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This study presents an analysis of daily temperature and precipitation extremes with return periods ranging from 2 to 50 years in phase 6 of the Coupled Model Intercomparison Project (CMIP6) multimodel ensemble of simulations. Judged by similarity with reanalyses, the new-generation models simulate the present-day temperature and precipitation extremes reasonably well. In line with previous CMIP simulations, the new simulations continue to project a large-scale picture of more frequent and more intense hot temperature extremes and precipitation extremes and vanishing cold extremes under continued global warming. Changes in temperature extremes outpace changes in global annual mean surface air temperature (GSAT) over most landmasses, while changes in precipitation extremes follow changes in GSAT globally at roughly the Clausius–Clapeyron rate of ~7% °C−1. Changes in temperature and precipitation extremes normalized with respect to GSAT do not depend strongly on the choice of forcing scenario or model climate sensitivity, and do not vary strongly over time, but with notable regional variations. Over the majority of land regions, the projected intensity increases and relative frequency increases tend to be larger for more extreme hot temperature and precipitation events than for weaker events. To obtain robust estimates of these changes at local scales, large initial-condition ensemble simulations are needed. Appropriate spatial pooling of data from neighboring grid cells within individual simulations can, to some extent, reduce the needed ensemble size.

Increases in the intensity and frequency of hydroclimatic extremes associated with climate change can cause significant socioeconomic problems. Assessments of projected extremes using only a limited number of general circulation model (GCM) simulations can undermine the capacity to differentiate and communicate the contribution of internal climate variability (ICV) and external forcing and result in an underestimation of associated risks. In this study, we assess the impacts of climate change on extreme temperature and precipitation and quantify the contribution of internal variability over the Columbia, Fraser, Peace and Campbell River basins in northwestern North America (NWNA). Seven GCMs that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and a large ensemble of CanESM2 model simulations (50 members) are downscaled to 1/16° spatial resolution using Bias Correction Constructed Analogues with Quantile mapping reordering version 2 (BCCAQ2). Spatial and temporal changes of climate extreme indices, representing the frequency and intensity of extreme temperature and precipitation, are assessed over the historical (1981–2010) and future (2060–2089) periods under the Representative Concentration Pathway (RCP) 8.5. The influence of ICV on the estimated trends of extreme indices is characterised. Overall, both the frequency and intensity of extreme temperature and precipitation events are projected to increase in NWNA indicating more severe dry days and wet conditions in the future. High-elevation Rocky and the Coast Mountains are at larger risks of extreme precipitation, while the Columbia basin, which already faces drought issues, is expected to experience severe dry conditions. Internal climate variability plays a significant role, particularly in the trends of precipitation-related indices. The signal to internal noise ratio analyses suggest that higher elevations experience stronger forcing signals for precipitation-based indices compared to the other regions.

This paper provides an updated analysis of observed changes in extreme precipitation using high quality station data up to 2018. We examine changes in extreme precipitation represented by annual maxima of one day (Rx1day) and five-day (Rx5day) precipitation accumulations at different spatial scales and attempt to address whether the signal in extreme precipitation has strengthened with several years of additional observations. Extreme precipitation has increased at about two thirds of stations and the percentage of stations with significantly increasing trends is significantly larger than that can be expected by chance for the globe, continents including Asia, Europe, and North America, and regions including C. North-America, E. North-America, N. Central-America, N. Europe, Russian-Far-East, E.C. Asia, and E. Asia. The percentage of stations with significantly decreasing trends is not different from that expected by chance. Fitting extreme precipitation to generalized extreme value distributions with global mean surface temperature (GMST) as a co-variate reaffirms the statistically significant connections between extreme precipitation and temperature. The global median sensitivity, percent change in extreme precipitation per Kelvin increase in GMST is 6.6% (5.1 to 8.2%, 5–95% confidence interval) for Rx1day and is slightly smaller at 5.7% (5.0 to 8.0%) for Rx5day. The comparison of results based on observations ending in 2018 with those from data ending in 2000–2009 shows a consistent median rate of increase, but a larger percentage of stations with statistically significant increasing trends, indicating an increase in the detectability of extreme precipitation intensification, likely due to the use of longer records.

