River Flow Simulation Research Articles

Safety Assessment of Gas Pipelines Crossing River through Hydrodynamic Analysis

Gas pipelines are buried and installed across rivers to supply the gas necessary for daily life. When crossing rivers, gas pipelines are typically installed on bridges; however, when installation on bridges is not feasible, the pipelines are buried in riverbeds. This study utilized both a one-dimensional model (HEC-RAS) and two-dimensional models (SMS) to simulate river flow and estimate the potential for scour and deposition around buried pipelines. The hydrodynamic simulations considered critical factors, including sediment transport, river discharge, and geological characteristics, to derive the maximum scour depth and assess the risk of pipeline exposure. The findings from the long-term and short-term simulations confirmed that riverbed changes due to natural hydrological events do not exceed the minimum burial depth standards, thereby ensuring pipeline safety. In addition, the study proposed specific reinforcement measures tailored to local site conditions, addressing concerns of continuous subsidence and ensuring long-term structural stability. This research offers important insights into pipeline risk management and contributes to the development of more effective regulatory standards for gas pipelines buried in riverbeds, enhancing both their safety and environmental sustainability

River flow simulation based on empirical mode function signals and random forest algorithm

River flow simulation based on empirical mode function signals and random forest algorithm

Application of the Soil and Water Assessment Tool (SWAT) Model for hydrological modelling in the Upper Oum Er Rbia watershed (Middle Atlas, Morocco)

Abstract In the study assessing the impact of global change on hydrosystems, particularly water resources, calibrated and validated tools are deemed crucial for accurate representation of the area’s characteristics. The Soil and Water Assessment Tool “SWAT” model is identified through literature review as well-suited for evaluating changes in land use and climate effects on water balance components. Applied to the Upper Oum Er Rbia watershed and its sub-basins, namely Oum Er Rbia, Srou, and Ouaoumana, the SWAT model demonstrates its effectiveness over the 1982-2019 period. Employing the sequential uncertainty adjustment method (SUFI-2) in SWAT-CUP software for calibration and validation, the model achieves good performance in simulating river flows. Statistical tests, including “R²,” “NSE,” “PBIAS,” and “RSR,” reveal consistent accuracy across various stations. The positive outcomes affirm the SWAT model’s operational capability for diagnostic assessments, risk evaluation, adaptation measures, and potential future projections in these three sub-basins.

Daily river flow simulation using ensemble disjoint aggregating M5-Prime model

Accurate prediction of daily river flow (Qt) remains a challenging yet essential task in hydrological modeling, particularly crucial for flood mitigation and water resource management. This study introduces an advanced M5 Prime (M5P) predictive model designed to estimate Qt as well as one- and two-day-ahead river flow forecasts (i.e. Qt+1 and Qt+2). The predictive performance of M5P ensembles incorporating Bootstrap Aggregation (BA), Disjoint Aggregating (DA), Additive Regression (AR), Vote (V), Iterative classifier optimizer (ICO), Random Subspace (RS), and Rotation Forest (ROF) were comprehensively evaluated. The proposed models were applied to a case study data in Tuolumne County, US, using a dataset comprising measured precipitation (Pt), evaporation (Et), and Qt. A wide range of input scenarios were explored for predicting Qt, Qt+1, and Qt+2. Results indicate that Pt and Qt significantly influence prediction accuracy. Notably, relying solely on the most correlated variable (e.g., Qt-1) does not guarantee robust prediction of Qt. However, extending the forecast horizon mitigates the influence of low-correlation input variables on model accuracy. Performance metrics indicate that the DA-M5P model achieves superior results, with Nash-Sutcliff Efficiency of 0.916 and root mean square error of 23 m3/s, followed by ROF-M5P, BA-M5P, AR-M5P, AR-M5P, RS-M5P, V-M5P, ICO-M5P, and the standalone M5P model. The ensemble M5P modeling framework enhanced the predictive capability of the stand-alone M5P algorithm by 1.2 %–22.6 %, underscoring its efficacy and potential for advancing hydrological forecasting.

