High Flows and Freshet Timing in Canada: Observed Trends CCRR 42

High Flows and Freshet Timing in Canada: 42 Observed Trends CLIMATE CHANGE RESEARCH REPORT CCRR-42 Sustainability in a Changing Climate: An Overview of MNRF’s Climate Change Strategy (2011–2014)

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Nicholas E. Jones, Ian C. Petreman, and Bastian J. Schmidt

Aquatic Research and Monitoring Section Ontario Ministry of Natural Resources and Forestry

2015

Science and Research Branch • Ministry of Natural Resources and Forestry © 2015, Queen’s Printer for Ontario Printed in Ontario, Canada

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Cover photo: Spanish River, Spooner Collins

Cite this report as: Jones, N.E., I.C. Petreman and B.J. Schmidt. 2015. High Flows and Freshet Timng in Canada: Observed Trends. Ontario Ministry of Natural Resources and Forestry, Science and Research Branch, Peterborough, Ontario. Climate Change Research Report CCRR-42.

This paper contains recycled materials. i

Summary The frequency and timing of flood events strongly contributes to the fundamental nature of rivers. We examined trends in timing and spatial distribution of highflow events across Canada’s Reference Hydrometric Basin Network, with a specific focus on spring freshet events. Freshets were clearly defined within a 4 month window for 65 of 82 stations, and of these, 49 stations had more than 30 years of data and were used in subsequent freshet trends analyses. Rivers at progressively higher latitudes had fewer highflow events throughout the year, especially in winter and there was a single well-defined freshet month. Along the southern border of Canada, particularly in the Atlantic provinces, Quebec, and Ontario, rivers could experience highflow events during any time of the year. Many rivers that had a large freshet timing window were located in the Atlantic provinces, coastal British Columbia, northern Saskatchewan and Alberta, and northwestern and southern Ontario. Thirty-seven stations (76%) had negative slopes, showing earlier freshet, of which 11 (30%) and 14 (38%) were significant at the 0.05 and 0.10 alpha level, respectively. Twelve stations (24%) had positive slopes, showing later freshet, but only 1 showed a significant trend at the 0.05 alpha level and 2 at the 0.10 alpha level. Sen’s slope estimates suggest that freshets for stations with significant negative trends are occurring earlier in spring at an average rate of 0.3 days per year. Earlier timing of freshets was readily apparent in the Atlantic provinces and Quebec. The trend for earlier freshets in British Columbia and Ontario was not yet statistically significant. Future warming will result in a major northward shift of the temperate zone and a new set of rivers that will be exposed to a shorter winter, shifts in the timing and frequency of highflow and lowflow events, and ice jamming. Depending on adaptive capacity, changes in stream flow may have potential impacts on aquatic organisms including shifts in distributions, asynchrony with important environmental cues, and changes in community interactions.

Rèsumè La fréquence des crues et le moment où elles se produisent contribuent fortement à la nature fondamentale des cours d’eau. Nous avons examiné les tendances concernant la répartition géographique des crues abondantes et le moment où elles se produisent dans l’ensemble du réseau hydrométrique de référence du Canada, en nous intéressant particulièrement aux crues nivales printanières. Celles-ci sont nettement circonscrites à une période de 4 mois pour 65 des 82 stations, et dans le cas de 49 de celles- ci, plus 30 années de données ont été utilisées pour effectuer des analyses ultérieures des tendances en ce qui concerne les crues nivales. Les cours d’eau se situant à des latitudes progressivement plus élevées connaissaient moins d’occurrences de haut débit au cours de l’année, particulièrement en hiver, et les crues nivales se produisaient au cours d’un unique mois bien délimité. Le long de la frontière sud du Canada, particulièrement dans les provinces de l’Atlantique, au Québec et en Ontario, les cours d’eau peuvent être en crue à n’importe quel moment de l’année. Il y a de nombreux cours d’eau où la période de crues nivales est longue dans les provinces de l’Atlantique, sur la côte de la Colombie-Britannique, en Saskatchewan et en Alberta, ainsi que dans le nord-ouest et le sud de l’Ontario. Trente-sept (76 %) des stations avaient une pente négative, les crues nivales y étant plus hâtives, dont 11 (30 %) et 14 (38 %) ayant un niveau de signification alpha de 0,05 et de 0,10 respectivement. Douze stations (24 %) avaient une pente positive, les crues nivales y étant plus tardives, mais le niveau de signification alpha était de 0,5 à une seule de ces stations et de 0,10 à deux. Des estimations de la pente de Sen semblent indiquer que les crues nivales pour les stations ayant des tendances négatives significatives se produisent plus tôt au printemps, à un rythme de 0,3 jours par an environ. L’arrivée hâtive des crues nivales était manifeste dans les provinces de l’Atlantique et au Québec. Par contre, la tendance à des crues nivales plus hâtives n’était pas encore statistiquement significative en Colombie-Britannique et en Ontario. Le réchauffement ii

futur aura pour conséquence un déplacement vers le nord de la zone tempérée, de nouveaux cours d’eaux étant exposés à des hivers plus courts, une modification de la fréquence des épisodes de haut et de faible débits et des moments où ils se produiront, et la formation d’embâcles. En fonction de leur capacité d’adaptation, ces modifications des débits des cours d’eaux pourraient avoir une incidence sur les organismes aquatiques, notamment des changements dans leur répartition, leur asynchronie avec les signaux environnementaux et des modifications des interactions au sein de la communauté.

Acknowledgements Funding for this project was provided by the Ontario Ministry of Natural Resources and Forestry’s Climate Change Program. Special thanks to Andrew Piggott and David Harvey at Environment Canada for discussions about freshet trend detection and the Reference Hydrometric Basin Network. iii

Contents

Summary...... i

Rèsumè...... i

Acknowledgements...... ii

Introduction...... 1

Methods ...... 2

Data extraction...... 2

Highflow events ...... 2

Freshet timing definitions...... 4

Detecting temporal trends in spring freshets...... 5

Results...... 5

Discussion...... 12

References...... 14

Appendicies...... 16 iv Climate Change Research Report CCRR-42 1

Introduction The frequency and timing of flood events strongly contributes to the fundamental nature of rivers and is a key determinant of freshwater biodiversity, life history characteristics, ecological traits of stream organisms, and physical processes in streams (Poff et al. 1997, Bunn and Arthington 2002). The natural flow regime is predicated on the idea that various patterns of flooding and drought (e.g., the magnitude, frequency and predictability of flow events) result in different degrees of physical control over biotic organization in streams (Poff et al. 1997). Floods can reconnect habitats such as isolated pools, side channels, and floodplains (Junk et al. 1989; King et al. 2003). Rising waters inundate neighbouring lands, renew soils, add valuable woody debris, and redistribute sediment creating riffles and pools. Floods can also lead to the death or displacement of stream organisms and the modification of habitat.

