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TRENDS AND ELEVATIONAL DEPENDENCE OF THE HYDROCLIMATOLOGY OF THE CARIBOO MOUNTAINS, BRITISH COLUMBIA
by
Aseem Raj Sharma
B.Sc., Tri-Chandra Multiple Campus, 2005 M.Sc., Tribhuvan University, 2007
THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE IN
NATURAL RESOURCES AND ENVIRONMENTAL STUDIES
UNIVERSITY OF NORTHERN BRITISH COLUMBIA
December 2014
© Aseem Raj Sharma, 2014
UMI Number: 1526513
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Abstract
Pristine mountain environments are more sensitive to climate change than other land
surfaces. The understanding of climatic variations in mountainous terrain is still uncertain.
Previous studies reveal inconsistent findings on the elevational dependency of warming in
the mountains. In this study, the trends and elevational dependence of climatic variables in
the Cariboo Mountains Region (CMR) of British Columbia are explored. A high resolution
10 km x 10 km gridded data set of climate variables over the period of 1950-2010 is used.
The Mann-Kendall test is performed for evaluation of trends and their significance. The
CMR is warming at a faster rate in recent decades than regional and global warming. The
minimum air temperature trend shows significant amplified warming at higher elevations.
Precipitation does not show any significant trend across the study area. The possible physical
mechanisms for such wanning trends and the potential impacts of these changes on the
endangered mountain caribou and water resources of the area are discussed.
Table o f Contents
Abstract....................................................................................................................................................i
Table of Contents...................................................................................................................................ii
List of Tables........................................................................................................................................vi
List of Figures......................................................................................................................................vii
List of Appendices.............................................................................................................................. xii
Dedication...........................................................................................................................................xiii
Acknowledgements............................................................................................................................ xiv
1. Introduction.................................................................................................................................... 1
1.1 Overview............................................................................................................................1
1.2 Background....................................................................................................................... 3
1.2.1 Climate change and mountains.................................................................................3
1.2.2 Physical processes for climate change in mountains..............................................9
1.3 Goal and research questions of the thesis...................................................................... 13
1.4 Structure of the thesis......................................................................................................13
2. Study Area: The Cariboo Mountains Region..............................................................................15
3. Data Collection and Methods..................................................................................................... 20
3.1 Data Collection................................................................................................................ 20
3.1.1 Data Sources............................................................................................................22
3.1.1.1 The Cariboo Alpine Mesonet Observed Data...................................................... 24
3.1.1.2 ANUSPLIN Canadian Climate Coverage gridded data..................................... 25
3.1.2 Data quality assessment and control.......................................................................27
3.2 Methods...........................................................................................................................28
3.2.1 Tools........................................................................................................................28
3.2.2 General statistics.....................................................................................................29
3.2.3 Validation................................................................................................................ 32
3.2.4 Lapse Rate................................................................................................................32
3.2.5 Anomaly...................................................................................................................33
3.2.6 Trend Analysis........................................................................................................ 33
3.2.6.1 Mann-Kendall Trend test...................................................................................... 34
3.2.6.2 Theil-Sen trend estimate (Median based trend estimate)................................... 36
3.2.6.3 Trend Distribution................................................................................................. 37
3.2.6.4 Hydroclimatic trends with elevation....................................................................37
3.2.6.5 LOESS.................................................................................................................... 38
4. Results.......................................................................................................................................... 39
4.1 Observed air temperature/precipitation variations.......................................................39
4.2 Validation of Interpolated (Gridded) Data................................................................... 46
4.3 General hydroclimatology of the region...................................................................... 52
4.3.1 General statistics..................................................................................................... 52
4.3.2 Average Hydroclimatic variation......................................................................... 54
4.4 Hydroclimatic trends in the Cariboo Mountains Region............................................. 61
4.4.1 Temporal hydroclimatic trends in the CMR......................................................... 61
iii
4.4.1.1 Annual Trends....................................................................................................... 61
4.4.1.2 Seasonal Trends..................................................................................................... 64
4.4.2 Spatial hydroclimatological trends in the CMR....................................................69
4.4.2.1 Annual Trends....................................................................................................... 69
4.4.2.2 Seasonal trends...................................................................................................... 73
4.5 Elevation dependence of hydroclimatic trends............................................................77
4.5.1 Elevational dependence of annual trends.............................................................. 78
4.5.2 Elevational dependence of seasonal trends........................................................... 80
4.5.3 Elevational dependence of precipitation trends....................................................82
5. Discussion.....................................................................................................................................85
5.1 Hydroclimatic trends......................................................................................................85
5.1.1 Magnitude and significance of linear trends.........................................................85
5.1.2 Magnitude and distribution of spatial trends........................................................86
5.2 Physical mechanisms associated with trends............................................................... 92
5.2.1 Snow-albedo feedback (SAF)................................................................................ 94
5.2.2 Clouds......................................................................................................................95
5.2.3 Soil moisture and specific humidity......................................................................96
5.2.4 Other Factors........................................................................................................... 97
5.3 Elevational dependency of hydroclimatic trends.........................................................98
5.4 Implications of trends...................................................................................................100
5.4.1 Seasonal shifting................................................................................................... 100
iv
5.4.2 Implications to the ecohydrology..........................................................................104
5.4.2.1 Impacts on water resources..................................................................................104
5.4.2.2 Impacts on animal species................................................................................... 106
5.5 Limitations of this study..............................................................................................107
6. Conclusions, Recommendations and Future W ork.................................................................. 109
6.1 Conclusion......................................................................................................................109
6.1.1 General Hydroclimatology.................................................................................. 109
6.1.2 Linear and spatial trends....................................................................................... 110
6.1.3 Elevational dependency of hydroclimate.............................................................112
6.1.4 Impacts of hydroclimatic variations..................................................................... 113
6.2 Recommendations and Future Work..........................................................................114
7. References................................................................................................................................. 117
8. Appendices..................................................................................................................................128
v
List of Tables
Table
Table
Table
Table
Table
Table
Table
Table
1: Primary and secondary climatic effects of the basic controls of mountain climate.
The effects refer to an increase in the factor listed (Barry, 1992)............................... 10
2: Climatic drivers and associated mechanisms that can produce elevation sensitive air
temperature responses with seasonal variations at the land surface. Tmin and Tmax are
daily minimum and daily maximum air temperatures, respectively (Rangwala &
Miller, 2012)...................................................................................................................... 11
3: Sources and agencies collecting a.) Observed and b.) Gridded climate data in the
Cariboo Mountains........................................................................................................... 22
f: Details of the stations used for the preliminaiy analysis.............................................41
5: Monthly lapse rate in the Cariboo Mountains..............................................................45
5: Elevation of stations used for validation with elevation difference from gridded data.
............................................................................................................................................47
7: Basic statistics of hydroclimatic parameters in the CMR, 1950-2010....................... 52
3: Average minimum and maximum air temperatures trends over the CMR, 1950-
2010. In parenthesis are percentage of the significant values across the domain of the
CMR...................................................................................................................................86
Table 9: Possible factors responsible for elevational warming in the CMR.............................93
List of Figures
Figure
Figure
Figure
Figure
Figure
Figure
Figure
Figure
1: Global air temperature trend and decadal average as given in IPCC working group I
AR5 report (IPCC, 2013). The y-axis represents the temperature anomaly (°C)
relative to 1961-1990..........................................................................................................4
2: The Cariboo Mountains as seen from Browntop Mountain [elevation 2031 m] 15
3: Topographic map of the Cariboo Mountains Region (CMR) and its location within
BC, Canada.......................................................................................................................17
4: Weather stations (both active and inactive) operated by different weather agencies
in the CMR. The elevational distribution of these weather stations is given in Figure
5 .........................................................................................................................................................................21
5 : Distribution of weather stations with elevation operated by different agencies in the
CMR. The spatial distribution and the agencies operating these stations are shown in
Figure 4.............................................................................................................................. 23
6: DEMs and corresponding histograms of elevational distribution of the CMR. a.
