ABSTRACT
A remote sensing approach was used to assess sedimentation in Tuli-Makwe Dam in the semi-arid Mzingwane Catchment in the Matebeleland South province of Zimbabwe. The loss in reservoir gross capacity due to sediment deposition for a period of 47 years since the construction of the dam in 1966 to 2013 was determined to be 3.371 Mm3 which translate to 40.84 % gross capacity loss. The revised capacity of the dam is estimated at 4.883 Mm3. The annual rate of sedimentation was calculated to be 0.87 % per annum which translates to 0.0717 Mm3 per annum. The specific sediment yield over Tuli-Makwe catchment was calculated to be 110.63 tonnes / km2 / year. The result of the sedimentation analysis is typical of small reservoirs in semi-arid regions in Southern Africa. The sedimentation results for Tuli-Makwe reservoir using the remote sensing approach for 2013 are comparable with the sedimentation results from the 2012 hydrographic survey. The results further confirm the applicability of remote sensing for sedimentation analysis for small reservoirs in semi-arid regions. Assuming a uniform sedimentation rate, current trends suggest that Tuli-Makwe reservoir may be filled up in the next sixty eight years from 2013, however the useful capacity of the reservoir may be lost in much less time.
Key words: Reservoir sedimentation, remote sensing, reservoir capacity loss.
Reservoir sedimentation is a serious problem in many parts of the world compromising the useful lifespan of reservoirs (Andredaki et al., 2015). The removal of accumulated sediments is usually a prohibitively expensive undertaking (Minear and Kondolf, 2009), while on the other hand, the construction of new reservoirs to offset the loss of reservoir capacity from sedimentation is becoming a less viable option since most reservoirs have already been constructed at the most suitable sites (Rashid et al., 2014). The cumulative loss of reservoir storage capacity disrupts the economic model of planned investments for the reservoir (Odhiambo and Ricker, 2011). Reservoir sedimentation has other negative environmental implications both upstream and downstream of the dam wall. Suspended sediments in the reservoir reduce water quality, reduce light penetration and cause eutrophication which affect biotic life. Sediment trapping behind dams has significant implications for downstream ecosystems as sediments play an important role in influencing river and coastal geomorphic processes in large river systems (Syvitski, 2003; Vorosmarty et al., 2003).
Sedimentation causes the loss of approximately 0.5 to 2.0% of the world reservoir volume annually (Hasan et al., 2011; Issa et al., 2015), while sediment deposition rate varies from 0.1 to 2.3% for large dams worldwide (Rashid et al., 2015). However depending on the nature of the catchment, small reservoirs in semi-arid to arid areas experience much higher levels of sediment deposition. Mambo and Archer (2007) suggest that in Zimbabwe, sediment load exceed the normal design limits in many reservoirs. Reservoir sedimentation surveys of seventeen small dams in semi-arid areas in Zimbabwe and Tanzania showed annual sedimentation rates ranging between 0.5 and 50% with a median of 2.6% translating to sedimentation life span for a “typical” small dam of 38 years, and an even shorter “useful” life span (Wallingford, 2004).
Wallingford (2004) further suggested that 15% of the small reservoirs are silted up in the first twenty years of construction. The effects of sedimentation are especially precarious in semi-arid and arid environments as many small reservoirs have been built to improve rural livelihoods (Senzanje and Chimbari, 2002). Small reservoirs provide water for multiple functions such as small-scale irrigation, domestic water supply, livestock watering, building and other socio-economic activities (Eilander et al., 2014).
In addition to improving the livelihood of the people, small dams also contribute to the sustainable management of the environment. Dalu et al. (2013) identified reservoir sedimentation as a major problem reducing the storage capacity and life span of agricultural dams in Botswana. When reservoirs are silted up, rural communities are deprived of their livelihood.