The freshet, the spring thaw resulting from snow and ice melt, is typically the most significant flood of the year both in magnitude and importance to aquatic biota and humans. Many fishes spawn as flows descend from the peak freshet flow. Concurrent with decreasing freshet flow is an increase in water temperature signaling the onset of spawning. The highflow conditions also provide an opportunity for fishes to migrate upstream over obstacles that prevent passage during low flow conditions. Of concern, however, is the break-up of ice and ice jams which can have serious ecological and socioeconomic impacts including flooding, damage to property and infrastructure, and interruption of hydroelectric production (Beltaos 2002).

In northern latitudes, the formation, duration, and break-up of river ice affects stream fish communities (Prowse 2001a, 2001b, Beltaos et al. 1993, Beltaos and Burrell 2003, Huusko et al. 2007). Empirical evidence and predictions suggest that many areas of Canada are warming which will consequently affect the timing and magnitude of freshet and winter thaw flows. Ice jams are expected to increase under a changing climate as a result of more frequent winter thaws, increases in the amount of winter rain, and higher winter flows (Beltaos 2002). The duration of ice cover is expected to decrease, with later freeze-up and earlier thaw. Spring break-up and the peak of freshet is occurring earlier in many parts of the world (Rannie 1983: 11 days per century (d/c), Beltaos 2002: 11–15 d/c in Canada, Zachrisson 1989: 19 d/c in Sweden, Soldatova 1993: up to 11 d/c, Hodgkins and Dudley 2006: 5–9 d/c in eastern North America).

Fishes have responded to earlier freshets and warmer temperatures by spawning earlier. Focusing on water temperature, Wedekind and Kung (2010) found that by 2009 the spawning season for grayling (Thymallus thymallus) was 3–4 weeks earlier than in the early 1960s. The shift in the timing of spawning was mirrored by temperature trends at a regional scale. However, they noted that despite shifts to earlier timing of spawning the spring water temperatures have been rising more slowly which could have impacts on critical sex determination and pathogen resistance life stages. Moreover, in summer the increasingly warmer temperatures may reach stressful levels for grayling fry (Wedekind and Kung 2010). Quinn and Adams (1996) noted that American shad (Alosa sapidissima) and sockeye salmon (Oncorhynchus nerka) migrate up the Columbia River approximately 38 d and 6 d earlier, respectively, than they did in 1940s. They remarked that the differences between the responses of the 2 species stem from differences in migration patterns. The shad spawn shortly after entering the river and their eggs hatch after only 3–8 days. In turn, there is a tighter connection between environmental conditions experienced by adults and young which strongly affect larval survival and thus allow greater behavioral response to a changing climate. Sockeye salmon on the other hand, spawn much further upstream and many days after entering the river. The salmon migration is strongly controlled by photoperiod, migrating consistently at a time which is typically best because environmental conditions when entering the river, will not be indicative of conditions emergent embryos will experience months later (Quinn and Adams 1996). The life history events of many fishes correspond to photoperiod and will likely become desynchronized with flow and temperature changes in rivers (Graham and Harrod 2009, Turner et al. 2010, Shuter et al. 2012). Such asynchrony 2 Climate Change Research Report CCRR-42

commonly results in increases in egg and larval mortality and changes in community interactions. Northern fish communities are at risk because their physiological and behavioural traits have been shaped by the harsh environmental conditions that are likely to change (Shuter et al. 2012). Throughout much of the world the climatic gradient from the equator to the poles of progressively shorter summers and longer winters is accompanied by predictable changes in population characteristics of many freshwater fish species (e.g., spawning time, growth, size and age at reproduction, lifespan; e.g., Zhao et al. 2008, McDermid et al. 2010). How fishes and other aquatic biota respond to changes in climate, temperature, and freshet timing is not clear and will depend on species-specific tolerances, adaptive capacity, and phenotypic plasticity (Crozier et al. 2011). Biota will respond either directly to climate related shifts in environmental conditions or indirectly to changes that are brought on through community-level interactions with other taxa e.g., prey suppression and release. The ability of biota to adapt to our future climate will vary among species (Graham and Harrod 2009).

To understand trends in freshet timing we examined flow data from Environment Canada’s Reference Hydrometric Basin Network (RHBN). The RHBN is a subset of the national network that has been identified for use in the detection, monitoring, and assessment of climate change (Harvey et al. 1999). Our main objectives were to determine the timing of highflow events, including the annual freshet, and examine the spatial and temporal patterns of these events across Canada. This analysis includes 14 years of additional flow data since Zhang et al. (2001) was published. We identified highflow events as the date of greatest flow magnitude between 2 troughs of lower flow, where events had to be above a given peak-to-trough magnitude ratio, and include rising and falling rates of change above specified thresholds. We determined which rivers had highflow events in 7–12 consecutive months of the year and identified the largest events (i.e., peak of freshet and dates) within a freshet timing window for each station and then determined if there were trends in the dates of the peak of freshet. Methods

Data extraction Historical streamflow data were queried from the Government of Canada’s HYDAT database (accessed online October 2013) up to and including 2010 data. The RHBN hydrometric stations were shortlisted to 82 stations by removing spatially redundant stations, stations with less than 1000 km2 drainage area, and any station which contained fewer than twenty contiguous calendar years of daily discharge data. Fifty-five percent of the streamflow stations have more than 40 years of records (up to a maximum of 99 years), while the average record length is 46 years. Occurrences of February 29 (leap-year days) were deleted from the dataset during this step to facilitate analysis.

