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Food-energy-water (FEW) nexus research emphasizes the links among food, energy, and water resource systems and is introduced as an attempt to overcome independent single-sector approaches that dominate the governing of resources today. Focus on multiple sectors and links among them is a key concept to the systems approach and methodology for its implementation by identifying and quantifying the connections between food, energy, and water sectors; modeling the FEW nexus system; and simulation of future management scenarios for decision-making assistance. This paper investigates the FEW nexus methodology within the system context by comparing simulation results obtained by the fully integrated model of global change, named ANEMI, and its modification, ANEMI_FEW, obtained by isolating nexus sectors from the rest of the model. Developing ANEMI_FEW requires breaking multiple feedback relationships that define the dynamic behavior of the modeled systems. A comparison of the simulation results confirms that the nexus model structure generates misleading results due to the break of significant feedbacks between FEW nexus and other sectors of the global change model. Obtained results are strongly suggesting that the nexus approach should be replaced with a more comprehensive structure of the global change model. The global ANEMI model is used as an example to investigate the research question. The findings generated by simulations of ANEMI and ANEMI_FEW are applicable to modeling at any scale (global, regional, or local). [ABSTRACT FROM AUTHOR]

The Yangtze Economic Belt (hereafter, the Belt) is one of the most dynamic regions in China in terms of population growth, economic progress, industrialization, and urbanization. It faces many resource constraints (land, food, energy) and environmental challenges (pollution, biodiversity loss) under rapid population growth and economic development. Interactions between human and natural systems are at the heart of the challenges facing the sustainable development of the Belt. By adopting systematic thinking and the methodology of system dynamics simulation, an integrated system-dynamics-based simulation model for the Belt, named ANEMI_Yangtze, has been developed based on the third version of ANEMI3. The nine sectors of population, economy, land, food, energy, water, carbon, nutrients, and fish are currently included in ANEMI_Yangtze. This paper presents the ANEMI_Yangtze model description, which includes (i) the identification of the cross-sectoral interactions and feedbacks involved in shaping the Belt's system behavior over time; (ii) the identification of the feedbacks within each sector that drive the state variables in that sector; and (iii) the description of a new fish sector and modifications to the population, food, energy, and water sectors, including the underlying theoretical basis for model equations. The validation and robustness tests confirm that the ANEMI_Yangtze model can be used to support scenario development, policy assessment, and decision-making. This study aims to improve the understanding of the complex interactions among coupled human–natural systems in the Belt to provide the foundation for science-based policies for the sustainable development of the Belt. [ABSTRACT FROM AUTHOR]

By running deterministic simulations of potential system planning, design and operating conditions, the system dynamics model facilitates the investigation of non-linear behaviour in complex water resources infrastructure systems. I Robustness i of the system (defined as the minimum value of the remaining system performance after the disturbance) and I rapidity i (duration of system performance under the disturbance) are clearly illustrated with the system resilience level at time I t i SB 1 sb and difference in time between I t i SB 0 sb and I t SB r sb i , respectively. Such an approach is very flexible in developing tools and methods to evaluate the entire water resources system management process. [Extracted from the article]

Yangtze Economic Belt is one of the most dynamic regions in China in terms of population growth, economic progress, industrialization, and urbanization. It faces many resource constraints (food, energy) and environmental challenges (pollution, biodiversity loss) under rapid population growth and economic development. Interactions between human and natural systems are at the heart of the challenges facing the sustainable development of the Yangtze Economic Belt. Understanding these interactions poses challenges because human and natural systems evolve in response to a wide range of influences. Accounting for these complex dynamics requires a system tool that can represent the fundamental drivers of change and responses of the individual system as well as how different systems interact and co-evolve. By adopting the system thinking and the methodology of system dynamics simulation, an integrated assessment model for the Yangtze Economic Belt, named ANEMI_Yangtze, is developed based on the third version of the global integrated assessment model, ANEMI. Nine sectors of population, economy, land, food, energy, water, carbon, nutrients, and fish are currently included in ANEMI_Yangtze. This paper identifies the opportunities and challenges facing the Yangtze Economic Belt and presents the ANEMI_Yangtze model structure. It also includes: (i) the identification of the cross-sectoral interactions and feedbacks involved in shaping Yangtze Economic Belt's system behaviour over time; (ii) the identification of the feedbacks within each sector that drive the state variables in that sector; and (iii) the explanation of the theoretical and mathematical basis for those feedbacks. ANEMI_Yangtze was developed and calibrated sector by sector before coupling them together into complete ANEMI_Yangtze model. After the validation and robustness test, the ANEMI_Yangtze model can be used to support decision making, policy assessment, and scenario development. This study aims to improve the understanding of the complex interactions among human and natural systems in the Yangtze Economic Belt to provide foundation for science-based policies for the sustainable development of the economic belt. [ABSTRACT FROM AUTHOR]

The ANEMI model is an integrated assessment model of global change that emphasizes the role of water resources. The model is based on the principles of system dynamics simulation to analyze changes in the Earth system using feedback processes. Securing water resources for the future is a key issue of global change, and ties into global systems of population growth, climate change, carbon cycle, hydrologic cycle, economy, energy production, land use and pollution generation. Here the third iteration of the model–ANEMI3 is described, along with the methods used for parameter estimation and model testing. The main differences between ANEMI3 and previous versions include: (i) implementation of the energy-economy system based on the principles of system dynamics simulation; (ii) incorporation of water supply as an additional sector in the global economy that parallels the production of energy; (iii) inclusion of climate change effects on land yield and potentially arable land for food production, and (iv) addition of nitrogen and phosphorus based nutrient cycles as indicators of global water quality, which affect the development of surface water supplies. The model is intended for analyzing long-term global feedbacks which drive global change. Because of this, there are limitations related to the spatial scale that is used. However, the model's simplicity can be considered a strength, as it allows for the driving feedbacks to be more easily identified. The model in its current form allows for a variety of scenarios to be created to address global issues such as climate change from an integrated perspective, or to examine the change in one model sector on Earth system behaviour. The endogenous structure of the model allows for global change to be driven entirely by model structure rather than exogenous inputs. The new additions to the ANEMI3 model are found to capture long term trends associated with global change, while allowing for the development of water supplies to be represented using an integrated approach considering global economy and surface water quality. [ABSTRACT FROM AUTHOR]

