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Systematically evaluating the emission intensity and total emission of industries is indispensable for understanding energy and environmental sector performance in general and to support scientific climate change policy-making. In this study, an environmentally extended input-output (EEIO) model with a detailed disaggregation of energy sectors is developed to investigate the life-cycle environmental impacts of different industries. A special case study of the Province of Saskatchewan, Canada, is conducted to illustrate the potential benefits of its use in the environmental policy-making field. The I–O table is transformed and disaggregated based on the energy use patterns and the underlying economic structure. Key GHG emissions, including CO2, CH4 and N2O, are considered and the CO2 equivalent intensities of different economic sectors are calculated. An in-depth analysis of key industries is conducted to further investigate the interactions between different industries. It is founded that the Province of Saskatchewan is a trade exposed and emission intense economy. The emission intensity of agriculture is higher than the mean level, and is difficult to reduce due to the large farm machines used in agricultural production. Fossil-fuel electric power generation, as an intermediate input, has a strong effect on other industries and is a key factor for emission reduction.

Influent flow of wastewater treatment plants (WWTPs) is a crucial variable for plant operation and management. In this study, a random forest (RF) model was applied for daily wastewater inflow prediction, and a new probabilistic prediction approach was, for the first time, applied for quantifying the uncertainties associated with wastewater inflow prediction. The RF model uses regression trees to capture the nonlinear relationship between wastewater inflow and various influencing factors, such as weather features and domestic water usage patterns. The proposed model was applied to the daily wastewater inflow prediction for two WWTPs (i.e., Humber and one confidential plant) in Ontario, Canada. For the confidential WWTP, the coefficient of determination ( $$\varvec{R}^{2}$$ ) values for training and testing were 0.971 and 0.722, respectively. The $$\varvec{R}^{2}$$ values at the Humber WWTP were 0.957 and 0.584 for training and testing, respectively. In comparison with other approaches such as the multilayer perceptron neural networks (MLP) models and autoregressive integrated moving average models, the results show that the RF model performs well on predicting inflow. In addition, probabilistic prediction of daily inflow was generated. For the Humber station, 93.56% of the total testing samples fall into its corresponding predicted interval. For the confidential plant, 78 observed values of the total 89 samples fall into its corresponding interval, accounting for 87.64% of the total testing samples. The results show that the probabilistic approach can provide robust decision support for the operation, management, and optimization of WWTPs.

Facing the potential conflict between economic and environmental challenges, it is essential to investigate the integrated GHG emissions and the emission relationships of all industries in a socio-economic system to support formulation of industrially related legislation. In this study, a disaggregated ecologically-extended input-output (DECEIO) model is developed to investigate integrated GHG emissions and the emission relationships of various industries. A special case study for the Province of Saskatchewan, Canada, is conducted to illustrate the potential benefits of its use in the formulation of industrially related legislation. A disaggregated analysis that contains three GHG types and four emission sources is conducted to gain more insight into the complicated interactions between different industries. It is found that all kinds of emission sources and GHG types should be considered to comprehensively identify the characteristics of emission flows in the socio-economic system. The competitive relationships reflect good interactions in the GHG emission flows and a mutualism relationship reveals effective pathways to mitigate carbon emissions in two sectors simultaneously. In the Province of Saskatchewan, the Agriculture and Forestry sector, Electric Power Generation, Transmission and Distribution sector, Construction sector and Household Consumption sector all rank at the top for GHG emissions and their relationships are mutualistic. Thus, it is vital to propose effective industrial legislation for these industries to realize GHG emission reduction targets.

Feature importance has been a popular approach for machine learning models to investigate the relative significance of model predictors. In this study, we developed a Wilk's feature importance (WFI) method for hydrological inference. Compared with conventional feature importance methods such as permutation feature importance (PFI) and mean decrease in impurity (MDI), the proposed WFI aims to provide more reliable importance scores that could partially address the equifinality problem in hydrology. To achieve this, the WFI measures the importance scores based on Wilk's Ʌ (a test-statistic that can be used to distinguish the differences between two or more groups of variables) throughout a decision tree. The WFI has an advantage over PFI and MDI as it does not account for predictive accuracy so the risk of overfitting will be greatly reduced. The proposed WFI was applied to three interconnected irrigated watersheds located in the Yellow River Basin, China. By employing the recursive feature elimination approach, our results indicated that the WFI could generate more stable relative importance scores in response to the reduction of irrelevant predictors, as compared with PFI and MDI embedded in three different machine learning algorithms. In addition, the comparative study also shows that the predictors identified by WFI achieved the highest predictive accuracy on the testing dataset, which indicates the proposed WFI could identify more informative predictors among many irrelevant ones. We also extended the WFI to the local importance scores for reflecting the varying characteristics of a predictor in the hydrological processes. The related findings could help to gain insights into different hydrological behaviours.