Rainfall-runoff modeling using artificial neural networks in the Mono River basin (Benin, West Africa)

Hydrological models are developed to simulate river flows over a watershed for many practical applications in the field of water resource management. However, the rainfall-runoff models mostly used in the Mono river basin struggle to better simulate high river flows. This paper presents a modeling approach based on Artificial Neural Networks (ANN) under different input meteorological parameters in the Mono River basin to better take into account the non-linearity of the relationship between rainfall and runoff. To this end, precipitation, potential evapotranspiration, and previously observed flow have been used for the daily flow simulation. The Levenberg-Marquardt algorithm is used to train the ANN rainfall-runoff models over the other optimization training algorithms mostly implemented in the study area. The analysis of the rainfall-runoff variability allowed us to show the strong correlation between rainfall and runoff and the impact of the Nangbéto dam on the flows at Athiémé. The results obtained after the training, validation, and testing of the ANN models are satisfactory (e.g., the coefficient of correlation varies between 0.93 and 0.99). The most efficient model has been identified and implemented in the Mono river basin at Nangbéto. The satisfactory results obtained show that ANN models can be considered good alternatives for traditional rainfall-runoff modeling approaches.

Rainfall-runoff modeling using artificial neural networks in the Mono River basin (Benin, West Africa)

Hydrological models are developed to simulate river flows over a watershed for many practical applications in the field of water resource management. However, the rainfall-runoff models mostly used in the Mono river basin struggle to better simulate high river flows. This paper presents a modeling approach based on Artificial Neural Networks (ANN) under different input meteorological parameters in the Mono River basin to better take into account the non-linearity of the relationship between rainfall and runoff. To this end, precipitation, potential evapotranspiration, and previously observed flow have been used for the daily flow simulation. The Levenberg-Marquardt algorithm is used to train the ANN rainfall-runoff models over the other optimization training algorithms mostly implemented in the study area. The analysis of the rainfall-runoff variability allowed us to show the strong correlation between rainfall and runoff and the impact of the Nangbéto dam on the flows at Athiémé. The results obtained after the training, validation, and testing of the ANN models are satisfactory (e.g., the coefficient of correlation varies between 0.93 and 0.99). The most efficient model has been identified and implemented in the Mono river basin at Nangbéto. The satisfactory results obtained show that ANN models can be considered good alternatives for traditional rainfall-runoff modeling approaches.

The compound impact of rainfall, river flow and sea level on a watercourse through a coastal city: Methodology in making

Due to climate change, future weather conditions will become more extreme. During recent years, several severe damages have been caused by heavy rainfalls in combination with riverine events. Even though the effects of compound events are known to be influential for flood hazard, the method for investigating these types of events is a novel area of expertise. In this study, a methodology was developed to investigate a watercourse, acting as a part of a stormwater drainage system in an urban coastal area, in a hydrodynamic model to find areas prone to flooding. The method was applied for Ståstorpsån in Trelleborg, Sweden. The model was a unified model for seasonal variability and compound events with scenarios developed based on series of data representing normal values of the boundary conditions rainfall, river flow and sea level. The result was analysed graphically and statistically as a flood hazard. The data used was based on data collected during the past 10 years for rain and sea level and 16 years of simulated river flow. The constructed rain events from gauge data all had a return time of less than 10 years. Therefore, the chosen events are considered to represent normal levels. For Trelleborg, the results from the hydrodynamic model indicate that compound events will increase the flood hazard with anincreasing time horizon. The visual analysis converges with earlier flood events, and hotspots are generally seen around bridges and culverts. For the studied area, there is a large seasonal variation in the flood hazard and with climate change, all seasons will cause more severe flood hazards. The effects experienced during a summer event, which is the most severe event today, are to be expected for all seasons in 2100. The effect seen during summer eventsis a combination of all three drivers. However, rain intensity is likely to be more influential for normal events. When a certain threshold value for sea level is reached, sea level becomes the most influential driver, overtaking the other drivers in importance.

Evaluation of the Effect of Agricultural Return Flow on Water Quality, Water Quantity and Aquatic Ecology in Downstream Rivers

Agricultural water serves various functions, including public interest purposes, beyond its primary role in agricultural production. In order to evaluate the various public interest purposes of agricultural water, a quantified study of the effect of agricultural water on river flow, water quality, and aquatic ecosystems is needed. Therefore, this study quantified the impact of agricultural water on the environmental and ecological maintenance function of downstream rivers, taking into account the return flow of agricultural water in rural areas. To this end, first, the effect of agricultural return flow on river maintenance function was evaluated by comparing the return flow quantity calculated using the reservoir supply data with the simulated river flow rate through the SWAT model. Second, the effect of the agricultural return flow on the downstream river environmental ecological function was analyzed using the optimal flow rate results calculated through the PHABSIM model. The lastly, the effect of agricultural water by farming period on the water quality of downstream rivers was analyzed. As a result of the analysis, it was found that the return flow of agricultural water had a large effect on the river flow rate in the case of the non-rainy season, but the optimal ecological flow rate was not satisfied. In the case of river water quality, it was confirmed that the effect of agricultural water (mainly considered as a pollutant) was not significant, except for the drainage duration of rice paddies. Therefore, it can be understood that agricultural water is not only used for the purpose of production but can also have a positive impact on the aquatic ecology of downstream rivers.

