Evidence map›Paper›PMID 41173890›Full record

ArticleScientific reports2025

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

Afshin Shahbazi, Yusuf Aydin, Guluzar Duygu Semiz, Elifnaz Torun, Babak Vaheddoost, Neda Beirami, Alper Unal, Kaveh Madani, Amir AghaKouchak

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Afshin ShahbaziGraduate School of Natural and Applied Sciences, Ankara University, Ankara, 06110, Turkey.
Yusuf AydinDepartment of Climate and Marine Sciences, Eurasia Institute of Earth Sciences, Istanbul Technical University, Maslak, 34469, Istanbul, Turkey. alizade@itu.edu.tr.
Guluzar Duygu SemizDepartment of Farm Structures and Irrigation, Ankara University, Agricultural Engineering Faculty, Ankara, Turkey.
Elifnaz TorunDepartment of Farm Structures and Irrigation, Ankara University, Agricultural Engineering Faculty, Ankara, Turkey.
Babak VaheddoostDepartment of Civil Engineering, Bursa Technical University, Bursa, 16310, Turkey.
Neda BeiramiDepartment of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Turkey.
Alper UnalDepartment of Climate and Marine Sciences, Eurasia Institute of Earth Sciences, Istanbul Technical University, Maslak, 34469, Istanbul, Turkey.
Kaveh MadaniUnited Nations University Institute for Water, Environment, and Health (UNU-INWEH), Richmond Hill, Ontario, Canada.
Amir AghaKouchakUnited Nations University Institute for Water, Environment, and Health (UNU-INWEH), Richmond Hill, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lake 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.

Indexed as

Agricultural water useConvolutional neural networkLake urmiaMultiple linear regressionResponse surface modelRiver inflowsSnow dynamics

Identifiers

PMID41173890
PMCPMC12578938

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.