The coincidence of floods and coronavirus disease 2019 (COVID‐19) is a genuine multihazard problem. Since the beginning of 2020, many regions around the World have been experiencing this double hazard of serious flooding and the pandemic. There have been 70 countries with flood events occurring after detection of the country's first COVID‐19 case and hundreds of thousands of people have been evacuated. The main objective of this article is to assess challenges that arise from complex intersections between the threat multipliers and to provide guidance on how to address them effectively. We consider the limitations of our knowledge including "unknown unknowns." During emergency evacuation, practicing social distancing can be very difficult. However, people are going to take action to respond to rising waters, even if it means breaking quarantine. This is an emergency manager's nightmare scenario: two potentially serious emergencies happening at once. During this unprecedented year (2020), we are experiencing one of the most challenging flood seasons we have seen in a while. Practical examples of issues and guides for managing floods and COVID‐19 are presented. We feel that a new approach is needed in dealing with multiple hazards. Our main messages are: a resilience approach is needed whether in response to floods or a pandemic; preparation is vital, in addition to defense; the responsible actors must be prepared with actions plans and command structure, while the general population must be involved in the discussions so that they are aware of the risk and the reasons for the actions they must take. This article is categorized under:Engineering Water > Methods [ABSTRACT FROM AUTHOR]

Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluatedwith reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2-4 day), and long (more than 5-day) precipitation events is projected. [ABSTRACT FROM AUTHOR]

The identification of homogeneous precipitation regions has value in many water resources engineering applications (infrastructure planning, design, operations; climate forecasting, modelling). The objective of this paper is to assess the sensitivity of precipitation regions to the temporal resolution (monthly, seasonal, annual and the annual maximum series) of the data. The presented method uses the fuzzyc-means clustering algorithm to partition climate sites into statistically homogeneous precipitation regions. The regions are validated using an approach based on L-moment statistics. The method is conducted in two climatically different study areas in western and eastern Canada. There does not appear to be a relationship between the spatial distributions of the regions formed using different temporal resolutions of the precipitation data. It is recommended to delineate precipitation regions that are specific to the task at hand, and to select a temporal resolution that is consistent with the final application of the regional precipitation dataset.EDITOR A. Castellarin; ASSOCIATE EDITOR T. Kjeldsen [ABSTRACT FROM PUBLISHER]

ABSTRACT Physical scaling ( SP) method is a statistical downscaling approach where model-based climate data are downscaled taking into consideration large-scale climate, elevation and land-cover at the location of interest. In this study, the downscaling skills of an ensemble of SP method and its variants and Statistical DownScaling Model ( SDSM) towards downscaling North American Regional Reanalysis ( NARR) temperature data are compared. Two downscaling approaches: direct and indirect, two versions: SP and surrounding pixel information and three functional forms: linear regression, quantile regression and generalized additive models are considered to prepare the method ensemble. To evaluate method performance, a leave-one-out cross-validation approach is adopted. Results indicate that SP method and its variants have comparable skill to SDSM. Further method skill is found to be only marginally influenced by the choice of method version and functional form, and considerably influenced by the choice of approach. The ensemble of models is thereafter used to downscale future air temperature projections made by a global climate model: FGOALS-s2. It is found that downscaled future projections are most significantly influenced by the choice of version, followed by the choice of approach and the choice of functional form in the decreasing order of importance. [ABSTRACT FROM AUTHOR]

ABSTRACT The objective of this study is to investigate the vulnerability of different land-cover types to climate change. To this end, land-cover specific temperature change factors are quantified for the southern Saskatchewan region using a novel statistical downscaling model: physical scaling ( SP). SP model considers large-scale climate and regional physical characteristics like land-cover, elevation in its formulation and hence can be used to predict future temperature for different land-cover types under changing large-scale climatic and land-cover conditions. The model is validated by assessing its ability to downscale North American Regional Reanalysis ( NARR) derived surface (skin) temperature from an initial resolution of 32 km to 500 m. The downscaled NARR data are evaluated using a cross-validation approach over the period 2006-2013 with reference to MODerate-resolution Imaging Spectroradiometer ( MODIS) derived surface temperature estimates and satisfactory model performance is obtained (average RMSE = 0.03 K). The validated model is used to predict future surface temperature across the study region. Future land-cover projections are derived by downscaling land-use projections for Representative Concentration Pathways ( RCPs) 2.6 and 8.5 made by integrated assessment models: IMAGE and MESSAGE, respectively. An analysis of land-cover specific temperature changes between historical (2006-2013) and future (2081-2100) timelines indicate variations of up to 2 K between different land-cover classes. Vulnerability pattern of different land-cover classes differ significantly between day- and night-time. Further, variations of upto 1 K in projected changes are observed among different forest cover types. Closed shrubland is obtained as the most vulnerable forest-cover class whereas evergreen broadleaf forest is found to be the least vulnerable. [ABSTRACT FROM AUTHOR]

Climate change is expected to alter the frequency and intensity of extreme rainfall events, affecting the rainfall intensity-durationfrequency (IDF) curve information used in the design, maintenance, and operation of water infrastructure in Canada. Presented in this study are analyses of precipitation data from 567 Environment Canada hydro-meteorological stations using the web-based IDF_CC tool, which applies a novel equidistance quantile-matching downscaling method to generate future IDF curve information. Results for the year 2100 based on The Second Generation Canadian Earth System Model (CanESM2) and a multimodel ensemble median of 24 global climate models (GCMs) were generated. A natural neighbor spatial interpolation method was used to generate results for ungauged locations. One in 5-year, 2-h and one in 100-year, 24-h precipitation events were explored. Results based on CanESM2 indicated a reduction in extreme precipitation in central regions of Canada under specific analyses and increases in other regions. Relative to the multimodel ensemble median approach, the CanESM2 results suggested more spatial variability in change of IDFs, and the ensemble median generated generally lower values than CanESM2. By using the median value that lowers the importance of extreme outputs, the ensemble median approach obscured uncertainty associated with GCM outputs. While the IDF_CC tool helps fill an important gap related to accessing local climate change information, it is important to consider uncertainty in GCM outputs when making climate change adaptation decisions. [ABSTRACT FROM AUTHOR]

Stakeholder involvement can serve to increase the quality of decision support systems (DSSs) and increase the perceived legitimacy of DSS outputs. Involving those who are ultimately affected by the outputs of DSSs in system design and development also reflects democratic principles. Importantly, stakeholder involvement can help ensure that the outputs of DSSs are used in decision-making processes. However, DSSs often fail due to poor engagement of stakeholder and end-user communities in the development and design of systems. The stakeholder engagement process applied in the development of the Computerized Tool for the Development of Intensity Duration Frequency Curves under Climate Change described here followed many of the tenants of best practices identified in the literature. While the engagement strategy was generally considered successful, over- and under-representation of some stakeholder groups and long term funding issues were weaknesses in the engagement process. [ABSTRACT FROM AUTHOR]