Industrial GHG mitigation policies are prevalent across the world to realize global greenhouse gas (GHG) emissions targets. It is essential to simulate the impacts of different policies on various industries in the socio-economic system to find out the most effective emission reduction pathways. In this study, an Environmentally-Extended Input-Output Simulation (EEIOS) model is developed to facilitate integrated GHG mitigation policy development for multiple industries from both production and consumption sides. In addition, a Production-Consumption Rate is proposed to reflect the differences between Production-Based Policies (PBP) and Consumption-Based Policies (CBP) for a certain industry, which further supports the optimized and systematic emission reduction strategy development. A special case study of the Province in Saskatchewan, Canada, is conducted to illustrate the applicability and superiority of the Environmentally-Extended Input-Output Simulation model. It is found that Production-Based Policies applied to primary industries will lead to larger GHG reductions, and that Consumption-Based Policies should be applied to industries that are located at the end of industrial chains. The results provide a solid scientific basis for supporting industrial greenhouse gas mitigation policy development for each industry and identifying the optimized emission reduction pathways for the entire socio-economic system.

This study developed an inexact optimization modelling approach for supporting regional energy systems decision-making and greenhouse gas emission mitigation under uncertainty. The developed model integrates multiple inexact optimization programming approaches, incorporating interval linear programming, mixed-integer programming, and chance-constrained programming in an optimization framework. Uncertainties expressed as interval values and probabilistic distributions can be effectively handled. This is the first attempt that applies an optimization-based modelling approach to Yukon Territory, Canada. Three scenarios and one business-as-usual scenario are evaluated. System costs are minimized in this model. Results obtained from this model can help identify optimal patterns of renewable energy expansions in the Yukon. The interval solutions obtained could help decision makers to identify desirable renewable energy polices and emission reductions.

Increasing urbanization in the world brings tremendous social, economic and environmental challenges. It is essential to fully analyze urban GHG emissions metabolism systems to reveal economic emissions reduction pathways and support sustainable development. In this study, a factorial-based ecologically-extended input-output (FEEIO) model is developed to facilitate urban GHG emissions metabolism analysis. A special case study of the Province in Saskatchewan, Canada, is conducted to illustrate the potential benefits of its use in urban metabolism system health diagnosis. A factorial analysis is introduced to further investigate the effects of the main factors and their interactions. It is found that an urban GHG emissions metabolism system differs from other metabolism systems in regards to its special structure. A high efficiency represents limited emissions pathways in an urban GHG emissions metabolism system, which further provides good opportunities to realize GHG emissions mitigation. In the Province of Saskatchewan, the urban GHG emissions system has high redundancy and low efficiency across twenty scenarios. The GHG emissions from other sources are much simpler than emissions from coal, which further indicates that the emissions from other sources are easier to control through technology improvements or industrial regulations for specific sectors.

Temperature-related health metrics are often determined not only by temperatures but also by multiple climate variables. Temperatures compounded by other climate variables are of significant concern in the assessment of climate change impacts on public health. Temperatures, wind speeds and their combined effects are investigated here for a comprehensive study of how measured temperatures, perceived temperature, and their related extremes will change in China under climate change conditions. Future projections of combined temperatures and wind speeds over China are generated through the PRECIS regional climate modeling system. Results indicate that temperatures can increase nearly 6 °C over China by the end of the twenty-first century from the baseline period (1976–2005) without considering the wind speed changes. However, by considering the combined effect of temperature and wind speed, the perceived temperatures over China are projected to decrease by 4.8 °C relative to the observed values in the baseline period. This unexpected drop in the future perceived temperatures suggests the projected warming is likely to be offset to a large extent by a potential increase in wind speed. This may be related to the RCM’s high-resolution making the thermal contrast distribute at finer scales. The mechanism behind this result needs to be further investigated to help understand the related physical processes and the associated uncertainties at regional scales. As for low-temperature extremes, China is projected to experience an apparent decrease in the frequency and duration of extreme cold events in the future compared to the baseline period without considering the combined wind chill effect. Considering the wind chill effect, an opposite trend for extreme cold events is detected, with an increase by 21% in the frequency of temperatures below − 20 °C.

A carbon tax has been proposed or applied in many countries and regions around the world to reduce greenhouse gas (GHG) emissions. In this study, a Computable General Equilibrium (CGE) model for the Province of Saskatchewan is first developed to examine and analyze a series of direct and indirect socio-economic impacts of a carbon tax. The energy sector is further disaggregated based on the production structure and energy use pattern to obtain robust results. Different carbon tax rates are simulated to quantify the inter-relationships of the carbon tax, GHG emission reduction, and economic growth. In-depth examinations are also conducted to investigate some other macroeconomic impacts and responses from specific economic sectors. The results show that the GDP change is mainly caused by consumption reduction and import increases, due to the income decline and relatively low tariff rates. Changes in coal and petroleum product production and processes result in the greatest GHG emissions among all sectors. This suggests that clean coal and petroleum technologies may be the crucial issues for realizing both national and provincial environmental and economic objectives. It is expected that the results will provide a solid basis for supporting the application of an effective Pan-Canadian carbon pricing strategy.

