Physics and Chemistry of the Earth 33 (2008) 788–799
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Developing suitability maps for rainwater harvesting in South Africa J. Mwenge Kahinda a,b,*, E.S.B. Lillie b, A.E. Taigbenu a, M. Taute b, R.J. Boroto b a b
School of Civil and Environmental Engineering, Wits University, Private Bag X3, Wits 2050, South Africa Source Strategic Focus (Pty) Ltd., P.O. Box 2857, Pretoria 0001, South Africa
a r t i c l e
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Available online 10 July 2008 Keywords: Rainwater harvesting Socio-economic factors Geographic information system Suitability map
a b s t r a c t Dry spells are a direct consequence of spatial and temporal variability of rainfall, and these jeopardise the success of rainfed agriculture by causing crop yield reduction and crop failure in rural South Africa. The potential of rainwater harvesting (RWH) to mitigate the spatial and temporal variability of rainfall has brought about its revival during the last two decades. For planning and implementation purposes, it is critical to be able to identify areas suitable for RWH. The paper presents a methodology that enable water managers to assess the suitability of RWH for any given area of South Africa. Previous methodologies developed to assess RWH suitability recognised the importance of the socio-economic factors but did not incorporate them in their assessment. This non-integration of socio-economic factors is pointed as the main cause of failure of rainwater harvesting projects. In this study, in-field RWH and ex-field RWH suitability maps are developed based on a combination of physical, ecological and socio-economic factors. Model Builder, an extension of ArcView 3.3 that enables a weighted overlay of datasets, is used to create the suitability model, comprising the physical, ecological and vulnerability sub-models from which the physical, the ecological and the vulnerability maps are derived respectively. Results indicate that about 30% is highly suitable for in-field RWH and 25% is highly suitable for ex-field RWH. Details of the proposed method as well as the suitability maps produced are presented in this paper. The implementation of this method is envisaged to support any policy shifts towards wide spread adoption of RWH. Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction To improve the reliability of rural water supply and the productivity of small-scale rainfed agriculture, South Africa needs to further investigate unconventional water sources. Aridity and climatic uncertainty are the major challenges faced by small-scale farmers who rely on rainfed agriculture as the low crop yield they experience is mainly attributed to poor temporal and spatial rainfall distribution rather than acute water shortage. Rainwater harvesting (RWH) is described as the collection, storage and use of rainwater for small-scale productive purposes. It has been identified at a number of international fora as one of the important interventions necessary towards meeting the Millennium Development Goals in Africa. RWH enhances water productivity by mitigating temporal and spatial variability of rainfall (Mwenge Kahinda et al., 2007a; Rockström and Barron, 2007) and provide water for basic human needs and other small-scale productive activities (Mwenge Kahinda et al., 2007b). In areas with dispersed populations or where the costs of developing surface or groundwater resources are high, RWH and storage have proved to be an affordable and sustainable intervention (Mati et al., 2006).
* Corresponding author. Tel.: +27 723969799; fax: +27 117177045. E-mail address:
[email protected] (J. Mwenge Kahinda). 1474-7065/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.pce.2008.06.047
Three broad categories of RWH can be distinguished when it is classified according to the type of catchment surface used: in-field RWH (IRWH), ex-field RWH (XRWH), and Domestic RWH (DRWH). DRWH systems collect water from rooftops, courtyards, compacted or treated surfaces, store it in RWH tanks for domestic uses. IRWH systems use part of the target area as the catchment area, while XRWH systems use an uncultivated area as its catchment area. The focus of this paper is on both IRWH and XRWH that will refer to as field RWH (FRWH). Although non-governmental organisations, faith-based groups and networks are advocating the use of RWH, its adoption rate is slow. The reasons might be that, firstly, there is inadequate attention paid to social factors (Patrick, 1997; Rockström, 2000) and secondly, there is lack of scientifically verified information which can be used to indicate areas where rainwater harvesting can be applied (Mati et al., 2006). DRWH is the least used water source in South Africa with only 1% of rural households currently using it as their main water source (Mwenge Kahinda et al., 2007c). IRWH is mostly practiced at household level in the backyards while XRWH is rarely implemented. The South African Agricultural Research Council (ARC) has had a programme of IRWH in the Taba Nchu area for over a decade but the technique has not extended beyond small plots around homestead with total adoption of about 0.5% of the Upper Middle Modder River Basin.