Intensity Duration Frequency (IDF) curves are among the most common tools used in water resources management. They are derived from historical rainfall records under the assumption of stationarity. Change of climatic conditions makes the use of historical data for development of IDFs for the future unjustifiable. The IDF_CC , a web based tool, is designed, developed and implemented to allow local water professionals to quickly develop estimates related to the impact of climate change on IDF curves for almost any local rain monitoring station in Canada. The primary objective of the presented work was to standardize the IDF update process and make the results of current research on climate change impacts on IDF curves accessible to everyone. The tool is developed in the form of a decision support system (DSS) and represents an important step in increasing the capacity of Canadian water professionals to respond to the impacts of climate change. [ABSTRACT FROM AUTHOR]

Two modelling approaches are presented in this article for spatial and temporal analysis of water resources risk. Major sources of uncertainty in water resources management are spatial and temporal variability. Spatial variability occurs when values fluctuate with the location of an area and temporal variability occurs when values fluctuate with time. System dynamics (SD) simulation and hydrodynamic modelling are presented in this article as tools for modelling the dynamic characteristics of flood risk and its spatial variability. The first modelling framework presents SD simulation coupled with 3D fuzzy set theory. Whereas the second modelling framework presents hydrodynamic modelling coupled with 3D fuzzy set theory. The two integrated modelling frameworks are illustrated and compared using the Red River flood of 1997 (Manitoba, Canada) as a case study. For the 1997 Red River case study, SD simulation proved to be efficient modelling approach for capturing the feedback-based dynamic processes occurring in the watershed. However, the SD simulation has limited capacity for adequately representing spatial processes. The SD simulation coupled with 3D fuzzy set theory and geographic information system-based spatial analysis tool provides an integrated approach capable of modelling the spatial and temporal variability of flood risk. With respect to the topography of Red River basin, the hydrodynamic modelling coupled with 3D fuzzy set theory proved to be an accurate approach for predicting spatial and temporal variation of water surface elevation, velocity, flux, etc. The integrated approach demonstrates high accuracy in predicting spatial and temporal variability of flood risk. [ABSTRACT FROM PUBLISHER]

This paper presents a coupled self-adaptive multiobjective differential evolution and network flow algorithm for the optimal operation of complex multipurpose reservoir systems. The developed algorithm (i.e., self-adaptive multiobjective differential evolution) is compared to nondominated sorting genetic algorithm II using a set of common test problems and a real-world case study. An out-of- kilter method for minimal-cost flow problems is used to optimize the water resource system from self-adaptive multiobjective differential evolution inputs driven by the evolutionary process. Self-adaptive multiobjective differential evolution is then used to evaluate objective functions based on the outputs from out-of-kilter algorithm and the process continues until the stop criterion is met. The advantages of the proposed approach include (1) flexible evolutionary algorithms for solving highly complex objective function, and (2) efficient network flow method for dealing with large and highly constrained problems. The case study includes one part of a complex water supply system located in southwestern Brazil that provides water for almost 20 million people in Sao Paulo metropolitan area. The objectives of the case study include minimization of demand shortage (the difference between demand for water and available water supply), maximization of water quality (or minimization of the deviation from the water quality standards), and minimization of pumping cost. The coupled model is applied to the case study using one inflow scenario representing a drought period with inflows below historical average. Multiobjective analyses are performed by comparing two pairs of objective functions, as follows: (1) minimization of demand shortage versus minimization of pumping cost, and (2) minimization of demand shortage versus minimization of the deviation from the water quality standards. The problem constraints include reservoir capacity, capacity of tunnels, channel flow limitations, and minimum downstream release for all reservoirs within the system. The proposed coupled model (self-adaptive multiobjective differential evolution and out-of-kilter) is outperforming both pure self-adaptive multiobjective differential evolution and nondominated sorting genetic algorithm II, as it requires significantly smaller number of generations to derive the Pareto front. In addition, the proposed approach is capable of handling larger problems without major computational burden. The coupled model and self-adaptive multiobjective differential evolution also converge closer to, and provide better coverage of the true Pareto front than, nondominated sorting genetic algorithm II. [ABSTRACT FROM AUTHOR]

Stochastic time series models are very useful in many environmental domains. In this paper, an analytical procedure for multi-site, multi-season streamflow generation using maximum entropy bootstrap stochastic model (M3EB) is developed that can implicitly preserve both the spatial and temporal dependence structure, in addition to the other statistical characteristics present in the historical time series. The proposed model is computationally less demanding and simple in terms of modeling complexity. The maximum entropy bootstrap (MEB) generates random samples from the empirical cumulative distribution function (ECDF) and rearranges the random series based on the rank ordering of the historical time series. The modeling structure of MEB implicitly satisfies the ergodic theorem (preservation of summary statistics) and guarantees the reproduction of the time dependent structure of an underlying process. The orthogonal transformation is used with M3EB to capture the spatial dependence present in the multi-site collinear data. The performance of M3EB is verified by comparing the statistical characteristics between the observed and synthetically generated streamflows. Three case studies from Colorado River Basin, USA; Red River Basin, USA and Canada; and Cauvery River Basin, India; are used to demonstrate the advantages of M3EB. The statistical measures adopted for evaluation of M3EB performance include monthly statistics (mean, standard deviation and skewness), temporal and spatial correlation, smoothing (flows other than present in historical data) and extrapolation (flows outside the range of historical data). The M3EB model shows (i) a high level of accuracy in preserving the statistics; and (ii) a high computational efficiency. Since M3EB can be used for multiple variable problems, the model can be easily extended to other environmental or hydroclimatic time series data. [ABSTRACT FROM AUTHOR]

In Brazil, little is known about climate change impacts on extreme events at local scale. Here, we explored ways of communicating it, in the form of intensity-duration-frequency (IDF) curves, using the nonstationary equidistant quantile matching (EQMNS) method. EQMNS was applied in two urbanized areas of Brazil: São Paulo and Porto Alegre. This research was conducted using recorded and modeled precipitation data, the latter using the Regional Climate Model Eta. Results indicated an increase in rainfall intensity for rare events, which were intensified under nonstationarity. The use of stationarity misrepresented the trends related to precipitation, being able to mask the direction of change in future conditions. Future IDFs with Eta's 3-hourly resolution appeared to be intensified when compared with those from daily resolution. The increase in rainfall intensity was more significant for short durations than for daily ones, bringing concerns about associated hazards and their impacts on society and urban infrastructure. [ABSTRACT FROM AUTHOR]

