# Impact of flow on the deposition and sediment trapping capacity in a West African reservoir (case of the Lobo River reservoir, west-central of Côte d'Ivoire)

Bérenger Koffi, Martin Sanchez, Kouadio Jean Olivier Kouamé, Hilaire

Amemou, Gnibga Issoufou Yangouliba, Michal Habel, Dibi Brou, Kouakou

Lazare Kouassi

► To cite this version:

Bérenger Koffi, Martin Sanchez, Kouadio Jean Olivier Kouamé, Hilaire Amemou, Gnibga Issoufou Yangouliba, et al.. Impact of flow on the deposition and sediment trapping capacity in a West African reservoir (case of the Lobo River reservoir, west-central of Côte d'Ivoire). Environmental Earth Sciences, 2023, 82 (19), pp.442. 10.1007/s12665-023-11144-2. hal-04229690

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# Two-dimensional Numerical Simulation of Suspended Sediment Transport in Lobo Reservoir(Western-central of Côte D'ivoire)

Bérenger Koffi (koffiberen@gmail.com)

Nantes University, UMR 6112 CNRS

Martin Sanchez

Nantes University, UMR 6112 CNRS

Kouadio Jean Olivier Kouamé

Ecole Supérieure des Mines de Saint-Étienne

Hilaire Amemou

Gnibga Issoufou Yangouliba

University of Abomey Calavi

Kouakou Lazare Kouassi

## Research Article

**Keywords:** Sediment transport, Trapping efficiencies, Modeling, Sedimentation, Lobo reservoir

**Posted Date:** September 30th, 2022

**DOI:** https://doi.org/10.21203/rs.3.rs-2110363/v1

**License:** This work is licensed under a Creative Commons Attribution 4.0 International License.

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# TWO-DIMENSIONAL NUMERICAL SIMULATION OF SUSPENDED SEDIMENT TRANSPORT IN LOBO RESERVOIR (WESTERN-CENTRAL OF CÔTE D'IVOIRE)

Bérenger Koffi¹,² · Martin Sanchez² · Kouadio Jean Olivier Kouamé¹,³ · Hilaire Amemou⁴ · Gnibga Issoufou

Yangouliba⁵ · Kouakou Lazare Kouassi¹

¹Jean Lorougnon Guédé University, Laboratory of science and Technology of Environment, BP 150 Daloa (Côte d'Ivoire) ;

²Nantes University, UMR 6112 CNRS, LPG (Lab. Planetologie et de Géosciences), 2 rue de la Houssinie`re, BP 92208, 44322 Nantes Cedex 3, France

³Ecole Supérieure des Mines de Saint-Étienne, 158 Cours Fauriel, 42023 Saint-Étienne, France

⁴Université de San-Pédro, Côte d'Ivoire, UFR des Sciences de la mer,

⁵Doctoral Research Program in Climate Change and Water Resources, National Water Institute, University of Abomey Calavi, P.O. Box 4521, Cotonou, Benin

*Correspondence : koffiberen@gmail.com

Bérenger KOFFI https://orcid.org/0000-0002-4835-2975

## Abstract

Built to improve the water supply of the populations of the city of Daloa, the water reservoir of the Lobo River is no longer functioning properly due to sedimentation phenomena. This study aims to understand the hydro-sedimentary functioning of the reservoir for better management. Based on the river flows, bathymetric surveys, suspended solids concentrations, settling velocity, a two-dimensional modelling of the flows and sedimentation of the Lobo River reservoir was carried out using the MIKE 21FM/MT software. The simulation results indicate that the hydrodynamic model could reproduce the variation of water levels at the free surface of the reservoir. The results are very sensitive to the boundary conditions of the model but also to the initial conditions. A strong influence of the initial conditions on the long-term stability of the models was observed. It could be seen that in all cases the deposition rate tends towards a final value which only depends on the river flow. The trapping efficiency of the reservoir is between 26.7% and 80.4%. The different simulation scenarios indicate that the deposition thicknesses are between 0.02 and 48 mm and that the deposition occurs when the flow is loaded, during the rise and fall phase of the hydrograph. The old intake, the new intake and the weir are the preferred areas for sediment deposition in the Lobo reservoir.