Highflow events Extracted data were imported to Streamflow Analysis and Assessment Software (SAAS v4, 2014, Metcalfe and Schmidt 2014) to calculate highflow events (HFEs) based on comparisons of sequential streamflow time series measurements (e.g., daily average flow). HFE peak dates are defined as the day of greatest flow between 2 neighbouring flow troughs (e.g., Station 01AD002, Figure 1A). Given this simple definition of a highflow event, an HFE can theoretically occur as often as every other day if the daily discharge increases and decreases regularly. By default, all flow reversals (i.e., days where flow is higher than 2 neighbours) are identified as peaks. As such, SAAS includes a filter criterion that allows the user to specify a relative minimum adjacent peak magnitude referred to here as the relative peak filter (Appendix 1). The relative peak filter excludes any peaks which are less than the specified factor times the magnitude of its adjacent troughs (Figure 1B). By adjusting the relative peak filter settings, minor fluctuations in daily Climate Change Research Report CCRR-42 3

Figure 1. Example single-year hydrograph and highflow event (HFE) definition calculation. Station 01AD002 Saint John River at Fort Kent, 1954’s daily average discharge is shown. A — raw hydrograph with all discharge peaks and troughs highlighted. B — 25% of peaks remain after filtering out all peaks which are not double the magnitude of their adjacent troughs. C — highflow event start and end dates are moved closer to their peaks by specifying minimum rising and falling rates of change equal to the 30th percentile of the record’s positive and negative rate of change exceedance curves (1.71 m3s-1hr-1 and -0.875 m3s-1hr-1 respectively). 4 Climate Change Research Report CCRR-42

flows were prevented from being identified as HFEs. The optimal value of the relative peak filter varies among streams with different flow regimes making it inappropriate for a single universal relative peak filter value to be applied for every station. The flow regime classes developed by Jones et al. (2014) were used to guide the selection of peak filter values such that values were consistent within classes. In many cases the same stations used by Jones et al. (2014) were used in the freshet assessment. Care was taken to select a relative peak filter value for each flow regime class that would result in at least 1 HFE per year of the record as well as any additional HFEs which may be biologically significant to fishes.

By default, an HFE end date will occur on the same date that the next HFE begins; as their positions coincide with the trough between 2 peaks. SAAS can apply a minimum rising and falling flow rates of change filter for improved HFE start and end date placement, respectively (Figure 1C). These SAAS settings will be referred to as rate filters herein (Appendix 1). HFE start and end dates were universally defined using the 30% exceedance values of the rising positive and negative falling rate of change duration curves created using the rates of all rising and falling event limbs throughout the period of record, respectively. In implementing the rate filters, HFE start and end date placement was sometimes confounded by short but sharp rising and falling rates not associated with an HFE; this can occur even during prolonged winter low flow periods. Visual inspection of the results showed that without further adjustments start/end dates of events could be placed a month or more before or after their true dates, but still satisfying the HFE start date definition of the rate filters. To counteract this effect, SAAS was configured to ignore these irrelevant rates of change in magnitude by applying a universal threshold below which the rate filters simply ignore trivial rates of flow change. This threshold was set to the 70% exceedance for each station’s period of record flow duration curve (Appendix 1). The magnitude and timing of events as defined by SAAS using all 3 filters were used for further analyses.

Freshet timing definitions All highflow event frequencies were binned by month of occurrence of the peaks. For each station, the monthly frequency bins were then plotted on a map of Canada using a radio bar chart. In addition, the HFE frequencies of greatest annual magnitude were superimposed on the radio bar chart. Spring freshet months were identified for each station where the annual maximum flow occurred during a window of 4 or less months (Appendix 2). For example, station 04GB004 (Ogoki River above Whiteclay Lake in northern Ontario) has HFEs occurring 7 months of the year; however, it consistently has its annual maximum flow date occurring between May and July. These months agree with the snowmelt period for the region. As a second example, station 08MG005 (Lillooet River in the central Canadian Rocky Mountains) shows 8 contiguous months each with many HFEs. It consistently has its annual maximum flow date occurring over a period of 7 months (i.e., April to October). Consequently the spring freshet for this station cannot be clearly defined using this methodology and this station was removed from further freshet timing trend analyses. The HFE peak dates of annual maxima for each station were used in the following analyses. Freshet peak dates (day of year) for each year were closely examined to find incorrect dates. We visually assessed 5% of the freshet dates (random 140 spot checks of 2876 dates) for accuracy. We also plotted all the trends to identify and assess potential outliers that would weigh heavily on the trend analyses. These outliers were then manually assessed via hydrographs to confirm or adjust the freshet dates.

Detecting temporal trends in spring freshets We used the Mann-Kendall nonparametric trend test to assess the statistical significance of possible monotonic trends in the spring freshet timing (Mann 1945, Kendall 1973). The Mann-Kendall test is known

for its disproportionate rejection of the null hypothesis (H0 = no trend) when the observed data are serially correlated (Yue et al. 2002). To this effect, the serial correlation was explored; 11 of 65 time series were Climate Change Research Report CCRR-42 5

found to be lag-1 significantly, serially correlated (α = 0.05). The mean r value of the statistically significant correlations was 0.38 (range 0.26 – 0.47, s = 0.08). A practice to reduce a serial correlation’s false detection of trend rate (type 1 error) is to employ a prewhitening procedure to the data before employing the Mann-Kendall test. Prewhitening often results in reduction of power in the Mann-Kendall test. Bayazit and Önöz (2007) explored the combinations of the parameters where prewhitening causes a real loss of power with a negligible increase in type 1 error (e.g., sample size, lag-1 autocorrelation coefficient, coefficient of variation, and linear slope). Given our estimates of these parameters, it was determined not to prewhiten because it would cause a loss of statistical power and that the serial correlation (where present), would have a negligible effect on the rejection rate of the Mann-Kendall tests while spuriously altering the magnitude of trend (Appendix 3). We employed Sen’s slope estimation to determine the linear slopes and intercepts of the spring freshet time series. Matlab was used to perform the Mann-Kendall and Sen’s slope analyses. Results There was a strong spatial pattern in which stations had high flow during the months of the year (Figure 2). Along the southern border of Canada, particularly in the Atlantic provinces, Quebec, and Ontario, rivers experience HFEs during any time of the year. Rivers at progressively higher latitudes had progressively fewer HFEs throughout the year, especially in winter.

All HFEs are spatially summarized as radial graphs that span 12 months of the year (Figure 3). Many of the rivers with a large freshet timing window are located in the Atlantic provinces, coastal British Columbia, northern Saskatchewan and Alberta, and northwestern and southern Ontario. Freshets occurred within a window of 4 months or less in 65 of 82 stations (80%) and were used in subsequent analyses. We also increased the number of years of data required from 20 to 30 years or greater leaving a total of 49 stations for freshet trend analyses. High latitude rivers (e.g., Yukon, Northwest Territories, Nunavut) typically had a single well-defined freshet month with few other HFEs occurring outside this period (Figure 3). The number of months with HFEs outside the typical freshet window increased in a southerly direction, particularly in the Atlantic provinces (Figure 3). The data quality checking of freshet peak dates (day of year) identified 54 station-years that were potentially incorrect out of 2876. Of the 54 potentially incorrect station-years, 25 required new dates which were obtained by manually checking station hydrographs.

Trends in the timing of freshets for each river were examined and were found well within the better- to-not-prewhiten zone sensu Bayazit and Önöz (2007). This was especially true for the large magnitude trends which were observed (Appendix 3). Each combination of parameter values of n ( ¯x = 43, range 12–99, s = 20); r ( ¯x = 0.171, range 0.001–0.47, s = 0.139); CV ( ¯x = 0.10, range 0.045–0.265, s = 0.037); and b ( ¯x = -0.47, range -0.21– -1, s = 0.17) were within the calculated zones for better to not prewhiten posited by Bayazit and Önöz (2007).

