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The Prairie landscape consists of numerous pothole depressions which produce complex fill-and-spill runoff generation processes that result in intermittent hydrologic connectivity and dynamic contributing areas (DCA). We investigated the effect of including DCA in the modified version of the Soil and Water Assessment Tool (SWAT) model and its implication on future streamflow projection for the pothole dominated Assiniboine River Basin (ARB). The fill-and-spill processes that lead to DCA were captured using a physically-based approach, with a volumetric threshold to reduce the computational demand. Despite the challenges in accurately simulating prairie pothole hydrology, both in terms of timing and volume of runoff, the modified approach improved streamflow modelling performance, and reduced model uncertainty. Further, we evaluated the effects of representing DCA on projecting future streamflow by using eight statistically downscaled CMIP5 GCMs, forced with the RCP4.5 and RCP8.5 scenarios. End of century projections indicate increases in annual precipitation and temperature across the ARB, with decreasing summer precipitation relative to the 1976–2005 baseline period. Compared to the standard SWAT setup that does not allow for DCA, the modified model was found to be more responsive to climatic change with relatively larger projected increases in seasonal and annual flows at the majority of evaluated stations. This advance in DCA modelling will facilitate longer-term large basin-scale simulations that are more representative for the Prairie region.

This study assesses snow response in the Assiniboine-Red River basin, located in the Lake Winnipeg watershed, due to anthropogenic climate change. We use a process-based distributed snow model driven by an ensemble of eight statistically downscaled global climate models (GCMs) to project future changes under policy-relevant global mean temperature (GMT) increases of 1.0 °C to 3.0 °C above the pre-industrial period. Results indicate that basin scale seasonal warmings generally exceed the GMT increases, with greater warming in winter months. The majority of GCMs project wetter winters and springs, and drier summers, while autumn could become either drier or wetter. An analysis of snow water equivalent (SWE) responses under GMT changes reveal higher correlations of snow cover duration (SCD), snowmelt rate, maximum SWE (SWEmax) and timing of SWEmax with winter and spring temperatures compared to precipitation, implying that these variables are predominantly temperature controlled. Consequently, under the GMT increases from 1.0 °C to 3.0 °C, the basin will experience successively shorter SCD, slower snowmelt, smaller monthly SWE and SWEmax, earlier SWEmax, and a transition from snow-dominated to rain-snow hybrid regime. Further, while the winter precipitation increases for some GCMs compensate the temperature-driven changes in SWE, the increases for most GCMs occur as rainfall, thus limiting the positive contribution to snow storage. Overall, this study provides a detailed diagnosis of the snow regime changes under the policy-relevant GMT changes, and a basis for further investigations on water quantity and quality changes.

Oil sands development in the lower Athabasca River watershed has raised considerable public and scientific concerns regarding perceived effects on environmental health. To address this issue for tributaries and the mainstem of the Athabasca River in the Athabasca Oil Sands Region, the Water Component of the Joint Oil Sands Monitoring (JOSM) plan produced monitoring assessments for seven integrated themes: atmospheric deposition, tributary water quality, river mainstem water quality, groundwater quality and quantity, water quality and quantity modelling, benthic invertebrate condition, and fish health. Our review integrates and synthesizes the large and diverse datasets assembled in the seven JOSM theme assessments to (i) evaluate possible environmental effects based on known sources and candidate proximal causes and (ii) determine the importance of cause-and-effect pathways related to contaminant, sediment, and nutrient inputs. Although JOSM research identified ecological effects that appear to be associated with contaminant exposure, the source of this exposure is confounded by co-location of, and inability to differentiate between, oil sands operations (principally released by atmospheric emission) and inputs from the natural bitumen outcrops (e.g., erosional material transported by surface and groundwater flows). Nutrient enrichment from treated municipal sewage effluent was the dominant ecological effect observed for the mainstem Athabasca River, associated with increased fish size and changes in invertebrate assemblages, likely because this pollution source is discharged directly into the river. If the direct release of treated oil sands process water occurs in the future, then the potential ecological impact of these direct industry releases will need to be evaluated carefully. The ecological causal assessment method proved to be a useful tool for better understanding how stressor sources relate to ecological effects through candidate proximate causes. Factors that confound our ability to assess the ecological effects of oil sands development focus on our inability to adequately differentiate between contaminants supplied from natural and anthropogenic contaminant sources. Our causal synthesis identifies options for changes in future monitoring to better anticipate and detect degradation in the ecosystem health of the lower Athabasca River and its tributaries.

This study developed and applied a framework for assessing the vulnerability of pan-Canadian water resources to permafrost thaw. The national-scale work addresses a key, but neglected, information gap, as previous research has focused on small scale physical processes and circumpolar trends. The framework was applied to develop the Canadian Water Resources Vulnerability Index to Permafrost Thaw (CWRVIPT) and map the index across the Canadian North. The CWRVIPT is a linearly additive index of permafrost, terrain, disturbance, and climatic conditions and stressors that influence water budgets and aquatic chemistry. Initial results imply water resources in the western Northwest Territories and Hudson Bay Lowlands are most vulnerable to permafrost thaw; however, water resources on Banks, Victoria and Baffin Islands are also relatively vulnerable. Although terrain and permafrost sub-indices are the largest component of the CWRVIPT across a wide swath from the Mackenzie River Delta to the Hudson Bay Lowlands, the climate sub-index is most important farther north over parts of the southern portion of the Arctic Archipelago. The index can be used to identify areas of water resource vulnerability on which to focus observation and research in the Canadian North.

