Cameroon’s primary hydroelectric infrastructure has been selected based on installed capacity geographical location and significant influence on regional hydrological regimes. Dams under scrutiny encompass Lom Pangar Memve’ele Nachtigal and Lagdo representing a range of characteristics in terms of scale and catchment area variably impacting ecosystem . Energy capacity of dams gets determined by installed capacity which ensures representativeness of national energy issues for dams exceeding 100 MW. Hydrological context varies greatly with numerous river regimes present including rain-fed perennial rivers and mixed regimes with wildly fluctuating discharge patterns. Such diversity facilitates analysis comparatively. Presence of wetlands and sensitive aquatic ecosystems alongside endemic fish communities indicates considerable ecological vulnerability pretty clearly in many regions . Table 1 presents the key characteristics of selected hydroelectric dams and monitoring stations in Cameroon.

Explanatory Notes

Hydrological regime classification was derived from long-term river discharge series (1980-2020) and accounts for seasonal rainfall variability and flow persistence.

Monitoring stations were selected based on spatial proximity, availability of multi-decadal environmental data, and accessibility for field calibration.

Reservoir areas were verified using Landsat 8 OLI satellite imagery and adjusted through supervised classification (2022-2023). Fish diversity monitoring incorporated IUCN status updates and local ichthyofaunal inventories.

Rigorous spatio-temporal quantification of environmental impacts relies heavily on systematic integration of multidisciplinary data via remote sensing and hydrosedimentary modelling alongside ecological analyses.

Acquisition of high-resolution multispectral satellite images namely Sentinel-2 and Landsat 8 was undertaken throughout a lengthy period from 2000 up to 2024 . Extraction of key spectral indices like NDWI happens fairly often for detecting changes in water surfaces rapidly over time $\text{NDWI}=\frac{\text{ρNIR}-\text{ρSWIR}}{\text{ρNIR}+\text{ρSWIR}}$. $\text{ρNIR}$ denotes reflectance in near infrared region whereas $\text{ρSWIR}$ signifies reflectance pretty deeply in shortwave infrared spectrum of light obviously. Normalized Difference Vegetation Index aka NDVI quantifies vegetation quite effectively and detects subtle changes in land cover rapidly nowadays $\text{NDVI}=\frac{\text{ρNIR}-\text{ρRed}}{\text{ρNIR}+\text{ρRed}}$. Image processing occurs via utilisation of supervised classification methodologies namely Random Forest and Support Vector Machines fairly effectively nowadays . Methodologies get calibrated by field data thereby facilitating generation of thematic maps illustrating wildly dynamic aquatic ecosystems and terrestrial ones pretty effectively. Biodiversity and aquatic fauna should be considered carefully alongside various other ecological data points under certain conditions naturally. Systematic biological inventories of aquatic species like fish and macroinvertebrates are undertaken alongside terrestrial species with biodiversity characterised according to ecological indices . Temporal monitoring of fish populations achieves assessment of disturbances quite effectively incorporating demographic models based on modified Lotka-Volterra ordinary differential equations . Effects of hydrological regime are accounted for by equation $\frac{\text{d}N}{\text{d}t}=rN\left(1-\frac{K\left(t\right)}{N}\right)-m\left(t\right)N$ somehow generally. Population size N represents a crucial metric here while intrinsic growth rate r gets denoted by a simple symbol and carrying capacity K varies with flow regime intricately over time t and mortality term m adjusts drastically under fluctuating hydrological conditions at time t.

Standardized sampling protocols adapted carefully to specific site conditions ensured scientific rigor and validity of results related to aquatic biodiversity. Standardized multimesh gill nets are utilized alongside portable electrofishing gear in shallow zones ensuring fairly comprehensive fish community coverage pretty effectively. Four annual campaigns were conducted across hydrological seasons capturing seasonal population dynamics during two dry seasons and two wet seasons. Three sampling stations per site are positioned upstream and adjacent to dams and downstream covering a minimum of 5 kilometers of river at each end. Gill nets stretching 100 meters with mesh sizes between 10 mm and 60 mm were deployed under international standards fairly rigorously. Electrofishing gets done with a generator that is pretty portable and pumps out quite a few volts adapted locally. Manual collection occurs on various substrates like sand and rocks in areas having moderate current velocity using a kick-net with 500 µm mesh size. Biannual sampling campaigns occur in spring and autumn corresponding roughly to critical phases within macroinvertebrate life cycles mostly. Five sampling points are distributed fairly evenly within wetland ecosystem affected by dam ensuring somewhat homogeneous representation spatially. Samples get preserved immediately in seventy percent ethanol stabilizing organisms effectively. Taxonomic identification occurs painstakingly in laboratories via identification keys locally validated with considerable rigor and great fastidiousness. Physicochemical parameters like temperature pH and electrical conductivity were measured in situ alongside turbidity and dissolved oxygen levels and nutrient concentrations. Measurements were taken every month without fail over a span of two years detecting variations both short-term and somewhat longer-term fairly continuously. Samples were collected at surface level and 1 meter depth at each hydrological station upstream downriver and near dam vicinity. Portable multiparameter sondes such as YSI ProDSS or equivalent are needed for instantaneous measurements alongside sterile bottles for collecting samples destined for analysis in labs accredited and certified. Protocols implemented facilitate thorough coverage spatially and temporally enabling quite precise assessment of ecological impacts stemming from hydroelectric infrastructure on riverine ecosystems in Cameroon.

