Search Results (33)

The Peace-Athabasca Delta (PAD) in northern Alberta, Canada, is one of the world’s largest inland freshwater deltas and is largely located within the Wood Buffalo National Park, a UNESCO World Heritage Site. Owing to its ecological and socioeconomic significance, the PAD has been designated a Ramsar wetland of international importance. A paucity of large-scale Peace River ice jam flooding and concurrent drying trend during the past five decades has motivated various studies on relevant processes and on possible remedial action. In turn, many of these studies are informed by a flood record that was compiled in 1995, based on Historical information and Traditional Knowledge (H-TK flood record). Later work has expressed occasional reservations regarding the accuracy of this record, while much more is now known about the physical and hydroclimatic controls of PAD ice jams. This paper examines the 20th century portion of the H-TK record in the light of recent scientific advances made since the 1990s and of a wealth of hydrometric and climatic indicators, along with eyewitness corroborations, that extend back to the early 1900s. Systematic observational data and monitoring reports that have become available since the 1990s have also provided valuable documentation of PAD flooding. It is concluded that the record of major ice-jam floods is reliable, while the possibility of “missed” events cannot be precluded. The record of minor ice jam floods, which is largely inferred from reversed tributary flows entering Lake Athabasca, may not be reliable because more than half of the reported events might not have occurred at all. The value of the H-TK record is primarily in the major events, which generate overland inundation and can amply recharge various ponds, lakes, and wetlands of the PAD. Implications of the results for pre- and post-regulation flood frequencies and for future park management are discussed. Full article

The Athabasca River flows through the Lower Athabasca Region (LAR) in Alberta, Canada, which is characterized by variable inter-annual weather, long winters and short summers. LAR is important for the extraction of energy resources and industrial activities that lead to environmental concerns, including river pollution and exploitation. This study attempts to forecast the Athabasca River at Fort McMurray and understand the suitability of HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) in cold weather regions, characterized by poorly gauged streams. Daily temperature and precipitation records (1971–2014) were employed in two calibration–validation schemes: (1) a temporally dependent partition (1971–2000 for calibration; 2001–2014 for validation) and (2) a temporally independent partition (alternating years assigned to calibration and validation). The temporally independent approach achieved superior performance, with a Nash–Sutcliffe efficiency of 0.88, outperforming previously developed regional models. HEC-HMS successfully reproduced hydrologic dynamics and peak discharge events under conditions of sparse hydroclimatic data and limited computational inputs, underscoring its robustness for operational forecasting in data-scarce, cold-climate catchments. However, long-term projections may be subject to uncertainty due to the exclusion of anticipated changes in land use and climate forcing. These results substantiate the applicability of HEC-HMS as a cost-effective and reliable tool for hydrological modeling and flow forecasting in support of water resource management, particularly in regions subject to industrial pressures and associated environmental impacts. Full article

The Athabasca River Basin (ARB) is the location of the Canadian oil sands industry and 70.8% of global estimated bitumen deposits. The Athabasca River is the water source for highly water-intensive bitumen processing. Our objective is to project ARB temperature, precipitation, total runoff, climate moisture index (CMI), and standardized precipitation evapotranspiration index (SPEI) for 2011–2100 using the superior modelling skill of seven regional climate models (RCMs) from Coordinated Regional Climate Downscaling Experiment (CORDEX). These projections show an average 6 °C annual temperature increase for 2071–2100 under RCP 8.5 relative to 1971–2000. Resulting increases in evapotranspiration may be partially offset by an average 0.3 mm/day annual precipitation increase. The projected precipitation increases are in the winter, spring, and autumn, with declines in summer. CORDEX RCMs project a slight increase (0.04 mm/day) in annual averaged runoff, with a shift to an earlier springtime melt pulse. However, these are countered by projected declines in summer and early autumn runoff. There will be significant decreases in annual and summertime CMI and annual SPEI. We conclude that there will be increasingly stressed ARB water availability, particularly in summer, doubtless resulting in repercussions on ARB industrial activities with their extensive water allocations and withdrawals. Full article

Dissolved oxygen (DO) is one of the most critical water quality constituents in cold region rivers. Harsh winter conditions pose significant challenges for DO sampling, making numerical modeling a valuable tool for gaining insights into DO concentrations during winter. Sensitivity analysis is essential for understanding the relative importance of the model parameters to the DO concentrations; however, such studies are rare. This study conducted a DO sensitivity analysis in the Lower Athabasca River, Canada, using a water quality model with ice effects in the MIKE HYDRO River. The simulated flow, water level, water temperature and DO concentrations closely matched observed values along the study reach. A bidirectional perturbation analysis was conducted to assess the sensitivity of DO concentrations to 14 model parameters. The results indicate that photosynthesis and respiration are the two most influential processes affecting river DO under winter conditions despite lower biomass activity compared to open-water conditions. A distinct seasonal pattern was observed for most parameters, with DO sensitivity during winter ice-covered periods being significantly higher than in summer open-water conditions. The study provides valuable insights for the development of integrated water quality and ice models for cold region rivers. Full article

