Streamflow regimes

 

Streamflow regime refers to the seasonal distribution of flow, influenced predominantly by the prevailing climate in the region (e.g., Moore et al., 2017). Temperature affects the type of precipitation (rain versus snow), the accumulation of a snowpack, and the timing and amount of ice and snowmelt runoff. Precipitation determines the potential magnitude of flow generated during different periods of the year. In Canada, streamflow regimes are classified as nival (snowmelt-dominated), glacial (glacier-dominated), pluvial (rainfall-dominated), or mixed. Across much of the country, most of the winter precipitation falls as snow and melts during spring and early summer. As a result, the vast majority of rivers are nival. These regimes exhibit high flows in spring and early summer (due to snowmelt), and the timing depends on geographic location (since snowmelt is later farther north or at higher elevations) and on the size of the catchment. Glacial regimes are confined to mountainous regions of western Canada and the high Arctic islands, where glaciers and ice caps are present. These regimes are associated with an initial snowmelt runoff, followed by continued flow into late summer sustained from ice melt. Pluvial regimes are driven by the seasonal distribution of rainfall. At lower elevations on the west coast of Canada, this consists of high flows during winter and low flows during summer and autumn (see Figure 6.7c). On the east coast, higher flows are most common in spring and autumn. Combinations of these regimes (known as mixed regimes) are also found in Canada (see Figure 6.7d). For instance, nivo-pluvial regimes are influenced by both snow and rainfall, the exact proportion depending on the location of the stream. In British Columbia, for example, the seasonal flow patterns transition from pluvial (rain-dominated) in coastal/low-elevations to nival (snow-dominated) toward the continental interior of the province and higher elevations (Moore et al., Nival catchments are predominantly found in northern and western Canada, while pluvial basins are located on the east and west coasts, and mixed catchments are mainly in southern Ontario and Quebec and Atlantic Canada. Glacial regimes were not identified in this analysis (Burn et al., 2016). The characterization of regimes is based on longer-term hydroclimatic averages, but, in most of Canada, there is considerable year-to-year variability in these patterns.

Canada’s hydrometric network

 

Hydrometric stations are located on lakes, rivers, and streams of many sizes, ranging from drainage basins as small as a few hectares to large watersheds such as the Mackenzie Basin (1,680,000 km2). Over 2600 active water-level and streamflow stations are currently operated under federal-provincial and federal-territorial cost-sharing agreements. Streamflow is the volume of water flowing past a point on a river in a unit of time (e.g., cubic metres per second). Most stations are located in the southern part of the country; as a result, the network is often inadequate to describe water characteristics and trends in northern Canada. The Reference Hydrometric Basin Network (RHBN) is a subset of stations from the national network that are used primarily for the detection, monitoring, and assessment of climate change (ECCC, 2017). These stations are characterized by near-pristine or stable hydrological conditions and have been active for at least 20 years (Harvey et al., 1999) (see Figure 6.2). However, the RHBN is also unevenly distributed across Canada (with almost no representation of the high Arctic Islands), and the length of data records varies (Whitfield et al., 2012).

The influence of human-induced climate change on extreme low Arctic sea ice extent in 2012

 

The Arctic experienced a record-low sea ice extent (SIE) in September 2012. Extreme low SIEs can have impacts on Arctic communities, ecosystems, and economic activities. Determining the role of human-induced climate change in extreme low Arctic SIEs is important, because understanding the role of anthropogenic greenhouse gases compared to natural variability provides a basis for understanding future projections and potential adaptation measures.

Event attribution methods are used to determine the influence of human-induced climate change on the occurrence (or intensity) of extreme events (NASEM, 2016). The probability of a particular extreme event is compared between two different sets of climate model simulations: those that include the contribution from human activities and those that include only natural factors. The difference in these probabilities indicates the effect of human-induced climate change on the event. Attribution studies are described in more detail in Chapter 4, Section 4.4.

Increasing temperatures in the Arctic have been attributed to human-induced factors in many studies (Gillett et al., 2008; Najafi et al., 2015; Min et al., 2008). Furthermore, attribution studies show that the record-low SIE in 2012 was extremely unlikely to be due to natural variability in the climate system alone (Zhang and Knutson, 2013) and that it would not have occurred without human influence on climate (Kirchmeier-Young et al., 2017). Figure 5.9a shows September Arctic SIE over time from climate model simulations using only natural factors (blue line) and simulations that also include the human-induced component (red line). Simulations that include the human-induced component show a strong decreasing trend, similar to the observed decline in SIE (black line). On the other hand, the simulations with only natural forcings show similar year-to-year variability but no trend.

To compare the probability of the 2012 event from each set of simulations, probability distributions are shown in Figure 5.9b. The distributions describe possible values that might be expected in each scenario and how likely they are. The observed 2012 record-low SIE event (vertical dashed line) is within the distribution from the simulations that include the human-induced component and is much lower than any values in the distribution with only natural forcings. With the human-induced component included, there is a 10.3% possibility of a September SIE more extreme than the observed 2012 event. For the natural-only simulations, this probability is extremely small. Therefore, the record-low September SIE in 2012 was extremely unlikely to be due to only natural variability of the climate and would not have occurred without the human influence on climate (Kirchmeier-Young et al., 2017).

Methods for event attribution

 

Event attribution is used to quantify how human-influenced climate change affects the occurrence of a particular type (or class) of extreme event. Its goals are similar to those of the detection and attribution process described in Chapter 2 (see Section 2.3.4), but it focuses on individual events. Event attribution analyses (NASEM, 2016) compare the likelihood of a particular class of events (e.g., all events as extreme, or more extreme, than the event defined in the study) between a factual world, which includes the human component, and a counter-factual world that comprises only natural factors — that is, the “climate that might have been” in the absence of the human component.

To demonstrate, Figure 4.21 shows distributions of possible values of a climate variable for the world without the human contribution in blue, and for a scenario like the one we have experienced with the human contribution in red. The shaded regions represent the probability that a particular extreme event (an outcome as extreme, or more so, than the one indicated by the vertical bar) will occur in each scenario. The probability of the event increases when the human contribution is included — from 1 in 60 to 1 in 5. The ratio of the probability with the human contribution to the probability without the human contribution is referred to as a “risk ratio.” Although this event could occur in the absence of human influence, it is 12 times as likely (risk ratio of 12) when the human component is included.

The conclusions of an event attribution analysis often depend on how the question is posed. This includes the choices made when defining events and designing the analysis approach. For example, the change in probability between the two scenarios in Figure 4.21 depends on the placement of the vertical bar, or the lower bound on the magnitude that defines the chosen event. Changes in the probabilities of temperature and precipitation extremes depend on the probability of the events in the current climate, with larger risk ratios corresponding to more extreme (rarer) events (Kharin et al., 2018). The uncertainty in the risk ratio (i.e., the event attribution result) becomes larger for rarer events, as it is more difficult to estimate the probabilities of these very rare events. The choice of the variable and/or region to determine the distributions also has an impact on the results.

Two types of questions have been asked in event attribution analyses: How has the probability of the extreme event (frequency) changed, and how has the intensity of the event (magnitude) changed? As an example, event attribution for a flood-producing heavy rainfall event may try to answer, “Has human-induced climate change made this type of heavy rainfall event occur more often?” (frequency) or “Has human-induced climate change increased the amount of rainfall in these types of storms?” (magnitude). The human influence could have a different impact on the frequency than on the magnitude of a particular event. It is thus important to understand the characteristics of the event being assessed and to interpret the results of an event attribution analysis in context.

The impact of combined changes in temperature and precipitation on observed and projected changes in fire weather

 

Changes in temperature and in precipitation each have impacts across many sectors. However, combined changes in temperature and precipitation can have additional impacts, and some sectors rely on information regarding concurrent changes in these two variables. An example is fire weather. Changing precipitation and temperature (along with changing wind) alter the risk of extreme wildfires that can result from hot, dry, and windy conditions. Understanding changes in both temperature and precipitation lends insight into changes in wildfire risk and how it might evolve in the future.

The Canadian Forest Fire Weather Index (FWI) System is a collection of indices that use weather variables, including temperature and precipitation, to characterize fire risk. It includes an index, labelled FWI, that synthesizes information from the collection of indices to quantify day-to-day changes in the risk of a spreading fire. A threshold of this index is often used to define days conducive to fire spread (Wang et al., 2015; Jain et al., 2017). In addition, three of the most commonly used indices are moisture codes, describing the dryness of different categories of fuels (Wotton, 2009). All of the FWI indices represent factors affecting fire potential, with larger values indicating greater fire potential, although the occurrence of a large wildfire also depends on ignition sources, fuel characteristics, and fire management actions.

A few studies have looked at trends in these indices across Canada. Large year-to-year variability in the FWI indices hinders detection of trends (Amiro et al., 2004; Girardin et al. 2004). Trends may sometimes be discerned from a very long record of data, as is the case with increases in the Drought Code21 in northern Canada and decreases in the Drought Code in western Canada and parts of eastern Canada during the 20th century (Girardin and Wotton, 2009). Another study found that the mean number of fire spread days across Canada increased over 1979 to 2002, although the trends varied regionally, and only some were significant (Jain et al., 2017). Despite inconsistent trends in the FWI indices, there has been a significant increase in annual area burned across Canada (Podur et al., 2002; Gillett et al., 2004).

Higher temperatures in the future will contribute to increased values of the FWI indices and, therefore, increased fire risk. The increase in precipitation that would be required to offset warming for most of the FWI indices exceeds both projected and reasonable precipitation changes (Flannigan et al. 2016). Increases in extreme values of the Duff Moisture Code22 are projected across most of the forested ecozones of Canada by 2090 (Wotton et al., 2010). Increases in fire spread days and extreme values of the FWI are projected, with the largest changes in the western Prairies (Wang et al., 2015). Several other studies also project increases in the FWI indices and the length of the fire season in Canada in the future (Flannigan et al., 2009; de Groot et al., 2013; Flannigan et al., 2013; Kochtubajda et al., 2006). Although the magnitude of projected changes varied among these studies, most project increases in the FWI indices that correspond to higher fire risk.