The western cordillera supplies freshwater across much of western Canada mainly through meltwater from snow and ice. This “alpine water tower” has been, and is projected to be, associated with changes in the seasonality and amount of freshwater availability, which are critical in supporting the societal and environmental flow needs of the region. This study incorporates existing information to synthesize and evaluate current and future freshwater supplies and demands across major north-, west-, and east-flowing sub-basins of the Canadian western cordillera. The assessment of supply indicators reveals several historical changes that are projected to continue, and be exacerbated, particularly by the end of this century and under a high emission scenario. The greatest and most widespread impact is the seasonality of streamflow characterized by earlier spring freshets, increased winter, and decreased summer flow. Future winter and spring warming over all basins will result in decreases in end of season snow and glacier mass balance with greatest declines in more southern regions. In many areas, there will be a greater likelihood of summer freshwater shortages. All sub-basins have environmental and economic freshwater demands and pressures, especially in more southern watersheds where population and infrastructure are more prevalent and industrial, agricultural, and water energy needs are higher. Concerns regarding the continued ability to maintain suitable aquatic habitats and adequate water quality are issues across all regions. These water supply changes along with continued and increasing demands will combine to create a variety of freshwater vulnerabilities across all regions of western Canada. Southern basins including the South Saskatchewan and Okanagan are likely to experience the greatest vulnerabilities due to future summer freshwater supply shortages and increasing economic demands. In more northern areas, vulnerabilities primarily relate to how the rapidly changing landscape (mainly associated with permafrost thaw) impacts freshwater quantity and quality. These vulnerabilities will require various adaptation measures in response to alterations in the timing and amount of future freshwater supplies and demands.

River ice, like open-water conditions, is an integral component of the cold-climate hydrological cycle. The annual succession of river ice formation, growth, decay and clearance can include low flows and ice jams, as well as midwinter and spring break-up events. Reports and associated data of river ice occurrence are often limited to single locations or regional assessments, are season-specific, and use readily available data. Within Canada, the National Hydrometric Program (NHP) operates a network of gauging stations with water level as the primary measured variable to derive discharge. In the late 1990s, the Water Science and Technology Directorate of Environment and Climate Change Canada initiated a long-term effort to compile, archive and extract river-ice-related information from NHP hydrometric records. This data article describes the original research data set produced by this near 20-year effort: the Canadian River Ice Database (CRID). The CRID holds almost 73 000 recorded variables from a subset of 196 NHP stations throughout Canada that were in operation within the period 1894 to 2015. Over 100 000 paper and digital files were reviewed, representing 10 378 station years of active operation. The task of compiling this database involved manual extraction and input of more than 460 000 data entries on water level, discharge, ice thickness, date, time and data quality rating. Guidelines on the data extraction, rating procedure and challenges are provided. At each location, time series of up to 15 variables specific to the occurrence of freeze-up and winter-low events, midwinter break-up, ice thickness, spring break-up, and maximum open-water level were compiled. This database follows up on several earlier efforts to compile information on river ice, which are summarized herein, and expands the scope and detail for use in Canadian river ice research and applications. Following the Government of Canada Open Data initiative, this original river ice data set is available at https://doi.org/10.18164/c21e1852-ba8e-44af-bc13-48eeedfcf2f4 (de Rham et al., 2020).

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.

Land use/land cover change (LUCC) combined with gold mining activity (GMA) have reportedly affected the water quality of Amazonian rivers by intensifying surface soil erosion and increasing rivers’ sediment concentration (SC). However, the role of LUCC on river siltation in comparison to that caused by GMA has not been assessed, and predictions of SC in the rivers accounting for future LUCC are scarce. This study applied a sediment modeling approach on Crepori Basin, located at eastern Amazon Basin, to simulate the impacts of past and future LUCC on SC in the river, comparing their impacts to those caused by GMA. Between 1973 and 2012, the expansion of deforested areas in the region had increased sheet erosion-driven SC in the river, especially during the high-water season, by up to 73.3%. LUCC projections for 2050 suggest future increases in sheet erosion-driven SC during the high-water season by more than three times of that caused by the land cover scenario of 1998–2012. Comparison between SC driven by sheet erosion to that caused by GMA has also shown that during the high-water and the low-water seasons, respectively, only about 14% and 6% of the total SC in the Crepori River resulted from laminar soil erosion, with the remaining proportion resulting from GMA.

The rising global temperature is shifting the runoff patterns of snowmelt-dominated alpine watersheds, resulting in increased cold season flows, earlier spring peak flows, and reduced summer runoff. Projections of future runoff are beneficial in preparing for the anticipated changes in streamflow regimes. This study applied the degree–day Snowmelt Runoff Model (SRM) in combination with the MODIS to remotely sense snow cover observations for modeling the snowmelt runoff response of the Upper Athabasca River Basin in western Canada. After assessing its ability to simulate the observed historical flows, the SRM was applied for projecting future runoff in the basin. The inclusion of a spatial and temporal variation in the degree–day factor (DDF) and separation of the DDF for glaciated and non-glaciated areas were found to be important for improved simulation of varying snow conditions over multiple years. The SRM simulations, driven by an ensemble of six statistically downscaled GCM runs under the RCP8.5 scenario for the future period (2070–2080), show a consistent pattern in projected runoff change, with substantial increases in May runoff, smaller increases over the winter months, and decreased runoff in the summer months (June–August). Despite the SRM’s relative simplicity and requirement of only a few input variables, the model performed well in simulating historical flows, and provides runoff projections consistent with historical trends and previous modeling studies.

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 &gt, 70% of basin areas upstream of the hydrometric stations, while precipitation exhibited increases for &gt, 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.

Arctic wetlands can occur as isolated patches with areas of 1–10 km2, or they can cover extensive areas in the landscape. Wetlands exert a strong influence on the hydrological cycle as they can both store and release water to streams, other wetlands (ponds) and replenish groundwater reserves. Arctic landscapes are also rich with lakes and their occurrence depends on geology, geomorphic or anthropogenic setting. Like ponds, lake sustainability over time depends on inputs exceeding losses. Hence, their capacity to hold water and the nature of shifting storage capacity especially in light of climate warming (shifts in precipitation/evaporation), or stream/river connectivity, together with growing expansion of mining, oil development are critical issues for northern ecosystems and communities. To survive over time, a wetland or lake needs positive water storage to maintain a high degree of saturation (wetland) or storage capacity (lake). Snow is particularly important, and for Arctic wetlands and lakes, it is the total winter snow accumulation that is of major hydrological consequence. Summer rainfall in most permafrost areas is not high, and often is insufficient to match evaporation rates in lakes. However, the frequency and duration of rainfall can be important for arctic wetlands. Climate warming has been tied to an increase in ground thaw, resulting in both the loss of lakes/ponds and expansion of ponds/lakes depending on permafrost conditions. The timing of ice cover on lakes can affect evaporation, where shallower lakes that experience bed-fast ice during the winter become ice-free sooner in the warm season, which leads to longer open-water seasons and greater amounts of evaporation. Northern lakes have shown shifts towards shorter ice cover duration during the cold seasons, resulting in longer open-water seasons. Lake ice modelling suggests continued shifts towards earlier break-up and later freeze-up may be expected.

