The objective of the study was to evaluate the performance of three different aquatic macrophytes for treatment of municipal wastewater collected from Taxila (Pakistan). A physical model of treatment plant was constructed and was operated for six experimental runs with each species of macrophyte. Every experimental run consist of thirty days period. Regular monitoring of influent and effluent concentrations were made during each experimental run. For the treatment locally available macrophyte species i.e. water hyacinth, duckweed & water lettuce were selected to use. To evaluate the treatment performance of each macrophyte, BOD5, COD, and Nutrients (Nitrogen and Phosphorus) were monitored in effluent from model at different detention time of every experimental run after ensuring steady state conditions. The average reduction of effluent value of each parameter using water hyacinth were 50.61% for BOD5, 46.38% for COD, 40.34% for Nitrogen and 18.76% for Phosphorus. For duckweed the average removal efficiency for selected parameters were 33.43% for BOD5, 26.37% for COD, 17.59% for Nitrogen and 15.25% for Phosphorus and for Water Lettuce the average removal efficiency were 33.43% for BOD5, 26.37% for COD, 17.59% for Nitrogen and 15.25% for Phosphorus. The mechanisms of pollutant removal in this system include both aerobic and anaerobic microbiological conversions, sorption, sedimentation, volatilization and chemical transformations. The rapid growth of the biomass was measured within first ten days detention time. It was also observed that performance of macrophytes is influenced by variation of pH and Temperature. A pH of 6-9 and Temperature of 15-38°C is most favorable for treatment of wastewater by macrophytes. The option of macrophytes for treatment of Municipal sewage under local environmental conditions can be explored by further verifying the removal efficiency under variation of different environmental conditions. Also this is need of time that macrophyte system should be used for treatment of wastewater because their performance is comparable to conventional wastewater treatment plants and also the system has very low O&M costs.

Previous experimental research on utilizing pier modifications as countermeasures against local scour has focused primarily on circular pier. It is of utmost importance to further investigate the most suitable pier shape for pier modification countermeasure separately and in combination. This experimental study aims to reduce the stagnation of the flow and vortex formation in front of the bridge pier by providing a collar, a hooked collar, a cable, and openings separately and in combination around a suitable pier shape. Therefore, six different pier shapes were utilized to find out the influence of pier shape on local scouring for a length–width ratio smaller than or equal to 3. A plain octagonal shape was shown as having more satisfactory results in reducing scour compared to other pier shapes. Furthermore, the efficiency of pier modification was then evaluated by testing different combinations of collar, hooked collar, cable, and openings within the octagonal bridge pier, which was compared to an unprotected octagonal pier without any modification. The results show that by applying such modifications, the scour depth reduced significantly. The best combination was found to be a hooked collar with cable and openings around an octagonal pier. It was revealed that the best combination reduced almost 53% of scour depth, as compared to an unprotected octagonal pier.

As a major component of the hydrologic cycle, rainfall runoff plays a key role in water resources management and sustainable development. Conceptual models of the rainfall-runoff process are governed by parameters that can rarely be directly determined for use in distributed models, but should be either inferred through good judgment or calibrated against the historical record. Artificial neural network (ANN) models require comparatively fewer such parameters, but their accuracy needs to be checked. This paper compares a Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS) conceptual model and an ANN model based on the conjugate gradient method for streamflow prediction. Daily precipitation, temperature, and streamflow data of the Upper Indus River for a period of 20 years (1985–2004) are used as input for calibrating in the case of the HEC-HMS, and for training in case of the ANN. Ten years of data (2005–2014) are used to validate the HEC-HMS model and test the ANN. The performance of the models is assessed using different statistical indicators such as the Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean bias error (MBE), and the coefficient of determination (R²). The results show good predictions for streamflow in the case of both HEC-HMS and ANN models. A parametric study is conducted using Monte Carlo analysis and finds that the most important parameters for HEC-HMS models are the storage coefficient and the time of concentration; while for ANN models, input combinations are the most important. This study investigates the sensitivity of these parameters, which can be used to determine preliminary estimation ranges of their values for future modeling. Finally, evaluating the impact of the simulated streamflow’s accuracy on the flow duration curve shows that the curve is significantly affected by any streamflow simulation inaccuracy.

Future predictions of rainfall patterns in water-scarce regions are highly important for effective water resource management. Global circulation models (GCMs) are commonly used to make such predictions, but these models are highly complex and expensive. Furthermore, their results are associated with uncertainties and variations for different GCMs for various greenhouse gas emission scenarios. Data-driven models including artificial neural networks (ANNs) and adaptive neuro fuzzy inference systems (ANFISs) can be used to predict long-term future changes in rainfall and temperature, which is a challenging task and has limitations including the impact of greenhouse gas emission scenarios. Therefore, in this research, results from various GCMs and data-driven models were investigated to study the changes in temperature and rainfall of the Qassim region in Saudi Arabia. Thirty years of monthly climatic data were used for trend analysis using Mann–Kendall test and simulating the changes in temperature and rainfall using three GCMs (namely, HADCM3, INCM3, and MPEH5) for the A1B, A2, and B1 emissions scenarios as well as two data-driven models (ANN: feed-forward-multilayer, perceptron and ANFIS) without the impact of any emissions scenario. The results of the GCM were downscaled for the Qassim region using the Long Ashton Research Station’s Weather Generator 5.5. The coefficient of determination (R²) and Akaike’s information criterion (AIC) were used to compare the performance of the models. Results showed that the ANNs could outperform the ANFIS for predicting long-term future temperature and rainfall with acceptable accuracy. All nine GCM predictions (three models with three emissions scenarios) differed significantly from one another. Overall, the future predictions showed that the temperatures of the Qassim region will increase with a specified pattern from 2011 to 2099, whereas the changes in rainfall will differ over various spans of the future.

Municipalities in the Kingdom of Saudi Arabia (KSA) are putting serious efforts into reducing the water losses to around 8% (i.e., national benchmark) of supplied water through active leakage control (ALC). In this research, a framework is conceptualized and developed to establish economic level of leakage (ELL), keeping in view the specific conditions of arid regions. In general, water loss control actions, including ALC, passive leakage control, pressure management, and infrastructure asset management, are performed by the municipalities up to the service connections until the cost of these actions becomes equal to the cost of the water lost. For the explicit scenario of KSA, the framework also investigates the possibilities of water losses beyond the property line where water is intermittently supplied for a certain duration in a week and stored in underground tanks to ensure a continuous supply within the house. Night flow analyses were conducted to establish the relationship between pressure and water losses. The framework is implemented on a case study in the Alryan neighborhood of Buraydah (Qassim, KSA) to evaluate its practicality. Scenario analyses are conducted to appraise the economic viability of different management actions for establishing ELL. The study results reveal that conventional ALC can reduce the infrastructure leakage index from around 20 to 7.28, and an additional 43% decrease can be economically achieved by implementing pressure management and control measures on the homeowner side. Environmental valuation analysis demonstrates that upgrading the effectiveness of ALC with a small increase in water prices could further improve the ELL.