An example of climate data inhomogeneity

 

The record of observed temperature at Amos, Quebec, shows how changes in sites and their surrounding environment can affect the estimation of long-term changes in the climate. Between 1927 and 1963, the Stevenson screen at the Amos station was located at the bottom of a hill (see Figure 4.2a) and was moved after 1963 (see Figure 4.2b) to level ground several metres away from its original place. The site was sheltered by trees and a building between 1927 and 1963, which could have prevented the cold air from draining freely during nighttime. The current site has an open exposure and is more representative of its surrounding region. Careful comparison of the temperature data at this site with those at a nearby station revealed two step-changes, one of −0.8ºC in 1927 and another of 1.3ºC in 1963 (see Figure 4.2c). The station history files do not provide information on the cause of the first step, but it is possible that the screen was also relocated at that time. These differences resulted in the original temperature data showing an increasing trend of 2.4ºC for 1951–1995 (see Figure 4.2d), whereas, after the artifact in temperature reading was removed, a warming of only 0.8ºC was shown

Short-lived climate forcers

 

Climate forcers, also referred to as climate forcing agents, act directly to change climate and include both natural and human contributors. They are often distinguished as short- or long-lived, according to their lifetime in the atmosphere. For example, carbon dioxide (CO2), the largest climate forcer from human activity, is considered long-lived. Although often described as having a lifetime of a century or more, a single lifetime value is not strictly applicable (owing to its complex interactions with the Earth system), but an estimated 15%– 40% of CO2 emitted by the year 2100 will remain in the atmosphere, and continue to exert a climate warming effect, for more than 1000 years (Ciais et al., 2013). Short-lived climate forcers are those with a lifetime of a few days to a few decades and include sulphate aerosols and black carbon (soot) with lifetimes of a few days; tropospheric ozone and various hydrofluorocarbons, with a lifetime of a few weeks; and methane, with a lifetime of a decade or so. Reducing emissions of short-lived substances leads to lower atmospheric concentrations of these substances shortly thereafter. Many of these short-lived species contribute to poor air quality. Those that have a climate warming effect are also referred to as short-lived climate pollutants (http://www. ccacoalition.org/en/science-resources) and include black carbon, methane, and tropospheric ozone. In some cases, aerosols that have a cooling effect are co-emitted with short-lived warming agents (Arctic Council, 2011), complicating estimates of the near-term effectiveness of emission reductions. Short-lived climate forcers are important in climate policy discussions because targeted mitigation of those with warming effects can both slow global temperature increase and improve human health by improving air quality.

Model projections and weighting

 

Climate change projections are generally based on an ensemble of climate models representing the state-ofthe-art in understanding and modelling climate. The reason for using an ensemble of models is that no single model can be considered the best, since different models exhibit varying levels of realism in simulating climate, depending on the region and variable of interest. Even if a single best model could be determined, there is no guarantee that its present-day performance would cause it to give more reliable projections of future climate.

Climate change projections differ from weather forecasts in several crucial respects. One important difference is that, while we learn the accuracy of weather forecasts in the next few days, the true performance of future climate projections will remain unknown until many decades from now (Weigel et al., 2010). In the absence of a consensus on which models are the best, common practice has been to rely on “model democracy,” whereby each model in a multi-model ensemble is treated equally. This equal-weighting method assumes that each model is different and yet equally plausible.

In recent years, however, there is increasing evidence in the scientific literature that model democracy has some drawbacks. Accurate present-day model performance may not guarantee future performance, but poor performance clearly does not inspire confidence (for example, models that severely underestimate current Arctic sea ice coverage may not be reliable in projecting future changes in sea ice coverage). As a result, there is a growing appreciation that some performance-based weighting of model projections may be appropriate. Indeed, the IPCC Fifth Assessment illustrated this for the case of Arctic sea ice (Collins et al., 2013). However, a clear consensus on to how to weight models has not yet emerged.

A further drawback of model democracy is that it assumes each model is independent. However, climate models often share common features because one model may use computer code adopted from another model with minor adjustments, or two models may have been developed from a common earlier model. Although schemes to account for model performance and independence are being developed and tested (e.g., Sanderson et al., 2017; Knutti et al., 2017), this is still an emerging area of research. Initial exploration of weighting approaches suggests that differences between weighted and unweighted projections for Canada are small, and so traditional, unweighted multi-model projections are presented in this report.

The Coupled Model Intercomparison Project

 

All models used to project climate have some uncertainty associated with them, owing to approximations that must be made in representing certain physical processes. To understand the uncertainty in models, scientists compare them with other models and evaluate how much the models differ in their projections. To determine this, an ensemble of models is needed, allowing a range of simulations and projections to be analyzed and compared. The World Climate Research Programme has established the Coupled Model Intercomparison Project (CMIP) specifically for this purpose. An agreed-upon suite of historical simulations and future climate projections are performed using the same external forcing (changing GHGs, land-use, etc.). The outputs from the models are archived in a common format for analysis by the climate research community (Taylor et al., 2012). Previous versions of CMIP have provided model results assessed in earlier IPCC Assessment Reports. The most recent, fifth phase of this project, CMIP5, provided climate model results that were assessed in the IPCC Fifth Assessment Report (IPCC, 2013), and many of these results are available from the Canadian Climate Data and Scenarios website. Future climate projections in CMIP5 used the Representative Concentration Pathways emission scenarios (see Section 3.2) (van Vuuren et al., 2011). A new version, CMIP6, is currently underway and will serve as input to the IPCC Sixth Assessment.

Modes of climate variability

 

“Modes of climate variability” are distinct and robust features of variability in the climate system with identifiable characteristics, affecting particular regions over certain time periods. Generally, these features alternate or “oscillate” between one set of patterns and an alternate set. A familiar example is the El Niño–Southern Oscillation (ENSO), but there are other modes of variability also discussed in this report.

 

El Niño–Southern Oscillation and Indian Ocean Dipole

ENSO is a quasi-periodic variation in sea surface temperature and other related variables, such as surface pressure and surface wind, lasting about three to five years and situated mainly over the tropical eastern Pacific Ocean. ENSO affects much of the tropics and subtropics, but also influences the mid-latitudes of both hemispheres, including Canada. The warm phase of ENSO is known as El Niño (warm waters in the tropical eastern Pacific Ocean) and the cool phase as La Niña (cool waters in the tropical eastern Pacific Ocean). The warm phase tends to be associated with warmer winter air temperatures and drier conditions over much of Canada. The opposite is true during La Niña. Related to ENSO is the Indian Ocean Dipole (IOD), a variation in sea surface temperature centred in the Indian Ocean, with a typical timescale of about two years.

 

Pacific Decadal Oscillation and Interdecadal Pacific Oscillation

The Pacific Decadal Oscillation (PDO) is a recurring pattern of sea surface temperature variability centred over the northern mid-latitude Pacific Ocean. The PDO has varied irregularly, with a characteristic timescale ranging from as short as a few years to as long as several decades. As with ENSO, the warm (positive) phase of the PDO tends to be associated with warmer winter air temperatures over much of Canada (Shabbar and Yu, 2012). At times over the past century, this mode of variability has exerted a strong influence on continental surface air temperature and precipitation, from California to Alaska. The Interdecadal Pacific Oscillation (IPO) is related to the PDO, but with a much wider geographic range of influence (Salinger et al., 2001).

 

Arctic Oscillation and North Atlantic Oscillation

The Arctic Oscillation (AO), sometimes referred to as the Northern Annular Mode, is the dominant pattern of variability of sea level pressure and atmospheric pressure north of about 20º north latitude. If the pressures are high over the Arctic, they are low over the mid-latitudes, and vice versa. The AO varies over time, with no particular periodicity. The positive phase of the AO tends to be associated in winter with warmer air temperatures over western Canada, and colder temperatures in the north and east. The North Atlantic Oscillation (NAO) is related to the AO but is centred over the North Atlantic Ocean rather than the whole of the Northern Hemisphere. The NAO has a strong influence on the strength and direction of westerly winds and the location of the storm track over the North Atlantic Ocean. The positive phase of the NAO is also associated with warm winter temperatures over much of western Canada, and cold winter temperatures over eastern Canada.

 

Atlantic Multi-decadal Oscillation

The Atlantic Multi-decadal Oscillation (AMO) is a recurring pattern of sea surface temperature of the North Atlantic Ocean (north of the equator and south of about 80º north latitude), with a characteristic timescale of 60 to 80 years. The AMO has been known to influence hurricane activity, as well as rainfall patterns and intensity, across the North Atlantic Ocean.

Canadian atmospheric greenhouse gas monitoring

 

The Canadian Greenhouse Gas Measurement Program operates stations that precisely monitor atmospheric levels of greenhouse gases (GHGs) carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) in all regions of the country. The most remote site, at Alert, Nunavut, contributes measurements to the Global Atmosphere Watch Programme of the World Meteorological Organization, which tracks changes in global GHG concentrations. Northern Hemisphere GHG concentrations, such as those observed at Canadian sites, are slightly higher than the global average because of larger sources of emissions in the Northern Hemisphere. The long-term trends from all Canadian sites closely track the increasing global CO2 concentration trend, while also showing clear seasonal cycles of CO2 concentration due to photosynthesis (plants remove CO2 from the atmosphere) and biogenic respiration (plants and animals breathe out CO2).