This paper presents an application of the Multiobjective Differential Evolution (MoDE) algorithm for the optimal operation of a complex multipurpose reservoir system. The developed algorithm (MoDE) is compared with the Genetic Algorithm (NSGA-II) using a set of common test problems and the case study. The case study includes part of a complex water supply system located in southwest Brazil that provides water for approximately 20 million people in the Sao Paulo metropolitan area (SPMA). The objectives of the case study include minimization of demand shortage (the difference between demand for water and available water supply), maximization of water quality (or minimization of the deviation from the water quality standards), and minimization of pumping cost. MoDE is applied to the case study using two inflow scenarios: (1) a drought period with inflows below historical average; and (2) a wet period with inflows above historical average. Multiobjective analysis is done by comparing two pairs of objective functions: minimization of demand shortage versus minimization of pumping cost and minimization of demand shortage versus minimization of the deviation from water quality standards. The constraints for the analysis are reservoir capacity, tunnels and channel limitations, and minimum downstream flow for all reservoirs. The proposed MoDE algorithm is outperforming NSGA-II as it converges closer to and provides better spread coverage of the true Pareto front. [ABSTRACT FROM AUTHOR]

Abstract: Awareness of increasing water scarcity has driven efforts to model global water resources for improved insight into water resources infrastructure and management strategies. Most water resources models focus explicitly on water systems and represent socio-economic and environmental change as external drivers. In contrast, the system dynamics-based integrated assessment model employed here, ANEMI, incorporates dynamic representations of these systems, so that their broader changes affect and are affected by water resources systems through feedbacks. Sectors in ANEMI therefore include the global climate system, carbon cycle, economy, population, land use and agriculture, and novel versions of the hydrological cycle, global water use and water quality. Since the model focus is on their interconnections through explicit nonlinear feedbacks, simulations with ANEMI provide insight into the nature and structure of connections between water resources and socio-economic and environmental change. Of particular interest to water resources researchers and modelers will be the simulated effects of a new water stress definition that incorporates both water quality and water quantity effects into the measurement of water scarcity. Five simulation runs demonstrate the value of wastewater treatment and reuse programs and the feedback-effects of irrigated agriculture and greater consumption of animal products. [Copyright &y& Elsevier]

Many climate change impact studies have been conducted using a top-down approach. First, outputs from global circulation models (GCMs) are considered which are downscaled in a second step to the river basin scale using either a statistical/empirical or a dynamic approach. The local climatic signal that is obtained is then used as input into a hydrological model to assess the direct consequences in the basin. Problems related to this approach include: a high degree of uncertainty associated with GCM outputs and an increase in uncertainty due to the downscaling approach. An inverse approach is proposed in this article to improve the understanding of the processes leading to hydrological hazards including both flood and drought events. The approach analyses of existing guidelines and management practices in a river basin with respect to critical hydrological exposures that may lead to failure of the water resources system or parts thereof. Critical hydrologic exposures (flood levels for example) are then transformed into corresponding critical meteorological conditions (extreme precipitation events for example). These local weather scenarios are then statistically linked to possible large-scale climate conditions that are available from the GCMs. The proposed procedure allows for the assessment of the vulnerability of river basins with respect to climate forcing. It also provides a tool for identifying the spatial distribution of the vulnerability and risk. [ABSTRACT FROM AUTHOR]

This paper evaluates the National Centers for Environmental Prediction - National Center for Atmospheric Research (NCEP-NCAR) reanalyses hydroclimatic data as an initial check for assessment of hydrologic impacts of climate change at the basin scale. A reanalysis dataset for daily precipitation, maximum temperature, and minimum temperature from the NCEP-NCAR global (NNGR) and regional (North American Regional Reanalysis or NARR) reanalysis project has been used as input into the semidistributed hydrologic model (Hydrologic Engineering Center Hydraulic Modeling System or HEC-HMS) for the period 1980-2005. An extensive analysis has been performed for assessing the performance of the reanalysis data generated flows compared with the observed inputs during May-November. The stream flows generated from the NARR dataset show encouraging results in simulating summertime low flows with less variability and fewer errors. The results indicate that NNGR results are less accurate and highly variable. This study suggests that NARR can be adequately used as an alternative in data-scarce regions. Le présent article évalue les nouvelles analyses des « NCEP-NCAR (« National Centers for Environmental Prediction-National Center for Atmospheric Research) » de données hydroclimatiques pour une vérification initiale de l’évaluation des impacts hydrologiques des changements climatiques à l’échelle du bassin. L’ensemble de données pour la nouvelle analyse des précipitations quotidiennes, de la température maximale et de la température minimale du projet de nouvelle analyse « NCEP-NCAR » (a) global (« NNGR ») et (b) régional (« NARR ») a été utilisé comme intrant dans le modèle hydrologique semi-réparti (« HEC-HMS ») pour la période 1980-2005. Une analyse approfondie a été réalisée afin d’évaluer le rendement des données des débits générés par la nouvelle analyse par rapport aux intrants observés entre mai et novembre. Les débits d’eau générés à partir de l’ensemble de données NARR montrent un résultat encourageant pour la simulation des faibles débits d’étiage comportant moins de variabilité et d’erreurs. Les résultats ont indiqué que les résultats NNGR sont moins précis et très variables. La présente étude suggère que NARR peut être utilisé adéquatement comme solution de remplacement dans les régions pour lesquelles les données sont peu abondantes. [ABSTRACT FROM AUTHOR]

The discharge and stage measurements in a river system are characterized by a number of sources of uncertainty, which affects the accuracy of a rating curve established from measurements. This paper presents a fuzzy set theory based methodology for consideration of different sources of uncertainty in the stage and discharge measurements and their aggregation into a combined uncertainty. The uncertainty in individual measurements of stage and discharge is represented using triangular fuzzy numbers, and their spread is determined according to the International Organization for Standardization (ISO) standard 748 guidelines. The extension principle based fuzzy arithmetic is used for the aggregation of various uncertainties into overall stage-discharge measurement uncertainty. In addition, a fuzzified form of ISO 748 formulation is used for the calculation of combined uncertainty and comparison with the fuzzy aggregation method. The methodology developed in this paper is illustrated with a case study of the Thompson River near Spences Bridge in British Columbia, Canada. The results of the case study show that the selection of number of velocity measurement points on a vertical is the largest source of uncertainty in discharge measurement. An increase in the number of velocity measurement points provides the most effective reduction in the overall uncertainty. The next most important source of uncertainty for the case study location is the number of verticals used for velocity measurements. The study also shows that fuzzy set theory provides a suitable methodology for the uncertainty analysis of stage-discharge measurements. [ABSTRACT FROM AUTHOR]

