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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]

Anthropogenic climate change is affecting the snowpack freshwater storage, with socioeconomic and ecological impacts. We present an assessment of maximum snow water equivalent (SWEmax) change in large river basins of the northwestern North America region using the Canadian Regional Climate Model 50-member ensemble under 1.0 °C to 4.0 °C global warming thresholds above the pre-industrial period. The projections indicate steep SWEmax decline in the warmer coastal/southern basins (i.e., Skeena, Fraser and Columbia), moderate decline in the milder interior basins (i.e., Peace, Athabasca and Saskatchewan), and either a small increase or decrease in the colder northern basins (i.e., Yukon, Peel, and Liard). A key factor for these spatial differences is the proximity of winter mean temperature to the freeze/melt threshold, with larger SWEmax declines for the basins closer to the threshold. Using the random forests machine-learning model, we find that the SWEmax change is primarily temperature controlled, especially for warmer basins. Further, under a categorical framework of below-normal SWEmax defined as snow drought (SD), we find that above-normal temperature and precipitation are the dominant conditions for SD occurrences under higher global warming thresholds. This implies a limited capacity of precipitation increase to compensate the temperature-driven snowpack decline. Additionally, the frequency and severity of SD occurrences are projected to be most extreme in the southern basins where current water demands are highest. Overall, the results of this study, including insights on snowpack changes, their climatic controls, and the framework for SD classification, are applicable for basins spanning a range of hydro-climatological regimes. [ABSTRACT FROM AUTHOR]

The article reports that western cordillera supplies freshwater across much of western Canada mainly through meltwater from snow and ice. Topics include the study incorporates existing information to synthesize and future freshwater supplies and demands across major north-, west-, and eastflowing sub-basins of the Canadian western cordillera, and the greatest and most widespread impact is the seasonality of streamflow has characterized by earlier spring freshets, and decreased summerflow.

This study provides an improved statistical modelling framework for understanding historical variability and trends in water constituent fluxes in subarctic western Canada. We evaluated total phosphorus (TP) and dissolved organic carbon (DOC) fluxes for the Hay, Liard and Peel tributaries of the Mackenzie River. The TP and DOC concentrations primarily exhibit chemodynamic relationships with discharge, with the exception of the chemostatic relationship between DOC and discharge for the Hay River. With this understanding, we explored a number of enhancements in the load estimation model that included the use of (i) linear regression and logarithmic models, (ii) air-temperature as an alternate input variable and (iii) quantile mapping for bias-correction. Further, we evaluated uncertainties in the simulation of fluxes and trends by using a bootstrapping method. The modelled TP and DOC fluxes show considerable seasonal and interannual variability that generally follow the runoff dynamics. The annual and seasonal trends are mostly small and insignificant, with the largest significant increases occurring in the winter months. These trends are amplified compared with discharge, suggesting the possibility of pronounced changes with large changes in discharge. Additionally, the results provide evidence that directly using limited water constituent samples for trend analysis can be problematic. [ABSTRACT FROM AUTHOR]

We describe a state-of-the-art framework for projecting hydrologic impacts due to enhanced warming and amplified moisture fluxes in the subarctic environment under anthropogenic climate change. We projected future hydrologic changes based on phase 5 of the Coupled Model Intercomparison Project global climate model simulations using the Variable Infiltration Capacity hydrologic model and a multivariate bias correction/downscaling method for the Liard basin in subarctic northwestern Canada. Subsequently, the variable importance of key climatic controls on a set of hydrologic indicators was analyzed using the random forests statistical model. Results indicate that enhanced warming and wetness by the end of century would lead to pronounced declines in annual and monthly snow water equivalent (SWE) and earlier maximum SWE. Prominent changes in the streamflow regime include increased annual mean and minimum flows, earlier maximum flows, and either increased or decreased maximum flows depending on interactions between temperature, precipitation, and snow. Using the variable importance analysis, we find that precipitation exerts the primary control on maximum SWE and annual mean and maximum flows, and temperature has the main influence on timings of maximum SWE and flow, and minimum flow. Given these climatic controls, the changes in the hydrologic indicators become progressively larger under the scenarios of 1.5°, 2.0°, and 3.0°C global mean temperature increases above the preindustrial period. Hence, the framework presented in this study provides a detailed diagnosis of the hydrologic changes as well as controls and interactions of the climatic variables, which could be generalized for understanding regional scale changes in subarctic/nival basins. [ABSTRACT FROM AUTHOR]