Spatiotemporal changes in China's carbon emissions during the 11th and 12th Five‐Year Plan periods are quantified for the first time through a reconstructed nationwide high‐resolution gridded data set. The hot spots of carbon emissions in China have expanded by 28.5% (toward the west) in the north and shrunk by 18.7% in the south; meanwhile, the emission densities in North and South China have increased by 15.7% and 49.9%, respectively. This suggests a clear transition to a more intensive economic growth model in South China as a result of the energy conservation and emission reduction policies, while the expanded carbon hot spots in North China are mainly dominated by the Grand Western Development Program. The results also show that China's carbon emissions exhibit a typical spatially intensive, high‐emission pattern, which has undergone a slight relaxation (up to 3%) from 2007 to 2012 due to a typical urbanization process.

The ensemble Kalman filter (EnKF) is recognized as a powerful data assimilation technique that generates an ensemble of model variables through stochastic perturbations of forcing data and observations. However, relatively little guidance exists with regard to the proper specification of the magnitude of the perturbation and the ensemble size, posing a significant challenge in optimally implementing the EnKF. This paper presents a robust data assimilation system (RDAS), in which a multi-factorial design of the EnKF experiments is first proposed for hydrologic ensemble predictions. A multi-way analysis of variance is then used to examine potential interactions among factors affecting the EnKF experiments, achieving optimality of the RDAS with maximized performance of hydrologic predictions. The RDAS is applied to the Xiangxi River watershed which is the most representative watershed in China’s Three Gorges Reservoir region to demonstrate its validity and applicability. Results reveal that the pairwise interaction between perturbed precipitation and streamflow observations has the most significant impact on the performance of the EnKF system, and their interactions vary dynamically across different settings of the ensemble size and the evapotranspiration perturbation. In addition, the interactions among experimental factors vary greatly in magnitude and direction depending on different statistical metrics for model evaluation including the Nash–Sutcliffe efficiency and the Box–Cox transformed root-mean-square error. It is thus necessary to test various evaluation metrics in order to enhance the robustness of hydrologic prediction systems.

In this study, an interval two-stage robust optimization method (ITRM) is developed for planning carbon-emission trading between ecosystem and industrial systems under uncertainty. The developed ITRM incorporates interval-parameter programming (IPP) and two-stage stochastic programming (TSP) within a robust optimization (RO) framework to deal with uncertainties presented as both probabilities and intervals and to reflect economic penalties as corrective measures or recourse against any infeasibilities arising due to a particular realization of an uncertain event. Compared with the traditional TSP, ITRM can effectively reflect the risk generated by stochastic programming process and enhance the robustness of the model, such that it is suitable for risk-aversive planners under high-variability conditions. The ITRM is applied to a case of carbon sink trading of Zhangjiakou and carbon dioxide (CO 2 ) emission planning under uncertainty. The results obtained reveal that carbon trading mechanism can greatly optimize the allocation of resources and reduce the cost of emission abatement. The results also reveal that the contribution of forest ecosystems to carbon sinks and ecosystem services than others. Moreover, the system benefit would decrease as the robustness level is raised. Results indicate that when the robustness level is relatively low, the decision makers would pay more attention to the economic benefit of the system and neglect the stability of the system.

In this study, a Bayesian estimation-based simulation-optimization modeling approach (BESMA) is developed for identifying effluent trading strategies. BESMA incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian estimation, and probabilistic–possibilistic interval programming with fuzzy random coefficients (PPI-FRC) within a general framework. Based on the water quality protocols provided by SWAT, posterior distributions of parameters can be analyzed through Bayesian estimation; stochastic characteristic of nutrient loading can be investigated which provides the inputs for the decision making. PPI-FRC can address multiple uncertainties in the form of intervals with fuzzy random boundaries and the associated system risk through incorporating the concept of possibility and necessity measures. The possibility and necessity measures are suitable for optimistic and pessimistic decision making, respectively. BESMA is applied to a real case of effluent trading planning in the Xiangxihe watershed, China. A number of decision alternatives can be obtained under different trading ratios and treatment rates. The results can not only facilitate identification of optimal effluent-trading schemes, but also gain insight into the effects of trading ratio and treatment rate on decision making. The results also reveal that decision maker's preference towards risk would affect decision alternatives on trading scheme as well as system benefit. Compared with the conventional optimization methods, it is proved that BESMA is advantageous in (i) dealing with multiple uncertainties associated with randomness and fuzziness in effluent-trading planning within a multi-source, multi-reach and multi-period context; (ii) reflecting uncertainties existing in nutrient transport behaviors to improve the accuracy in water quality prediction; and (iii) supporting pessimistic and optimistic decision making for effluent trading as well as promoting diversity of decision alternatives.

