Open access peer-reviewed chapter - ONLINE FIRST
Abstract
The proliferation and extent of small dams is a significant issue for water resources management. South Africa has an extensive spatial database of farm dams; however, uncertainties remain when estimating the water volume held, although satellite-based techniques offer some means of measurement. This chapter compares various datasets on the occurrence of farm dams in southern Africa and summarises the research on their impacts. Correlations between high-density of small dams and the decline of macroinvertebrate communities, resulting from compromised water quality and low flows, have been established in South Africa. Despite this, the assessment of the cumulative impact of farm dams on riverine ecosystems proves complex due to various uncertainties. The degree of impact varies by dam type, with off-channel dams exhibiting a lower influence on connectivity and sediment dynamics. Efforts to manage and mitigate the effects of small dams is being addressed through a variety of integrated approaches, including: a spatial cost-benefit framework, a model that incorporates different value systems with equitable allocation criteria, and agent-based modelling. The viability of these techniques is dependent upon securing agreement among stakeholders. The chapter concludes with some recommendations for the ways forward.
Keywords
- environmental impacts of small dams
- cumulative impacts
- South Africa
- occurrence and extent of small dams
- managing and mitigating impacts
1. Introduction
Farm dams have been a part of the agricultural landscape of some parts of southern Africa for a long time. Most of the farm dams were developed and are currently owned and operated by single farming units, although there are some that have been designed to service the water requirements of larger communities of (mostly) subsistence farmers. Small dams play crucial roles in rural communities across many African and Asian countries, particularly during the dry season. Senzanje et al. [1] highlighted their importance in Zimbabwe and advocated for collaboration between communities, NGOs and government to manage and conserve communal small dams. The authors highlighted that these small dams serve multiple purposes including domestic use, irrigation and livestock watering, and brick making, making them invaluable for supporting rural livelihoods and ensuring the long-term sustainability of small-scale farmers. Many of the differences in the occurrence of farm dams across the countries of the southern Africa region are likely related to the evolution of agricultural practices and specifically the differences between those areas dominated by commercial farming and those where communal farming practices dominate. The proliferation of farm dams in parts of South Africa are almost certainly associated with the greater proportion of commercial farms under private ownership relative to other countries. Agriculture in the other countries appears to be mostly based on dryland, non-irrigated farming practices, although a scan of Google Earth imagery suggests that Zambia and parts of Tanzania have several large-scale irrigation schemes that are partly supported by small dams.
In India, Rajendra Singh, an Indian conservationist, has successfully revived the use of
Farm dams dominate the landscape of many regions, and Mantel et al. [4] considered them to be convenient surrogates for indicating the level of impact of catchment transformation in rural and peri-urban areas. Farm dams are, however, not high on the research agenda, and therefore there are challenges and uncertainties in quantifying their impacts on riverine ecosystems and their connectivity. This chapter reviews the available information about the occurrence of farm dams in southern Africa, highlights the potential impacts on downstream users and aquatic ecology, and discusses the ways in which these impacts may be assessed and mitigated or managed.
2. The occurrence of farm dams in southern Africa
2.1 Farm dam characterisation
There are different types of farm dams, but most of them fall into the four broad categories illustrated in Figure 1. ‘No channel’ dams are almost always quite small (typically <1 ha) in area and depth and are designed to harvest surface runoff from upslope hillsides. They are mostly used for stock watering and rarely have sufficient storage, and they are too infrequently recharged, for irrigation purposes. The second type is referred to in Figure 1 as ‘On-channel weirs’ and represent situations where a river is dammed with a weir and where the storage is confined to the river channel itself. These can vary in size depending upon the gradient and cross-sectional area of the upstream channel. ‘On-channel dams’ represent those where the dam wall extends beyond the main river channel and where the full supply inundated area is far more than the upstream channel itself. ‘Off-channel dams’ are typically built close to a river channel which is used to recharge the storage through either pumping, or gravitational flow along a canal connecting the dam with an upstream section of the river. These may also receive some recharge water from upslope surface runoff. The last three can vary in size from quite small to very large, and they are used mostly for irrigation or local domestic water supply.
Figure 1.
The main different types of farm dams found in South Africa.