The paper describes the development of a DENET computer model that involves the application of an evolutionary optimization technique, differential evolution, linked to the hydraulic simulation solver, EPANET, for optimal design of water distribution networks. A model is formulated with the objective of minimizing cost and this formulation is applied to two benchmark water distribution system optimization problems—New York water supply system and Hanoi water distribution network. The study yielded promising results as compared with earlier studies in the literature and encouraged to reformulate the model for a new objective of maximizing network resilience. The results of the analysis demonstrate that DENET can be considered as a potential alternative tool for economical and reliable water distribution network planning and management. [ABSTRACT FROM AUTHOR]

Abstract: A multiciteria decision making problem in water resource is addressed through the generation of fuzzy Pareto optimal set. Methodology is using positive and negative ideal solutions (Lai, Y.-J., Liu, T.-Y., Hwang, C.L. (1994). TOPSIS for MODM. European Journal of Operational Research 76, 486–500) and a set of weights assigned to the objective functions in fuzzy triangular form. Solution of the problem is obtained by transforming each objective function into a set of three objective functions. A planning problem of multicriteria waste water treatment from the literature is used as an illustrative example to demonstrate the utility of the proposed methodology. The obtained fuzzy Pareto solution set has been compared with the deterministic solution set. It is shown that the proposed approach can: (a) capture the uncertainty associated with the assignment of weights; and (b) provide the decision makers with a wider range of solutions to select from. [Copyright &y& Elsevier]

Abstract: Floodplain management is a subject in which everyone in the floodplain is a stakeholder. Real participation only takes place when stakeholders are part of the decision-making process. This can occur directly when local communities come together to make floodplain management choices or if democratically elected or otherwise accountable agencies or groups can represent stakeholders. Real participation is more than consultation and requires that stakeholders at all levels of the social structure have an impact on decisions at different levels of floodplain management. A participatory approach is the only means for achieving long-lasting consensus and common agreement. However, for this to occur, stakeholders and officials from water management agencies have to recognize that the sustainability of floodplain management decisions is a common problem and that all parties are going to have to sacrifice some desires for the common good. There is common responsibility for making participation possible. This involves (a) the creation of mechanisms for stakeholder consultation at various scales (from local, over watershed to national) and (b) the creation of participatory capacity, particularly amongst marginalized social groups. This may include awareness raising, confidence building and education, as well as the provision of the economic resources needed to facilitate participation and the establishment of good and transparent sources of information. This work focuses on the methodology for stakeholder participation in floodplain management. Floodplain management problems are characterized by multiple objectives and large number of stakeholders. The solution methodologies adapted for multi-criteria multi participant decision problems are generally based on aggregation of decisions obtained for individual decision makers. This approach seems somewhat inadequate when the number of stakeholders is very large. We have developed a methodology to include the views of multiple stakeholders using fuzzy set theory and fuzzy logic. Three possible different response types: scale (crisp), linguistic (fuzzy), and conditional (fuzzy) are analyzed to obtain the resultant input by using Fuzzy Expected Value. Fuzzy Expected Value input is used with the multi-criteria decision-making tool named Fuzzy Compromise Programming. The methodology has been applied to floodplain management in the Red River Basin, Canada that faces periodical flooding. We have demonstrated that the empowerment of stakeholders can improve the floodplain management process and provide decisions acceptable to a wider group of stakeholders. [Copyright &y& Elsevier]

Most engineering systems are subject to a wide range of possible uncertain future conditions. The probabilistic reliability analysis usually fails to address the problems of human error, subjectivity, and lack of system performance history and records. This paper explores the utility of the following fuzzy performance measures for evaluating the performance of a complex water supply system: (i) combined reliability–vulnerability, (ii) robustness, and (iii) resiliency. The regional water supply system for the City of London, Ontario, Canada, is used as the case study. The computational requirements for the implementation of the fuzzy performance measures and their sensitivity to different shapes of fuzzy membership functions are investigated. The study illustrates the capability of the fuzzy performance measures to handle uncertainty and identify critical system components. This can be of value in identifying the optimal level of improvement that will increase the overall system performance. [ABSTRACT FROM AUTHOR]

Abstract: Conceptual models are considered to be the best choice for describing the runoff process in a watershed. However, enormous requirements for topographic, hydrologic and meteorological data and extensive time commitment for calibration of conceptual models (especially for distributed models) are often prohibitive factors in their practical applications. Artificial neural networks (ANN) can be an efficient way of modeling the runoff process in situations where explicit knowledge of the internal hydrologic processes is not available. An ANN is a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data sets. This paper presents the use of ANN for predicting the peak flow, timing and shape of runoff hydrograph, based on causal meteorological parameters. Antecedent precipitation index, melt index, winter precipitation, spring precipitation, and timing are the five input parameters used to develop runoff hydrograph for the Red River in Manitoba, Canada. A feed-forward artificial neural network is trained by using back-percolation algorithm. Peak flow, time of peak, width of hydrograph at 75 and 50% of peak, base flow, and timing of rising and falling sides of hydrograph are the output parameters obtained from the neural network model to describe a runoff hydrograph. The ANN generated results are evaluated using statistical parameters: percentage error and correlation. For six flood events for which forecasts are made the average absolute error in peak flow and time of peak is 6% and 4 days, respectively. Correlation between observed and simulated values of peak flow and time of peak is 0.99 and 0.88, respectively. [Copyright &y& Elsevier]

Abstract: Flood management decision-making problems are often associated with multiple objectives and multiple stakeholders. The challenge remains on how to obtain the diversified opinions of a large number of stakeholders where uncertainty plays a major role. In response to this challenge, a methodology has been proposed to capture the views of multiple stakeholders using fuzzy set theory and fuzzy logic. Three possible forms of individual stakeholders'' input, scale (crisp), linguistic (fuzzy), and conditional (fuzzy), are analyzed to obtain the aggregated input by using Fuzzy Expected Value. The methodology is applied to flood management in the Red River Basin, Manitoba, Canada. The outputs derived from the three different input types show good correlation. Thus, the proposed methodology shows alternative ways of soliciting the opinion of a large number of stakeholders and aggregating those opinions for use in multi-objective decision making. [Copyright &y& Elsevier]

