Multi-Temporal Built-Up Grids of Brazilian Cities: How Trends and Dynamic Modelling Could Help on Resilience Challenges?

Iana Rufino, Slobodan Djordjević, Higor Costa de Brito and Priscila Barros Ramalho Alves
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Iana Rufino: PPGECA, Center for Natural Resources and Technology, Federal University of Campina Grande, Campina Grande, PB 58428-830, Brazil
Slobodan Djordjević: Centre for Water Systems, University of Exeter, Exeter EX4 4QF, UK
Higor Costa de Brito: PPGECA, Center for Natural Resources and Technology, Federal University of Campina Grande, Campina Grande, PB 58428-830, Brazil
Priscila Barros Ramalho Alves: Centre for Water Systems, University of Exeter, Exeter EX4 4QF, UK

Sustainability, 2021, vol. 13, issue 2, 1-21

Abstract: The 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.

Keywords: dynamic modelling; urban land-use scenarios; water security (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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