Jianxun He

Characterizing Physicochemical Quality of Storm-Water Runoff from an Urban Area in Calgary, Alberta

Understanding storm-water runoff quality is required to develop effective urban storm-water runoff management for regions of semiarid climate. In this study, the quality of storm-water runoff from a semiarid, urban residential catchment, draining through separated storm-water sewers was investigated in 2006 and 2007. Water temperature, conductivity, pH, dissolved oxygen, and turbidity were continuously measured during 16 storm events. Storm-water runoff quality was characterized in terms of event mean values (EMVs), loads, and first flush (FF) loads and their relationships with rainfall characteristics. Discharge of total suspended solids (TSSs) is in general governed by the flow magnitude in storms and no significant relationships exist between the FF loads of TSS and rainfall intensity. The discharge of dissolved solids is independent of the flow magnitude. Strong FF effect for dissolved solids and weak FF effect for TSS were observed. This semiarid region provided no relationship between the EMVs of both TSS and conductivity and the antecedent dry period. This raises doubts on storm-water runoff being more heavily loaded with pollutants after a longer dry period in semiarid regions.

Characteristics of suspended solids, microorganisms, and chemical water quality in event-based stormwater runoff from an urban residential area.

Temporal evolution of microbiological, physical, and chemical quality of stormwater runoff from a stormwater drain in an urban residential area in Calgary, Canada, was investigated from May to September, 2006 and 2007. Investigating event mean concentrations and their correlations with rainfall characteristics revealed that intensive rainfall events produced highly polluted stormwater runoff when pollutant source limitation did not occur. Inconsistent event-based correlations between total suspended solids (TSS) concentrations and water quality parameters were observed. During storms, the loading of TSS exhibited a flow-dependent nature, whereas microorganism discharge appeared to be governed by a flow-independent mechanism. No strong first-flush effect was observed in either TSS or microorganisms, on average. No correlations of first-flush loads of TSS with rainfall characteristics were identified. Moderate negative correlations between first-flush loads of microorganisms and rainfall depth and intensity indicated that first flush of microorganisms tended to occur in small storms.

Three Types of Permeable Pavements in Cold Climates: Hydraulic and Environmental Performance

AbstractThis paper examined and compared the hydraulic and environmental performance of permeable interlocking pavers (PICPs), porous asphalt (PA), and porous concrete (PC) under cold climate conditions in Calgary, Alberta, Canada. Assessments were made of their hydraulic performance in terms of storm runoff reduction and surface infiltration capacity, and environmental performance in terms of the removal of several pollutants including total suspended solids (TSS), total nitrogen (TN), total phosphorous (TP) and, heavy metals: copper (Cu), lead (Pb), and zinc (Zn). Results from this paper demonstrated that PA, PC, and PICPs are all effective in mitigating storm runoff under cold climate conditions. Surface infiltration rate was substantially affected by winter sanding materials for PA, PC, and PICPs. Pressure washing was demonstrated to be able to partially restore surface infiltration rates for all three types of pavements. All pavement types in general have the same level of performance in removing TSS...

Quantification of Prediction Uncertainty in Artificial Neural Network Models

The research towards improving the prediction and forecasting of artificial neural network (ANN) based models has gained significant interest while solving various engineering problems. Consequently, different approaches for the development of ANN models have been proposed. However, the point estimation of ANN forecasts seldom explains the actual mechanism that brings the relationship among modeled variables. This raises the question on the model output while making decisions due to the inherent variability or uncertainty associated. The standard procedure though available for the quantification of uncertainty, their applications in ANN model are still limited. In this chapter, commonly employed uncertainty methods such as bootstrap and Bayesian are applied in ANN and demonstrated through a case example of flood forecasting models. It also discusses the merits and limitations of bootstrap ANN (BTANN) and Bayesian ANN (BANN) models in terms of convergence of parameter and quality of prediction interval evaluated using uncertainty indices.

Bias compensation in flood frequency analysis

Abstract Flood frequency analysis (FFA) is essential for water resources management. Long flow records improve the precision of estimated quantiles; however, in some cases, sample size in one location is not sufficient to achieve a reliable estimate of the statistical parameters and thus, regional FFA is commonly used to decrease the uncertainty in the prediction. In this paper, the bias of several commonly used parameter estimators, including L-moment, probability weighted moment and maximum likelihood estimation, applied to the general extreme value (GEV) distribution is evaluated using a Monte Carlo simulation. Two bias compensation approaches: compensation based on the shape parameter, and compensation using three GEV parameters, are proposed based on the analysis and the models are then applied to streamflow records in southern Alberta. Compensation efficiency varies among estimators and between compensation approaches. The results overall suggest that compensation of the bias due to the estimator and short sample size would significantly improve the accuracy of the quantile estimation. In addition, at-site FFA is able to provide reliable estimation based on short data, when accounting for the bias in the estimator appropriately. Editor D. Koutsoyiannis; Associate editor Sheng Yue

River flood prediction using fuzzy neural networks: an investigation on automated network architecture

Urban floods are one of the most devastating natural disasters globally and improved flood prediction is essential for better flood management. Today, high-resolution real-time datasets for flood-related variables are widely available. These data can be used to create data-driven models for improved real-time flood prediction. However, data-driven models have uncertainty stemming from a number of issues: the selection of input data, the optimisation of model architecture, estimation of model parameters, and model output. Addressing these sources of uncertainty will improve flood prediction. In this research, a fuzzy neural network is proposed to predict peak flow in an urban river. The network uses fuzzy numbers to account for the uncertainty in the output and model parameters. An algorithm that uses possibility theory is used to train the network. An adaptation of the automated neural pathway strength feature selection (ANPSFS) method is used to select the input features. A search and optimisation algorithm is used to select the network architecture. Data for the Bow River in Calgary, Canada are used to train and test the network.