Thirty-seven stations (76%) had negative slopes of which 11 (30%) and 14 (38%) were significant at the 0.05 and 0.10 alpha level, respectively (Table 1). The average slope was -0.21 for significant and non-significant negatively sloping relationships. The average slope for stations with statistically significant negative trends was higher (b = -0.33 for stations significant at an α = 0.05; -0.31, for stations significant at an α = 0.10). Twelve (24%) had positive slopes (average slope = 0.26) but only 1 showed a significant trend at α = 0.05 and 2 at α = 0.10. Five stations had a slope of 0. Sen’s slope estimates of the statistically significant negative trends suggest that these stations’ freshets are occurring earlier in spring at an average rate of 0.33 days per year (maximum = 0.58, minimum = 0.14 days per year; s = 0.17). Earlier timing of freshets was readily apparent in the Atlantic provinces and Quebec (Figure 4). British Columbia and Ontario showed negative trends, though not yet significant. Several stations with steeper slopes 6 Climate Change Research Report CCRR-42

Figure 2. Annual timing of highflow events (HFE) for rivers in the Reference Hydrometric Basin Network. Yellow station markers indicate stations that can have a highflow event occur during any month of the year. Grey markers indicate stations that are free of highflow events during 1–7 months of the year. Climate Change Research Report CCRR-42 7

Figure 3. Not all highflow events occur during the spring freshet period. The timing of highflow events summarized as radial graphs that span 12 months of the year for all 82 flow stations examined in the Reference Hydrometric Basin Network. Each station’s highflow events are binned by month, where the limits of the radial axis are always equal to the highest highflow count of all months for grey wedges, and separately, the highest annual maxima count of all months for black wedges. For rivers which have 1 highflow event per year (i.e., always the annual maximum), no grey wedges are visible as there is perfect overlap between grey and black wedges. Rivers that have graphs with bold black outlines are where the maximum annual flows occur in at least 4 different months of the year. 8 Climate Change Research Report CCRR-42

Table 1. Mann-Kendall statistics for 49 flow stations used to assess trends in the timing of spring freshet. Significance at α = 0.1 are indicated in bold.

Station ID tau-b MKp MKslope n 01AD002 -0.19 0.0135 -0.13 84 01BC001 -0.36 0.0003 -0.40 48 01BE001 -0.21 0.0139 -0.18 66 01BP001 -0.29 0.0012 -0.42 61 02BF002 -0.16 0.1219 -0.25 45 02EC002 0.01 0.8617 0.00 96 02JC008 -0.17 0.1425 -0.22 37 02KB001 -0.26 0.0002 -0.19 94 02NE011 -0.07 0.5916 -0.10 32 02NF003 -0.09 0.3357 -0.12 56 02QA002 -0.33 0.0079 -0.58 33 02RF001 -0.18 0.1315 -0.22 37 02VC001 -0.35 0.0018 -0.42 39 02YL001 -0.20 0.0283 -0.24 59 03MB002 -0.25 0.0453 -0.46 33 03QC001 -0.03 0.8302 -0.06 30 03QC002 -0.10 0.4143 -0.14 34 04DA001 0.09 0.4216 0.11 42 04JC002 -0.12 0.1905 -0.12 60 04KA001 -0.04 0.7436 -0.03 34 04LJ001 -0.14 0.0596 -0.08 90 04NA001 0.00 0.9955 0.00 65 05AA023 0.10 0.2828 0.12 59 05BB001 0.00 1.0000 0.00 100 05DA009 -0.08 0.4951 -0.18 35 05LH005 -0.16 0.0957 -0.32 51 06CD002 0.25 0.0285 0.70 39 07AA002 0.08 0.4647 0.13 41 07EC002 0.01 0.9348 0.00 36 07FB001 -0.09 0.3781 -0.23 46 07LE002 -0.02 0.8864 -0.13 30 Climate Change Research Report CCRR-42 9

Table 1. Continued.

Station ID tau-b MKp MKslope n 08FB006 -0.13 0.2712 -0.16 37 08JE001 -0.20 0.0258 -0.14 60 08LA001 -0.09 0.3243 -0.11 57 08LD001 -0.04 0.7367 -0.04 42 08MA002 -0.14 0.2041 -0.33 41 08MB006 0.05 0.6850 0.11 37 08NB005 -0.07 0.4595 -0.12 51 08ND013 -0.02 0.8379 -0.04 48 08NL007 0.00 0.9779 0.00 66 09AA006 0.07 0.4960 0.11 43 09AC001 -0.26 0.0129 -0.50 45 09BC001 -0.14 0.1593 -0.14 51 10BE004 -0.06 0.5847 -0.14 44 10CB001 -0.11 0.2994 -0.17 47 10CD001 0.18 0.0899 0.53 46 10EB001 -0.09 0.4133 -0.16 38 10PB001 -0.14 0.2495 -0.25 35 10RC001 -0.23 0.0711 -0.25 32 10 Climate Change Research Report CCRR-42

(larger triangles) are significant at the 0.10 level (Figure 4). Waterhen River near Waterhen, Manitoba (05LH005) has a non-significant negative slope (p = 0.096, slope = -0.32). Muskwa River near Fort Nelson British Columbia (10CD001) showed a positive trend that was significant at the 0.10 level (p = 0.090). Churchill River above Otter Rapids (06CD002) in Saskatchewan showed a strong 0.53 days per year significant positive trend (p = 0.028). There are few stations to report on in northern Quebec, Manitoba, Saskatchewan, and Alberta. Figure 5 shows 3 examples of significant trends from across Canada.