**Keywords :** Sediment transport, Trapping efficiencies, Modeling, Sedimentation, Lobo reservoir
## 38 Introduction

39 Reservoirs and dams are essential for the sustainable health and well-being of populations as it provides water for 40 human consumption, irrigation, fishing, energy production, recreation, flood control and drought (Buyukyildiz et 41 al., 2014; Luís and Cabral, 2021). However, all reservoirs in the world are increasingly threatened by sedimentation 42 phenomena which impact their storage capacities and operations (Miranda et al., 2021; Schleiss et al., 2016; 43 Shrestha et al., 2022). Sedimentation is a completely natural phenomenon that is not contemporary. Nevertheless, 44 human action can accelerate this process and reduce the life span of water reservoirs (Guertault et al., 2018; Jia et 45 al., 2021). The exposure of reservoirs catchments to deforestation and intense agricultural activities favors erosion 46 phenomena, leading to a regular sediments load deposit in the reservoir during runoff processes (Gharbi et al., 47 2016; Ghernaout et al., 2020). Recent studies reported that about 0.5% ± 1% of the world's reservoir storage 48 capacity is lost each year due to sedimentation (Schleiss et al., 2016; Ren et al., 2021). This loss of reservoir 49 capacity threatens the sustainability of water supply as water demand increases (Liu et al., 2021). Therefore 50 controlling the sedimentation rate and maintaining reservoirs sustainability have become a major concern 51 worldwide (Fu et al., 2008; Chen et al., 2017), especially in Africa where land use and land cover change is very 52 rapid (Asenso Barnieh et al., 2020; Bullock et al., 2021; Findell et al., 2017). According to studies on the impact 53 of dam morphology on the Zambezi River in Africa, the reduction of waterflow seems to have an immediate effect 54 downstream by initially fostering the sediment deposition (Ronco et al., 2010). In Algeria, water erosion reduces 55 the storage capacity of dams by 0.5 to 3% each year as a result of silting due to sediment transport and deposition 56 (Ghernaout et al., 2020). Estimating sedimentation in reservoirs helps in the management of the reservoir's useful 57 capacity (Tadesse and Dai, 2019).

58 Many reservoirs in Côte d'Ivoire are concern by sedimentation and eutrophication phenomena (Kouassi et al., 59 2013; Meledje et al., 2021). The country's reservoirs are under an advanced eutrophication and siltation, making 60 difficult drinking water supply to some populations (Anoh et al., 2021; Koffi et al., 2020). For instance, the Lobo 61 reservoir, which is the main source of drinking water supply for Daloa faces a problem of water storage quantity 62 and quality. In terms of quality, a study by Groga et al. (2017) showed an evidence of pollution in the Lobo River 63 by pesticides due to the very intensive agricultural activities in the catchment area, which led to an eutrophication 64 of the reservoir. On the quantitative level, a dredging work was carried out in 2011 due to the high decrease of its 65 storage capacity. Despite the efforts of the competent authorities, a proliferation of aquatic plant on the Lobo 66 reservoir is observed (Koffi et al., 2020). Few studies have attempted to explain the hydrodynamic and 67 sedimentological processes of reservoirs in Cote d'Ivoire (Kouassi et al., 2013), making difficult their 68 management. Maiga et al. (2001) highlighted that the Lobo reservoir water level decreased by 3 m between 1985 69 and 1999 due to the siltation. However, there is no scientific evidence on how the hydrodynamic and sedimentation 70 processes in the Lobo reservoir occur. Several methods exist to address these problems. They are mainly 71 composed of hydrological models that estimate water erosion on catchments versants (Hallouz et al., 2018; Hunink 72 et al., 2013; Szalińska et al., 2020) and numerical simulations using hydraulic models that estimate the preferential 73 areas of sediment deposition in the dam reservoir (Isaac and Eldho, 2016; Ran et al., 2021). Hydrological and 74 hydraulic model coupling is also a possibility to estimate erosion and sediment deposition in a dam (Shrestha et 75 al., 2022; Tadesse and Dai, 2019). Numerical simulations are nowadays powerful tools to understand and manage 76 the physical processes related to river flows and solid transport (Kouassi et al., 2013; Liu et al., 2019; Winterwerp 77 and Van Kesteren, 2004). In addition to their ability to predict flow conditions and sedimentation processes, they
help to minimize costs and investment time. However, the implementation of these models depends strongly on the boundary and the initial conditions imposed when these are not known. The objective of this study is to model the hydrosedimentary functioning of the Lobo reservoir using a two-dimensional numerical model.

# Material et Methods

## Study area

The Lobo River impoundment is located 25 km from Daloa (Fig. 1). This river is one of the tributaries of the Sassandra River (Koffi et al., 2020; Yao et al., 2012). Its catchment has an area about 7000 km² and is characterised by a monotonous relief with an altitude ranging between 192 and 612 m.a.s.l (Avenard, 1974). The climate is equatorial with a rainy season from March to October and an average annual rainfall of about 1150 mm. The Lobo reservoir was built in 1976 and is the main source of drinking water supply for the population of Daloa (Maiga et al., 2001). It has a depth of 3.5 m, a width of 74 m, a length of 4.3 km and a surface of 0.3 km². The volume at normal elevation (231.03 m) of the dam is 360,000 m³. The characteristics of the Lobo reservoir are summarized in Table 1. Anthropogenic activities in the catchment are very diverse. However, agriculture remains the main activity of the population.

