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Research paper thumbnail of Modelling of climate-induced hydrologic changes in the Lake Winnipeg watershed

Lancet

The hydrologic regime of the Lake Winnipeg watershed (LWW), Canada, is dominated by spring snowme... more The hydrologic regime of the Lake Winnipeg watershed (LWW), Canada, is dominated by spring snowmelt runoff, often occurring over frozen ground. Analyses of regional climate models (RCMs) based on future climate projections presented in a companion paper of this special issue (Dibike et al., 2011) show future increases in annual precipitation and temperature in various seasons and regions of this catchment. Such changes are expected to influence the volume of snow accumulation and melt, as well as the timing and intensity of runoff. This paper presents results of modelling climate-induced hydrologic changes in two representative sub-catchments of the Red and Assiniboine basins in the LWW. The hydrologic model, Soil and Water Assessment Tool (SWAT), was employed to simulate a 21-year baseline and future (2042-2062) climate based on climate forcings derived from 3 RCMs. The effects of future changes in climatic variables, specifically precipitation and temperature, are clearly evident in the resulting snowmelt and runoff regimes. The most significant changes include higher total runoff, and earlier snowmelt and discharge peaks. Some of the results also revealed increases in peak discharge intensities. Such changes will have significant implications for water availability and nutrient transport regimes in the LWW.

Research paper thumbnail of Simulation of flood flow in a river system using artificial neural networks

Hydrology and Earth System Sciences, 2005

Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow sim... more Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow simulation models. An important criterion for the wider applicability of the ANNs is the ability to generalise the events outside the range of training data sets. With respect to flood flow simulation, the ability to extrapolate beyond the range of calibrated data sets is of crucial importance. This study explores methods for improving generalisation of the ANNs using three different flood events data sets from the Neckar River in Germany. An ANN-based model is formulated to simulate flows at certain locations in the river reach, based on the flows at upstream locations. Network training data sets consist of time series of flows from observation stations. Simulated flows from a one-dimensional hydrodynamic numerical model are integrated for network training and validation, at a river section where no measurements are available. Network structures with different activation functions are considered for improving generalisation. The training algorithm involved backpropagation with the Levenberg-Marquardt approximation. The ability of the trained networks to extrapolate is assessed using flow data beyond the range of the training data sets. The results of this study indicate that the ANN in a suitable configuration can extend forecasting capability to a certain extent beyond the range of calibrated data sets.

Research paper thumbnail of A Fuzzy Set Theory Based Methodology for Analysis of Uncertainties in Stage-Discharge Measurements and Rating Curve

River stage and discharge records are essential for hydrological and hydraulic analyses.

Research paper thumbnail of Fuzzy Nonlinear Regression Approach to Stage-Discharge Analyses: Case Study

Journal of Hydrologic Engineering, 2010

River discharge is typically derived from a single valued stage-discharge relationship. However, ... more River discharge is typically derived from a single valued stage-discharge relationship. However, the relationship is affected by different sources of uncertainty, especially, in the measurement of discharge and stage values. The measurement uncertainty propagates into stage-discharge relationship curve and affects the discharge values derived from the relation. A fuzzy set theory based methodology is investigated in this paper for the analysis of uncertainty in the stage-discharge relationship. Individual components of stage and discharge measurement are considered as a fuzzy numbers and the overall stage and discharge uncertainty is obtained through the aggregation of all uncertainties using fuzzy arithmetic. Building on the previous work-fuzzy discharge and stage measurements, we use fuzzy nonlinear regression-in this case study for the analysis of uncertainty in the stage-discharge relationship. The methodology is based on fuzzy extension principle and considers input and output variables as well as the coefficients of the stage-discharge relationship as fuzzy numbers. Two different criteria are used for the evaluation of output fuzziness: ͑1͒ minimum spread and ͑2͒ least absolute deviation criteria. The results of the fuzzy regression analysis lead to a definition of lower and upper uncertainty bounds of the stage-discharge relationship and representation of discharge value as a fuzzy number. The methodology developed in this work is illustrated with a case study of Thompson River near Spences Bridge in British Columbia, Canada.

Research paper thumbnail of Fuzzy set theory based methodology for the analysis of measurement uncertainties in river discharge and stage

Canadian Journal of Civil Engineering, 2010

The discharge and stage measurements in a river system are characterized by a number of sources o... more 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.

Research paper thumbnail of Simulation of flood flow in a river system using ar tificial neural networks 313 Simulation of flood flow in a river system using artificial neural networks

Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow sim... more Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow simulation models. An important criterion for the wider applicability of the ANNs is the ability to generalise the events outside the range of training data sets. With respect to flood flow simulation, the ability to extrapolate beyond the range of calibrated data sets is of crucial importance. This study explores methods for improving generalisation of the ANNs using three different flood events data sets from the Neckar River in Germany. An ANN-based model is formulated to simulate flows at certain locations in the river reach, based on the flows at upstream locations. Network training data sets consist of time series of flows from observation stations. Simulated flows from a one-dimensional hydrodynamic numerical model are integrated for network training and validation, at a river section where no measurements are available. Network structures with different activation functions are considered for improving generalisation. The training algorithm involved backpropagation with the Levenberg-Marquardt approximation. The ability of the trained networks to extrapolate is assessed using flow data beyond the range of the training data sets. The results of this study indicate that the ANN in a suitable configuration can extend forecasting capability to a certain extent beyond the range of calibrated data sets.