Figure 4. Changes in freshet timing as calculated in temporal Mann-Kendall trend analysis for 49 flow stations examined in the Reference Hydrometric Basin Network. Black triangles indicates statistical significance at a α = 0.05. Climate Change Research Report CCRR-42 11

Figure 5. Examples of significant negative trends in the timing of freshet from across Canada: (A) Saint John River at Fort Kent, New Brunswick 01AD002, (B) Petawawa River near Petawawa, Ontario 02KB001, and (C) Stuart River near Fort St. James, British Columbia 08JE001. 12 Climate Change Research Report CCRR-42

Discussion We examined the timing and spatial distribution of HFEs across Canada’s Reference Hydrometric Basin Network. Climate plays a significant role in the timing and frequency of high flows and freshet flows as influenced by latitude, elevation, and the climate regions of Canada (e.g., Atlantic Canada, prairies). We found that spring freshets are occurring earlier in many rivers across Canada. This finding is in agreement with the research of others from Canada and abroad (Rannie 1983; Zachrisson 1989; Soldatova 1993; Beltaos 2002, Hodgkins and Dudley 2006). Using just stations showing significant negative slopes, freshets are occurring earlier in spring at an average rate of 0.33 days per year or 33 d/c. This value is considerably larger than that reported by others including Rannie (1983) 11 d/c; Beltaos (2002) 11–15 d/c; Zachrisson (1989) 19 d/c; Soldatova (1993) up to 11 d/c; Hodgkins and Dudley (2006) 6 d/c. The inconsistencies may stem from differences in the period of record used in our analysis which includes data from the last 20 years which is considered to be the warmest years on record. In addition, if we recalculate the rate of change using all river stations including those with positive and non-significant change, the spring freshets occurs 0.21 days per year or 21 d/c earlier. This rate of change is more consistent with an estimate of 14– 28 days per century earlier made by Prowse et al. (2002) and Zhang et al. (2001) for the start of freshet.

Trends in flow data were challenging to detect because there is often much variability in the annual date of freshet (Figure 5). Average range in the date of freshet for the 49 flow stations was 62 days with a coefficient of variation of 9%. The ability to detect change increases with larger samples sizes (e.g., n = 75 years +) and larger slopes indicating change and a trend. Using longer periods of record will be important in assessing change in freshet timing and likely other aspects of flow regime. Our analysis found a greater proportion of stations (76%) showing negative trends (significant and non-significant) which may also be used as evidence of earlier freshet timing.

Using water temperature data in combination with flow data may be useful in pin-pointing the timing of freshet maxima because water temperatures appear to rise quickly once freshet flows begin to decrease. This may be particularly useful in southern latitudes where determining the timing of freshets is difficult because winter high flows are common.

Generally, rivers at lower latitudes in Canada have HFEs during the winter months. As climate change progresses we can expect that rivers in more northerly latitudes will also start to have mid-winter thaws and shorter winter ice periods. Such changes may also lead to more frequent ice jam and scour events that could be of socioeconomic and ecological concern (Beltaos 2002). In turn, rivers at lower latitudes may not freeze at all or for only brief periods — taking on hydrothermal regimes of rivers in the United States (e.g., West Virginia).

There are only 8 RHBN stations in Ontario and 2 in the Great Lakes Basin, Goulais River near Searchmont (02BF002) and Black River near Washago (02EC002). The Goulais had a non-significant trend for earlier freshets. The Black River showed no trend likely because it is close to southern Ontario where high flows can occur over a wide range of dates in the spring making trend detection more difficult. There is a third station if you include the Petawawa River near Petawawa (02KB001) draining into the Ottawa River. This river showed a significant shift to earlier freshets.

Future warming will result in a major northward shift of the temperate zone and a new set of rivers that will be exposed to a shorter winter, shifts in the timing and frequency of HFEs, and ice jamming (Prowse et al. 2002). Recent data analyses suggest that many fishes are now more likely to occur in lakes where climate was once limiting. The northern range limits of warm and coolwater fishes have shifted significantly northward over nearly 30 years at a rate of approximately 13–18 km per decade (Alofs et al. 2013). Similar shifts in the distribution of aquatic biota have been shown by others (e.g., Chu et al. 2005, Climate Change Research Report CCRR-42 13

Isaak and Rieman 2013); however, these studies mainly focus on changes in thermal regime or more strictly temperature. Research on climate change impact on freshwater species has focused mainly on temperature, overlooking important drivers such as flow regime (Jager et al. 1999; Wenger et al. 2011). In the western United States, Wenger et al. (2011) noted that fall-spawning brook trout Salvelinus fontinalis and brown trout Salmo trutta showed a strong negative relationship with high flow frequency in the winter – likely due to redd (fish nest) scour. Jager et al. (1999) modelled the ecological responses to climate change as a shift in peak flows from spring to winter and an increase in stream temperature in Sierra Nevada streams. They noted that while scouring mortality did occur under the new flow regime, the seasonal shift in flow also reduced dewatering of redds, perhaps compensating for scour. They also found that changes in water temperature meant losses of thermal habitat in lower (warmer) reaches but gains in upper (colder) reaches. Interestingly, they stated that the combination of flow and thermal changes produced threshold and non-additive effects in rainbow trout abundance. They concluded that concentrating on 1 factor alone (e.g., temperature) may not be adequate to predict climate change effects in rivers.

The temporal patterns of flow and thermal regimes are strongly linked. Changes in air temperature lead to changes in ice dynamics and the thermal habitat of aquatic biota. Future research should explore the consequences of earlier freshets and increases in the frequency of HFEs on the ecology of biota and how these changes impact other potentially stressful periods in a river including the low flow summer periods when water temperatures are typically highest and access to cold tributaries might be limited (Jones and Petreman 2012). How fishes and other aquatic biota respond to changes in climate, flow, and temperature is not clear and will depend on species-specific thermal tolerances, adaptive capacity, and phenotypic plasticity (Crozier et al. 2011). Aquatic biota will respond either directly to shifts in climatic conditions or indirectly to changes brought on through community-level interactions with other taxa (fundamental vs. realized niches, Wenger et al. 2011). The ability to adapt to our future climate will vary among species; there will be both winners and losers (Graham and Harrod 2009). Understanding the temporal and spatial patterns of these gains and losses will be pivotal in guiding how governments and other conservation organizations respond in adapting to climate change. 14 Climate Change Research Report CCRR-42