The particle filtering techniques have been receiving increasing attention from the hydrologic community due to its ability to properly estimate model parameters and states of nonlinear and non-Gaussian systems. To facilitate a robust quantification of uncertainty in hydrologic predictions, it is necessary to explicitly examine the forward propagation and evolution of parameter uncertainties and their interactions that affect the predictive performance. This paper presents a unified probabilistic framework that merges the strengths of particle Markov chain Monte Carlo (PMCMC) and factorial polynomial chaos expansion (FPCE) algorithms to robustly quantify and reduce uncertainties in hydrologic predictions. A Gaussian anamorphosis technique is used to establish a seamless bridge between the data assimilation using the PMCMC and the uncertainty propagation using the FPCE through a straightforward transformation of posterior distributions of model parameters. The unified probabilistic framework is applied to the Xiangxi River watershed of the Three Gorges Reservoir (TGR) region in China to demonstrate its validity and applicability. Results reveal that the degree of spatial variability of soil moisture capacity is the most identifiable model parameter with the fastest convergence through the streamflow assimilation process. The potential interaction between the spatial variability in soil moisture conditions and the maximum soil moisture capacity has the most significant effect on the performance of streamflow predictions. In addition, parameter sensitivities and interactions vary in magnitude and direction over time due to temporal and spatial dynamics of hydrologic processes.

Agricultural water management has become an essential problem in recent years due to the increasing water demands. Irrigation water resources allocation is a dynamic decision making process associated with various uncertainties, which often exist in a complex and composite format. In this study, a new uncertainty quantification technique, the cloud model, is introduced to a dual-objective nonlinear programming (DONP) framework, and a cloud-based dual-objective nonlinear programming (CDONP) model is developed to support irrigation water allocation and agricultural water planning under composite uncertainties. The cloud model is applied to address the complex composite uncertainties associated with reference evapotranspiration (ET0) and surface water availability (SWA). A case study of the Yingke irrigation district (YID) in Northwest China is conducted to demonstrate the applicability of the developed model. The results show that the net economic profit (ENP) and irrigation system efficiency (ISE) are influenced by ET0 more than SWA. The obtained results are also compared to those of a traditional dual-objective nonlinear programming model to illustrate the advantages of the proposed CDONP model. In addition, four water shortage scenarios are built and discussed for risk analysis.

As the largest surface fresh water system on earth, the Great Lakes is facing the threat of climate change. Understanding how the hydrologic cycle in the Great Lakes region would be affected by human-induced global warming is important for developing informed adaptation strategies. In this study, high-resolution regional climate ensemble simulations based upon the PRECIS modeling system are conducted to project future climatic changes over the Great Lakes Basin. The results show that the Great Lakes Basin is very likely to experience a continuous warming-up throughout the 21st century. Particularly, mean air temperatures will rise by 2.6 °C in the forthcoming decades (i.e., 2030s), 3.8 °C in the middle of the century (i.e., 2050s), and 5.6 °C to the end of the century (i.e., 2080s), respectively. The warming air temperatures are very likely to result in more precipitation over the entire basin. The annual total precipitation over the Great Lakes Basin is projected to increase by 8.9% in the 2030s and 12.2% in the 2050s, while the magnitude of precipitation increase would decline to 7.1% in the 2080s. The slow-down of the precipitation increase from the 2050s to the 2080s indicates a shift from the aggressive increase of precipitation before and in the middle of this century to the eventual decrease by the end of this century, suggesting that a nonlinear response relationship between precipitation and temperature may exist in the Great Lakes Basin and such a relationship is also likely to vary in response to global warming.

Sustainable or green building practices have been adopted by most higher education institutions for their new campus buildings, major renovations and daily operations. This paper provides a synthesis of opinions and existing practices related to water conservation in institutional green buildings of member institutions of APPA (formerly the Association of Physical Plant Administrators). A specific focus regarding waterless urinals and their operation was attempted. A web-based survey and follow-up one-to-one interviews were utilized to extract information and data from these industry professionals. The survey evaluated the institution's use of policy related to sustainable building practices and focused on their approaches to water conservation. Regional preferences are provided and barriers to some water conservation practices and approaches have been identified. Operational challenges are evident, particularly as they relate to waterless urinals. It is clear that higher education institutions are engaging in water conservation practices across Canada and the United States. This work contributes to a foundation for future research and analysis related to best-management practices for water conservation in the higher education sector.

This paper presents a factorial possibilistic–probabilistic inference (FPI) framework for estimation of hydrologic parameters and characterization of interactive uncertainties. FPI is capable of incorporating expert knowledge into the parameter adjustment procedure for enhancing the understanding of the nature of the calibration problem. As a component of the FPI framework, a Monte Carlo–based fractional fuzzy–factorial analysis (MFA) method is also proposed to identify the best parameter set and its underlying probability distributions in a fuzzy probability space. Factorial analysis of variance (ANOVA) coupled with its multivariate extensions are performed to explore potential interactions among model parameters and among hydrological metrics in a systematic manner. The proposed methodology is applied to the Xiangxi River watershed by using the conceptual hydrological model (HYMOD) to demonstrate its validity and applicability. Results reveal that MFA is capable of deriving probability density functions (PDFs) of hydrologic model parameters. Moreover, the sequential inferences derived from the F test and its multivariate approximations disclose the statistical significance of parametric interactions affecting individual and multiple hydrological metrics, respectively. The findings presented here indicate that parametric interactions are complex in a fuzzy stochastic environment, and the magnitude and direction of interaction effects vary in different regions of the parameter space as well as vary temporally because of the dynamic behavior of hydrologic systems.

