This study projects water availability and sustainability in Nigeria due to climate change. This study used Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage data (TWS), Global Precipitation Climatology Center (GPCC) precipitation data and Climate Research Unit (CRU) temperature data. Four general circulation models (GCMs) of the Coupled Model Intercomparison Project 5 were downscaled using the best of four downscaling methods. Two machine learning (ML) models, RF and SVM, were developed to simulate GRACE TWS data for the period 2002–2016 and were then used for the projection of spatiotemporal changes in TWS. The projected TWS data were used to assess the spatiotemporal changes in water availability and sustainability based on the reliability–resiliency–vulnerability (RRV) concept. This study revealed that linear scaling was the best for downscaling over Nigeria. RF had better performance than SVM in modeling TWS for the study area. This study also revealed there would be decreases in water storage during the wet season (June–September) and increases in the dry season (January–May). Decreases in projected water availability were in the range of 0–12 mm for the periods 2010–2039, 2040–2069, and 2070–2099 under RCP2.6 and in the range of 0–17 mm under RCP8.5 during the wet season. Spatially, annual changes in water storage are expected to increase in the northern part and decrease in the south, particularly in the country’s southeast. Groundwater sustainability was higher during the period 2070–2099 under all RCPs compared to the other periods and this can be attributed to the expected increases in rainfall during this period.

This study compares multi model ensemble (MME) projections of rainfall using general quantile mapping, gamma quantile mapping, Power Transformation and Linear Scaling bias correction (BC) methods for representative concentration pathways (RCPs) 4.5 and 8.5 of the Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models (GCMs). Using the Global Precipitation Climatology Centre historical period (1961–2005) rainfall data as the reference, projection was conducted over 323 grid points of Nigeria for the periods 2010–2039, 2040–2069 and 2070–2099. The performances of the different BC methods in removing biases from the GCMs were assessed using different statistical indices. The computation of the MME of the projected rainfall was conducted by aggregation of 20 GCMs using random forest regression method. The percentage differences in the future rainfall relative to the historical period were estimated for all BC methods. Spatial projection of the percentage changes in rainfall for Linear scaling, which was the best performing BC method, showed increases in rainfall of 5.5–6.9% under RCPs 4.5 and 8.5, respectively, while the decrease range was −3.2–−4.2% respectively during the wet season. The range of annual increases in precipitation was 5.7–7.3% for RCP 4.5 and 8.5, respectively, while the decrease range was −1.0–−4.3%. This study also revealed monthly rainfall within the country will decrease during the wet season between June and September, which is a significant period where most crops need the water for growth. Findings from this study can be of importance to policy makers in the management of changes in hydrological processes due to climate change and management of related disasters such as floods and droughts.

The feasibility of using the velocity-based method for calculating the transverse mixing coefficient of the two-dimensional contaminant transport model was studied to substitute the concentration-based method in which the mixing coefficient is calculated from the concentration curves obtained via the costly tracer experiment. To calculate the transverse mixing coefficients, the hydraulic data and concentration data of the electric conductivity (EC) were collected at the confluence of tributary rivers in the Nakdong River, South Korea. The experimental results showed that the transverse mixing was controlled by secondary currents that were caused by the merging of the tributary rivers as well as the meandering of the main channel in the study area. The comparison of the flow-weighted transverse mixing coefficients estimated from velocity measurements with values estimated from concentration measurements revealed that a linear relationship with a fitted slope of 1.310 was found between the two values. This discrepancy occurred because the velocity-based method contains only shear effects due to the vertical velocity deviation, while the concentration-based method embraces other mixing effects, such as channel irregularities, storage zone effects, and turbulent diffusion, as well as the shear effect.

The maintenance of the performance of sump pumps is important to mitigate flood damage in urban areas and lowlands. However, the air-entraining vortex in the sump leads to undesirable performance degradation. Thus, in this study, the newly designed floating anti-vortex device (F-AVD) was employed in the intake pipe to enhance the efficiency of water intake in the sump by decreasing the surface vortex. The performance of the F-AVD was evaluated from the model experiments, in which the sump model was designed to represent the pump station that operates in Korea. The flow in the sump was measured using the particle image velocimetry (PIV) technique, and the velocity and vorticity distributions were compared both with and without the adoption of the F-AVD. The experimental results indicated that the vortex structures behind the intake pipe were effectively mitigated by installing the F-AVD. The vorticity magnitude behind the intake pipe was reduced in range of 24.8–52.5% after the installation of the F-AVD. However, in the case of a flow rate increase, the efficiency of the F-AVD decreased because of the strong vortex. Thus, an additional anti-vortex device (AVD), which is attached to the backwall or the floor in the sump, is required to prevent the air entrainment in conditions with high flow rates.

River meanders form complex 3D flow patterns, including secondary flows and flow separation. In particular, the flow separation traps solutes and delays their transport via storage effects associated with recirculating flows. The simulation of the separated flows highly relies in the performance of turbulence models. Thus, these closure schemes can control dispersion behaviors simulated in rivers. This study performs 3D simulations to quantify the impact of the turbulence models on solute transport simulations in channels under different sinuosity conditions. The 3D Reynolds-averaged Navier-Stokes equations coupled with the $k-\epsilon$ , $k-\omega$ and SST $k-\omega$ models are adopted for flow simulations. The 3D Lagrangian particle-tracking model simulates solute transport. An increase in sinuosity causes strong transverse gradients of mean velocity, thereby driving the onset of the separated flow recirculation along the outer bank. Here, the onset and extent of the flow separation are strongly influenced by the turbulence models. The $k-\epsilon$ model fails to reproduce the flow separation or underestimates its size. As a result, the $k-\epsilon$ model yields residence times shorter than those of other models. In contrast, the SST $k-\omega$ model exhibits a strong tailing of breakthrough curves by generating more pronounced flow separation.