Sand dams [5, 6] are promoted by the UN Framework Convention on Climate Change (UNFCC) as a sustainable, low maintenance, climate-resilient option for water-scarce areas (http://bitly.ws/Rk7o; accessed 8 Aug 2023), and they are being built in Eswatini through the support of UNDP. These dams are designed to store water in the sediments trapped behind the dam wall, so that they are subject to lower rates of evaporative loss than conventional dams. They are understood to exist in Namibia [7], and possibly other arid parts of the region, but there appears to be no information available on the number and size range.
There are relatively few dams in southern Africa that have been constructed for the purpose of generating hydro-electric power. According to a database compiled by Klunne [8] there are 51 active hydropower stations in South Africa. Most of the larger ones are associated with multi-purpose dams (e.g., the Gariep and Van der Kloof dams on the Orange River) or pump-storage schemes (e.g., the Drakensberg scheme linking the Thukela and Vaal river systems). Most of the smaller schemes are based on run-of-river flows with or without diversion canals from the rivers supplying the water. The situation appears to be similar in other southern Africa countries. While there may be potential for further hydropower development [8, 9], it seems unlikely that this will be associated with many new impoundments that will substantially impact on downstream flow regimes. At the same time, we acknowledge the warning about the global proliferation and impacts of small hydropower plants referred to by others [10, 11, 12, 13]. It is interesting to note that the assessment of potential sites summarised in Figure 6. and 7 of the Korkovelos et al. [9] study include quite a large number in the very arid Karoo region of South Africa (the west central parts of the country), where the flow regimes are highly ephemeral and very unlikely to justify the costs of even run-of-river hydropower installations.
2.2 Databases and information to assess impacts of farm dams
In order to assess or manage the impact of farm dams in any given catchment, information is required about the upstream catchment area (to assess runoff inputs), surface area (to assess evaporation losses), storage capacity and patterns of abstraction [14]. Surface areas can be estimated quite accurately using satellite imagery and appropriate spatial analysis techniques [15], while upstream catchment areas can, theoretically, be estimated using high resolution digital elevation models and automated catchment definition tools. Patterns of abstraction can normally be estimated on the basis of documented crop requirements where the main water supply purpose is irrigation. However, the actual temporal patterns of abstraction can be highly variable. The main source of uncertainty in most impact assessments of farm dams will typically lie in the estimations of storage capacity [16, 17], in the absence of
McCully [20] estimated that the number of small dams globally was approximately 800,000, using the ratio of large to small dams in the USA (cited in [21]). However, a more recent estimate of the number of low-head dams (with height < 7.6 m) was in excess of 2 million (vs 87,000 large dams) in the USA alone [22]. There are two main local sources of information about the number and location of farm dams in South Africa (including Lesotho and Eswatini), while some farm dams in the region are included as part of the georeferenced global dams and reservoirs (GeoDAR) database [23]. The South African GIS dam coverage and the associated database (AGIS) [24] includes surface area and storage capacity for 3940 ‘minor dams’, but only 152 of these have surface areas of less than 1 ha. The AGIS database is primarily based on information collected from the registered owners of the dams by the Dam Safety Office, and using Google Earth to supplement the database. The HydAreas database, derived from a combination of topographic maps and satellite data, is available from the National Geo-Spatial Information of the South African Department of Agriculture, Land Reform and Rural Development [4]. This database contains surface area information (no capacity data) for 165,728 dams and 67.4% (111,700) of these have surface areas of less than 1 ha. The satellite-derived global GeoDAR database only includes 845 sites for South Africa, many of which are large dams, and is therefore of limited use for understanding the status of farm dam development in South Africa. Figure 2 illustrates the frequency distributions of surface area for all three databases (excluding those that can be considered as large dams from the GeoDAR database) and storage capacity for the limited sample available in AGIS. The total surface area of the HydAreas small dams is 275,000 ha. Using the AGIS data to calculate the mean depth at full supply capacity (i.e. volume/area), the median depth is some 3 m. Applying a median depth of between 2 m and 4 m (allowing for uncertainty in the actual depths of individual dams) to all the dams in the more complete HydAreas dataset suggests a national total storage capacity of between 5500 × 106 m3 and 11,000 × 106 m3. To put these values into perspective, South Africa’s largest dam (Gariep Dam on the Orange River) has an area of 36,000 ha and a capacity of 5674 × 106 m3, while the total area and capacity for the 212 major dams listed in the AGIS database are 302,621 ha and 35,541 × 106 m3, respectively. Therefore, while farm dams represent a similar total surface area compared with large dams, they only represent between 16% and 31% of the total storage capacity of large dams.