DEM and elevational distribution of CCC data with mean elevation, b. High
resolution DEM (~ 1 km x 1 km) and its elevational distribution with mean elevation.
Elevations are in metres a.s.l............................................................................................26
7: Location of the weather stations used for preliminary analysis of hydroclimatology
of the CMR........................................................................................................................40
8: Cross correlation of daily mean air temperature among the selected stations
analyzed for the study period of September 2011 to June 2012................................... 42
Figure 9: Daily air temperature variations in the Quesnel River Basin (QRB), on the western
slopes of the CMR, September 2011 to June 2012........................................................ 43
Figure 10: Elevational dependence of mean monthly air temperature on the western slopes of
the CMR (monthly average of September 2011 - June 2012 observational data) 44
Figure 11: Monthly total precipitation at the Likely RS and QRRC stations in the QRB that
lies on the western slopes of the CMR, September 2011 to June 2012........................46
Figure 12: Monthly minimum air temperature (CCC and CAMnet) with NSE, RMSE, r, and
p values.............................................................................................................................. 48
Figure 13: Monthly maximum air temperature (CCC and CAMnet) with NSE, RMSE, r, and
p values.............................................................................................................................. 49
Figure 14: Comparison of mean monthly minimum and maximum air temperature between
CAMnet observed and CCC gridded data for the period of 2007 - 2010 at four
locations in the CMR. The solid diagonal line is the 1:1 line........................................50
Figure 15: CCC and CAMnet monthly precipitation data with NSE, RMSE, r, and p values.
Gaps are due to missing observational data as remote CAMnet stations record only
rainfall................................................................................................................................51
Figure 16: Box plots of monthly maximum (top) and minimum (bottom) air temperatures
over the CMR, 1950-2010. The small white diamond in the centre shows the mean,
the black line in the centre shows the median, the notch shows the 95% confidence
interval of the median, the vertical line shows range of minimum and maximum
values excluding outliers, and the black dot shows the outliers defined as the values
greater than 1.5 times the interquartile range of corresponding air temperature 55
Figure 17: Mean annual minimum and maximum air temperatures and standard deviations
(SD) over the CMR, 1950-2010.......................................................................................56
Figure 18: Seasonal mean minimum and maximum air temperatures and their SD over the
CMR, 1950-2010...............................................................................................................57
Figure 19: Boxplot of monthly precipitation in the CMR, 1950-2010. The small white
diamond in the centre shows the mean, the black line in the centre shows the median,
the notch shows 95% confidence interval of the median, the vertical line shows range
of minimum and maximum values excluding outliers, and the black dot shows the
outliers defined as the values greater than 1.5 times the interquartile range of the
precipitation.......................................................................................................................58
Figure 20: Mean annual precipitation and variation over the CMR, 1950-2010......................59
Figure 21: Mean seasonal precipitation with seasonal variation expressed by the CV (%),
1950-2010..........................................................................................................................60
Figure 22: Annual minimum air temperature anomalies and trend for the CMR, 1950-2010.
61
Figure 23: Annual maximum air temperature anomalies and trend with moving average for
the CMR, 1950-2010........................................................................................................ 62
Figure 24: Annual precipitation anomalies and trend with 5-year moving average in the
CMR, 1950-2010...............................................................................................................63
Figure 25: Seasonal minimum air temperature anomalies and trends in the CMR, 1950-2010.
............................................................................................................................................ 64
Figure 26: Seasonal maximum air temperature anomalies and trends in the CMR, 1950-2010.
66
ix
Figure 27: Seasonal total precipitation anomalies and trends over the CMR, 1950-2010...... 68
Figure 28: Minimum air temperature trends and their significance over the CMR, 1950-2010.
............................................................................................................................................ 69
Figure 29: Maximum air temperature trends and their significance over the CMR, 1950-2010.
............................................................................................................................................ 70
Figure 30: Annual total precipitation trends and their significance over the CMR, 1950-2010.
............................................................................................................................................ 72
Figure 31: Spatial variation of seasonal minimum air temperature trends over the CMR,
1950-2010.......................................................................................................................... 73
Figure 32: Spatial variation of seasonal maximum air temperature trends over the CMR,
1950-2010.......................................................................................................................... 75
Figure 33: Spatial variation of seasonal precipitation trends over the CMR, 1950-2010........76
Figure 34: Minimum air temperature trend versus elevation, 1950-2010. Each point
represents the elevation of a grid cell while the solid blue line represents the LOESS
fit.........................................................................................................................................78
Figure 35: Maximum air temperature trend versus elevation, 1950-2010. Each point
represents the elevation of a grid cell while the solid blue line represents the LOESS
fit.........................................................................................................................................79
Figure 36: Elevational dependence of seasonal minimum air temperature trends, 1950-2010.
The solid blue line shows the LOESS fit of trends with elevation................................80
Figure 37: Elevational dependence of seasonal maximum air temperature trends, 1950-2010.
The solid blue line shows the LOESS fit of trends with elevation................................81
Figure 38: Annual precipitation trend versus elevation, 1950-2010. The solid blue line shows
the LOESS fit of trends with elevation............................................................................83
Figure 39: Elevational dependence of seasonal precipitation trends, 1950-2010. The solid
blue line shows the LOESS fit of trends with elevation................................................ 84
Figure 40: Histogram of the distribution of air temperature trend magnitudes with mean trend
(°C decade'1) and its standard deviation (SD) (°C decade'1) over the CMR, 1950-
2010. The solid red line shows the mean trend magnitude and the solid black curve
shows the Gaussian distribution of the trend magnitudes..............................................88
Figure 41: Histogram of the distribution of annual total precipitation trend magnitudes with
mean trend (mm decade'1) and its standard deviation (SD) (mm decade'1) over the
CMR, 1950-2010. The solid red line shows the mean trend magnitude and the solid
black curve shows the Gaussian distribution of the trend magnitudes......................... 89
Figure 42: Seasonal trend distributions for minimum and maximum air temperatures in the
CMR, 1950-2010. The solid red lines show seasonal mean trends for each season.
The mean (°C decade'1) and SD (°C decade'1) of each season is given. Relative
magnitudes of seasonal trends are also presented...........................................................90
Figure 43: Linear trends of start of days with greater than 0°C temperature threshold for
minimum, maximum, and mean air temperature (areal average) in the CMR, 1950-
2010 101
Figure 44: Linear trend of start of freezing days with temperature threshold of less than 0°C
for minimum, maximum, and mean air temperature (areal average) over the CMR,
1950-2010....................................................................................................................... 102
xi
List of Appendices
Appendix A Validation of gridded data with observed data (daily frequency)...................... 128
Appendix B Spatial plots of monthly minimum and maximum air temperatures.................. 130
Appendix C Spatial plots of monthly precipitation...................................................................132
Appendix D Minimum and maximum annual temperature and trend..................................... 133
Appendix E Mean annual air temperature anomaly and trend................................................. 134
Appendix F Seasonal minimum, maximum, and mean temperatures and trends................... 135
Appendix G Spatial trend of mean annual air temperature...................................................... 138
Appendix H Spatial trend of mean seasonal temperature in the CMR, 1950-2010.............. 139
Appendix I Annual and seasonal mean temperature trends vs elevation................................ 140
Dedication
I would like to respectfully dedicate this work to my late mother Harimaya Ghimire.
