Canadian Watersheds Research Articles

Temporal Assessment of GCM-Driven Hydroclimatic Conditions for the Alberta Oil Sands Region, Canada

Abstract Freshwater supplies in most western Canadian watersheds are threatened by the warming of temperatures because it alters the snow-dominated hydrologic patterns that characterize these cold regions. In this study, we used datasets from 12 climate simulations, which are associated with seven global climate models and four future scenarios and are participating in phase 6 of the Coupled Model Intercomparison Project, to calculate and assess the historical and future temporal patterns of 13 hydroclimate indicators relevant to water resources management. We conducted linear long-term trend and change analyses on their annual time series to provide insight into the potential regional impacts of the detected changes on water availability for all users. We implemented our framework on the Alberta oil sands region in Canada to support the monitoring of environmental changes in this region, relative to the established baseline 1985–2014. Our analysis indicates a persistent increase in the occurrence of extreme hot temperatures, fewer extreme cold temperatures, and an increase in warm spells and heatwaves, while precipitation-related indices show minor changes. Consequently, deficits in regional water availability during summer and water-year periods, as depicted by the standardized precipitation evapotranspiration indices, are expected. The combined effects of the strong climate warming signals and the small increases in precipitation annual amounts generally detected in this study suggest that drier conditions may become severe and frequent in the Alberta oil sands region. The challenging climate change risks identified for this region should therefore be continuously monitored, updated, and integrated to support a sustainable management for all water users.

Sustainable Water Management and the 2030 Agenda: Comparing Rain Forest Watersheds in Canada and Brazil by Applying an Innovative Sustainability Indicator System

Watershed management varies greatly across the world. Local conditions are generally dictated by how watershed management is regulated at national, regional, and local scales. Both multisectoral and community-based participatory involvement in watershed management can positively impact the quality and effectiveness of outcomes. This localization can also be vital to the achievement of the UN’s Sustainable Development Goals. In recent years, the term “sustainability” has become overused, has limited quantifiable meaning, and can create “fuzzy” targets. We suggest that an outcome that focuses on “thrivability” is more appropriate; this refers to the ability to not only sustain positive conditions for future generations but to create conditions that allow for all living things (present and future) to have the ability and opportunity to thrive. A thrivability approach aligns with the 2030 Agenda’s ultimate goal: prosperity for all beings on earth. This study uses a thrivability lens to compare two study sites. Primary and secondary data were collected for both the Regional District of Nanaimo (RDN), Canada, and Hydrographic Region VIII (HR-VIII), Brazil, and have been input and analyzed through our Thrivability Appraisal to determine each region’s watershed thrivability score. The Thrivability Appraisal uses seven sustainability principles as the overarching framework. These are then related to four individual subcomponents of watershed health and three common interest tests based on primary environmental perception and secondary technical data as inputs. Assuming the centricity of water for prosperity, the final scoring is a culmination of the 49 total indicators. A comparison is then drawn to the regions’ capacity to achieve the eight targets for UN Sustainable Development Goal (SDG) 6. The outcome illustrates each region’s water management strengths and weaknesses, allowing for lessons to be learned and transferred to other multijurisdictional watersheds.

The Effects Atmospheric Changes Have on Runoff

Over the last century, the earth has seen unprecedented atmospheric concentrations contaminate our ecosystems due to human activity. Predictions state the introduction of carbon dioxide, methane, nitrous oxide, and chlorofluorocarbons (CFCs), will increase temperatures, and change the amount and location of precipitation causing more runoff. This could potentially result in disturbance events such as floods, to be more frequent and severe. This study aims to perform an assessment of the effects of a range of hypothetical climate changes on runoff in the North-east Pond River watershed, located in Newfoundland.
 To carry this out a watershed runoff model simulates runoff in the basin for current climatic conditions and for hypothetical climatic conditions that represent a range of possible climate changes (Bobba et al., 1997). The hypothetical changes in climate will showcase the effects of a 2oC increase in temperature on the total annual precipitation. This will then be compared to flood forecasting models to analyze how runoff will be affected by various climatic conditions, inducing unusual flooding events (Wijayarathne & Coulibaly, 2020). Previous studies have indicated the runoff sensitivity in watersheds to changes in temperatures which raises concerns as to the adverse effects this may cause in limiting water resources in the semi-arid regions in parts of Canada and the U.S. Thus, there is a need to increase the understanding of the sensitivity of water resources in Canadian watersheds to climate variability and climate change as effects of this magnitude on the North-east Pond River could have significant environmental implications.

