A GIS-based approach for identifying suitable sites for rainwater harvesting technologies in Kasungu District, Malawi

Abstract

A GIS-based approach for identifying suitable sites for rainwater harvesting (RWH) technologies was developed and applied in Kasungu District, Malawi. Data were obtained from reports, socio-economic survey documents of the area and maps. Field surveys were conducted in the villages of Chipala Extension Planning Area (EPA), in order to identify and evaluate the performance of existing RWH interventions, and determine factors for locating suitable areas for RWH. Observed soil moisture content was used to assess the water retention performance of the prevalent RWH technologies: contour tied ridging and soil mulching. A GIS-based Soil Conservation Service Curve Number (SCS-CN) method was used to map runoff potential for areas with RWH technologies, using physical factors of rainfall, land use, soil type and slope to estimate runoff potential. This was then integrated in a GIS database, with social-economic factors in the form of household income level and environmental factors, including impacts of implementing RWH, to determine the suitability of land areas for RWH in Kasungu District. One way analysis of variance (ANOVA) was used to test the impact of identified technologies by comparing the moisture content measurements for each of the identified technologies at 5% level of significance. The ANOVA results showed a statistically significant difference in the moisture measurements for the three technologies identified (P < 0.05). The RWH suitability map for the study area showed that 0.2% of the area considered had very high potential, 33.5% high, 55.9% moderate, 10.1% marginal and 0.3% not suitable for in-field RWH. The model was verified by locating the existing RWH on the suitability map obtained from GIS: 81% of RWH were located in the highly and moderately suitable areas whilst only 13% were located in areas of low suitability. Hence the developed model can reliably be used to predict potential areas for RWH.

Assessing the potential of Rainwater Harvesting (RWH) for sustaining small-scale irrigated coffee farming in the Bigasha watershed, Uganda

“…However, it is important to note that the onset and recession dates of the seasonal rainfalls were inconsistent across the years. In general, the annual average rainfall received in the Bigasha watershed lies within the 100 -1 000 mm range recommended by FAO for RWH [38]. Therefore, these rains were adequate for this research project's analyses.…”

Identifikasi Lokasi Prioritas Untuk Pembangunan Akuifer Buatan Simpan Air Hujan (Absah) Di Wilayah Sungai Citanduy, Jawa Barat

“…Untuk skala yang lebih besar, pemilihan lokasi dan jenis teknologi yang digunakan untuk memanen air hujan menjadi tantangan tersendiri (Prinz et al, 1998). Beberapa penelitian telah dilakukan untuk mengidentifikasi lokasi potensial pemanenan air hujan dengan menggunakan berbagai metode, misalnya: (Aklan et al, 2022;Al-Adamat et al, 2012;Bakir & Xing-nan, 2008;Mahmoud & Alazba, 2015;Maina & Raude, 2016;Mbilinyi et al, 2007;Nyirenda et al, 2021;Prinz et al, 1998;Wu et al, 2018;Ziadat et al, 2012).…”

Use of remote sensing to determine rainwater harvesting sites for piped micro-irrigation schemes in Chimanimani District, Zimbabwe

“…Methods and tools that have been used for identifying suitable sites for RWH include field surveys, and GIS-based multi-criteria evaluations (Nyirenda et al, 2021). Field surveys are appropriate for selecting suitable RWH sites for small and accessible areas.…”

“…Thus, given the challenges and difficulties of conducting field surveys, novel methods which integrate GIS and remote sensing offer relatively cheap, simple and quick ways to identify suitable sites for RWH even in inaccessible areas. For example, the use of remote-sensing based spectral indices such as the topographic wetness index (TWI), normalized difference vegetation index (NDVI) and the normalised difference moisture index (NDMI) aid in understanding the soil moisture regimes in an area (Kahinda et al, 2008;Nyirenda et al, 2021). TWI requires very high-resolution input data which, however, are not readily available in arid and semi-arid regions such as Zimbabwe.…”