I miss you Aama!
Acknowledgements
There are a large number of people without whom this thesis might not have been
written, and to whom I am greatly indebted. First and foremost, I would like to thank
sincerely my supervisor, Dr. Stephen Dery, for providing me an opportunity to work with
him. His guidance, encouragements, and inspiration through in-depth knowledge of
research and professionalism had a great influence on this research. It has expanded my
horizons beyond what I imagined when I began this degree. I also thank my supervisory
committee members, Dr. Peter Jackson and Dr. Margot Parkes, for their valuable
comments on the proposal and thesis. In addition, thanks go to Dr. Andrew Bush,
University of Alberta, for his valuable comments on this thesis.
I would like to thank Dr. Ian Picketts (UNBC) and Wyatt Klopp (UNBC) for
helping me proof read the thesis and Stefanie LaZerte (UNBC) who always helped me to
fix R problems during my data analysis. I would like to acknowledge my colleagues and
the members of the Northern Hydrometeorology Group (NHG) of UNBC: Dr. Do Hyuk
Kang, Michael Allchin, Pabitra J. Gurung, Joseph B. McGrath (Ben), and James Fraser.
The data for this analysis were generously provided by Dr. Alex Cannon, Pacific Climate
Impacts Consortium (PCIC). I am thankful to him. Special thanks are due to the Natural
Sciences and Engineering Research Council of Canada (NSERC) for financial support.
I am obliged to Bunu, my wife whose love, help and care are invaluable. I could
not have completed this work without a supportive family and friends, I acknowledge
them. Thank you all ©.
Aseem Raj Sharma
xiv
1. Introduction
1.1 Overview
Mountain regions are often pristine environments that play a vital role in the
sustainable functioning of the earth system. It is generally accepted that mountainous regions
are more sensitive to global scale climate change than other land surfaces at the same latitude
(Barry, 2008; Beniston, Diaz, & Bradley, 1997; Beniston, 2003; Rangwala & Miller, 2012).
There are limited studies that examine how higher elevation regions are warming compared
to lower elevations. Those studies that analyze the elevational dependence of climate change
do not necessarily show consistent findings (Rangwala & Miller, 2012). Thus, there is a need
to further explore how climate variables are interrelated and change with time, space, and
elevation in mountainous regions. Information on how the leeward/windward aspects of
mountains influence the climate variables will help to better understand their complex
environment. The mechanisms that drive enhanced warming in the mountains and change in
orographic precipitation will improve knowledge of mountain climate and its role in the
environment as a whole. Furthermore, development of the altitudinal profiles of climate
parameters for mountainous terrain will lead to a better understanding of the mountain
climate and environment in an integrated approach.
Mountains cover about 25% of the earth’s land surface and have substantial
environmental and social significance; they are important sources of natural resources such
as: freshwater, biodiversity and an essential part of the global ecosystem (Barry, 2008, 2012;
Beniston, 2003). However, mountains are experiencing the compounding impacts of climate
change more than any other land surfaces (Beniston, 2003). Paucity of observation-based
data in mountainous regions, the model limitations to capture the complex mountain
topography, and high uncertainties on model outputs make the understanding of climate
variations and trends in these regions uncertain (Bradley, Keimig, & Diaz, 2004; Luce,
Abatzoglou, & Holden, 2013; Rangwala & Miller, 2012; Q. Wang, Fan, & Wang, 2013). In
this context, it is important to investigate how the principal climate variables such as air
temperature and precipitation differ with elevation in mountains.
To further understand this complex interaction between the mountains and climate
variables, the Cariboo Mountains Region (CMR) of northern British Columbia (BC) is
selected as a study site. This study incorporates historical daily climatological measurements
interpolated spatially from observed data from different data generating agencies such as
Environment Canada. Furthermore, hydrometeorological data collected at the Cariboo Alpine
Mesonet (CAMnet) sites are used to compare with interpolated data in the CMR of northern
BC. A longitudinal and elevational profile of air temperature and precipitation of the CMR is
developed. Spatial variation of the CMR is of special interest because the mountains are
oriented approximately Northwest (NW)-Southeast (SE) with prevailing upper winds from
the west that make spatial variations prominent in the region.
Windward/leeward effects and the scales at which these processes operate are
explored by contrasting the meteorological conditions on the eastern and western slopes of
the mountains. Long-term time series of climate data are analyzed to observe the temporal
and spatial patterns of climate variability and trends over the region. Elevational dependency
on the historical climate data is analyzed. The mechanisms responsible for trends of climate
variables at different elevations are explored and analyzed. The potential consequences of
such changes over the local ecological and hydrological processes in the region are
discussed.
1.2 Background
This section describes climate change and discusses the studies that explore its relation to
mountains. Furthermore, different physical processes responsible for differences in mountain
climate are discussed. Many contributions to the literature show enhanced warming in the
higher elevation regions. This study looks at the case of the CMR, a typical mountain range
of northwestern North America with limited climate studies, and explores how different
physical phenomena have acted to cause hydroclimatological variations in the region.
1.2.1 Climate change and mountains
It is generally accepted that the earth is warming at a faster rate in recent decades than
any time during the past thousands of years. The 5th assessment report of the
Intergovernmental Panel on Climate Change (IPCC, 2013) states that “ warming o f the
climate system is unequivocal and since the 1950s, many o f the observed changes are
unprecedented over decades to millennia, the atmosphere and ocean have warmed".
Observed globally averaged combined land and ocean surface tem perature anomaly 1850—2012
o.eAnnual average
0.4
0.2
0.0
- 0.2
- 0.4
0.6Decadal average
0 . 0
- 0.6
18 SO 2000Year
Figure l : Global air temperature trend and decadal average as given in IPCC working group I AR5
report (IPCC, 2013). The y-axis represents the temperature anomaly (°C) relative to 1961-1990.
Figure l shows the global air temperature trend and decadal averages from 1850 to
2010 as given in the IPCC AR5 report. Globally-averaged land and ocean combined
temperatures show a warming trend of 0.72 [0.49 to 0.89]°C over the period 1951-2012
(Stocker et al., 2013). Recent decades, especially after the 1990s, are consecutively warmer
than previous decades globally. The compounding impacts of such warming in different
components of the earth system are already observed and projected to intensify.
Mountain climate is still not well understood, although there are many recent
advances in several areas of research in mountain climatology (Barry, 2012). Elevation,
latitude, continentality, topography, and wind are the major controlling factors of the
mountain climate. How atmospheric climate variables, namely surface air temperature and
precipitation are influenced by these controlling factors is still uncertain (Barry, 2008, 2012;
Beniston & Fox, 1995). The relation of climate variables among each other and how they are
influenced by elevation, slope, and aspect of the mountains is still not entirely clear (Pepin &
Lundquist, 2008; T. Zhang, Osterkamp, & Stamnes, 1996). In this context, the CMR is
selected as a study area to better understand the influence of major controls of mountains on
climate variables. In particular, there are few studies on the climatology of the Cariboo
Mountains of BC. An exception includes Burford, Dery, & Holmes (2009) who analyze the
long term hydroclimatology of the Quesnel River Basin (QRB). They find an increasing
mean minimum air temperature trend, an earlier spring freshet, and increasing river runoff in
the QRB. Most of the climate studies and information available for the Cariboo Mountains
are supplementary to other studies such as those on the ecology or cryosphere of the area.
