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

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 & 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). As of 2011, the Global Reservoir and Dam estimated about 16.7 million dams with a total storage capacity of about 8,070 km³ in the world (Lehner et al. 2011).

Despite the numerous importance of dams, one of the critical problems in their operation and management is the continuous deposition of sediments in their reservoirs via inflows (Chihombori et al. 2013). The construction of a dam and its reservoir on rivers and streams automatically cuts off sediment transport to the downstream side of the dam (Salimi et al. 2013), and this development has made the reservoir become a natural means for retention of the transported sediment (Hasan et al. 2011). Sedimentation results in loss of reservoir depth, storage capacity and reduction of designed lifespan of reservoirs (Basson 2010) and blocks irrigation canals, damages power generating turbines, and degrades reservoir water quality (Halcrow 2001). 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 the 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 their storage capacities to sedimentation at a rate of 0.26 to 0.91% per year.

Modelling and forecasting of reservoir sedimentation has been the subject of several empirical studies since the 1950s (Jothiprakash & Garg 2009). In recent years, the artificial neural network (ANN) technique has shown excellent performance in regression, especially when used for pattern recognition, function estimation, forecasting and modelling (ASCE 2000). Jothiprakash & Garg (2009) noted that ANN is a highly non-linear mathematical model that can capture complex interactions among the input and output variables without any prior knowledge about the nature of these interactions. In the fields of hydrology and water resources engineering, ANNs have been extensively applied because of their ability to model both linear and non-linear systems without the need to make assumptions as are done in most conventional statistical approaches (Nourani 2009). Sarangi & Bhattacharya (2005) compared the performance of ANN models for sediment yield prediction with a linear regression model for the Banha watershed in India. Sarangi et al. (2005) developed ANN and linear regression models using watershed geomorphologic parameters to predict surface runoff and sediment yield of the St. Esprit Watershed, Quebec, Canada. Jothiprakash & Garg (2008) found that linear regression techniques sometimes underestimate or overestimate observed values and need modifications. Jothiprakash & Garg (2009) modelled the annual volume of sediment retained in the Gobindsagar Reservoir in India using the ANN and linear regression models. Salimi et al. (2013) also applied both ANN and linear regression models to model the sedimentation rate of the Karaj Dam Reservoir in Iran.

In this study, an ANN, linear and non-linear exponential regression models were explored and developed for estimation of annual rate of sedimentation of nine (9) reservoirs in the Guinea Savannah Ecological Zone of Ghana. The novelty of this study is that it used input parameters (Figure 2) that have direct influence in the sedimentation process of reservoirs in the Guinea Savannah Ecological Zone of Ghana in developing the ANN models for estimating and forecasting reservoir sedimentation in the study area and other areas with similar characteristics.

The study was carried out in nine (9) reservoirs in the Guinea Savannah Ecological Zone (GSEZ) of northern Ghana, namely Daffiama, Karni and Sankana reservoirs in the Upper West Region; Bontanga, Golinga and Libga reservoirs in the Northern Region; and Gambibgo, Tono and Vea reservoirs in the Upper East Region. Figure 1 is a map showing the study reservoirs whilst the principal characteristics of the reservoirs are presented in Table 1. The study reservoirs represent 3% of the number of reservoirs in the GSEZ of northern Ghana with all the three (3) large-sized reservoirs covered and with six (6) being small- and medium-sized reservoirs. Small, medium and large reservoirs are reservoirs with maximum storage capacities of <1 Mm³, 1 to 3 Mm³ and >3 Mm³ respectively (Basson 2007; Kolala et al. 2015).

Linear regression (LR) and non-linear exponential regression (NER) analyses were performed, relating the output parameter (annual deposited sediment volume) and the input parameters (annual inflow, annual rainfall, annual reservoir trap efficiency and age of reservoir) using Minitab software, version 16.0 and R software, version 3.6.3 respectively.

In order to train and validate the proposed ANN model, the split-sample method was applied, in which 70% of the data was used for training the model and then the remaining 30% was used for validation. This split of dataset (70% training and 30% validation) gave the best accuracies in the training and validation stages. Similarly, Salimi et al. (2013) and Sultana & Naik (2016) recorded the highest accuracies using the 70% training and 30% validation split. The feed-forward back-propagation (FFBP) ANN training algorithm presented in Figure 2 was used to model the data.

Twenty (20) years (1999–2018) rainfall data for the catchments of the various study reservoirs was obtained from the Ghana Meteorological Agency.

