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1000 Titel
  • The performance of rainwater harvesting systems in the context of deep uncertainties
1000 Autor/in
  1. Pacheco, Gabriela Cristina Ribeiro |
  2. Alves, Conceição de Maria Albuquerque |
1000 Verlag
  • Copernicus Publications
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-04-18
1000 Erschienen in
1000 Quellenangabe
  • 385:11-16
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.5194/piahs-385-11-2024 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:p>Abstract. Rainwater harvesting systems (RHS) are a relevant alternative of water supply in urban areas with increasing water demand and limited water availability. But these systems depend on several parameters that present uncertainties as well-characterized uncertainties whose probability functions are known and deep uncertainty factors that doesn't have analytical representation of their variability. This study evaluates the influence of water demand, tariff and discount rate (deep uncertain factors) on the feasibility of RHS for different scenarios of uncertainties. The systems were evaluated using the following performance criteria: Satisfied Demand, Reliability, Percentage of Rainwater Harvesting, Net Present Value, Net Present Value Volume and Benefit Cost Rate. We simulated the RHS performance for sixteen system configurations, comprised of eight categories of residential buildings according to representative water consumption (ranging from 4.748 to 44.673 m3 per month) and two typical catchment areas for each of the eight groups of demands (ranging from 60 to 400 m2) in the city of Rio Verde located in the central of Brazil. Each system was evaluated under the context of 1000 States of the World (SOWs) defined using the Latin Hypercube Sampling (LHS) method (in the case of the deep uncertainty factors) and bootstrapping resampling (for precipitation). Results showed slight difference on performance criteria among precipitation scenarios, maybe due to the fact that the synthetic rainfall series preserved the pattern and the total rainfall volume among the series which is reasonable for the location. However, the water tariff and discount rate showed a significant influence in the performance criteria confirming the relevance of deep uncertainty factors in the evaluation of RHS performances. </jats:p>
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/UGFjaGVjbywgR2FicmllbGEgQ3Jpc3RpbmEgUmliZWlybw==|https://frl.publisso.de/adhoc/uri/QWx2ZXMsIENvbmNlacOnw6NvIGRlIE1hcmlhIEFsYnVxdWVycXVl
1000 Hinweis
  • DeepGreen-ID: f569153429e6418b8c9da73477c03992 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
1000 Label
1000 Förderer
  1. Universidade de Brasília |
  2. Instituto Federal Goiás |
1000 Fördernummer
  1. -
  2. -
1000 Förderprogramm
  1. -
  2. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Universidade de Brasília |
    1000 Förderprogramm -
    1000 Fördernummer -
  2. 1000 joinedFunding-child
    1000 Förderer Instituto Federal Goiás |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6481995.rdf
1000 Erstellt am 2024-05-24T01:44:18.103+0200
1000 Erstellt von 322
1000 beschreibt frl:6481995
1000 Zuletzt bearbeitet 2024-05-27T13:04:25.611+0200
1000 Objekt bearb. Mon May 27 13:04:25 CEST 2024
1000 Vgl. frl:6481995
1000 Oai Id
  1. oai:frl.publisso.de:frl:6481995 |
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