These studies cover either a wide range of mountains of that region or relatively small
specific areas such as particular glaciers (Beedle, 2014; Burford et al., 2009; Davis & Reed,
2013; Dery, Clifton, MacLeod, & Beedle, 2010; Eyles, 1995; Hageli & McClung, 2003;
Maurer et al., 2012; Tong, Dery, & Jackson, 2009a).
Mountains are an important area of climate research not only because of their
presence, but also because they are not much disturbed by human influence and are a good
indicator of the state of the planet with regards to global warming (Barry, 2012; Pepin &
Lundquist, 2008; Whiteman, 2000). Environmental behaviour and rapidly changing
biodiversity and hydrology along with the changing elevation in the short horizontal span of
mountains can be better understood with mountain climate studies (Beniston, 2003;
Whiteman, 2000). Detailed studies of the changes of climatic variables in the pristine
mountain environments remain limited despite their direct and indirect importance (Bradley
et al., 2004).
The global surface air temperature has been rising over the past century and it is
expected that there will be amplified warming in mountainous regions. The extent of the
impacts of these changes is highly uncertain (Rangwala & Miller, 2012). Most of the climate
downscaling approaches do not capture details of the complex mountain topography,
increasing the uncertainties of their projections (Bradley et al., 2004). There are studies that
show variations in the warming trend with elevation (Rangwala & Miller, 2012). Some
studies show the elevation dependency of surface warming with greater warming rates at
higher altitudes (Bradley et al., 2004; Ceppi, Scherrer, Fischer, & Appenzeller, 2012; Daly et
al., 2008; Dong, Huang, Qu, Tao, & Fan, 2014; Ghatak, Sinsky, & Miller, 2014; Liu, Cheng,
Yan, & Yin, 2009; Pepin & Lundquist, 2008; Rangwala, Miller, & Xu, 2009; Rangwala,
Sinsky, & Miller, 2013; Williams, Losleben, Caine, & Greenland, 1996); however, there are
other studies where either no elevation dependent warming is observed (Ceppi et al., 2012;
Pepin & Lundquist, 2008; Vuille, Bradley, Werner, & Keiming, 2003; Vuille & Bradley,
2000; You et al., 2010) or even decreasing air temperature trends with elevation are reported
(Beniston & Rebetez, 1996; Ceppi et al., 2012; Lu, Kang, Li, & Theakstone, 2010; Vuille &
Bradley, 2000).
Some of the studies that compare mountain air temperature trends with global air
temperature trends find that mountains are warming faster than low-lying areas. In addition,
mountain air temperature trends are higher than the same latitude Northern Hemisphere air
temperature trends (Beniston et al., 1997; Liu & Chen, 2000). The Southern Hemisphere
mountain regions’ air temperature trends are also higher than the trends in low lying areas
(Urrutia & Vuille, 2009; Vuille et al., 2008). However, differences in spatial scales and the
paucity of climate stations in mountain regions make comparison of climate trends among
different land surfaces with mountains difficult. Pepin & Lundquist (2008) used
comprehensive, homogeneity-adjusted air temperature records from over 1000 high elevation
stations across the globe to analyze air temperature trends at high elevations. They could not
find a global relationship between elevation and warming rates. However, they find that
mountains show more spatially consistent air temperature trends. Furthermore, they argue
that high mountains are representative indicators of the status of global warming on the earth.
An annual mean air temperature analysis shows significant altitudinal amplification
of warming trends compared to low-lying areas globally (Q. Wang et al., 2013). The surface
mean temperature lapse rate has decreased at a rate of about 0.2°C km'1 over the last 50 years
(Q. Wang et al., 2013). This indicates that higher elevations have experienced more warming
compared to lower elevations, i.e. the air temperature decreases less rapidly with height than
previously. A strong positive correlation, especially in the minimum air temperature and
increasing elevation, is observed in the winter months on the Tibetan Plateau using the
Coupled Model Intercomparison Project Phase 5 (CMIP5) by Rangwala et al. (2013).
Fyfe & Flato (1999) using the Coupled Climate Model (CCM) showed that there is
an elevation dependency in surface climate change in the winter and spring seasons in the
Rocky Mountains. Increased air temperatures with increasing elevation is observed along the
American Cordillera (Alaska to Chile) through the analysis of seven General Circulation
Models (GCMs) with simulations at 2 x CO2 levels (Bradley et al., 2004). Based on limited
available observational data, Beniston et al. (1997) find higher increases in daily minimum
air temperature at higher altitudes, especially in Europe and Asia.
Using long term observed data along a 2077 m elevational transect in the Rocky
Mountain Front Range of Colorado, USA, McGuire et al. (2012) showed that warming
signals are strongest at mid-elevations over the long-term (56 years) and short-term (20
years) temporal scales. Based on observational data, Rangwala, Miller, & Xu (2009) found
that the Tibetan Plateau high elevation regions are warming at a greater rate than the low
lying regions. Further, they suggested that a continuous warming of the atmosphere caused
by an increasing greenhouse gas forcing would further increase the atmospheric water vapour
content. Therefore, for most of the 21st century, they expect relatively large rates of warming
over the Tibetan Plateau. Scherrer, Ceppi, Croci-Maspoli, & Appenzeller (2012) used
observational data in the Swiss Alps and found that the snow-albedo feedback (SAF) is
responsible for an increasing trend of air temperature during spring in that region.
An analysis based on observational data shows a general decrease of warming trends
with elevation in the Tropical Andes. Using surface data of daily maximum and minimum air
temperatures, Pepin & Losleben (2002) found the local surface air temperature trends at high
elevation are opposite to global increasing warming trends. Lu, Kang, Li, & Theakstone
(2010) through the analysis of 46-year January mean observed air temperature data between
1000 m and 5000 m in elevation, found significant altitudinal effects of temperature warming
onset time on the Tibetan Plateau. They concluded that the warming in high elevations
(below 5000 m) is less sensitive than low lying nearby areas. They suggested that could be
caused by high albedo and the large thermal capacity of ice/snow cover on the higher part of
the plateau and a possible heat island effect over the lower part.
Furthermore, many studies focused on recent warming show earlier melting of snow
and significant decline in snow cover in the mountains of North America and an increased
number of snow-free days in recent years (Choi, Robinson, & Kang, 2010; Shi, Dery,
Groisman, & Lettenmaier, 2013; Tong, Dery, & Jackson, 2009b). Tong, Dery, & Jackson
(2009b) show linkage between snow cover fraction and the hydrology of the QRB region
using MODIS snow products. However, there are no specific studies on how the snow cover
days and melting and freezing days are changing in the Cariboo Mountains and the
surrounding region.
These studies raise a concern about whether or not there exists an elevation
dependency on warming. Furthermore, it is possible that some regions may be experiencing
elevational warming and other regions may not be at a global level. This study, therefore,
examines important questions regarding elevational dependence on warming in the CMR,
including:
• Is this type of warming different with elevation and the leeward and windward
sides of a given mountain region?
• What are the physical processes responsible for such different outcomes in the
mountain environment?
