Effects of cross-section on infiltration and seepage in permeable stormwater channels

Factors affecting the infiltration rate have been studied fairly well by many researches; however, the effects of the cross-section of a permeable stormwater channel on the surface water depth reduction due to infiltration and seepage have largely been neglected. In the present study, towards improving the efficiency of permeable channels, the effects of the three components of a trapezoidal section, namely, the water depth, side slope, and base width, on the infiltration and unsteady seepage rates were investigated. Laboratory studies using models of the channel with unsaturated soil were performed under ponding condition using various initial water levels, base widths, and side slopes for two soil textures, namely, sandy loam and loamy sand. The results showed that the rate of surface water depth reduction by infiltration and seepage increases with increasing water level irrespective of the base width and side slope. In addition, an increase of the side slope increases the infiltration rate, with the effect becoming more significant with increasing initial water level, while the effect of varying the base width is insignificant

Design and analysis of a canal section for minimum water loss

Seepage and evaporation are the most serious forms of water loss in an irrigation canal network. Seepage loss depends on the channel geometry, while evaporation loss is proportional to the area of free surface. In this paper, a methodology to determine the optimal canal dimensions for a particular discharge is developed. The nonlinear water loss function, for the canal, which comprises seepage and evaporation loss, was derived. Two constraints (minimum permissible velocity as a limit for sedimentation and maximum permissible velocity as a limit for erosion of canal) have been taken into consideration in the canal design procedure. Using Lagrange’s method of undetermined multipliers, the optimal canal dimensions were obtained for minimum water loss. A computer program was developed to carry out design calculation for the optimal canal dimensions. The results are plotted in form of a set of design charts. The proposed charts facilitate easy design of the optimal canal dimensions guaranteeing minimum water loss. Water loss from the canal section can be estimated from these charts without going through the conventional and cumbersome trial and error method. Sensitivity analysis had been included to demonstrate the impact of important parameters

Runoff forecasting by artificial neural network and conventional model

Rainfall runoff models are highly useful for water resources planning and development. In the present study rainfall–runoff model based on Artificial Neural Networks (ANNs) was developed and applied on a watershed in Pakistan. The model was developed to suite the conditions in which the collected dataset is short and the quality of dataset is questionable. The results of ANN models were compared with a mathematical conceptual model. The cross validation approach was adopted for the generalization of ANN models. The precipitation used data was collected from Meteorological Department Karachi Pakistan. The results confirmed that ANN model is an important alternative to conceptual models and it can be used when the range of collected dataset is short and data is of low standard

Future Predictions of Rainfall and Temperature Using GCM and ANN for Arid Regions: A Case Study for the Qassim Region, Saudi Arabia

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

Evaluation of a Low-Cost Ceramic Filter for Sustainable Reuse of Urban Stormwater in Arid Environments

Sustainable reuse of urban stormwater is inevitable in the fight against water crises in arid regions. This research aimed to evaluate the effectiveness of a low-cost ceramic filtration process for reuse applications of urban stormwater. Stormwater was collected from a storage pond located in Buraydah (Qassim, Saudi Arabia) for laboratory experiments. The filtration tests were performed in a continuous mode with constant pressure using a low-cost ceramic filter made of clay soil and rice bran. The removal rates of the contaminants (heavy metals) as well as the turbidity, suspended solids, and nutrients of the stormwater were assessed. High removal efficiencies for turbidity (97.4%), suspended solids (97.0%), BOD5 (78.4%), and COD (76.1%) were achieved while low removals were achieved for the nutrients: 19.7% for total nitrogen, 25.3% for nitrate, and 8.6% for phosphate. Removal efficiencies ranged between 36.2% and 99.9% for the heavy metals, i.e., iron, manganese, lead, zinc, nickel, copper, cadmium, selenium, and barium. Contaminant removal rates observed for the ceramic filter were also compared with the alum coagulation process operated in a continuous mode at an optimum alum dose of 50 mg/L. Similar removal behaviors for removal of turbidity, suspended solids, organics, nutrients, and heavy metals suggested that both ceramic filtration and alum coagulation can be effectively used for stormwater treatment. Effluent qualities of both the ceramic filter and alum coagulation met the standards, for recycling/reuse of wastewater, set by the Kingdom of Saudi Arabia and World Health Organization for unrestricted irrigation and toilet flushing. The study results revealed that ceramic filtration is a low-cost, energy efficient, and easy to maintain technology which can be complimentary to best management practices for stormwater

Identification of Parameters of Evaporation Equations Using an Optimization Technique Based on Pan Evaporation

Countries in arid regions are presently facing challenges in managing their limited water resources. Assessing the evaporation losses from various sources of water is a daunting task that is inevitable for the sustainability of water resource management schemes in these regions. Although several techniques are available for simulating evaporation rates, identifying the parameters of various evaporation equations still needs to be further investigated. The main goal of this research was to develop a framework for determining the parameters influencing the evaporation rate of evaporation pans. Four different equations, including those of Hamon, Penman, Jensen–Haise, and Makkink, were chosen to estimate evaporation from the evaporation pans installed in the Qassim Region of Saudi Arabia. The parameters of these four equations were identified by a state-of-the-art optimization technique, known as the general reduced gradient (GRG). Three types of objective functions used for optimization were tested. Forty-year monitoring records for pan evaporation, temperature, relative humidity, and sunshine hours were collected from the Municipality of Buraydah Al Qassim, for the period of 1976 to 2016. These data were mainly manually recorded at a weather station situated in the Buraydah city. Preliminary data analysis was performed using the Mann–Kendall and Sen’s slope tests to study the trends. The first 20-year (1976–1995) data were used for calibrating the equations by employing an optimization technique and the remaining data were used for validation purposes. Four new equations were finally developed and their performance, along with the performance of the four original equations, was evaluated using the Nash and Sutcliffe Efficiency (NSE) and the Mean Biased Error (MBE). The study revealed that among the original equations, the Penman equation performed better than the other three equations. Additionally, among the new equations, the Hamon method performed better than the remaining three equations