A new approach called spatial system dynamics (SSD) is presented to model feedback based dynamic processes in time and space. This approach is grounded in control theory for distributed parameter systems. System dynamics and geographic information system (GIS) are coupled to develop this modeling approach. The SSD modeling approach offers a single modeling framework for developing conceptually different models. It also provides the much-needed capability to model feedback based complex dynamic processes in time and space while giving insight into the interactions among different components of the system. The proposed approach is superior to existing techniques for dynamic modeling such as cellular automata and GIS and addresses most of the limitations present in these approaches. The SSD approach can be used to model a variety of physical and natural processes where the main interest is the space–time interaction, e.g., environmental/water resources processes, natural resources management, climate change, and disaster management. The applicability of the proposed approach is demonstrated with an application to flood management in the Red River basin in Manitoba, Canada. [ABSTRACT FROM AUTHOR]

An original modeling framework for the assessment of climate variation and change impacts on the performance of a complex flood protection system has been developed for the city of Winnipeg in the Red River basin, Manitoba, Canada. The modeling framework allows for the evaluation of different climate change scenarios generated by the global climate models. Temperature and precipitation are used as the main factors affecting flood flow generation. The main contribution of the reported work is the use of a system dynamics modeling and simulation approach in the development of a system performance assessment model. The assessment-modeling framework is based on flood flows, capacity of flood control structures, and failure flow levels at different locations in the basin. The results of this study (shown only to illustrate the methodology) indicate that the capacity of the existing Red River flood protection system is sufficient to accommodate future climate variability and change. [ABSTRACT FROM AUTHOR]

A multicriteria technique named fuzzy compromise programming is combined with a methodology known as group decision making under fuzziness to come up with a new technique that supports decision making with multiple criteria and multiple participants (or experts). All criteria (qualitative and quantitative) are modeled by way of fuzzy sets, utilizing the fact that criteria values in most water resources problems are vague, imprecise and/or ill defined. The involvement of multiple experts in the decision process is achieved by incorporating each participant's perception, of criteria weights, best and worst criteria values, relative degrees of risk acceptance, as well as other parameters into the problem. The proposed methodology is illustrated with a case study taken from the literature, combined with the input of four expert individuals with diverse backgrounds. After processing the input from the experts, a group compromise decision is formulated. [ABSTRACT FROM AUTHOR]

A new multicriteria technique, fuzzy compromise programming (FCP), is used to evaluate discrete alternatives in the context of water resources decision-making. All uncertain variables (subjective and objective) are modeled by way of fuzzy sets. Fuzzy set ranking methods are employed to compare, rank, and (or) sort the fuzzy output produced by FCP. The literature suggests that many ranking methods are available; however, not all may be appropriate for water resources decision-making. The objective of this paper is to compare fuzzy set ranking methods that can be implemented with FCP. Nine such ranking methods are considered in this research, two of which are fully tested using case studies from the literature. It was found that for all case studies, the ranking of alternatives was not very sensitive to changes in the degree of risk acceptance by experts or changes in the ranking methods themselves.Key words: fuzzy set ranking methods, risk preferences, compromise decision, water resources systems.Une nouvelle technique à critères multiples, soit la programmation par compromis flous (« fuzzy compromise programming : FCP »), est employée afin d'évaluer des alternatives discrètes dans le contexte du processus de prise de décision en ressources hydriques. Toutes les variables incertaines (subjectives and objectives) sont modélisées par le biais d'ensembles flous. Des méthodes de classement d'ensembles flous sont employées afin de comparer, classer et (ou) arranger dans un ordre spécifique les résultats flous produits par la FCP. La littérature implique que plusieurs méthodes de classement sont disponibles, mais elles pourraient ne pas être toutes appropriées pour le processus de prise de décision en ressources hydriques. L'objectif de cet article est de comparer des méthodes de classement d'ensembles flous qui peuvent être mises en place avec la FCP. Un total de neuf méthodes de classement sont considérées dans cette recherche, desquelles deux sont pleinement testées en utilisant des cas d'études provenant de la littérature. Pour tous les cas d'études, il a été trouvé que les méthodes de classement ne sont pas très sensibles aux changements du degrée d'acceptation du risque par l'expert, pas plus qu'aux changements à même des méthodes de classement.Mots clés : méthodes de classement d'ensembles flous, préférences de risques, décision par compromis, systèmes de ressources hydriques.[Traduit par la Rédaction] [ABSTRACT FROM AUTHOR]

Develops dynamics model for the global world water balance that captures the dynamic character of the main variables affecting water availability. Relation between water availability and different aspects of water development; Consequences of two extreme scenarios on the global water balance.

Heuristic knowledge has been integrated with analytical tools to support decision making for flood management. Development of an expert system called Intelligent Flood Management System for the selection of appropriate flood damage reduction measures for a given area is described. The selection of flood damage reduction measures is based on hydraulic, hydrological, geotechnical, environmental, and economic factors related to the river system and the area to be protected from floods. The knowledge base of the Intelligent Flood Management System is generic and can be used to identify a suitable flood management option for any area. The model base of the Intelligent Flood Management System consists of the hydraulic analysis package HEC-RAS, the flood damage analysis program HEC-FDA, and a model for economic analysis. The graphical user interface is developed for effective communication with the system. The developed system has been implemented to identify appropriate flood damage reduction options for the town of Ste. Agathe in Manitoba, Canada using data from 1997 flood in the Red River Basin.Key words: flood control, flood management, structural measures, heuristic knowledge, decision support systems, expert systems.Des connaissances heuristiques ont été intégrées avec des outils analytiques afin d'aider à la prise de décisions pour la gestion des crues. Le développement d'un système expert appelé « Intelligent Flood Management System (IFMS) » (système intelligent de gestion des crues) pour la sélection de mesures appropriées de réduction de dommages dues aux crues sur une région donnée est décrit. La sélection de mesures de réduction de dommages dues aux crues est basée sur des facteurs hydrauliques, hydrologiques, géotechniques, environnementaux et économiques reliés au système de la rivière et à la région qui doit être protégée des crues. La base de connaissances du IFMS est générique et peut être employée pour identifier une option appropriée quelle que soit la région. Le bloc de modélisation comprend l'ensemble d'analyse hydraulique HEC-RAS, le programme d'analyse des dommages dues aux crues HEC-FDA et un modèle pour analyses économiques. L'interface graphique usager est développé pour permettre une communcation efficace avec le système. Le système développé a été appliqué pour identifier les options appropriées de reduction des dommages dues aux crues pour le village de Ste-Agathe au Manitoba, Canada, en utilisant les donnés de la crue du bassin de la rivière Rouge en 1997.Mots clés : contrôle des crues, gestion des crues, mesures structurales, connaissances heuristiques, systèmes d'aide à la décision, systèmes expert.[Traduit par la Rédaction] [ABSTRACT FROM AUTHOR]

Discusses a study on the application of systems analysis techniques for reservoir operations for flood management. Overview of the system dynamics modeling of reservoir operations; Model architecture; Case study application; Results and discussion.