1.2.2 Physical processes for climate change in mountains
There are a number of factors associated with elevation that may lead to different
responses to global warming. Indeed, different physical mechanisms and processes
associated with either elevational differences or sensitivity of surface warming may be
responsible for enhanced air temperature trends at high elevations (Rangwala & Miller,
2012). Some of these factors are universal for all land surfaces while others are particular to
the mountains. Some are seasonal mechanisms and others operate year-round (Barry, 2008;
Rangwala & Miller, 2012). Physical mechanisms along with the atmospheric circulation such
as the SAF, cloud formation/concentration, soil moisture, etc. may contribute to seasonal
trends (Ceppi, Scherrer, Fischer, & Appenzeller, 2012). Table 1 shows a summary of climatic
effects of the basic control of the mountain climate system. The altitude, continentality, and
topography are the major physical factors that affect, alone or in combinations, the climate of
mountain regions and therefore make them unique and sensitive to changes.
Table 1: Primary and secondary climatic effects o f the basic controls o f mountain climate. The effects
refer to an increase in the factor listed (Barry, 1992).
Altitude
reduced air density;
vapour pressure;
increased solar radiation receipts (cloud
dependent);
lower air temperatures.
increased wind velocity;
increase precipitation (mid
latitude);
reduced evaporation;
physiological stress.
Continentality
annual/diurnal air temperature range
increased;
cloud and precipitation regimes modified.
snow line altitude rises.
Latitudeday length and solar radiation totals vary
seasonally.
snowfall proportion increases;
annual air temperatures
decrease.
Topography
spatial contrasts in solar radiation and air
temperature regimes, and precipitation as
a result of slope and aspect.
diurnal wind regimes;
snow cover related to
topography.
Table 2 shows some of the possible climate drivers and mechanisms responsible for
enhanced air temperature responses at high elevations. This study will further explore how
these drivers are acting on the micro-scale and meso-scale in the Cariboo Mountains and how
they influence air temperature variations in that region at different elevations, longitudes and
exposures.
Table 2: Climatic drivers and associated mechanisms that can produce elevation sensitive air
temperature responses with seasonal variations at the land surface. Tm,„and Tmaxare daily minimum
and daily maximum air temperatures, respectively (Rangwala & Miller, 2012).
Decreases in
Snow/Ice Albedo
Increases surface
absorption of
insolation
Primarily spring; but also
important in winter at
lower elevations, summer
at higher elevations, in
association with the 0°C
isotherm
Increases Tmax;
suppressed effect
if soil moisture
also increases and
causes daytime
evaporative
cooling
Increases in Cloud
Cover (Daytime)
Decreases surface
insolation
All seasons but greater
effects in summer
Decreases Tmax;
strongest effect
when the cloud
base is low
Increases in Cloud
Cover (Nighttime)
Increases
downwelling
longwave radiation
All seasons but greater
effects in winterIncreases Tnun
Increases in
Specific
Humidity (q)
Increases
downwelling
longwave radiation
Primarily winter; smaller
effects are possible in fall
and spring
Increases Tmin
| H|
Increases in
Aerosols
(non-absorbing)
Decreases surface
insolation;
Increases cloud
albedo and cloud
lifetime
Dependent on seasonal
emissions
Decreases Tmax;
Small increases in
Tmin when cloud
lifetime is
enhanced;
Effect is
somewhat
localized to near
the emission
source
Increases in
Aerosols
(absorbing)
Decreases surface
insolation but
increases mid-
tropospheric heating;
Decreases albedo of
clouds;
Decreases albedo of
snow on ground;
Decreases cloud
cover
Dependent on seasonal
emissions and insolation
Increases T mjn;
Increases T max
when cloud cover
is reduced;
Effect is
somewhat
localized to near
the emission
source
Increases in Soil
Moisture
Increases latent heat
fluxes and decreases
sensible heat fluxes
during the day
Snowmelt effects are
strongest in spring and
winter; rainfall effects
are strongest in summer
Decreases diurnal
temperature
range; Strong Tmax
- soil moisture
link in summer
SAF, cloud cover, soil moisture, and aerosols all play important roles for air
temperature trends in elevated areas. This study identifies the major climate drivers for the
CMR and discusses their role in the region.
1.3 Goal and research questions of the thesis
The goal of this study is to develop the longitudinal and elevational profiles of trends
in hydroclimatic parameters across the spine of the Cariboo Mountains. To achieve this goal
the following research questions are addressed:
1. What are the variability and trends (annual and seasonal) of climate variables at
different elevations across the spine of the Cariboo Mountains? Do leeward/windward
effects of the Cariboo Mountains influence this variability?
2. Is there an enhanced climate change signal in the higher elevations of the Cariboo
Mountains compared to lower elevations? If so, what are the possible climatic drivers
for this enhancement? Do these enhancements differ between the leeward and
windward sites?
Further, this study discusses the potential consequences of these trends to the
hydrological system and local ecosystems such as on the endangered mountain caribou of the
region.
1.4 Structure of the thesis
This thesis is structured as follows: the first chapter introduces mountain climate research
and current knowledge gaps in understanding climate change and elevational warming
related processes in mountains. The basics of climate change and hydroclimatic variations
and the physical mechanisms associated with mountain climate are described in this chapter.
In addition, the rationale and goal of the thesis are discussed. Chapter 2 provides an
introduction to the characteristics of the study area that makes it especially relevant to learn
about elevational dependence of hydroclimatology. Chapter 3 describes the nature of the
data, tools, and methods used for analysis. The remainder of this thesis consists of the results
of analysis and discussions based on the results. Chapter 4 details the results of local climate
for short periods, comparisons of observed data with interpolated data, and temporal and
spatial trends, and elevation dependency of trends. Further explanations and comparison of
the results with other similar studies are presented in Chapter 5. A description of the role of
different physical factors responsible for the mountain climate trends is given in this chapter.
Furthermore, the impacts of these trends are discussed. The first part of Chapter 6 is the
concluding summary of the results and discussions while the second part proposes some
recommendations and future work.
2. Study Area: The Cariboo Mountains Region
The study area is the CMR of east-central BC, Canada. Located to the east of the Quesnel
Highlands, these mountains form the northern extension of the Columbia Mountains and lie
between the interior plateau and the Rocky Mountain Trench in BC. Figure 2 shows a typical
view of the Cariboo Mountains from Browntop Mountain. The study domain of the CMR
spans approximately 245 km in length and 44,150 km in area. The study area extends from
51°37’00” N to 53°30’00” N latitude and 119°6’00” W to 122°33’00” W longitude with
elevations ranging from 330 m to the highest peak at 3,520 m average sea level (a.s.l.) at Mt.
Sir Wilfrid Laurier (Figure 3).
Figure 2: The Cariboo Mountains as seen from Browntop Mountain [elevation 2031 m].
Generally, the climate of BC including the CMR is influenced by the presence of
mountainous regions, the coastline of the Pacific Ocean, and its location in the Northern
Hemisphere (latitude, longitude). The steep elevational gradient across the Cariboo
Mountains also shows variation in soils, climate, and vegetation. The geology of these
mountains is composed of sedimentary and metamorphosed sedimentary rocks, dominantly
quartzite and some limestone. Valleys in this region are steep sided with shallow surficial
deposits (MoF, 1979).
Location o f the Cariboo Mountains Region
A AlbertaBrrfeah Cohim&ia
U s A
LegendA Prince George
zbb*
Mt.Sir Wilfrid Laurier j
# Castle Creek Glaciers
A Barkerville
I Quesnel River Basin
Elevation (m)High: 3496
Low: 3820 25 50 100 km
I i i i
123°0’0"W 122°30'0“W 122°6'0”W 121"30*0*W 121°0'0"W 120°30,0’W 12000’0"W 119"30'0*W
Figure 3: Topographic map o f the Cariboo Mountains Region (CMR) and its location within BC,
Canada.