Canadian monitoring sites are also used to track changes in regional GHG emissions and removals due to the impact of the changing climate on vulnerable ecosystems, such as the tundra and boreal forest. The vast Canadian boreal forest (2.7 million km2) typically takes up a net 28 megatonnes of carbon from the atmosphere per year (Kurz et al., 2013). Fraserdale, situated close to the boreal forest, is influenced quite strongly by forest processes that affect atmospheric CO2 levels. Lower concentrations of CO2 are evident in summer (dominated by photosynthesis) and higher concentrations are evident in winter (dominated by respiration) compared with the more distant site at Alert that is not surrounded by significant vegetation. Research has found that the net amount of carbon taken up in the Canadian boreal forest has increased in warmer years (Chen et al., 2006). In contrast, studies in Scandinavian boreal forests have found that the net uptake of carbon has decreased in recent years (i.e., 1999–2013) (Hadden and Grelle, 2016). This highlights the value of performing specific atmospheric observations in the Canadian boreal forest. Furthermore, atmospheric observations of CH4 in the Arctic could detect any rapid changes in emissions due to thawing of permafrost.

In summary, atmospheric observations play a key role in tracking global trends in GHG concentrations, in monitoring changes resulting from global GHG mitigation efforts, and in understanding the climate feedback of Canadian ecosystems.

Positive feedbacks that amplify climate change

 

The water vapour feedback

Water vapour is a greenhouse gas (GHG), as it absorbs outgoing longwave radiation (heat radiation) from Earth. Unlike other GHGs such as carbon dioxide (CO2) and methane, water vapour levels in the atmosphere cannot be controlled or altered directly by human activity. Instead, the amount of water vapour in the atmosphere is a function of the temperature of the atmosphere. There is a physical limit to how much water vapour air can hold at a given temperature, with warmer air able to hold more moisture than cooler air. For every additional degree Celsius in air temperature, the atmosphere can hold about 7% more water vapour. When air becomes saturated with water vapour, the water vapour condenses and falls as rain or snow, which means that water vapour does not reside for long in the atmosphere. When an external forcing agent, such as increases in atmospheric CO2, causes climate warming, the rise in temperature both increases the evaporation of water from the surface of the Earth and increases the atmospheric water vapour concentrations. This increased water vapour, in turn, amplifies the warming from the initial CO2-induced forcing. Therefore, water vapour provides a strong positive climate feedback in response to changes initiated by human emissions of other GHGs (Boucher et al., 2013).

 

The snow/ice albedo feedback

Snow and ice are bright, highly reflective surfaces. While open water reflects only about 6% of incoming solar radiation and absorbs the rest, snow-covered sea ice reflects as much as 90% of incoming radiation. This value decreases to 40%–70% during the melt season, due to melt ponds on the ice surface (see Figure 2.4; Perovich et al., 1998; Perovich et al., 2007). Climate warming decreases the amount of snow and ice cover on Earth, reducing the Earth’s albedo (reflectivity). Darker land and water surfaces exposed by melting snow and ice absorb more incoming solar radiation, adding more heat to the climate system and amplifying the initial warming, in turn causing further melting of snow and ice. Increased absorption of solar energy over the ocean is particularly important, as this additional heat must be dissipated in the autumn before ice can form again, thereby delaying the date of freeze-up. This positive climate feedback is particularly important in the Northern Hemisphere, where declines in Arctic Ocean sea ice and snow cover have been strong (see Chapter 5, Sections 5.2 and 5.3). In combination with other feedbacks involving the ocean, atmosphere, and clouds, the snow/ice albedo feedback explains why temperatures across the Arctic have warmed at approximately twice the rate of the rest of planet (Overland et al., 2017; Pithan and Mauritsen, 2014; Serreze and Barry, 2011).

Sources of the main greenhouse gases

 

The main greenhouse gases (GHGs) have both natural sources and anthropogenic sources — from human activity — with the exception of the group of GHGs referred to as halocarbons, which are human-made. Since anthropogenic sources add emissions to the atmosphere at a rate greater than natural processes can remove them from the atmosphere, atmospheric levels of GHGs are building up.

 

Carbon dioxide

Carbon dioxide (CO2), along with methane (CH4), is part of Earth’s carbon cycle, which involves the movement of carbon among the atmosphere, the land, the ocean, and living things. CO2 enters the atmosphere from a variety of natural sources, most notably as a result of plant and animal respiration, and is removed from the atmosphere through the photosynthesis of plants and uptake by the ocean. The main anthropogenic sources of CO2 are the burning of carbon-containing fossil fuels (coal, oil, and natural gas) and deforestation / land clearing. Land clearing can involve either burning trees and other vegetation, which releases CO2 immediately, or allowing cut vegetation to decay, which releases CO2 slowly. The manufacture of cement is another important source, as it involves heating limestone (calcium carbonate), the main component of cement, in a process that releases CO2

 

Methane

The main sources of CH4 — a carbon-containing GHG — are from decomposition of organic matter by micro-organisms under low-oxygen conditions. Wetlands are by far the largest natural source of CH4. Anthropogenic sources include rice paddies, landfills, and sewage; fermentation in the gut of ruminant animals; and artificial wetlands. Along with other pollutants, CH4 is also produced when fossil fuels and trees are burned with insufficient oxygen for combustion to be complete. It also leaks or is vented to the atmosphere from geological sources, mainly during the extraction, processing, and transportation of fossil fuels, although natural leaks also occur.

 

Nitrous oxide

Nitrous oxide (N2O) is part of Earth’s nitrogen cycle. Anthropogenic sources are mainly related to the use of nitrogen-based synthetic fertilizers and manure to improve crop productivity, and the cultivation of certain crops that enhance biological nitrogen fixation. These sources have added significant amounts of reactive nitrogen to Earth’s ecosystems, some of which is converted to N2O and released to the atmosphere. Some N2O is also released to the atmosphere during the combustion of fossil fuels and biomass (e.g., trees or woodbased fuels) and from some industrial sources.

 

Halocarbons

Halocarbons are a group of synthetic chemicals containing a halogen (e.g., fluorine, chlorine, and bromine) and carbon. There are a variety of industrial sources.

 

Water vapour

Water vapour is the most important naturally occurring GHG. Human activities do not directly influence the amount of water vapour in the atmosphere to any significant degree. However, the amount of water vapour in the atmosphere changes with temperature, and changes in water vapour are considered a feedback in the climate system.

The greenhouse effect and drivers of climate change

 

The Earth’s climate system is powered by energy from the sun reaching the Earth in the form of sunlight. Some of the incoming solar radiation is reflected back to space, but the rest is absorbed by the atmosphere and at the Earth’s surface, which warms the planet. The Earth cools down by emitting radiation back to space at a rate that depends on the temperature of Earth. Since the Earth is much colder than the sun, it emits infrared radiation in the lower-energy, longwave part of the energy spectrum (infrared radiation, invisible to the human eye), whereas the sun emits mainly high-energy, shortwave radiation (visible and ultraviolet light).

The Earth’s average temperature is determined by the overall balance between the amount of absorbed incoming energy (as light) from the sun and the amount of outgoing energy (as infrared radiation) from Earth to space. Only a portion of the incoming energy from the sun is used to warm the Earth, as some of it is reflected by the Earth’s atmosphere and surface. About two-thirds of the incoming solar energy (about 240 W/ m2) is absorbed and used to warm the planet (Hartman et al., 2013). Some of the outgoing infrared radiation (heat radiation) is absorbed and then re-emitted by clouds and GHGs in the lower atmosphere. This process is known as the greenhouse effect, and it leads to heat being trapped in the lower atmosphere, which warms the Earth’s surface. Naturally occurring GHGs in the atmosphere — mainly water vapour and CO2 from natural sources — produce a natural greenhouse effect that raises the Earth’s mean surface temperature from about −16ºC to about +15ºC (Lacis et al., 2010). This higher temperature creates conditions favourable for life on Earth and also increases the flow of heat from Earth to space (to about 240 W/m2) so that it balances the flow of incoming solar energy.

In a stable climate, global average temperature remains roughly constant because of this balance between incoming and outgoing energy. However, the Earth’s energy balance can be perturbed. Factors that disrupt this balance and cause climate warming or cooling are called climate drivers or climate forcing agents. Climate drivers can be either natural or human-caused. They can disrupt the Earth’s energy balance by 1) changing the amount of incoming solar radiation; 2) changing the Earth’s albedo, that is, how much incoming solar radiation is reflected from the Earth’s surface and atmosphere; and 3) changing the amount of outgoing infrared radiation by changing the composition of the atmosphere.

Climate Change Canada

 

6.4: Soil moisture and drought

Periodic droughts have occurred across much of Canada, but no long-term changes are evident. Future droughts and soil moisture deficits are projected to be more frequent and intense across the southern Canadian Prairies and interior British Columbia during summer, and to be more prominent at the end of the century under a high emission scenario (medium confidence).

 

Soil moisture directly influences runoff and flooding, since it strongly affects the amount of precipitation/ snowmelt that makes its way into surface water bodies. It also determines the exchange of water and heat energy between the land surface and the atmosphere through evaporation and plant transpiration, and influences occurrence of precipitation through the recycling of moisture (see Seneviratne et al., 2010 for a detailed explanation of soil moisture–climate interactions). There are few direct measurements of soil moisture in Canada, and amounts are therefore estimated through remote sensing (e.g., with satellites) and/or modelling. The lack of an extensive monitoring network makes it difficult to make large-scale assessments of past trends (e.g., Mortsch et al., 2015). Future changes in soil moisture are primarily assessed using direct soil moisture output from GCMs. These changes are influenced by future precipitation and evaporation (the latter of which may be affected by changes in vegetation). However, modelled soil moisture is associated with large uncertainties, due to complexities in the representation of actual evapotranspiration, vegetation growth, and water use efficiency under enhanced atmospheric carbon dioxide concentrations (e.g., Seneviratne et al., 2010; Wehner et al., 2017). Longer-term climate variability, including droughts and excessive wet periods, are often directly related to soil moisture (and other aspects of freshwater availability). As a result, this section also assesses past and future changes to relevant indicators of drought.