Over the recent decades, the warming in Arctic has affected changes in the terrestrial hydrologic processes. Unfortunately, the number of hydrometeorological observing stations in the region has decreased. To reduce the limitation in observation, a number of process-based and distributed models have been developed for simulating the hydrological processes in a changing climate. The current generations of models are able to reasonably reproduce the prominent cold region hydrologic processes, such as degrading permafrost, decreasing snow extent, increasing river discharge and evapotranspiration, and increasing streamflow temperature. These models enhance our understanding of the response of Arctic terrestrial processes to climate change and variation. However, the model representations for some of the Arctic hydrological processes are still not yet sufficient and need further improvements. This chapter provides an overview of changes in key processes and conditions of the Arctic terrestrial hydrology based on a synthesis of observations and model simulations, and presents recommendations for further development and improvement of cold region hydrologic models.

Within the Oil-Sands industry in Alberta, Canada, tailings ponds are used as water recycling and tailings storage facilities (TSF) for mining activities. However, there could be possible circumstances under which a sudden breach of an embankment confining one of the TSFs may occur. Such a tailings pond breach would result in a sudden release of a huge volume of Oil Sands process-affected water (OSPW) and sediment slurry containing substantial amount of chemical constituents that would follow the downstream drainage paths and subsequently enter into the Lower Athabasca River (LAR). This study investigates the implications of OS tailings release on the water and sediment quality of the LAR by simulating the fate of sediment and associated chemicals corresponding to a hypothetical breach and release scenarios from a select set of tailings ponds using a two-dimensional hydrodynamic and constituent transport model. After predicting the total volume, time evolution and concentration of sediment and associated chemicals (metals, polycyclic aromatic hydrocarbons (PAHs) and naphthenic acids (NAs)) reaching the LAR, the transport and deposition of these materials within the study reach is simulated. The results show that, depending on tailings release locations, between 40 and 70% of the sediment and associated chemicals get deposited onto the river bed of the 160 km study reach while the rest leaves the study domain during the first three days following the release event. These sediment/chemicals deposited during the initial spill may also have long-term effects on the water quality and aquatic ecosystem of the river and the downstream delta. However, care has to be taken in interpreting the results as further analysis has shown that the outcomes of such model simulations are very sensitive to the various underlying assumptions as well as the values assigned to some model parameters representing the physical properties of the tailings material.

The potential effects of climate change on the hydrodynamic and sediment transport regime of the lower Athabasca River (LAR) in Alberta, Canada, is investigated. Future climate projections for the region suggest a potential increase in mean air temperature and precipitation by about 2.8–7.1 °C and 8–25%, respectively, by the end of this century. Implications of these climatic changes on the hydrologic regime of the LAR are found to be significant with spring flows expected to increase by about 11–62% and 26–71% by the end of the century for a moderate and high emissions scenarios respectively with corresponding decreases in summer flows. The effects of such changes are examined using the MIKE‐11 hydrodynamic and sediment transport modelling system with inflow boundary conditions corresponding to the changing hydro‐climatic regime. The results suggest that there will be an overall increase in flow velocity, water level, and suspended sediment concentration and transport for most seasons except in the summer months when there may be some decreases. The projected changes in suspended sediment concentration will result in an overall increase in mean annual sediment load in the LAR and to the Peace Athabasca Delta by over 50% towards the latter part of this century (2080s) compared with the 1980s base‐line period. Implications of such potential changes in the transport characteristics of the river system to the mobilization and transport of various chemical constituents and their effects on the region's aquatic ecosystems are subjects of other ongoing investigations.

Study region The Athabasca River basin (ARB) with its head-waters located within the Canadian Rockies. Study focus Investigating the snow response of the Athabasca watershed to projected climate using the Variable Infiltration Capacity (VIC) hydrologic model and statistically downscaled future climate data from a selected set of CMIP5 GCMs forced with RCP4.5 and RCP8.5 emissions scenarios. New hydrological insights for the region High resolution end-of-century projections of SWE over the Athabasca watershed show an overall decreasing trend in the mean monthly SWE over the watershed, with the largest decreases occurring in March and April, especially in the high-elevation sub-basin. There are also widespread decreases in annual maximum SWE (SWEmax), with the middle-basin showing slight increases under the RCP4.5 scenario. The dates of SWEmax are generally getting earlier, with RCP4.5 showing a less linear response than RCP8.5. Increases in early spring snowmelt are followed by decreases during the late spring and summer months mainly as a result of earlier start of snowmelt. An overall decrease in snow-cover duration of up to fifty days is projected with the largest decrease occurring in the high elevation sub-basin. Such projected declines in snow water storage and a shift to earlier peak SWE and snowmelt over the ARB have significant implications for the magnitude and timing of the watershed soil-moisture content and hydrologic regime of the Athabasca River.

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 [...]

The hydrologic response of the Athabasca River Basin (ARB) in Alberta to projected changes in the future climate is investigated using the Variable Infiltration Capacity (VIC) process-based and distributed hydrologic model. The model forcings are derived from a selected set of GCMs from the latest Coupled Model Intercomparison Project (CMIP5) statistically downscaled to a higher resolution (10 km) over Canada. Twelve hydrologic indicators that represent the magnitude and timing of the hydrologic regimes are evaluated for three 30-year time periods centered at the 1990s, 2050s and 2080s to identify significant alterations of hydrologic regimes between the reference and the two future periods using a t-test at 5% significance level. Hydrologic alteration factors (HAF) are also evaluated for each hydrologic indicator using the range of variability approach (RVA) to investigate projected changes in the distribution of these indicators. The results show increases in spring and winter flows for the two future periods at all hydrometric stations within the basin, resulting in an extended period of spring freshet. A higher rate of increase is projected for the stations located at the upper reach of the river because of the combined effects of increased precipitation and earlier snowmelt resulting from a warming climate. By contrast, summer flows are projected to decrease by up to 21% on average in the 2080s over most of the mainstem stations because of earlier snowmelt, increased evapotranspiration and no significant increase in summer precipitation. A water-management rule that optimizes impacts of water withdrawal from the lower reach of the Athabasca River under the current condition is also applied to the future scenarios to assess its relative performance under the projected climate conditions. The results indicate possible improvement in the water resources system performance in terms of increased reliability and resilience and reduced vulnerability during the two future periods as compared with those in the reference period mainly because of the projected increases in spring and winter flows, which has the potential to offset an expected future water deficit.