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FAO (2003) lists six key factors when identifying RWH sites: climate (rainfall), hydrology (rainfall–runoff relationship and intermittent watercourses), topography (slope), agronomy (crop characteristics), soils (texture, structure and depth) and socio-economic (population density, work force, people’s priority, experience with RWH, land tenure, water laws, accessibility and related costs). A number of studies present methods for assessing RWH suitability of a given area. Those studies commonly use physical factors such as rainfall, land cover/use, soil characteristics and topography for the assessment of suitability. For instance, Mbilinyi et al. (2006) used rainfall, soil depth, soil texture, differential global positioning system points, aerial photos, ground truthing and topographic maps while Mati et al. (2006) used baseline thematic maps (rainfall, topography, soils, population density and land use) to produce composite maps that show attributes or ‘‘development domains” that serve as indicators of suitability for targeted RWH interventions. To determine index maps of RWH potential (ponds) in Africa, Senay and Verdin (2004) used runoff data derived from rainfall data using the SCS curve number methods and delineated watersheds from the Africa-wide Hydro-1K digital elevation model. Mou et al. (1999) used rainfall (average rainfall, annual rain days, annual rainfall fluctuation rate) and topography. Prinz et al. (1998) used remote sensed data and thematic maps in conjunction with field investigations. In his iterative decision tree, Patrick (1997) incorporated socio-economic factors after the mapping. The need to integrate socio-economic factors into deciding the suitability of an area to RWH largely underscores the failure or success of RWH systems (Critchley and Siegert, 1991; Oweis et al., 2001). The quality, reliability and availability of data often limit the setup of Geographic Information systems (GIS). Errors in the final results may originate from any stage of the process; from the collection of the source data to the interpretation of the final results (Store and Kangas, 2001). Also error propagation or the accumulation of errors from various sources affects the results of analysis. Therefore, the accuracy of the RWH suitability maps depends on the quality of the different layers used as well as the quality of the spatial data analysis. This paper presents a GIS-based model, which combines physical, ecological and socio-economic attributes, to assess the suitability of a given area for FRWH in South Africa.
Table 1 Datasets used in the RSM No.
Attributes
Layers
Resolution
Source
1
Physical
Aridity zones
2 km 2 km
Annual rainfall with 80% Probability of Exceedance National land cover 2000
2 km 2 km
Agricultural Geo-referenced Information System (AGIS) Schulze et al. (1996)
Soil texture
1/250,000
Soil depth Ecological Importance and Sensitivity Category % Households without piped water % Households without proper sanitation % Household below poverty datum % Population not economically active Rivers Dams and lakes Roads Railways Conservation areas Urban areas
1/250,000 1/250,000
2
3
4 5 6
7
Ecological
Socioeconomic
8
9
10
11 12 13 14 15 16
Constraint
1/250,000
1/250,000
Agricultural Research Council (ARC) & Council for Scientific and Industrial Research (CSIR) Derived from Land type of the ARC Schulze (2006) Department of Water Affairs and Forestry (1999)
Statistics SA (2003)
1/250,000
1/250,000
1/250,000
1/250,000 1/250,000 1/250,000 1/250,000 1/250,000
Department of Water Affairs and Forestry
1/250,000
Thereafter, pixel values of each physical, ecological and socio-economic layer were reclassified from 1 to 5. The most suitable parameters were classified as 5, while the least suitable were classified as 1. To precustomise the RSM, the conversion to grid theme and the reclassification was done during the data processing using Spatial Analyst, another extension of ArcView 3.3.
2. Methods 2.1. The scale of the RSM The RWH suitability model (RSM) was developed using model builder, an extension of ArcView 3.3. The physical, ecological, socio-economic as well as constraint layers used in the RSM are listed in Table 1. Only the best available datasets at national level were used in the RSM. As model builder works in the raster environment with grid format layers, vector themes were converted into grid themes of cell size 100 m 100 m. Different suitability values were assigned to the physical, ecological and socio-economic layers based on literature review.
The datasets were collected in different years by different sources and therefore have different resolutions (Table 1). The rainfall and the aridity zones datasets that have coarser resolution (2 km 2 km) were resampled to produce new datasets with smaller pixel (100 m 100 m). The model prediction is generalised because it is based on country scaled data. Fig. 1a and b shows how the generalisation reduces both the number of objects, and the amount of detail of the land use dataset.
Fig. 1. Land use for the Potshini catchment: (a) at 1:50,000 resolution (de Winnaar et al., 2007), (b) from the National Land cover 2000 with 1:250,000 resolution.