## Data

### Hydrometric data

The estimation of inflows and solids inputs to the Lobo River Reservoir required the installation of a hydrometric station upstream of the Lobo River Reservoir (Fig. 3a). This activity first consisted of identifying a site that meets the conditions for the installation of a hydrometric station. The installation of the water level gauges is based on levelling (altimetry) in order to set the gauge elements in relation to each other. The hydrometric data of the Andjoukro-Sikaboutoutation were at daily time step (Figure 3 b). Water level data read from the Lobo reservoir was also used. All hydrometric data used in this study are at daily time step on the period 2019-2021.

### Suspended solids data

The assessment of suspended sediment transport in the Lobo River impoundment required field data collection. Therefore, twenty (20) water samples were collected in the impoundment and at the Andjoukro-Sikaboutou station. The samples were taken at different depths (surface, mid-depth and bottom) using a Van Dorn hydrological bottle (Fig. 2). In addition, ten sediment samples were collected with a Van Veen grab from the Lobo River impoundment to in order characterize the bottom sediments and determine the density of the bed layer.

# Methods

## Bathymetry

The bathymetric surveys were carried out using a Lowrance Elite Ti 9 echo sounder. This device consists of three parts. Bathymetric measurements were made at a frequency of 450 kHz to allow the waves emitted by the echo sounder not to penetrate the sediment (Lowrance, 2019). The development of a good hydraulic and sediment transport model is highly dependent on good bathymetry. To this end, the areas covered by the bathymetry take
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into account the fluvial and lacustrine part of the Lobo reservoir. In order to obtain a regular grid of points, the
data set was interpolated by the nearest neighbor method using the MIKE 21 software (Fig. 4).

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Settling velocity

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In order to study the distribution of the settling velocity under normal water conditions, four tests were carried out on water samples taken from the surface, mid-depth and the bottom of the water column. The local distribution of suspended solids in terms of settling velocity was examined by the Owen tube method. The Owen (1976) tube is positioned vertically. Samples of one tenth of the initial volume are then taken from the bottom of the tube at different times. The samples are distributed over time in order to differentiate speed groups and estimate the quantity of particles settling in each interval (2 min, 4 min, 8 min, 15 min, 30 min, 60 min, 120 min, 240 min, 480 min). Finally, a last sample is taken to recover the remaining liquid in the tube in order to estimate the overall solid mass of the sample. Suspended solids concentrations were then determined after filtration of the water samples filtration manifold using pre-weighed WHATMAN GF/F circular filters of 47 mm diameter and 0.45 µm porosity. After being dried at 105 °C, the filters were carefully recovered and marked in order to determine the total suspended solids concentrations expressed in kg/m³ (AFNOR, 1994). A distribution curve representing the percentage by weight (V%) of grains with a settling velocity lower than Vd = H/t where H is the fall height of the sediment and t is the sedimentation time measured from the beginning of the test was drawn. The median settling velocity V50% and the settling velocity V90% are obtained from the settling velocity distribution function curves. The settling velocity V of the suspended solids is studied as a stochastic variable. According to Sanchez et al. (2005), the settling velocity follows a probability law in accordance with the Gamma law with a shape parameter r that characterizes the range of values of the fall velocity for all the aggregates simultaneously in suspension. All suspended particles fall with a variable fall velocity depending on their concentrations and weights.

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Hydrodynamic and sediment transport model

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Hydrodynamic Model

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The distribution of current and water levels in the Lobo reservoir was assessed through the development of 2DH numerical modelling using the MIKE 21 FM software (DHI, 2017). MIKE 21 showed its capability to simulate water levels, flows and sediment transport variations in two dimensions (2D) (Kouassi et al., 2018; Tessier et al., 2012). It solves the classical two-dimensional Saint-Venant equations by the finite volume method on a flexible mesh grid. The vertically integrated conservation of mass and momentum equations used by the MIKE 21 model are used to describe the flow and water level variations (DHI, 2017). A triangular mesh was used to generate the topography of the 2D model. Afterwards, the mesh is smoothed to obtain the largest possible angles and feature area. Then, the mesh is interpolated with the initial bathymetry using Natural Neighbor Interpolation (NNI). When generating the model mesh, a correction is made to ensure a good mesh quality. The mesh of the dam is composed of 8125 nodes and 14628 elements (Fig. 5).