For sustainable management of reservoirs, an up to date knowledge of sedimentation levels is fundamental (Chitata et al., 2014; Onwuegbunam et al., 2013). Accurate quantification of sediment trapping in reservoirs improves the estimates of river sediment export, allows the useful life of reservoirs to be determined and provides insights into sediment transport and dynamics of watersheds (Lewis et al., 2013). This requires efficient and cost effective tools for systematic and timely reservoir capacity surveys to identify reservoirs that are most vulnerable to rapid sedimentation (Minear and Kondolf, 2009).Analysis of sedimentation has traditionally been done through direct and indirect methods. Direct methods include actual measurement of volume of sediments deposited in the reservoir through mainly hydrographic surveys (Vente et al., 2003). Indirect methods include sediment budgets which involve the analysis of the inflow and outflow sediment samples collected at gauging stations upstream and downstream of the reservoir, and also the various models that have been developed to estimate sediment yield in to reservoirs (Adam et al., 2014; Bronstert et al., 2014; Weerakoon, 2005).
Hydrographic surveys involve measuring the depth from the water surface to the settled sediments at the bottom of the reservoir along range lines. Measurements are typically taken from a boat using echo sounders or a tape measure with a weight attached at its bottom end. At the end of the survey a new capacity of the reservoir is calculated based on the bathymetry data collected. The difference between the previously known capacity of the reservoir and the new capacity represents reservoir capacity lost due to sedimentation. Depending on the size of the reservoir, hydrographic surveys may take from a few weeks to a few years. While hydrographic surveys have been proven to be quite accurate in sedimentation analysis (Vente et al., 2003), specialized equipment such as boats, echo sounders and GPS receivers are not readily available in many developing countries. Hydrographic methods are also cumbersome and time consuming. In this regard, hydrographic surveys are seldom done or are done after long periods of time such as one in 15 to 25 years.
On the other hand, sediment budget methods require manned station so that sediment samples are collected and sent for analysis consistently during periods of river flow. However, in many developing countries there is lack of well-developed institutional infrastructure to operate monitoring systems efficiently in terms of manpower development and financial constraints in the procurement of equipment and materials required for a monitoring system and for data analysis. Chikwanha (1996) highlights that in many developing countries, the process of sediment samples collection is fraught with challenges arising mainly from financial constraints which in the end results in poorly collected data with too many gaps. While a large number of models have been developed and are available for estimation of reservoir sedimentation process in catchments, the data requirements and computational modelling skills required make the application sediment yield models difficult in most catchments (Jothiprakash and Garg, 2008). As a result, despite their crucial importance, many reservoirs are not monitored for sedimentation regularly. The situation is much direr for small reservoirs in developing countries where governments seldom avail resources to setup and manage sediment monitoring networks (Amitrano et al., 2014) making up to date information about sedimentation for timely interventions unavailable. There is a need to apply cost effective, simple, reliable and sufficiently accurate methods, which require less time, depend on less field data and are easily adaptable to different catchments (Jain et al., 2002; Weerakoon, 2005).
Remote sensing techniques have emerged as an alternative expedient and proficient option to assess sediment deposition and distribution pattern in reservoirs. Remote sensing data provides repetitive, synoptic, timely and relatively cheap information for detection of water bodies that is useful for assessment of sedimentation (Arledler et al., 2010). Assessment of sedimentation using remote sensing is premised on the fact that water spread area of a reservoir at given elevation reduces with the sedimentation indicating deposition of sediments at that elevation (Pandey et al., 2014). Water spread area at different elevations is used to calculate new elevation-area-capacity relationship for the reservoir based on methods such as the prismoidal formula, the Simpson formula and the trapezoidal formula. Satellite data from remote sensing techniques provide important capabilities to map surface water features and monitor the dynamics of surface water (Ji et al., 2009).
Dalu et al. (2013) used the normalized difference water index (NDWI) developed by McFeeters (1996) to successfully map surface water bodies in the Yangtze River Basin and the Huaihe River Basin in China using the recent Landsat 8 Operational Land Imager (OLI) multispectral images and obtained overall accuracy of above 95%, kappa coefficient of 0.89 and producer’s accuracy of 95%. Rathore et al. (2006) used water index (WI) to identify water pixels for sedimentation analysis of Harkud dam in India. The NDWI and WI are derived from arithmetic operations based on the Near Infra-red (NIR), green and red wavelengths of the electromagnetic spectrum to enhance the presence of water surface in satellite data. An appropriate threshold for each index is established to separate water bodies from other land-cover features based on spectral characteristics. The establishment of spectral indices that delineate water bodies are based on the fact that water absorbs energy at near-infrared (NIR) while it reflects more in the optical range of the green and red wavelengths (Xu, 2006). However, studies show that the method yields best results for deeper water while they do not do so well for shallower water bodies (Du et al., 2014). The water spectral indices not only enhances the spectral signals of water, but also cancels out portions of the noise components that are common in different wavelength regions such as soil and terrestrial vegetation features.