Figure 2.
Frequency distributions of surface area (ha) for the farm dams identified by different data sources (HydAreas, AGIS and GeoDAR) and for capacity (m3 × 103) based on the AGIS data.
Inevitably, the impact of farm dams in any given catchment depends on the total number and size, as well as the proportion of the catchment area that lies upstream of the impoundments. Hughes and Mantel [16] referred to three example areas in South Africa where impacts associated with dams used for both irrigation and intensive stock rearing are expected to be quite large. In the headwaters of the Breede River (666 km2), they reported a total of 376 dams with a combined surface area of 1109 ha, estimated total storage of 48.6 × 106 m3, and with approximately 55% of the total catchment area draining into the dams. A relatively high proportion of the Breede River dams appear to fall into the ‘Off-channel’ category (Figure 1). Figure 3 illustrates a further extreme example located in one of the headwater tributaries of the Kouga River in the Eastern Cape, South Africa. Within a 45 km2 part of this catchment (centred on 33.787°S, 23.613°E) there are at least 55 identifiable (using Google Earth imagery) dams ranging in surface area from ~0.1 ha to ~15 ha, all of them being either ‘No channel’ or ‘On-channel’ dams. These dams largely support large scale commercial deciduous fruit cultivation and many of them appear to have been developed after 1985 and prior to 2004, based on the available historical Google Earth imagery. To illustrate the limitations of some of the available databases, the AGIS database only includes 20 dams for a similar area, while the global GeoDAR database includes even fewer. The flow regimes of the more arid parts of the southern Africa are generally too ephemeral to justify the costs of even small dam development, although Hughes and Mantel [16] included an example from the Hartebeest River in the arid Karoo region of South Africa, where they identified 49 small dams in a 378 km2 catchment that are used mostly for irrigated fodder crop cultivation.
Figure 3.
Google Earth imagery (2023) for a headwater area of the Kouga River, Eastern Cape Province, South Africa, identifying the existing farm dams used for irrigation of deciduous fruit.
2.3 Farm dams in southern Africa and database reliability
While there do not seem to be any comparable datasets on farm dams for other countries of southern Africa, a visual assessment (using Google Earth imagery) of the main agricultural areas of Angola, Malawi, Mozambique, Tanzania, Zambia and Zimbabwe suggest that there are fewer areas with the same level of intensive small dam development that are found in several regions of South Africa. Zimbabwe appears to have the next highest level of small dam development and Senzanje et al. [1] suggest that there are some 7000 relatively small dams in the country, although no size statistics are provided to enable comparison with the South African data. Figure 4 illustrates an area of about 3500 km2 in the upper reaches of the Hunyani River (a tributary of the Zambezi River) located to the west of Harare and downstream of Lake Manyame. A visual inspection of Google Earth imagery for 2023 suggests that there are at least 84 dams varying in surface area from about 0.5 ha to about 100 ha, but mostly in the range of 5–20 ha. Many of these lie upstream of two major dams in this region (Mazvikadei and Biri dams).
Figure 4.
Google Earth imagery (2023) for a headwater area of the Hunyani River, Mashonaland West, Zimbabwe, identifying the existing farms dams.
Figure 5 illustrates the distribution of the number of farm dams per quaternary catchment area (the standard water management area used in the region) across the whole of South Africa, Lesotho and Eswatini. Using a different metric, that measures the cumulative upstream impact potential, Mantel et al. [4] noted that 52% of the quaternary catchments have a density of small dams that exceeds a threshold where downstream river functionality is likely to be compromised in some way. Figure 6 shows the spatial distribution of farm dams across South Africa, Lesotho, Eswatini (Swaziland) and Zimbabwe using the GeoDAR dataset based on the level 6 catchment areas from HydroSHEDS [26]. The map suggests that there are similar densities of small dams in Zimbabwe and most of South Africa, but also illustrates that there are some parts of South Africa with substantially higher densities. However, a comparison of Figure 6 with Figure 5 and the detailed Google Earth views in Figures 3 and 4, indicates that the GeoDAR data cannot be used to reliably assess the occurrence of farm dams in southern Africa.