Influence of large wood dynamics on flow and channel morphology in a forest stream

The riparian zone is a dynamic ecosystem formed by the interaction of aquatic and terrestrial components of rivers and serves as a source of the large wood (LW). We aimed to understand the LW dynamics and the LW influence on river morphology, sediment distribution, and flow velocity within three reaches of a low-order stream. The study was performed in a segment (770 m in length) of the Perdizes stream, located in Aparados da Serra National Park, southern Brazil. We framed the study by focusing three issues: i) the processes explaining the LW dynamics in the Araucaria Forest biome; ii) the effects of LW on the flow velocity along the study reaches, and iii) the presence of LW affecting the sediment longitudinal connectivity. The methodology consisted of: (i) collection of hydrological data (rainfall, discharge, and streamflow velocity), (ii) Perdizes stream characterization by topo-bathymetric survey and river-flow simulation with HEC-RAS 5.0.7 1D. and (iii) detailed field surveys of LW and bed sediments. The LW descriptions were performed at four moments (July 10, 2019; November 19, 2019; July 24, 2020; and December 18, 2020). Abundant LW, mainly from araucaria (Araucaria angustifolia (Bertol.) Kuntze), was observed before meanders. A notable reduction in LW volume and an increased proportion of araucaria, compared to deciduous trees, were observed within the channel. Mass balance analysis indicated fluctuations in riparian vegetation biomass, emphasizing the connectivity between vegetation and the river. Streamflow velocity decreased in LW presence, from occurrence to the margin. Bed sediment diameters were consistently smaller in cross-sections with LW presence. The accumulation of smaller sediments with LW indicated partial downstream sediment disconnectivity. HEC-RAS simulations confirmed increased LW deposition in lower velocity zones. This understanding of fluvial dynamics contributes to effective natural environment management, especially in the Araucaria Forest context.

Simulation of monthly river flow using SVR neural network improved with population-based optimization algorithms

Simulation of monthly river flow using SVR neural network improved with population-based optimization algorithms

Evaluation of Hydraulic Structures for Agricultural Discharge Optimization

The objective of the research was to evaluate the hydraulic structures on the Al-Gharraf River in southern Iraq and their ability to achieve the required discharge for agricultural areas that depend on them. Al-Gharraf head regulator discharges unstable volumes of water, ranging from 280 m3/s in winter to 100 m3/s in summer. The research aimed to determine whether the operational discharges are achievable for the offtakes branching from the Al-Gharraf River when the river's discharge ranges from 60% to 100% of the operational discharge. The researchers utilized a simulation of the irrigation channel (SIC) model to simulate river flow. The researchers used hydraulic indicators such as Delivery Performance Ratio (DPR), Discharge Deviation (∆Q), and Sensitivity (S) to evaluate the work of the hydraulic structures (regulators), determine the more and less efficient regulators in delivering water to the offtakes, and determine the reasons for inefficiency. Depending on the discharge values for each offtake from simulation results by SIC and calculating the hydraulic indicators, it is observed that some offtakes exceed their operational discharges, such as Al-Zydia and Al-Sabila. Also, some offtakes do not receive their operational discharge (Al-Dawaiya, Shatt Al-Shatra, and Al-Basra) projects, which failed to reach even 10% of their operational discharges. The researchers suggest redesigning some offtakes and ensuring reasonable control of gate openings for other offtakes to make the water distribution proportional. Doi: 10.28991/CEJ-2024-010-05-010 Full Text: PDF