The CMR experiences a transitional climate and is wetter than the Rocky Mountains
to the east and interior plateau to the west but drier than the Coast Mountains further to the
west (Beedle, Menounos, & Wheate, 2014). The Cariboo Mountains lie in the prevailing
westerlies of the Northern Hemisphere mid-latitudes and form a natural obstacle to the
transport of moisture from the Pacific Ocean, forcing it upward. As a result, sharp
longitudinal and elevational gradients in air temperature, precipitation, and snow
accumulation are observed in the CMR.
Furthermore, the Northern Hydrometeorology Group (NHG) of the University of
Northern British Columbia (UNBC) operates the Cariboo Alpine Mesonet (CAMnet) weather
stations in the region since 2006. The hydroclimatic data generated from these stations are
useful to perform short-term hydrometeorological analyses of the region.
The inland temperate rainforest is unique to this region and lies in and around the
CMR providing habitat to mountain caribou and many other diverse plant and animal species
(ForestEthics, 2014; Stevenson et al., 2011). Cedar (Thujaplicata) and hemlock
(Tsuga heterophylla) dominated forests are found in the lower, nutrient-rich valleys of the
region, logdepole pine (Pinus contorta), Engelmann spruce (Picea engelmannii), and
subalpine fir (Abies lasiocarpa) are found in the mid-altitudes, and only alpine meadows and
lichens are found above the tree line (1700 m a.s.l.) (Dery et al., 2010). The Cariboo
Mountains form habitat of the endangered mountain caribou, a mountain ecotype of
woodland caribou (Rangifer tarandus caribou) (Mountain Caribou Science Team, 2005).
Tributaries of the Fraser River around the CMR such as the Quesnel River are
important habitats of keystone salmon species. These are the spawning habitat of keystone
salmon species, especially sockeye salmon (Oncorhynchus nerka) and Chinook salmon (O.
tshawysscha) (Beacham et al., 2004; Dery, Hemandez-Henriquez, Owens, Parkes, &
Petticrew, 2012; English et al., 2005).
The Fraser River is an important source of freshwater for downstream regions in BC’s
interior and plays a significant role in its ecohydrological system (Burford et al., 2009). Thus
the study of climatic variability and trends over the Cariboo Mountains would not only make
a contribution to a further understanding of mountain climate, but will also have broader
implications to the ecohydrology of the surrounding areas. There are many glaciers (536 in
the Cariboo Mountains; Bolch, Menounos, & Wheate, 2010) such as Castle Creek,
Quanstrom, and Premier glaciers, in the CMR that form the headwaters of the Fraser, the
North Thompson, and the Columbia Rivers (Beedle et al., 2014; Maurer et al., 2012).
The CMR has great environmental and socio-economic importance. The QRB and
historical places such as Barkerville lie within the western slopes of the Cariboo Mountains.
th tV\Barkerville was the focus of the Cariboo Gold Rush in BC during the late 19 and early 20
centuries and these days it exists as one of the famous historical ghost towns of Canada
(GeoBC, 2014; Wikipedia, 2014). Some of the major settlements in and around the CMR
include Likely, Horsefly, McBride, Quesnel, Wells, Clearwater, and Williams Lake. These
settlements lie within the Cariboo Regional District of BC and have a total population of
about 60,000 (Statistics Canada, 2014). The CMR also provides high recreational values
through scenic areas such as the Bowron Lake Provincial Park, Cariboo Mountains
Provincial Park, and Wells Gray Provincial Park (Figure 3) (BC Parks, 2014; Wikipedia,
2013).
I 19 1
3. Data Collection and Methods
This chapter describes the data and methods used in analyzing hydroclimatic
variations in the CMR. The first part looks at different sources of data considered for this
study and a description of the data used for analysis. The second part is about the methods
used for long-term trend analyses and their statistical significance.
3.1 Data Collection
The climate system is the fundamental driver of life on earth and the understanding of
such a complex system comes through the analysis of records of climate variables at different
locations (Goosse, Barriat, Lefebvre, Loutre, & Zunz, 2010; McKenney et al., 2011).
Unfortunately, the challenge for climatic studies in remote regions is the lack of sufficient
weather stations to monitor the local climate (Beniston, 2006; Bradley et al., 2004). This
limitation is also evident in the chosen study area of the CMR where there are few stations,
especially in the higher elevations, with both long term and/or reliable quality data (Figure
Weather stations in the Cariboo Mountains Region
ZbO"o3
OO.COoCOlO
oO'CO
o©.co6CNtO
oO'CNlO
N
A
o©co
Fa M» 1
£ &
LegendA CAMnet Stations
A Environment Canada Stations
A BCRFC's Snow Pillow Stations
A wihtfire Mgmt. Branch Stations
Elevation (m)High: 3496
Low: 382
0 25 50j i i L_
100 kmj i I
- 123°0'0"W 122°30'0"W 1 2 2 W W 12r30'0"W 1 2 1 W W 120°30’0'*W 120o0'0''W 119“30’0"W w
Figure 4: Weather stations (both active and inactive) operated by different weather agencies in the
CMR. The elevational distribution o f these weather stations is given in Figure 5.
Furthermore, historical daily climate data provide information on past daily air
temperature and precipitation over a selected period. Such data are particularly valuable as
input for trend analysis, model simulation processes such as plant growth, fire severity, and
plant phenology (McKenney et al., 2011). In this study, both observed and gridded climate
data from different sources are collected and compiled. The gridded climate data are then
validated using the observation-based, independent dataset before analysis.
3.1.1 Data Sources
Different types of data, both observed and modelled, from different sources are
collected and examined to select long-term good quality hydroclimatic data of the CMR for
detailed analysis (Table 3). Detailed inspections of the observed data from various agencies
(presented in Table 3) show that there are large data gaps and lack of continuous data beyond
30 years from the present in the CMR.
Table 3: Sources and agencies collecting a.) Observed and b.) Gridded climate data in the Cariboo
Mountains.
The Cariboo Alpine Mesonet (CAMnet) ANUSPLIN observed gridded data
(Macleod & D&y, 2007) (NRCan, 2014)
Environment Canada
(Environment Canada, 2014)
BC River Forecast Centre’s snow pillow stations
(BCRFC, 2014)
BC Ministry of Forests, Lands, and Natural
Resource Operations’ (MLNRO) Wildfire
Management Branch (WMB) (WMB, 2014)
There are few operational weather stations in the CMR covering higher elevations
(Figure 5). Most stations lie below 1500 m elevation and do not have long-term continuous
data. Therefore, data from those stations are not used for detailed hydroclimatic trend
analysis of the CMR.
Distribution of weather stations with elevation[m] in the Cariboo Region
0 5 10 15 20 25 30 35 40 45 50 55 60 65No. of Stations
Figure 5: Distribution o f weather stations with elevation operated by different agencies in the CMR.
The spatial distribution and the agencies operating these stations are shown in Figure 4.
The source of gridded data for the region is the Australian National University
SPLINe (ANUSPLIN) observed Canadian Coverage Climate (CCC) data developed by
Natural Resources Canada (NRCan). The CCC data were available at the Pacific Climate
Impacts Consortium (PCIC) web portal
http://medusa.pcic.uvic.ca/dataportal/bcsd downscale canada/map. They can also be
obtained through special request to Natural Resources Canada (NRC, 2014). These are the
interpolated data from observed station data. The CCC data are used instead of other gridded
data available for the region such as ClimateWNA (T. Wang, Hamann, Spittlehouse, &
Murdock, 2012) because they are available at a higher temporal resolution (daily frequency)
and based on solely observed data.