 

6.4.1: Soil moisture

Quantifying soil moisture over large domains is challenging, as a result of the variability of soil moisture over time and among regions (Famiglietti et al., 2008). Several national-scale soil moisture networks exist globally (Doringo et al., 2011), including two in the United States (Schaefer et al., 2007; Bell et al., 2013). While there is no national network across Canada, there are some regional/provincial sites. For example, Alberta has monitored drought for the past 15 years, including soil moisture conditions, over a large network within the province, while Saskatchewan, Manitoba, and Ontario have established soil moisture and weather monitoring stations for selected regions. These networks have been used for validation of remote sensing data (described below) (e.g., Adams et al., 2015; Pacheco et al., 2015; Champagne et al., 2016) and for calibration and validation of hydrological models (Hayashi et al., 2010). Due to the difficulties (including high costs) of direct soil moisture monitoring, numerous remote sensing approaches have been used (Chan et al., 2016; Colliander et al., 2017). At present, continuous estimates of soil moisture for Canada as a whole are available from the Soil Moisture and Ocean Salinity (SMOS) satellite mission (2010–present) and more recently as part of the Soil Moisture Active Passive (SMAP) Mission (2015–present) (e.g., Champagne et al., 2011, 2012). SMOS data are distributed by Agriculture and Agri Food Canada.

A limitation to estimates of soil moisture from remote sensing is the relatively shallow observation depth, which is generally limited to the top few centimetres from the surface. Deeper values within the root zone (i.e., the top metre) are often determined using data assimilation systems, in which soil moisture data from satellite sensors are merged with estimates from a hydrological model (e.g., Reichle et al., 2017). In Canada, this is done operationally and nationally as part of the Canadian Land Data Assimilation System (Carrera et al., 2015). Due to the relatively short record, no studies have examined trends in these data. Daily soil moisture values in the Canadian prairie provinces for three soil layer depths (0–20 cm, 20–100 cm and 0–100 cm) were, however, reconstructed from 1950 to 2009 using the Variable Infiltration Capacity (VIC) land-surface hydrology model. The reconstructed soil moisture matched past observations across the prairies, but no trends were reported (Wen et al., 2011).

There have been a few global studies of future soil moisture using GCM output. An ensemble of 15 CMIP3 GCMs projected a decrease in June–August soil moisture for most of Canada for the late century under a medium-high emission scenario (SRES 1Ab) (Wang, 2005). Projected late-century changes in surface, total, and layer-by-layer soil moisture from 25 GCMs included in CMIP5 under a high emission scenario (RCP8.5) indicated that, in most mid-latitudes of the Northern Hemisphere, including southern Canada, the top 10 cm of soil will become drier during the summer, but the remainder of the soil, down to 3 m, will stay wet (Berg et al., 2016; Wehner et al., 2017).

 

6.4.2: Drought

Drought is often defined as a period of abnormally dry weather long enough to cause a serious hydrological imbalance (e.g., Seneviratne et al., 2012) and therefore impacts on several components of the water cycle. These impacts can also be exacerbated by increases in evapotranspiration associated with high temperatures. Drought impacts differ, however, depending on their timing. In general, warm-season droughts affect not only agricultural production (usually due to soil moisture deficits) but also surface and subsurface water levels. Precipitation deficits associated with the runoff season (including winter snow accumulation) primarily affect the replenishment of freshwater systems.

Numerous indices of drought (which also identify moisture surplus) have been used to characterize their occurrence and intensity. The indices incorporate various hydroclimatic inputs (e.g., precipitation, temperature, streamflow, groundwater, and snowpack), and each index has its own strengths and weaknesses (see WMO, 2016 for a comprehensive list). Some indices are based on precipitation alone (e.g., the Standardized Precipitation Index [SPI] (McKee et al., 1993)) and do not take into account that higher temperatures are often associated with below-normal precipitation. As a result, enhanced evapotranspiration is not considered. A few indices incorporate precipitation and estimates of potential evapotranspiration (based on air temperature) — for example, the Palmer Drought Severity Index (PDSI) (Palmer, 1965) and the Standardized Precipitation Evapotranspiration Index (SPEI) (Vicente-Serrano et al., 2010). A shortcoming of these indices is that they use potential evapotranspiration as a proxy for actual evapotranspiration and, thus, do not consider how soil moisture and vegetation may limit evapotranspiration and subsequent drought development. This can lead to overestimation of drought intensity, particularly for climate change projections (e.g., Donohue et al., 2010; Milly and Dunne, 2011, 2016; Shaw and Riha, 2011). The vast majority of global-scale and Canadian analyses of historical trends and projected future changes to drought have used indices based on precipitation alone or on the combined effects of temperature and precipitation (e.g., Bonsal et al., 2011), and these are the focus of this assessment.

A few global studies have highlighted past trends in specific regions, including, for example, drying over mid-latitude regions of Canada from 1950 to 2008 (Dai, 2011 using PDSI). However, since the beginning of the 20th century, the frequency of global drought remains generally unchanged; it appears that, over this longer period, increases in global temperature and potential evapotranspiration have been offset by increases in annual precipitation (e.g., Sheffield et al., 2012; McCabe and Wolock, 2015). Trend analyses in Canadian drought are fragmented, with no comprehensive country-wide analyses to date. The majority have focused on the Prairie region, because of the greater frequency of drought in this region (e.g., Mortsch et al., 2015). A Canadian drought review (Bonsal et al., 2011) provided examples of 20th-century changes in PDSI for individual stations in various regions of the country (1900 to 2007) (see Figure 6.14). Considerable multi-year variability is evident, with no discernible long-term trends. This variability was also apparent in regional studies of SPEI (1900–2011) in summer (June–August) and over the “water year” (October–September) in southeastern Alberta and southwestern Saskatchewan (Bonsal et al., 2017) and the Athabasca River Basin (Bonsal and Cuell, 2017). Other Canadian Prairie region drought studies have highlighted periodic droughts during the 1890s, 1910s, 1930s, 1980s, and early 2000s (e.g., Chipanshi et al., 2006; Bonsal and Regier, 2007; Bonsal et al., 2013). From the mid-to-late 2000s to approximately 2014, the Prairie region has experienced exceptionally wet conditions, highlighting the high variability in this region (e.g., Bonsal et al., 2017).

In other areas of the country, the Canadian Drought Code (based on maximum temperature and precipitation) showed that drought severity over the southern boreal forest regions of Canada was variable, with no longterm trend from 1913 to 1998 (Girardin et al., 2004). A more recent analysis using PDSI and the Climate Moisture Index (difference between annual precipitation and annual potential evapotranspiration) indicated that, for the Canadian boreal zone as a whole, several regions experienced significant drying between 1951 and 2010, but there were also some areas with significant wetting (Wang et al., 2014). An analysis of 20th century (1920–1999) drought events in southern Ontario revealed occurrences in 1930, 1933, 1934, 1936, 1963, 1998, and 1999, with no long-term trend (Klaassen, 2002). Canada-wide trends in actual evapotranspiration from 1960 to 2000 showed significant increasing values at 35% of the station locations, mainly on the Pacific and Atlantic coasts and in the Laurentian Great Lakes/St. Lawrence zones (Fernandes et al., 2007). Other studies found that annual actual evapotranspiration trends in the Prairie region were mixed (e.g., Gan, 1998). Observed pan evaporation and estimated potential evapotranspiration for 11 Prairie region sites from the 1960s to early 2000s showed significant decreasing and increasing trends at different sites. Overall, more locations had decreases in potential evapotranspiration, and these were concentrated during June and July (Burn and Hesch, 2006).

No Canadian studies have attempted to directly attribute past trends in drought to anthropogenic climate change, although there has been some research on the 2015 extreme drought event in western Canada. Anthropogenic climate change increased the likelihood of the extremely warm spring, but no human influence was detected on the persistent drought-producing weather pattern (Szeto et al., 2016).

To date, no Canada-wide studies of future drought projections have been carried out. There are, however, several regional-scale analyses, with the majority focusing on the Prairie region and incorporating one or more drought indices. For example, output from three CMIP3 GCMs incorporating high (A2), medium-high (A1B), and medium (B2) emission scenarios were used to project future (2011–2100) summer PDSI over the southern Canadian prairies. More persistent droughts are projected, particularly after 2040, and multi-year droughts of 10 or more years are projected to become more probable (Bonsal et al., 2013). Similarly, the Canadian Regional Climate Model, under a high emission (A2) scenario, projected that long droughts of six to 10 months will increase and become more severe by mid-century across southern Manitoba and Saskatchewan and the eastern slopes of the Rocky Mountains. However, in the northern Prairie region, long drought events will be less severe and less frequent (PaiMazumder et al., 2012). A number of other studies of the Prairie region have examined drought changes for the mid-century period using several climate models that are part of the North American Regional Climate Change Assessment Program (Mearns et al., 2009). For the southern Prairie region, results under a high emission scenario (A2) indicated an overall increased drought risk for both summer and winter. There were considerable differences among models, with projections ranging from a substantial increase in drought with a higher degree of year-to-year variability, to relatively no change from current conditions (Jeong et al., 2014; Masud et al., 2017; Bonsal et al., 2017). Further north, in the Athabasca River Basin, projections revealed an average change toward more summer drought, but, again, there was a substantial range among the climate models (Bonsal and Cuell, 2017). Future annual and summer SPEI changes over all western Canadian river basins were assessed with six CMIP5 GCMs for the periods 2041–2070 and 2071–2100 (relative to 1971–2000) using medium emission (RCP4.5) and high emission (RCP8.5) scenarios. Southern watersheds showed a gradual increase in annual water deficit throughout the 21st century, while the opposite was true for northern basins. For summer, however, all river basins except those in the extreme north are expected to experience decreasing water availability (see Figure 6.15) (Dibike et al., 2017). Twelve CMIP3 GCMs incorporating medium (B1), medium-high (A1B), and high (A2) emission scenarios showed that, by the end of the 21st century, the combined changes in precipitation and temperature will lead to generally drier conditions in much of the boreal forest region of western Canada and to a higher likelihood of drought. However, some regions in the east may become slightly wetter (Wang et al., 2014).