Study region The Muskeg River Basin located in the Oil-Sands region of northern Alberta, Canada. Study focus An integrated modelling framework, which combines a process-based distributed hydrologic model with a dynamic land-cover simulation model is used to evaluate the effects of climate and land-cover changes on the hydrological regime in the basin. Land-cover types corresponding to three hypothetical levels of future industrial expansion are synthesized based on the current lease holdings for the Oil-Sands development in the region. An ensemble of hydrologic simulations based on multiple climate-change projections is performed with future land-cover scenarios during a baseline (1980–2010) and two future (2050 s and 2080 s) periods. The effects of climate and land-cover changes are quantified through various hydrologic indicators using a range of variability approach. New hydrological insights for the region Analysis of the relative contribution of inter-annual climate variability and land-cover change to the historical streamflow demonstrates the necessity to consider both in evaluating future water availability in the basin. Results indicate that modification to evapotranspiration rates caused from land-cover change affect spring and summer flows. Wetter and warmer conditions in the projected climate are found to increase spring and winter streamflows. Sensitivity analysis of the hydrologic indicators computed from the simulated flows shows that land-cover change may play a larger role in affecting the hydrologic regime than climate change, except that of spring runoff.

Tailings dams, used for containing the residue of mining processes, are very important elements of the Alberta oil-sands industry in Canada. Potential breach of any of these dams can have catastrophic impact on the environment, economy and human health and safety. Therefore, understanding the after-breach processes is a crucial step in hazard analysis and response planning. This paper studies the potential consequence of a hypothetical oil-sands tailings dam breach by performing numerical simulations of the runout and non-Newtonian overland flow of tailings, including the resulting flooding condition and subsequent spill to nearby water bodies. A non-Newtonian dam-breach model with a visco-plastic rheological relationship is used for this purpose. The model is first validated using the 2014 Mount Polley tailings dam breach in British Columbia, before its application to investigate the flooding volume, extent, and downstream hydrograph of a hypothetical breach from a selected oil-sands tailings dam. The validation results show that the model is able to reproduce the flooding extent and water level variation (due to breach wave) at a downstream lake. The oil-sands tailings spill simulation study demonstrated the importance of considering the non-Newtonian behaviour of tailings materials as the non-Newtonian approach resulted in twice as long flood travel time and slightly less spill volume to the downstream river (i.e. Lower Athabasca River) as that of a Newtonian fluid (i.e. water). The results are also found to be highly sensitive to the rheological parameters of the tailings materials such as their viscosity and yield stress that need to be determined through proper calibration.

Spring freshet is the dominant annual discharge event in all major Arctic draining rivers with large contributions to freshwater inflow to the Arctic Ocean. Research has shown that the total freshwater influx to the Arctic Ocean has been increasing, while at the same time, the rate of change in the Arctic climate is significantly higher than in other parts of the globe. This study assesses the large-scale atmospheric and surface climatic conditions affecting the magnitude, timing and regional variability of the spring freshets by analyzing historic daily discharges from sub-basins within the four largest Arctic-draining watersheds (Mackenzie, Ob, Lena and Yenisei). Results reveal that climatic variations closely match the observed regional trends of increasing cold-season flows and earlier freshets. Flow regulation appears to suppress the effects of climatic drivers on freshet volume but does not have a significant impact on peak freshet magnitude or timing measures. Spring freshet characteristics are also influenced by El Ni&ntilde, o-Southern Oscillation, the Pacific Decadal Oscillation, the Arctic Oscillation and the North Atlantic Oscillation, particularly in their positive phases. The majority of significant relationships are found in unregulated stations. This study provides a key insight into the climatic drivers of observed trends in freshet characteristics, whilst clarifying the effects of regulation versus climate at the sub-basin scale.

Precipitation, temperature, and evaporative demand are the most dominant factors affecting water availability in a region. This study examines projected changes in these hydro-climatic variables over western Canada under two greenhouse gas emissions scenarios using statistically downscaled, high resolution climate data generated by six Global Climate Models (GCMs) from the latest Coupled Model Intercomparison Project (CMIP5). Potential changes in the spatial and seasonal distributions of water availability over nine major western Canadian river basins are examined by computing the 3- and 12-month standardized precipitation and evapotranspiration indices (SPEI-3 and SPEI-12). While individual GCM projections vary on the rate and seasonality of changes, they all indicate similar spatial and temporal patterns. The highest projected increases in precipitation and temperature are primarily in the northern basins, with some decreases in summer precipitation in the southern basins. The evolution of the SPEI-12 values for the southern basins such as Columbia, Saskatchewan, Fraser and Athabasca indicate a gradual increase in the magnitude and duration of water deficit, while the reverse was found for most of the northern basins such as Peel/Lower Mackenzie, Liard, and Northern Pacific that show a gradual increase in water surplus on an annual basis. The SPEI-3, however, shows that almost all river basins in western Canada, with the exception of Peel/Lower Mackenzie that are located in the extreme north of the study region, are projected to experience decreasing water availability in summer. In general, the study highlights the potential changes in the spatial and seasonal distribution of western Canadian water resources and sets the stage for a more detailed and process based hydro-climate modelling study to be conducted in the region.

There is a great deal of interest to determine the state and variations of water quality parameters in the lower Athabasca River (LAR) ecosystem, northern Alberta, Canada, due to industrial developments in the region. As a cold region river, the annual cycle of ice cover formation and breakup play a key role in water quality transformation and transportation processes. An integrated deterministic numerical modelling framework is developed and applied for long-term and detailed simulation of the state and variation (spatial and temporal) of major water quality constituents both in open-water and ice covered conditions in the lower Athabasca River (LAR). The framework is based on the a 1D and a 2D hydrodynamic and water quality models externally coupled with the 1D river ice process models to account for the cold season effects. The models are calibrated/validated using available measured data and applied for simulation of dissolved oxygen (DO) and nutrients (i.e., nitrogen and phosphorus). The results show the effect of winter ice cover on reducing the DO concentration, and a fluctuating temporal trend for DO and nutrients during summer periods with substantial differences in concentration between the main channel and flood plains. This numerical frame work can be the basis for future water quality scenario-based studies in the LAR.