The results obtained from the application of remote analysis have been found to be accurate in comparison with hydrographic surveys. Rodrigues et al. (2012) used Landsat images with spatial resolution of 30 m to estimate small reservoir storage volumes in Preto surface area. The method was validated with a subset of reservoirs for which surface areas, shapes and depths were determined with ground-based survey measurements and the results were found to be accurate. Pandey et al. (2014) used the Normalized Difference Water Index (NDWI) to delineate open water spread area to assess reservoir sedimentation of the Patratu Reservoir in India from 2006 to 2012 using Landsat TM satellite data and reported that the area-capacity curves derived using remote sensing data were similar to the curves obtained from hydrographic methods.
Jain et al. (2002) used supervised image classification on the IRS – 1B LISS II satellite data with a spatial resolution of 36,25m to determine water spread area to carry out a study on the sedimentation in Bhakra reservoir in India. The results obtained from remote sensing technique were slightly higher at 25.23 Mm3 / year over the 32 years study period compared to the hydrographic survey results 20.84 Mm3 / year over the same period. The higher results were attributed to the lower accuracy attained in determination of water spread area due to the lower spatial resolution of the satellite data used. Mixed pixels around the edges of the reservoir that are occupied by water and other land cover types greatly affect the classification exercise. The accuracy of the remote sensing technology for reservoir sedimentation analysis is hinged on accurately determining the water spread area at different reservoir levels. This can be achieved with the use of higher spatial resolution images.
Remote sensing therefore proves to be a powerful technology allowing a reduction of costs and time necessary to obtain relevant information for an effective reservoir sediment management. This situation has been made more possible thanks to the availability of free imagery such as Landsat imagery (Amitrano et al., 2014). However, remote sensing techniques can be used to analyse sedimentation only within the water level fluctuation zone. Information on water level below the minimum draw down level (MDDL) is usually not available as most reservoirs rarely reach this level except when extended drought periods are experienced. Thus sedimentation analysis using remote sensing is rarely used to assess sedimentation below the MDDL. Information on capacity below MDDL that is, in the dead zone could be taken from the most recently conducted hydrographic survey.
The objectives of this study were to apply a remote sensing approach to assess the rate of reservoir sedimentation of Tuli-Makwe Dam and to define a current elevation-capacity relationship for the dam for the year 2013.
The SRTM DEM was used to generate the drainage area of Tuli-Makwe Dam using Arc Hydro Tools in order to identify the drainage area for the dam. This represents the area that contributes sediments to Tuli-Makwe Dam. A detailed description of the processes followed is found in (Li, 2014).
Water spread area was analysed from the downloaded Landsat images. Prior to water spread area analysis all the images were geometrically corrected based on the Universal Transverse Mercator (UTM) projection and the WGS84 datum. Georeferencing was done using the nearest neighbour resampling method using ground truth data obtained from the catchment using a hand held GPS receiver and from Google Earth. A root mean square error of less than 0.18 pixels was achieved for all the images. Water spread area of the reservoir was analysed using the supervised image classification method and the water index method. Table 1 shows the dates of satellite pass corresponding with the reduced reservoir water levels selected for the study.
Supervised image classification
Landsat imagery bands corresponding to the blue, green, red and near infrared (NIR) wavelengths of the electromagnetic spectrum were selected and combined into a multiband image using layer stacking in ENVI 4.7 software. The area covering Tuli-Makwe Dam and surroundings areas were extracted by masking from the multiband images using image sub-setting. A false colour composite with band combination of NIR, Red and Green in the (Red; Green; Blue) format was adopted prior to image the classification. The adopted false colour composite enhances visualisation of vegetation pixels with a red colour and water pixels with dark pixels. Supervised maximum likelihood classification algorithm was used for image classification as it had a good separation of water pixels. The images were classified into three classes (water; vegetation and other). The producer and user accuracy for the water class for all classified images were all above 85%.
