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CLIMATE
CHANGE
RESEARCH
REPORT
CCRR-42
High Flows and Freshet
Timing in Canada:
Observed Trends
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High Flows and Freshet Timing in
Canada: Observed Trends
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
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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 cellesci, 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
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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.
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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
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Climate Change Research Report CCRR-42
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
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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
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).
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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
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 betterto-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 ( x̄ = 43, range
12–99, s = 20); r ( x̄ = 0.171, range 0.001–0.47, s = 0.139); CV ( x̄ = 0.10, range 0.045–0.265, s = 0.037);
and b ( x̄ = -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
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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
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.
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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
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
9
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
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.
11
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
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.
13
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).
Station Number
Flow
Class
HFE
criterion 1
HFE criterion
2a
(m3s-1d-1)
HFE criterion
2b
(m3s-1d-1)
HFE criterion
3
(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
Appendix 1. Continued.
Station Number
Flow
Class
HFE
criterion 1
HFE criterion
2a
(m3s-1d-1)
HFE criterion
2b
(m3s-1d-1)
HFE criterion
3
(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
17
18
Climate Change Research Report CCRR-42
Appendix 1. Continued.
Station Number
Flow
Class
HFE
criterion 1
HFE criterion
2a
(m3s-1d-1)
HFE criterion
2b
(m3s-1d-1)
HFE criterion
3
(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
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
Start Month
Accepted Freshet Window
End Month
# of adjacent greatest
magnitude HFE frequency
months
01AD002
March
May
3
01BC001
March
May
3
01BE001
March
May
3
01BP001
March
May
3
01AP004
5
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
19
20
Climate Change Research Report CCRR-42
Appendix 2. Continued.
Station Number
Accepted Freshet Window
Start Month
Accepted Freshet Window
End Month
# of adjacent greatest
magnitude HFE frequency
months
05DA009
June
August
3
05LH005
April
July
4
05SA002
March
June
4
05TD001
June
July
2
05PB014
7
06BD001
06CD002
6
May
August
06DA004
4
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
07RD001
August
October
3
08CD001
May
June
2
08FB006
May
June
2
08JE001
June
July
2
08LA001
May
June
2
07OB001
3
5
08CG001
6
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
Appendix 2. Continued.
Station Number
Accepted Freshet Window
Start Month
Accepted Freshet Window
End Month
# of adjacent greatest
magnitude HFE frequency
months
10CD001
May
August
4
10EB001
May
July
3
10GB006
April
May
2
10NC001
May
June
2
10PB001
July
September
3
10QD001
May
June
2
10RC001
June
July
2
10FA002
6
21
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
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)
23
Climate Change Report Series
CCRR-01 Wotton, M., K. Logan and R. McAlpine. 2005. Climate
Change and the Future Fire Environment in Ontario: Fire Occurrence
and Fire Management Impacts in Ontario Under a Changing Climate.
CCRR-02 Boivin, J., J.-N. Candau, J. Chen, S. Colombo and M.
Ter-Mikaelian. 2005. The Ontario Ministry of Natural Resources LargeScale Forest Carbon Project: A Summary.
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T. Cai. 2005. The Effects of Forest Management on Carbon Storage in
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Vulnerability Analysis.
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Blakemore and L. Ferguson. 2012. Climate Change Vulnerability
Assessment and Adaptation Options for Ontario’s Clay Belt – A Case
Study.
CCRR-25 Bowman, J. and C. Sadowski. 2012. Vulnerability of
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CCRR-04 Hunt, L.M. and J. Moore. 2006. The Potential Impacts
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CCRR-26 Rempel, R.S. 2012. Effects of Climate Change on
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CCRR-05 Colombo, S.J., D.W. McKenney, K.M. Lawrence and
P.A. Gray. 2007. Climate Change Projections for Ontario: Practical
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CCRR-27 Minns, C.K., B.J. Shuter and S. Fung. 2012. Regional
Projections of Climate Change Effects on Ice Cover and Open-Water
Duration for Ontario Lakes
CCRR-06 Lemieux, C.J., D.J. Scott, P.A. Gray and R.G. Davis.
