Ecological Drought in Central Asia: Predictive Modeling and Projections for 2030

<p dir="ltr">In recent decades, Central Asia has increasingly faced risks of ecological drought, threatening ecosystems, agriculture, and livelihoods. However, forward-looking spatial predictions are limited. This study models the impacts of ecological drought across Kazakhstan, Uzbe...

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Հիմնական հեղինակ: Omid Shobairi (22317805) (author)
Այլ հեղինակներ: Yaning Chen (2987694) (author), Hossein Azadi (12239929) (author)
Հրապարակվել է: 2025
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_version_ 1851482479350775808
author Omid Shobairi (22317805)
author2 Yaning Chen (2987694)
Hossein Azadi (12239929)
author2_role author
author
author_facet Omid Shobairi (22317805)
Yaning Chen (2987694)
Hossein Azadi (12239929)
author_role author
dc.creator.none.fl_str_mv Omid Shobairi (22317805)
Yaning Chen (2987694)
Hossein Azadi (12239929)
dc.date.none.fl_str_mv 2025-09-27T07:25:09Z
dc.identifier.none.fl_str_mv 10.6084/m9.figshare.30225292.v1
dc.relation.none.fl_str_mv https://figshare.com/articles/dataset/Ecological_Drought_in_Central_Asia_Predictive_Modeling_and_Projections_for_2030/30225292
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Climate change processes
Geospatial information systems and geospatial data modelling
Landscape ecology
Environmental management
Drought Monitoring
Satellite Time Series Analysis
Predictive Vegetation Modeling
Climate Incidents
Ecological Resilience
dc.title.none.fl_str_mv Ecological Drought in Central Asia: Predictive Modeling and Projections for 2030
dc.type.none.fl_str_mv Dataset
info:eu-repo/semantics/publishedVersion
dataset
description <p dir="ltr">In recent decades, Central Asia has increasingly faced risks of ecological drought, threatening ecosystems, agriculture, and livelihoods. However, forward-looking spatial predictions are limited. This study models the impacts of ecological drought across Kazakhstan, Uzbekistan, Turkmenistan, Kyrgyzstan, and Tajikistan from 2023 to 2030 using satellite data and time series analysis. Employing MODIS-derived vegetation indices, nighttime light data, and climate variables, the research reveals significant deterioration in ecosystem health. Random forest models utilizing a harmonized dataset from 1990 to 2022 were validated with 2023 data, leading to projections for 2030. Findings indicate ongoing degradation in southern arid/semi-arid regions and around the Aral Sea, while northern steppes and high-elevation areas remain stable. The study also provides networked forecasts, threshold-aware hotspot maps, and skill recognition classifications, offering valuable tools for early warning and contingency planning for future droughts.</p>
eu_rights_str_mv openAccess
id Manara_1c45eebe10ec4e313cdda986fc6a45c5
identifier_str_mv 10.6084/m9.figshare.30225292.v1
network_acronym_str Manara
network_name_str ManaraRepo
oai_identifier_str oai:figshare.com:article/30225292
publishDate 2025
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv CC BY 4.0
spelling Ecological Drought in Central Asia: Predictive Modeling and Projections for 2030Omid Shobairi (22317805)Yaning Chen (2987694)Hossein Azadi (12239929)Climate change processesGeospatial information systems and geospatial data modellingLandscape ecologyEnvironmental managementDrought MonitoringSatellite Time Series AnalysisPredictive Vegetation ModelingClimate IncidentsEcological Resilience<p dir="ltr">In recent decades, Central Asia has increasingly faced risks of ecological drought, threatening ecosystems, agriculture, and livelihoods. However, forward-looking spatial predictions are limited. This study models the impacts of ecological drought across Kazakhstan, Uzbekistan, Turkmenistan, Kyrgyzstan, and Tajikistan from 2023 to 2030 using satellite data and time series analysis. Employing MODIS-derived vegetation indices, nighttime light data, and climate variables, the research reveals significant deterioration in ecosystem health. Random forest models utilizing a harmonized dataset from 1990 to 2022 were validated with 2023 data, leading to projections for 2030. Findings indicate ongoing degradation in southern arid/semi-arid regions and around the Aral Sea, while northern steppes and high-elevation areas remain stable. The study also provides networked forecasts, threshold-aware hotspot maps, and skill recognition classifications, offering valuable tools for early warning and contingency planning for future droughts.</p>2025-09-27T07:25:09ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.6084/m9.figshare.30225292.v1https://figshare.com/articles/dataset/Ecological_Drought_in_Central_Asia_Predictive_Modeling_and_Projections_for_2030/30225292CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/302252922025-09-27T07:25:09Z
spellingShingle Ecological Drought in Central Asia: Predictive Modeling and Projections for 2030
Omid Shobairi (22317805)
Climate change processes
Geospatial information systems and geospatial data modelling
Landscape ecology
Environmental management
Drought Monitoring
Satellite Time Series Analysis
Predictive Vegetation Modeling
Climate Incidents
Ecological Resilience
status_str publishedVersion
title Ecological Drought in Central Asia: Predictive Modeling and Projections for 2030
title_full Ecological Drought in Central Asia: Predictive Modeling and Projections for 2030
title_fullStr Ecological Drought in Central Asia: Predictive Modeling and Projections for 2030
title_full_unstemmed Ecological Drought in Central Asia: Predictive Modeling and Projections for 2030
title_short Ecological Drought in Central Asia: Predictive Modeling and Projections for 2030
title_sort Ecological Drought in Central Asia: Predictive Modeling and Projections for 2030
topic Climate change processes
Geospatial information systems and geospatial data modelling
Landscape ecology
Environmental management
Drought Monitoring
Satellite Time Series Analysis
Predictive Vegetation Modeling
Climate Incidents
Ecological Resilience