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In this study future flooding frequencies have been estimated for the Grand River catchment located in south - western Ontario, Canada. Historical and future climatic projections made by fifteen Coupled Model Inter - comparison Project - 3 climate models are bias - corrected and downscaled before they are used to obtain mid - and end of 21 st century streamflow projections. By comparing the future projected and historically observed precipitation and temperature record s it is found that the mean and extreme temperature events will intensify in future across the catchment. The increase is more drastic in the case of extreme events than the mean events. The sign of change in future precipitation is uncertain. Further flow extremes are expected to increase in magnitude and frequency in future across the catchment. The confidence in the projection is more for low return period (<10 years) extreme events than higher return period (10 - 100 years) events. It can be expected that increases in temperature will play a dominant role in increasing the magnitude of low return period flooding events while precipitation seems to play an important role in shaping the high return period events.
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The contemporary definition of integrated water resources management (IWRM) is introduced to promote a holistic approach in water engineering practices. IWRM deals with planning, design and operation of complex systems in order to control the quantity, quality, temporal and spatial distribution of water with the main objective of meeting human and ecological needs and providing protection from water related disasters. This paper examines the existing decision making support in IWRM practice, analyses the advantages and limitations of existing tools, and, as a result, suggests a generic multi-method modeling framework that has the main goal to capture all structural complexities of, and interactions within, a water resources system. Since the traditional tools do not provide sufficient support, this framework uses multi-method simulation technique to examine the codependence between water resources system and socioeconomic environment. Designed framework consists of (i) a spatial database, (ii) a traditional process-based model to represent the physical environment and changing conditions, and (iii) an agent-based spatially explicit model of socio-economic environment. The multi-agent model provides for building virtual complex systems composed of autonomous entities, which operate on local knowledge, possess limited abilities, affect and are affected by local environment, and thus, enact the desired global system behavior. Agent-based model is used in the presented work to analyze spatial dynamics of complex physical-social-economic-biologic systems. Based on the architecture of the generic multi-method modeling framework, an operational model for the Upper Thames River basin, Southwestern Ontario, Canada, is developed in cooperation with the local conservation authority. Six different experiments are designed by combining three climate and two socio-economic scenarios to analyze spatial dynamics of a complex physical-social-economic system of the Upper Thames River basin. Obtained results show strong dependence between changes in hydrologic regime, in this case surface runoff and groundwater recharge rates, and regional socio-economic activities.
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ABSTRACTTwo 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 oc...
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The KnnCAD Version 4 weather generator algorithm for nonparametric, multisite simulations of temperature and precipitation data is presented. The K-nearest neighbor weather generator essentially reshuffles the historical data, with replacement. In KnnCAD Version 4, a block resampling scheme is introduced to preserve the temporal correlation structure in temperature data. Perturbation of the reshuffled variable data is also added to enhance the generation of extreme values. The Upper Thames River Basin in Ontario, Canada isused as a case study and the model is shown to simulate effectively the historical characteristics at the site. The KnnCAD Version 4 approach is shown to improve on the previous versions of the model and offers a major advantage over many parametric and semiparametric weather generators in that multisite use can be easily achieved without making statistical assumptions dealing with the spatial correlations and probability distributions of each variable.