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Appendicies Appendix 1. SAAS settings by flow station used in the calculation of highflow events (HFEs). Criteria 2a and 2b are the calculated 30 % exceedance values of the positive and negative daily average rate of change duration curves. Flow class from (Jones et al. 2014). HFE criterion HFE criterion HFE criterion Station Number Flow HFE 2a 2b 3 Class criterion 1 (m3s-1d-1) (m3s-1d-1) (m3s-1) 01BC001 0 2.2 0.400 -0.160 21.1 01BE001 0 2.2 0.223 -0.100 11.4 01BP001 0 2.2 0.292 -0.113 11.2 01EF001 0 2.2 0.296 -0.142 11.4 02BF002 0 2.2 0.120 -0.040 5.65 02EC002 0 2.2 0.092 -0.058 6.09 02JC008 0 2.2 0.083 -0.042 7.4 02LH004 0 2.2 0.063 -0.029 8.25 02NF003 0 2.2 0.130 -0.050 11 02YL001 0 2.2 0.750 -0.329 26.1 02YQ001 0 2.2 0.450 -0.292 54.7 03NF001 0 2.2 0.750 -0.170 30.65 03QC002 0 2.2 0.500 -0.142 12.4 04LJ001 0 2.2 0.438 -0.188 23 05DA009 0 2.2 0.246 -0.146 6.65 07AA002 0 2.2 0.417 -0.250 14 07EC002 0 2.2 0.288 -0.140 19.7 07FB001 0 2.2 0.625 -0.500 46 08CD001 0 2.2 0.100 -0.100 9.17 08CG001 0 2.2 2.830 -1.670 99.6 08MG005 0 2.2 0.583 -0.500 35.4 08ND013 0 2.2 0.279 -0.179 11.9 10BE004 0 2.2 0.096 -0.083 10.2 10CB001 0 2.2 0.058 -0.058 5.9 10CD001 0 2.2 0.625 -0.542 29.8 10EB001 0 2.2 0.830 -0.460 42.9 10GB006 0 2.2 0.210 -0.130 5.8 10RC001 0 2.2 3.500 -0.375 43.6 01AP004 1 2.4 0.350 -0.129 7.5 01EO001 1 2.4 0.742 -0.283 13.3 02AA001 1 2.4 0.083 -0.046 3.94 02QA002 1 2.4 0.167 -0.075 7.96 04KA001 1 2.4 0.199 -0.088 5.7 02KB001 2 2.4 0.096 -0.079 20.7 02RF001 2 2.4 1.000 -0.542 127 02UC002 2 2.4 1.625 -0.667 153 Climate Change Research Report CCRR-42 17

Appendix 1. Continued.

HFE criterion HFE criterion HFE criterion Station Number Flow HFE 2a 2b 3 Class criterion 1 (m3s-1d-1) (m3s-1d-1) (m3s-1) 02VC001 2 2.4 0.92 -0.500 105 02ZF001 2 2.4 0.25 -0.100 23 03MB002 2 2.4 1.667 -0.500 102 03QC001 2 2.4 0.920 -0.330 63.5 04DA001 2 2.4 0.170 -0.063 19 04JC002 2 2.4 0.075 -0.038 7.8 05BB001 2 2.4 0.092 -0.083 15 08FB006 2 2.4 0.067 -0.038 15 08LA001 2 2.4 0.420 -0.333 59.2 08NB005 2 2.4 0.290 -0.290 42.2 09AC001 2 2.4 0.0833 -0.079 14 09AE003 2 2.4 0.140 -0.075 13.2 09BC001 2 2.4 1.040 -0.583 79.3 10NC001 2 2.4 0.250 -0.125 18.75 07FC003 3 1.8 0.019 -0.100 0.025 07GG001 3 1.8 0.012 -0.017 0.56 10FA002 3 1.8 0.117 -0.083 3.41 10QD001 3 1.8 0.750 -0.292 0 02FC001 4 2.5 0.375 -0.238 20.7 04GB004 5 1.8 0.133 -0.080 56.9 04NA001 5 1.8 0.120 -0.085 28.9 05PB014 5 1.8 0.080 -0.046 17.2 06BD001 5 1.8 0.025 -0.020 9.57 06DA004 5 1.8 0.046 -0.0335 31.7 06KC003* 5 1.7* 0.460 -0.125 274 06LC001 5 1.8 0.420 -0.330 144 07CD001 5 1.8 0.130 -0.130 62.3 08JE001 5 1.8 0.170 -0.080 64.3 08LD001 5 1.8 0.083 -0.058 26.1 08MA002 5 1.8 0.060 -0.050 13.3 09AA006 5 1.8 0.080 -0.046 50.07 10PB001 5 1.8 3.000 -1.000 52.3 05AA023 6 2 0.030 -0.029 3.04 08MB006 6 2 0.025 -0.020 1.01 08NL007 6 2 0.083 -0.054 4.53 05SA002 7 2.6 0.008 -0.009 0.12 07OB001 7 2.6 0.208 -0.167 7.79 03FA003 8 1.2 0.083 -0.050 117 18 Climate Change Research Report CCRR-42

Appendix 1. Continued.

HFE criterion HFE criterion HFE criterion Station Number Flow HFE 2a 2b 3 Class criterion 1 (m3s-1d-1) (m3s-1d-1) (m3s-1) 04GA002 8 1.2 0.038 -0.020 27.4 05TD001* 8 1.4* 0.083 -0.046 45.1 07LE002 8 1.2 0.167 -0.125 241 07RD001 8 1.2 0.083 -0.042 101 01AD002 9 2.7 1.708 -0.875 80.1 02NE011 9 2.7 0.179 -0.083 10.8 05LH005 10 1.25 0.250 -0.170 32.9 06CD002 10 1.25 0.125 -0.083 194 Climate Change Research Report CCRR-42 19

Appendix 2. Freshet timing windows by station. Stations with more than 4 months in which highflow events occur were excluded from the subsequent freshet trend analyses.

Station Number Accepted Freshet Window Accepted Freshet Window # of adjacent greatest Start Month End Month magnitude HFE frequency months 01AD002 March May 3 01AP004 5 01BC001 March May 3 01BE001 March May 3 01BP001 March May 3 01EF001 9 01EO001 10 02AA001 6 02BF002 March June 4 02EC002 February April 3 02FC001 5 02JC008 March May 3 02KB001 March May 3 02LH004 March May 3 02NE011 April May 2 02NF003 March May 3 02QA002 April May 2 02RF001 April May 2 02UC002 April June 3 02VC001 May June 2 02YL001 May July 3 02YQ001 6 02ZF001 7 03FA003 June July 2 03MB002 May June 2 03NF001 May June 2 03QC001 May June 2 03QC002 April June 3 04DA001 April July 4 04GA002 May July 3 04GB004 May July 3 04JC002 April July 4 04KA001 March May 3 04LJ001 March June 4 04NA001 April May 2 05AA023 April June 3 05BB001 May July 3 20 Climate Change Research Report CCRR-42

Appendix 2. Continued.

Station Number Accepted Freshet Window Accepted Freshet Window # of adjacent greatest Start Month End Month magnitude HFE frequency months 05DA009 June August 3 05LH005 April July 4 05PB014 7 05SA002 March June 4 05TD001 June July 2 06BD001 6 06CD002 May August 4 06DA004 5 06KC003 June July 2 06LC001 June July 2 07AA002 May July 3 07CD001 6 07EC002 May June 2 07FB001 May July 3 07FC003 6 07GG001 5 07LE002 May July 3 07OB001 5 07RD001 August October 3 08CD001 May June 2 08CG001 6 08FB006 May June 2 08JE001 June July 2 08LA001 May June 2 08LD001 May July 3 08MA002 June August 3 08MB006 May July 3 08MG005 7 08NB005 May July 3 08ND013 May July 3 08NL007 May June 2 09AA006 August September 2 09AC001 June August 3 09AE003 May June 2 09BC001 May June 2 10BE004 May July 3 10CB001 May August 4 Climate Change Research Report CCRR-42 21

Appendix 2. Continued.

