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The lower Athabasca River (Canada) has experienced notable declines in streamflow and increasing oil sands development since the 1970s. This study investigates the potential impacts of climate change on navigability using both observed historical and projected future flows derived via hydrological simulations driven by an ensemble of statistically downscaled general circulation model climate data. Our use of proposed indices that form the Aboriginal Navigation Index (ANI) and a new index based on percentage over threshold (POT) occurrences yielded novel insights into anticipated changes to the flow regime. Comparisons of near (2041–2070) and far (2071–2100) future periods with the historical baseline (1981–2010) yielded results that project significant reductions in the 500 m3 s−1 POT during the fall navigability period spanning weeks 34 to 43, as well as reductions in the integrated ANIFall. These results indicate that challenging navigational conditions may become more frequent in the second half of the 21st century, not only during this fall period but also earlier into the summer, due to a shift in the flow regime, with potentially severe impacts on the users of the river channels. Our assessment approach is transferable to other regional study areas and should be considered in water management and environmental flow frameworks. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change--induced snowpack loss is affecting streamflow predictability, as it becomes less dependent on the initial snowpack conditions and more dependent on meteorological forecasts. We assess future changes to seasonal streamflow predictability over two large river basins, Liard and Athabasca in western Canada, by approximating streamflow response from the Variable Infiltration Capacity (VIC) hydrologic model with the Bayesian regularized neutral network (BRNN) machine learning emulator. We employ the BRNN emulator in a testbed ensemble streamflow prediction system by treating VIC-simulated snow water equivalent (SWE) as a known predictor and precipitation and temperature from GCMs as ensemble forecasts, thereby isolating the effect of SWE on streamflow predictability. We assess warm-season mean and maximum flow predictability over 2041--70 and 2071--2100 future periods against the1981--2010 historical period. The results indicate contrasting patterns of change, with the predictive skills for mean flow generally declining for the two basins, and marginally increasing or decreasing for the headwater subbasins. The predictive skill for maximum flow declines for the relatively warmer Athabasca basin and improves for the colder Liard basin and headwater subbasins. While the decreasing skill for the Athabasca is attributable to substantial loss in SWE, the improvement for the Liard and headwaters can be attributed to an earlier maximum flow timing that reduces the forecast horizon and offsets the effect of SWE loss. Overall, while the future change in SWE does affect the streamflow prediction skill, the loss of SWE alone is not a sufficient condition for the reduction in streamflow predictability. [ABSTRACT FROM AUTHOR]

Shrestha, Rajesh R., Yonas B. Dibike, and Terry D. Prowse, 2011. Modeling Climate Change Impacts on Hydrology and Nutrient Loading in the Upper Assiniboine Catchment. Journal of the American Water Resources Association (JAWRA) 48(1): 74-89. DOI: 10.1111/j.1752-1688.2011.00592.x Abstract: This paper presents a modeling study on climate-induced changes in hydrologic and nutrient fluxes in the Upper Assiniboine catchment, located in the Lake Winnipeg watershed. The hydrologic and agricultural chemical yield model, Soil and Water Assessment Tool (SWAT) was employed to model a 21-year baseline (1980-2000) and future (2042-2062) periods with model forcings for future climates derived from three regional climate models (RCMs) and their ensemble means. The modeled future scenarios reveal that potential future changes in the climatic regime are likely to modify considerably hydrologic and nutrient fluxes. The effects of future changes in climatic variables, especially precipitation and temperature, are clearly evident in the resulting snowmelt and runoff regimes. The future hydrologic scenarios consistently show earlier onsets of spring snowmelt and discharge peaks, and higher total runoff volumes. The simulated nutrient loads closely match the dynamics of the future runoff for both nitrogen and phosphorus, in terms of earlier timing of peak loads and higher total loads. However, nutrient concentrations could decrease due to the higher rate of runoff increase. Overall, the effects of these changes on the nutrient transport regime need to be considered together with possible future changes in land use, crop type, fertilizer application, and transformation processes in the receiving water bodies. [ABSTRACT FROM AUTHOR]

The article discusses the difficulties associated with downscaling meteorological data in order to model climate changes. Simulations involving the WatFlood and HBV-96 models are discussed and the outcomes of simulations of the the Saguenay watershed, located in Quebec, Canada, are described. Data that was downscaled using the Statistical Down-Scaling Model (SDSM), a regression-based method, produced variations in the predictive power of the two models. The significance of such variations for attempts to accurately estimate the hydrological effects of climate change are discussed.

The issues of downscaling the outputs of a global climate model (GCM) to a scale that is appropriate to hydrological impact studies are investigated using a temporal neural network approach. The time-lagged feed-forward neural network (TLFN) is proposed for downscaling daily total precipitation and daily maximum and minimum temperature series for the Serpent River watershed in northern Quebec (Canada). The downscaling models are developed and validated using large-scale predictor variables derived from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset. Atmospheric predictors such as specific humidity, wind velocity, and geopotential height are identified as the most relevant inputs to the downscaling models. The performance of the TLFN downscaling model is also compared to a statistical downscaling model (SDSM). The downscaling results suggest that the TLFN is an efficient method for downscaling both daily precipitation and temperature series. The best downscaling models were then applied to the outputs of the Canadian Global Climate Model (CGCM1), forced with the Intergovernmental Panel on Climate Change (IPCC) IS92a scenario. Changes in average precipitation between the current and the future scenarios predicted by the TLFN are generally found to be smaller than those predicted by the SDSM model. Furthermore, application of the downscaled data for hydrologic impact analysis in the Serpent River resulted in an overall increasing trend in mean annual flow as well as earlier spring peak flow. The results also demonstrate the emphasis that should be given in identifying the appropriate downscaling tools for impact studies by showing how a future climate scenario downscaled with different downscaling methods could result in significantly different hydrologic impact simulation results for the same watershed. [ABSTRACT FROM AUTHOR]