Climate change may alter energy demand as well as energy supply, thus posing a threat to energy security. This study investigates the long-term energy security responses to climate change for Ontario from a planning perspective. A regional climate model (RCM) is employed to assess the climate-driven changes in energy sectors at a 25 km × 25 km resolution. Reliable projections of changes in climatic variables are provided to assess their impacts on cooling degree days, heating degree days, and energy availability. Quantified sensitivities of residential and commercial energy consumptions to degree days are incorporated with future projections to estimate energy demand changes. We then estimate the impact of climate change on the primary power sources, including nuclear power, hydropower, gas, wind energy, and solar energy from a capacity planning perspective. Results indicate that winter warms more rapidly than summer in Ontario. This leads to heating degree days decreasing 2 times faster than cooling degree days increasing. Changes in degree days result in an increase in summer electricity demand and a reduction in winter gas consumption. We also find that efficiencies of hydropower and wind energy could be reduced in different scales because of decreased resource availability. The efficiency of nuclear power is sensitive to the temperature rise, but relatively less reduced compared to other energy sources. Solar energy production can benefit from climate change for the perspective of a decrease in rainy and cloudy days. With the increased electricity demand and decreased availability of water and wind resources, more green energy capacities are expected to build to ensure the long-term energy security for Ontario.

Changing climate is one of the most challenging environment issues worldwide. The objective of this paper is to develop a Multi-Dimensional Hypothetical Fuzzy Risk Simulation Model to facilitate the Greenhouse Gases mitigation policy development and multi-dimensional risk simulation. In detail, the comprehensive performances of various industries are evaluated and analyzed through Hypothetical Extraction Method. The preferences of decision-makers are considered through Analytic Hierarchy Process and Fuzzy Technique for Order Preference by Similarities to Ideal Solution method to develop the optimized Greenhouse Gases mitigation policies. The multi-dimensional risks of optimized Greenhouse Gases mitigation policies are simulated through RAS method. A detailed case study of the Province of Saskatchewan, Canada, is conducted to illustrate the potential benefits of the proposed model and support the Greenhouse Gases mitigation policy development. It is found that Electric power generation, transmission and distribution sector is the key industry in Saskatchewan. The government supports are suggested to be allocated to the Electric power generation, transmission and distribution sector, since it will benefit the province from environmental, economic, and urban metabolic perspectives.

Climate ensembles utilize outputs from multiple climate models to estimate future climate patterns. These multi-model ensembles generally outperform individual climate models. In this paper, the performance of seven global climate model and regional climate model combinations were evaluated for Ontario, Canada. Two multi-model ensembles were developed and tested, one based on the mean of the seven combinations and the other based on the median of the same seven models. The performance of the multi-model ensembles were evaluated on 12 meteorological stations, as well as for the entire domain of Ontario, using three temperature variables (average surface temperature, maximum surface temperature, and minimum surface temperature). Climate data for developing and validating the multi-model ensembles were collected from three major sources: the North American Coordinated Regional Downscaling Experiment, the Digital Archive of Canadian Climatological Data, and the Climactic Research Unit’s TS v4.00 dataset. The results showed that the climate ensemble based on the mean generally outperformed the one based on the median, as well as each of the individual models. Future predictions under the Representative Concentration Pathway 4.5 (RCP4.5) scenario were generated using the multi-model ensemble based on the mean. This study provides credible and useful information for climate change mitigation and adaption in Ontario.

Summary This paper presents a polynomial chaos ensemble hydrologic prediction system (PCEHPS) for an efficient and robust uncertainty assessment of model parameters and predictions, in which possibilistic reasoning is infused into probabilistic parameter inference with simultaneous consideration of randomness and fuzziness. The PCEHPS is developed through a two-stage factorial polynomial chaos expansion (PCE) framework, which consists of an ensemble of PCEs to approximate the behavior of the hydrologic model, significantly speeding up the exhaustive sampling of the parameter space. Multiple hypothesis testing is then conducted to construct an ensemble of reduced-dimensionality PCEs with only the most influential terms, which is meaningful for achieving uncertainty reduction and further acceleration of parameter inference. The PCEHPS is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability. A detailed comparison between the HYMOD hydrologic model, the ensemble of PCEs, and the ensemble of reduced PCEs is performed in terms of accuracy and efficiency. Results reveal temporal and spatial variations in parameter sensitivities due to the dynamic behavior of hydrologic systems, and the effects (magnitude and direction) of parametric interactions depending on different hydrological metrics. The case study demonstrates that the PCEHPS is capable not only of capturing both expert knowledge and probabilistic information in the calibration process, but also of implementing an acceleration of more than 10 times faster than the hydrologic model without compromising the predictive accuracy.

