GIS-based decision support system for identifying potential sites for rainwater harvesting

摘要

Identification of potential sites for rainwater harvesting (RWH) is an important step towards maximizing water availability and land productivity in the semi-arid areas. However, selection of appropriate sites for different RWH technologies on a large scale presents a great challenge, since the necessary biophysical data and infrastructure are often lacking. This paper presents a geographic information system (GIS)-based decision support system (DSS) that uses remote sensing (RS), limited field survey to identify potential sites for RWH technologies. The input into the DSS include maps of rainfall, slope, soil texture, soil depth, drainage and land use/cover and the outputs are maps showing potential sites of water storage systems (ndiva), stone terraces, bench terraces and borders. The Model Builder in the Arc View GIS was used as a platform for the DSS. Two sites in the Makanya watershed, in Kilimanjaro Region, Tanzania, were used for testing and validation of the DSS. The results reflect specific suitability levels of parameters and weight of factors; for example, near streams (drainage) with slope ranges from moderately steep to steep (10°-30°) are potential sites for ndiva locations whereas moderately undulating to steep slopes (5°-30°) with unstable soils are potential sites for stone terraces. Moderately undulating slopes (5°-10°) with clay, silt clay and sandy clay soils are potential sites for bench terrace and gently undulating slopes (2°-5°) with clay, silt clay and sandy clay soils are potential sites for borders. The results from testing and validation of the developed DSS indicated that the tool can be used reliably to predict potential sites for RWH technologies in semi-arid areas. Most of predicted RWH technologies during testing were found within very highly and highly suitable locations (41.4% and 40%, respectively) also in validation 36.9% of RWH technologies were found within the moderately suitable followed by very highly suitable and highly suitable both with 23.6%. Despite the good results, it is recommended that more work be carried out to refine the model and to include other pertinent ancillary data like socio-economic factors to increase its usefulness.