Research paper thumbnail of Modelling of climate-induced hydrologic changes in the Lake Winnipeg watershed

Lancet

The hydrologic regime of the Lake Winnipeg watershed (LWW), Canada, is dominated by spring snowme... more The hydrologic regime of the Lake Winnipeg watershed (LWW), Canada, is dominated by spring snowmelt runoff, often occurring over frozen ground. Analyses of regional climate models (RCMs) based on future climate projections presented in a companion paper of this special issue (Dibike et al., 2011) show future increases in annual precipitation and temperature in various seasons and regions of this catchment. Such changes are expected to influence the volume of snow accumulation and melt, as well as the timing and intensity of runoff. This paper presents results of modelling climate-induced hydrologic changes in two representative sub-catchments of the Red and Assiniboine basins in the LWW. The hydrologic model, Soil and Water Assessment Tool (SWAT), was employed to simulate a 21-year baseline and future (2042-2062) climate based on climate forcings derived from 3 RCMs. The effects of future changes in climatic variables, specifically precipitation and temperature, are clearly evident in the resulting snowmelt and runoff regimes. The most significant changes include higher total runoff, and earlier snowmelt and discharge peaks. Some of the results also revealed increases in peak discharge intensities. Such changes will have significant implications for water availability and nutrient transport regimes in the LWW.

Research paper thumbnail of Simulation of flood flow in a river system using artificial neural networks

Hydrology and Earth System Sciences, 2005

Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow sim... more Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow simulation models. An important criterion for the wider applicability of the ANNs is the ability to generalise the events outside the range of training data sets. With respect to flood flow simulation, the ability to extrapolate beyond the range of calibrated data sets is of crucial importance. This study explores methods for improving generalisation of the ANNs using three different flood events data sets from the Neckar River in Germany. An ANN-based model is formulated to simulate flows at certain locations in the river reach, based on the flows at upstream locations. Network training data sets consist of time series of flows from observation stations. Simulated flows from a one-dimensional hydrodynamic numerical model are integrated for network training and validation, at a river section where no measurements are available. Network structures with different activation functions are considered for improving generalisation. The training algorithm involved backpropagation with the Levenberg-Marquardt approximation. The ability of the trained networks to extrapolate is assessed using flow data beyond the range of the training data sets. The results of this study indicate that the ANN in a suitable configuration can extend forecasting capability to a certain extent beyond the range of calibrated data sets.

Research paper thumbnail of A Fuzzy Set Theory Based Methodology for Analysis of Uncertainties in Stage-Discharge Measurements and Rating Curve

River stage and discharge records are essential for hydrological and hydraulic analyses.

Research paper thumbnail of Fuzzy Nonlinear Regression Approach to Stage-Discharge Analyses: Case Study

Journal of Hydrologic Engineering, 2010

River discharge is typically derived from a single valued stage-discharge relationship. However, ... more River discharge is typically derived from a single valued stage-discharge relationship. However, the relationship is affected by different sources of uncertainty, especially, in the measurement of discharge and stage values. The measurement uncertainty propagates into stage-discharge relationship curve and affects the discharge values derived from the relation. A fuzzy set theory based methodology is investigated in this paper for the analysis of uncertainty in the stage-discharge relationship. Individual components of stage and discharge measurement are considered as a fuzzy numbers and the overall stage and discharge uncertainty is obtained through the aggregation of all uncertainties using fuzzy arithmetic. Building on the previous work-fuzzy discharge and stage measurements, we use fuzzy nonlinear regression-in this case study for the analysis of uncertainty in the stage-discharge relationship. The methodology is based on fuzzy extension principle and considers input and output variables as well as the coefficients of the stage-discharge relationship as fuzzy numbers. Two different criteria are used for the evaluation of output fuzziness: ͑1͒ minimum spread and ͑2͒ least absolute deviation criteria. The results of the fuzzy regression analysis lead to a definition of lower and upper uncertainty bounds of the stage-discharge relationship and representation of discharge value as a fuzzy number. The methodology developed in this work is illustrated with a case study of Thompson River near Spences Bridge in British Columbia, Canada.

Research paper thumbnail of Fuzzy set theory based methodology for the analysis of measurement uncertainties in river discharge and stage

Canadian Journal of Civil Engineering, 2010

The discharge and stage measurements in a river system are characterized by a number of sources o... more 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.

Research paper thumbnail of Simulation of flood flow in a river system using ar tificial neural networks 313 Simulation of flood flow in a river system using artificial neural networks

Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow sim... more Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow simulation models. An important criterion for the wider applicability of the ANNs is the ability to generalise the events outside the range of training data sets. With respect to flood flow simulation, the ability to extrapolate beyond the range of calibrated data sets is of crucial importance. This study explores methods for improving generalisation of the ANNs using three different flood events data sets from the Neckar River in Germany. An ANN-based model is formulated to simulate flows at certain locations in the river reach, based on the flows at upstream locations. Network training data sets consist of time series of flows from observation stations. Simulated flows from a one-dimensional hydrodynamic numerical model are integrated for network training and validation, at a river section where no measurements are available. Network structures with different activation functions are considered for improving generalisation. The training algorithm involved backpropagation with the Levenberg-Marquardt approximation. The ability of the trained networks to extrapolate is assessed using flow data beyond the range of the training data sets. The results of this study indicate that the ANN in a suitable configuration can extend forecasting capability to a certain extent beyond the range of calibrated data sets.