Flooding is one of the leading causes of loss due to natural catastrophes, and at least one third of all losses due to natural forces can be attributed to flooding. Flood management systems involve a variety of complexities, such as multiple uncertainties, dynamic variations, and policy implications. This paper presents an inexact probabilistic–possibilistic programming with fuzzy random coefficients (IPP-FRC) model for flood management in a hybrid uncertain environment. IPP-FRC is capable not only of tackling multiple uncertainties in the form of intervals with fuzzy random boundaries but of addressing the dynamic complexity through capacity expansion planning within a multi-region, multi-flood-level, and multi-option context. The possibility and necessity measures used in IPP-FRC are suitable for risk-seeking and risk-averse decision making, respectively. A case study is used to demonstrate the applicability of the proposed methodology for facilitating flood management. The results indicate that the inexact degrees of possibility and necessity would decrease with increased probabilities of occurrence, implying a potential tradeoff between fulfillment of objectives and associated risks. A number of decision alternatives can be obtained under different policy scenarios. They are helpful for decision makers to formulate the appropriate flood management policy according to practical situations. The performance of IPP-FRC is analyzed and compared with a possibility-based fractile model.

This paper describes a GIS-based land-use diversity measure for residential neighbourhoods – the land-use diversity index (or LDI) model – as a possible urban sustainability criterion. The term ‘land-use diversity’ is proposed as representative of many physical attributes of neighbourhood form opposite to typical sprawl patterns. A diverse neighbourhood is one with a mixture of compatible land uses and housing types, containing an array of amenities in reasonable proximity to where people live. The prototype version of the LDI model incorporates 34 input variables, structured around four sub-indices. Its range of expected values are explored through four case study applications. Theoretically, index values can vary between 0 and 1, where 1 represents a condition of greater ‘land-use diversity’. The two traditional urban neighbourhoods fared well (index values ranging between 0.627 and 0.726) because they have a greater range of land uses and neighbourhood amenities, a better integration of housing types and are more concentrated. These two neighbourhoods meet many of the ‘exuberant diversity’ criteria described by Jacobs. The two suburban neighbourhoods scored lower index values (between 0.250 and 0.363), indicating variables different to those for traditional urban forms. The LDI model differs from existing sprawl measures fundamentally, as it attempts to measure sprawl at a finer resolution (i.e. at the neighbourhood scale). It is anticipated the LDI model will assist with planning new, and reconfiguring old, neighbourhoods as they strive to meet smart growth criteria now being considered by many cities.

A drainage area is often divided into sub-areas for estimating the area's time of concentration. If runoff from the sub-areas concentrates in parallel to the drainage outlet, the time of concentration of the drainage area is usually estimated as the longest time of travel through the individual sub-areas. Findings reported here show that this conventionally used method of estimating time of concentration for parallel drainage areas is inappropriate and may result in underestimation of flood quantiles. A more suitable method of estimating the time of concentration for areas comprised of parallel sub-areas is to use a runoff-weighted average of the times of travel through the parallel sub-areas. The runoff weight of each sub-area can be calculated as the product of its runoff coefficient and area. This proposed method was tested and shown to provide more accurate design flood estimates. Also compared in this study are design flood estimates obtained through continuous simulation and design storm modelling.

This study improved hydrologic data assimilation through integrating the capabilities of particle filter (PF) and ensemble Kalman filter (EnKF) methods, leading to two integrated data assimilation schemes: the coupled EnKF and PF (CEnPF) and parallelized EnKF and PF (PEnPF) approaches. The applicability and usefulness of CEnPF and PEnPF were demonstrated using a conceptual rainfall-runoff model. The performance of two new developed data assimilation methods and traditional EnKF and PF approaches was tested through a synthetic experiment and two real-world cases with one located in the Jing River basin and one located in the Yangtze River basin. The results show that both PEnPF and CEnPF approaches have more opportunities to provide better results for both deterministic and probabilistic predictions than traditional EnKF and PF approaches. Moreover, the computational time of the two integrated methods is manageable. But the proposed PEnPF may need much more time for some large-scale or time-consuming hydrologic models since it generally needs three times of model runs used by EnKF, PF and CEnPF.

National Natural Science Foundation of China, the National Key Research and Development Plan, and the Natural Sciences and Engineering Research Council of Canada

The development of a holistic approach for the implementation of reuse strategies on vacant urban lands is essential if cities are to optimize the potential utility of these untapped resources as public amenity spaces, at a neighbourhood-, community- or city-wide planning scale. Many cities across the globe struggle with the presence of vacant and underutilized land in the urban environment. This is a wasted resource that has significant potential to contribute to a city’s green infrastructure/amenity capacity if the suitability of reuse strategies can be better understood. This paper presents a prototype community-based decision support tool to assist neighbourhood groups in completing a user-customizable vacant and underutilized land inventory at a neighbourhood, community or city-wide planning scale. The purpose of this research is to create an inventory that captures the relevant neighbourhood and site attributes in a way that can be conveyed to the user as a ‘site suitability index’ (or score). This ...