Deals with a short-term operation model developed for optimal operation of hydraulically coupled hydro-power plants. Definition of a reservoir or a plant; Features of the model; Description on the Energy Management and Maintenance Analysis model.

Describes a study which presented a systems approach for collaborative group planning of water development projects. Public decision processes; Selection of feedback mechanism for the planning process; Issues for collaborative decision support implementation; Details on the use of systems approach.

• Four widely used atmospheric reanalyses used as hydraulic inputs to a global flood model. • Performance in floodplain mapping is tested over six test-basins in Canada. • NARR simulated floodplain maps perform exemplarily among all reanalyses. • Historic flood events are also described satisfactorily by NARR fed simulations. • Need for judicious selection of reanalysis for floodplain mapping over large regions. Surface runoff estimates from atmospheric reanalysis datasets are increasingly preferred by hydrologists for modeling floods in regions where traditional observations are not sufficiently available. This study explores the fidelity of four widely used reanalyses runoff products from CFSR (6 hourly), ERA-Interim (3 hourly), MERRA (1 hourly), and NARR (3 hourly) as hydraulic forcings to a flood inundation model in describing inundation dynamics over Canada. The reanalysis datasets (duration: 1970 to 2010) are fed to the Catchment-based Macroscale Floodplain (CaMa-Flood) global hydrodynamic model, along with other relevant river and floodplain characteristic inputs, to derive high-resolution floodplain maps for 100 and 200-yr return periods. The floodplain maps derived from each reanalysis dataset are compared with the regional developed or 'benchmark floodplain maps' over six selected flood-prone basins (test basins) in Canada through a set of performance statistics. The NARR and ERA-Interim simulated flood maps are found to capture the high and very-high inundation spots fittingly over entire Canada than the remaining reanalysis datasets. While examining inundation dynamics over the six test regions, the NARR simulated floodplain maps are found to capture the majority of inundated areas by exhibiting the highest Hit-Rates between 0.78 and 0.88, and Critical Success Indexes between 0.7 and 0.82. ERA-Interim followed NARR in most of the cases, while CFSR and MERRA did not perform well on several occasions. On the basis of superior performance of NARR, a few historic flood events over the test basins are simulated and subsequently compared with MODIS satellite-derived floodplain information. We notice that more than 75% of the inundation is precisely captured for these events, leaving behind a few spots either due to the presence of a complex terrain and river geometry or due to impacts from other flood drivers such as snow-melt and tide inflow, which are not considered in the present analysis. This study strongly supports the need for careful selection of a reanalysis dataset while performing inundation modeling for large regions. Otherwise, an arbitrary choice may result in larger uncertainties in damage costs, population exposure, and flood risk values during the process. We hope that this study will provide additional insights into floodplain management practices and assist flood modelers and practitioners in a quest for selection of appropriate reanalysis data inputs for floodplain mapping. [ABSTRACT FROM AUTHOR]

In recent years, geospatial data (e.g. remote sensing imagery), and other relevant ancillary datasets (e.g. land use land cover, climate conditions) have been utilized through sophisticated algorithms to produce global population datasets. With a handful of such datasets, their performances and skill in flood exposure assessment have not been explored. This study proposes a comprehensive framework to understand the dynamics and differences in population flood exposure over Canada by employing four global population datasets alongside the census data from Statistics Canada as the reference. The flood exposure is quantified based on a set of floodplain maps (for 2015, 1 in 100-yr and 1 in 200-yr event) for Canada derived from the CaMa-Flood global flood model. To obtain further insights at the regional level, the methodology is implemented over six flood-prone River Basins in Canada. We find that about 9% (3.31 million) and 11% (3.90 million) of the Canadian population resides within 1 in 100-yr and 1 in 200-yr floodplains. We notice an excellent performance of WorldPop, and LandScan in most of the cases, which is unaffected by the representation of flood hazard, while Global Human Settlement and Gridded Population of the World showed large deviations. At last, we determined the long-term dynamics of population flood exposure and vulnerability from 2006 to 2019. Through this analysis, we also identify the regions that contain a significantly larger population exposed to floods. The relevant conclusions derived from the study highlight the need for careful selection of population datasets for preventing further amplification of uncertainties in flood risk. We recommend a detailed assessment of the severely exposed regions by including precise ground-level information. The results derived from this study may be useful not only for flood risk management but also contribute to understanding other disaster impacts on human-environment interrelationships. Unlabelled Image • Five population datasets are considered for quantifying flood exposure over Canada. • WorldPop and LandScan provide the closest estimates when compared with census data. • Skill of population datasets is tested over six flood-prone River Basins of Canada. • Long-term changes in degree of exposure is characterized at census-division level. • Highly exposed divisions are identified for ensuring detailed flood-risk assessment [ABSTRACT FROM AUTHOR]

Deals with minimizing the total cost of pumping in a liquid pipeline. Overview; Problem formulations; Strengths and weaknesses of common solution methods; Genetic algorithms; Approach to pumping cost optimization; Numerical example; Conclusions.

Focuses on the lessons learned from the numerous large-scale water resources projects. Integrated water resources model for Egypt; Sihu basin water management in China; Red river flooding.

• A system dynamics simulation approach (SDSA) for reservoir operation is proposed. • Scenario based simulation analyses are performed with the SDSA model. • Impacts of placing environmental priorities over power production are investigated. • Research results can contribute to environmentally friendly operation of reservoir. Multipurpose reservoir operation is a very complex decision-making process because of its multiple and usually conflicting targets. A system view of the interactions between various reservoir purposes (like for example, hydropower production and environmental impacts such as fish habitats, sediment flushing, and landslide stability) is a prerequisite to attain the full benefits from reservoir operations. System dynamics simulation is a well suited approach for dealing with complex time-varying systems and is widely used in water resources management and analyses of global change. This paper presents the development of a generic system dynamics simulation approach (SDSA) for the analyses of interactions between reservoir's various functions. The implementation of SDSA includes two steps. First, the causal diagram of the reservoir system is used to map and analyze the main interactions among reservoir system's different components, identify driving feedback relationships and use the system structure to obtain insights' into system behavior. In the second step, the stock and flow diagraming is used to quantify those feedbacks and interactions by introducing a set of physical and empirical relationships required to simulate the model. The developed SDSA is applied to the Three Gorges Reservoir in China. Scenario based simulation analyses are performed with the SDSA model to increase the understanding of placing different priorities on the interactions between hydropower production and environmental impacts of reservoir operations. Results from model validation and sensitivity analyses suggest that the developed SDSA model is capable of simulating interactions among reservoir's various functions, and the case study application demonstrates the utility of the model for capturing the main characteristics of reservoir system dynamic behavior. The insights gained from model simulations can assist in environmentally friendly operations of the reservoir. [ABSTRACT FROM AUTHOR]

