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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2025
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Ավելացրեք ցուցիչ
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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 |