Added value of seasonal hindcasts to create UK hydrological drought storylines

Abstract. The UK has experienced recurring periods of hydrological droughts in the past, including the drought declared in summer 2022. Seasonal hindcasts, consisting of a large sample of plausible weather sequences, can be used to create drought storylines and add value to existing approaches to water resources planning. In this study, the drivers of winter rainfall in the Anglian region in England are investigated using the ECMWF SEAS5 hindcast dataset, which includes 2850 plausible winters across 25 ensemble members and 3 lead times. Four winter clusters are defined using the hindcast winters based on possible combinations of various atmospheric circulation indices (such as the North Atlantic Oscillation, NAO; East Atlantic, EA, pattern; and El Niño–Southern Oscillation). Using the 2022 drought as a case study, we demonstrate how storylines representing alternative ways the event could have unfolded can be used to explore plausible worst-case scenarios over winter 2022/23 and beyond. The winter clusters span a range of temperature and rainfall response in the study region and represent circulation storylines that could have happened over winter 2022/23. River flow and groundwater level simulations with the large sample of plausible hindcast winters show that drier-than-average winters characterised by predominantly NAO−/EA− and NAO+/EA− circulation patterns could have resulted in the continuation of the drought with a high likelihood of below-normal to low river flows across all selected catchments and boreholes by spring and summer 2023. Catchments in Norfolk were particularly vulnerable to a dry summer in 2023 as river flows were not estimated to recover to normal levels even with wet winters characterised predominantly by NAO−/EA+ and NAO+/EA+ circulation patterns, due to insufficient rainfall to overcome previous dry conditions and the slow response nature of groundwater-dominated catchments. Through this analysis, we aim to demonstrate the added value of this approach to create drought storylines during an ongoing event. Storylines constructed in this way supplement traditional weather forecasts and hydrological outlooks, in order to explore a wider range of plausible outcomes.

ResORR: A globally scalable and satellite data-driven algorithm for river flow regulation due to reservoir operations

We propose a globally scalable algorithm, ResORR (Reservoir Operations driven River Regulation), to predict regulated river flow and tested it over the heavily regulated basin of the Cumberland River in the US. ResORR was found able to model regulated river flow due to upstream reservoir operations of the Cumberland River. Over a mountainous basin dominated by high rainfall, ResORR was effective in capturing extreme flooding modified by upstream hydropower dam operations. On average, ResORR improved regulated river flow simulation by more than 50% across all performance metrics when compared to a hydrologic model without a regulation module. ResORR is a timely software algorithm for understanding human regulation of surface water as satellite-estimated reservoir state is expected to improve globally with the recently launched Surface Water and Ocean Topography (SWOT) mission.

Effect of water movement in frozen soil on long-term hydrological simulation of Soil and Water Assessment Tool in cold climate watershed in Hokkaido, Japan

ABSTRACT Despite the unique hydrological properties exhibited by frozen soil, its characteristics are often oversimplified in most distributed hydrological models, resulting in major errors in material cycle predictions in cold climate regions. This study aims to modify the Soil and Water Assessment Tool (SWAT), a semi-distributed hydrological model that originally assumes no water movement in frozen soil layers, by incorporating the dynamic change of soil permeability based on the degree of soil freezing, and to understand the effect of water movement in frozen soil on watershed hydrology. SWAT with frozen soil permeability (SWAT-FSP) was developed by adding two modules to the original SWAT: a heat conduction equation that considers the phase change of ice and liquid water and a frozen-soil percolation model that considers dynamic decrease in soil permeability due to soil freezing. The original SWAT and SWAT-FSP were set up in the Tokoro River watershed in northern Japan. The river flow and soil water dynamics during the snowmelt period (March–May) simulated using the original SWAT and SWAT-FSP were compared. The modified model significantly alters the soil water dynamics simulation to generate earlier soil water drainage and reduces the soil water content during the snowmelt period in years featuring weak soil freezing. Consequently, the SWAT-FSP estimated an earlier increase in river flow at the beginning of the snowmelt period and a smaller river flow in the middle of the snowmelt period as compared with the original SWAT. The modified model improved the overall performance in river flow simulation for the snowmelt period (March–May) of 2012–2019. The Nash-Sutcliffe efficiency obtained during the calibration period by the original SWAT was 0.602, and during the validation period it was 0.367. For the SWAT-FSP, it was 0.659 during calibration, and 0.639 during validation. Meanwhile, the SWAT-FSP showed unsatisfactory performance in 2014 and 2015, with underestimated snow water equivalent, indicating that improvements in the snowmelt/accumulation module may further enhance its accuracy. Our results showed that the dynamic change of soil permeability governed by the degree of soil freezing is an important factor affecting the water cycle of cold climate watersheds with seasonally frozen soil.