Sustainability Evaluation Framework of Urban Stormwater Drainage Options for Arid Environments Using Hydraulic Modeling and Multicriteria Decision-Making

Stormwater drainage systems in urban areas located in arid environmental regions generally consist of storm-sewer networks and man-made ponds for the collection and disposal of runoff, respectively. Due to expansion in cities’ boundaries as a result of population growth, the capacity of existing drainage systems has been exhausted. Therefore, such systems overflow even during the smaller (than the design) return period floods. At the same time, changing rainfall patterns and flash floods due to climate change are other phenomena that need appropriate attention. Consequently, the municipalities in arid environmental regions are facing challenges for effective decision-making concerning (i) improvement needs for drainage networks for safe collection of stormwater, (ii) selection of most feasible locations for additional ponds, and (iii) evaluation of other suitable options, such as micro-tunneling. In this research, a framework has been developed to evaluate different stormwater drainage options for urban areas of arid regions. Rainfall-runoff modeling was performed with the help of Hydrological-Engineering-Centre, Hydrological-Modelling-System (HEC-HMS). To evaluate the efficacy of each option for handling a given design flood, hydraulic-modeling was performed using SewerGEMS. Meteorological and topographical data was gathered from the Municipality of Buraydah and processed to generate different inputs required for hydraulic modeling. Finally, multicriteria decision-making (MCDM) was performed to evaluate all the options on the basis of four sustainability criteria, i.e., flood risk, economic viability, environmental impacts, and technical constraints. Criteria weights were established through group decision-making using the Analytic Hierarchy Process (AHP). Preference-Ranking-Organization-Method for Enrichment-Evaluation (PROMETHEE II) was used for final ranking of stormwater drainage options. The proposed framework has been implemented on a case of Buraydah City, Qassim, Saudi Arabia, to evaluate its pragmatism. Micro-tunnelling was found to be the most sustainable option

Simulation of Pan-Evaporation Using Penman and Hamon Equations and Artificial Intelligence Techniques

The evaporation losses are very high in warm-arid regions and their accurate evaluation is vital for the sustainable management of water resources. The assessment of such losses involves extremely difficult and original tasks because of the scarcity of data in countries with an arid climate. The main objective of this paper is to develop models for the simulation of pan-evaporation with the help of Penman and Hamon’s equations, Artificial Neural Networks (ANNs), and the Artificial Neuro Fuzzy Inference System (ANFIS). The results from five types of ANN models with different training functions were compared to find the best possible training function. The impact of using various input variables was investigated as an original contribution of this research. The average temperature and mean wind speed were found to be the most influential parameters. The estimation of parameters for Penman and Hamon’s equations was quite a daunting task. These parameters were estimated using a state of the art optimization algorithm, namely General Reduced Gradient Technique. The results of the Penman and Hamon’s equations, ANN, and ANFIS were compared. Thirty-eight years (from 1980 to 2018) of manually recorded pan-evaporation data regarding mean daily values of a month, including the relative humidity, wind speed, sunshine duration, and temperature, were collected from three gauging stations situated in Al Qassim, Saudi Arabia. The Nash and Sutcliffe Efficiency (NSE) and Mean Square Error (MSE) evaluated the performance of pan-evaporation modeling techniques. The study shows that the ANFIS simulation results were better than those of ANN and Penman and Hamon’s equations. The findings of the present research will help managers, engineers, and decision makers to sustainability manage natural water resources in warm-arid regions

Risk-Based Inspection and Rehabilitation Planning of Service Connections in Intermittent Water Supply Systems for Leakage Management in Arid Regions

Most of the leakage in water distribution systems operating with plastic pipes occurs at service connections (SCs), while the existing tools plan rehabilitation of pipes. With limited water resources, intermittent supplies in arid regions further enhance the failure vulnerability of metal fittings on water mains due to scale formation and large pressure transients. The present research developed a risk-based methodology for the proactive maintenance of SCs in intermittent water supply systems. A five-generation bottom-up hierarchical approach aggregated the basic hydraulic, physical, and water quality factors to determine the vulnerability of structural failures of SCs. Hydraulic parameters (pressure and velocity) were estimated by simulating a distribution network of 366 water mains of diameters ranging from 110 mm to 225 mm serving 371 SCs in a residential neighborhood located in the Qassim region of Saudi Arabia. Age, depth, and length of SCs’ estimated the condition index, while soil corrosivity and condition of the water mains were also counted when assessing the structural failure index for each SC. Water quality parameters, e.g., pH, turbidity, and iron, that can contribute to the vulnerability of an SC’s failure were also included. Fuzzy-based methods first assessed the relative importance weights of the basic input parameters at the bottom of the hierarchy and the risk factors in the middle of the hierarchy. Subsequently, the performance and condition scores were aggregated to develop respective indices. As the consequence of structural failure is high for the SCs serving households with a large number of residents, the final risk index aggregates the vulnerability and consequence at the hierarchy’s top. The developed model was effectively validated by comparing the SCs of high priority with the leaking and repaired SCs in the past. The method will be a useful tool for planning proactive inspection and rehabilitation of SCs of intermittent supply systems to minimize water losses (less than 8% of the national benchmark) in Saudi Arabia and elsewhere