Search Results (12)

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

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

The Peace–Athabasca Delta (PAD) in northern Alberta, Canada is one of the world’s largest inland freshwater deltas, home to large populations of waterfowl, muskrat, beaver, and free-ranging wood bison. The delta region has been designated a Ramsar wetland of international importance and is largely located within the Wood Buffalo National Park, itself being a UNESCO World Heritage Site. Indigenous residents have depended on the delta for centuries to sustain their culture and lifeways. In the past five decades, the PAD has experienced prolonged dry periods in-between rare floods, accompanied by reduction in the area covered by lakes and ponds that provide habitat for aquatic life. Recharge of the higher-elevation, or “perched”, basins depends on overland flooding generated by major spring ice jams that occasionally form in the lower reaches of the Peace and Athabasca Rivers and in their various distributaries. Indigenous Traditional Knowledge and Historical Records for the unregulated Athabasca River are relatively scarce, but conclusively demonstrate the role of ice jams in replenishing perched basins of the Athabasca sector of the PAD. Similar information, coupled with extensive hydrometric and observational data for the regulated Peace River have enabled elucidation of the physical mechanisms that lead to ice-jam flooding of the Peace sector and assessment of regulation impacts on flood frequency. Such understanding can inform design of remedial strategies to moderate or arrest the drying trend of the delta. Climate-related projections to future scenarios suggest reduced frequency of ice-jam floods, albeit with uncertainty. Full article

Herein, the focus was on the identification of similarities in the weather parameters collected within 19 stations, consisting of 3 weather networks located in the Lower Athabasca River Basin operated under the Oil Sands Monitoring program. These stations were then categorised into seven distinct groups based on comparable topography and land cover. With regard to weather parameters, these were air temperature (AT), precipitation (PR), relative humidity (RH), solar radiation (SR), atmospheric/barometric pressure (BP), snowfall depth (SD), and wind speed/direction (WSD). For all seven groups, relational analysis was conducted for every station pair using Pearson’s coefficient (r) and average absolute error (AAE), except for wind direction and wind speed. Similarity analysis was also performed for each station pair across all seven groups using percentage of similarity (PS) measures. Our similarity analysis revealed that there were no similarities (i.e., PS value < 75%) for: (i) SR, PR, and WSD for all groups; (ii) AT for all groups except group G3; (iii) RH for group G7; and (iv) BP for group G1. This study could potentially be decisive in optimizing or rationalising existing weather networks. Furthermore, it could be constructive in the development of meteorological prediction models for any place and that requires input from surrounding stations. Full article

Changes in water resources within basins can significantly impact ecosystems, agriculture, and biodiversity, among others. Basins in northern Canada have a cold climate, and the recent changes in climate can have a profound impact on water resources in these basins. Therefore, it is crucial to study long term trends in water flow as well as their influential factors, such as temperature and precipitation. This study focused on analyzing long term trends in water flow across the Athabasca River Basin (ARB) and Peace River Basin (PRB). Long term trends in temperature and precipitation within these basins were also studied. Water flow data from 18 hydrometric stations provided by Water Survey of Canada were analyzed using the Mann-Kendall test and Sen’s slope. In addition, hybrid climate data provided by Alberta Environment and Parks at approximately 10 km spatial resolution were analyzed for the ARB and its surrounding regions during 1950–2019. Trend analysis was performed on the water flow data on monthly, seasonal, and annual scales, and the results were cross-checked with trends in temperature and precipitation and land use and land cover data. The overall temperature across the basins has been increasing since 1950, while precipitation showed an insignificant decrease during this period. Winter water flow in the upper ARB has been slowly and steadily increasing since 1956 because of the rising temperatures and the subsequent slow melting of snowpacks/glaciers. The warm season flows in the middle and lower subregions declined up to 1981, then started to show an increasing trend. The middle and lower ARB exhibited a rapid increase in warm-season water flow since 2015. A similar trend change was also observed in the PRB. The gradual increase in water flow observed in the recent decades may continue by the mid-century, which is beneficial for agriculture, forestry, fishery, and industry. However, climate and land cover changes may alter the trend of water flow in the future; therefore, it is important to have a proper management plan for water usage in the next decades. Full article