Recent developments in the field of computational intelligence (CI) techniques are helping to solve various problems of water resources modeling and management. These techniques have also shown their potential as an alternative approach to conventional controllers. In this paper, artificial neural networks (ANN) and fuzzy systems (FS) are shown to be efficient alternatives to using optimal control algorithms in real-time control of the polder water system of Overwaard in The Netherlands. The relation between the optimal decision or action and the influencing parameters are learned by ANN and FS and then used to derive the decisions and control actions in real-time. It was possible to reproduce the centralized behavior (in terms of water levels and corresponding discharges) of optimal control action by using easily measurable local information. Moreover, it is demonstrated that model simulation with external intelligent controllers is ten times faster than that with the optimal control. [ABSTRACT FROM AUTHOR]

Presents a study which investigated the possibility of using a machine learning paradigm based on the theory of statistical learning, namely that of the support vector machine (SVM). Basic ideas underlying statistical learning theory and SVM; Potential of the SVM for feature classification and multiple regression problems; Analysis of the performance of the SVM.

The important aspect of changing hydro-climatic extremes is addressed in studies examining projected changes to flood-generating mechanisms associated with snowmelt across Canada, and historical and projected changes to severe Canadian Prairie droughts. The earth has vast amounts of surface and sub-surface freshwater in the form of lakes, reservoirs, rivers, wetlands, soil water, groundwater, as well as water stored in snowpacks, glaciers, and permafrost. Hydro-climatic extremes such as floods and droughts have significant impacts on freshwater availability and are projected to increase in frequency as the result of climate change. Summary of This Special Issue This special issue includes two invited critical reviews related to hydro-climatic processes associated with freshwater availability. [Extracted from the article]

Flows originating from cold and mountainous watersheds are highly dependent on temperature and precipitation patterns, and the resulting snow accumulation and melt conditions, affecting the magnitude and timing of annual peak flows. This study applied a multiple linear regression (MLR) modelling framework to investigate spatial variations and relative importance of hydroclimatic drivers of annual maximum flows (AMF) and mean spring flows (MAMJflow) in 25 river basins across western Canada. The results show that basin average maximum snow water equivalent (SWEmax), April 1st SWE and spring precipitation (MAMJprc) are the most important predictors of both AMF and MAMJflow, with the proportion of explained variance averaging 51.7%, 44.0% and 33.5%, respectively. The MLR models' abilities to project future changes in AMF and MAMJflow in response to changes to the hydroclimatic controls are also examined using the Canadian Regional Climate Model (CanRCM4) output for RCP 4.5 and RCP8.5 scenarios. The results show considerable spatial variations depending on individual watershed characteristics with projected changes in AMF ranging from −69% to +126% and those of MAMJflow ranging from −48% to +81% by the end of this century. In general, the study demonstrates that the MLR framework is a useful approach for assessing the spatial variation in hydroclimatic controls of annual maximum and mean spring flows in the western Canadian river basins. However, there is a need to exercise caution in applying MLR models for projecting changes in future flows, especially for regulated basins. [ABSTRACT FROM AUTHOR]

Climatic change is affecting streamflow regimes of the permafrost region, altering mean and extreme streamflow conditions. In this study, we analyzed historical trends in annual mean flow (Qmean), minimum flow (Qmin), maximum flow (Qmax) and Qmax timing across 84 hydrometric stations in the permafrost region of Canada. Furthermore, we related streamflow trends with temperature and precipitation trends, and used a multiple linear regression (MLR) framework to evaluate climatic controls on streamflow components. The results revealed spatially varied trends across the region, with significantly increasing (at 10% level) Qmin for 43% of stations as the most prominent trend, and a relatively smaller number of stations with significant Qmean, Qmax and Qmax timing trends. Temperatures over both the cold and warm seasons showed significant warming for >70% of basin areas upstream of the hydrometric stations, while precipitation exhibited increases for >15% of the basins. Comparisons of the 1976 to 2005 basin-averaged climatological means of streamflow variables with precipitation and temperature revealed a positive correlation between Qmean and seasonal precipitation, and a negative correlation between Qmean and seasonal temperature. The basin-averaged streamflow, precipitation and temperature trends showed weak correlations that included a positive correlation between Qmin and October to March precipitation trends, and negative correlations of Qmax timing with October to March and April to September temperature trends. The MLR-based variable importance analysis revealed the dominant controls of precipitation on Qmean and Qmax, and temperature on Qmin. Overall, this study contributes towards an enhanced understanding of ongoing changes in streamflow regimes and their climatic controls across the Canadian permafrost region, which could be generalized for the broader pan-Arctic regions. [ABSTRACT FROM AUTHOR]