Bahram Gharabaghi

Sediment-Removal Efficiency of Vegetative Filter Strips

Field experiments on vegetative filter strips (VFS) showed average sediment-removal efficiency varied from 50 to 98% as flowpath length increased from 2.44 to 19.52 m. Almost all of the easily removable aggregates (i.e. aggregates larger that 40 mm in diameter) can be captured within the first five meters of the filter strip. However, the remaining small-size aggregates are very difficult to remove by filtering flow through grass media, as even relatively low levels of turbulent energy in the water is sufficient to keep the finer sediments in suspension. The only effective mechanism for removal of small-size sediments is infiltration. Experiments with appreciable infiltration (low to moderate flow rates on the longer plot lengths), showed removal efficiencies of 90% or higher. The sediment-removal efficiency of the filter strip does not increase much by increasing the width of the filter strip beyond ten meters. Improved efficiency of VFS can be achieved through the installation of a drainage system to increase infiltration.

Assessment of the Contributions of Traditional Qanats in Sustainable Water Resources Management

Qanats (Kanats) have been an ancient, sustainable system facilitating the harvesting of water for centuries in Iran, and more than 34 additional countries of the world. These subterranean channels have been used for the transference of snowmelt water from the mountainous terrain for thousands of years. Agricultural, industrial and urban demands for fresh water have brought about increasing demands for water, the elixir of life. In response, the harvesting of water via deep groundwater wells throughout arid zones has disturbed the aquifers, and resulted in the abandonment of some qanats. Qanats in the province of Yazd City are witnessing this depletion. This paper introduces qanats, objectives causing their creation, construction materials, locations and their importance in different times, as well as their present role in Iran. Further, current qualitative and quantitative analyses of the qanats in Tehran are identified.

Predicting the Timing of Water Main Failure Using Artificial Neural Networks

AbstractEffective management of aging water distribution infrastructure is essential for preserving the economic vitality of North American municipalities. Historical failures within Scarborough, Ontario, Canada, reveal a seasonal pattern to water main failures, with the majority of failures occurring during the very cold winter months. Extensive installation of cement mortar lining and cathodic protection have extended the life span of aging water mains and reduced escalating failure rates. Artificial neural networks are found to be capable of predicting the time to failure for individual pipes using a range of pipe-specific attributes, including diameter, length, soil type, construction year, and the number of previous failures. The developed models have correlation coefficients ranging from 0.70–0.82 on instances reserved for evaluating predictive performance and have utility on an asset-by-asset basis when planning water main inspection, maintenance, and rehabilitation. Simulated failure scenarios ind...

Evaluation of Weather Generator ClimGen for Southern Ontario

Modelling of soil water systems has become an important tool for water quality management and source water protection. Application of such models requires long-term continuous weather data from several weather stations distributed across the watershed. However, historic weather data often have missing records, large gaps in data or the length of the record may not be sufficient to include flood or drought conditions of interest. This has led to the development and use of weather generation procedures and tools. In this paper, the weather generator ClimGen has been evaluated for generation of daily precipitation, air temperature, solar radiation, wind speed, and relative humidity for southern Ontario conditions. The comparison of simulated weather data with 30 years of weather data for six stations indicated that ClimGen performed with reasonable accuracy with some limitations in generating rainfall intensities and solar radiation, particularly for the winter months.

Application of Weather Radar in Estimation of Bulk Atmospheric Deposition of Total Phosphorus Over Lake Simcoe

The decline of Lake Simcoe water quality has been attributed to high phosphorus inputs that result in excessive algae and macrophyte growth subsequently contributing to end-of-summer hypolimnetic dissolved oxygen depletion and loss of fish habitat. Out of the estimated 53 to 67 tonne/annum (1998 to 2004 water years) of phosphorus entering the lake, atmospheric deposition is believed to be responsible for 16 to 38 tonne/annum. Historical estimates for atmospheric deposition involved averaging rain gauge (rainfall depth) and rain quality (phosphorus concentration) station data. Through use of this procedure, any spatial variability in the data (quality and quantity) is lost as each gauge is given an equal weighting. This study proposes a methodology to use Next Generation Radar (NEXRAD) to spatially represent rainfall data and a method to correct radar-rainfall estimates to rainfall recorded by local rain gauges. From this analysis it was found that the radar generally represented localized rainfall well, with the majority of correlation coefficients (R2) being over 0.90. Radar related issues that resulted in poor R2 values included virga, overshooting beam, beam attenuation, range related issues and ground clutter. For large bulk atmospheric total phosphorus (TP) deposition events the dominant parameter in calculating TP loads was rainfall depth. Results from this analysis demonstrated a large (−88% to +44%) difference between historical and revised estimates of bulk atmospheric deposition of phosphorus over Lake Simcoe.

Urban stormwater thermal gene expression models for protection of sensitive receiving streams

Thermal impact of typical high-density residential, industrial, and commercial land uses is a major concern for the health of aquatic life in urban watersheds, especially in smaller, cold and cool-water streams. This is the first study of its kind that provides simple easy-to-use equations, developed using gene expression programming (GEP), that can guide the assessment and design of urban stormwater management systems to protect thermally sensitive receiving streams. We developed three GEP models using data collected during three years 2009-2011 from four urban catchments; the first GEP model predicts event mean temperature at the inlet of the pond; the second model predicts the stormwater temperature at the outlet of the pond; and the third model predicts the temperature of the stormwater after flowing through a cooling trench and before discharging to the receiving stream. The new models have high correlation coefficients of 0.90-0.94 and low prediction uncertainty of less than 4% of the median value of the predicted runoff temperatures. Sensitivity analysis shows that climatic factors have the highest influence on the thermal enrichment followed by the catchment characteristics and the key design variables of the stormwater pond and the cooling trench. The general method presented here is easily transferable to other regions of the world (but not necessarily the exact equations developed here); also through sensitivity and parametric analysis we gained insight on the key factors and their relative importance in modelling thermal enrichment of urban stromwater runoff.