2007. Climate Change and Ontario’s Provincial Parks: Towards an
Adaptation Strategy.
CCRR-28 Lemieux, C.J., P. A. Gray, D.J. Scott, D.W. McKenney
and S. MacFarlane. 2012. Climate Change and the Lake Simcoe
Watershed: A Vulnerability Assessment of Natural Heritage Areas and
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CCRR-07 Carter, T., W. Gunter, M. Lazorek and R. Craig. 2007.
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CCRR-08 Browne, S.A. and L.M Hunt. 2007. Climate Change
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CCRR-09 Varrin, R. J. Bowman and P.A. Gray. 2007. The Known
and Potential Effects of Climate Change on Biodiversity in Ontario’s
Terrestrial Ecosystems: Case Studies and Recommendations for
Adaptation.
CCRR-29 Hunt, L.M. and B. Kolman. 2012. Selected Social
Implications of Climate Change for Ontario’s Ecodistrict 3E-1 (The Clay
Belt).
CCRR-30 Chu, C. and F. Fischer. 2012. Climate Change
Vulnerability Assessment for Aquatic Ecosystems in the Clay Belt
Ecodistrict (3E-1) of Northeastern Ontario.
CCRR-31 Brinker, S. and C. Jones. 2012. The Vulnerability of
Provincially Rare Species (Species at Risk) to Climate Change in the
Lake Simcoe Watershed, Ontario, Canada
CCRR-11 Dove-Thompson, D. C. Lewis, P.A. Gray, C. Chu and
W. Dunlop. 2011. A Summary of the Effects of Climate Change on
Ontario’s Aquatic Ecosystems.
CCRR-32 Parker, W.C., S. J. Colombo and M. Sharma. 2012. An
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CCRR-12 Colombo, S.J. 2008. Ontario’s Forests and Forestry in
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Wood Products from Ontario and Canada.
CCRR-13 Candau, J.-N. and R. Fleming. 2008. Forecasting the
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CCRR-14 Minns, C.K., B.J. Shuter and J.L. McDermid. 2009.
Regional Projections of Climate Change Effects on Ontario Lake Trout
(Salvelinus namaycush) Populations.
CCRR-15 Subedi, N., M. Sharma, and J. Parton. 2009. An
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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
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CCRR-18 Scoular, M., R. Suffling, D. Matthews, M. Gluck and
P. Elkie. 2010. Comparing Various Approaches for Estimating Fire
Frequency: The Case of Quetico Provincial Park.
CCRR-19 Eskelin, N., W. C. Parker, S.J. Colombo and P. Lu.
2011. Assessing Assisted Migration as a Climate Change Adaptation
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CCRR-20 Stocks, B.J. and P.C. Ward. 2011. Climate Change,
Carbon Sequestration, and Forest Fire Protection in the Canadian
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CCRR-21 Chu, C. 2011. Potential Effects of Climate Change and
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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
CCRR-34 McLaughlin, J. and K. Webster. 2013. Effects of a
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Ontario.
CCRR-36 Nituch, L. and J. Bowman. 2013. Community-Level
Effects of Climate Change on Ontario’s Terrestrial Biodiversity.
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Anderson and S. MacRitchie. Responding to the Effects of Climate
Change in the Lake Simcoe Watershed: A Pilot Study to Inform
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CCRR-38 Furrer, M., M. Gillis, R. Mussakowski, T. Cowie and
T. Veer.Monitoring Programs Sponsored by the Ontario Ministry of
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CCRR-39 McKechnie, J., J. Chen, D. Vakalis and H. MacLean.
Energy Use and Greenhouse Gas Inventory Model for Harvested
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CCRR-40 Minns, C.K., Shuter, B.J. and S. R. Fung. 2014.
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Models.
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Regional Projections of Climate Change Effects on Thermal Habitat
Space for Fishes in Stratified Ontario Lakes.
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