Much of the freshwater in western Canada originates in the Rocky Mountains as snowpack. Temperature and precipitation patterns throughout the region control the amount of snow accumulated and stored throughout the winter, and the intensity and timing of melt during the spring freshet. Therefore, changes in temperature, precipitation, snow depth, and snowmelt over western Canada are examined through comparison of output from the current and future periods of a series of regional climate models for the time periods 1971–2000 and 2041–2070. Temporal and spatial analyses of these hydroclimatic variables indicate that minimum temperature is likely to increase more than maximum temperature, particularly during the cold season, possibly contributing to earlier spring melt. Precipitation is projected to increase, particularly in the north. In the coldest months of the year snow depth is expected to increase in northern areas and decrease across the rest of study area. Snowmelt results indicate increases in mid-winter melt events and an earlier onset of the spring freshet. This study provides a summary of potential future climate using key hydroclimatic variables across western Canada with regard to the effects these changes may have on streamflow and the spring freshet, and thus water resources, throughout the study area.

Statistical downscaling methods have been actively developed to improve the applicability of global climate models’ (GCMs) outputs to impact studies at a local scale. This study presents a multivariate bias correction (MBC) method that preserves GCM-driven climate change signals and the interdependence between hydro-climate variables by combining the quantile delta mapping (QDM) method with distribution-free shuffle approach, refer to as MBCDS. Since MBCDS also employs the Gaussian rank correlation directly, it does not require an iterative process to improve the Gaussianity as used in existing MBC methods. This study evaluated the effects of interdependence on univariate- and multivariate-distribution criteria and hydrologic indicators during a historical baseline period. Furthermore, this study examined potential changes under selected CMIP5 climate projections for the three future time windows. Application of the downscaling outputs to hydrologic simulations showed that the univariate method underestimated snowfall considerably during the snowfall period, which leads to less snow accumulation and subsequently resulting in less spring and summer flows. On the contrary, the multivariate methods improved all of multivariate-distribution criteria, hydrologic indicators, and the ability in reproducing snowfall depending on multiple hydro-climate variables (i.e., precipitation and temperature). Moreover, the univariate method projected less increase in winter snowfall compared to those of multivariate methods, indicating that the univariate method may result in biased future conditions. In particular, MBCDS showed better and comparative performance compared to the existing methods with regard to climatic and hydrologic performance measures and considerably improved the computational time, with up to 53% less computing time required than those of other existing methods which include an iterative process. Therefore, this study proved that MBCDS can be a good alternative to provide high-resolution climate projections for impact studies at local scales. Further, the study also demonstrated that consideration of the interdependence between hydro-climate variables using multivariate methods is essential to avoid erroneous assessment of climate change impacts for snow-dominated watersheds such as those in Alberta, Canada.

Runoff from Arctic rivers constitutes a major freshwater influx to the Arctic Ocean. In these nival-dominated river systems, the majority of annual discharge is released during the spring snowmelt period. The circulation regime of the salinity-stratified Arctic Ocean is connected to global earth&ndash, ocean dynamics through thermohaline circulation, hence, variability in freshwater input from the Arctic flowing rivers has important implications for the global climate system. Daily discharge data from each of the four largest Arctic-draining river watersheds (Mackenzie, Ob, Lena and Yenisei, herein referred to as MOLY) are analyzed to identify historic changes in the magnitude and timing of freshwater input to the Arctic Ocean with emphasis on the spring freshet. Results show that the total freshwater influx to the Arctic Ocean increased by 89 km3/decade, amounting to a 14% increase during the 30-year period from 1980 to 2009. A distinct shift towards earlier melt timing is also indicated by proportional increases in fall, winter and spring discharges (by 2.5%, 1.3% and 2.5% respectively) followed by a decrease (by 5.8%) in summer discharge as a percentage of the mean annual flow. This seasonal increase in discharge and earlier pulse onset dates indicates a general shift towards a flatter, broad-based hydrograph with earlier peak discharges. The study also reveals that the increasing trend in freshwater discharge to the Arctic Ocean is not solely due to increased spring freshet discharge, but is a combination of increases in all seasons except that of the summer.

Land cover changes (LCC) affect the water balance (WB), changing surface runoff (SurfQ), evapotranspiration (ET), groundwater (GW) regimes, and streamflow (Q). The Tapaj&oacute, s Basin (southeastern Amazon) has experienced LCC over the last 40 years, with increasing LCC rates projected for the near future. Several studies have addressed the effects of climate changes on the region&rsquo, s hydrology, but few have explored the effects of LCC on its hydrological regime. In this study, the Soil and Water Assessment Tool (SWAT) was applied to model the LCC effects on the hydrology of the Upper Crepori River Basin (medium Tapaj&oacute, s Basin), using historical and projected LCC based on conservation policies (GOV_2050) and on the &ldquo, Business as Usual&rdquo, trend (BAU_2050). LCC that occurred from 1973 to 2012, increased Q by 2.5%, without noticeably altering the average annual WB. The future GOV_2050 and BAU_2050 scenarios increased SurfQ by 238.87% and 300.90%, and Q by 2.53% and 2.97%, respectively, and reduced GW by 4.00% and 5.21%, and ET by 2.07% and 2.43%, respectively. Results suggest that the increase in deforestation will intensify floods and low-flow events, and that the conservation policies considered in the GOV_2050 scenario may still compromise the region&rsquo, s hydrology at a comparable level to that of the BAU_2050.