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Characteristic of the weir

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The weir of the Lobo River reservoir is 47 m long with a height of about 10 m. For the hydrodynamic model, the
level of the weir crest is considered as the zero point (Z= 0 m) (Fig. 6) of the hydraulic model. A good knowledge
of the weir and the choice of its coefficient (C₁) is very important because the weir is the structure that controls
the water level variation in the reservoir. A method has been developed to determine the most appropriate weir coefficient for the Lobo reservoir in order to reduce the model calibration time. For this purpose, the water level curve as a function of the fluvial flow of the Lobo River in stationary regime with five values of weir coefficient projected on Fig. 7 allowed to choose the most appropriate weir coefficient. To calculate the weir coefficient, the type of weir was taken into account. An evaluation was made based on the theoretical formulation and five types of weir coefficient below or above the C1 =1.838 coefficient Honma (1940) formulation were used. The comparison of the water level and river flow data of the Lobo River shows a good synchronization of the data to the weir coefficient C1=1.838 (Fig. 7). It fits better to the water level and flow data of the Lobo River. Due to the uncertainties, a sensitivity study was carried out on the coefficient values of the chosen weir. The weir formulation of Honma (1940) is described as follows:

$$Q = C_1 W (H_{us} - H_w) \sqrt{H_{us} - H_w} \qquad (1)$$

$$Q = C_1 W \Delta H \sqrt{\Delta H} \quad \text{for} \quad H_{us} - H_w / H_w < 2/3 \qquad (2)$$

Where:

Q is the discharge through structure, W is the width, C1 is the weir coefficient 1, and C1 the weir coefficient 2 is calculated according to, Hus is the upstream water level and Hw is weir level.

## Sediment transport model

The silt transport module (MIKE 21 MT) was used for the sediment transport modelling. The majority of the sediments in the reservoir were very fine sediments and silt. The hydrodynamic actions are not large enough to cause significant coarse sediment transport and therefore the potential for sand transport by scour is practically zero. A value of 500 kg/m³ as the dry density of sediment deposition was used. The dry density was derived from analyses of sediment samples that were taken, weighed and then subjected to a temperature of 105 °C in an oven. Partheniades (1965) law was used for the critical erosion stress with a value of 2 N/m Krone (1986) law was used to evaluate the deposition. The critical deposition stress of 0.5 N/m² was chosen according to the recommendations of DHI (2017). The bottom roughness in the sense of Nikuradse taken is 0.001 m and the dispersion coefficient along the x-direction (Dx) and y-direction (Dy) is equal to 1 m²/s. The solution assumption of Teeter (1986) was used. The concentration profile results from the competition between particle fall and turbulence.

$$C_b = \frac{1}{C} \left( 1 + \frac{p_e^i}{1.25 + 4.75 p_D^{i,2.5}} \right) \qquad (3)$$

$$p_e^i = 6 \frac{W_s^i}{K U_f} \qquad (4)$$

With,

Pc : Peclet number ; Uf : friction velocity, and k, the constant of von Karman
## Boundary and initial conditions

Two boundary conditions were imposed in the development of the models. The flows and the average concentration of suspended solids were imposed as boundary conditions upstream of the hydraulic and sediment transport model. a steady and a non-steady states models were run. For the upstream boundary conditions of the model, river flows and average suspended solids concentration were used. The minimum, maximum, and mean river flows for the steady-state simulations and the daily flows according to the low and high-water periods, from 10 to 26 September 2020 for the calibration and from 1 to 16 June 2020 for the validation. As for the downstream boundary, a constant water level (-1 m) and the spillway of the reservoir were used to control the hydrodynamic model. An assumed zero gradient was imposed as a condition on the downstream boundary of the sediment transport model. A water level (-1 m?) was imposed as an initial condition throughout the hydrodynamic model domain. Due to the lack of knowledge about the relationship between suspended solids and river flow, a constant value for the suspended solids concentration of 0.031 kg/m³ was used in this study. For this reason, the siltation rate for a permanent hydro-sedimentary regime must be directly related to the fluid flow. To find a relationship between the siltation rate and the river flow corresponding to a steady state, an original method consisting in verifying that the siltation rate obtained from a model is independent of the initial conditions was developed. This verification was possible by imposing an initial concentration in the study area with two extreme values: Cᵢ = Cᵣiver; Cᵢ = 0. Wind speed, rainfall and evaporation at the daily time step (2019-2020) at the synoptic station of Daloa were used. The model calibration consisted in playing on the mesh size, the initial condition, the model start-up, the weir coefficient and the Strickler coefficient of 35 m¹³/s.