Evaluating soil moisture simulations from a national-scale gridded hydrological model over Great Britain

Study RegionThe study covers sites across Great Britain. Study FocusSoil moisture information is important for a range of applications including flood and drought monitoring, seasonal hydrological forecasting, and agricultural management. However, spatially distributed soil moisture estimates for sub-surface soils are scarce despite their importance. The Grid-to-Grid hydrological model (G2G) was primarily developed to simulate river flows at a national scale, but can also output simulated depth-integrated soil moisture on a 1 km grid. Here, we evaluate G2G soil moisture simulations against in situ neutron probe (NP) observations at 85 sites across Great Britain, to evaluate whether modelled soil moisture outputs have value and to identify areas for improvement. New Hydrological Insights for the Region:Despite large uncertainties in observed soil moisture within a site, there was a good temporal correlation between observed and modelled soil moisture, with Pearson correlation values exceeding 0.7 for 77% of sites. However, the model tended to under-predict soil moisture values (median bias of −12 cm/m) and over-predict variation (median standard deviation error of 2 cm/m). Model agreement with observations was generally better for areas with deep or mid-depth mineral soils and worst in areas of peat. Based on this evaluation against NP observations, we demonstrate that G2G soil moisture is a useful resource for estimating relative wetness of the soil, but not necessarily the soil moisture content values themselves.

Historical climate impact attribution of changes in river flow and sediment loads at selected gauging stations in the Nile basin

The Nile basin is the second largest basin in Africa and one of the regions experiencing high climatic diversity with variability of precipitation and deteriorating water resources. As climate change is affecting most of the hydroclimatic variables across the world, this study assesses whether historical changes in river flow and sediment loads at selected gauges in the Nile basin can be attributed to climate change. An impact attribution approach is employed by constraining a process-based model with a set of factual and counterfactual climate forcing data for 69 years (1951–2019), from the impact attribution setup of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). To quantify the role of climate change, we use the non-parametric Mann-Kendall test to identify trends and calculate the differences in long-term mean annual river flow and sediment load simulations between a model setup using factual and counterfactual climate forcing data. Results for selected river stations in the Lake Victoria basin show reasonable evidence of a long-term historical increase in river flows (two stations) and sediment load (one station), largely attributed to changes in climate. In contrast, within the Blue Nile and Main Nile basins, there is a slight decrease of river flows at four selected stations under factual climate, which can be attributed to climate change, but no significant changes in sediment load (one station). These findings show spatial differences in the impacts of climate change on river flows and sediment load in the study area for the historical period.

Water and carbon balances in a hemi-boreal forest

Abstract The carbon and water fluxes and their inter-relations are key aspects of ecosystem dynamics. In this study, regionalization was used in transferring parameters from the GR4J-Cemaneige model calibrated in Reola hydrographic basin to predict daily flows in Kalli basin; both watersheds are located in the southeast of Estonia. Evapotranspiration data was collected from the MODIS sensor of the Terra satellite and from the Station for Measuring Ecosystem-Atmosphere Relations (SMEAR Estonia). Precipitation data was collected from Tartu–Tõravere and SMEAR Estonia stations and river flow from Reola hydrometric station. The year 2011 was used for model warm-up, model calibration was done in 2012–2017 and the 2018–2020 period was used for validation. The GR4J-Cemaneige model was calibrated at Reola Basin, with a Nash-Sutcliffe Efficiency index of 0.73. The 6 constants of Reola subbasin were transferred to Kalli subbasin for river flow simulation. Net ecosystem exchange (NEE) was measured at the 70 m SMEAR tower with the eddy covariance technique. The balances indicate that the ecosystem at Kalli watershed is slowly becoming a source of carbon and less water is available at the catchment reservoir. NEE has increased from -1.23 μmol m-2 s-1 in 2015 to -0.62 μmol m-2 s-1 in 2020, while the delta water storage decreased from 0.24 mm in 2015 to -0.05 mm in 2020. This behavior may increase soil drying and oxidation, and it will probably release more carbon in the future. This research allows a better understanding of the Järvselja hemi-boreal forest water-carbon dynamics.

Assimilating Soil Moisture Information to Improve the Performance of SWAT Hydrological Model

