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7 most recent application of GIS in the identification of suitable location for rain water harvesting

The water crisis has been rapidly increasing these days in many parts of the world which emphasize the importance of refinement in water management systems. Harvesting of rainwater(or rainwater harvesting(RWH)), which involves the collection and storage of rainwater for future use, is an area of increasing interest and one of the most suitable measures for ensuring water availability in the future days. The location of RWH is of utmost importance for the success of the water harvesting project. Locations for the construction of RWH structures like check dams, percolation tanks, bench terraces, contour ridges, and contour bunds can be determined by the application of Geographical Information System(GIS), Multi-Criteria Decision Making(MCDM), and different predictive models. Below are the seven recent case studies which demonstrate the procedure of location selection for RWH.

1) Case Study of Kiambu County-Kenya

The ideal sites for five rainwater harvesting structures (bench terraces, check dams, percolation tanks, contour bunds, and contour ridges) have been selected for potential runoff harvesting by the application of remotely sensed imageries and GIS-based spatial analysis. 

Input: Soil(from KENSOTER soil database), Precipitation(TRMM monthly precipitation), Runoff depth(generated by SCS CN method)
Output: Thematic layers are generated to depict the suitable location for RWH. Weights are assigned based on the capacity of infiltration and runoff characteristics.
Imageries Used:SRTM GDEM and Landsat 8
Software Used: ENVI, PCI Geomatica

2)Case Study of Haqlan valley basin in the western part of Iraq

This study was carried out in Haqlan valley basin in the western part of Iraq. The suitable location for rainwater harvesting was selected based on different key determinates such as environment, hydrology, socio-economic, and topography as well as the estimation of the storage volume and the surface area. Geographical Information System (GIS) and remote sensing with multi-criteria decision techniques were used.

Input: Soil map, vegetation cover, land use/land cover, slope, and digital elevation model.
Output: Weighted thematic layers of suitability was generated. Weights are determined based on the drainage network and the contour line map. 

3) Case study of Chemoga watershed, Ethiopia

Ethiopia is Africa's second-most populous country, after Nigeria, and is primarily a farming community with low productivity that is heavily reliant on rain-fed agriculture. The goal of this study was to use remote sensing and geographic information system (GIS) techniques in conjunction with the dam suitability stream model and multi-criteria decision analysis to identify potential sites for multi-purpose dam construction. 

Input: topography; climate; hydrology; soils; agronomy; and socioeconomics and distance from the road
Output : Weighted thematic layers of the suitability of RWH refined by topography and land use.
Software/Technique Used:  ArcGIS,MCDA, and dam suitability stream model (DSSM) 

4) Case study of Turkey

The main goal of this paper is to recognize the proper location for a rainwater harvesting structure using a suitability model generated with ModelBuilder in ArcGIS. 

Input: Six thematic layers i.e. soil structure, slope, drainage density, vegetation cover, distance to the roads, and runoff depth
Output: Weighted layer representing suitability of installation of RWH

5) Case study of Egypt

In the present study, suitable areas for sustainable stormwater harvesting and storage in Egypt were identified using remote sensing for land cover data - location assessment linked to a decision support system (DSS).

Input: Thematic layers such as rainfall surplus, slope, potential runoff coefficient (PRC), land cover/use, and soil texture. 
Output: Weighted layer representing suitability of installation of RWH
Software/Technique Used: AHP MCDM

More Info : 

6) Case study of Wadi Watir, Egypt 

This research aims to determine the optimal implementation of RWH systems considering the biophysical and socioeconomic characteristics of the study area. This research combines geographic information systems, remote sensing, multi-criteria analysis, and hydrological modeling in a case study in Wadi Watir in the Sinai Peninsula, Egypt. The study's findings are linked to the sustainable development goals to develop a sustainable RWH plan for the first time. 

Input: Thematic Layers of Runoff, Slope, Basin Area, Drainage density,  Landuse, FN, LFD, TWI, MFD
Output: Weighted layer representing suitability of installation of RWH based on the combination of  Boolean analysis, weighted linear combination, and depression depth technique generated RWH map
Software/Technique Used: ArcGIS, WMS, ENVI, Geomatica

More Info : 

7) Case study of Malappuram district in Kerala 

Since the effectiveness of RWH depends on site selection here, remote sensing and GIS were successfully used as a tool to identify the most suitable location for the installation of a rainwater harvesting system in Malappuram district of Kerala 

Input: Run-off potential (ArcCN Runoff generation tool), slope, storage loss, and drainage 
Output : Thematic Layer of Suitability for RWH installation

My Comments: There are various studies that have attempted to delineate the suitable locations for the installation of RWH. Most of them have used Rainfall, Slope, watershed area, and Landuse, which are the most common parameters as input and the thematic layer of suitability as output. The outputs are often the weighted average of the input parameters. The majority of the studies have utilized the ArcINFO GIS software and AHP  MCDM techniques. The weights are determined with the help of MCDM software in the majority of the studies based on the storage capacity of the catchment or socioeconomic upliftment of the adjacent societies.

The most prominent lacunae of these studies are they do not attempt to use more than one MCDM technique for validating the results and there is a lack of  Artificial Neural Network applications that can improve the accuracy of the results. Also, these studies have not considered the water quality parameters as one of the factors which can be included in future research studies on similar objectives.

Thanking you,

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