Evaluating community science sampling for microplastics in shore sediments of large river watersheds

A community science project in the Ottawa River Watershed in Canada interacted with an existing volunteer base to collect sediment from 68 locations in the watershed over approximately 750 km. Ninety-one percent of the distributed kits were returned with 42 volunteers taking part in the project. After analysis, particle concentrations were relatively low compared to previous freshwater microplastic sediment research, with contributing factors including (but not limited to) the large size of the watershed, a lower population base compared to other researched freshwater watersheds, the relative size and discharge of the Ottawa River and the large seasonal fluxes experienced in the river basin. Utilising community science for sampling large freshwater watersheds demonstrated its advantages in the research, especially spatially. However, careful consideration to research design and implementation is essential for community science projects examining microplastics in freshwater sediments. Research teams should ensure they are responsible for strict quality assurance and quality control protocols, especially in the laboratory with sample preparation and processing. Nonetheless, community science is potentially an extremely useful approach for researchers to use for microplastic sampling projects over large spatial areas.

Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models.

The main aim of this study was to predict current and future flood susceptibility under three climate change scenarios of RCP2.6 (i.e., optimistic), RCP4.5 (i.e., business as usual), and RCP8.5 (i.e., pessimistic) employing four machine learning models, including Gradient Boosting Machine (GBM), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), and Naïve Bayes (NB). The study was conducted for two watersheds in Canada, namely Lower Nicola River, BC and Loup, QC. Three statistical metrics were used to validate the models: Receiver Operating Characteristic Curve, Figure of Merit, and F1-score. Findings indicated that the RF model had the highest accuracy in providing the flood susceptibility maps (FSMs). Moreover, the provided FSMs indicated that flooding is more likely to occur in the Lower Nicola River watershed than the Loup watershed. Following the RCP4.5 scenario, the area percentages of the flood susceptibility classes in the Loup watershed in 2050 and 2080 have changed by the following percentages from the year 2020 and 2050, respectively: Very Low = -1.68%, Low = -5.82%, Moderate = +6.19%, High = +0.71%, and Very High = +0.6% and Very Low = -1.61%, Low = +2.98%, Moderate = -3.49%, High = +1.29%, and Very High = +0.83%. Likewise, in the Lower Nicola River watershed, the changes between the years 2020 and 2050 and between the years 2050 and 2080 were: Very Low = -0.38%, Low = -0.81%, Moderate = -0.95%, High = +1.72%, and Very High = +0.42% and Very Low = -1.31%, Low = -1.35%, Moderate = -1.81%, High = +2.37%, and Very High = +2.1%, respectively. The impact of climate changes on future flood-prone places revealed that the regions designated as highly and very highly susceptible to flooding, grow in the forecasts for both watersheds. The main contribution of this study lies in the novel insights it provides concerning the flood susceptibility of watersheds in British Columbia and Quebec over time and under various climate change scenarios.

Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism

This study investigates the capability of sequence-to-sequence machine learning (ML) architectures in an effort to develop streamflow forecasting tools for Canadian watersheds. Such tools are useful to inform local and region-specific water management and flood forecasting related activities. Two powerful deep-learning variants of the Recurrent Neural Network were investigated, namely the standard and attention-based encoder-decoder long short-term memory (LSTM) models. Both models were forced with past hydro-meteorological states and daily meteorological data with a look-back time window of several days. These models were tested for 10 different watersheds from the Ottawa River watershed, located within the Great Lakes Saint-Lawrence region of Canada, an economic powerhouse of the country. The results of training and testing phases suggest that both models are able to simulate overall hydrograph patterns well when compared to observational records. Between the two models, the attention model significantly outperforms the standard model in all watersheds, suggesting the importance and usefulness of the attention mechanism in ML architectures, not well explored for hydrological applications. The mean performance accuracy of the attention model on unseen data, when assessed in terms of mean Nash–Sutcliffe Efficiency and Kling-Gupta Efficiency is, respectively, found to be 0.985 and 0.954 for these watersheds. Streamflow forecasts with lead times of up to 5 days with the attention model demonstrate overall skillful performance with well above the benchmark accuracy of 70%. The results of the study suggest that the encoder–decoder LSTM, with attention mechanism, is a powerful modelling choice for developing streamflow forecasting systems for Canadian watersheds.

Contribution of rain events to surface water loading in 3 watersheds in Canada’s Alberta Oil Sands region

Study regionIn the oil sands region of Canada, hydrology of rivers is strongly influenced by snowmelt and a shifting climate resulting in shortening of snow cover duration and increases in the frequency of rain events. Study focusTo evaluate the importance of rain events to these rivers, discharge and water quality sampling was conducted at river sites in this region. Two approaches were used to collect water quality samples: (1) event-based automated samplers that triggered repeated sampling in response to 10% increases in surface flow, and (2) an intensive program of routine sampling at daily, weekly or biweekly intervals from April 2013 to January 2014. New hydrological insights for the regionThe importance of rain events as contributors to loading of nutrients and priority pollutants differed temporally. During fall, when baseflow conditions prevailed, average daily loads differed when calculated using routine only versus routine + rain-event data. However, on an annual basis, the inclusion of rain-event sampling did not change pollutant loads. Thus, when assessing loads on an annual basis, rain event sampling may not provide enough benefit to warrant the additional logistical considerations for this remote region. However, when monitoring focuses on capturing fall conditions, efforts should be expanded to include rain event sampling in order to capture spikes in chemical concentration during this ecologically important time period. Predictions of a shorter snow season with more precipitation falling as rain rather than snow reinforce the need to adapt monitoring schemes to ensure that the timing and frequency of water chemistry sampling reflects changing climate and hydrological patterns.

Identification of flood seasonality and drivers across Canada

AbstractFloods are the most frequently occurring natural hazard in Canada. An in‐depth understanding of flood seasonality and its drivers at a national scale is essential. Here, a circular, statistics‐based approach is implemented to understand the seasonality of annual‐maximum floods (streamflow) and to identify their responsible drivers across Canada. Nearly 80% and 70% of flood events were found to occur during spring and summer in eastern and western watersheds across Canada, respectively. Flooding in the eastern and western watersheds was primarily driven by snowmelt and extreme precipitation, respectively. This observation suggests that increases in temperature have led to early spring snowmelt‐induced floods throughout eastern Canada. Our results indicate that precipitation (snowmelt) variability can exert large controls on the magnitude of flood peaks in western (eastern) watersheds in Canada. Further, the nonstationarity of flood peaks is modelled to account for impact of the dynamic behaviour of the identified flood drivers on extreme‐flood magnitude by using a cluster of 74 generalized additive models for location scale and shape models, which can capture both the linear and nonlinear characteristics of flood‐peak changes and can model its dependence on external covariates. Using nonstationary frequency analysis, we find that increasing precipitation and snowmelt magnitudes directly resulted in a significant increase in 50‐year streamflow. Our results highlight an east–west asymmetry in flood seasonality, indicating the existence of a climate signal in flood observations. The understating of flood seasonality and flood responses under the dynamic characteristics of precipitation and snowmelt extremes may facilitate the predictability of such events, which can aid in predicting and managing their impacts.