These future projections are consistent with other North American and global-scale studies using similar drought indices. For instance, drought projections using numerous CMIP5 GCMs (medium emission (RCP4.5) scenario) showed that the frequency of severe-to-extreme drought conditions is expected to increase by the late 21st century for much of southern Canada, including southeast British Columbia, the prairies and Ontario (as measured by PDSI and soil moisture) (Dai, 2012; Zhao and Dai, 2015, 2016). Similar results have been projected using PDSI and SPEI under a high emission (RCP 8.5) scenario (Cook et al., 2014; Touma et al., 2015). This included increases in drought magnitude and frequency over western, central, and eastern North America, with the greatest change over western and central regions. Year-to-year variability in SPI was projected to increase by the end of century (2080–2099) in various regions of North America, suggesting more extremes; however, there was considerable uncertainty in these results, due to large differences among regions and among the 21 CMIP5 GCMs (Swain and Hayhoe, 2015). Although there is overall consistency regarding the increased likelihood of future drought over southern interior continental regions of Canada, there is uncertainty concerning the magnitude of these changes. This is primarily due to shortcomings of the indices that estimate potential evapotranspiration, which may lead to an overestimation of drought intensity (e.g., Sheffield et al., 2012; Trenberth et al., 2014; Milly and Dunne, 2016)

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6.3.2: Other lakes

Although levels of most other large lakes in Canada (e.g., Lakes Winnipeg, Athabasca, and Great Slave Lake) are monitored, these lakes are influenced by human regulation, making it difficult to assess past climate-related trends. An exception is Great Bear Lake in the Northwest Territories, which is unregulated. Figure 6.11 illustrates recurring high and low levels of this lake, with no discernible long-term trend. The levels have varied, in part, due to regional climatic conditions. In particular, the driest years were in the late 1940s and early 1950s, when water levels reached an all-time low, with another low recorded in the mid-1990s. The wettest years and highest levels were in the early to mid-1960s, with another peak in the early 1970s (MacDonald et al., 2004).

In the Prairie region, glaciation and dry climate have resulted in numerous closed-basin saline lakes, which drain internally and rarely spill runoff. Water storage in these lakes is sensitive to climate, driven by precipitation, local runoff, and evaporation. From 1910 to 2006, levels in several closed-basin lakes across the Prairie region showed an overall decline of 4 to 10 m (see Figure 6.12), due, in part, to higher warm-season temperatures (and resulting increased evaporation) and declining snowmelt runoff to the lakes. However, climate variables alone did not explain the declines, and other contributing factors, such as land-use changes (dams, ditches, wetland drainage, and dugouts) and changes in agricultural practices, were also involved (van der Kamp et al., 2008). From the late 2000s through 2016, there has been an abrupt reversal in levels of many of these lakes (a rise of as much as 6 to 8 m), reflecting the exceptionally wet conditions on the Prairies over these years (e.g., Bonsal et al., 2017). The reversal has resulted in several cases of overland flooding, exemplifying the natural hydroclimatic variability in this region and the susceptibility of surface water bodies to precipitation extremes, both dry and wet. Although no studies have investigated future climate impacts on these lake levels, they will continue to be affected by dry and wet extremes. However, given the projected higher temperatures and resulting increased evaporation, future levels are expected to decline, although the magnitude will depend on how much future precipitation increases will offset evaporation.

Smaller lakes and ponds are a characteristic feature of the Canadian Arctic, with large numbers of permafrost thaw lakes found in northern Yukon and the Northwest Territories. These water bodies are variable in size, with diameters of 10 to 10,000 m and depths of 1 to 20 m (Plug et al., 2008; Vincent et al., 2012). Warming due to Arctic amplification at high latitudes can affect the size of permafrost lakes. In particular, those in continuous permafrost may expand due to acceleration of the permafrost thaw processes that formed them, whereas those in discontinuous permafrost (i.e., patches of permafrost) may shrink and even disappear due to rapid drainage as the underlying permafrost completely thaws (e.g., Hinzman et al., 2005; Smith et al., 2005). Some evidence for these processes has been observed in certain high-latitude regions, including Canada. For example, total lake area in the Old Crow Flats (Yukon) declined by approximately 6000 hectares between 1951 and 2007, with close to half of this loss being caused by rapid and persistent drainage of 38 large lakes. This drainage also resulted in the formation of numerous smaller residual ponds. Catastrophic lake drainages in this region have become more than five times more frequent in recent decades, and it has been suggested that these changes are associated with increases in regional temperature and precipitation (Lantz and Turner, 2015). This observation is consistent with local perceptions that lakes in the Old Crow Flats are showing declining water levels (e.g., Wolfe et al., 2011). However, other Canadian Arctic studies have revealed mixed results. For example, aerial photographs and topographic maps showed that, in a 10,000 km2 region east of the Mackenzie delta in the Northwest Territories, 41 lakes drained between 1950 and 2000, but the rate of drainage has decreased over time (Marsh et al., 2009). Similarly, total lake area on the Tuktoyaktuk Peninsula on the Arctic Ocean coast of the Northwest Territories from 1978 to 2001 ranged from a 14% increase to an 11% decrease. The increases occurred primarily between 1978 and 1992 and decreases between 1992 and 2001, depending strongly on annual precipitation (Plug et al., 2008).

Future warming and further permafrost thaw (see Chapter 5, Section 5.6.2) are anticipated to have a substantial impact on surface water in the Arctic. Permafrost thaw lakes currently have natural cycles of expansion, erosion, drainage, and reformation (e.g., van Huissteden et al., 2011), which may accelerate under warmer climate conditions. GCMs project increased precipitation over the Canadian Arctic (see Chapter 4, Section 4.3.1.3); however, these increases will be partially offset by greater evaporation due to both warmer temperatures in summer and decreased duration of ice cover. In addition, many high Arctic lakes depend on year-round snow and glaciers and are thus vulnerable to the rapid warming of the cryosphere. As a result, the extent of northern lakes is highly vulnerable to change as a result of increased water loss from evaporation and/or drainage (e.g., Vincent et al., 2012).

 

6.3.3: Wetlands and deltas

Wetlands are land saturated with water all or most of the time, with poorly drained soils and vegetation adapted to wet environments. They are often associated with standing surface water, and depths are generally less than 2 m. Canada has approximately 1.5 million km2 of wetlands — commonly referred to as swamps, marshes, bogs, muskegs, ponds, and sloughs — representing about 16% of the country’s landmass (National Wetlands Working Group, 1988, 1997). The majority of wetlands are peatlands in the Arctic, sub-Arctic, boreal, prairie, and temperate regions (van der Kamp and Marsh, 2004). Canada also has several deltas that form from sediments deposited by rivers entering a large lake or ocean. The most prominent examples include the Mackenzie (with more than 25,000 shallow lakes and wetlands), Fraser, Peace–Athabasca, Slave, Saskatchewan, and St. Clair river deltas. Critical to the resilience of delta ecosystems are occasional low- and high-water events. High-water events can result in overland flow (ice jam and open-water flooding) and are a crucial source of water replenishment to disconnected water bodies perched above the main flow system (see below; Peters et al., 2013).

By storing water and releasing it slowly, wetlands and deltas are important to Canada’s freshwater availability. Under certain conditions, wetlands can alleviate floods, maintain groundwater levels and streamflow, filter sediments and pollutants, cycle nutrients, and sequester carbon (Federal, Provincial and Territorial Governments of Canada, 2010). They are closely linked with climate, as they gain water from direct precipitation, runoff from surrounding uplands, and groundwater inflow. They lose water via evapotranspiration and surface/ groundwater outflow. Some wetlands also owe their existence in part to cold Canadian winters and resulting permafrost, snowmelt, and river ice jams. Thus, both shorter winters and increased evaporation due to longer summers will increase stress on wetland environments, unless increases in precipitation offset the loss of water through evaporation (van der Kamp and Marsh, 2004).

Despite the importance of wetlands, a comprehensive inventory or monitoring program for the entire country does not exist (Fournier et al., 2007). However, since 1979, Ducks Unlimited Canada has used aerial photography and satellite imagery to inventory millions of hectares of wetlands across Canada. In addition, the US Fish and Wildlife Service produces an annual report that summarizes the status of North American waterfowl populations and their habitats, with input from Canada (US Fish and Wildlife Service, 2017). Figure 6.13 shows Canadian prairie pond counts during May from 1961 to 2017. The series shows substantial multi-year variability and no long-term trends. The levels closely correspond to long-term precipitation variability in the region. In many regions of Canada, wetlands are being lost due to land conversion, water-level control, and climate change (e.g., Watmough and Schmoll, 2007; Ducks Unlimited Canada, 2010).

Many small lakes in freshwater delta systems are “perched basins,” located at a higher elevation than the nearby rivers. These basins typically experience declines in water levels during drier periods and replenishment during flood events in a continuous cycle (e.g., Marsh and Lesack, 1996; Peters et al., 2006; Lesack and Marsh, 2010). For example, in the Peace–Athabasca delta, evaporation exceeded precipitation from 1900 to 1940; opposite conditions prevailed from 1940 to the mid-1970s; and this was followed by a return to drier conditions that has continued through 2009 (Peters et al., 2006; Peters, 2013). The Mackenzie, Slave, and Saskatchewan river deltas had similar variability (e.g., Lesack and Marsh, 2010; Peters, 2013). Under a warmer and wetter future climate (2070–2099; ensemble of CMIP3 GCMs; high emission (A2) and medium emission (B2) scenarios), a shorter ice season (by two to four weeks), thinner ice cover, and depletion of the snowpack by mid-winter melt events are projected to lead to a major reduction in the frequency of spring ice jam flooding in the Peace–Athabasca delta (Beltaos et al., 2006). This reduction would have serious ecological implications, including accelerated loss of aquatic habitat, unless summer flood levels can reach the perched basins (Peters et al., 2006).