Figure 5.
Number of small dams per quaternary catchment for South Africa, Lesotho and Swaziland (Eswatini) using the HydAreas database. The locations of the 19 flagship, free-flowing rivers, identified by Nel et al. [
Figure 6.
Number of farms dams for South Africa, Lesotho, Swaziland (Eswatini) and Zimbabwe using catchment areas based on HydroSHEDS and the GeoDAR dams data (the specific catchments referred to in the text and
3. Impacts of farm dams
3.1 Cumulative impacts of farm dams
Individually, small dams have a limited impact on downstream users and environment, such as effects on flow connectivity [27], faunal assemblages [28, 29] and on geomorphological footprints [22]. Inevitably, different dam types (Figure 1) will have different individual potential impacts. For example, off-channel dams may have similar impacts on downstream flow reduction (through pumping from the river) as on-channel dams, but they will not impact on flow connectivity or sediment dynamics. However, when the density of small dams surpasses a certain threshold, impairment of downstream ecosystems through cumulative impacts on both water quality (physico-chemistry), macroinvertebrate communities, and reduced water availability during dry periods have been documented in South African rivers [3, 30]. Hughes and Mantel [16] estimated a reduction in mean streamflow of up to 35% in the intensively irrigated headwaters of the Breede River (Western Cape Province, South Africa). The impacts on high flows (exceeded <10% of the time) were expected to be low, but much higher for moderate to low flows (e.g. reductions in the 90% exceeded flow of up to 71%). Within the semi-arid parts of the region natural flow events are infrequent and evaporation losses from the dams are high, suggesting that the impacts on downstream flow regimes are also expected to be high. For the Hartebeest River, referred to earlier, Hughes and Mantel [16] estimated reductions in mean monthly flow of between 30% and 40%, and reductions in both the 10% and 50% exceeded flows of between 50% and 60%. A comparable assessment in Western France [31] reached similar conclusions and suggested that the efficiency of small farms dams in supplying irrigation water and their downstream impacts might decrease due to climate change. Unlike the
3.2 Farm dam impacts on rivers and beyond
Although the impacts of farm dams on the hydrological regime of a river can only be estimated with quite a high degree of uncertainty [16, 17], there is little doubt that they can be substantial. Mantel et al. [3, 30] used multivariate analysis to study the impacts of small dams on the physico-chemistry and macroinvertebrate biology of rivers in two different climate regions of South Africa (one winter and one summer rainfall region) and the results suggested that impacts were more highly correlated with small dam density than with dam volume. Reduced baseflows due to high densities of small dams were correlated with increased total dissolved salts and shifts to more opportunistic and slow-current taxa in macroinvertebrate communities in South African rivers [30]. Impacts on downstream discharge reductions and changes in the index of macroinvertebrate health appeared to be greater in the winter rainfall region. However, they acknowledged that attributing impacts to the farm dams themselves is not conclusive as other anthropogenic landscape changes (such as land-use changes) are highly correlated with the density of farm dams. These impacts will vary depending on whether the dams are on- or off-channel.
Apart from the likely impacts on the ecology of the downstream river, farm dams may also impact on downstream water users. For example, the dams in the Kouga River (Figure 3) lie upstream of one of the major dams supplying the water requirements of the city of Port Elizabeth to the east. There are, therefore, largely unquantified, conflicts of interest between the supply of water to support a local economy, water to support a nearby large city, and environmental sustainability of the upper reaches of the Kouga River.