Water index method
Using the water index method (Rathore et al., 2006), water pixels were identified by calculating the band ratio of Green/Near Infrared to be very low compared to DN values in the Green band. The ratio distinctly separates water bodies from soil and vegetation with very bright pixels. The WI image was then reclassified to show water pixels as a separate class by assigning the value 1 for water pixels and 0 for the remaining area which is not covered by water.
Water spread area in each image was calculated in ArcGIS by multiplying the number of water pixels and the pixel area. Isolated water pixels noted around the reservoir and along the tributary rivers were not considered to be part of the reservoir. Finally water spread area for each reduced reservoir level was obtained by averaging water spread area for that level from the two methods used. Reservoir water storage capacity between consecutive levels was calculated using the trapezoidal formula as follows:
Where V12 is the volume of water present in the dam between two consecutive water levels taken as H1 and H2. H12 is the difference in water levels between consecutive water level H1 and H2. A1 and A2 are spread area at water level H1 and H2 respectively (Figure 1). Storage capacities between consecutive levels were summed up to arrive at the revised capacity at the full supply level.
Study area
Tuli-Makwe Dam is located 30 kilometres from Gwanda town due West in the Mzingwane catchment in the agro-ecological region (Natural Region) IV in Zimbabwe. This is a semi-arid agro-ecological zone characterised by low and erratic rainfall which makes rain-fed agriculture difficult. Thus surface runoff harvesting through dam construction is an important strategy for the region that has been adopted by the Government of Zimbabwe (GoZ) to increase the level of water security and improve livelihood for the region (GoZ, 2000). The dam was constructed in 1966 by the then Ministry of Water Development. The dam wall spans a natural rocky gorge at approximately two kilometres from the confluence of the Thuli and the Mtsheleli rivers. Thuli River is a major tributary of the Shashe River. Major tributaries of the Thuli River are Mtshabezi, Mtsheleli and Mwewe rivers. The reservoir capacity at construction was 8 254 000 m3 and has a catchment area of 778 km2 Figure 1.
Thuli-Makwe Dam provide many benefits and contribute significantly to the socio-economic development of the local rural communities (Rusere, 2005). The dam was designed to supply irrigation water to the 229 hectare Makwe and 50 hectare Thuli irrigation schemes and for other socio-economic activities such as domestic water supply, livestock watering, building water and fisheries. A nearby mining enterprise (Freda Rebecca Mine) also abstracts water from the dam. Natural region IV receives low annual rainfall that varies from 250 to 550 mm per annum. It is also characterised by a low rainfall-runoff conversion with mean runoff varying from 17 mm to 19 mm per annum and high evaporation losses with a mean evaporation of 1800 mm per annum (Love et al., 2005). As a result of these factors, Mzingwane catchment experiences water scarcity (Mazvimavi, 2003). The mean annual temperatures ranges from 12°C to 29°C with the lowest temperatures recorded between June and July, and the highest during October. The catchment is characterised by low open woodland of Combretum-acacia- terminalia associated with granitic or gneissic derived sandy soils on the upper part. The upper part normally experiences moderately high rainfall. On the lower part of the catchment is sparse low Mopane woodland gradually replaced by terminalia- sericea and open woodland. Mzingwane catchment has been characterised by shifts in land uses since the construction of the dam. Government initiated land resettlement programmes including the Fast Track Land Reform (FTLR) programme that started in 2000 and was aimed at de-congesting some communal areas and redistributing land to landless natives had negative impacts on the catchment. The programme has left most of the country forests facing serious threat of deforestation increasing from 1.41% (1990 to 2000) to 16.4% (2000 to 2005) (Dalu et al., 2013). More communal areas have been introduced into the catchment. The sparse natural vegetation in the catchment has been converted into other land uses such as arable land, grazing land and mining as a result of anthropogenic activities. Alluvial mining is also a major practice along river courses within the catchment. The region is characterized by deforestation that makes reservoir sedimentation a major threat to the economic life span of reservoirs
Since the dam construction in 1966, two hydrographic surveys have been conducted on Tuli-Makwe Dam. The results of the hydrographic surveys carried out in 1991and 2012 by department responsible for water resources management in the country showed that the dam had a revised gross capacities of 6.182 Mm3 and 5.206 Mm3 respectively down from the original design gross capacity of 8.254 Mm3 highlighted above.