Microlitter in the water, sediments, and mussels of the Saint John River (Wolastoq) watershed, Atlantic Canada

Microlitter is a widespread contaminant with implications for aquatic health; however, knowledge of its distribution in fresh waters is limited. We examined microlitter in surface water, sediment, and mussels within the Saint John River and four major tributaries. Microlitter was present at all 89 sites, with concentrations in water equivalent to other Canadian watersheds. Microlitter concentrations likely reflected differences in land use and development. Comparisons between rivers generally revealed distinct water microlitter particle compositions, suggesting that input sources were specific to each river, while microlitter in sediments and mussels was largely homogeneous among rivers. There was little similarity in concentration and composition of microlitter among the three matrices sampled. Microlitter in sediments and mussels may not reliably track concentration and composition in aquatic environments. Microlitter concentration was not significantly greater downstream of wastewater treatment plants. Airborne microlitter transportation was evident, as remote sites in two tributary rivers demonstrated the highest concentrations of microlitter. Our data highlights the prevalence of microlitter in rural rivers, underscoring the ubiquity of this emerging contaminant and its integration into food webs.

Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy

Abstract. Data-driven flow-forecasting models, such as artificial neural networks (ANNs), are increasingly featured in research for their potential use in operational riverine flood warning systems. However, the distributions of observed flow data are imbalanced, resulting in poor prediction accuracy on high flows in terms of both amplitude and timing error. Resampling and ensemble techniques have been shown to improve model performance on imbalanced datasets. However, the efficacy of these methods (individually or combined) has not been explicitly evaluated for improving high-flow forecasts. In this research, we systematically evaluate and compare three resampling methods, random undersampling (RUS), random oversampling (ROS), and the synthetic minority oversampling technique for regression (SMOTER), and four ensemble techniques, randomised weights and biases, bagging, adaptive boosting (AdaBoost), and least-squares boosting (LSBoost), on their ability to improve high stage prediction accuracy using ANNs. These methods are implemented both independently and in combined hybrid techniques, where the resampling methods are embedded within the ensemble methods. This systematic approach for embedding resampling methods is a novel contribution. This research presents the first analysis of the effects of combining these methods on high stage prediction accuracy. Data from two Canadian watersheds (the Bow River in Alberta and the Don River in Ontario), representing distinct hydrological systems, are used as the basis for the comparison of the methods. The models are evaluated on overall performance and on typical and high stage subsets. The results of this research indicate that resampling produces marginal improvements to high stage prediction accuracy, whereas ensemble methods produce more substantial improvements, with or without resampling. Many of the techniques used produced an asymmetric trade-off between typical and high stage performance; reduction of high stage error resulted in disproportionately larger error on a typical stage. The methods proposed in this study highlight the diversity-in-learning concept and help support future studies on adapting ensemble algorithms for resampling. This research contains many of the first instances of such methods for flow forecasting and, moreover, their efficacy in addressing the imbalance problem and heteroscedasticity, which are commonly observed in high-flow and flood-forecasting models.

Impacts of climate variations on non-stationarity of streamflow over Canada

With climate change, understanding and assessing the impact of climate variations on non-stationary changes of streamflow is of importance in the hydrologic and atmospheric sciences. In this study, tempo-spatial and scaling effects in the impacts of 18 climate variations on nonstationary streamflow for 279 watersheds across Canada are explored. Specifically, the change point and trends of streamflow are examined through Pettitt's test and Mann-Kendall test. Spatial patterns of correlations between the climate variations and flow rates over Canada, especially their non-stationarity, are investigated at seasonal and decadal scales. The patterns are also quantified by seven spatial classification algorithms under method uncertainty. A series of findings regarding the impacts are revealed. For instance, nonstationary changes of streamflow exist for approximately 9% of Canadian watersheds and most of them are located in Prairie Provinces and the eastern coast. The Atlantic Multidecadal Oscillation, Niño 12, Niño 3, Niño 4, and Niño 3.4 pose significant impacts on Canadian streamflow, which vary with watersheds and seasons. The impacts are closely associated with human activities, e.g., significant impacts of climate variations on populated-area streamflow over Canada. Different climatic variations have different time-varying effects on streamflow. All watersheds have obvious clustering characteristics and four spatial patterns are identified, which is insensitive with classification algorithm. These findings are conducive to understanding the hydrological impacts of atmospheric circulation and enhancing the reliability of hydrological prediction.