This paper presents research that was undertaken to determine whether planting deciduous trees, using intensive tree planting schemes, on vacant and underutilized urban land provides significant hydrologic benefits. This work contributes to an ongoing discussion on how to use vacant and underutilized land productively, and may be important to land use decision-makers, whose policies support the use of green infrastructure for stormwater management. Tree growth parameters for four monoculture planting schemes were modeled (all trees had a 50.8 mm caliper at planting) and included (i) 450 Ginkgo biloba , (ii) 92 Platanus × acerifolia , (iii) 120 Acer saccharinum , and (iv) 434 Liquidambar styraciflua , on a 1.6-acre parcel. i-Tree Hydro (formerly UFORE-Hydro) was used to derive a simplified Microsoft Excel-based water balance model to quantify the canopy interception potential and evaporation, based on 7 years (2002–2008) of historical hourly rainfall and mean temperature data in Hamilton, Ontario, Canada. This study revealed that three of the species responded similarly, while one species ( L. styraciflua ) performed significantly better with respect to total canopy storage potential and evaporation, capturing and evaporating 2.9 m 3 /tree over the 7 years analyzed, or 1280 m 3 for the total tree stand of 434 trees. The analyses presented herein demonstrate that the tree canopy layer was able to intercept and evaporate approximately 6.5%–11% of the total rainfall that falls onto the crown across the 7 years studied, for the G. biloba , P. × acerifolia and A. saccharinum tree stands and 17%–27% for the L. styraciflua tree stand. This study revealed that the rate at which a species grows, the leaf area index of the species as it matures, and the total number of trees to be planted need to be determined to truly understand the behavior and potential benefits of different planting schemes; had the mature leaf area been used as the sole indicator of the stormwater attenuating potential for each species, the A. saccharinum would have been the selected species. Also, had attenuation and evaporation per unit of tree been the only measurement reported, the P. × acerifolia stand would have been deemed the best performing tree, attenuating and evaporating 8.1 m 3 /tree. While the actual values presented herein may be uncertain because of a lack of locally-derived tree growth models, the approach described warrants further investigation.

The need for more efficient water use has increased in importance with growing water scarcity and increasing competition among water users. Measuring the economic efficiency of water use has become a useful indicator for water resources management at all levels. This study proposes a multilevel factorial fractional programming model to support water resources management under uncertainty. Linear fractional programming is introduced to provide a practical way for taking into account the ratio of economic benefit to water consumption in the modeling process. This approach allows water allocation plans to be developed on the basis of the optimal economic efficiency of water use rather than economic incentives. A multilevel factorial analysis technique is integrated within linear fractional programming framework to deal with data uncertainty. This technique can quantify the individual and interactive effects of uncertain parameters on system performance and help decision makers gain improved insight i...

Continuous-simulation water balance models may be used to study the soil moisture dynamics of small urban areas. These models require as input many soil-texture and land-use-related parameters. Difficulties encountered in determining the values of these input parameters warrant an investigation on their relative importance. In this study, a series of global sensitivity analyses were performed to evaluate the response of selected outputs from a continuous-simulation soil moisture model to variations of specified input parameters. Using randomly generated input parameter values representing various site conditions, the soil moisture model was run with meteorological data from Toronto, Ontario, Canada. Three output statistics, namely, average soil moisture, the standard deviation, and skewness of the output daily soil moisture distributions, were determined from each model run. Four types of sensitivity indices between the output statistics and the input parameters were calculated. Based on these sensitivity...

While there has been research on rainwater quality and quantity from green roofs and some conventional roof systems, there does not appear to be any significant study regarding the quality of rainwater harvested from selected white membrane roof systems and subsequently treated for potable use in an urban, institutional setting. A new Leadership in Energy and Environmental Design (LEED®) Canada Gold facility on the campus of McMaster University in Hamilton, Canada offered an excellent opportunity to analyze the quality of rainwater from different roof assemblies. Field research was undertaken on the evaluation of three white roof membranes: modified bitumen finish ply, polyvinyl chloride (PVC), and thermoplastic polyolefin (TPO); and their effects on the runoff water quality were studied. An analysis of the quality of runoff was performed from each of these three membranes and compared with Ontario provincial drinking water standards. This paper provides the results of runoff quality testing on these membranes and their suitability for future institutional green building applications.

Sustainable or green building practices have been adopted recently by many higher education institutions for their new campus buildings and major renovations. To date, no formal study has been conducted to determine if policy is essential for sustainable building practices and the implementation of LEED®for these institutional green buildings in North America. A mixed-methods approach consisting of a quantitative survey and qualitative interviews was undertaken with senior facility professionals at higher education institutions in North America. The survey evaluated the institution's use of a policy, guideline, standard, law or goal related to sustainable building practices and the interview identified specific practices as well as issues such as leadership, policy compliance and barriers to adopting sustainable building policies. This paper provides a framework for an institutional sustainable building policy that is suitable to use as a template for senior facility professionals and their specific policy development. This work contributes to a foundation for future research related to sustainable/green building policy development and its application to the higher education sector.