## Model Performance

The performance of the models was evaluated based on the parameters of root mean square error (RMSE), coefficient of determination (R²), Nash-Sutcliffe Efficiency coefficient (NSE) (Nash and Sutcliffe, 1970) and Bias (Gupta et al., 1999; Tuo et al., 2018).

$$RMSE = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (h_{obs,i} - h_{sim,i})^2} \qquad (5)$$

$$R^2 = \frac{\sum_{i=1}^{n} (h_{obs,i} - \bar{h}_{obs})(h_{sim,i} - \bar{h}_{sim})}{\sqrt{\sum_{i=1}^{n} (h_{obs,i} - \bar{h}_{obs})^2 \sum_{i=1}^{n} (h_{sim,i} - \bar{h}_{sim})^2}} \qquad (6)$$

$$NSE = 1 - \frac{\sum_{i=1}^{n} (h_{obs,i} - h_{sim,i})^2}{\sum_{i=1}^{n} (h_{obs,i} - \bar{h}_{obs,i})^2} \qquad (7)$$

$$Biais = \frac{\sum_{i=1}^{N} (h_{obs,i} - h_{sim,i})}{N} \qquad (8)$$
With,
$\bar{h}_{obs}$ and $\bar{h}_{sim}$ are the observed and calculated values of water level changes, $\bar{h}_{obs}$; $\bar{h}_{sim}$ are the average of the observed and simulated values of water level variations.

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## Trapping efficiency

The sediment trapping of a reservoir depends on the characteristics of the incoming sediment and the time of the water is kept in the reservoir. Furthermore, sediment trapping is controlled by the reservoir geometry and runoff characteristics (Verstraeten and Poesen, 2000). The sediment inflow into the reservoir is expressed in equation 9:

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$$Q_{inf} = Q_D + Q_{out} \qquad (9)$$

The trapping efficiency is described by equation 10.

$$\eta(\%) = \left( \frac{Q_D}{Q_{inf}} \right) \times 100 \qquad (10)$$

With,
$Q_{inf}$ (kg/s): Inflow sediment discharge; $Q_D$ (kg/s): Sediment deposited in the reservoir; $Q_{out}$ (kg/s): Outflow sediment discharge.

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## Results

### Settling velocity

The distribution functions of the suspended solids velocity for tests 1 to 4 are shown in the Fig. 8. Overall, the distribution functions of the suspended solids vary from test to test. This spatial variation in fall velocity shows that the vertical distribution of suspended solids in the Lobo River can be considered to be gradually varied with time as a function of suspended solids concentration. The results show that the mean fall velocities are significantly higher than the median fall velocities (V50%). Thus, according to test 1, a fall velocity of suspended solids is 0.107 mm/s, then test 2 and 3 increase to reach 0.137 and 0.221 mm/s. A decrease in the falling velocity is then observed during the test 4 phase and it is 0.117 mm/s. The variation of the fall velocity is also confirmed by the shape parameter "r". This parameter depends on the range of values of the falling velocities of the different aggregates of suspended matter simultaneously. The values of the shape parameter r are 0.58; 0.46; 0.5 and 0.84 for tests 1; 2; 3 and 4, respectively (Table 2).

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### Calibration and validation of hydrodynamic model

The comparison of observed and simulated water levels shows that the hydraulic model correctly reproduces well the observed water level variation of the reservoir although certain peaks are underestimated and overestimated (Fig. 9 a; 9 b; 9 c). This is the case for the simulated floods over the calibration period (15 to 20 September 2020) and the validation period (1 to 9 June 2020). Likewise, the model fails to correctly reproduce water level during the dry years. Despite the deviations, the variation of water level at the Lobo reservoir is satisfactorily reproduced. The results of the statistical indicators confirm this good agreement between the observed and simulated water levels during the model calibration phase (NSE= 0.91; RMSE= 0.015 m; BIAS= -0.05, R²= 0.91; r= 0.95). The statistical indicators also show that the model perfectly reproduces the hydrodynamic parameters during the model

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validation phase. During the first model validation phase, the results of the simulation from 1 to 16 June 2020 were acceptable (NSE = 0.94; RMSE = 0.023 m; BIAS = -1.32; R² = 0.96; r = 0.98). During the second model validation period (11/1/2020 to 11/16/2020), the results were significantly lower than in the first phase (NSE = 0.91; RMSE = 0.012 m; BIAS = 0.23; R² = 0.95; r = 0.97) (Table 3). This difference between the simulated and observed values does not affect the quality of the model results. The graphical and statistical indicators show that the model perfectly reproduces the hydrodynamic parameters of the water reservoir. This is a good basis for the implementation of the sediment transport model into the Lobo reservoir.