Station Number Accepted Freshet Window Accepted Freshet Window # of adjacent greatest Start Month End Month magnitude HFE frequency months 10CD001 May August 4 10EB001 May July 3 10FA002 6 10GB006 April May 2 10NC001 May June 2 10PB001 July September 3 10QD001 May June 2 10RC001 June July 2 22 Climate Change Research Report CCRR-42

Appendix 3. Parameters relevant to prewhitening and resultant Mann-Kendall test. r represents the lag-1 serial correlation coefficient. High n (≥ 50) and high slope (|b| ≥ 0.01) and low CV (≤ 0.20) are situations where prewhitening is undesirable. MK-p represents the p-value of the resulting Mann-Kendall test.

Station Number n r CV b MK-p 01AD002 84 *0.449 0.134 -0.385 <0.001 01BC001 48 *0.299 0.110 -0.650 <0.001 01BE001 66 0.190 0.118 -0.421 <0.001 01BP001 61 0.044 0.147 -0.653 <0.001 02BF002 75 *0.357 0.174 -0.614 <0.001 02EC002 45 0.005 0.174 -0.213 <0.001 02JC008 96 0.273 0.113 -0.458 0.007 02KB001 96 0.135 0.162 -0.438 <0.001 02LH004 37 0.009 0.129 0.000 0.888 02NE011 94 0.102 0.087 -0.333 0.153 02NF003 25 0.077 0.118 -0.143 0.183 02QA002 32 0.255 0.105 -0.818 0.001 02RF001 56 0.085 0.077 -0.500 0.002 02UC002 33 *0.457 0.090 -1.000 0.003 02VC001 37 *0.468 0.076 -0.667 <0.001 02YL001 24 0.030 0.109 -0.296 0.008 03FA003 40 0.374 0.062 0.250 0.762 03MB002 59 0.361 0.068 -0.041 0.820 03NF001 18 0.028 0.089 -0.168 0.730 03QC001 33 0.344 0.116 1.000 0.098 03QC002 29 0.071 0.080 -0.389 0.054 04DA001 30 0.085 0.152 -0.146 0.397 04GA002 34 0.116 0.093 0.333 0.672 04GB004 42 0.152 0.126 0.183 0.516 04JC002 22 0.028 0.148 -0.282 0.007 04KA001 20 0.171 0.091 -0.286 0.077 04LJ001 61 *0.386 0.129 -0.375 <0.001 04NA001 34 0.092 0.093 -0.250 0.001 05AA023 90 0.013 0.105 -0.128 0.216 05BB001 65 *0.271 0.094 -0.235 <0.001 05DA009 59 0.324 0.097 -0.429 0.147 05LH005 99 0.177 0.151 -0.250 0.287 05SA002 35 0.143 0.265 -0.786 0.211 05TD001 51 0.032 0.066 0.765 0.092 06CD002 22 0.024 0.134 0.333 0.164 06KC003 31 0.019 0.059 -0.188 0.673 06LC001 41 0.073 0.071 0.513 0.172 07AA002 25 0.001 0.091 -0.110 0.637 Climate Change Research Report CCRR-42 23

Appendix 3. Continued.

Station Number n r CV b MK-p 07EC002 24 0.133 0.067 -0.233 0.117 07FB001 41 0.035 0.142 -0.457 0.035 07LE002 36 0.081 0.114 -0.400 0.475 07RD001 46 0.069 0.096 -0.425 0.465 08CD001 30 0.070 0.067 -0.191 0.467 08FB006 43 0.224 0.076 -0.440 0.009 08JE001 28 *0.466 0.061 -0.400 <0.001 08LA001 25 *0.263 0.085 -0.350 0.001 08LD001 37 *0.343 0.080 -0.304 0.101 08MA002 60 0.276 0.100 -0.583 0.037 08MB006 58 0.129 0.114 -0.138 0.619 08NB005 42 0.135 0.088 -0.368 0.017 08ND013 41 0.244 0.113 -0.286 0.174 08NL007 37 0.047 0.089 -0.100 0.275 09AA006 51 0.026 0.052 -0.143 0.402 09AC001 48 0.217 0.078 -0.500 0.008 09AE003 66 0.199 0.064 -0.286 0.153 09BC001 43 *0.424 0.073 -0.400 <0.001 10BE004 45 0.004 0.099 -0.393 0.085 10CB001 31 0.041 0.118 -0.417 0.019 10CD001 51 0.204 0.144 0.273 0.389 10EB001 44 0.098 0.094 -0.400 0.049 10GB006 47 0.045 0.059 -0.286 0.419 10NC001 38 0.272 0.063 -0.500 0.052 10PB001 40 0.170 0.088 -0.375 0.118 10QD001 20 0.026 0.045 -0.463 0.044 10RC001 26 0.340 0.046 -0.500 0.001 *significant lag-1 serial correlation detected (α=0.05) Climate Change Report Series