The prototype decision support system (PDSS) described within this paper represents one of the first of its kind in the urban forest/civil engineering research arena. The primary objective of this research was to develop a user-friendly, intuitive PDSS that provides users with tools for improved micro-management of the urban forest canopy. The secondary objective was to generate further discussion with respect to the relevance of viewing the urban forest as a municipal infrastructure, and designing and implementing sound management plans with the same rigor and attention to detail that is adhered to in the design and implementation of its civil engineering counterparts. The PDSS is divided into seven modules: (1) determination of potential native and non-native tree species available for planting; (2) defining the plantable and non-plantable areas in a defined region; (3) determination of planting locations; (4) species diversity assignment; (5) evaluating age distribution; (6) evaluating canopy cover, and (7) shadow analysis. Within each module the PDSS provides flexibility with respect to user input and constraints and for ease of use contains downloadable user reference guides. The PDSS was developed in three components, using three commonly used software programs: (1) SMODT, which is a south to south-central Ontario, Canada database of trees, developed in Microsoft Access Database; (2) ArcTrees, which is a GIS-based application that uses customized tools to map the plantable and non-plantable area in the urban environment, and (3) TreeModules, which uses a customized user interface in Microsoft Excel to carry out functions for Module 1 and Modules 3 through 7 inclusive. The PDSS uses rule-based algorithms that interact with expert knowledge and heuristics to draw inferences based on guided user inputs, through customized user-interfaces.

Using the past 45 years of climate data in south-western Ontario, Canada and a deterministic continuous simulation model, this study investigates the long-term variability in rain-fed soil moisture in urban areas as influenced by climate change. Statistical analyses of four variables, i.e., soil moisture, precipitation, temperature and evapotranspiration were carried out. As found from other studies for other locations, these analyses confirm in creasing temperatures and average growing season precipitation in south-western Ontario. Results show that both overall soil moisture and evapotranspiration have increased throughout the 45-year period. The probability/frequency distributions of soil moisture were obtained and the analysis shows an increasing average growing season soil moisture availability from the 1960's to the 1990's. The direct influence of precipitation and temperature on soil moisture and evapotranspiration were examined, revealing a stronger relationship of soil moisture and evapotranspiration with precipitation rather than temperature. Overall increasing average growing season soil moistures have very likely resulted from overall increasing rainfall during the growing seasons in south-western Ontario.

This technical note uses an integer programming formulation and GIS tools to explore the feasibility of eliminating door-to-door solid waste collection in a community and replacing this municipal service with resident haulage of solid wastes to centralized depots. Obtained results show technical feasibility and considerable cost savings, which could be considered in the future by municipalities exploring options for their solid waste infrastructure systems.

Hydrological system analyses are challenged by complexities of irregular nonlinearities, data uncertainties, and multivariate dependencies. Among them, the irregular nonlinearities mainly represent inexistence of regular functions for robustly simulating highly complicated relationships between variables. Few existing studies can enable reliable simulation of hydrological processes under these complexities. This may lead to decreased robustness of the constructed models, unfeasibility of suggestions for human activities, and damages to socio-economy and eco-environment. In the first of two companion papers, a discrete principal-monotonicity inference (DPMI) method is proposed for hydrological systems analysis under these complexities. Normalization of non-normally distributed samples and invertible restoration of modelling results are enabled through a discrete distribution transformation approach. To mitigate data uncertainties, statistical inference is employed to assess the significance of differences among samples. The irregular nonlinearity between the influencing factors (i.e. predictors) and the hydrological variable of interest (i.e. the predictand) is interpreted as piecewise monotonicity. Monotonicity is further represented as principal monotonicity under multivariate dependencies. Based on stepwise classification and cluster analyses, all paired samples representing the responsive relationship between the predictors and the predictand are discretized as a series of end nodes. A prediction approach is advanced for estimating the predictand value given any combination of predictors. The DPMI method can reveal evolvement rules of hydrological systems under these complexities. Reliance of existing hydro-system analysis methods on predefined functional forms is removed, avoiding artificial disturbances, e.g. empiricism in selecting model functions under irregular nonlinearities, on the modelling process. Both local and global significances of predictors in driving the evolution of hydrological variables are identified. An analysis of interactions among these complexities is also achieved. The understanding obtained from the DPMI process and associated results can facilitate hydrological prediction, guide water resources management, improve hydro-system analysis methods, or support hydrological systems analysis in other cases. The effectiveness and advantages of DPMI will be demonstrated through a case study of streamflow simulation in Xingshan Watershed, China, in another paper. Copyright © 2016 John Wiley & Sons, Ltd.