Machine learning projections of Iran’s water scarcity response to climate-land use synergies

Iran faces severe water stress with over 88 million people and 14.2 million hectares of cultivated land. Agriculture consumes over 87 % of national water, with total withdrawals exceeding 80 % of renewable resources. From 2003–2019, total water storage declined by over 200 BCM, exceeding annual national consumption. This study analyzed the impacts of climate change and cultivated area on Iran’s total water storage. Country-wide actual evapotranspiration (ET a ) in agricultural and non-agricultural sectors was simulated using machine learning and statistical analysis. Changes in total water storage were then projected via the water balance equation using bootstrap sampling, under combined climate change and cultivated area management scenarios. Projections indicate that by 2100, climate change will increase nationwide ET a by up to 7.7 % and agricultural ET a by 23.8 %. Even 10 % reduction in cultivated area cannot offset the impacts of changing climate. Under high emissions scenarios, nationwide ET a will increase by 37.1 BCM (21 %) and agricultural ET a by 17.5 BCM (39 %) compared to the 2009–2014 baseline. Given current consumption trends and high withdrawal ratios (>80 %), aquifer deficits are projected to increase by 300 BCM (42 %) to 578 BCM (174 %) over the next two decades, depending on climate scenarios. These findings warn that failure to manage demand and improve irrigation efficiency in agriculture will seriously threaten the country’s water and food security. • Random Forest yielded excellent performance in predicting agricultural ET a (NSE=0.96). • By 2100, climate change raises national ET a by 7.7 % and agricultural ET a by 23.8 %. • 10 % less cultivated area cannot offset climate-driven actual evapotranspiration increases. • Aquifer deficits projected to grow 300–578 BCM (42 %-174 %) in next 20 years.

Authors

Institutions

Publication Details

Journal
Journal of Hydrology Regional Studies
Published
2025-07-24
DOI
https://doi.org/10.1016/j.ejrh.2025.102638
Citations
1
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
1.11
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Machine learning projections of Iran’s water scarcity response to climate-land use synergies

Fahimeh Shakeri, Nooshdokht Bayat-Afshary, Mohammad Danesh‐Yazdi
1 citations
Journal of Hydrology Regional Studies
Climate variability and models
1.11
article

Machine learning projections of Iran’s water scarcity response to climate-land use synergies

Fahimeh Shakeri, Nooshdokht Bayat-Afshary, Mohammad Danesh‐Yazdi
article en
1 citations

Abstract

Iran faces severe water stress with over 88 million people and 14.2 million hectares of cultivated land. Agriculture consumes over 87 % of national water, with total withdrawals exceeding 80 % of renewable resources. From 2003–2019, total water storage declined by over 200 BCM, exceeding annual national consumption. This study analyzed the impacts of climate change and cultivated area on Iran’s total water storage. Country-wide actual evapotranspiration (ET a ) in agricultural and non-agricultural sectors was simulated using machine learning and statistical analysis. Changes in total water storage were then projected via the water balance equation using bootstrap sampling, under combined climate change and cultivated area management scenarios. Projections indicate that by 2100, climate change will increase nationwide ET a by up to 7.7 % and agricultural ET a by 23.8 %. Even 10 % reduction in cultivated area cannot offset the impacts of changing climate. Under high emissions scenarios, nationwide ET a will increase by 37.1 BCM (21 %) and agricultural ET a by 17.5 BCM (39 %) compared to the 2009–2014 baseline. Given current consumption trends and high withdrawal ratios (>80 %), aquifer deficits are projected to increase by 300 BCM (42 %) to 578 BCM (174 %) over the next two decades, depending on climate scenarios. These findings warn that failure to manage demand and improve irrigation efficiency in agriculture will seriously threaten the country’s water and food security. • Random Forest yielded excellent performance in predicting agricultural ET a (NSE=0.96). • By 2100, climate change raises national ET a by 7.7 % and agricultural ET a by 23.8 %. • 10 % less cultivated area cannot offset climate-driven actual evapotranspiration increases. • Aquifer deficits projected to grow 300–578 BCM (42 %-174 %) in next 20 years.

Journal of Hydrology Regional StudiesVol. 61
Sharif University of Technology (IR)
Climate action
Openalex Percentile: Top 22%
Climate variability and models
1.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Machine learning projections of Iran’s water scarcity response to climate-land use synergies — Fahimeh Shakeri, Nooshdokht Bayat-Afshary, et al. · Journal of Hydrology Regional Studies (2025) | TGRS Research Map | TGRS