Background 
Food- and water-borne pathogens exhibit spatial heterogeneity, but attribution to specific environmental processes is lacking while anthropogenic climate change alters these processes. The goal of this study was to investigate ecology, land-use and health associations of these pathogens and to make future disease projections. 

Methods
The rates of five acute gastrointestinal illnesses (AGIs) (campylobacteriosis, Verotoxin- producing Escherichia coli, salmonellosis, giardiasis and cryptosporidiosis) from 2000 to 2013 in British Columbia, Canada, were calculated across three environmental variables: ecological zone, land use, and aquifer type. A correlation analysis investigated relationships between 19 climatic factors and AGI. Mean annual temperature at the ecological zone scale was used in a univariate regression model to calculate annual relative AGI risk per 1 °C increase. Future cases attributable to climate change were estimated into the 2080s.

Findings
Each of the bacterial AGI rates was correlated with several annual temperature-related factors while the protozoan AGIs were not. In the regression model, combined relative risk for the three bacterial AGIs was 1.1 [95% CI: 1.02–1.21] for every 1 °C in mean annual temperature. Campylobacteriosis, salmonellosis and giardiasis rates were significantly higher (p < 0.05) in the urban land use class than in the rural one. In rural areas, bacteria and protozoan AGIs had significantly higher rates in the unconsolidated aquifers. Verotoxin-producing Escherichia coli rates were significantly higher in watersheds with more agricultural land, while rates of campylobacteriosis, salmonellosis and giardiasis were significantly lower in agricultural watersheds. Ecological zones with higher bacterial AGI rates were generally projected to expand in range by the 2080s.

Interpretation
These findings suggest that risk of AGI can vary across ecosystem, land use and aquifer type, and that warming temperatures may be associated with an increased risk of food-borne AGI. In addition, spatial patterns of these diseases are projected to shift under climate change.

Cold climate regions offer various ecosystem services. The water quality parameters such as dissolved oxygen (DO), water temperature (Tw), and dissolved organic carbon (DOC) have considerable impacts on the aquatic ecosystem species. Any impairments in water quality such as elevated water temperature, and low DO concentrations can limit the survival of aquatic ecosystems and its species, such as walleye, northern pike and salmon. Therefore, a good understanding of the aquatic ecosystem of rivers is essential for effective and sustainable river basin and watershed management of fisheries and aquatic resources. The objectives of this study is to improve a watershed scale module of water quality (DO, DOC and Fecal coliforms (FC) in the SWAT model to examine the spatiotemporal patterns and their impacts on aquatic ecosystem and water quality processes in the Athabasca River Basin (ARB), Alberta, Canada. The calibration and validation results of DO, DOC and FC show that the improved Soil and Water Assessment Tool (SWAT) model achieved successfully with a varied range (satisfactory to vey-good) of accuracy at the daily temporal scales. The results showed that concentrations of DO for the selected stations (spring and summer) reduced far below the thresholds for ecosystems survival. In concurrent reduction with DO, the FC concentration considerably varied in the different monitoring stations of ARB. These results highlight that DO, DOC and FC variability in the ARB may drive changes in water quality and ecosystem services that have to be understood on the specific research scale for designing adaptive management scenarios. This study reveals that the new SWAT model can be applied to other similar regions of the worlds.

Landslide hazards in British Columbia are mainly caused by precipitation and can result in significant damage and fatalities. Anthropogenic climate change is expected to increase precipitation frequency and intensity in the winter, spring, and fall in British Columbia (BC), potentially resulting in increased frequency of landslide hazard. Quantifying the effect of changing precipitation on future landslide hazard across the varying topographic and climatic conditions in BC requires detailed projections of future precipitation. Here, the operational Landslide Hazard Assessment for Situational Awareness (LHASA) model is used with high-resolution, statistically downscaled daily precipitation to generate detailed simulations of landslide hazard in BC over the twenty-first century. Historical evaluation of the LHASA model is performed using a station-based, gridded observational precipitation dataset. Classification of observed landslide dates and locations as hazard events occurs as successfully as, or slightly better than, when LHASA is applied globally with satellite precipitation. Using the LHASA model with precipitation projections from 12 downscaled global climate models following RCP8.5 indicates that future landslide hazard frequency will increase from 16 days per year to 21 days per year (32%) on average by the 2050s for landslide susceptible regions in the province. Areas of the province currently with the most frequent landslide hazards (18 to 21 days per year), including the west coast and northern Rocky Mountains, are expected to see between 8 and 11 additional hazardous days (49 to 61% increases) per year. Most of the increased hazard frequency occurs during winter and fall, reflecting those seasons with the largest projected increases in single and multi-day precipitation. Risk assessments for regions in British Columbia vulnerable to landslides will need to account for increasing hazard due to climate change altered precipitation.

