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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]
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Database: GreenFILE
Description
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]
ISSN:01959255
DOI:10.1016/j.eiar.2025.108205