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Water harvesting through ponds in the Arco Seco region of the Republic of Panama : decision support system for pond storage capacity estimation

The 'Arco Seco' or 'Dry Arc' region of the Republic of Panama is considered to be the driest in the country, where many areas of this region experience severe water stress during the months of January through May. This study was conducted to develop a tool for the assessment of sustainable implementation of water harvesting through ponds for agricultural purposes in the region. A computer based Decision Support System (DSS) has been developed specifically for the Arco Seco region in order to facilitate pond storage capacity estimation. As part of the DSS, four computer programs have been designed for four different case scenarios; the first one is for sites that have high water demand and no topographical restrictions for pond size; the second is for fairly high water demand, no topographical restrictions for pond size, and for farmers who wish to have a backup of water to use mostly during drier years; the third is for low water demand, usage during the dry season only, and topographical restrictions for pond size, and finally the fourth is for constant water demand throughout the year, and for sites where runoff is the only water source.* / *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation).

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.80908
Date January 2004
CreatorsDesrochers, Anne
ContributorsBonnell, Robert (advisor)
PublisherMcGill University
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Formatapplication/pdf
CoverageMaster of Science (Department of Bioresource Engineering.)
RightsAll items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated.
Relationalephsysno: 002152004, proquestno: AAIMQ98773, Theses scanned by UMI/ProQuest.

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