Currents Status, Challenges, and Future Directions in Identifying Critical Source Areas for Non-Point Source Pollution in Canadian Conditions

Non-point source (NPS) pollution is an important problem that has been threatening freshwater resources throughout the world. Best Management Practices (BMPs) can reduce NPS pollution delivery to receiving waters. For economic reasons, BMPs should be placed at critical source areas (CSAs), which are the areas contributing most of the NPS pollution. The CSAs are the areas in a watershed where source coincides with transport factors, such as runoff, erosion, subsurface flow, and channel processes. Methods ranging from simple index-based to detailed hydrologic and water quality (HWQ) models are being used to identify CSAs. However, application of these methods for Canadian watersheds remains challenging due to the diversified hydrological conditions, which are not fully incorporated into most existing methods. The aim of this work is to review potential methods and challenges in identifying CSAs under Canadian conditions. As such, this study: (a) reviews different methods for identifying CSAs; (b) discusses challenges and the current state of CSA identification; and (c) highlights future research directions to address limitations of currently available methods. It appears that applications of both simple index-based methods and detailed HWQ models to determine CSAs are limited in Canadian conditions. As no single method/model is perfect, it is recommended to develop a ‘Toolbox’ that can host a variety of methods to identify CSAs so as to allow flexibility to the end users on the choice of the methods.

Evaluation of Snowmelt Estimation Techniques for Enhanced Spring Peak Flow Prediction

Water resources management and planning requires accurate and reliable spring flood forecasts. In cold and snowy countries, particularly in snow-dominated watersheds, enhanced flood prediction requires adequate snowmelt estimation techniques. Whereas the majority of the studies on snow modeling have focused on comparing the performance of empirical techniques and physically based methods, very few studies have investigated empirical models and conceptual models for improving spring peak flow prediction. The objective of this study is to investigate the potential of empirical degree-day method (DDM) to effectively and accurately predict peak flows compared to sophisticated and conceptual SNOW-17 model at two watersheds in Canada: the La-Grande River Basin (LGRB) and the Upper Assiniboine river at Shellmouth Reservoir (UASR). Additional insightful contributions include the evaluation of a seasonal model calibration approach, an annual model calibration method, and two hydrological models: McMaster University Hydrologiska Byrans Vattenbalansavdelning (MAC-HBV) and Sacramento Soil Moisture Accounting model (SAC-SMA). A total of eight model scenarios were considered for each watershed. Results indicate that DDM was very competitive with SNOW-17 at both the study sites, whereas it showed significant improvement in prediction accuracy at UASR. Moreover, the seasonally calibrated model appears to be an effective alternative to an annual model calibration approach, while the SAC-SMA model outperformed the MAC-HBV model, no matter which snowmelt computation method, calibration approach, or study basin is used. Conclusively, the DDM and seasonal model calibration approach coupled with the SAC-SMA hydrologic model appears to be a robust model combination for spring peak flow estimation.

Impact of the spatial density of weather stations on the performance of distributed and lumped hydrological models

This study aimed to quantify the ability of distributed and lumped hydrological models to use high-resolution precipitation and temperature data to improve streamflow simulation at watershed outlets. To that end, a 40-year, high-resolution, spatially distributed, meteorological dataset was extracted from a 15-km resolution regional climate model simulation (from the Canadian Regional Climate Model – CRCM v.4.2.4 driven by ERA40 reanalysis). This dataset was used to feed one distributed and four lumped hydrological models. The five models were calibrated on 192 watersheds located in the province of Quebec (Canada) using five different meteorological network densities of pseudo-stations. These densities ranged from one single station (located at the centre of gravity of the watershed) up to the maximum grid density of 1 station per 225 km2 (15 km × 15 km which corresponds to the CRCM spatial resolution). No significant decrease in validation performance for both types of hydrological models was observed when using any of the tested station densities. Similar results were also obtained when investigating the subsets of 54 smaller (≤2,500 km2) and 84 medium-sized (2,500 < area <10,000 km2) watersheds. However, for the 54 larger watersheds (≥10,000 km2), the decrease in performance was statistically significant for the distributed model when using one single station. While all lumped models showed a noticeable drop in performance only when using a single station, the distributed model was the only model to show a gradual decrease in performance as the network density decreased. These results indicate that when dealing with large watersheds, distributed models could benefit up to some extent from a larger meteorological network density. These conclusions are likely to be relevant to Canadian watersheds with similar physiographic characteristics and hydroclimatic conditions as the ones included in the Quebec database that was studied.