Temperature and dissolved oxygen (DO) are of critical importance for sustainable aquatic ecosystem and biodiversity in the river systems. This study aims to develop a data-driven model for forecasting water quality in the Athabasca River using a seasonal autoregressive integrated moving average model (SARIMA) for forecasting monthly DO and water temperature. DO and water temperature observed at Fort McMurray and Athabasca from 1960 to 2023 were used to train and test the model. The results show the satisfied model performance of DO with a coefficient of determination (R²) value of 0.76 and an RMSE value of 0.79 for training and 0.67 and 0.92 for testing, respectively, at the Fort McMurray station. At the Town of Athabasca station, the RMSE and R² of DO were 0.92 and 0.72 for training and 0.77 and 0.86 for testing, respectively. For the modeled temperature, RMSE and R² were 2.7 and 0.87 for training and 2.2 and 0.95 for testing, respectively, at Fort McMurray and were 2.0 and 0.93 for training and 1.8 and 0.97 for testing, respectively, in the Town of Athabasca. The results show that DO concentration is inversely proportional to the temperature. This implies that the DO could be related to water temperature, which, in turn, is correlated with air temperature. Therefore, the SARIMA model performed reasonably well in representing the dynamics of water temperature and DO in the cold climate river. Such a model can be used in practice to reduce the risk of low DO events. Full article

Climate change (CC) and land use/land cover change (LULCC) are significant drivers of hydrological change, and an effective watershed management requires a detailed understanding of their individual and the combined impact. This study focused on the Athabasca River Basin (ARB), Canada, and investigated how the basin responded to their changes using the MIKE SHE-MIKE Hydro River. Our findings revealed novel insights into ARB hydrological changes, including increment in non-vegetated lands (0.26%), savannas (1.28%), forests (0.53%), and urban areas (0.02%) while grasslands (2.07%) and shrublands (0.03%) decreased. Moreover, the basin experienced rising annual minimum (1.01 °C) and maximum (0.85 °C) temperatures but declining precipitation (6.2%). The findings suggested a significant impact of CC compared to LULCC as CC caused annual reduction in streamflow (7.9%), evapotranspiration (4.8%), and recharge (6.9%). Meanwhile, LULCC reduced streamflow (0.2%) and recharge (0.4%) but increased evapotranspiration (0.1%). The study revealed spatiotemporal variability across the ARB, with temperature impacts stronger in winter and precipitation influencing other seasons. Full article

Ice jamming is the primary mechanism that can generate overland flooding and recharge the isolated basins of the Peace–Athabasca Delta (PAD), a valuable ecosystem of international importance and the ancient homeland of the Indigenous Peoples of the region. Focusing on the regulated Peace River and the Peace Sector of the delta, which has been experiencing a drying trend in between rare ice-jam floods over the last ~50 years, this study describes recent notable breakup events, associated observational data, and numerical applications to determine river discharge during the breakup events. Synthesis and interpretation of this material provide a new physical understanding that can inform the ongoing development of a protocol for strategic flow releases toward enhancing basin recharge in years when major ice jams are likely to form near the PAD. Additionally, several recommendations are made for future monitoring activities and improvements in proposed antecedent criteria for early identification of “promising” breakup events. Full article

Streamflow forecasting is of great importance in water resource management and flood warnings. Machine learning techniques can be utilized to assist with river flow forecasting. By analyzing historical time-series data on river flows, weather patterns, and other relevant factors, machine learning models can learn patterns and relationships to present predictions about future river flows. In this study, an autoregressive integrated moving average (ARIMA) model was constructed to predict the monthly flows of the Athabasca River at three monitoring stations: Hinton, Athabasca, and Fort MacMurray in Alberta, Canada. The three monitoring stations upstream, midstream, and downstream were selected to represent the different climatological regimes of the Athabasca River. Time-series data were used for model training to identify patterns and correlations using moving averages, exponential smoothing, and Holt–Winters’ method. The model’s forecasting was compared against the observed data. The results show that the determination coefficients were 0.99 at all three stations, indicating strong correlations. The root mean square errors (RMSEs) were 26.19 at Hinton, 61.1 at Athabasca, and 15.703 at Fort MacMurray, respectively, and the mean absolute percentage errors (MAPEs) were 0.34%, 0.44%, and 0.14%, respectively. Therefore, the ARIMA model captured the seasonality patterns and trends in the stream flows at all three stations and demonstrated a robust performance for hydrological forecasting. This provides insights and predictions for water resource management and flood warnings. Full article