Recent improvements in forecast skill of the climate system by dynamical climate models could lead to improvements in seasonal streamflow predictions. This study evaluates the hydrologic prediction skill of a dynamical climate model-driven hydrologic prediction system (CM-HPS), based on an ensemble of statistically downscaled outputs from the Canadian Seasonal to Interannual Prediction System (CanSIPS). For comparison, historical and future climate traces-driven ensemble streamflow prediction (ESP) was employed. The Variable Infiltration Capacity model (VIC) hydrologic model setup for the Fraser River basin, British Columbia, Canada, was used as a test bed for the two systems. In both cases, results revealed limited precipitation prediction skill. For streamflow prediction, the ESP approach has very limited or no correlation skill beyond the months influenced by initial hydrologic conditions, while the CM-HPS has moderately better correlation skill, attributable to the enhanced temperature prediction skill that results from CanSIPS's ability to predict El Niño-Southern Oscillation (ENSO) and its teleconnections. The root-mean-square error, bias, and categorical skills for the two methods are mostly similar. Hydrologic modeling uncertainty also affects the prediction skill, and in some cases prediction skill is constrained by hydrologic model skill. Overall, the CM-HPS shows potential for seasonal streamflow prediction, and further enhancements in climate models could potentially to lead to more skillful hydrologic predictions. [ABSTRACT FROM AUTHOR]

This study analyzed potential hydroclimatic change in the Peace River basin in the province of British Columbia, Canada, based on two structurally different approaches: (i) statistically downscaled global climate models (GCMs) using the bias-corrected spatial disaggregation (BCSD) and (ii) dynamically downscaled GCM with the Canadian Regional Climate Model (CRCM). Additionally, simulated hydrologic changes from the GCM-BCSD-driven Variable Infiltration Capacity (VIC) model were compared to the CRCM integrated Canadian Land Surface Scheme (CLASS) output. The results show good agreements of the GCM-BCSD-VIC simulated precipitation, temperature, and runoff with observations, while the CRCM-simulated results differ substantially from observations. Nevertheless, differences (between the 2050s and 1970s) obtained from the two approaches are qualitatively similar for precipitation and temperature, although they are substantially different for snow water equivalent and runoff. The results obtained from the five Coupled Global Climate Model, version 3, (CGCM3)-driven CRCM runs are similar, suggesting that the multidecadal internal variability is not a large source of uncertainty for the Peace River basin. Overall, the GCM-BCSD-VIC approach, for now, remains the preferred approach for projecting basin-scale future hydrologic changes, provided that it explicitly accounts for the biases and includes plausible snow and runoff parameterizations. However, even with the GCM-BCSD-VIC approach, projections differ considerably depending on which of an ensemble of eight GCMs is used. Such differences reemphasize the uncertain nature of future hydroclimatic projections. [ABSTRACT FROM AUTHOR]

This study uses a high-frequency discharge and nitrate concentration dataset from the Weida catchment in Germany for the catchment scale hydrologic response analysis. Nitrate transport in the catchment is mostly conservative as indicated by the nitrate stable isotope (δ15N and δ18O) analysis. Discharge-nitrate concentration data from the catchment show distinctive patterns, suggesting flushing and dilution response. A self-organizing feature map-based methodology was employed to identify such patterns or cluster in the datasets. Based on knowledge of the catchment conditions and prevailing understanding of discharge-nitrate concentration relationship, the clusters were characterized into five qualitative flow responses: (1) baseflow; (2) subsurface flow increase; (3) surface runoff increase; (4) surface runoff recession; and (5) subsurface flow decrease. Such qualitative flowpaths were used as soft data for a multi-objective calibration of a hydrological model (WaSiM-ETH). The calibration led to a reasonable simulation of overall discharge (Nash-Sutcliffe coefficient: 0.84) and qualitative flowpaths (76% agreement). A prerequisite for using such methodology is limited biogeochemical transformation of nitrate (such as denitrification). [ABSTRACT FROM AUTHOR]