Many measurements, tools, and approaches are used to identify and track the influence of human activities on the physicochemical status of streams. Commonly, chemical concentrations are utilized, but in some areas, such as downstream of coal mines, capacity indices such as specific conductivity have also been used to estimate exposure and risk. However, straightforward tools such as conductivity may not identify human influences in areas with saline groundwater inputs, diffuse exposure pathways, and few discharges of industrial wastewater. Researchers have further suggested in conductivity relative to alkalinity may also reveal human influences, but little has been done to evaluate the utility and necessity of this approach. Using data from 16 example sites in the Peace, Athabasca, and Slave Rivers in northern Alberta (but focusing on tributaries in Canada’s oil sands region) available from multiple regional, provincial, and national monitoring programs, we calculated residual conductivity and determined if it could identify the potential influence of human activity on streams in northern Alberta. To account for unequal sampling intervals within the compiled datasets, but also to include multiple covariates, we calculated residual conductivity using the Generalized Estimating Equation (GEE). The Pearson residuals of the GEEs were then plotted over time along with three smoothers (two locally weighted regressions and one General Additive Model) and a linear model to estimate temporal patterns remaining relative to known changes in human activity in the region or adjacent to the study locations. Although there are some inconsistencies in the results and large gaps in the data at some sites, many increases in residual conductivity correspond with known events in northern Alberta, including the potential influence of site preparation at oil sands mines, reductions in particulate emissions, mining, spills, petroleum coke combustion at one oil sands plant, and hydroelectric development in the Peace basin. Some differences in raw conductivity measurements over time were also indicated. Overall, these analyses suggest residual conductivity may identify broad influences of human activity and be a suitable tool for augmenting broad surveillance monitoring of water bodies alongside current approaches. However, some anomalous increases without apparent explanations were also observed suggesting changes in residual conductivity may also be well-suited for prompting additional and more detailed studies or analyses of existing data. Full article

The restoration and protection of wetlands are crucial in reducing greenhouse gas emissions. In this research, the SWAT model was modified to investigate and estimate the groundwater table, net ecosystem exchange (NEE), and soil respiration impact on carbon dioxide (CO₂) emission in the cold regions in Alberta. There is a lack of a process-based model that accounts explicitly for the CO₂ emission and ecosystem exchange resulting from interactions between hydrological and biogeochemical processes. The SWAT model is modified to make unique contributions to wetlands by estimating CO₂ emissions, soil temperature, and soil respiration that account for the dynamics of water tables and the relationship between subsurface and surface water storage. The modified model results predicted daily NEE with a very good model fit resulting in an R² (Coefficient of determination), NSE (Nash-Sutcliffe Efficiency), PBIAS (percent bias), and RMSE (root mean square error) of 0.88, 0.72, 2.5, and 0.45 in the calibration period and 0.82, 0.67, −1.8, and 0.56 for the validation period, respectively. The prediction result indicated that the modified model performed well in predicting soil temperature, the groundwater table, and ecosystem respiration in the calibration and validation periods. In general, this study concluded that the modified model has the capability of representing the effects of water table dynamics on CO₂ emissions and NEE in cold wetlands. Full article

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. Full article

Catchments located in cold weather regions are highly influenced by the natural seasonality that dictates all hydrological processes. This represents a challenge in the development of river flow forecasting models, which often require complex software that use multiple explanatory variables and a large amount of data to forecast such seasonality. The Athabasca River Basin (ARB) in Alberta, Canada, receives no or very little rainfall and snowmelt during the winter and an abundant rainfall–runoff and snowmelt during the spring/summer. Using the ARB as a case study, this paper proposes a novel simplistic method for short-term (i.e., 6 days) river flow forecasting in cold regions and compares existing hydrological modelling techniques to demonstrate that it is possible to achieve a good level of accuracy using simple modelling. In particular, the performance of a regression model (RM), base difference model (BDM), and the newly developed flow difference model (FDM) were evaluated and compared. The results showed that the FDM could accurately forecast river flow (E_NS = 0.95) using limited data inputs and calibration parameters. Moreover, the newly proposed FDM had similar performance to artificial intelligence (AI) techniques, demonstrating the capability of simplistic methods to forecast river flow while bypassing the fundamental processes that govern the natural annual river cycle. Full article