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Geographic data are rarely distinguished by regularly spaced shapes. Hence, variation in any of the layers within the cells is lost and a single value of each layer represents the cell area. Though the conversion of the data into the raster format was a step necessary for the progression of the analysis, it reduced the quality and accuracy of the results generated by the model. In South Africa, the average size of subsistence farmers’ crop field is about 10,000 m2 (Botha et al., 2003; de Winnaar et al., 2007). Since the size of the pixel should be half of the smallest distance to be represented, a 50 m 50 m pixel would have represented RWH more appropriately. However, to strike a balance between large data volume resulting from too fine cell size and over generalisation resulting from too coarse cell size, taking account the resolution of the dataset available, a cell size of 100 m 100 m was adopted. It should be noted that the RSM works at two scales: Country scale and Water management area scale. A Water Management Area is a management unit in the South African National Water Resource Strategy within which a catchment management agency oversees the use, protection, development, conservation, management and control of water resources (DWAF, 2004). There are 19 such water management areas in South Africa. 2.2. Spatial data layers 2.2.1. Physical layers 2.2.1.1. Aridity zones. Six aridity zones (Table 2) were delineated based on the ratio of precipitation to potential evapotranspiration (PET). About 91% of the country has a ratio rainfall to PET that lies between 0.05 and 0.65. In general, RWH is suitable in arid, semiarid and dry sub-humid areas (FAO, 2003). 2.2.1.2. Rainfall. The magnitude of rainfall plays a significant role in assessing the suitability of RWH for a given area. Rainfalls in semiarid and arid regions of South Africa are of short duration, relatively high intensity, limited spatial extent and large variability. It is therefore inadequate to use the mean annual precipitation to find suitable sites for RWH since it does not account for its variability. To account for this variability, FAO (2003) recommends the use of design rainfall of 67% Probability of Exceedance. In this study, the Annual rainfall with 80% Probability of Exceedance was used. Promoting RWH in areas receiving less than 100 mm/year or more than 1000 mm/year of rains (Table 3) is not common or not recommended (Mou et al., 1999; FAO, 2003; Mati et al., 2006). There is hardly any Productive water-based activity are not feasible in areas that receive less than 100 mm/year of rain while there is no incentive for to implement RWH schemes in areas with annual rains in excess of 1000 mm/year. However, DRWH does take place in high rainfall areas due to socio-economic conditions of underdevelopment in scattered settlements where centralised piped systems are uneconomically viable. This again brings to light the socio-economic dimension in RWH. In this model, the user has the option of using either the aridity zones or the rainfall as a physical layer.
Table 3 Rainfall suitability ranking No.
Rainfall (mm)
IRWH suitability
XRWH suitability
1 2 3 4
0–100 100–200 200–400 400–600 600–800 800–1000 >1000
1 2 5 4 3 3 1
1 2 3 4 5 3 1
5 6
2.2.1.3. National land cover 2000. There is a correlation between the land cover and the runoff produced by a given area for a rainfall event. Increase in the vegetation density results in a corresponding increase in interception, and infiltration rates thereby reducing runoff (Thompson, 1999). Thus the design of RWH takes cognisance of the runoff component of the hydrological process. The national land cover (NLC2000), developed by the ARC & the Council for Scientific and Industrial Research (CSIR) in South Africa, is the latest and most detailed land cover dataset at national level. The 52 land cover classes in the dataset were ranked according to their suitability to RWH. 2.2.1.4. Soil texture. The soil texture was derived from the land type dataset of the ARC. The land type dataset has six clay classes to which a textural class is associated (Table 4). It is assumed that most South African soils have silt content of about 10% (Schulze, 2006 citing Hutson, 1984). Each textural class was then reclassified (Table 4) following the FAO (2003) soil texture triangle (Fig. 2), which indicates the relative proportion of sand, silt and clay suitable for RWH either as catchment area (Runoff area) or crop basin (Runon area). 2.2.1.5. Soil depth. The total soil depth was calculated by adding the thicknesses of topsoil and subsoil horizons. Most topsoils in South Africa, especially in the eastern regions, extend to a depth of 0.25– 0.30 m, with slightly thinner topsoils at 0.20–0.25 m in the semiarid central-west regions and smaller tracts at 0.65
39 5 4
5 3 5
1 1 2
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Fig. 2. Soil texture triangle with RWH Runon and Runoff area (FAO, 2003).
Table 5 Soil depth suitability ranking No.
Soil depth class
Depth
IRWH suitability
1 2 3 4 5
Very deep Deep Moderately shallow Shallow Very shallow
>0.75 0.4–0.75 0.3–0.4 0.2–0.3