The four (4) input parameters used to model the rate of sedimentation of the reservoirs were annual rainfall, annual water inflow, annual trap efficiency and age of reservoir, whilst the output parameter was annual volume of sediment deposit (Table 2). These input parameters were selected because of their high influence in the sedimentation process of a reservoir. Salimi et al. (2013) and Sultana & Naik (2016) noted that reservoir sedimentation cannot occur without the influence of rainfall, water inflows, sediment inflows and the sediment trapping efficiency of the reservoir. As presented in Table 2, the annual rainfall in the reservoirs was found to vary between 617.20 mm and 1,382.30 mm for the period of 2009–2018. The recorded annual water inflows to the reservoirs ranged from 197,836 m³ at Daffiama to 55,371,500 m³ at Tono. This variation of annual water inflows among the various catchments was noted to be influenced by catchment size, vegetation cover, antecedent moisture content, rainfall amount, land uses and soil characteristics. The trap efficiency of the reservoirs was found to range from 45.56 to 99.91%, with the least and highest trap efficiencies being recorded at Gambibgo and Tono reservoirs, respectively. The small and medium reservoirs were noted to have lower trap efficiencies than the large reservoirs and this could be due to their frequent annual spillage, hence higher sediment outflows. In a similar study by Sultana & Naik (2016), a trap efficiency range of 41.80–99.99% for the Sriramsagar reservoir, a large reservoir in Nizamabad was obtained. The annual volume of sediment retained in the reservoirs was estimated to range from 1,594.42 m³ at Daffiama reservoir to 355,016.9 m³ at Tono reservoir. These levels of sediment retention were noted to be influenced by poor land use practices in the catchments and buffer zones of the reservoirs causing high levels of sediment production and inflows into the reservoirs. The reservoirs also lacked the facilities to flush out sediment, hence little sediment outflow from the reservoirs.

Relating the output parameter to the four (4) input parameters in Minitab version 16.0, the overall response of the multi variate linear regression analysis for each of the study reservoirs is presented in Table 3 and the non-linear exponential regression models in Table 4. The linear regression (LR) models recorded an R² of 0.763–0.902 (Table 3), thus suggesting that the selected input variables accounted for about 76.3 to 90.2% of the volume of sediment retained in the reservoirs, whilst other factors accounted for about 9.8–23.7%. The results also showed that an increase in the annual rainfall by a unit resulted in an increase in the volume of sediment retention in the reservoirs by 6.05–42.02% (Table 3). A unit increase in annual water inflows also resulted in an increase in volume of sediment retention in the reservoirs by 0.49–1.08%. For annual trap efficiency, the model established that a unit increase resulted in an increase in volume of sediment retention in the reservoirs by 16.01–87.23%. A unit increase in age of reservoir resulted in a 0.55–6.35% increase in volume of sediment retention in the reservoirs. Equations (2)–(10) presented in Table 3 can be used as the models for estimating annual volume of sediment retention in reservoirs with accuracy between 76.3 and 90.2%.

Table 4 presents the expression for SV, which indicates that reservoir capacity loss increases with an increase of the independent variables; that is, AR, AI, ATE, and Ra. The exponent of Ra in Equations (11)–(19) is less than one, thus indicating that the rate at which sediment is retained in the reservoirs is a decreasing function of time. This is expected because those areas of the reservoir that are conducive to settlement of fine sediments will fill quickly in the early years and then no longer be available for deposition. Shrinkage of deposited silt also takes place from weathering action and the superimposed loads of additional sediment, thereby reducing the sedimentation rate with time (Froehlich et al. 2017).

The study developed Generalised Linear Regression (GLR) and Non-linear Exponential Regression (NER) models for application on different reservoirs in the Guinea Savannah Ecological Zone (GSEZ) of northern Ghana using the data of the nine (9) study reservoirs. The details of the GLR model are presented in Table 5, whilst the details of the NER model are presented in Table 6. It can be observed that the accuracy of the models increased with increased number of input parameters. The models with the highest R² are Equation (28) for the GLR model and Equation (37) for the NER model. These models were therefore considered the best models for predicting annual rate of reservoir sedimentation in the GSEZ. The R² of 0.913 for the GLR model and 0.941 for the NER model indicate that the input parameters account for about 91.3% and 94.1%, respectively, of the variations in annual reservoir sedimentation in the GSEZ. Both models show that annual volume of sediment retention increases as all the independent variables (AR, AI, ATE and Ra) increase. In Equation (37), as presented in Table 6, the exponent of Ra is less than one (1) and this indicates that the rate of sedimentation is a decreasing function of time. This is because those areas of the reservoir that are conducive for settlement of fine sediments will fill quickly in the early years and then no longer be available for deposition. Also, shrinkage of deposited silt takes place from weathering action and the superimposed loads of additional sediments thereby reducing the sedimentation rate with time.

A Multi-layer Perceptron (MLP) ANN architecture model consisting of three (3) layers was developed for each reservoir using the R tool. Since there was a large variation among the data sets, all the data were normalised between a range of 0 and 1. The model was trained using 70% of the data and the remaining 30% was used for validation. A satisfactory model was selected based on the minimum MSE values and the optimum values of R² generated during the training and validation stages. Performance statistics for the ANN model for each reservoir are presented in Table 7.