Data
Daily observed water level data for Tuli-Makwe Dam for the period from January, 2012 to December, 2013 was obtained from the Zimbabwe National Water Authority (ZINWA), Data and Research Office. The observed data ranged from reservoir reduced level of
96.00 m to 100.000 m at reservoir full supply level (FSL). The FSL at Tuli-Makwe Dam and many other state owned dams in Zimbabwe is given an arbitrary value of 100.00 m. Revised reservoir capacities from the hydrographic surveys carried out in 1991 and 2012 were obtained from the same office. However,
reservoir elevation-capacity relationship for 2012 hydrographic survey and the original design elevation-capacity relationship for the year 1966 could not be located.
Satellite imagery from Landsat Thematic Mapper (TM) and Landsat Operational Land Imager (OLI) taken over Tuli-Makwe Dam on the days corresponding with selected observed levels were downloaded from the United States Geological Survey (USGS), Global Visualisation Viewer (GloVis, website: www.glovis.usgs.gov). The images covered Landsat path 170 and row 75.
Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) at 90 m spatial resolution was obtained from the Consortium for Spatial Information (CGIAR-CSI) website. The STRM data is provided “finished grade” meaning it has been processed to fill data voids. Digital elevation models (DEM) covering all of the countries of the world, are available for download on this site.
Table 1 shows the observed reduced reservoir water levels and the corresponding water spread area obtained from remote sensing for 2013. The table also shows the revised reservoir capacity calculated at the different levels from remote sensing for the same period. Figure 2 shows the revised elevation-capacity curve for 2013 obtained using the remote sensing approach prepared from Table 1. Reservoir water capacity at the minimum observed water level of 96 m and below were extrapolated. The elevation-capacity curve is compared with the elevation-capacity curve based on the hydrographic survey of 1991. The original design capacity as well as the 2012 hydrographic survey capacities are also shown.
The results show that using the remote sensing approach, Tuli-Makwe Dam has a revised gross reservoir capacity of 4.883 Mm3 for the year 2013 at the full supply level of reduced reservoir level of 100.00 m. It is thusobserved that during its 47 years of operation since its construction in the year 1966 to 2013, the reservoir lost 3.371 Mm3 through sedimentation thus reducing the original gross design capacity of 8.254 Mm3 by 40.84%. The annual rate of capacity loss is calculated to be 0.87% per annum which translates to 0.0717 Mm3 per annum.
The equation for the elevation-capacity curve for 2013 obtained from the remote sensing approach is similar to the equation obtained for the 1991 hydrographic survey, and follows the general reservoir capacity parabolic function as outlined by Kaveh et al. (2013). The general form of the capacity equation has three coefficients as follows:
Where Vx is the reservoir capacity at depth x and k, m, and n are coefficients, respectively. The depth x represents the water depth above the stream bed or the water level elevation as used in Figure 3. Using the equation, the capacity of the reservoir can be calculated for any level of the reservoir. The difference between the curves at any level represents the loss of capacity due to sedimentation at that level from 1991 to 2013.
The revised capacity for Tuli-Makwe reservoir using the remote sensing approach for 2013 is comparable with the results from the 2012 hydrographic survey which suggests that the reservoir had a capacity of 5.201 Mm3 in 2012. The two methods show a difference of 3.85% for the estimated reservoir capacity lost in a period of 47 years since construction. This agreement further confirms that remote sensing approach can be adequately used to assess sedimentation in reservoirs as highlighted by various authors (Jain et al., 2002; Rodrigues et al., 2012). The results of the remote sensing approach are also in agreement with the assertion from Zirebwa and Twomlow (1999) that small reservoirs in Southern Africa lose about 30% of their capacity over a period of 40 years due to siltation. This assertion results in an annual reservoir capacity loss of 0.75% per annum which is comparable to the 0.87% per annum calculated for the 47 years of reservoir operation. Assuming a uniform sedimentation rate, current trends suggest that Tuli-Makwe reservoir may be completely filled up in the next sixty eight years from 2013, however the useful capacity of the reservoir may be lost in much less time.