The potential
4. Managing and mitigating impacts
4.1 Various integrated approaches to manage impacts
Figure 7 represents an attempt to summarise the steps in an integrated approach to evaluating water resources management options to achieve sustainable solutions from the multiple perspectives of various socio-economic and environmental concerns. The starting point is a hydrological model that can adequately represent both the natural and modified catchment system, and which includes the simulation of both surface and ground water resources. A number of such models have been applied across many of the countries of southern Africa [34, 35, 36, 37, 38]. Given that many parts of the sub-continent are data-scarce regions and given many of the uncertainties in future climate projections, the outputs of these models are frequently in the form of ensembles of plausible, but uncertain outcomes [39, 40]. While the choice of which model to use may depend on a number of factors (including user preference and experience), it is clearly important that the model includes components that simulate all the main anthropogenic impacts, including large dams and smaller farm dams [16, 17]. Many countries of southern Africa have some form of policy and legislation that regulates water use to ensure a defined level of protection for aquatic ecosystems, and methods have been developed to undertake the necessary assessments [41, 42, 43] and generate information that is compatible with integrated water resources yield models [44, 45]. The lower part of the diagram represents the main decision-making part of the whole process where various scenarios can be considered in the context of the requirements and attitudes of the full range of stakeholders.
Figure 7.
Simplified summary of the steps in an integrated approach to evaluation of water management options to achieve sustainable solutions.
Hughes and Mallory [46] used a water resources yield model to demonstrate how downstream impacts of dams on river ecology [47] and other water users can be mitigated by managed releases, but this approach is generally only applicable to larger dams that include bottom release capabilities and that are subject to some form of water sharing agreement. Many of the dams constructed in South Africa since the new water act of 1998 are theoretically subject to licencing that includes the need to conform with legislation controlling downstream environmental flow requirements, while the only legislation prior to that date focused on dam safety and was applicable to dams with wall heights over 5 m and capacities exceeding 50 × 103 m3. However, the majority of the existing farm dams were probably constructed prior to 1998, while others may have been built or extended without approval by the national controlling department (Department of Water and Sanitation; DWS). Even where more recently constructed dams have been designed with the potential to release low flows through a bottom outlet, there is the real possibility that releases are rarely made in the absence of any policing by DWS officials.
Crossman et al. [48] applied a spatial targeting approach within a cost-benefit framework to manage irrigation allocations in Australia. They report multiple benefits that include a reduction in agricultural water use coupled with an increase in the value of agriculture, as well as an increase in the provision of ecosystem services. Pienaar and Hughes [49] suggested an approach that accounts for the integration of different value systems across different water users (e.g. purely economic values related to crop yield, or qualitative values based on perceived benefits of water for social welfare and health), which has recently been extended and tested in a commercial agricultural region of the Western Cape Province of South Africa where there is intense competition for water [50]. Xoxo and co-authors applied an analytic hierarchical process (AHP) to determine equity-based allocation criteria for weighting the needs of various community stakeholders under water deficit conditions. The combination of the approaches suggested by Pienaar and Hughes [49] and Xoxo et al. [50] contribute to the emerging field of socio-hydrology [51] for decision-making that is participatory, practical and with potential to mitigate conflict, thus ensuring greater chance of success of management plans. Another socio-hydrological approach for getting stakeholder buy-in is agent-based modelling [52] which includes agent behaviours to understand how the system can be managed under different scenarios, and these behaviours can be used to develop an appropriate management plan.
4.2 Challenges and uncertainties in assessing farm dam impacts
Arguably, one of the key issues that affects almost all the components of Figure 7 is the presence of both aleatory and epistemic uncertainties [49] associated with both data and model limitations. Two key challenges are associated with (a) difficulty in assessing how these dams affect downstream hydrological systems due to uncertainty regarding water volumes and abstraction patterns, as well as a lack of information about dam attributes like wall height, age, and condition; (b) lack of accurate information on the location of small dams in relation to rivers due to the resolution of geographic information system (GIS) datasets. Hughes and Mantel [17] estimated the uncertainties specifically associated with farm dam impacts. While it is important to represent these uncertainties in the whole process [53], not all of the available modelling tools are designed to include uncertainty analysis, and it is not always straightforward to communicate uncertainties to stakeholder groups [54].