The Canadian Large Ensembles Adjusted Dataset version 1 (CanLEADv1) contains 50‐member ensembles of bias‐adjusted near‐surface global and regional climate model variables on a 0.5° grid over North America for historical and future scenarios (1950–2100). Canadian Earth System Model Large Ensembles (CanESM2 LE) and Canadian Regional Climate Model Large Ensemble (CanRCM4 LE) datasets are bias‐corrected using a multivariate quantile‐mapping algorithm for statistical consistency – in terms of marginal distributions and multivariate dependence structure – with two observationally constrained historical meteorological forcing datasets. For each observational dataset, bias‐adjusted variables are provided for two sets of 50‐member initial‐condition CanESM2 ensembles (historical plus RCP8.5 scenarios, 1950–2005 and 2006–2100, respectively; and historicalNAT scenario, 1950–2020, which excludes anthropogenic forcings), and one 50‐member CanRCM4 ensemble (historical plus RCP8.5). The archive includes daily minimum temperature, maximum temperature, precipitation, relative humidity, surface pressure, wind speed, incoming shortwave radiation and incoming longwave radiation. Intended uses include hydrological and land surface impact modelling, as well as related event attribution studies. [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 discharge and stage measurements in a river system are characterized by a number of sources of uncertainty, which affects the accuracy of a rating curve established from measurements. This paper presents a fuzzy set theory based methodology for consideration of different sources of uncertainty in the stage and discharge measurements and their aggregation into a combined uncertainty. The uncertainty in individual measurements of stage and discharge is represented using triangular fuzzy numbers, and their spread is determined according to the International Organization for Standardization (ISO) standard 748 guidelines. The extension principle based fuzzy arithmetic is used for the aggregation of various uncertainties into overall stage-discharge measurement uncertainty. In addition, a fuzzified form of ISO 748 formulation is used for the calculation of combined uncertainty and comparison with the fuzzy aggregation method. The methodology developed in this paper is illustrated with a case study of the Thompson River near Spences Bridge in British Columbia, Canada. The results of the case study show that the selection of number of velocity measurement points on a vertical is the largest source of uncertainty in discharge measurement. An increase in the number of velocity measurement points provides the most effective reduction in the overall uncertainty. The next most important source of uncertainty for the case study location is the number of verticals used for velocity measurements. The study also shows that fuzzy set theory provides a suitable methodology for the uncertainty analysis of stage-discharge measurements. [ABSTRACT FROM AUTHOR]

This study examined heat flux from 15 Canadian northern rivers that drain to the Arctic Ocean and the Hudson/James Bay. Based on statistical analysis of available water temperature and discharge data, we determined patterns and characteristics of discharge, water temperature, and heat flux in relation to seasonal air temperature and precipitation. We found similar seasonal cycles of discharge and water temperature across the study region, i.e. most rivers experiencing maximum discharge in June/July and highest water temperatures in July/August. The mean flows during the open water season (May to Oct.) vary from west to east along the Arctic Coast (with higher yield from the Mackenzie and Peel rivers), while river flows are higher with warmer water temperatures in western and southern Hudson Bay. The summed heat flux for the studied rivers was about 10.0 × 1012 MJ along the Arctic Coast and 2.0x1012MJ around the Hudson Bay. Among the 9 rivers flowing directly into the Arctic Ocean, the Mackenzie River with the highest flow and warmest water temperature delivered the highest heat flux, i.e. average 9.5 × 1012 MJ over the open water season during 1960–2015. These observed patterns in discharge, water temperature and heat flux were generally consistent with CHANGE model simulations for most rivers in northern Canada. The outcomes of our study provide critical knowledge of river thermal condition and heat transport to the northern seas, which will be useful for large-scale climate and ocean model development and validation, and climate/hydrology change investigations in the broader northern regions. • Determined patterns of discharge (Q), water temperature (WT), and heat flux (HF) for 15 northern rivers in Canada. • Similar seasonal cycles of Q, WT and HF for the rivers, most rivers with max Q in June/July and highest WT in July/August. • Total warm season HF of 10.0x1012 MJ from the 9 rivers along Arctic Coast and 2.0x1012MJ for the 6 rivers into Hudson Bay. • Mackenzie River with highest Q and warmest WT delivered highest HF, 9.5x1012 MJ in open water season during 1960-2015. • Observations and CHANGE model HF simulations were generally consistent for most rivers in northern Canada. [ABSTRACT FROM AUTHOR]

Obtaining reliable water balance estimates remains a major challenge in Canada for large regions with scarce in situ measurements. Various remote sensing products can be used to complement observation-based datasets and provide an estimate of the water balance at river basin or regional scales. This study provides an assessment of the water balance using combinations of various remote sensing– and data assimilation–based products and quantifies the nonclosure errors for river basins across Canada, ranging from 90 900 to 1 679 100 km2, for the period from 2002 to 2015. A water balance equation combines the following to estimate the monthly water balance closure: multiple sources of data for each water budget component, including two precipitation products—the global product WATCH Forcing Data ERA-Interim (WFDEI), and the Canadian Precipitation Analysis (CaPA); two evapotranspiration products—MODIS, and Global Land surface Evaporation: The Amsterdam Methodology (GLEAM); one source of water storage data—GRACE from three different centers; and observed discharge data from hydrometric stations (HYDAT). The nonclosure error is attributed to the different data products using a constrained Kalman filter. Results show that the combination of CaPA, GLEAM, and the JPL mascon GRACE product tended to outperform other combinations across Canadian river basins. Overall, the error attributions of precipitation, evapotranspiration, water storage change, and runoff were 36.7%, 33.2%, 17.8%, and 12.2%, which corresponded to 8.1, 7.9, 4.2, and 1.4 mm month−1, respectively. In particular, the nonclosure error from precipitation dominated in Western Canada, whereas that from evapotranspiration contributed most in the Mackenzie River basin. [ABSTRACT FROM AUTHOR]