CCRR-01 Wotton, M., K. Logan and R. McAlpine. 2005. Climate Earthworms in Ontario’s Forested Ecosystems: A Preliminary Change and the Future Fire Environment in Ontario: Fire Occurrence Vulnerability Analysis. and Fire Management Impacts in Ontario Under a Changing Climate. CCRR-24 Lalonde, R., J. Gleeson, P.A. Gray, A. Douglas, C. CCRR-02 Boivin, J., J.-N. Candau, J. Chen, S. Colombo and M. Blakemore and L. Ferguson. 2012. Climate Change Vulnerability Ter-Mikaelian. 2005. The Ontario Ministry of Natural Resources Large- Assessment and Adaptation Options for Ontario’s Clay Belt – A Case Scale Forest Carbon Project: A Summary. Study. CCRR-03 Colombo, S.J., W.C. Parker, N. Luckai, Q. Dang and CCRR-25 Bowman, J. and C. Sadowski. 2012. Vulnerability of T. Cai. 2005. The Effects of Forest Management on Carbon Storage in Furbearers in the Clay Belt to Climate Change. Ontario’s Forests. CCRR-26 Rempel, R.S. 2012. Effects of Climate Change on CCRR-04 Hunt, L.M. and J. Moore. 2006. The Potential Impacts Moose Populations: A Vulnerability Analysis for the Clay Belt Ecodistrict of Climate Change on Recreational Fishing in Northern Ontario. (3E-1) in Northeastern Ontario. CCRR-05 Colombo, S.J., D.W. McKenney, K.M. Lawrence and CCRR-27 Minns, C.K., B.J. Shuter and S. Fung. 2012. Regional P.A. Gray. 2007. Climate Change Projections for Ontario: Practical Projections of Climate Change Effects on Ice Cover and Open-Water Information for Policymakers and Planners. Duration for Ontario Lakes CCRR-06 Lemieux, C.J., D.J. Scott, P.A. Gray and R.G. Davis. CCRR-28 Lemieux, C.J., P. A. Gray, D.J. Scott, D.W. McKenney 2007. Climate Change and Ontario’s Provincial Parks: Towards an and S. MacFarlane. 2012. Climate Change and the Lake Simcoe Adaptation Strategy. Watershed: A Vulnerability Assessment of Natural Heritage Areas and Nature-Based Tourism. CCRR-07 Carter, T., W. Gunter, M. Lazorek and R. Craig. 2007. Geological Sequestration of Carbon Dioxide: A Technology Review and CCRR-29 Hunt, L.M. and B. Kolman. 2012. Selected Social Analysis of Opportunities in Ontario. Implications of Climate Change for Ontario’s Ecodistrict 3E-1 (The Clay Belt). CCRR-08 Browne, S.A. and L.M Hunt. 2007. Climate Change and Nature-based Tourism, Outdoor Recreation, and Forestry in CCRR-30 Chu, C. and F. Fischer. 2012. Climate Change Ontario: Potential Effects and Adaptation Strategies. Vulnerability Assessment for Aquatic Ecosystems in the Clay Belt Ecodistrict (3E-1) of Northeastern Ontario. CCRR-09 Varrin, R. J. Bowman and P.A. Gray. 2007. The Known and Potential Effects of Climate Change on Biodiversity in Ontario’s CCRR-31 Brinker, S. and C. Jones. 2012. The Vulnerability of Terrestrial Ecosystems: Case Studies and Recommendations for Provincially Rare Species (Species at Risk) to Climate Change in the Adaptation. Lake Simcoe Watershed, Ontario, Canada CCRR-11 Dove-Thompson, D. C. Lewis, P.A. Gray, C. Chu and CCRR-32 Parker, W.C., S. J. Colombo and M. Sharma. 2012. An W. Dunlop. 2011. A Summary of the Effects of Climate Change on Assessment of the Vulnerability of Forest Vegetation of Ontario’s Clay Ontario’s Aquatic Ecosystems. Belt (Ecodistrict 3E-1) to Climate Change. CCRR-12 Colombo, S.J. 2008. Ontario’s Forests and Forestry in CCRR-33 Chen, J, S.J. Colombo, and M.T. Ter-Mikaelian. 2013. a Changing Climate. Carbon Stocks and Flows From Harvest to Disposal in Harvested Wood Products from Ontario and Canada. CCRR-13 Candau, J.-N. and R. Fleming. 2008. Forecasting the Response to Climate Change of the Major Natural Biotic Disturbance CCRR-34 McLaughlin, J. and K. Webster. 2013. Effects of a Regime in Ontario’s Forests: The Spruce Budworm. Changing Climate on Peatlands in Permafrost Zones: A Literature Review and Application to Ontario’s Far North. CCRR-14 Minns, C.K., B.J. Shuter and J.L. McDermid. 2009. Regional Projections of Climate Change Effects on Ontario Lake Trout CCRR-35 Lafleur, B., N.J. Fenton and Y. Bergeron. 2013. The (Salvelinus namaycush) Populations. Potential Effects of Climate Change on the Growth and Development of Forested Peatlands in the Clay Belt (Ecodistrict 3E-1) of Northeastern CCRR-15 Subedi, N., M. Sharma, and J. Parton. 2009. An Ontario. Evaluation of Site Index Models for Young Black Spruce and Jack Pine Plantations in a Changing Climate. CCRR-36 Nituch, L. and J. Bowman. 2013. Community-Level Effects of Climate Change on Ontario’s Terrestrial Biodiversity. CCRR-16 McKenney, D.W., J.H. Pedlar, K. Lawrence, P.A. Gray, S.J. Colombo and W.J. Crins. 2010. Current and Projected Future CCRR-37 Douglas, A., C. Lemieux, G. Nielsen, P. Gray, V Climatic Conditions for Ecoregions and Selected Natural Heritage Anderson and S. MacRitchie. Responding to the Effects of Climate Areas in Ontario. Change in the Lake Simcoe Watershed: A Pilot Study to Inform Development of an Adaptation Strategy on a Watershed Basis CCRR-17 Hasnain, S.S., C.K. Minns and B.J. Shuter. 2010. Key Ecological Temperature Metrics for Canadian Freshwater Fishes. CCRR-38 Furrer, M., M. Gillis, R. Mussakowski, T. Cowie and T. Veer.Monitoring Programs Sponsored by the Ontario Ministry of CCRR-18 Scoular, M., R. Suffling, D. Matthews, M. Gluck and Natural Resources and their Relevance to Climate Change. P. Elkie. 2010. Comparing Various Approaches for Estimating Fire Frequency: The Case of Quetico Provincial Park. CCRR-39 McKechnie, J., J. Chen, D. Vakalis and H. MacLean. Energy Use and Greenhouse Gas Inventory Model for Harvested CCRR-19 Eskelin, N., W. C. Parker, S.J. Colombo and P. Lu. Wood Product Manufacture in Ontario. 2011. Assessing Assisted Migration as a Climate Change Adaptation Strategy for Ontario’s Forests: Project Overview and Bibliography. CCRR-40 Minns, C.K., Shuter, B.J. and S. R. Fung. 2014. Regional Projections of Climate Change Effects on Ice Cover and CCRR-20 Stocks, B.J. and P.C. Ward. 2011. Climate Change, Open-Water Duration for Ontario Lakes Using Updated Ice Date Carbon Sequestration, and Forest Fire Protection in the Canadian Models. Boreal Zone. CCRR-41 Minns, C.K., Shuter, B.J. and S. R. Fung. 2014. CCRR-21 Chu, C. 2011. Potential Effects of Climate Change and Regional Projections of Climate Change Effects on Thermal Habitat Adaptive Strategies for Lake Simcoe and the Wetlands and Streams Space for Fishes in Stratified Ontario Lakes. within the Watershed. CCRR-22 Walpole, A and J. Bowman. 2011. Wildlife Vulnerability to Climate Change: An Assessment for the Lake Simcoe Watershed. CCRR-23 Evers, A.K., A.M. Gordon, P.A. Gray and W.I. Dunlop. 2012. Implications of a Potential Range Expansion of Invasive

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