The following hydrosedimentary data is presented: flow rate and sedimentation. The objective of the present study is to undertake in situ measurements and the acquisition of time series of river flows (Q) and sediment flows (S), expressed in tonnes per day (t/d). The modelling of hydrosedimentary systems is predicated on the utilisation of coupled advection-dispersion equations :

$\frac{\partial C}{\partial t}+u\frac{\partial C}{\partial x}=\frac{{\partial }^{2}C}{\partial x^2}-\lambda C+S_{\text{sources}}-S_{\text{puits}}$ (1)

Variables of interest are defined here: C denotes sediment concentration, u signifies flow velocity, DDD represents dispersion coefficient, λ is settling rate and $S_{\text{sources}}/S_{\text{puits}}$ signify local sediment sources and losses respectively somehow. Integration of model into coupled 2D/3D hydrodynamic model holds particular significance in this study remarkably well nowadays. Numerical solutions of Navier-Stokes equations underpin 2D and 3D hydrodynamic models used in river flow studies with significant academic merit existing within broader research frameworks . Model simulates spatial distribution of sediments and critical accumulation areas or erosion zones downstream of man-made structures quite effectively.

Advanced geomatics processing chain combining multispectral optical remote sensing data from sources like Sentinel-2 MSI and Landsat 8 OLI with Geographic Information Systems underpins spatio-temporal analysis of dam-induced environmental changes. Processing involves various methodologies including radiometric and geometric techniques employed during preliminary image processing stages quite thoroughly nowadays. Supervised classification gets a boost from machine learning algorithms like Random Forest and Convolutional Neural Networks facilitating distinction of various land cover classes including wetlands and agricultural land. Calculation of NDVI, NDWI and EVI spectral indices and their temporal monitoring is utilised largely for assessing vegetation dynamics and water resource fluctuations effectively . Variation in water surface area is crudely modelled by some wonky parametric time series function over surprisingly long periods:

$\Delta A_t=f\left({\text{NDWI}}_t\right)=A_t-A_{t-1}$(2)

Modelling environmental change gradients involves utilizing spatialisation functions like ordinary kriging and spline interpolation ordinarily within geographical contexts slowly.

Gas chromatography-mass spectrometry analysed concentration of heavy metals namely As Hg Pb Cd Cr and persistent organic pollutants quite thoroughly. Water quality index was reckoned via a rather complex weighted formula below.

$\text{IQE}={\displaystyle {\sum }_{i=1}^n{w}_i\cdot Q_i}$(3)

$Q_i$ represents relative quality of parameter i variously expressed as BOD₅ or perhaps nitrates and sometimes turbidity in this particular study. Weight assigned to parameter i denoted by $w_i$ gets determined according to its ecological importance pretty much straightforwardly. Bioassays on sentinel species like Tilapia nilotica and Daphnia magna offer a fairly methodical way of assessing rather subtle sublethal effects . Sigmoidal dose-response models are implemented initially and refined subsequently through application of logistic regression in a rather convoluted process:

$R\left(d\right)=\frac{R_{\max }}{1+{\text{e}}^{-k\left(d-d_{50}\right)\text{}}}$ (4)

In this formula, the dose is denoted by the letter d, the effective dose at 50% by d₅₀, and the slope coefficient by k.

Hydrosedimentary modelling plays a crucial role in assessing environmental impacts of dams with considerable ecological and socio-economic repercussions downstream. HEC-RAS SWAT and MIKE11 hydrological models were employed rather vigorously in this quite intricate study at surprisingly varying scales . Models were validated by comparing them with time series data and grain size profiles measured in situ and cross-calibrated subsequently.

