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Integrating System Dynamics and Remote Sensing to Estimate Future Water Usage and Average Surface Runoff in Lagos, Nigeria

Author(s):


Medium: journal article
Language(s): English
Published in: Civil Engineering Journal, , n. 2, v. 4
Page(s): 378
DOI: 10.28991/cej-030998
Abstract:

The goal of this study was twofold; first analyze the patterns of water consumption in Lagos, Nigeria and use them in a System Dynamics (SD) model to make projections about future demand. The second part used remote sensing to quantify the contribution of extensive land use/cover change to urban flooding. Land use/cover dynamics over the past decade was analyzed using satellite imagery provided by Landsat Thematic Mapping (TM). Unsupervised classification was performed with false color composite using the Iterative Self-Organizing Data Analysis (ISODATA) technique in a Geographic Information Systems (GIS). The study area was divided into four different land use types during image classification: bare land, built-up area, water bodies, and vegetation. For water demand, two different scenarios of population growth including 5.5% and 2.75 % annual increase were considered. The results showed that water demand dropped by 67% of its current value when losses in distribution were reduced by 20% and population annual growth rate kept at 2.75% over the study period. Bare land and water bodies lost 1.31% and 1.61% of their current area respectively while built-up area grew by 1.11%. These changes in land use/cover changes led to a 64% increase in average surface runoff, mostly attributable to increasing surface imperviousness and the absence of an adequate urban drainage system.

Copyright: © 2018 Gilles A Kandissounon, Ajay Kalra, Sajjad Ahmad
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

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  • About this
    data sheet
  • Reference-ID
    10341072
  • Published on:
    14/08/2019
  • Last updated on:
    02/06/2021
 
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