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6.3: Surface water levels: lakes and wetlands

In regions of Canada where there are sufficient data, there is no indication of long-term changes to lake and wetland levels. Future levels may decline in southern Canada, where increased evaporation may exceed increased precipitation (low confidence). Projected warming and thawing permafrost has the potential to cause future changes in many northern Canadian lakes, including rapid drainage (medium confidence).

 

Canada has more than 2 million lakes covering 7.6% of the country’s area, with 578 having an area greater than 100 km2 (Canadian National Committee, 1975; Monk and Baird, 2011). There is a wide range of lake types, including the Laurentian Great Lakes (Superior, Michigan, Huron, Erie, and Ontario) and Mackenzie Great Lakes (Great Slave and Great Bear), Arctic and sub-Arctic lakes, glacial, boreal, prairie, and shallow enclosed saline lakes (Schertzer et al., 2004). Some lake levels are monitored by Canada’s Hydrometric Network (see Box 6.1). Other than in a select few cases, there is limited information on past trends and projected future changes in lake levels. Furthermore, many of the larger lakes are regulated by humans, and there is no comprehensive national dataset for unregulated lakes. Thus, a Canada-wide assessment of past trends and future changes is challenging. This section focuses on major lakes and water bodies, reflecting the available literature and monitoring data.

The main inputs include river inflow (runoff), direct precipitation onto the water body, snowmelt, and groundwater inflow. Outputs involve river outflow, evaporation, and exchange with groundwater. The contribution from these variables varies greatly with the size of the water body. Larger lakes within very large drainage basins are affected by events far upstream, in addition to local/regional climate. Smaller lakes and wetlands are more responsive to local climatic conditions. Surface water bodies in Canada are becoming increasingly vulnerable to a variety of stresses, both climate-related and from human management (flow regulation and land-use change) (e.g., Schertzer et al., 2004).

 

6.3.1: Laurentian Great Lakes

Given their importance to Canada and the United States, the Laurentian Great Lakes are among the most studied water bodies in North America. Levels of these lakes have been monitored for more than 100 years by Canadian and US federal agencies. The levels show a large degree of variability due to natural climate variations, as well as to direct human management (e.g., dredging, diversions). These fluctuations have significant impacts on shoreline erosion, flooding of property, navigation, recreation, economy, aquatic ecosystems, and human health. Seasonally, water levels typically progress from a summer maximum to a minimum in the winter/spring (Argyilan and Forman, 2003). The lakes also exhibit year-to-year and multi-year fluctuations of less than 2.0 m, varying by lake (Wilcox et al., 2007; DFO, 2013).

All of the Laurentian Great Lakes have experienced considerable variability in overall NBS and its primary individual components (basin-wide precipitation, lake evaporation, and river runoff) during the last several decades. This year-to-year and multi-year variability is significantly influenced by naturally occurring large-scale modes of climate variability including PDO, AO, and the Atlantic Multi-decadal Oscillation (see Chapter 2, Box 2.5) (e.g., Ghanbari and Bravo, 2008; Hanrahan et al., 2010). Given the large geographic expanse of the Laurentian Great Lakes basin, trends in NBS and individual components vary from one lake to another. In Lake Superior, evaporation is increasing and runoff is decreasing, resulting in a significant decrease in NBS. These trends are also seen for Lake Erie (although not at statistically significant levels). In Lake Ontario, NBS has increased significantly, mainly due to increases in precipitation and runoff, although changes in these individual components are not significant. For the other lakes, trends are insignificant and mixed. For example, runoff is declining for Lake Erie, but rising for Lakes Michigan, Huron, and Ontario. Evaporation has increased over the last 70 years in Lakes Superior and Erie but shows relatively little change in the other lakes (although values have been higher since around 1998). Precipitation has increased in Lake Ontario but decreased in Lake Superior, while no trend is evident in the other lakes.

From 1998 to 2013, all the Laurentian Great Lakes experienced a long period of low levels, including record lows in Lakes Michigan and Huron in December 2012 and January 2013. This period ended with a quick rise in all lake levels starting in 2013. September 2014 was the first month since 1998 that all lakes were above long-term (1918–2013) average levels. The 2013 rise was attributed to increased precipitation, while the 2014 rise resulted from a combination of below-average evaporation and above-average precipitation and runoff (Gronewold et al., 2016). During spring 2017, a series of above-average precipitation events caused the level of Lake Ontario to reach its highest level since reliable measurements began in 1918 (IJC, 2017). These two opposite extremes, occurring within a few years of each other, reveal the variability in the Laurentian Great Lakes’ levels and illustrate the difficulty in projecting future lake levels in response to climate change.

Most studies of future levels have been based on CMIP3 GCM projections (see Chapter 3, Box 3.1) that have been run through RCMs (Angel and Kunkel, 2010; Hayhoe et al., 2010; IUGLS, 2012; MacKay and Seglenieks, 2013). RCMs are essential for modelling the Laurentian Great Lakes, since their finer spatial resolution (typically around 50 km versus GCM grids of around 200 to 250 km; see Chapter 3, Section 3.5) allows explicit modelling of the individual lakes. As a result, models include phenomena that can have significant effects on water balance, such as lake-effect snow, which transfers large amounts of water from the lake to the land surface. Projected NBS shows considerable changes to the seasonal cycle of Lakes Michigan and Huron for 2041–2070 compared with 1961–2000 (see Figure 6.10). These changes include an increase in NBS during the winter and early spring and a decrease in summer and early fall, largely due to projected changes in seasonal precipitation. Other lakes have similar results. Overall, these projected seasonal changes are expected to result in a decrease in NBS of 1.7% to 3.9% in Lakes Superior, Michigan, Huron, and Erie, and of 0.7% in Lake Ontario (IUGLS, 2012). On average, under a range of emission scenarios, most RCM studies project a lowering of future lake levels by 0.2 m for the 30-year time period centred on the 2050s, as compared to the 1971–2000 mean. However, there is a considerable range (from a 0.1 m increase to a 0.5 m decrease) (Angel and Kunkel, 2010; Hayhoe et al., 2010; IUGLS, 2012). These changes are less than those projected using statistically downscaled GCM output that does not incorporate the individual lakes (MacKay and Seglenieks, 2013). All studies agree that there will continue to be large year-to-year and multi-year variability in lake levels, possibly even above and below the historically observed extremes (IUGLS, 2012; Music et al., 2015).

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6.2.2: Streamflow timing

Reliable streamflow is important for water users and aquatic ecosystems, which have become accustomed to having adequate water supplies at certain times of the year. As a result, streamflow timing and related streamflow regimes (see Section 6.2.3) are important indicators of freshwater availability. The timing of streamflow events is significantly influenced by climate. Such events include the spring freshet, when flow dramatically increases due to snowmelt, and shorter-duration (usually one- to seven-day) maximum and minimum flows during the year. Pan-Canadian studies have generally reported that the spring high-flow season is coming earlier (Zhang et al., 2001; Déry et al., 2009; Vincent et al., 2015). This finding is supported by a study using 49 RHBN hydrometric stations with more than 30 years of data up to 2010 (Jones et al., 2015; see Figure 6.6). The average rate of change for stations with earlier trends was approximately two days per decade, consistent with other studies showing earlier freshets (e.g., Prowse et al., 2002).

Several regional studies in western Canada, including the Northwest Territories, have also found an earlier onset of spring freshet over the past several decades (Burn et al., 2004a, 2004b; Abdul Aziz and Burn, 2006; Burn, 2008; Rood et al., 2008; Cunderlik and Ouarda, 2009). For example, the Fraser River in British Columbia displayed a trend toward smaller mountain snowpacks and earlier melt onsets that resulted in a 10-day advance of the spring freshet (with subsequent reductions in summer flows) for the 1949–2006 period (Kang et al., 2016). In the Mackenzie Basin (British Columbia, Alberta, and Northwest Territories), spring freshet advanced by approximately 2.7 days per decade over the last 25 years (Woo and Thorne, 2003). These trends are consistent with increasing spring temperatures (see Chapter 4, Section 4.2.1.1) and the resulting earlier spring snowmelt (e.g., DeBeer et al., 2016).

The timing of annual low flows of various durations (one, seven, 15, and 30 days) was significantly earlier in the year over the 1954–2003 period in southern British Columbia, central and southwestern Alberta, central Saskatchewan, much of Ontario, as well as Quebec and the Atlantic provinces. Northern British Columbia, Yukon, Northwest Territories, Nunavut, and the Laurentian Great Lakes region had significant trends toward later dates. Similar spatial results were also observed for winter and summer low flows (Khaliq et al., 2008). For the timing of high flows, summer rainfall-driven peak events in some regions of the Prairies were found to occur earlier (Burn et al., 2008); however, western Canada as a whole showed no consistent trends in the timing of rainfall-dominated high flows (e.g., Cunderlik and Ouarda, 2009).

No Canadian studies have directly attributed change in streamflow timing to anthropogenic climate change. However, since earlier spring freshets are the result of strong winter and spring warming, and most the observed warming in Canada is due to human influence, there is strong reasoning that observed changes in streamflow seasonality are at least partly attributable to anthropogenic warming. Furthermore, trends toward earlier snowmelt-driven streamflow in the western United States since 1950 (including the Columbia River basin that extends into southern British Columbia) have been attributed to anthropogenic climate warming (Hidalgo et al., 2009).

There are few studies of future streamflow timing in Canada. An earlier snowmelt peak and resulting spring freshet is projected for mid-century (2041–2070) over western Canada, particularly for northern basins, using the Canadian Regional Climate Model and a high emission scenario (A2). For the majority of western Canada basins, this earlier shift was also projected for the end-of-winter low-flow events (Poitras et al., 2011). Earlier spring freshet flows for the mid-century period (2041–2070) are also projected using several CMIP5 models under a medium (RCP4.5) and a high (RCP8.5) emission scenario. Spring freshets are projected to advance by an average of 25 days (RCP4.5 and RCP8.5) in the Fraser River, British Columbia (Islam et al., 2017), and by 15 days (RCP4.5) and 20 days (RCP8.5) for rivers in southern Quebec (CEHQ, 2015). Given the continued spring warming projected for Canada (see Chapter 4, Section 4.2.1.3), earlier spring freshet flows in the future are also probable in other regions of Canada.

