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dc.contributor.authorRufino, I
dc.contributor.authorDjordjević, S
dc.contributor.authorde Brito, HC
dc.contributor.authorAlves, PBR
dc.date.accessioned2022-04-05T08:37:19Z
dc.date.issued2021-01-14
dc.date.updated2022-04-05T02:59:28Z
dc.description.abstractThe northeastern Brazilian region has been vulnerable to hydrometeorological extremes, especially droughts, for centuries. A combination of natural climate variability (most of the area is semi-arid) and water governance problems increases extreme events’ impacts, especially in urban areas. Spatial analysis and visualisation of possible land-use change (LUC) zones and trends (urban growth vectors) can be useful for planning actions or decision-making policies for sustainable development. The Global Human Settlement Layer (GHSL) produces global spatial information, evidence-based analytics, and knowledge describing Earth’s human presence. In this work, the GHSL built-up grids for selected Brazilian cities were used to generate urban models using GIS (geographic information system) technologies and cellular automata for spatial pattern simulations of urban growth. In this work, six Brazilian cities were selected to generate urban models using GIS technologies and cellular automata for spatial pattern simulations of urban sprawl. The main goal was to provide predictive scenarios for water management (including simulations) and urban planning in a region highly susceptible to extreme hazards, such as floods and droughts. The northeastern Brazilian cities’ analysis raises more significant challenges because of the lack of land-use change field data. Findings and conclusions show the potential of dynamic modelling to predict scenarios and support water sensitive urban planning, increasing cities’ coping capacity for extreme hazards.en_GB
dc.format.extent748-
dc.identifier.citationVol. 13(2), article 748en_GB
dc.identifier.doihttps://doi.org/10.3390/su13020748
dc.identifier.urihttp://hdl.handle.net/10871/129272
dc.language.isoenen_GB
dc.publisherMDPIen_GB
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_GB
dc.subjectdynamic modellingen_GB
dc.subjecturban land-use scenariosen_GB
dc.subjectwater securityen_GB
dc.titleMulti-Temporal Built-Up Grids of Brazilian Cities: How Trends and Dynamic Modelling Could Help on Resilience Challenges?en_GB
dc.typeArticleen_GB
dc.date.available2022-04-05T08:37:19Z
dc.identifier.issn2071-1050
exeter.article-numberARTN 748
dc.descriptionThis is the final version. Available on open access from MDPI via the DOI in this recorden_GB
dc.descriptionData Availability Statement: The data presented in this study are available on request from the corresponding author.en_GB
dc.identifier.eissn2071-1050
dc.identifier.journalSustainabilityen_GB
dc.relation.ispartofSustainability, 13(2)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2021-01-11
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2021-01-14
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2022-04-05T08:35:46Z
refterms.versionFCDVoR
refterms.dateFOA2022-04-05T08:37:33Z
refterms.panelBen_GB
refterms.dateFirstOnline2021-01-14


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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's licence is described as © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).