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

Since human activities and ecosystem health require adequate, reliable water supplies, hydro‐climatic variability and extremes pose serious threats to society and the environment. Previous studies have shown that the Canadian Prairies normally experience considerable hydro‐climatic variability, including periodic droughts and excessive moisture conditions, which are mainly caused by mid‐tropospheric circulations that disrupt expected precipitation and temperature patterns. However, no investigations have specifically focused on both past and future hydro‐climatology over watersheds within the region. Evaluation of the Standardized Precipitation Evapotranspiration Index reveals considerable inter‐annual and decadal‐scale variability over the study regions of the Oldman and Swift Current Creek Watersheds, with no discernible trends during the last ∼100 years. There is also an indication of increased variability since the mid‐1980s. Assessment of the 500 hPa circulation patterns associated with identified hydro‐climatic extremes shows that major droughts are related with higher frequencies of distinctive ridging patterns over the Prairie region, and lower incidences of zonal/troughing patterns (the former significantly increasing over the last 60 years). Excessive moisture conditions have opposite patterns. Projections from two (a drier/warmer and wetter/cooler) Regional Climate Models indicate an uncertain future in the selected watersheds ranging from a substantial increase in drought with a higher degree of inter‐annual variability, to relatively no change from current conditions. Furthermore, future changes to key atmospheric circulations suggest that those patterns associated with extreme dry conditions will continue in the future and in some cases, increase in frequency. Results from this analysis have improved the understanding of historical hydro‐climatic extremes in western Canada and have provided insight into potential future occurrences of these extremes as driven by changes to key surface climate and synoptic‐scale atmospheric circulation patterns.

This study evaluates General Circulation Models (GCMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) for their ability in simulating historical means and extremes of daily precipitation (P), and daily maximum (Tmax), and minimum temperature (Tmin). Models are evaluated against hybrid observations at 2255 sub-basins across Alberta, Canada using established statistical metrics for the 1983–2014 period. Three extreme indices including consecutive wet days (CWD), summer days (SD), and warm nights (WN) are defined based on the peak over the threshold approach and characterized by duration and frequency. The tail behaviour of extremes is evaluated using the Generalized Pareto Distribution. Regional evaluations are also conducted for four climate sub-regions across the study area. For both mean annual precipitation and mean annual daily temperature, most GCMs more accurately reproduce the observations in northern Alberta and follow a gradient toward the south having the poorest representation in the western mountainous area. Model simulations show statistically better performance in reproducing mean annual daily Tmax than Tmin, and in reproducing annual mean duration compared to the frequency of extreme indices across the province. The Kernel density curves of duration and frequency as simulated by GCMs show closer agreement to that of observations in the case of CWD. However, it is slightly (completely) overestimated (underestimated) by GCMs for warm nights (summer days). The tail behaviour of extremes indicates that GCMs may not incorporate some local processes such as the convective parameterization scheme in the simulation of daily precipitation. Model performances in each of the four sub-regions are quite similar to their performances at the provincial scale. Bias-corrected and downscaled GCM simulations using a hybrid approach show that the downscaled GCM simulations better represent the means and extremes of P characteristics compared to Tmax and Tmin. There is no clear indication of an improved tail behaviour of GPD based on downscaled simulations.

Large-area, long-duration droughts are among Canada’s costliest natural disasters. A particularly vulnerable region includes the Canadian Prairies where droughts have, and are projected to continue to have, major impacts. However, individual droughts often differ in their stages such as onset, growth, persistence, retreat, and duration. Using the Standardized Precipitation Evapotranspiration Index, this study assesses historical and projected future changes to the stages and other characteristics of severe drought occurrence across the agricultural region of the Canadian Prairies. Ten severe droughts occurred during the 1900–2014 period with each having unique temporal and spatial characteristics. Projected changes from 29 global climate models (GCMs) with three representative concentration pathways reveal an increase in severe drought occurrence, particularly toward the end of this century with a high emissions scenario. For the most part, the overall duration and intensity of future severe drought conditions is projected to increase mainly due to longer persistence stages, while growth and retreat stages are generally shorter. Considerable variability exists among individual GCM projections, including their ability to simulate observed severe drought characteristics. This study has increased understanding in potential future changes to a little studied aspect of droughts, namely, their stages and associated characteristics. This knowledge can aid in developing future adaptation strategies.

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Recent advances in artificial intelligence (AI) and explainable AI (XAI) have created opportunities to better predict and understand drought processes. This study uses a machine learning approach for understanding the drivers of drought severity and extent in the Canadian Prairies from 2005 to 2019 using climate and satellite data. The model is trained on the Canadian Drought Monitor (CDM), an extensive dataset produced by expert analysis of drought impacts across various sectors that enables a more comprehensive understanding of drought. Shapley additive explanation (SHAP) is used to understand model predictions during emerging or worsening drought conditions, providing insight into the key determinants of drought. The results demonstrate the importance of capturing spatiotemporal autocorrelation structures for accurate drought characterization and elucidates the drought time scales and thresholds that optimally separate each CDM severity category. In general, there is a positive relationship between the severity of drought and the time scale of the anomalies. However, high-severity droughts are also more complex and driven by a multitude of factors. It was found that satellite-based evaporative stress index (ESI), soil moisture, and groundwater were effective predictors of drought onset and intensification. Similarly, anomalous phases of large-scale atmosphere–ocean dynamics exhibit teleconnections with Prairie drought. Overall, this investigation provides a better understanding of the physical mechanisms responsible for drought in the Prairies, provides data-driven thresholds for estimating drought severity that could improve future drought assessments, and offers a set of early warning indicators that may be useful for drought adaptation and mitigation. Significance Statement: This work is significant because it identifies drivers of drought onset and intensification in an agriculturally and economically important region of Canada. This information can be used in the future to improve early warning for adaptation and mitigation. It also uses state-of-the-art machine learning techniques to understand drought, including a novel approach called SHAP probability values to improve interpretability. This provides evidence that machine learning models are not black boxes and should be more widely considered for understanding drought and other hydrometeorological phenomena.