Probabilistic and ensemble simulation approaches for input uncertainty quantification of artificial neural network hydrological models

ABSTRACT Artificial neural network (ANN) has been demonstrated to be a promising modelling tool for the improved prediction/forecasting of hydrological variables. However, the quantification of uncertainty in ANN is a major issue, as high uncertainty would hinder the reliable application of these models. While several sources have been ascribed, the quantification of input uncertainty in ANN has received little attention. The reason is that each measured input quantity is likely to vary uniquely, which prevents quantification of a reliable prediction uncertainty. In this paper, an optimization method, which integrates probabilistic and ensemble simulation approaches, is proposed for the quantification of input uncertainty of ANN models. The proposed approach is demonstrated through rainfall-runoff modelling for the Leaf River watershed, USA. The results suggest that ignoring explicit quantification of input uncertainty leads to under/over estimation of model prediction uncertainty. It also facilitates identification of appropriate model parameters for better characterizing the hydrological processes.

Relative importance of P and N in macrophyte and epilithic algae biomass in a wastewater-impacted oligotrophic river.

The role of nutrient loading on biomass growth in wastewater-impacted rivers is important in order to effectively optimize wastewater treatment to avoid excessive biomass growth in the receiving water body. This paper directly relates wastewater treatment plant (WWTP) effluent nutrients (including ammonia (NH3-N), nitrate (NO3-N) and total phosphorus (TP)) to the temporal and spatial distribution of epilithic algae and macrophyte biomass in an oligotrophic river. Annual macrophyte biomass, epilithic algae data and WWTP effluent nutrient data from 1980 to 2012 were statistically analysed. Because discharge can affect aquatic biomass growth, locally weighted scatterplot smoothing (LOWESS) was used to remove the influence of river discharge from the aquatic biomass (macrophytes and algae) data before further analysis was conducted. The results from LOWESS indicated that aquatic biomass did not increase beyond site-specific threshold discharge values in the river. The LOWESS-estimated biomass residuals showed a variable response to different nutrients. Macrophyte biomass residuals showed a decreasing trend concurrent with enhanced nutrient removal at the WWTP and decreased effluent P loading, whereas epilithic algae biomass residuals showed greater response to enhanced N removal. Correlation analysis between effluent nutrient concentrations and the biomass residuals (both epilithic algae and macrophytes) suggested that aquatic biomass is nitrogen limited, especially by NH3-N, at most sampling sites. The response of aquatic biomass residuals to effluent nutrient concentrations did not change with increasing distance to the WWTP but was different for P and N, allowing for additional conclusions about nutrient limitation in specific river reaches. The data further showed that the mixing process between the effluent and the river has an influence on the spatial distribution of biomass growth.

Changes in Water Quality Characteristics and Pollutant Sources Along a Major River Basin in Canada

Temporal and spatial variations of water quality along the Bow River (Alberta, Canada) were investigated using monthly water quality data (chloride, sulphate, nitrate, sodium, and conductivity) collected from 2004 to 2011. Non-point and point (notably three wastewater treatment plants) pollutant loads were characterized along the river. The river was divided into three reaches, namely, the Upper river reach, the Calgary reach, and the Downstream river reach, based on the distribution of point pollutant sources and geographic conditions. A mass balance approach and statistical analyses were employed to analyze water quality. The results demonstrated that the point sources, Calgary’s three wastewater treatment plants (WWTPs), are largely responsible for the observed spatial and temporal trends in the investigated quality parameters. However, the contribution of non-point sources appears to vary along the river, which might be related to the flow pathways taken by non-point pollutants discharging into the river and the geochemical characteristics of the groundwater within the alluvial aquifer that is hydraulically connected to the river. Apart from the identified point and non-point sources, the effects of other processes such as biological reactions need to be further ascertained and quantified for a better assessment of pollutant loads, in particular nutrients. Further understanding of these issues will allow a more accurate quantification of pollutant loads and consequently, better knowledge for formulating reliable water quality management strategies.

Response of green roof performance to multiple hydrologic and design variables: a laboratory investigation

Multiple factors affect green roof performance and their effects might vary at different stages of operation. This paper aimed to link green roof performance to hydrologic variables (antecedent moisture condition (AMC) and rainfall intensity) and design variables (growing medium (GM) type and depth) under multiple dimensions at the early stage of operation using laboratory experiment data. The results showed that the AMC is the most influential factor of hydrologic performance, whereas the GM type appeared to primarily affect the nutrient levels of the outflow. The significant main effects of other variables and interaction effects between two variables point to challenges in green roof design.