Remote Sensing Data Based Rainwater Harvesting Approach for Remote Rural Areas - A Case Study, Blue Nile Area, Sudan

Mohamed, Gar Al-Nabi Ibrahim (2020) Remote Sensing Data Based Rainwater Harvesting Approach for Remote Rural Areas - A Case Study, Blue Nile Area, Sudan. Journal of Scientific Research and Reports, 26 (7). pp. 75-82. ISSN 2320-0227

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Abstract

The method of water harvesting is very crucial to the collection of the volume of water required for sustainable water supply. Most physical water scarcity remote rural areas worldwide are characterized by a reasonable amount of rainfall. These areas lack the suitable Surface Rainwater Harvesting System (SRHS) to collect the required water volume. To address this problem a remote sensing data based Surface Rainwater Harvesting Approach (SRHA) was proposed. The proposed approach was tested on existing surface rainwater harvesting systems (SRHS) in residential and agricultural areas inside the study area. The study area is bounded by latitudes 11°-12° N and longitudes 33°-34° E, with an approximate area of 11,000 km2. The SRTM90 DEM data of the study area was processed using QGIS application program hydrological modules. The hydrological model of the area was created, the catchment areas were derived and draining capacities for the specific test sites were calculated. The results revealed that the remote sensing data based approach is capable of locating sites with draining capacities 82 and 8 times those of the traditional systems in the residential and agricultural areas respectively. These results demonstrated that the proposed approach can facilitate locating optimum surface rainwater harvesting sites that would provide sustainable water supply and mitigate physical water scarcity problem in remote rural areas.

Item Type: Article
Subjects: Digital Open Archives > Multidisciplinary
Depositing User: Unnamed user with email support@digiopenarchives.com
Date Deposited: 21 Feb 2023 08:19
Last Modified: 01 Aug 2024 08:46
URI: http://geographical.openuniversityarchive.com/id/eprint/396

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