Hydrological modeling for mining water availability in a Mexican watershed

ABSTRACT Assessment of water availability is important to mining operations. Efficient mineral extraction, processing, and refining is related to a proper water management plan, especially in areas where water is scarce. Hydrological models such as the land surface scheme have been successfully applied to Canadian watersheds to estimate surface streamflow. This paper presents the application of the Modélisation Environnementale communautaire – Surface Hydrology (MESH) model for mining water management using the Coatzacoalcos River basin in Mexico as a case study. The model provided reasonable estimates of the basin water budget (2002–2010) and magnitude and pattern of the peakflows during the wet season (summer). However, the modeled baseflows tended to be lower than observed during the dry season. Additional studies are warranted to refine the characterization of the basin feeding the model and obtain more accurate water budget results that can be used in mining surface water management.

Chlorophyll-a, dissolved organic carbon, turbidity and other variables of ecological importance in river basins in southern Ontario and British Columbia, Canada.

Optical sensing of chlorophyll-a (chl-a), turbidity, and fluorescent dissolved organic matter (fDOM) is often used to characterize the quality of water. There are many site-specific factors and environmental conditions that can affect optically sensed readings; notwithstanding the comparative implication of different procedures used to measure these properties in the laboratory. In this study, we measured these water quality properties using standard laboratory methods, and in the field using optical sensors (sonde-based) at water quality monitoring sites located in four watersheds in Canada. The overall objective of this work was to explore the relationships among sonde-based and standard laboratory measurements of the aforementioned water properties, and evaluate associations among these eco-hydrological properties and land use, environmental, and ancillary water quality variables such as dissolved organic carbon (DOC) and total suspended solids (TSS). Differences among sonde versus laboratory relationships for chl-a suggest such relationships are impacted by laboratory methods and/or site specific conditions. Data mining analysis indicated that interactive site-specific factors predominately impacting chl-a values across sites were specific conductivity and turbidity (variables with positive global associations with chl-a). The overall linear regression predicting DOC from fDOM was relatively strong (R2 = 0.77). However, slope differences in the watershed-specific models suggest laboratory DOC versus fDOM relationships could be impacted by unknown localized water quality properties affecting fDOM readings, and/or the different standard laboratory methods used to estimate DOC. Artificial neural network analyses (ANN) indicated that higher relative chl-a concentrations were associated with low to no tree cover around sample sites and higher daily rainfall in the watersheds examined. Response surfaces derived from ANN indicated that chl-a concentrations were higher where combined agricultural and urban land uses were relatively higher.

Evaluating the utility of remotely sensed soil moisture for the characterization of runoff response over Canadian watersheds

Remotely sensed soil moisture measurements from satellite platforms are increasingly reliable, cost-effective and widely available data sources where in situ measurements are unavailable. This research uses the Soil Moisture and Ocean Salinity mission (SMOS) satellite-derived soil moisture anomalies over a database of 65 watersheds across Canada from 2011 to 2014 to analyze the soil moisture-runoff relationship. A spatial analysis of the variability and influences on the strength of this relationship revealed that 32% of catchments showed significant (1 tailed, p < 0.05) correlations between the weekly antecedent soil moisture state of the catchment and the weekly runoff ratio. Regions of strongest correlation were related to the topographic variables of slope and elevation. These results support the use of coarse-scale satellite remote sensing as a valuable data source in hydrological studies, but recommend caution when applying the data to regions where the accuracy of satellite soil moisture data sets is less certain (such as wetlands and areas with high topography) or areas where the runoff generation mechanisms are complex (frozen soils, wetlands or prairie environments).