The oil sands industry in Canada uses soil–vegetation–atmosphere-transfer (SVAT) water balance models, calibrated against short-term (less than 10 years) field monitoring data, to evaluate long-term (≈60 years) reclamation cover design performance. These evaluations use long-term historical climate data; however, the effects of climate change should also be incorporated in these analyses. Although statistical downscaling of global climate change projections is commonly used to obtain local, site-specific climate, high resolution dynamical downscaling can also be used. The value of this latter approach to obtain local site-specific projections for mine reclamation covers has not been evaluated previously. This study explored the differences in key water balance components of three reclamation covers and three natural sites in northern Alberta, Canada, under future, site-specific, statistical, and dynamical climate change projections. Historical meteorological records were used to establish baseline periods. Temperature datasets were used to calculate potential evapotranspiration (PET) using the Hargreaves–Samani method. Statistical downscaling uses the Long Ashton Research Station Weather Generator (LARS-WG) and global circulation model (GCM) projections of temperature and precipitation. Dynamical climate change projections were generated on a 4 km grid using the weather research and forecasting (WRF) model. These climate projections were applied to a physically-based water balance model (i.e. Hydrus-1D) to simulate actual evapotranspiration (AET) and net percolation (NP) for the baseline and future periods. The key findings were: (a) LARS-WG outperformed WRF in simulating baseline temperatures and precipitation; (b) both downscaling methods showed similar directional shifts in the future temperatures and precipitation; (c) this, in turn, created similar directional shifts in future growing season median AET and NP, although the increase in future NP for LARS-WG was higher than that for WRF. The relative increases in future NP were much higher than the relative increases in future AET, particularly for the reclamation covers.

Quantifying bacteria fluxes and contaminants from the point and nonpoint sources in a watershed are important for the management of water quality and safeguard public health. Therefore, the appropriate characterization of bacteria from different sources is necessary for understanding of fate and transport of bacteria in watersheds. However, it is challenging to simulate the effects of pH on bacteria, such as Escherichia coli (E. coli) in the original version of Soil and Water Assessment Tool (SWAT). This study aimed to augment SWAT-bacteria module to evaluate the potential effect of pH on E. coli concentrations. We modified SWAT-bacteria module to incorporate pH factor and to check E. coli observations from four sites of Athabasca River Basin. The modified SWAT-bacteria model demonstrated a linear relationship between observed and simulated daily E. coli data with R2 values found between 0.70 and 0.80; NSE: 0.59 and 0.68; PBIAS: 7.94% and 17.85% during calibration for all monitoring sites (2010–2012). While during the validation (2013–2014) the performance statistics found to be: R2: 0.59–0.72; NSE: 0.55–0.66; PBIAS: 10–22%. The results of the sensitivity analysis confirmed that pH is one of the most significant fate factors of E. coli. The modified SWAT-bacteria module provides an improved estimate of E. coli concentration from the river basin. This study contributes new insight to E. coli modelling. Therefore, the modified SWAT-bacteria model could be a powerful tool for the future regional to global scale model of E. coli concentrations thus significantly contribute for the application of effective river basin management.

Change point detection methods have an important role in many hydrological and hydraulic studies of river basins. These methods are very useful to characterize changes in hydrological regimes and can, therefore, lead to better understanding changes in extreme flows behavior. Flood events are generally characterized by a finite number of characteristics that may not include the entire information available in a discharge time series. The aim of the current work is to present a new approach to detect changes in flood events based on a functional data analysis framework. The use of the functional approach allows taking into account the whole information contained in the discharge time series of flood events. The presented methodology is illustrated on a flood analysis case study, from the province of Quebec, Canada. Obtained results using the proposed approach are consistent with those obtained using a traditional change point method, and demonstrate the capability of the functional framework to simultaneously consider several flood features and, therefore, presenting a comprehensive way for a better exploitation of the information contained in a discharge time series.