Simultaneous integration of HEC-RAS, SWAT and MIKE11 models is crucial for thoroughly analyzing hydrological impacts associated with dams on various scales. HEC-RAS excels at painstakingly detailed one-dimensional hydraulic modeling of river flows enabling rather precise simulation of water levels locally. SWAT captures long-term hydrological processes like runoff generation and sediment transport at watershed scale incorporating land-water interactions over extensive spatial domains. MIKE11 ultimately bolsters this framework with flexible 1D or 2D hydraulic modeling capabilities geared towards simulating gnarly sediment transport within river networks. Rigorous evaluation occurs across disparate temporal resolutions and spatial scales thereby assessing physical processes alongside quite complex biogeochemical phenomena. Primary calibration parameters focus on time step typically ranging from 5 minutes or so up to 1 hour or thereabouts depending on dynamics under study and spatial mesh resolution on order of meters in critical zones for HEC-RAS or several hundred meters at watershed scale for SWAT. Model parameters get tweaked via iterative calibration against field data and hydrometric observations striking a weird balance between accuracy and efficiency pretty optimally.

1) HEC-RAS (Hydrologic Engineering Center-River Analysis System)

HEC-RAS model gets utilised pretty extensively nowadays for simulating hydrodynamic profiles and sediment flows in dam-impacted channels generally. Achievement occurs in regimes both one-dimensional and rather curiously in two-dimensional ones too ordinarily under certain conditions . Fundamental premise of this model hinges on coupled resolution of Saint-Venant’s equations for hydrodynamics and Exner’s equation governing sediment balance roughly:

$\{\begin{array}{l}\frac{\partial A}{\partial t}+\frac{\partial Q}{\partial x}=0\\ \frac{\partial Q}{\partial t}+\frac{\partial }{\partial x}\left(\frac{Q^2}{A}+gAh\right)=gA\left(S_0-S_f\right)\\ \frac{\partial z_b}{\partial t}+\frac{1}{1-{\lambda }_p}\frac{\partial q_s}{\partial x}=0\end{array}$ (5)

Subscripts A and Q denote cross-sectional area and flow rate respectively in this particular formula. Bed elevation denoted by $z_b$ and sediment porosity by ${\lambda }_p$ whereas solid flow rate is represented by $q_s$; natural and friction bed slopes $S_0$ and $S_f$ respectively being indicated. Grain size fractions dynamics are coupled with a transport law namely Meyer-Peter Müller or Yang according to local distribution of grain size.

2) SWAT (Soil and Water Assessment Tool)

SWAT facilitates emulation of various hydrological processes and sedimentary events occurring extensively at a relatively large watershed scale suddenly . Daily mass balance combines infiltration runoff erosion sediment transport and nutrient cycling modelling each hydrological unit with considerable environmental intricacy. Erosion simulation unfolds via utilisation of modified RUSLE with equation E = R × K × L × S × C × P calculating requisite parameters pretty accurately. The location is as follows: The variables employed in this study are as follows: E is soil loss (t/ha/year), R is the rainfall erosivity index, K is the soil erodibility factor, LS is the slope factor, C is the cover factor, and P is the conservation practices factor. Sediments generated thus are subsequently processed via a semi-Lagrange scheme incorporating critical suspension concentration and morphology of hydrographic network.

3) MIKE11 (DHI Software)

MIKE11 model gets employed in high-resolution hydrodynamic modelling of river basins incorporating direct coupling between hydrosediment transport and thermal dynamics alongside water quality parameters somehow. A concise overview of implicit solver for Saint-Venant equations in transient regime is provided in ensuing text quite elaborately therein . A SED module facilitates modelling particle transport encompassing both fine fractions and coarse particulate matter effectively. Coupling with ECOLab modules facilitates studying interactions between sediment and biogeochemistry extensively in various environmental conditions normally . Modular approaches facilitated precise spatialisation of critical silting areas and erosion hotspots downstream of various manmade structures quite effectively over time.