Biogeochemical asynchrony: Ecosystem drivers of seasonal concentration regimes across the Great Lakes Basin

AbstractChanges in seasonal nutrient dynamics are occurring across a range of climates and land use types. Although it is known that seasonal patterns in nutrient availability are key drivers of both stream metabolism and eutrophication, there has been little success in developing a comprehensive understanding of seasonal variations in nutrient export across watersheds or of the relationship between nutrient seasonality and watershed characteristics. In the present study, we have used concentration and discharge data from more than 200 stations across U.S. and Canadian watersheds to identify (1) archetypal seasonal concentration regimes for nitrate, soluble reactive phosphorus (SRP), and total phosphorus, and (2) dominant watershed controls on these regimes across a gradient of climate, land use, and topography. Our analysis shows that less impacted watersheds, with more forested and wetland area, most commonly exhibit concentration regimes that are in phase with discharge, with concentration lows occurring during summer low‐flow periods. Agricultural watersheds also commonly exhibit in‐phase behavior, though the seasonality is usually muted compared to that seen in less impacted areas. With increasing urban area, however, nutrient concentrations frequently become essentially aseasonal or even exhibit clearly out‐of‐phase behavior. In addition, our data indicate that seasonal SRP concentration patterns may be strongly influenced by proximal controls such as the presence of dams and reservoirs. In all, these results suggest that human activity is significantly altering nutrient concentration regimes, with large potential consequences for both in‐stream metabolism and eutrophication risk in downstream waterbodies.

Implication of evaporative loss estimation methods in discharge and water temperature modelling in cool temperate climates

AbstractEvaporative flux is a key component of hydrological budgets. Water loss through evapotranspiration reduces volumes available for run‐off. The transition from liquid to water vapour on open water surfaces requires heat. Consequently, evaporation act as a cooling mechanism during summer. Both river discharge and water temperature simulations are thus influenced by the methods used to model evaporation. In this paper, the impact of evapotranspiration estimation methods on simulated discharge is assessed using a semidistributed model on two Canadian watersheds. The impact of evaporation estimation methods on water temperature simulations is also evaluated. Finally, the validity of using the same formulation to simulate both of these processes is verified. Five well‐known evapotranspiration models and five evaporation models with different wind functions were tested. Results show a large disparity (18–22% of mean annual total evapotranspiration) among the evapotranspiration methods, leading to important differences in simulated discharge (3–25% of observed discharge). Larger differences result from evaporation estimation methods with mean annual divergences of 34–48%. This translates into a difference in mean summer water temperature of 1–15%. Results also show that the choice of model parameter has less influence than the choice of evapotranspiration method in discharge simulations. However, the parameter values influence thermal simulations in the same order of magnitude as the choice of evaporation estimation method. Overall, the results of this study suggest that evapotranspiration and open water evaporation should be represented separately in a hydrological modelling framework, especially when water temperature simulations are required.

An overview of river flood forecasting procedures in Canadian watersheds

This paper discusses flood forecasting procedures currently practiced at the Canadian provincial river flood forecast centers. In Canada, each province is responsible for collecting and managing meteorological and hydrometric data (through provincial authorities and/or under Federal/Provincial agreements), developing suitable hydrological models, and providing information about river discharge and water level to the public. In case of an extreme event such as flood or drought, the forecast center is responsible for issuing alerts and supporting provincial emergency management. Due to the large diversity in landscape, weather, and hydrological features across the country, extreme events are triggered by different mechanisms such as snowmelt, heavy rainfall, rain on snow, etc., at different times of the year. Each river forecast center deals with unique challenges in data collection, hydrologic/hydraulic modeling, and flood forecasting. Thus, the focus of the Center in planning for developments and future directions could be significantly different from one province to another. In this paper, the significance of river flood forecasting in Canada, as well as the development in hydrological modelling procedures are highlighted. Moreover, an overview of the current approaches for streamflow/flood forecasting used by the Centers is provided. The content presented here is an outcome of our interaction with the forecast centers. This further resulted in identifying a number of research questions to help bridge the gap between ongoing research and needs of the Centers.