The present work aims to highlight the possibility of improving model performance by assimilating soil moisture information in the calibration and validation process. The Soil and Water Assessment Tool (SWAT) within QGIS, i.e., QSWAT, was used to simulate the hydrological processes within the test basin, i.e., Vosvozis River Basin (VRB) in NE Greece. The model calibration and validation were conducted via SWAT-CUP for a four-year period from 2019 to 2022, in three different ways, i.e., using the traditional calibration process with river flow measurements, using satellite-based soil moisture only in the calibration, and finally incorporating satellite-based soil moisture datasets and calibrating using simultaneously flow and soil moisture information. All modeling approaches used the same set of input data related to topography, land cover, and soil information. This study utilized the recently released global scale daily downscaled soil moisture at 1 km from the Soil Moisture Active Passive (SMAP) mission to generate soil moisture datasets. Two performance indicators were evaluated: Nash Sutcliffe (NS) and coefficient of determination (R2). Results showed that QSWAT successfully simulated river flow in VRB with NS = 0.61 and R2 = 0.69 for the calibration process using river flow measurements at the outlet of VRB. However, comparing satellite-based soil moisture, NS and R2 were considerably lower with an average derived from the 19 subbasins (NS = 0.55, R2 = 0.66), indicating lower performance related to the simulation of soil moisture regime. Subsequently, introducing satellite-derived soil moisture as an additional parameter in the calibration process along with flow improved the acquired average soil moisture results of the 19 subbasins (NS = 0.85, R2 = 0.91), while preserving the satisfactory performance related to flow simulation (NS = 0.57, R2 = 0.66). Our work thus demonstrates how assimilating available satellite-derived soil moisture information into the SWAT model may offer considerable improvement in the description of soil moisture conditions, keeping the satisfactory performance in flow simulation.

Cascaded-ANFIS to simulate nonlinear rainfall–runoff relationship

Hydrologic models require atmospheric, dynamic and static models to simulate river flow from catchments. Thus the accuracy of hydrologic modelling highly depends on the data quality. Therefore, simulation is always challenging in data-scarcity environments. In addition, physical flow measurements are infeasible in the Spatiotemporal domain, and soft computing techniques are helpful in river flow simulation in data-scarcity environments. In this research paper, an efficient and accurate Cascaded-ANFIS-based model for rainfall–runoff was proposed and evaluated using five case studies in three countries: Japan, Vietnam, and Sri Lanka. The investigation focused on predicting streamflow by the influence of past data, with each river’s dataset examined to determine the best configuration of past rainfalls affecting streamflow volume. The proposed algorithm was compared against six state-of-the-art regression algorithms. The results showed that it outperformed the other algorithms in every case study except the Kalu River dataset, with zero bias calculated. The developed R-R model can be considered a generic model for streamflow prediction in data-scarcity environments, with excellent acceptability of simulated river flows against measured river flows observed across different geographic and climatic regions.

Application of SWAT and WEAP Models for Sustainable Management of Water Resources in the Two Rivers Dam Catchment, Uasin Gishu County, Kenya

Kaptagat Forest, the main source of the Ellegerini River, which feeds the Ellegerini Dam and Two Rivers Dam, is under threat of extinction due to human activity. The Two Rivers Dam catchment that has over the years been a source of water in Uasin Gishu County is slowly depleting and urgent measures are required to restore it. Activities including commercial logging, charcoal burning and firewood harvesting have exerted a lot of pressure on the catchment, posing a great threat to the livelihoods of the people of Eldoret town who depend on the reservoirs for water supply. The main objective of this study was to customise SWAT and WEAP models for the sustainable management of water resources in the Two Rivers Dam catchment. The specific objectives were to set up and apply the SWAT model to generate simulated river flows draining to the Two Rivers and Ellegerini Reservoirs as an input to the WEAP model to determine the impact of land use change on the hydrological function of the Two Rivers Dam catchment, to set up, calibrate and validate a WEAP model for the Two Rivers Dam catchment and to apply the WEAP model in analyses of various management and infrastructural development projects scenarios to enhance river flow and water storage in the Two Rivers and Ellegerini Reservoirs. The goodness of fit SWAT model statistical evaluation indices attained during the calibration period was R2 = 0.854, NSE = 0.822 and Bias = 0.392. Additionally, for the validation period, the R2 = 0.786, NSE = 0.815 and Bias = 0.381. The modelled results indicate that the land use change resulted in decreased baseflow and increased surface runoff hence the high fluctuations of water levels in the Two Rivers and Ellegerini reservoirs. The WEAP model results for actual and simulated water demand in the calibration period of 2019, the R2 = 0.88, while during the validation period in the year 2020, the R2 = 0.85. The results of the model simulation indicated that the management option that had the most impact on all the scenarios was the reduction of unaccounted-for water, while the one with the least impact was increased water use efficiency. It was concluded that the models were able to simulate the observed conditions reasonably well and can therefore be used to effectively manage water resources and assist the relevant stakeholders in decision-making. The study recommended that forested areas need to be properly conserved in order to restore the hydrological function of the catchment.