Based on the minimum MSE values of 0.034–0.143 during the training stage and 0.069–0.187 during the validation stage (Table 7), it was found that the Feed Forward, Back Propagation (BP ANN) models with structures ranging from 4–6–1 at Karni reservoir to 4–12–1 at Tono reservoir generated the best trend of the observed volume of sediment retained in the reservoirs. The detailed architecture of the proposed models are illustrated in Figure 3. According to Jothiprakash & Garg (2009), the best ANN prediction model is the ANN structure that best captured the pattern of the observed data set. Also, the R² values of 0.857–0.996 and 0.814–0.931 during the training and validation stages, respectively, suggest that averagely, about 81.35 to 96.60% of the variables are explained by the output of the models during the training and validation stages. The average accuracy of the models ranged from 85.53 to 94.85%. In modelling the annual sedimentation rate of the Gobindsagar Reservoir in India, Jothiprakash & Garg (2009) observed that a Feed Forward Back Propagation ANN model with a structure of 3–5–1 with R² values of 0.970 (training stage) and 0.965 (validation stage) best followed the pattern of the observed sediment volume. Salimi et al. (2013) also had an ANN structure of 3–3–1 with R² values of 0.972 for training and 0.988 for testing in the Karaj reservoir in Iran while Sultana & Naik (2016) recorded an ANN structure of 3–10–1 with R² value of 0.922 for the Sriramsagar reservoir in Nizamabad. ANN structure indicates the stage at which high prediction accuracy can be attained.

The three (3) reservoir sedimentation prediction models were compared based on their accuracies (Table 8). The accuracy of the linear regression (LR) model ranged from 76.3 to 90.2%, non-linear exponential regression model ranged from 79.7 to 91.3% and the ANN model ranged from 83.5 to 94.9%. The results from the ANOVA showed no significant difference among the models, as can be seen in Figure 4. Therefore, all the models were observed to be efficient and suitable for predicting the annual sedimentation of the reservoirs, as they recorded accuracies above 76% (Table 8).

The developed linear regression (LR), non-linear exponential regression (NER) and artificial neural network (ANN) models were used to forecast the volume of sediment that would be retained in the reservoirs within the next 20 years; that is, by year 2038 and the results presented in Table 9 and Figure 5.

The ages of the reservoirs at 2038 (Table 9) would be 49 to 75 years with the least and highest being the Daffiama and Gambibgo reservoirs, respectively. The total volume of sediment predicted by the ANN model to be retained in the reservoirs by the year 2038 ranged from 122,264 m³ at Daffiama to 13,317,600 m³ at Tono, whilst the LR model predicted sediment volume deposits ranging from 125,891 m³ also at Daffiama and 12,861,900 m³ at Tono (Figure 5). This variation could be due largely to size of catchment and annual sediment inflows. The total storage capacities of the reservoirs forecasted to be lost within this time period ranged from 14.32 to 48.33% by the ANN model, 13.83 to 46.97% by the LR model and 15.54 to 50.07% by NER model (Table 9). The results from the three (3) models indicate that the storage capacities of the Golinga, Libga, Gambibgo, Daffiama, Karni and Sankana reservoirs would almost be 50% filled with sediment deposits by year 2038 if no sedimentation control measures are taken to curb the phenomenon. In all three (3) models, it was noted that the predicted storage capacity losses in the small and medium reservoirs were quite higher than those of the large sized reservoirs, as can be observed in Table 9. Across all the reservoirs, the trend of the observed sedimentation data was best followed by the ANN model than the LR and NER models, as illustrated in Figure 5. This suggests that the ANN model gives more accurate and efficient predictions when compared with the LR model. In forecasting the trap efficiency of the Sriramsagar reservoir (Nizamabad), Sultana & Naik (2016) observed that the pattern of the observed trap efficiencies was better followed by the ANN model than the LR model. Similarly, Jothiprakash & Garg (2009) and Salimi et al. (2013) found that the ANN model estimated the volume of sediment retained in the Gobindsagar Reservoir (India) and Karaj reservoir (Iran), respectively, with higher accuracy and less effort compared to the LR model.

The study developed two mathematical models using linear regression (LR) and non-linear exponential regression (NER) and artificial neural network (ANN) models with input parameters as annual rainfall, annual water inflows, annual trap efficiency and age of reservoir, for the forecast of annual sedimentation in the study reservoirs and other reservoirs in the Guinea Savannah Ecological Zone (GSEZ) of Ghana. The models revealed that annual sediment volume retention increases as all four (4) independent variables (input parameters) increased and the rate of sedimentation in the reservoirs also decreased with time. The developed mathematical models provide a straightforward and rapid means of predicting the annual storage capacity loss of reservoirs in the GESZ of Ghana. The feed-forward, back-propagation algorithm Multi-Layer Perceptron ANN 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.

Based on the accuracies of the models, the results from the ANOVA showed no significant difference among the models and therefore, all the three (3) models developed are noted to be efficient with accuracies above 76% and suitable for forecasting the annual sedimentation of reservoirs with characteristics like those in the GSEZ of Ghana.

Forecasted sedimentation up to the year 2038 using the developed models revealed that the total storage capacities of the reservoirs that would be lost ranged from 13.83 to 50.07%, with all three (3) models indicating that the small and medium reservoirs would almost be 50% filled with sediment deposits if no sedimentation control measures are taken to curb the phenomenon.

The authors declare that there is no conflict of interest. They have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Also, no financial support was received for the conduct of the research and/or preparation of the article.

All relevant data are included in the paper or its Supplementary Information.