Several different gridded climate data sets have recently been made available with the purpose of providing a consistent set of climatic data for many hydro-climatic studies. Recent advances in land-surface schemes and their implementation in fully distributed processes-based hydrologic models have demanded even higher-resolution gridded data. It remains, however, a challenge to identify the most reliable gridded climate data for hydrologic modelling, especially in mountainous headwater regions where there is significant spatial variability but few observing stations. Moreover, the accuracy of such climate forcing data applied to alpine headwaters directly affects the modelled hydrologic responses of the lower, downstream portions of river basins. This study evaluates the spatial and temporal differences in precipitation and temperature fields among three high-resolution climate data sets available in Canada, namely, the North American Regional Reanalysis, the Canadian Precipitation Analysis and the thin-plate smoothing splines (ANUSPLIN). Inter-comparison of the quality of these data sets was undertaken for the Athabasca River basin in western Canada. The hydrologic responses of this watershed with respect to each of the three gridded climate data sets were also evaluated using the Variable Infiltration Capacity model. Results indicate that the data sets have systematic differences, which vary with regional characteristics – the largest differences being for mountainous regions. The hydrologic model simulations corresponding to those three forcing data sets also show significant differences and more with North American Regional Reanalysis than those between Canadian Precipitation Analysis and ANUSPLIN. © 2014 Her Majesty the Queen in Right of Canada. Hydrological Processes © 2014 John Wiley & Sons Ltd.

Flows originating from alpine dominated cold region watersheds typically experience extended winter low flows followed by spring snowmelt and summer rainfall driven high flows. In a warmer climate, there will be a temperature-induced shift in precipitation from snowfall towards rain along with changes in precipitation intensity and snowmelt timing, resulting in alterations in the frequency and magnitude of peak flow events. This study examines the potential future changes in the frequency and severity of peak flow events in the Athabasca River watershed in Alberta, Canada. The analysis is based on simulated flow data by the variable infiltration capacity (VIC) hydrologic model driven by statistically downscaled climate change scenarios from the latest coupled model inter-comparison project (CMIP5). The hydrological model projections show an overall increase in mean annual streamflow in the watershed and a corresponding shift in the freshet timing to an earlier period. The river flow is projected to experience increases during the winter and spring seasons and decreases during the summer and early fall seasons, with an overall projected increase in peak flow, especially for low frequency events. Both stationary and non-stationary methods of peak flow analysis, performed at multiple points along the Athabasca River, show that projected changes in the 100-year peak flow event for the high emissions scenario by the 2080s range between 4% and 33% depending on the driving climate models and the statistical method of analysis. A closer examination of the results also reveals that the sensitivity of projected changes in peak flows to the statistical method of frequency analysis is relatively small compared to that resulting from inter-climate model variability.

While the cause of eutrophication in Lake Winnipeg is known to be excess nutrient loading from watershed sources, few comprehensive studies have attempted to quantify the effects of changes in temperature and precipitation on the hydrological and nutrient transport regimes in the region. In this paper, results of spatial and temporal analyses of observed and regional climate model (RCM) projections of precipitation and air-temperature regimes within the Lake Winnipeg watershed (LWW) are presented. The Mann–Kendall trend analysis of observed data over a recent time period of 1961 to 2003 shows that, while there has been a consistent increasing trend in the mean annual air temperature in the region, there is no significant trend in annual precipitation. Seasonally, precipitation exhibited a significantly decreasing trend in the Saskatchewan and Assiniboine river basins during the winter and an increasing trend in part of the Red and Assiniboine river basins during summer season. Air temperatures showed significant increases in winter, spring and summer throughout the LWW. Future climate projections from three RCMs driven by the SRES's A2 emission scenario indicate that by the 2050s (2041–2070), total annual precipitation (air temperature) increases by 5.5–7.7% (2.1–2.8 °C) compared to the 1980s (1971–2000). These observed and projected changes in the climate regimes of the LWW will have important implications for the hydrology and nutrient transport regimes of this region, which is the subject of additional investigation in a companion paper of this special issue (Shrestha et al., 2012).

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., 2012) 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 (1980–2000) 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.

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.

Climate change impact studies have generally downscaled large-scale global climate model (GCM) output data; however, few studies have considered downscaling regional climate model (RCM) data. It is unclear whether further downscaling raw RCM data could be beneficial or not in a hydrologic impact study. This study provides some experimental results to address that question. Raw Canadian regional climate model (CRCM4.2) data are downscaled by using a common statistical downscaling method (SDSM) and a data-driven technique called a time-lagged feedforward network (TLFN). Regardless of the downscaling methods and the predictands (e.g., precipitation, temperature), the downscaled CRCM4.2 data are found to be much closer to the observed data than the raw CRCM4.2 data. When the downscaled CRCM4.2 data are used in a hydrologic model (HBV), the model’s ability to accurately simulate streamflow and reservoir inflow is significantly improved as compared to the use of the raw CRCM4.2 data. Simulations of future river...

Freshwater ice plays an important role in physical, biological, and chemical processes affecting cold-region lakes. To examine the potential magnitude of climate-change-related impacts in ice-cover characteristics, particularly its formation, duration, breakup, thickness, and structural composition over North America, this study employs a one-dimensional, process-based, lake simulation model, ‘MyLake’ (Multi-year simulation model for Lake thermo- and phytoplankton dynamics). The model is first calibrated and validated for Baker Lake, Nunavut, Canada, and then used to simulate patterns of ice conditions using a set of hypothetical lakes of varying depth (5, 20, and 40 m) located at a 2° latitude/longitude grid pattern for the major cold-region portion of North America between 40 and 75° latitude. The model is driven by gridded atmospheric forcing data from the North American Regional Reanalysis (NARR) and the Canadian Regional Climate Model (CRCM) with projections of future climate corresponding to the SRES A2 emissions scenario. The NARR-based simulation results for the period 1979–2006 are consistent with observations of lake-ice thickness and phenology obtained from the Canadian Ice Database. The CRCM-based lake-ice simulation results for the baseline (1961–1990) and future (2041–2070) time periods indicate that projected air-temperature warming will advance break-up by 10–20 days and delay freeze-up by 5–15 days, thereby reducing lake-ice duration by about 15–35 days. Lake depth is also found to have significant influence on lake-ice freeze-up dates and maximum thicknesses but not on the break-up dates. Such changes are accompanied by a reduction in ice thickness of 10–30 cm. Cover composition is also altered as a result of changes in ice thickness and snow loading resulting in somewhat greater thicknesses of white-ice (1–5 cm) over most areas except towards the east and west coasts, as well as more southerly latitudes where it slightly decreased. Implications of such cover changes are also discussed. Copyright © 2011 Royal Meteorological Society and Crown in the right of Canada.