Life in the modern world depends on multiple critical services provided by infrastructure systems which are interdependent at multiple levels. To effectively respond to infrastructure failures, this paper proposes a model for developing optimal joint restoration strategy for interdependent infrastructure systems following a disruptive event. First, models for (i) describing structure of interdependent infrastructure system and (ii) their interaction process, are presented. Both models are considering the failure types, infrastructure operating rules and interdependencies among systems. Second, an optimization model for determining an optimal joint restoration strategy at infrastructure component level by minimizing the economic loss from the infrastructure failures, is proposed. The utility of the model is illustrated using a case study of electric-water systems. Results show that a small number of failed infrastructure components can trigger high level failures in interdependent systems; the optimal joint restoration strategy varies with failure occurrence time. The proposed models can help decision makers to understand the mechanisms of infrastructure interactions and search for optimal joint restoration strategy, which can significantly enhance safety of infrastructure systems. [ABSTRACT FROM AUTHOR]

Enhancing the resilience of infrastructure systems is critical to the sustainability of the society against multiple disruptive events. This paper develops an approach for allocating restoration resources to enhance resilience of interdependent infrastructure systems. According to Inoperability Input–Output Model, a resilience metric for infrastructure systems is developed, in which the performance loss of infrastructure systems resulting from a disruptive event is measured in economic loss and inoperability. Model for determining the optimal infrastructure restoration resources allocation is proposed with the objective of maximizing resilience. Infrastructure interdependence is modeled by the Dynamic Inoperability Input-Output Model (DIIM), which is an accepted economic model for describing the interconnected relationship of industry sectors. To investigate the utility of the restoration resource allocation model, numerical analysis is conducted with an example derived from the data provided by the US Bureau of Economic Analysis. The results show that: (1) the optimal restoration resource allocation varies with the resource budget; (2) for a specific disruptive event, there exists an optimal resource budget which can minimize the sum of restoration cost and the performance loss of infrastructure system; and (3) the significance of factors such as initial inoperability of infrastructure systems on the optimal allocation. The proposed model can assist the decision makers in (i) better understand the effects of resource allocation, and (ii) deciding which allocation strategies should be used following a disruptive event. [ABSTRACT FROM AUTHOR]

Surface Urban Heat Island (SUHI) is an urban climate phenomenon that is expected to respond to future climate and land-use land-cover change. It is important to further our understanding of physical mechanisms that govern SUHI phenomenon to enhance our ability to model future SUHI characteristics under changing geophysical conditions. In this study, SUHI phenomenon is quantified and modelled at 20 cities distributed across Canada. By analyzing MODerate Resolution Imaging Spectroradiometer (MODIS) sensed surface temperature at the cities over 2002–2012, it is found that 16 out of 20 selected cities have experienced a positive SUHI phenomenon while 4 cities located in the prairies region and high elevation locations have experienced a negative SUHI phenomenon in the past. A statistically significant relationship between observed SUHI magnitude and city elevation is also recorded over the observational period. A Physical Scaling downscaling model is then validated and used to downscale future surface temperature projections from 3 GCMs and 2 extreme Representative Concentration Pathways in the urban and rural areas of the cities. Future changes in SUHI magnitudes between historical (2006–2015) and future timelines: 2030s (2026–2035), 2050s (2046–2055), and 2090s (2091–2100) are estimated. Analysis of future projected changes indicate that 15 (13) out of 20 cities can be expected to experience increases in SUHI magnitudes in future under RCP 2.6 (RCP 8.5). A statistically significant relationship between projected future SUHI change and current size of the cities is also obtained. The study highlights the role of city properties (i.e. its size, elevation, and surrounding land-cover) towards shaping their current and future SUHI characteristics. The results from this analysis will help decision-makers to manage Canadian cities more efficiently under rapidly changing geophysical and demographical conditions. [ABSTRACT FROM AUTHOR]

The identification of homogeneous precipitation regions is essential in the planning, design and management of water resources systems. Regions are identified using a technique that partitions climate sites into groups based on the similarity of their attributes; the procedure is known as regionalization. In this paper the ability of four attribute sets to form large, coherent precipitation zones is assessed in terms of the regional homogeneity of precipitation statistics and computational efficiency. The outcomes provide guidance for effective attribute selection for future studies in Canada. The attributes under consideration include location parameters (latitude, longitude), distance to major water bodies, site elevation and atmospheric variables modelled at different pressure levels. The analysis is conducted in two diverse climate regions within Canada including the Prairie and the Great Lakes–St Lawrence lowlands regions. The method consists of four main steps: (i) formation of the attribute sets; (ii) determination of the preferred number of regions (selection of thec-value) into which the sites are partitioned; (iii) regionalization of climate sites using the fuzzyc-means clustering algorithm; and (iv) validation of regional homogeneity usingL-moment statistics. The results of the attribute formation,c-value selection, regionalization and validation processes are presented and discussed in a comparative analysis. Based on the results it is recommended for both regions to use location parameters including latitude, longitude and distance to water bodies (in the Great Lakes region) to form precipitation regions and to consider atmospheric variables for future (climate change) applications of the regionalization procedure. [ABSTRACT FROM PUBLISHER]

Summary Impacts of global climate change on water resources systems are assessed by downscaling coarse scale climate variables into regional scale hydro-climate variables. In this study, a new multisite statistical downscaling method based on beta regression (BR) is developed for generating synthetic precipitation series, which can preserve temporal and spatial dependence along with other historical statistics. The beta regression based downscaling method includes two main steps: (1) prediction of precipitation states for the study area using classification and regression trees, and (2) generation of precipitation at different stations in the study area conditioned on the precipitation states. Daily precipitation data for 53 years from the ANUSPLIN data set is used to predict precipitation states of the study area where predictor variables are extracted from the NCEP/NCAR reanalysis data set for the same interval. The proposed model is applied to downscaling daily precipitation at ten different stations in the Campbell River basin, British Columbia, Canada. Results show that the proposed downscaling model can capture spatial and temporal variability of local precipitation very well at various locations. The performance of the model is compared with a recently developed non-parametric kernel regression based downscaling model. The BR model performs better regarding extrapolation compared to the non-parametric kernel regression model. Future precipitation changes under different GHG (greenhouse gas) emission scenarios also projected with the developed downscaling model that reveals a significant amount of changes in future seasonal precipitation and number of wet days in the river basin. [ABSTRACT FROM AUTHOR]