The Development of Closure Dike as Countermeasure of Sedimentation in the Wonogiri Reservoir, Indonesia

The problem of reservoir sedimentation occurs worldwide, including in the Wonogiri reservoir, Indonesia. The reservoir was built from 1977 to 1980, but the dam faces a severe problem of sedimentation. Sediment inflow in the Wonogiri Reservoir comes from several sources, especially from Keduang River, a tributary from Mount Lawu. The sediment management problems are generally complicated and different in every place. In some conditions, it will be possible to prevent sediment from entering the reservoir by adding an internal barrier as applied in the Wonogiri Reservoir. The reservoir is divided into two reservoirs by a closure dike equipped with an overflow dike and a new spillway. This paper will describe the development of closure dike based on field observation and data obtained from the Ministry of Public Works and Housing. Moreover, this paper will also describe the sedimentation status of the Wonogiri reservoir based on the recent bathymetric data. The development of closure dike already completed, which consists of Closure Dike A (700 m), B (700 m), C (302 m), and Overflow Dike (298 m). The recent bathymetric survey revealed that the capacity of effective storage of the main reservoir is 322.84 MCM.

Sediment Yield and Reservoir Sedimentation in Highly Dynamic Watersheds: The Case of Koga Reservoir, Ethiopia

Soil erosion is exacerbated by unsustainable land-use activities and poor management practices, undermining reservoir storage capacity. To this effect, appropriate estimation of sediment would help to adopt sustainable land-use activities and best management practices that lead to efficient reservoir operations. This paper aims to investigate the spatial variability of sediment yield, amount of sediment delivery into the reservoir, and reservoir sedimentation in the Koga Reservoir using the Soil and Water Assessment Tool (SWAT). Sediment yield and the amount entered into the reservoir were also estimated using a rating curve, providing an alternative approach to spatially referenced SWAT generated suspended sediment load. SWAT was calibrated from 1991 to 2000 and validated from 2002 to 2007 using monthly observations. Model performance indicators showed acceptable values using Nash-Sutcliffe efficiency (NSE) correlation coefficient (R2), and percent bias (PBIAS) for flow (NSE = 0.75, R2 = 0.78, and PBIAS = 11.83%). There was also good agreement between measured and simulated sediment yields, with NSE, R2, and PBIAS validation values of 0.80, 0.79, and 6.4%, respectively. The measured rating curve and SWAT predictions showed comparable mean annual sediment values of 62,610.08 ton/yr and 58,012.87 ton/yr, respectively. This study provides an implication for the extent of management interventions required to meet sediment load targets to a receiving reservoir, providing a better understanding of catchment processes and responses to anthropogenic and natural stressors in mixed land use temperate climate catchments. Findings would benefit policymakers towards land and water management decisions and serve as a prototype for other catchments where management interventions may be implemented. Specifically, validating SWAT for the Koga Reservoir is a first step to support policymakers, who are faced with implementing land and water management decisions.

Reservoir sedimentation regime analysis: case study of Kedungombo reservoir and Sermo reservoir

The Kedungombo and the Sermo Reservoirs have problems in fulfilling basic services because of sedimentation. Sedimentation that occurs in each of the reservoirs would form a specific reservoir sedimentation pattern that is supposed to be similar because the hydrology and physiography conditions of the reservoir's catchment area are similar. This study aims to determine the dynamics of sedimentation patterns that occur in the dead storage for reviewing the characteristics/sedimentation regime of the two reservoirs. The analysis was carried out by processing bathymetrical data which were processed into a digital terrain model (DTM) using ArcGIS. Furthermore, the storage volume, sedimentation volume, storage percentage, and specific reservoir sedimentation rate are calculated. The results showed that the two reservoirs showed an increase in sedimentation volume each year so that the reservoir characteristic curve shifted from the plan graph. The dead storage capacity of Kedungombo Reservoir is 100% in 1989 to 43% in 2016 and 100% of Sermo Reservoir in 1997 to 58% in 2011. The specific reservoir sedimentation rate, i.e. 0.0031 and 0.0042 million m3/year/km2 for the Kedungombo Reservoir (between 1989 and 2016) and the Sermo Reservoir (between 1997 and 2011) respectively, indicating that the two reservoirs are in the same regime

Modelling and forecasting reservoir sedimentation of irrigation dams in the Guinea Savannah Ecological Zone of Ghana

Effective management of reservoir sedimentation requires models which can predict sedimentation of the reservoirs. In this study, linear regression, non-linear exponential regression and artificial neural network models have been developed for the forecasting of annual storage capacity loss of reservoirs in the Guinea Savannah Ecological Zone (GSEZ) of Ghana. Annual rainfall, inflows, trap efficiency and reservoir age were input parameters for the models whilst the output parameter was the annual sediment volume in the reservoirs. Twenty (20) years of reservoirs data with 70% data used for model training and 30% used for validation. The ANN model, the feed-forward, back-propagation algorithm Multi-Layer Perceptron model structure which best captured the pattern in the annual sediment volumes retained in the reservoirs ranged from 4-6-1 at Karni to 4-12-1 at Tono. The linear and nonlinear exponential regression models revealed that annual sediment volume retention increased with all four (4) input parameters whilst the rate of sedimentation in the reservoirs is a decreasing function of time. All the three (3) models developed were noted to be efficient and suitable for forecasting annual sedimentation of the studied reservoirs with accuracies above 76%. Forecasted sedimentation up to year 2038 (2019–2038) using the developed models revealed the total storage capacities of the reservoirs to be lost ranged from 13.83 to 50.07%, with 50% of the small and medium reservoirs filled with sediment deposits if no sedimentation control measures are taken to curb the phenomenon.