Many of the small dams in South Africa would have been constructed prior to the legislation (South African National Water Act, No. 36 of 1998) that accounted for downstream impacts and recognition of the need to maintain aquatic ecosystems and the benefits that they provide. Even now, some 25 years after the legislation was put into effect, it remains difficult to quantify the cumulative effects of farm dams in some parts of the country, partly due to a lack of resources to carry out such assessments together with the large number of catchments that are potentially affected by farm dams. This therefore makes it difficult to undertake assessments of the benefits and dis-benefits of farm dams and identify sustainable management practices. As an example, the South African National Freshwater Ecosystem Priority Areas project (NFEPA) [25] identified spatial priorities that are strategic for conserving water ecosystems for sustainable use of water resources (Second National Water Resources Strategy) [55]. Nineteen flagship, free-flowing rivers, have been identified and Nel et al. [25] recommended that further developments should be limited to ensure that they retain their free-flowing character. If the location of these 19 flagship rivers is compared with the distribution of farms dams (Figure 5) it is apparent that some reaches of these rivers overlap with quaternary catchments having more than 100 small dams, raising some concerns about the reality of their designation as ‘free-flowing’. Part of the reason for this discrepancy is that the NFEPA only included dams within a 50 m buffer of the main river channel, which effectively ignores the majority of farm dams [4].
5. Conclusions and recommendations
South Africa has a reasonably robust database on the presence of farm dams, far surpassing databases for its neighbouring countries and even global datasets, that often miss a notable portion of small dams. The estimation of water volume held by these dams is, however, considerably uncertain, although certain satellite-based techniques exist for this purpose. Uncertainties also plague our understanding of the operation and the extent of catchment area draining into these dams. These issues are closely linked to whether the dams are situated on- or off-channel.
Quantifying the effects of farm dams on riverine ecosystems presents challenges and uncertainties. These impacts vary based on factors like spatial density and dam type, with off-channel dams having a comparatively lower influence on connectivity and sediment dynamics than on-channel farm dams. Research conducted in South Africa has established correlations between the cumulative impacts of high density of small dams on the deterioration of macroinvertebrate communities. This degradation is connected to degraded water quality and impacted flow regimes, especially during low flow periods. These repercussions may extend to affect downstream water users. Thus, modelling the cumulative impacts of these farm dams and managing water resources at the catchment level are problematic due to a range of uncertainties in our understanding. Notably, the issue of impacted riverine connectivity is not restricted to small dams; it can also be associated with infrastructure such as road-river crossings that include culverts, bridges, fords [56]. Some regions are now focusing more on these infrastructure aspects, incorporating them into proactive planning and strategies for adaptation to climate change [57].
Managing and mitigating the impacts of small dams is being addressed through various integrated approaches, such as the cost-benefit framework used in Australia [48], equity-based allocations using gaming [49, 50] and agent-based modelling [58], both used in South Africa. The feasibility of these various techniques hinge on the considerable effort required to reach agreement among users, and the time required for implementation and the fact that the infrastructure required to manage releases does not exist for older farm dams. External pressures, such as requirements from international markets, may encourage cooperation. For instance, initiatives like the World Wide Fund for Nature’s WWF Basket (https://www.wwf.org.uk/wwf-basket) are designed to reduce the environmental impacts of food consumption in the UK. Such initiatives, if successful, can facilitate cooperation among farmers and aid in establishing adherence to environmental flow standards.
We propose two main recommendations for governance actors (government representatives at local, provincial and national levels and non-governmental organisations) involved in water resources management to effectively address uncertainty. Firstly, it is essential for relevant government departments to collate and update data for farm dams, in addition to large dams. Creating open-access databases that store information about the location, volume, and ideally, the operational status of various types of farm dams is highly advisable. These comprehensive databases are vital for accurately quantifying the cumulative effects of different farm dams and other infrastructure, such as road-river crossings. While remote sensing technology is improving and providing more detailed imagery, local databases remain indispensable for validating satellite data and supplementing information that remote sensing cannot capture.
Secondly, there is a need to promote integrated approaches that take into account social, economic, and environmental considerations and constraints when developing robust solutions for water resources planning and management. Embracing socio-hydrological modelling, which facilitates stakeholder engagement and consensus-building around mutually agreed-upon solutions, is particularly important in situations where water resources are contested. While South Africa has made some strides in terms of data availability and integrated modelling, there is still room for greater adoption of these techniques, and other southern African countries (and beyond) need to invest resources into similar datasets along with adoption of proactive management strategies.
Acknowledgments
We thank Rhodes University for supporting the postdoctoral fellowship for the first author during 2006–2008, when the original work [3, 30] was conceptualised and conducted. Rhodes University is also acknowledged for funding this publication.
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