Wallingford (2004) studied reservoir sedimentation in small to medium sized dams in semi-arid areas in Zimbabwe and Tanzania, and suggested that the settled density for sediment deposited in the reservoir could be adequately estimated at 1.2 tonnes/m3. Based on this assumption, and also assuming a uniform rate of sedimentation over 47 years, the specific sediment yield over Tuli-Makwe catchment is calculated at 110.59 tonnes / km2 / year. The sediment yield is comparable with the sediment yield results of 120 .1 tonnes / km2 / year obtained by (Dalu et al., 2013) in Malilangwe Dam catchment located in the Chiredzi district in the south-eastern lowveld of Zimbabwe. While in different geographic locations, Malilangwe and Tuli-Makwe dams are both in the semi-arid agro-ecological region IV of Zimbabwe. Wallingford (2004) also obtained specific sediment yields ranging from 120 tonnes / km2 / year to 3400 tonnes / km2 / year, with a median of 290 tonnes / km2 / year were for small dams. The slightly lower sediment yield for Tuli-Makwe catchment may be due to the low rainfall-runoff conversion due high rates of evaporation experienced in the catchment.
The hydrographic survey of 1991, shows that Tuli-Makwe reservoir lost 2.072 Mm3 which is 25.10% of its original capacity in the first 25 years of operation since construction in 1966. The new capacity of the dam was 6.182 Mm3 and the annual rate of capacity loss is 0.0829 Mm3 / year (1.004% per annum). The specific sediment yield is calculated at 127. 84 tonnes / km2 / year under the same period. The results from remote sensing approach suggest that the in period from 1991 to 2013 the reservoir lost a further 1.299 Mm3 equivalent to 15.74% of the original design capacity in 22 years. The annual sedimentation rate is calculated at 0.0591 Mm3 / year (0.715% per annum). The specific sediment yield is calculated at 91.07 tonnes / km2 / year under this period. Whilst the sedimentation rates after 1991 seem to be slightly lower than those prior to 1991, this situation may not necessarily imply that the Tuli-Makwe catchment is becoming more conserved due to appropriate soil and water conservation practices in the catchment. On the contrary such a scenario may point to the temporal variability of sediment trap efficiency. Trap efficiency measures the percentage of the incoming sediment trapped by a reservoir. Trap efficiency is usually 100% for the bed load sediment except for very low-head dams mainly designed for navigation purposes. However, suspended sediment trap efficiency varies roughly with the ratio of reservoir capacity to river inflow. Small reservoirs will have trap efficiency that decreases significantly over time as the reservoir capacity reduces. Vorosmarty et al. (2003) suggests the variability of trap efficiency over time is an important consideration that many reservoir sedimentation models have erroneously assumed to be constant. Another possible factor to consider is the reduction in the overall sediments reaching the reservoir as more reservoirs are constructed upstream of the catchment. Haregeweyn et al. (2012) and Minear and Kondolf (2009) attributed the relatively decreasing trend in sediment yield to the effects of sediment trapping by upstream reservoirs. This effect is particularly important in areas with numerous reservoirs within the same watershed.
Sedimentation results for 2013 from remote sensing techniques that are comparable with 2012 hydrographic survey further confirms the applicability of remote sensing for sedimentation analysis for small reservoirs in semi-arid regions. Small reservoirs play an important role in the livelihood of rural communities, and should be regularly monitored for sedimentation to ensure that corrective measures are taken in time. The results also show that sedimentation rates in Tuli-Makwe Dam are comparable with sedimentation rates recorded within the country and region. Corrective measures have to be put in place to ensure that the useful life of Tuli-Makwe reservoir in not compromised in the near future.
The authors have not declared any conflict of interests.
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