A multi-criteria decision-making approach was developed rigorously assessing cumulative environmental impacts associated with juxtaposition of several dams and integrating variables like hydrosedimentary and ecological factors alongside heritage value and ecosystem resilience . AHP method underpins this approach heavily augmented by fuzzy uncertainty informing a distinctly probabilistic treatment somehow:

1) Hierarchical breakdown of impacts:

2) Construction of the pairwise comparison matrix according to Saaty’s method:

$A=\left[\begin{array}{cccc}1& a_{12}& \cdots & a_{1n}\\ 1/a_{12}& 1& \cdots & a_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ 1/a_{1n}& 1/a_{2n}& \cdots & 1\end{array}\right]$ with $a_{ij}\in \left[1/9,9\right]$(6)

3) Calculation of eigenvectors and consistency index (CI):

$CI=\frac{{\lambda }_{\max }-n}{n-1}$ et $CR=\frac{CI\text{}}{RI}$ with $C_R<0.10$

The integration of environmental uncertainty is achieved through the implementation of fuzzy aggregation of preferences, utilising fuzzy logic to produce a cumulative risk map according to a spatio-temporal gradient.

Hydrosedimentary and ecological models utilised in this study require cross-validation for robustness and generalisation and pretty high reliability somehow. Bifocal methodology encompassing backcasting and inter-site spatial validation operates in tandem with probabilistic quantification of uncertainties rather haphazardly.

Hydrological sedimentological and ecological data from MINEE SNE and ARAA were utilised as reference for adjusting critical model parameters largely . Daily flows measured upstream and downstream of dams and suspended matter concentrations and total sediment fluxes are encompassed in this data set rather thoroughly. It also includes annual data on limnimetry and bathymetry.

1) Objective function and optimisation algorithm

Calibration of the HEC-RAS, SWAT and MIKE11 models was achieved by minimising a weighted global error function:

$F_{cal}={\displaystyle {\sum }_{i=1}^n{\alpha }_i\cdot \left(\frac{1}{N_i}{\displaystyle {\sum }_{i=1}^{N_i}{\left(\frac{O_{ij}-P_{ij}}{O_{ij}+\epsilon }\right)}^2}\right)}$ (7)

Observed values $O_{ij}$ and predictions $P_{ij}$ for variable i at specific time j are represented here basically. Key elements in this analysis include weight ${\alpha }_i$ assigned to variable i and regularisation term ε mostly under specific conditions normally. Optimisation process execution leveraged metaheuristic evolutionary algorithms like NSGA-II and PSO in highly non-linear multi-objective parametric spaces effectively .

2) Statistical performance indicators

The performance of the models was evaluated according to several standardised statistical metrics, which were chosen for their ability to provide a comprehensive assessment of the models’ effectiveness:

$\text{NSE}=1-\frac{{{\displaystyle \sum }}_{j=1}^N{\left(O_j-P_j\right)}^2}{{{\displaystyle \sum }}_{j=1}^N{\left(O_j-O_j\right)}^2}$ (8)

with NSE > 0.85 advisable.

$\text{RMSE}=\sqrt{\frac{1}{N}{{\displaystyle \sum }}_{j=1}^N{\left(O_j-P_j\right)}^2}$ (9)

${\text{R}}^2={\left(\frac{{\displaystyle \sum \left(O_j-\bar{O}\right)\left(P_j-\bar{P}\right)}}{\sqrt{{\displaystyle \sum {\left(O_j-\bar{O}\right)}^2}{\displaystyle \sum {\left(P_j-\bar{P}\right)}^2}}}\right)}^2$ (10)

The models were deemed valid if the Nash-Sutcliffe efficiency (NSE) was greater than zero, the coefficient of determination (R²) was greater than zero, and if root mean square error across all time series tested was minimal.

In addition to temporal validation, spatial inter-site validation was performed by comparing model predictions with observations from field campaigns at independent sites not used for calibration. This methodological approach facilitated the assessment of the geographical generalisability of the models, and the identification of areas exhibiting high structural bias. The spatial analysis was conducted using the following software: The spatial residual maps are defined as R(x, y) = O(x, y) − P(x, y) via kriging. Furthermore, Kolmogorov–Smirnov tests are employed for two samples. Finally, a MANOVA analysis of variance is conducted to check for structural biases .

A sensitivity analysis is to be conducted, with the Sobol method being utilised for this purpose: $S_i=\frac{V_i}{V_T}$; $S_{T_i}=\frac{V_{T_i}}{V_T}$.

