Spatial clustering of WEF-environment Nexus indicators for irrigation water operational performance: A feature-driven approach.
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| Title: | Spatial clustering of WEF-environment Nexus indicators for irrigation water operational performance: A feature-driven approach. |
|---|---|
| Authors: | Rahparast, Dorsa1 (AUTHOR), Hashemy Shahdany, Seied Mehdy1 (AUTHOR) mehdi.hashemy@ut.ac.ir, Roozbahani, Abbas1,2 (AUTHOR) abbas.roozbahani@nmbu.no |
| Source: | Environmental Impact Assessment Review. Mar2026, Vol. 117, pN.PAG-N.PAG. 1p. |
| Subject Terms: | *Water management, *Sustainability, *Environmental economics, Clustering algorithms, Irrigation efficiency, Feature selection, Hydraulic models |
| Geographic Terms: | Iran |
| Abstract: | The technical assessment of surface water distribution among water-right holders within an irrigation district under conditions of water supply stress did not yield a comprehensive operational appraisal. The Water–Energy–Food (WEF)–Environment nexus-assessment approach offers a holistic and pragmatic evaluation by incorporating technical assessment alongside considerations of energy and environmental trade-offs. This study presents an innovative clustering-based WEF-Environment spatial assessment methodology that combines hydraulic simulation and data-driven feature selection. An integrated hydraulic-operation model was developed to simulate daily operations under stressed scenarios. From this, nine key indicators were generated—such as Surface Water Delivery (SWD), Energy Consumption (EC), Carbon Emissions (CE), and Energy Productivity (EP). To enhance clustering accuracy, a cross-validated feature selection approach was applied, ranking indicators according to their contribution to clustering quality. The proposed methodology is implemented in Nekouabad Irrigation District, Iran, and the Nexus-based clustering and GIS-based spatial mapping were conducted using the selected features. Cross-validation across operational scenarios confirms the robustness of the feature-driven approach. SWD, CE, and Surface Water-Based Cultivated Area (SWCR) are key indicators that identify performance patterns. Under high stress, low-performing areas grew to cover over 90 % of the district. Environmental costs rose accordingly; in some zones, SWD dropped below 35 %, while carbon emissions exceeded 100,000 kg CO₂, indicating unsustainable operational trade-offs. Only a small portion of the district maintained balanced performance across delivery, efficiency, and emissions metrics. The study demonstrates that integrating feature selection with spatial clustering can identify priority areas for intervention, improve nexus assessments, and provide actionable insights for water managers. [Display omitted] • Practical K-means framework integrates WFEE indicators for surface water operations' performance assessment. • Combining feature selection with clustering yielded reasonable, reliable, spatially coherent results. • Optimum clusters determined upon cross-validation using Silhouette, Calinski-Harabasz, Davies-Bouldin, WCSS metrics. • Drought shifts nexus dominance from water-defined to energy/carbon-defined, raising sustainability costs. • Spatial clusters enable identifying high-priority zones for infrastructure upgrades & equity interventions. [ABSTRACT FROM AUTHOR] |
| Copyright of Environmental Impact Assessment Review is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 189665744 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Spatial clustering of WEF-environment Nexus indicators for irrigation water operational performance: A feature-driven approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rahparast%2C+Dorsa%22">Rahparast, Dorsa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hashemy+Shahdany%2C+Seied+Mehdy%22">Hashemy Shahdany, Seied Mehdy</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mehdi.hashemy@ut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Roozbahani%2C+Abbas%22">Roozbahani, Abbas</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> abbas.roozbahani@nmbu.no</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environmental+Impact+Assessment+Review%22">Environmental Impact Assessment Review</searchLink>. Mar2026, Vol. 117, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Water+management%22">Water management</searchLink><br />*<searchLink fieldCode="DE" term="%22Sustainability%22">Sustainability</searchLink><br />*<searchLink fieldCode="DE" term="%22Environmental+economics%22">Environmental economics</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Irrigation+efficiency%22">Irrigation efficiency</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Hydraulic+models%22">Hydraulic models</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Iran%22">Iran</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The technical assessment of surface water distribution among water-right holders within an irrigation district under conditions of water supply stress did not yield a comprehensive operational appraisal. The Water–Energy–Food (WEF)–Environment nexus-assessment approach offers a holistic and pragmatic evaluation by incorporating technical assessment alongside considerations of energy and environmental trade-offs. This study presents an innovative clustering-based WEF-Environment spatial assessment methodology that combines hydraulic simulation and data-driven feature selection. An integrated hydraulic-operation model was developed to simulate daily operations under stressed scenarios. From this, nine key indicators were generated—such as Surface Water Delivery (SWD), Energy Consumption (EC), Carbon Emissions (CE), and Energy Productivity (EP). To enhance clustering accuracy, a cross-validated feature selection approach was applied, ranking indicators according to their contribution to clustering quality. The proposed methodology is implemented in Nekouabad Irrigation District, Iran, and the Nexus-based clustering and GIS-based spatial mapping were conducted using the selected features. Cross-validation across operational scenarios confirms the robustness of the feature-driven approach. SWD, CE, and Surface Water-Based Cultivated Area (SWCR) are key indicators that identify performance patterns. Under high stress, low-performing areas grew to cover over 90 % of the district. Environmental costs rose accordingly; in some zones, SWD dropped below 35 %, while carbon emissions exceeded 100,000 kg CO₂, indicating unsustainable operational trade-offs. Only a small portion of the district maintained balanced performance across delivery, efficiency, and emissions metrics. The study demonstrates that integrating feature selection with spatial clustering can identify priority areas for intervention, improve nexus assessments, and provide actionable insights for water managers. [Display omitted] • Practical K-means framework integrates WFEE indicators for surface water operations' performance assessment. • Combining feature selection with clustering yielded reasonable, reliable, spatially coherent results. • Optimum clusters determined upon cross-validation using Silhouette, Calinski-Harabasz, Davies-Bouldin, WCSS metrics. • Drought shifts nexus dominance from water-defined to energy/carbon-defined, raising sustainability costs. • Spatial clusters enable identifying high-priority zones for infrastructure upgrades & equity interventions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Environmental Impact Assessment Review is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.eiar.2025.108205 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Water management Type: general – SubjectFull: Sustainability Type: general – SubjectFull: Environmental economics Type: general – SubjectFull: Clustering algorithms Type: general – SubjectFull: Irrigation efficiency Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Hydraulic models Type: general – SubjectFull: Iran Type: general Titles: – TitleFull: Spatial clustering of WEF-environment Nexus indicators for irrigation water operational performance: A feature-driven approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rahparast, Dorsa – PersonEntity: Name: NameFull: Hashemy Shahdany, Seied Mehdy – PersonEntity: Name: NameFull: Roozbahani, Abbas IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01959255 Numbering: – Type: volume Value: 117 Titles: – TitleFull: Environmental Impact Assessment Review Type: main |
| ResultId | 1 |