 

6.2.3: Streamflow regime

In a warming climate, the following changes to current streamflow regimes (see Box 6.2) are expected: (1) earlier onset of spring freshet; (2) smaller magnitude of snowmelt events; (3) more rainfall-generated flows; (4) a transition from nival catchments to mixed regimes and from mixed regimes to pluvial regimes (Burn et al., 2016). Regional studies have yielded similar results. For example, trends in southern areas of western Canada (Fraser and Columbia river watersheds) are associated with changes in runoff timing, including a shorter snow- and glacier-melt season, earlier onset of spring melt, and decreased summer flows during approximately the last 50 years (e.g., Rood et al., 2008; Déry et al., 2009). Shifts from nival to mixed or even pluvial regimes were observed for small prairie streams (Burn et al., 2008; Shook and Pomeroy, 2012; Dumanski et al., 2015).

 

6.2.4: Streamflow-related floods

A flood is the overflowing of the normal confines of a stream or other body of water, or the accumulation of water over areas that are not normally submerged. Flooding typically occurs at local to watershed scales. There are several types, including streamflow (fluvial), urban, flash, and coastal flooding. This section assesses only streamflow-related floods, although implications for urban floods are discussed. The main causes of streamflow floods are intense and/or long-lasting precipitation, snow/ice melt, rain-on-snow, river ice jams, or a combination of these causes. Flood risk is also affected by drainage basin conditions, such as pre-flood water levels in the rivers; the presence of snow and ice; soil character (e.g., whether it is frozen, its moisture content); urbanization; and the existence of dikes, dams, and reservoirs (e.g., Bates et al., 2008).

Streamflow flooding is a common and natural occurrence, but large events are often a costly disaster for Canadians (Buttle et al., 2016; Peters et al., 2016). Given the range of potential drivers, flooding can occur any time of the year somewhere in Canada. Flooding from snowmelt and ice jams typically occurs during the spring but can also result from mid-winter melts. Floods generated by intense and/or excessive rainfall typically occur in late spring and summer, when atmospheric convective precipitation (generally brief but intense rain showers resulting from heat convection forming cumulonimbus clouds) is more common. An example of a costly event was the June 2013 southern Alberta flood, which was driven mainly by extreme rainfall (including rain-on-snow at higher elevations) associated with an intense weather system (Liu et al., 2016; Teufel et al., 2017) (see Chapter 4, Section 4.4.1.1). By contrast, ice jams on the lower Peace and Athabasca Rivers in northern Alberta in 2014 led to widespread inundation of delta wetland areas, which was beneficial to maintaining the aquatic ecosystem in the region (Peters et al., 2016). In 2014 as well, a delayed spring onset of snowmelt and an extremely wet May and June resulted in major flooding in the southeastern Canadian prairies (Szeto et al., 2015).

Different areas of Canada are classified according to the type of floods they generally experience. Across the country, 32% of 136 stream gauge sites (1913–2006) are classified as spring freshet/ice breakup flood– dominated, 42% as open-water flood–dominated (i.e., during the warm season), and 23% as a mix of these two classes. The timing of ice-influenced peak water levels and ice breakup (which can lead to flooding) has shifted earlier since the late 1960s (von de Wall et al., 2009; 2010) (see also Chapter 5, Section 5.5). There are also areas of Canada, such as the Saint John River, New Brunswick, where floodplains have been subject to more frequent mid-winter ice jams and higher April flows, both of which increase the risk of major flooding (Beltaos, 2002). However, more recent analyses of both spring freshet- and open-water flood-dominated rivers across Canada revealed that changes in magnitude, timing, number, and duration of high-flow events showed varying trends across Canada, increasing in some cases and decreasing in others. For nival catchments, this included trends toward smaller and earlier flood events; both consistent with a reduction in winter snowpack (Burn and Whitfield, 2016). In addition, examination of the seasonality of past flood regimes in 132 RHBN stations over four periods ranging from 50 to 80 years revealed the decreased importance of snowmelt flood events and the increased importance of both rain-on-snow and rainfall-driven flood events (Burn et al., 2016). To the authors’ knowledge, no studies have assessed past trends in urban flooding across Canada.

Complex interactions among the many factors that lead to streamflow floods complicate the attribution of these events to anthropogenic climate change. An event-attribution study of the 2013 southern Alberta flood determined that human-induced warming increased the likelihood of extreme precipitation, at least as large as the amount observed during this event (Teuful et al., 2017). However, since the flood resulted from a combination of many meteorological and hydrological factors, human influence could not be detected for the flood itself (see Chapter 4, Section 4.4.1.1). Similarly, an event-attribution study of the 2014 flood in the southeastern prairies was unable to detect human influence on that flood, owing to multiple contributing factors (Szeto et al., 2015).

It is expected that a changing climate will impact several of the factors affecting future streamflow flood occurrence (see FAQ 6.1). These include precipitation amount, type, and intensity; the amount and duration of snow cover; the timing and frequency of ice jams; and the potential for rain-on-snow events. However, interactions between flood-generating factors at the watershed scale lead to large uncertainties regarding the frequency and intensity of future floods (Whitfield, 2012). Some studies have suggested that the contribution of snowmelt to spring floods is expected to generally decline due to depleted snowpacks (e.g., Whitfield and Cannon, 2000; Zhang et al., 2001; Peters et al., 2006). However, there are only a few watershed-scale studies on future streamflow flooding (and/or their related factors) in Canada, which use climate model projections as input into a hydrological model. For example, depletion of the snowpack by mid-winter melt events are projected to lead to a major reduction in the frequency of spring ice jam flooding, but could increase the potential for mid-winter ice jam flooding in the Peace–Athabasca delta in northern Alberta (Beltaos et al., 2006). Two British Columbia watersheds, one on the coast and one in the interior, are both projected to experience increased flooding potential, due to more rainfall and winter rain-on-snow events in the coastal watershed and to more spring rain and more rapid snowmelt events in the interior watershed (Loukas et al., 2000; 2002). For the Red River Basin in Manitoba, snow accumulation during winter is expected to decrease, while rainfall is expected to increase during the snowmelt period. However, due to the variability among climate models, it is difficult to project whether flood magnitude will increase or decrease (Rasmussen, 2015). In the Châteauguay watershed in Quebec, spring, summer, and autumn peak flood events are projected to be reduced in magnitude under a medium emission scenario (B2), but there are large differences among the three models used (Mareuil et al., 2007). The only study of projected changes in rain-on-snow events suggested general increases in these events from November to March for most of Canada by mid-century (2041–2070) for both medium (RCP4.5) and high (RCP8.5) emission scenarios (Jeong and Sushama, 2018). To the authors’ knowledge, no studies have assessed projected changes to urban floods across Canada; however, increases in extreme precipitation are considered a factor that will affect their future occurrence (e.g., Buttle et al., 2016; Sandink, 2016).

Climate Change Canada

 

5.6.2: Projected changes in permafrost

Climate models project large increases in mean surface temperature (approximately 8ºC) across present-day permafrost areas by the end of the 21st century under a high emission scenario (RCP8.5) (Koven et al., 2013) (see Chapter 3, Section 3.3.3). While this dramatic warming will no doubt affect permafrost temperatures and conditions (e.g., Slater and Lawrence, 2013; Guo and Wang, 2016; Chadburn et al., 2017), it is challenging to project associated reductions in permafrost extent from climate model simulations because of inadequate representation of soil properties (including ice content) and uncertainties in understanding the response of deep permafrost (which can exceed hundreds of metres below the surface). Simulations from a model considering deeper permafrost and driven by low and medium emission scenarios project that the area underlain by permafrost in Canada will decline by approximately 16%–20% by 2090, relative to a 1990 baseline (Zhang et al., 2008a). These declines are smaller than projections from other modelling studies that only examined near-surface ground temperature (Koven et al., 2013; Slater and Lawrence, 2013). These simulations also show that permafrost thaw would continue through the late 21st century, even if air temperatures stabilize by mid-century (Zhang et al., 2008b).

Other climate-related effects also influence the future response of permafrost to warming and complicate modelling of future conditions (e.g., Kokelj et al., 2017b; Romanovsky et al., 2017a). For example, intensification of rainfall appears to be strongly linked to thaw slumping (Kokelj et al., 2015). New shrub growth in the tundra can promote snow accumulation and lead to warmer winter ground conditions (Lantz et al., 2013). Thaw and collapse of peat plateaus and palsas into adjacent ponds increase overall permafrost degradation, and gullies that form because of degrading ice wedges can result in thermal erosion and further permafrost degradation (Mamet et al., 2017; Beck et al., 2015; Quinton and Baltzer, 2013; Godin et al., 2016; Perreault et al., 2017). Damage to vegetation and the organic layer due to wildfires (which are projected to occur more frequently under a warming climate) can lead to warming of the ground, increases in ALT, and degradation of permafrost (Smith et al., 2015c; Zhang et al., 2015; Fisher et al., 2016). Similarly, vegetation clearing and surface disturbance due to human activity and infrastructure construction can also lead to ground warming and thawing and enhance the effects of changing air temperatures on permafrost environments (Smith and Riseborough, 2010; Wolfe et al., 2015).

 

6.2: Surface runoff: streamflow

The seasonal timing of peak streamflow has shifted, driven by warming temperatures. Over the last several decades in Canada, spring peak streamflow following snowmelt has occurred earlier, with higher winter and early spring flows (high confidence). In some areas, reduced summer flows have been observed (medium confidence). These seasonal changes are projected to continue, with corresponding shifts from more snowmelt-dominated regimes toward rainfall-dominated regimes (high confidence).