In this study, $V_i$ is defined as the partial variance due to $X_i$, and $V_T$ is the total variance of the model. These indices facilitate the evaluation of the influence of the parameters on the variance of the outputs. Furthermore, an uncertainty analysis was integrated using Monte Carlo simulations (N = 10,000 draws) to estimate the 95% confidence intervals around the predictions . The confidence interval (CI) at the 95% level is calculated as follows: [μ − 1.96 · σ, μ + 1.96 · σ]. The Global Modelling Confidence Index (GMCI) was finally defined as follows:

The $\text{IGCM}=1-\frac{1}{n}{\displaystyle {\sum }_{i=1}^n\frac{\left|O_i-P_i\right|}{O_i+\epsilon }}$ is a measure of reliability, with a GMCI > 0.90 indicating excellent reliability. Figure 1 illustrates the numerical modelling of hydrosedimentary transport, including dynamic analysis and the resulting morphological impact.

A comprehensive numerical model simulates coupled hydrodynamic and sediment transport processes within three-dimensional fluvial domains under highly variable seasonal flow. Model breaks down representative river segment into varied layers horizontally and vertically enabling thorough resolution of velocity fields and sediment concentration gradients slowly. Simulation incorporates advection-diffusion transport mechanisms alongside gravitational settling with velocity field having sinusoidal spatial variability and seasonal modulation replicating varied flow regimes. Sediment concentration dynamics are computed via a finite difference approach integrating horizontal and vertical fluxes with explicit treatment of settling velocities. Bed morphology evolution unfolds via Exner equation capturing erosion and deposition through sediment flux divergence at sediment-water interface governed by porosity-dependent volumetric sediment conservation. Zones susceptible to deposition and entrainment get mapped spatially by critical thresholds for sediment settling and resuspension velocities informing sediment connectivity risks potentially of clogging. Decadal hydrological timescale simulation reveals heterogeneous sediment accumulation and erosion patterns driven by variability in flow and transport dynamics. Bed elevation evolves within realistic bounds emphasizing model robustness and stability pretty quickly under various physically plausible scenarios. Morphodynamic responses under hydrological forcing are scrutinized with this advanced predictive framework having significant implications for river management and infrastructure planning. High-resolution velocity profiles and sediment transport processes are melded rather thoroughly providing robust scientific basis for optimized ecological resilience strategies as seen in Figure 2 .

Three-dimensional numerical simulation intricately weaves together hydrodynamic processes and advective-diffusive transport with biogeochemical interactions between dissolved mercury and various sediment particles. Factors like vertical stratification and spatial heterogeneity of hydraulic velocities influence sorption parameters and reveal a complex dynamics of contamination over time and space. Maximum concentrations of dissolved Hg reside in low-shear zones near bed-water interfaces owing largely to gravitational sedimentation and vertical diffusion. Physical erosion and concentration gradients promote re-mobilisation of Hg trapped in sediments suggesting recurring cumulative toxic risk downstream during seasonal flow variations. Results confirm importance of hydro-biogeochemical couplings in detailed ecotoxicological risk assessments within river environments and furnish robust predictive tools for environmental management decision-making. Figure 3 illustrates the coupled analysis of NDVI and NDWI used to monitor seasonal eco-hydrological variations.

Non-linear time series from simulated satellite imagery facilitate accurate characterisation of cross-dynamics between vegetation cover and surface water extent variably. Results reveal areas of differentiated ecohydrological response under varied seasonal conditions and anthropogenic influences across heterogeneous spatial landscapes indicating ecological sensitivity. Three-dimensional visualisation techniques highlight topological signatures consistent with water level fluctuations and abrupt transitions during flooding or severe water stress events. An operational lever for anticipating environmental impacts and proactively managing floodplains emerges under this integrated approach leveraging spectral indices and deep temporal modelling heavily. Figure 4 illustrates the algorithmic optimization of ecological flow regimes designed to support sustainable hydropower and enhance environmental resilience.

The present study proposes a multi-criteria modelling approach that is innovative in nature. The aim of the approach is to optimise ecological flow restoration in a river system. This is intended to reconcile the maximisation of biodiversity, the minimisation of downstream sediment erosion and the maintenance of viable hydroelectric production. The approach is grounded in multi-objective evolutionary algorithms (NSGA-II), a sophisticated framework that facilitates the efficient exploration of the decision space and the characterisation of Pareto fronts, thereby integrating functional trade-offs between conflicting criteria. The simulations demonstrate the existence of optimal zones where ecological resilience is preserved without significantly compromising energy performance. Moreover, deliberations on prospective scenarios, encompassing the construction of new dams or the adjustment of flow rates, underscore the significance of integrated decision-making for the sustainable management of water resources. This methodological approach constitutes a significant contribution to the adaptive management of river systems by providing a robust quantitative framework for environmental and energy decision-making at the watershed scale.