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## Current velocities

The results of the velocity current distribution are presented in Fig. 10. A high variation of velocity currents in the reservoir is observed. The velocities decrease from upstream to downstream. The speeds are between 0.054 and 0.76 m/s (Fig. 10). It can be seen that the maximum speeds are observed at the entrance to the reservoir, where they can reach 0.76 m/s. The currents are directed from East to West. However, the simulated velocities are relatively low and comprised between 0.0051 and 0.36 m/s in the reservoir. It remains relatively weak at the level of the banks. In the vicinity of the spillway of the reservoir, the flow is accelerated by the presence of the weir and the currents are between 0.04 and 0.40 m/s (Fig. 10 c). A hydraulic load allows the water to create a hydraulic jump downstream of the weir with an increase in velocity of about 0.45 m/s.

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## Sediment transport

### Initialization of the hydraulic and sediment transport model

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The deposition rate tends towards a final value which depends only on the river flow whatever the case. The time needed to reach the stationary results depends mainly on the river flow (Fig. 11 a; 11 b). The results also show that the time to reach the stationary state of the hydraulic model and the sediment transport model are different. The sediment transport model tends to take longer to reach the steady state compared to the hydraulic model (Fig. 11). The sensitivity of the model to initial conditions cannot be neglected. A strong influence of the initial conditions on the stability of the models is not immediate but rather long-term. Indeed, for a 15-day simulation period with a flow rate Q = 55 m³/s, the hydraulic model takes 14 hours and 42 minutes to reach the stationary state and the sediment transport model takes 16 hours 31 minutes and 12 seconds. For a flow rate Q = 27.5 m³/s, the hydraulic model takes 14 hours 58 minutes 48 seconds to reach the steady state. However, the transport model takes 17 hours 30 minutes to reach the steady state.

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### Relationship between trapping efficiency and river flow

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The evolution of the trapping efficiency of the reservoir in relation to the river flows of the Lobo River in stationary and non-stationary regime is presented in Figure 12. The trapping efficiency of the reservoir is between 26.7% and 80.4%. For maximum floods there is a significant resuspension of particles in the water column, which leads to a decrease in the storage capacity of the reservoir. However, when moving from a maximum to a minimum flood, an increase in the storage capacity of the reservoir is observed (Figure 12 a). The trapping efficiency of the reservoir in the stationary and non-stationary regimes are the same order of magnitude (Figure 12 b). It can be assumed that
295 the assumption of very slow dynamics of the Lobo River is verified. Steady state modelling can also give reliable
296 results.

297 Sedimentation in the Lobo reservoir

298 The variations in sediment deposition thickness (m) over fifteen (15) days of simulation for the months of January;
299 September; October and November 2020 are presented in Figs. 13 a; 13 b; 13 c and 13 d. The deposition
300 thicknesses vary from month to month and from upstream to downstream of the Lobo River impoundment. During
301 the period of low flow (January), the decrease in water inflow is correlated with a decrease in the thickness of
302 deposits in the reservoir. These deposit thicknesses are between 0.01 mm and 0.056 mm. However, during the
303 period of high flow, the simulations also show that it is the high flows of September and October that remobilise
304 the maximum amount of sediment in the reservoir, and which then induces deposits in the area of the old water
305 intake and the new water intake. During the period of high flow, the thickness of the sediment deposits are between
306 1 mm and 15 mm in September and between 1 mm and 13 mm in October. It should also be noted that during the
307 flood phase in November there is a significant amount of deposition in the Lobo reservoir with values ranging
308 from 0.1 mm to 24 mm. The monthly deposits are between 0.02 and 48 mm. This gradual decrease in the flows
309 entering the reservoir allows a large part of the suspended sediments to settle. On the other hand, during peak
310 floods there is a decrease in deposition rates in the Lobo reservoir (Fig. 13 b; 13 c). In terms of spatial distribution,
311 sediment deposition is variable from one area to another. High deposition rates are recorded at the inlet of the
312 reservoir, at the new water intake and at the old water intake where velocities are relatively low. The low velocities
313 observed in the above areas would favour sedimentation in the reservoir. High rates of siltation are expected
314 towards the weir which is a flow barrier.