There have been no consistent trends in annual streamflow amounts across Canada as a whole. In the future, annual flows are projected to increase in most northern basins but decrease in southern interior continental regions (medium confidence).

treamflow-related floods result from many factors, and in Canada these mainly include excess precipitation, snowmelt, ice jams, rain-on-snow, or a combination of these factors. There have been no spatially consistent trends in these flood-causing factors or in flooding events across the country as a whole. Projected increases in extreme precipitation are expected to increase the potential for future urban flooding (high confidence). Projected higher temperatures will result in a shift toward earlier floods associated with spring snowmelt, ice jams, and rain-on-snow events (medium confidence). It is uncertain how projected higher temperatures and reductions in snow cover will combine to affect the frequency and magnitude of future snowmelt-related flooding.

Canada has more than 8500 rivers and streams of various lengths (Monk and Baird, 2011). Many are affected by human alterations, such as flow regulation (dams, weirs, and locks), water withdrawals, and diversions, often associated with hydroelectric facilities (CDA, 2016). Studies on climate-related past changes in streamflow rely heavily on data from streams that are not subject to these forms of human regulation (i.e., unregulated) or those with limited regulation. In a few cases, studies have attempted to account for regulation by determining naturalized flow using various hydrological models (Peters and Buttle, 2010). Future streamflow changes are assessed using climate output (e.g., precipitation and temperature) from various GCMs and/or RCMs that provide input to a hydrological model. The multitude of climate and hydrological models used in these studies adds uncertainty to future streamflow changes (e.g., Seneviratne et al., 2012).

 

6.2.1: Streamflow magnitude

Streamflow magnitude (runoff) is a key indicator for evaluating changes in surface water. It is assessed at monthly, seasonal, and annual timescales to determine changes in overall flow volumes, and on daily to weekly scales to assess high and low streamflow extremes. In all cases, pan-Canadian analyses are infrequent and, in most cases, older than regional studies. For Canada as a whole, annual streamflow trends were mixed. Significant declines occurred at 11% of stations and significant increases at 4% of stations for the 1967–1996 period. Most decreases were in southern Canada (Zhang et al., 2001; similar results in Burn and Hag Elnur, 2002). Seasonal runoff over the 1970–2005 period, in most of the 172 stations evaluated, was dominated by natural variability. Twelve per cent of stations showed significant increases in winter runoff (December to February), while only 5% had significant winter decreases. Spring and summer trends were mixed, with no spatial pattern (Monk et al., 2011). From 1960 to 1997, significant increases in April flow occurred at almost 20% of stations, and significant decreases in summer flow (May to September) were observed at 14% of the sites (Burn and Hag Elnur, 2002). The increases in April flow were also found (25% of stations) for the longer 1950–2012 period (Vincent et al., 2015).

Regional studies of trends in annual and seasonal streamflow magnitudes are summarized. Although these individual studies use different time periods, hydrometric stations, and analysis techniques, the findings are mostly consistent with the Canada-wide analyses. Annual flows over western Canada have varied from one region to another, with both increasing and decreasing trends since approximately the 1960s and 1970s (e.g., DeBeer et al., 2016). Most declines were observed in rivers draining the eastern slopes of the central/southern Rocky Mountains, including the Athabasca, Peace, Red Deer, Elbow, and Oldman rivers (Burn et al., 2004a; Rood et al., 2005; Schindler and Donahue, 2006; St. Jacques et al., 2010; Yip et al., 2012; Peters et al., 2013; Bawden et al., 2014). Long-term streamflow records (over more than 30 years) from the Northwest Territories, including the Mackenzie River, indicated increasing annual flows (St. Jacques and Sauchyn, 2009; Rood et al., 2017). However, annual runoff from rivers draining into northern Canada as a whole (western Arctic Ocean, western Hudson and James Bay, and Labrador Sea) showed no significant trends for the period 1964–2013 (Déry et al., 2016). Rivers in Yukon, British Columbia, Ontario, and Quebec reported mixed annual trends (Fleming and Clarke, 2003; Brabets and Walvoord, 2009; Fleming, 2010; Fleming and Weber, 2012; Déry et al., 2012; Nalley et al., 2012; Hernández-Henríquez et al., 2017).

Seasonally, there has been a consistent pattern of increasing winter flows in many regions (see Table 6.1), particularly for more northern basins, such as the Mackenzie and Yukon rivers and those draining into Hudson Bay. Summer flows have been generally declining over most regions of Canada, although the declines are not as widespread as for winter. Note that these studies are mostly consistent on the direction of change, but there are large differences in the rate of these changes. Many of these regional trends in flow were linked to precipitation trends or variability affecting the entire basin, although winter warming and associated snowmelt explained several of the increases in winter/early spring flow (e.g., DeBeer et al., 2016). In addition, several flows were associated with naturally occurring internal climate variability (mainly El Niño–Southern Oscillation, Pacific Decadal Oscillation [PDO], and Arctic Oscillation [AO]; see Chapter 2, Box 2.5), particularly for western Canada during winter (e.g., Bonsal and Shabbar, 2008; Whitfield et al., 2010), the Mackenzie Basin (St. Jacques and Sauchyn, 2009), and rivers draining into Hudson Bay (Déry and Wood, 2004).

Changes in extreme short-term streamflow are important indicators of flood risk. One-day maximum flow magnitudes (the highest one-day flow recorded during the year) from 1970 to 2005 revealed that 11% of hydrometric sites across Canada have significantly decreasing trends (lower maximum flow levels), while only less than 4% have increasing trends (higher maximum flow levels) (Monk et al., 2011). A more recent study using an expanded set of RHBN stations (280) for the 1961–2010 period yielded very similar results, with 10% of the sites showing significant decreases and less than 4% significant increases (Burn and Whitfield, 2016) (see Figure 6.3).

Equally important to aquatic ecosystems and society are low flows, since they represent periods of decreased water availability. More stations show significantly lower one-day minimum flow trends (18%) than show significantly higher ones (8%) (see Figure 6.4) (Monk et al., 2011). Results from a smaller subset of RHBN stations (Ehsanzadeh and Adamowski, 2007, for 1961–2000 and Burn et al., 2010, for 1967–2006) revealed similar tendencies in seven-day low flows, with more sites having significantly lower values (36% and 18% for each study, respectively) than higher values (7% and 5%, respectively).

Another indicator of freshwater availability is baseflow, the portion of streamflow resulting from seepage of water from the ground (related to groundwater; see Section 6.5). Baseflow often sustains river water supply during low-flow periods. For the vast majority of sites in Canada, annual baseflow trends did not significantly change from 1966 to 2005 (Rivard et al., 2009). However, an analysis of a baseflow index across Canada found some locations with significantly decreasing trends (11% of stations) and others with increasing trends (9%) (Monk et al., 2011). Additionally, in northwestern Canada, winter baseflow has increased significantly in 39% of the 23 rivers analyzed. The likely explanation is enhanced water infiltration from permafrost thawing due to climate warming (St. Jacques and Sauchyn, 2009).

Only one published study directly attributed changes in streamflow magnitude within Canada to anthropogenic climate change. This included recent observed declines in summer (June–August) streamflow in four British Columbia rivers (Najafi et al., 2017b). The decreases were due to smaller late-spring snowpacks (and consequently, lower summer runoff), which were attributed to the human influence on warming of cold-season temperatures (Najafi et al., 2017a) (see Chapter 4, Section 4.3.1.2).

Projected future changes in Canadian streamflow magnitudes have not been extensively examined on a national scale, although several regional assessments have been conducted (see Table 6.2 and Figure 6.5). The majority of these studies are based on the third phase of the Coupled Model Intercomparison Project (CMIP3) climate models and SRES emission scenarios (see Chapter 3, Section 3.3) unless otherwise specified. The findings are mostly consistent on the direction of change, although there are large uncertainties in magnitude. In general, for the mid-21st century, watersheds in British Columbia and northern Alberta are projected to have increases in annual and winter runoff, whereas some watersheds in Alberta, southwest British Columbia, and southern Ontario are projected to have declines in summer flow (Kerkhoven and Gan, 2011; Poitras et al., 2011; Bennett et al., 2012; Bohrn, 2012; Harma et al., 2012; Schnorbus et al., 2011; 2014; Shrestha et al., 2012a; Eum et al., 2017; Islam et al., 2017). In the Prairie region, most rivers in southern Alberta and Saskatchewan are projected to have decreases in both annual and summer runoff (Lapp et al., 2009; Shepherd et al., 2010; Forbes et al., 2011; Kerkhoven and Gan, 2011; Kienzle et al., 2012; Tanzeeba and Gan, 2012; St. Jacques et al., 2013, 2017). However, rivers in southern and northern Manitoba are projected to have increasing flow (Poitras et al., 2011; Shrestha et al., 2012b; Stantec, 2012). Projected changes in future annual runoff are mixed in Ontario (EBNFLO Environmental and AquaResource Inc., 2010; Grillakis et al., 2011), while in Quebec the majority of studies project increasing annual flows (Quilbe et al., 2008; Minville et al., 2008, 2010; Boyer et al., 2010; Chen et al., 2011; Guay et al., 2015). A Quebec study using several models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) found that mid-century (2041–2070) flows for southern rivers under both medium and high emission scenarios (RCP4.5 and RCP8.5) will be characterized by earlier and smaller spring peak flow and lower summer runoff. Annual mean flow is anticipated to increase in northern regions and decrease in the south (CEHQ, 2015). Annual streamflow is projected to increase for New Brunswick (El-Jabi et al., 2013) and Labrador (Roberts et al., 2012). In northwestern Canada, there is evidence that watersheds such as the Mackenzie and Yukon river basins will see an increase in annual flow, mainly due to the higher precipitation amounts projected at higher latitudes (e.g., Poitras et al., 2011; Thorne, 2011; Vetter et al., 2017).