Watershed runoff in areas with heavy seasonal snow cover is usually estimated using physically based conceptual hydrologic models. Such simulation models normally require a snowmelt algorithm consisting of a surface energy balance and some accounting of internal snowpack processes to be part of the modeling system. On the other hand, artificial neural networks are flexible mathematical structures that are capable of identifying such complex nonlinear relationships between input and output datasets from historical precipitation, temperature and streamflow records. This paper presents the findings of a study on using a form of time-delayed neural network, namely time-lagged feedforward neural network (TLFN), that implicitly accounts for snow accumulation and snowmelt processes through the use of logical values and tapped delay lines. The logical values (in the form of symbolic inputs) are used to implicitly include seasonal information in the TLFN model. The proposed method has been successfully applied for improved precipitation–runoff modeling of both the Chute-du-Diable reservoir inflows and the Serpent River flows in northeastern Canada where river flows and reservoir inflows are highly influenced by seasonal snowmelt effects. The study demonstrates that the TLFN with logical values is capable of modeling the precipitation–runoff process in a cold and snowy climate by relying on ‘logical input values’ and tapped delay lines to implicitly recognize the temporal input–output patterns in the historical data. The study results also show that, once the appropriate input patterns are identified, the time-lagged neural network based models performed quite well, especially for spring peak flows, and demonstrated comparable performance in simulating the precipitation–runoff processes to that of a physically based hydrological model, namely HBV.

s part of a wider study on the adaptation of agricultural land use to climate change (CC), this paper presents an assessment of possible future hydrological regimes of the Chaudiere River watershed, Quebec, Canada. We first present a review of the various methods used to integrate outputs of General Circulation Models (GCMs) into hydrological models that are applied at a local scale. Following this review, the delta method, statistical downscaling, and a combination of both methods were selected for this investigation. Data from different GCMs (in the case of the delta method) corresponding to different gas emission scenarios and simulation members were also considered to provide a range of possible future conditions. We used the integrated modelling system GIBSI, which is based on the distributed hydrological model HYDROTEL, to simulate streamflows for a reference period (1970-1999) and a short-term future period (2010-2039). For all three methods, results show a slight decrease in annual runoff (-5% on ...

Uncertainty analysis is used to make a quantitative evaluation of the reliability of statistically downscaled climate data representing local climate conditions in the northern coastlines of Canada. In this region, most global climate models (GCMs) have inherent weaknesses to adequately simulate the climate regime due to difficulty in resolving strong land/sea discontinuities or heterogeneous land cover. The performance of the multiple regression-based statistical downscaling model in reproducing the observed daily minimum/maximum temperature, and precipitation for a reference period (1961–1990) is evaluated using climate predictors derived from NCEP reanalysis data and those simulated by two coupled GCMs (the Canadian CGCM2 and the British HadCM3). The Wilcoxon Signed Rank test and bootstrap confidence-interval estimation techniques are used to perform uncertainty analysis on the downscaled meteorological variables. The results show that the NCEP-driven downscaling results mostly reproduced the mean and variability of the observed climate very well. Temperatures are satisfactorily downscaled from HadCM3 predictors while some of the temperatures downscaled from CGCM2 predictors are statistically significantly different from the observed. The uncertainty in precipitation downscaled with CGCM2 predictors is comparable to the ones downscaled from HadCM3. In general, all downscaling results reveal that the regression-based statistical downscaling method driven by accurate GCM predictors is able to reproduce the climate regime over these highly heterogeneous coastline areas of northern Canada. The study also shows the applicability of uncertainty analysis techniques in evaluating the reliability of the downscaled data for climate scenarios development.

Coarse resolution global climate models (GCMs) have inherent difficulty simulating a reliable climate regime in coastal areas, as in northern Canada, where sea ice and snow cover are highly sensitive to fine-scale climate forcings. As a result, strong biases are present in GCM temperature regimes in this region, and the direct use of raw-GCM climate change signals at the local scale is problematic. However, fine resolution climate change information for use in impact studies can be obtained via statistical downscaling (SD) methods. This study investigates the regression-based SDSM model with respect to its potential to simulate reliable and plausible changes in mean values as well as probabilities of extreme temperatures, in some specific locations in northern Canada. Four sets of independent climate predictors, from the outputs of two GCMs (i.e. CGCM2 and HadCM3) and using two SRES emission scenarios (i.e. A2 and B2), are used by the SDSM model to construct climate scenario information for this region over the period 2070-2099. The results demonstrate that the SD model is able to capture the major part of the temperature change signal, with a plausible climatic regime for higher warming in winter than in summer and in A2 than in B2 runs. The combination of relevant atmospheric predictors in the SD process is able to take into account most key factors of the temperature change signal, with strong convergence in the magnitude and the timing of the changes in all results. The downscaling signals are more consensual and physically-plausible in comparison with the raw GCM anomalies, with relatively better skill using HadCM3 predictors than those from CGCM2. The study also confirms that scrupulous analysis of the climate change regime and its temporal and spatial distribution at the scale of interest is essential for it to be useful in impact studies.

This paper presents the results of an investigation into the problems associated with using downscaled meteorological data for hydrological simulations of climate scenarios. The influence of both the hydrological models and the meteorological inputs driving these models on climate scenario simulation studies are investigated. A regression-based statistical tool (SDSM) is used to downscale the daily precipitation and temperature data based on climate predictors derived from the Canadian global climate model (CGCM1), and two types of hydrological model, namely the physically based watershed model WatFlood and the lumped-conceptual modelling system HBV-96, are used to simulate the flow regimes in the major rivers of the Saguenay watershed in Quebec. The models are validated with meteorological inputs from both the historical records and the statistically downscaled outputs. Although the two hydrological models demonstrated satisfactory performances in simulating stream flows in most of the rivers when provided with historic precipitation and temperature records, both performed less well and responded differently when provided with downscaled precipitation and temperature data. By demonstrating the problems in accurately simulating river flows based on downscaled data for the current climate, we discuss the difficulties associated with downscaling and hydrological models used in estimating the possible hydrological impact of climate change scenarios. Copyright © 2007 John Wiley & Sons, Ltd.