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316 Discussion

317 The Lobo reservoir is important for drinking water supply of Daloa's population. However, it faces serious
318 problems due to sedimentation in its reservoir and also to the proliferation of aquatic plants on the surface of the
319 water body. Hydro-sedimentation modelling using MIKE 21 FM/MT software was used to understand the
320 hydrodynamic functioning and to determine the preferential deposition zones in the Lobo reservoir. The simulated
321 current velocities are relatively low on the water body. They range from 0.054 to 0.76 m/s. The currents decrease
322 from upstream to downstream of the reservoir. However, near the water intake and after the weir, the current speed
323 is higher. This increase in currents at the new intake could be explained by the continuous abstraction of flows by
324 the Côte d'Ivoire Water Supply Company (SODECI). This finding is in agreement with those of Baruah and Sarma
325 (2020), and Smolar-Žvanut and Mikoš (2014) in Brahmaputra River in India, and Soča River in Slovenia,
326 respectively. In the vicinity of the reservoir's weir, the flow is accelerated by the presence of the weir and the
327 currents are between 0.04 and 0.40 m/s. This phenomenon could be explained by the hydraulic load which allows
328 the water to create a hydraulic jump downstream of the weir by increasing the velocity currents. However, a
329 decrease in currents due to head losses generated by the banks is observed in the vicinity of the banks of the
330 reservoir (Kouassi et al., 2021). In addition, a decrease in velocity currents is observed from upstream to
331 downstream of the Lobo reservoir. Many researchers (Guertault et al., 2018; Kouassi et al., 2013; Tang et al.,
332 2021; Teal et al., 2015) reported that when a river enters a reservoir its velocities decrease considerably allowing
333 suspended sediments to settle according to their sizes.
The study also showed that the results of the different models are very sensitive to the conditions at the boundaries of the model but also to the initial conditions. It is very difficult to know exactly the real initial conditions imposed in the model. For initialization reasons, an initial condition was imposed to start the simulations and reach the stationary state. At the end of the simulation of the sediment transport model, the amount of sedimentation (erosion/deposition) which can be expressed in terms of mass and thickness of deposits is calculated. The results show that the deposition rates in the Lobo reservoir are dependent on the river flows (Adongo et al., 2020; Ding and Langendoen, 2018). The deposition is higher during the high-flow period than the dry season. This can be explained by the fact that during the rainy season, the flows carry a lot of sediment due to the higher inflow (Dramais et al., 2018; Hountondji et al., 2019). This variability is also explained by the variation in vegetation cover (bare soil) during the year and the aggressive nature of the first rains at the beginning of the rainy season (Achite and Meddi, 2005). These two factors allow the first floods to carry considerable amounts of sediment after a long dry season characterized by high temperatures and the destruction of soil aggregates. The spatial distribution of sedimentation in the reservoir illustrated shows that the deposition varies spatially and temporarily. High deposition rates are observed at the entrance to the reservoir where velocities are abruptly reduced as the river enters the reservoir. High deposition rates are also observed at the old water intake. This area of the reservoir is a significant sediment trapping area. The old water intake was located in a concave area. The loss of head due to the presence of the meander causes a decrease in the velocity of the currents, which favours the fall of the sediments by contributing to the construction of a delta. The silt can flocculate and settle in the area protected by the counter-current cell, given the low velocities in this sector (Petit, 1975; Tiron Dutu et al., 2019; Yousefi et al., 2021). A proper relationship of the evolution of the trapping efficiency as a function of the fluvial flows of the Lobo River was obtained. This relationship shows that the deposition is proportional to the inflows into the reservoir. During the high water, there is a strong resuspension of particles in the water column, which leads to a decrease of the deposits in the reservoir. This strong resuspension of particles during the flood phase, caused by a decrease in deposits, seems to be favourable to the removal of sediments from the reservoir downstream of the weir. However, when moving from a high to low flows, an increase in deposition rate is observed (Fig. 13 d). The deposition rates in the stationary and non-stationary regimes evolve in the same order of magnitude (Fig. 13 b). The good synchronisation between the steady and non-steady states transport model explains the very slow dynamics of the Lobo River flows although a steady state modelling could also give reliable results.

362
363

## Conclusion

Sedimentation modelling of the Lobo reservoir was carried out in this study using the MIKE 21 FM software and its mud transport module (MT). The hydro-sedimentary functioning of the Lobo reservoir was simulated in steady state and non-steady state. The good agreement between the observed and simulated water levels by the hydraulic model allowed to set up a quality sediment transport model. The results show that the models are very sensitive to the boundary and the initial conditions of the model. Whatever the conditions, the deposition rate tends towards a final value which only depends on the river flow. The time needed to reach stationary results depends mainly on the river flow. The Lobo River impoundment experiences siltation with monthly deposit thicknesses ranging from 0.02 to 48 mm. The old water intake, the new water intake and the weir area are the preferred sites for sediment deposition. It seems that the deposition rate in stationary and non-stationary regimes evolve in the same order of magnitude. This justifies the very slow dynamics of the Lobo River. The trapping capacity of the reservoir is
between 26.7% and 80.4%. A specific equation to the parameters of this study was developed to understand the relationship between river flows and trapping efficiency. However, this relationship needs to be improved through a good chronicle of field data in order to allow long-term simulations for better management of the Lobo reservoir.