3.1.1.1 The Cariboo Alpine Mesonet Observed Data
To monitor the long-term, amplified impact of global warming on the
hydrometeorology of the mountainous terrain of northern BC, CAMnet, a mesoscale network
of weather stations, has been developed by the NHG at UNBC (Macleod & Dery, 2007). This
network of stations collects different climatic variables such as air temperature, rainfall, snow
depth, wind speed and direction, and atmospheric pressure at 15 minute intervals in the CMR
(Macleod & Dery, 2007). The CAMnet data, aggregated to daily totals or means, are used for
preliminary climatic analysis of the CMR and validation of interpolated data. The
preliminary analysis is conducted on the data from September 2011 to June 2012 because of
the availability of recent data at the time of analysis.
3.1.1.2 ANUSPLIN Canadian Climate Coverage gridded data
The observation-based ANUSPLIN interpolated grids developed by NRCan span all
of Canada (NRCan, 2014). The hydroclimatic data of this study domain are clipped from this
nation-wide CCC gridded data set. The CCC data are generated using the ANUSPLIN
climate modelling software (Hopkinson et al., 2011; McKenney et al., 2011). The
ANUSPLIN climate modelling method is a multidimensional, nonparametric surface fitting
method that is suitable for the interpolation of climate parameters such as minimum and
maximum air temperature, and precipitation. In ANUSPLIN a trivariate thin-plate smoothing
splines is applied to model the complex spatial patterns associated with daily data as spatially
continuous functions of longitude, latitude, and elevation (Hutchinson et al., 2009). This
method can be observed as a simplification of multivariate linear regression in which a
parametric model is replaced by a smooth nonparametric function (Hutchinson et al., 2009).
The ANUSPLIN method depends on every data point observed that gives the robust and
stable determination of dependencies on the variable, particularly in data sparse, high
elevation regions (McKenney et al., 2011). This type of interpolation can account for
spatially-varying dependence on elevation that has control over different physical factors and
hence overall climate (Daly, 2006). The details of the ANUSPLIN interpolation can be found
in Hopkinson et al. (2011), Hutchinson et al. (2009), and McKenney et al. (2011).
a.
*
t2000
1500
1000
oooCO
ootOCM
£ o C§ .2 CM
*55>0)UJg
b.
Mean * 1292 m
0 20 40 60 80 100Frequency
30002500200015001000500
§ 'OCO
£ o -C§ . 9 CM
ro>0UJg'
m r~ 0 2500 5000 7500 10000Frequency
12500
Figure 6: DEMs and corresponding histograms o f elevational distribution o f the CMR. a. DEM and
elevational distribution o f CCC data with mean elevation, b. High resolution DEM (~ 1 km x 1 km)
and its elevational distribution with mean elevation. Elevations are in metres a.s.l..
The Digital Elevational Model (DEM) and elevational distribution of the ANUSPLIN
data of the study domain along with high resolution DEM and elevational distribution are
shown in Figure 6. The majority of the grid cells lie in the elevations between 800 m a.s.l. to
2000 m a.s.l. with mean grid cell elevation of 1292 m for the ANUSPLIN data.
For this analysis, the ANUSPLIN interpolated data (daily models) based on station
data from Environment Canada are used (Hutchinson et al., 2009; McKenney et al., 2011;
NRCan, 2014). The ANUSPLIN gridded data comprise daily minimum temperature, daily
maximum temperature, and precipitation. The precipitation includes all form of precipitation
such as snow, rain, hail, etc. These daily frequency gridded data are at the resolution of 300
arc-seconds (0.0833° or about 10 km) for the period of 1950-2010.
3.1.2 Data quality assessment and control
Quality assessment of data is an important factor for hydroclimatic analysis. For
CAMnet observed station data, quality assessment is carried out for individual parameters at
each station through statistical quality control procedures, analysis of frequency intervals,
values, manual observations, etc. Erroneous values and outliers are detected by defining the
range of data values to be considered. Missing time gaps are identified and filled with “NA”
using codes developed in R (R Core Team, 2014). Data with frequency less than daily are
processed for quality control and summarized to their daily average for air temperature and
daily total for precipitation. Interpolated data are also assessed for their quality through visual
inspection and scatter plots.
Missing gaps are filled using different techniques. Air temperature data with short
gaps (<1 day), had their missing values filled in by performing linear interpolation over time.
Here the differences between the end points are computed and divided by the number of
times for which data are missing. A given meteorological quantity is in-filled by adding the
computed increment to the point at the beginning of the gap, and is done so iteratively until
the end of the gap. For longer gaps (>1 day), cross-correlation is used to fill the missing data.
Air temperature is calculated by correlating daily air temperatures seen at nearby stations.
Missing gaps are reconstructed based on linear regressions. Precipitation data gaps are not
filled because of lack of reference precipitation data from other nearby stations. During
interpretation of the results, this is clearly considered.
For the ANUSPLIN data, detailed inspection is conducted for missing gaps in all
parameters, namely daily minimum air temperature, daily maximum air temperature, and
precipitation at each grid cell within the study domain. The inspection shows that there are no
missing values in the data. Therefore, the ANUSPLIN observation based interpolated CCC
data are directly used for analysis after an independent validation.
3.2 Methods
3.2.1 Tools
All analyses/calculations in this work are performed using R and ArcGIS. R is a
freely available software language and environment for statistical computing and graphics
that provides a wide variety of statistical and graphical techniques through its base and
associated packages: ggplot2, raster, Kendall, etc. (Hijmans, 2014; Mcleod, 2013; R Core
Team, 2014; Wickham, 2009, 2011). The Environmental Systems Research Institute
(ESRI)’s ArcGIS 10.1 is a powerful tool for spatial analysis and presentation such as
watershed delineation, digital elevation model (DEM) analysis, area calculation, etc. (ESRI,
3.2.2 General statistics
Descriptive statistical values such as means, standard deviations (SDs), and
coefficient of variations (CVs) of the hydroclimatic data are calculated for seasonal and
annual time series. The four seasons mentioned in this study are: winter (December-
February), spring (March-May), summer (June -August), and fall (September-November).
The quality-controlled data are transferred to the R statistical software and functions
and codes are executed to calculate these statistics.
Means and Standard Deviations
Let Xi, i = 1 , , n be the series of the hydroclimatic data then the mean (x) and
standard deviation (a) of this time series are calculated as given in equations (1) and (2)
respectively (Dalgaard, 2008; Storch & Zwiers, 1999).
n
( i )i= 1
(2)
The percentage of the coefficient of variation (CV) of the data series is calculated as in
equation (3).
Correlation
The Pearson’s correlation coefficient (r) is calculated between observed and
modelled data to a single-valued measure of association.
with means x and y and standard deviations ax and ay respectively is given as in equation
The r is considered statistically significant ifp<0.05 in the present work.
The p ( P-value) is the probability quantifying the strength of the evidence against the
null hypothesis in favour of the alternative.
Moving Average
Five years moving average of the hydroclimatic time series is calculated to reduce the
effects of nonsystematic variations as below.
Let be the time series of the hydroclimatic data, then the five years moving average is a
new series obtained by taking the arithmetic mean of five consecutive values of x t as given in
equation (5) (McCuen, 2003).
