Assessment of reservoir sedimentation of irrigation dams in northern Ghana

Adongo, Thomas Apusiga; Kyei‐Baffour, Nicholas; Abagale, Felix K.; Agyare, Wilson Agyei

doi:10.1080/10402381.2019.1659461

Cited by 28 publications

(17 citation statements)

References 12 publications

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“…The constants c and d were determined from observed infiltration data by taking logarithm to both sides of Equation (14) to give Equation (15)…”

Section: 6985mentioning

confidence: 99%

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Estimation of Infiltration Models’ Parameters Using Regression Analysis in Irrigation Fields of Northern Ghana

Salifu¹,

Abagale²,

Kranjac-Berisavljevic³

2021

OJSS

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The quantification of soil infiltration is necessary for the estimation of water accessibility in soils for plant growth and development. Field infiltration tests runs were conducted on agricultural soils in three irrigation sites of Northern Ghana. The field data were fitted into Green and Ampt, Kostiakov, Philip, Holtan, Soil Conservation Service and Horton infiltration models for the determination of the unknown model parameters. Regression analysis at 95% confidence level using GraphPad Prism 8. Laboratory and field data on infiltration were used for the model fitting and the unknown parameters were determined using the calibrated models. The k and n parameters of Kostiakov model at Bontanga study site were found to be k = 28.0027 and n = 0.5902, k = 17.0294 and n = 0.4504 for Golinga and k = 23.0356 and n = 0.6339 for Libga. For all the models, the coefficient of determination ranged from 0.7612 to 0.9876 indicating strong relationships. Only Holtan model gave different values at all the three study sites. The parameter GIa and i c of the Holtan model drawn from hydrologic soil group were the same at all the study sites because of the same vegetative cover and surface conditions. The study observed the values of the parameters to have influenced the models' performance.

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“…The constants c and d were determined from observed infiltration data by taking logarithm to both sides of Equation (14) to give Equation (15)…”

Section: 6985mentioning

confidence: 99%

“…The Golinga Irrigation Scheme is situated in the Tolon District of the Northern Region of Ghana and it lies on latitude 9˚4'N and longitude 1˚0'W.The Libga Irrigation Scheme is located in the Savelugu Municipality in the Northern Region of Ghana. It lies between latitude 9˚59'N and longitude 0˚85'W[15].…”

mentioning

confidence: 99%

Estimation of Infiltration Models’ Parameters Using Regression Analysis in Irrigation Fields of Northern Ghana

Salifu¹,

Abagale²,

Kranjac-Berisavljevic³

2021

OJSS

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“…The development and management of dams have been become a necessity in recent years, due to the seasonality of rainfall and water scarcity in many parts of the world (Yusuf and Yusuf, 2012). Dams and their associated reservoirs store water during the rainy season and make it available for humans and animals to use in their immediate environment (Huang et al 2018;Adongo et al 2019). The contribution of irrigation dams towards boosting agricultural production is crucial, especially in dryland environments such as arid, semi-arid and other water scarce areas in the world (Chihombori et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…According to White (2001) and Schleiss et al (2016), the existing reservoirs worldwide lose between 0.5% and 1.0% of their total storage capacity yearly with Basson (2010) reporting, global average sedimentation rate as between 0.7% and 0.8% per annum with highest occurrence in arid and semi-arid regions. Adongo et al (2019) reported that reservoirs in northern Ghana are losing the storage capacities to sedimentation at a rate of 0.26 to 0.91% per year.…”

Section: Introductionmentioning

confidence: 99%

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

Adongo

Abagale

Agyare

2021

Water Practice and Technology

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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.

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“…Reservoir sediments, a byproduct of weathering and erosion of rocks, are transported through alluvial process and get deposited in reservoirs of dams [4]. Oman has constructed several recharge dams to recharge groundwater from non-perennial flows and the sediments being deposited in the reservoirs are causing water infiltration reduction problems in situ [5].…”

Section: Introductionmentioning

confidence: 99%

Use of Reservoir Sediments to Improve Engineering Properties of Dune Sand in Oman

et al. 2021

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Managing sediments dredged from reservoirs of recharge dams is an environmental issue, however, these sediments can be an abundant and economical source of fine-grained fill soil. This experimental investigation quantifies the geotechnical properties of a reservoir sediment used to improve engineering properties of a poorly graded dune sand in Oman. The binary mixes were prepared with different percentages (5, 10, 20, 50, 75, 90, 95%) of sediment with sand. Laboratory tests such as gradation, consistency limits, compaction, and unconfined compression tests were performed to measure the engineering characteristics of the binary mixtures. The results showed that the maximum dry density increases up to a sediment content of 50% and then decreases with further increase in the sediment content. The optimum water content increases with the increase in sediment content from 17% for pure sand to 22.5% for pure sediment. The optimum moisture content shows a good correlation with the plastic limit of the binary mixture of sand and sediment. The unconfined compressive strength substantially increases with sediment content up to 75% and then decreases with further increase in the sediment content. The binary mixture of sand sediment is sensitive to moisture, however, the order of strength stability against moisture is dune sand mixed with 75, 50, and 20% sediments. The addition of sediment to dune sand improved the uniformity coefficient to some extent with an increase in the maximum and minimum void ratios as well. The elemental analysis of the sediment confirms that the material is non-contaminated and can be employed in geotechnical engineering applications as a sustainable and environmentally friendly solution.

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Assessment of reservoir sedimentation of irrigation dams in northern Ghana

Cited by 28 publications

References 12 publications

Estimation of Infiltration Models’ Parameters Using Regression Analysis in Irrigation Fields of Northern Ghana

Estimation of Infiltration Models’ Parameters Using Regression Analysis in Irrigation Fields of Northern Ghana

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

Use of Reservoir Sediments to Improve Engineering Properties of Dune Sand in Oman

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