Timely release of flow from upstream hydropower generation facilities on the Peace River can enhance potential ice-jam flooding near the drying Peace–Athabasca Delta (PAD), a Ramsar wetland of international importance and homeland to Indigenous Peoples. An important consideration in deciding whether and when to commence a release is the celerity of the breakup front as it advances along the Peace River. Relevant historical data for a key stretch of the river are analyzed to determine average celerities, which can vary by an order of magnitude from year to year. Seven breakup events are identified that might have been candidates for a release, and the predictability of associated celerities is explored in terms of antecedent hydroclimatic variables, including cumulative winter snowfall, snow water equivalent on 1 April, ice cover thickness, coldness of the winter, and freezeup level. It is shown that celerity can be predicted to within a factor of two or less, with the freezeup level giving the best results. Three of the seven “promising” events culminated in PAD floods and were associated with the three highest celerities. The empirical findings are shown to generally align with physical understanding of breakup driving and resisting factors. Full article

Satellite altimetry has been proven to measure water levels in lakes and rivers effectively. The Sentinel-3A satellite is equipped with a dual-frequency synthetic aperture radar altimeter (SRAL), which allows for inland water levels to be measured with higher precision and improved spatial resolution. However, in regions at middle and high latitudes, where many lakes are covered by ice during the winter, the non-uniformity of the altimeter footprint can substantially impact the accuracy of water level estimates, resulting in abnormal readings when applying standard SRAL synthetic aperture radar (SAR) waveform retracking algorithms (retrackers). In this study, a modified method is proposed to determine the current surface type of lakes, analyzing changes in backscattering coefficients and brightness temperature. This method aligns with ground station observations and ensures consistent surface type classification. Additionally, a dual-threshold algorithm that addresses the limitations of the original bimodal algorithm by identifying multiple peaks without needing elevation correction is introduced. This innovative approach significantly enhances the precision of equivalent water level measurements for ice-covered lakes. The study retrieves and compares the water level data of nine North American lakes covered by ice from 2016–2019 using the dual-threshold and the SAMOSA-3 algorithm with in situ data. For Lake Athabasca, Cedar Lake, Great Slave Lake, Lake Winnipeg, and Lake Erie, the root mean square error (RMSE) of SAMOSA-3 is 39.58 cm, 46.18 cm, 45.75 cm, 42.64 cm, and 6.89 cm, respectively. However, the dual-threshold algorithm achieves an RMSE of 6.75 cm, 9.47 cm, 5.90 cm, 7.67 cm, and 5.01 cm, respectively, representing a decrease of 75%, 79%, 87%, 82%, and 27%, respectively, compared to SAMOSA-3. The dual-threshold algorithm can accurately estimate water levels in ice-covered lakes during winter. It offers a promising prospect for achieving long-term, continuous, and high-precision water level measurements for middle- and high-latitude lakes. Full article

The Peace–Athabasca Delta (PAD) in northern Alberta is one of the world’s largest inland freshwater deltas and is home to many species of fish, mammals, and birds. Over the past five decades, the PAD has experienced prolonged dry periods in between rare floods, accompanied by a reduction in the area comprised of lakes and ponds that provide a habitat for aquatic life. In the Peace sector of the PAD, this likely resulted from a reduced frequency of spring flooding caused by major ice jams that form in the lower Peace River. There is debate in the literature regarding the factors that promote or inhibit the formation of such ice jams, deriving from physical process studies, paleolimnological studies, and—recently—statistical analysis founded in logistic regression. Logistic regression attempts to quantify ice-jam flood (IJF) probability, given the values of assumed explanatory variables, involve considerable uncertainty. Herein, different sources of uncertainty are examined and their effects on statistical inferences are evaluated. It is shown that epistemic uncertainty can be addressed by selecting direct explanatory variables, such as breakup flow and ice cover thickness, rather than through more convenient, albeit weak, proxies that rely on winter precipitation and degree-days of frost. Structural uncertainty, which derives from the unknown mathematical relationship between IJF probability and the selected explanatory variables, leads to different probability predictions for different assumed relationships but does not modify assessments of statistical significance. The uncertainty associated with the relatively small sample size (number of years of record) may be complicated by known physical constraints on IJF occurrence. Overall, logistic regression corroborates physical understanding that points to breakup flow and freezeup level as primary controls of IJF occurrence. Additional influences, related to the thermal decay of the ice cover and the flow gradient during the advance of the breakup front towards the PAD, are difficult to quantify at present. Progress requires increased monitoring of processes and an enhanced numerical modelling capability. Full article

