Bathymetric Survey for Assessing and Forecasting the Rate of Sediment Deposits in Kainji Reservoir Across River Niger in Nigeria

Abstract

Sedimentation and siltation are gradual and continuous processes that fill up water bodies, especially reservoirs. The menace of sediment deposits can cause flooding or dam failure if left unchecked. This will be pronounced globally “especially in developing countries” due to the rising sea levels caused by climate emergencies, which will further exacerbate the destructive power of flooding. Therefore, science, international cooperation, preparedness and early action must be pivotal to safer people and communities (Antonio, 2021). To this end, accurately determining or estimating the amount of sediment deposits in a reservoir and possibly forecasting the future reservoir depth surface has been a global challenge if the activities of sedimentation remain the same. To ensure proper monitoring and management of reservoir, sounding operations to determine the amount of deposited lacustrine material to be dredged should be conducted regularly. Hence, bathymetric and sedimentation surveys were conducted in 2020 using a dual-frequency (20/200 kHz) echo sounder system in the Kainji Reservoir in New Bussa, Niger State, Nigeria. A Hi-Target V30 dual-frequency Differential Global Positioning System (DGPS) was used to delineate the reservoir shoreline at some strategic locations, including tidal measurement. The data were merged to provide detailed visualization and analyses of current depth distribution and thickness and to estimate the volume of lacustrine sediment, time average sediment accumulation rates, long-term average annual sediment flux, and water storage capacity. A recent day reservoir stand-alone information system was generated from 200 kHz sounding data. Linear regression of the reservoir depths was assessed from 1990 bathymetric datasets and 2020 datasets using profiles and cross-sections. The results show that the maximum observed depth is 71.2 m, indicating a 7.53% loss in depth from the 1990 archived data and a 16.24% depth loss to sedimentation from 1968 to 2020. The calculated long-term average sediment accumulation rates were used to model sediment infilling and the projected lifetime of the reservoir. The outcome from the echo sounder datasets suggests that the basin has a projected lifetime of 85.65 years due to sediment load. Furthermore, Markov chain and cellular automata (MC – CA) were used to forecast the future bathymetric surface of 2050, but the final surface generated some artefacts but well-predicted depths were achieved. Hence, Cellular Automata and Gradient Boosting Regression (CA – GBR) were specifically integrated for this research to model future bathymetric surfaces to evaluate the outcome of CA – Markov chains. The results show that CA – GBR effectively forecasted the future bathymetric surface of 2050 without any artefacts at a 95.7% accuracy rate. Furthermore, to investigate the scientific reasons for the high sedimentation rate in the study area, first, land use land cover classification was conducted to assess the land-use changes using Landsat data from 1990, 2005, and 2020 with five classification schemes: water, vegetation, built-up, forest, and bare-ground. The results suggested that forest and vegetation have lost a greater percentage of their area to bare ground, giving rise to erosion because the soil loses its resistivity over time without adequate vegetation cover. Second, drainage network and catchment area analyses were conducted. The results show that the catchment has 1 to 6 stream orders and 28 subbasins, again indicating a mass movement of sediment from the catchment area to the central basin. Third, the Universal Soil Loss Equation (USLE) model was employed to estimate the soil loss in ton/acre/year via rill and sheet erosion into the reservoir. The results show a high rate of soil loss of calculated annual sediment loss from USLE of 5.6% greater than the estimated yearly sediment flux using a low-frequency echo sounder. The small change between USLE and low-frequency sediment estimation suggests that USLE can be used to validate low-frequency lacustrine sediment measurements. Finally, the flood impact vulnerability map was modelled with a maximum elevation of 137 m above mean sea level. The results indicated that all the communities within 1500 km from the river centre are in great danger of flooding, while others are either medium- or low-risk and safe zones.