Three downscaling models namely Statistical Down-Scaling Model (SDSM), Long Ashton Research Station Weather Generator (LARS-WG) model and Artificial Neural Network (ANN) model have been compared in terms various uncertainty assessments exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperatures. In case of daily maximum and minimum temperature, uncertainty is assessed by comparing monthly mean and variance of downscaled and observed daily maximum and minimum temperature at each month of the year at 95% confidence level. In addition, uncertainties of the monthly means and variances of downscaled daily temperature have been calculated using 95% confidence intervals, which are compared with the observed uncertainties of means and variances. In daily precipitation downscaling, in addition to comparing means and variances, uncertainties have been assessed by comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions (cdfs) of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and downscaled daily precipitation. The study has been carried out using 40 years of observed and downscaled daily precipitation, daily maximum and minimum temperature data using NCEP (National Center for Environmental Prediction) reanalysis predictors starting from 1961 to 2000. The uncertainty assessment results indicate that the SDSM is the most capable of reproducing various statistical characteristics of observed data in its downscaled results with 95% confidence level, the ANN is the least capable in this respect, and the LARS-WG is in between SDSM and ANN.

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.

Changes in global climate will have significant impact on local and regional hydrological regimes, which will in turn affect ecological, social and economical systems. However, climate-change impact studies on hydrologic regime have been relatively rare until recently, mainly because Global Circulation Models, which are widely used to simulate future climate scenarios, do not provide hourly or daily rainfall reliable enough for hydrological modeling. Nevertheless, more reliable rainfall series corresponding to future climate scenarios can be derived from GCM outputs using the so called ‘downscaling techniques’. This study applies two types of statistical (a stochastic and a regression based) downscaling techniques to generate the possible future values of local meteorological variables such as precipitation and temperature in the Chute-du-Diable sub-basin of the Saguenay watershed in northern Quebec, Canada. The downscaled data is used as input to two different hydrologic models to simulate the corresponding future flow regime in the catchment. In addition to assessing the relative potential of the downscaling methods, the paper also provides comparative study results of the possible impact of climate change on river flow and total reservoir inflow in the Chute-du-Diable basin. Although the two downscaling techniques do not provide identical results, the time series generated by both methods indicates a general increasing trend in the mean daily temperature values. While the regression based downscaling technique resulted in an increasing trend in the mean and variability of daily precipitation values, such a trend is not obvious in the case of precipitation time series downscaled with the stochastic weather generator. Moreover, the hydrologic impact analysis made with the downscaled precipitation and temperature time series as input to the two hydrological models suggest an overall increasing trend in mean annual river flow and reservoir inflow as well as earlier spring peak flows in the basin.

The formation and break-up of ice-cover are important seasonal events in mid- to high-latitude cold-region lakes. There is increasing concern regarding how climate change will affect lake-water thermal structure and lake-ice characteristics, particularly ice formation, duration, break-up, thickness, and composition. This study employs a one-dimensional process-based multi-year lake ice model, MyLake, to simulate the evolution of the Northern Hemisphere lake-ice and thermal structure patterns under a changing climate. After testing the model on Baker Lake located in Nunavut Canada, large-scale simulations were conducted over the major land masses of the Northern Hemisphere subarctic regions between 40° and 75°N using hypothetical lakes positioned at 2·5° latitude and longitude resolution. For the baseline period of 1960–1999, the lake-ice model was driven by gridded atmospheric forcings from the ERA-40 global reanalysis data set while atmospheric model forcings corresponding to future (2040–2079) climate were obtained by modifying the ERA-40 data according to the Canadian Global Climate Model projection based on the SRES A2 emissions scenario. Analysis of the modelling results indicates that lake-ice freeze-up timing will be delayed by 5–20 days and break-up will be advanced by approximately 10–30 days, thereby resulting in an overall decrease in lake-ice duration by about 15–50 days. Maximum lake-ice thickness will also be reduced by 10–50 cm. The change in maximum snow depth on the lake-ice ranges between − 15 to + 5 cm, while the change in white-ice thickness ranges between − 20 to + 10 cm depending on the geographic location and other climate parameters. The future warming will also result in an overall increase in lake-water temperature, with summer stratification starting earlier and extending later into the year. Copyright © 2011 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.

[1] Concern exists about future changes to air-temperature gradients along large “northward”-flowing Arctic rivers having the potential to affect the timing and severity of spring river-ice breakup, and associated flooding events. To evaluate the significance of this concern, an analysis was conducted of temporal and spatial changes to the spring 0°C air-temperature isotherm (I0°C), which is also known to be a good index for the timing of spring melt/breakup conditions. Changes in I0°C were analyzed for the downstream 2000-km main-stem reaches of four large Arctic rivers: the Lena, Mackenzie, Ob and Yenisey. Current climatic conditions (1979–2008) were compared to those of two future climatic periods (2041–2070 and 2071–2100) projected by an ensemble of four Global Climate Models. Future projections show I0°C chronology patterns along the rivers that closely parallel current conditions, but with earlier dates varying from an ensemble mean of 7.5 (13.6) to 16.5 (25.5) days for the 2050s (2080s). Results also reveal a progressive downstream increase in warming under future climates. At the time when headwater temperatures reach 0°C, river mouth to headwater temperature differences for the four rivers decrease by an average of 0.8°C (2.4°C) to 2.1°C (3.7°C) for the 2050s (2080s). The implications of such decreases on the severity of spring ice-jam floods are discussed.

This paper presents an application of temporal neural networks for downscaling global climate models (GCMs) output. Because of computational constraints, GCMs are usually run at coarse grid resolution (in the order of 100s of kilometres) and as a result they are inherently unable to present local sub-grid scale features and dynamics. Consequently, outputs from these models cannot be used directly in many climate change impact studies. This research explored the issues of 'downscaling' the outputs of GCMs using a temporal neural network (TNN) approach. The method is proposed for downscaling daily precipitation and temperature series for a region in northern Quebec, Canada. The downscaling models are developed and validated using large-scale predictor variables derived from the National Center for Environmental Prediction (NCEP) reanalysis data set. The performance of the temporal neural network downscaling model is also compared to a regression-based statistical downscaling model with emphasis on their ability in reproducing the observed climate variability and extremes. The downscaling results for the base period (1961-2000) suggest that the TNN is an efficient method for downscaling both daily precipitation as well as daily maximum and minimum temperature series. Furthermore, the different model test results indicate that the TNN model mostly outperforms the statistical models for the downscaling of daily precipitation extremes and variability.