**Acknowledgments:** The hydrogical and sediment data used in this study were collected by the hydrogical team of the Environmental Sciences and Technology Laboratory of the Jean Lorougnon Guédé University, Daloa, whom we would like to thank.

**Acknowledgments:** We would like to thank the hydrogical team of the Environmental Sciences and Technology Laboratory of the Jean Lorougnon Guede University, Daloa, who help us to collect hydrogical and sediment data used in this study.

**Funding:** EPEAEP_Daloa project is funded by the Education and Research Ministry of Côte d'Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD.

**Conflicts of Interest:** On behalf of all authors, the corresponding author states that there is no conflict of interest.

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575
576
577 **Fig. 1** : Location of the Lobo catchment

578 **Fig. 2** (a) Installation of the Andjoukro hydrometric station upstream of the Lobo reservoir. (b) Average monthly
579 inflows in Lobo reservoir (2019-2020)

580 **Fig. 3** Water and sediment sampling in the Lobo River

581
582 **Fig. 4** Bathymetric map of the Lobo reservoir

583 **Fig. 5** Meshing of the study area

584 **Fig. 6** : Sketch of sedimentation in Lobo reservoir

585
586 **Fig. 7** Variation of the water level according to the river inflows in the Lobo Reservoir

587 **Fig. 8** : Distribution function of the velocity of settling of suspended solids of tests 1;2;3 and 4

588 **Fig. 9** Comparison of observed and simulated water levels during the calibration (a) and validation (b,c) phases

589 **Fig. 10** Distribution of velocity currents in the Lobo reservoir (a); the area of the old (b); the new (c) water intake

590 **Fig. 11** Curve of water level variation (a) and deposition rate as a function of time (b) in steady state

591
592
593 **Fig. 12** Evolution of the trapping efficiency of the water reservoir in relation to the river flows of the Lobo River
in stationary (a) and non-stationary (b) regime
594 **Fig. 13** Deposits during low flows (January (a)-November (d)) and high flows (September (b)-October (c)) in the
595 Lobo reservoir

596

597

598

599

600 **Table. 1** Characteristics of the Lobo Reservoir

601

**Table 2** Suspended Solids settling velocity

602

**Table 3** Numerical criteria for calibration and validation of the hydrodynamic model

603
Figures

<figure><img src="image_2.png" /><figcaption>Figure 1</figcaption></figure>

Location of the Lobo catchment
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /><figcaption>Figure 2</figcaption></figure>

(a) Installation of the Andjoukro hydrometric station upstream of the Lobo reservoir. (b) Average monthly inflows in Lobo reservoir (2019-2020)
<figure><img src="image_5.png" /><figcaption>**Figure 3**

Water and sediment sampling in the Lobo River</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 4</figcaption></figure>

Bathymetric map of the Lobo reservoir
<figure><img src="image_7.png" /><figcaption>Figure 5</figcaption></figure>

Meshing of the study area
<figure><img src="image_8.png" /><figcaption>Figure 6</figcaption></figure>

Sketch of sedimentation in Lobo reservoir
<figure><img src="image_9.png" /><figcaption>Figure 7</figcaption></figure>

Variation of the water level according to the river inflows in the Lobo Reservoir
<figure><img src="image_10.png" /><figcaption>Figure 8</figcaption></figure>

Distribution function of the velocity of settling of suspended solids of tests 1;2;3 and 4

<figure><img src="image_11.png" /></figure>
<caption>Figure 9</caption>

Comparison of observed and simulated water levels during the calibration (a) and validation (b,c) phases

<figure><img src="image_12.png" /><figcaption>Figure 10</figcaption></figure>

Distribution of velocity currents in the Lobo reservoir (a); the area of the old (b); the new (c) water intake
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>Figure 11</figcaption></figure>

Curve of water level variation (a) and deposition rate as a function of time (b) in steady state
<figure><img src="image_15.png" /><figcaption>Figure 12</figcaption></figure>

Evolution of the trapping efficiency of the water reservoir in relation to the river flows of the Lobo River in stationary (a) and non-stationary (b) regime
<figure><img src="image_16.png" /><figcaption>Figure 13</figcaption></figure>

Deposits during low flows (January (a)-November (d)) and high flows (September (b)-October (c)) in the Lobo reservoir