Pollutants Removal by Vegetative Filter Strips Planted with Different Grasses

Over the last few years, increasing occurrence of deadly pathogens and presence of various pollutants (nutrients, pesticides, other chemicals, and sediments) above the prescribed limit in water systems, clearly indicate alarmingly deteriorating quality of water resources. As a result, farming systems that are known to be the main non-point or diffuse pollution source are being reviewed microscopically. Vegetative Filter Strip (VFS) is considered to be one of the best management practices (BMPs) for effective control sediment and nutrient transport over agricultural lands. Many laboratory and field scale studies have also indicated the limited usefulness of VFS to control movement of bacteria in surface runoff. However, design of VFS under field conditions still remains a challenge due to variation in upland hydrological parameters and factors effecting movement of pollutants through VFS such as type of vegetation cover and density, width of strip, and land slope. Determination of trapping efficiency of VFS for bacteria is more complex due to the complex interaction of various factors governing the die-of and re-growth of bacteria under field condition, and release of bacteria from soil reserve. An extensive field experiment is being conducted at the research farm of University of Guelph in Southern Ontario, Canada, to evaluate to effectiveness of VFS under different vegetation cover, ground slope, width of filter strip, and in various seasons. Concentration of sediment reduced an average by 88.3% and almost 94.3% sediment mass was trapped in various filter strips. Higher trapping efficiencies for mass were observed for sediment bound nutrients (94.5% and 93.9% for N and P, respectively) compared to soluble forms (57.0% and 77.3% for N and P, respectively). Results for bacteria (Total Coliforms, Fecal Coliforms, and E. Coli) through VFSs were encouraging but not conclusive. In the present paper, experiment and results of the study are presented and discussed in details.

Prediction of Incipient Breaking Wave-Heights Using Artificial Neural Networks and Empirical Relationships

The accurate prediction of shallow water breaking heights is paramount to better understanding complex nonlinear near shore coastal processes. Over the past 150 years, numerous empirical relationships have been proposed based on scaled laboratory datasets. This study utilizes a newly available field collected full-scale dataset of breaking wave conditions to investigate the accuracy of published empirical models and a novel artificial neural networks (ANN) model in predicting the final breaking wave-height for laboratory-scaled and full-scaled ocean waves. Performance is measured by comparison against both the field datasets and 465 separate datasets from 11 independent laboratory studies. The relationship of Rattanapitikon and Shibayama [2000 “Verification and modification of breaker height formulas,” Coastal Eng. J. 42 (4), 389–406.] outperformed all available empirical models when tested against only laboratory datasets, but was superseded by the relationship of Robertson et al. [2015 “Remote sensing of irregular breaking wave parameters in field conditions,” J. Coastal Res. 31 (2), 348–363.] when tested against only field datasets. However, this study noted that models developed based on scaled laboratory tests tend to underestimate the ocean full-scale breaking wave-heights. The training and testing of the ANN model were accomplished using 75% and 25% of the combined field and laborartory datasets. The ANN models consistently outperformed predictive accuracy of empirical models. Sensitivity analysis of the trained ANN models quantified the relative impact of individual wave parameters on the final breaking wave-height.

Predicting Saturated Hydraulic Conductivity by Artificial Intelligence and Regression Models

Saturated hydraulic conductivity (), among other soil hydraulic properties, is important and necessary in water and mass transport models and irrigation and drainage studies. Although this property can be measured directly, its measurement is difficult and very variable in space and time. Thus pedotransfer functions (PTFs) provide an alternative way to predict the from easily available soil data. This study was done to predict the in Khuzestan province, southwest Iran. Three Intelligence models including (radial basis function neural networks (RBFNN), multi layer perceptron neural networks (MLPNN)), adaptive neuro-fuzzy inference system (ANFIS) and multiple-linear regression (MLR) to predict the were used. Input variable included sand, silt, and clay percents and bulk density. The total of 175 soil samples was divided into two groups as 130 for the training and 45 for the testing of PTFs. The results indicated that ANFIS and RBFNN are effective methods for prediction and have better accuracy compared with the MLPNN and MLR models. The correlation between predicted and measured values using ANFIS was better than artificial neural network (ANN). Mean square error values for ANFIS, ANN, and MLR were 0.005, 0.02, and 0.17, respectively, which shows that ANFIS model is a powerful tool and has better performance than ANN and MLR in prediction of .

Better management of construction sites to protect inland waters

Abstract Several areas within the Lake Simcoe watershed, Canada, are experiencing rapid urban development. The construction of new homes and businesses is frequently associated with elevated rates of soil erosion stemming from land clearing and grading activities. During development, rates of soil erosion can climb to levels that are typically 200 times above background conditions, with the eroded sediments entering waterways and causing harm to the biota living therein. This is a serious challenge for the communities around Lake Simcoe because the transport of sediment has previously been identified as a contributor to the eutrophication of the lake’s waters. To mitigate the negative impacts associated with development, many jurisdictions across North America and elsewhere have developed a suite of construction-phase stormwater management (CPSWM) guidelines, which entail the use of onsite best management practices that capture, detain, and treat sediment-laden surface runoff. Here, we review CPSWM guidelines for effluent discharge and receiving water quality and discuss the relative strengths and weaknesses of each approach. Finally, proposed revisions to the current Ontario guidelines are suggested based on a combination of field observations at predevelopment and active construction sites, as well as the reviewed literature. If adopted, the proposed revisions would help to reduce sediment transport from construction sites in rapidly urbanizing areas such as Lake Simcoe.