
dc.titleThe compounding effects of agricultural expansion and snow drought on lake urmia's drying crisis
dc.typeArticle

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