Abstract
Cities in South America face many of the urban water supply challenges experienced globally including increasingly extreme future hydroclimatic conditions and rapid population growth. These challenges are further exacerbated by historical socioeconomic inequity, informal land occupation, and poor water services management. In recent years, decision support tools that aid in structuring water supply management and infrastructure pathway policies that remain robust under deeply uncertain future scenarios have been developed. However, within the context of developing countries, failing to acknowledge the complex social and institutional dynamics and stark differences in residents' experiences of climate extremes may lead to uneven adaptation capacities across socioeconomic strata. Toward this end, our study extends the deeply uncertain pathways framework by applying multiobjective optimization, disaggregated service area-level assessments of performance and vulnerability across time, and exploratory visual analytics in the Federal District of Brazil (FDB). We highlight the performance and robustness disparities between two water supply service areas in the FDB that differ in socioeconomic standing to reveal the impacts of deeply uncertain future hydroclimatic and socioeconomic scenarios on vulnerable populations. We further show that historical inequity renders poorer residents significantly more vulnerable to deeply uncertain future conditions without urgent and significant infrastructure investments. Overall, the outcomes of our study are largely applicable to urban water utilities in regions with high levels of historical regional inequity seeking to develop water management and infrastructure planning policies that are robust, adaptive, and equitable.
Original language | English |
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Article number | 04024030 |
Journal | Journal of Water Resources Planning and Management |
Volume | 150 |
Issue number | 8 |
DOIs | |
Publication status | Published - 1 Aug 2024 |
Bibliographical note
Publisher Copyright:© 2024 This work is made available under the terms of the Creative Commons Attribution 4.0 International license.
Funding
This work used the computational resources of The Cube and Hopper clusters at the Cornell University Center for Advanced Computing. The authors thank Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) for financial support provided for this research. We would also like to thank Bernardo Trindade and Andressa Giacomazzo for earlier work on WaterPaths and CAESB (Diogo Gebrim) and ADASA (Jorge Lima and Gustavo Carneiro) for their consistent collaboration in discussing technical features concerning the FDB water supply system. Finally, we would like to thank the Editor, the Associate Editor, and the two anonymous reviewers for helping us improve the clarity and quality of the manuscript.
Funders | Funder number |
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Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) |