A clustering approach to improve spatial representation in water-energy-food models
Currently available water-energy-food (WEF) modelling frameworks to analyse cross-sectoral interactions often share one or more of the following gaps: (a) lack of integration between sectors, (b) coarse spatial representation, and (c) lack of reproducible methods of nexus assessment. In this paper,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2021-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/ac2ce9 |
| _version_ | 1827870023402651648 |
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| author | Abhishek Shivakumar Thomas Alfstad Taco Niet |
| author_facet | Abhishek Shivakumar Thomas Alfstad Taco Niet |
| author_sort | Abhishek Shivakumar |
| collection | DOAJ |
| description | Currently available water-energy-food (WEF) modelling frameworks to analyse cross-sectoral interactions often share one or more of the following gaps: (a) lack of integration between sectors, (b) coarse spatial representation, and (c) lack of reproducible methods of nexus assessment. In this paper, we present a novel clustering tool as an expansion to the Climate-Land-Energy-Water-Systems modelling framework used to quantify inter-sectoral linkages between water, energy, and food systems. The clustering tool uses Agglomerative Hierarchical clustering to aggregate spatial data related to the land and water sectors. Using clusters of aggregated data reconciles the need for a spatially resolved representation of the land-use and water sectors with the computational and data requirements to efficiently solve such a model. The aggregated clusters, combined together with energy system components, form an integrated resource planning structure. The modelling framework is underpinned by an open-source energy system modelling tool—OSeMOSYS—and uses publicly available data with global coverage. By doing so, the modelling framework allows for reproducible WEF nexus assessments. The approach is used to explore the inter-sectoral linkages between the energy, land-use, and water sectors of Viet Nam out to 2030. A validation of the clustering approach confirms that underlying trends actual crop yield data are preserved in the resultant clusters. Finally, changes in cultivated area of selected crops are observed and differences in levels of crop migration are identified. |
| first_indexed | 2024-03-12T15:51:53Z |
| format | Article |
| id | doaj.art-772c728af1034745b1b994e7c2f44c4c |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:51:53Z |
| publishDate | 2021-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-772c728af1034745b1b994e7c2f44c4c2023-08-09T15:06:15ZengIOP PublishingEnvironmental Research Letters1748-93262021-01-01161111402710.1088/1748-9326/ac2ce9A clustering approach to improve spatial representation in water-energy-food modelsAbhishek Shivakumar0https://orcid.org/0000-0002-2535-4134Thomas Alfstad1Taco Niet2https://orcid.org/0000-0003-0266-2705Department of Economic and Social Affairs, United Nations , New York, United States of AmericaDepartment of Economic and Social Affairs, United Nations , New York, United States of AmericaSchool of Sustainable Energy Engineering, Simon Fraser University , Surrey, BC, CanadaCurrently available water-energy-food (WEF) modelling frameworks to analyse cross-sectoral interactions often share one or more of the following gaps: (a) lack of integration between sectors, (b) coarse spatial representation, and (c) lack of reproducible methods of nexus assessment. In this paper, we present a novel clustering tool as an expansion to the Climate-Land-Energy-Water-Systems modelling framework used to quantify inter-sectoral linkages between water, energy, and food systems. The clustering tool uses Agglomerative Hierarchical clustering to aggregate spatial data related to the land and water sectors. Using clusters of aggregated data reconciles the need for a spatially resolved representation of the land-use and water sectors with the computational and data requirements to efficiently solve such a model. The aggregated clusters, combined together with energy system components, form an integrated resource planning structure. The modelling framework is underpinned by an open-source energy system modelling tool—OSeMOSYS—and uses publicly available data with global coverage. By doing so, the modelling framework allows for reproducible WEF nexus assessments. The approach is used to explore the inter-sectoral linkages between the energy, land-use, and water sectors of Viet Nam out to 2030. A validation of the clustering approach confirms that underlying trends actual crop yield data are preserved in the resultant clusters. Finally, changes in cultivated area of selected crops are observed and differences in levels of crop migration are identified.https://doi.org/10.1088/1748-9326/ac2ce9water-energy-food nexusopen-source modellingopen datasustainable developmenthierarchical clusteringoptimisation models |
| spellingShingle | Abhishek Shivakumar Thomas Alfstad Taco Niet A clustering approach to improve spatial representation in water-energy-food models Environmental Research Letters water-energy-food nexus open-source modelling open data sustainable development hierarchical clustering optimisation models |
| title | A clustering approach to improve spatial representation in water-energy-food models |
| title_full | A clustering approach to improve spatial representation in water-energy-food models |
| title_fullStr | A clustering approach to improve spatial representation in water-energy-food models |
| title_full_unstemmed | A clustering approach to improve spatial representation in water-energy-food models |
| title_short | A clustering approach to improve spatial representation in water-energy-food models |
| title_sort | clustering approach to improve spatial representation in water energy food models |
| topic | water-energy-food nexus open-source modelling open data sustainable development hierarchical clustering optimisation models |
| url | https://doi.org/10.1088/1748-9326/ac2ce9 |
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