Seismic lines are cleared corridors for the location mapping of subsurface bitumen. After use, the lines can be left to regenerate naturally with varying success. Wildfires, another prominent disturbance in the Boreal region, are propagated by continuous fuel distribution (coarse/fine), meteorological variables (e.g., wind speed, temperature, and precipitation), and the moisture content of the fuel and soil. However, little is known about seismic lines and the potential risk and severity of wildfires. This work presents a case study of wildfire variables on two paired (seismic line and adjacent natural area) sites near Fort McMurray, Alberta, Canada. Wind speed was increased on seismic lines, and the dominant wind direction changed. Higher precipitation, air temperature, and soil moisture and reduced water table depths were observed on seismic lines. Coarse fuel distribution was not continuous on seismic lines; however, fine fuels were. Although the Fire Weather Index (FWI) indicated an enhanced wildfire potential on one line (NS orientation), peat smouldering and ignition models (H_comb/H_ign) showed increased smouldering potential on both seismic lines compared to adjacent natural areas. Future work should focus on expanding the diversity of seismic line characterization, working towards the landscape-scale modelling of these variables. Full article

Having a complete hydrological time series is crucial for water-resources management and modeling. However, this can pose a challenge in data-scarce environments where data gaps are widespread. In such situations, recurring data gaps can lead to unfavorable outcomes such as loss of critical information, ineffective model calibration, inaccurate timing of peak flows, and biased statistical analysis in various applications. Despite its importance, predicting monthly streamflow can be a complex task due to its connection to random dynamics and uncertain phenomena, posing significant challenges. This study introduces an ensemble machine-learning regression framework for modeling and predicting monthly streamflow time series with a high degree of accuracy. The framework utilizes historical data from multiple monthly streamflow datasets in the same region to predict missing monthly streamflow data. The framework selects the best features from all available gap-free monthly streamflow time-series combinations and identifies the optimal model from a pool of 12 machine-learning models, including random forest regression, gradient boosting regression, and extra trees regressor, among others. The model selection is based on cross-validation train-and-test set scores, as well as the coefficient of determination. We conducted modeling on 26 monthly streamflow time series and found that the gradient boosting regressor with bagging regressor produced the highest accuracy in 7 of the 26 instances. Across all instances, the models using this method exhibited an overall accuracy range of 0.9737 to 0.9968. Additionally, the use of either a bagging regressor or an AdaBoost regressor improved both the tree-based and gradient-based models, resulting in these methods accounting for nearly 80% of the best models. Between January 1960 and December 2021, an average of 40% of the monthly streamflow data was missing for each of the 26 stations. Notably, two crucial stations located in the economically significant lower Athabasca Basin River in Alberta province, Canada, had approximately 70% of their monthly streamflow data missing. To address this issue, we employed our framework to accurately extend the missing data for all 26 stations. These accurate extensions also allow for further analysis, including grouping stations with similar monthly streamflow behavior using Pearson correlation. Full article

Stream and river monitoring have an influential role in agriculture, the fishing industry, land surveillance, the oil and gas industry, etc. Recognizing sudden changes in the behavior of streamflow could also provide tremendous insight for decision-making and administration purposes. The primary purpose of this study is to offer a new robust Regime Shift Change Detection (RSCD) algorithm which can identify periods and regime changes without any assumptions regarding the length of these periods. A regime shift algorithm using two different refined method approaches is proposed in this article. The RSCD with Relative Difference (RSCD-RD) and RSCD with Growth Rate (RSCD-GR) are the two main specializations of this regime shift algorithm. We compared these two specializations on train and test datasets and commented on the advantages and each specialization. RSCD-GR and RSCD-RD were equally effective in detecting regime changes when thresholds were pinpointed for each station and season. However, RSCD-RD outperformed RSCD-GR when general thresholds were used for cold and warm months. A strength of RSCD-GR is the ability to investigate newly observed data separately, while RSCD-RD may require re-investigation of historical data in some cases. A regime change was detected in the monthly streamflow data of the Athabasca River at Athabasca (07BE001) in May 2007, while no such change was observed in the monthly streamflow data of the Athabasca River below Fort McMurray (07DA001). The discrepancy could be attributed to factors such as the clarity of the river water from Saskatchewan or the utilization of industrial water. Additional investigation might be required to determine the underlying causes. Full article

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