The Pearson’s correlation (rxy) between two series x (, i = 1 , , n and y it i = 1 , , n
(4) (Wilks, 2011).
n - iZT-iK*IT=i[(*j - *)(y« - y ) ](4)
° x ° y
5
(5)
I 30 |
Root mean square error (RMSEt and Nash-Sutcliffe efficiency (NSE) coefficient
Root mean square error (RMSE) and Nash-Sutcliffe model efficiency (NSE)
coefficient values are calculated to assess how closely the gridded data represent independent
observations collected in the CMR. The RMSE is the square root of the average squared
difference between the gridded and observed hydroclimatic data pairs. Therefore, the RMSE
is calculated as given in equation (6) (Wilks, 2011).
R M S E =
n
i Y 7 _ V\ x obs,i x model,i)i=l
(6)
where xobs are observed values and x model are gridded values at time i.
The NSE coefficient measures the accuracy of a model’s prediction (Krause, Boyle,
& Base, 2005; Martinec & Rango, 1989). NSE coefficients range from — oo to 1; if the NSE
is closer to 1, the more accurate is the model. The NSE is calculated to see how accurately
the gridded data represent observed hydroclimatic parameters in the CMR. The NSE of
observed data (xobs) and gridded data (xmodei) for data series x t, i = 1 ,..........., n is given as
in equation (7) (Nash & Sutcliffe, 1970).
N S E = Xmodel,i) (7 )
Si=lC *o6s,i — x obs,i)
3.2.3 Validation
The interpolated CCC data are validated using independent data from CAMnet
weather stations operating in the Cariboo Mountains. Four CAMnet stations covering
different elevations and with maximum continuous data are selected to validate the gridded
data. The CAMnet 15 minute frequency data from 2007 to 2010 are averaged/summed to
daily values after quality control. The corresponding data from each grid nearest to the
CAMnet station are extracted from CCC time series for the same period, i.e. 2007 to 2010 for
daily and monthly comparisons. This validation period is limited by data availability from
both CAMnet (>2006) and CCC (< 2010) data.
3.2.4 Lapse Rate
The surface lapse rate of air temperature of the CMR is calculated to assess the
monthly rate of decrease of air temperature with elevation and the stability of the atmosphere
for different months.
The surface lapse rate, i.e. the rate of decrease of temperature with elevation at the earth’s
surface, is calculated as in equation (8) (Goosse et al., 2010):
where T is the temperature and AZ is the change of elevation. For multiple measurements at
different elevations, an average of values between neighbouring stations is used for surface
lapse rate calculations. The surface lapse rate is calculated for the period of September 2011
to June 2012 because of the availability of recent data at the time of analysis.
3.2.5 Anomaly
The anomalies of the hydroclimatic parameters are calculated so that the data are less
influenced by seasonal variations and therefore, more easily comparable. An anomaly, z, is
calculated by subtracting the mean of the raw data (for a given year or season) as given in
equation (9) (Wilks, 2011).
Let x lt i = 1 , ..........., n be the series of the hydroclimatic data than the anomaly, z t is
z i = x i - x (9)
where x is the mean of the x t time series.
3.2.6 Trend Analysis
The daily frequency CCC gridded data are summarized to annual and seasonal totals
or means for each grid cell. Temporal and spatial analysis of the summarized gridded data are
performed. As a first step for the temporal analyses, the mean of all the grid cells for each
year or seasons of each year is calculated. For spatial analysis, calculations are executed on
each grid cell.
Trend analysis of a time series considers the statistical significance and magnitude of
the trend. There are different techniques in analyzing these trends. In this study,
nonparametric techniques for calculating the significance and magnitude of the trend are
used. The nonparametric tests rely on fewer assumptions compared to their parametric
equivalents. They are applicable in the situations where less is known about the data in
question, and are robust. Therefore, the Mann-Kendall trend test is performed to test the
significance of the trend while the Theil-Sen trend estimate is executed to obtain the
magnitude of the trend.
3.2.6.1 Mann-Kendall Trend test
To detect the change in hydroclimate of the CMR, the Mann-Kendall trend test is
performed (Kendall, 1975; H. B. Mann, 1945). The Mann-Kendall trend test is a popular
nonparametric alternative for testing the presence of a trend or nonstationarity of the central
tendency of a time series (Dery, Stieglitz, McKenna, & Wood, 2005; Wilks, 2011).
The Mann-Kendall trend test is based on the relative ranking of data and not on the
data themselves. This robust, nonparametric test is resistant to outlier effects, influential data,
and non-normal data. This test shows lower sensitivity for non-homogeneous/inconsistent
data, it does not require the data sets to follow any particular distribution. This test is found
to be an excellent tool for trend detection by many researchers in similar hydroclimatic
studies (Burford et al., 2009; Bum & Elnur, 2002; Dery & Brown, 2007; Dery et al., 2005;
Dery & Wood, 2005; Gan & Kwong, 1992; Gocic & Trajkovic, 2013; Modarres & Sarhadi,
2009; Shi et al., 2013).
Let x t, i = 1 , ,n be the monotonically increasing time series with time index i,
then statistics for the Mann-Kendall trend test S is
n - i
t=l
where
S = 'Yusgn(.xi+1 -Xi ) (10)
'+ l,A x > 0 sg n ( Ax) = 0, Ax = 0
(—1, Ax < 0(11)
The statistics in equation (10) counts the number of adjacent data pairs in which the
first value is smaller than the second, and subtracts the number of data pairs in which the first
is larger than the second (Wilks, 2011). The sampling distribution of the test statistic 5 is
approximately Gaussian. If the null hypothesis of no trend is true, then Gaussian null
distribution will have zero mean. The variance of this distribution depends on whether all
the x ’s are distinct, or if some are repeated values. If there are no ties, the variance of the
sampling distribution of 5 is
„ s n (n - l) (2 n + 5)Var (S ) = ------- 15--------- (12)
Otherwise,
„ n ( n - l ) ( 2 n + 5 ) - E j =1t y f o - l ) ( 2 t ; + 5)Var(S) = ----------------------------—---------------------------- (13)
where / indicates the number of groups or repeated values, and t* is the number of repeated
values in the j th group. The test p-value is then evaluated using the standard normal
distribution as,
( S - 15 > 0
[Var(S)]20, 5 = 0 (1 4 )5 + 1
------------T, 5 < 0[Tar(5)]I
Now for this distribution in equation (14), the significance of a trend at a level p is
established if \ZS\ > p( f ) , where / is the standard normal distribution (Dery et al., 2005;
Wilks, 2011). A very high positive value of S is an indicator of an increasing trend, and a
large negative value indicates a decreasing trend. In this study, significance level (a) = 0.05
is used; the null hypothesis of no trend is rejected if the standard normal test statistics
\ZS\ > 1.96. This trend test has two parameters of importance for trend detection:
a) Significance level that indicates the strength; and
b) The slope magnitude estimate that indicates the direction as well as the magnitude
of the trend (Bum & Elnur, 2002).
The Mann-Kendall trend test does not calculate the trend magnitude; therefore, the
nonparametric median based technique, Thei 1-Sen trend estimate, is implemented for the
estimation of trend magnitude.
3.2.6.2 Theil-Sen trend estimate (Median based trend estimate)
Trend magnitude is calculated using the Theil-Sen trend estimate (Dery et al., 2005;
Mondal, Kundu, & Mukhopadhyay, 2012; Sen, 1968).
Let t be time, y is the climate variable and b a constant, then the Kendall-Theil
Robust Line develops a linear equation as
y - m t + b (15)
where m is the slope. To calculate this slope, the slopes m k for each tied group of a time
series are computed as
(1 6 )
where/c = 1,2, ,n (n — l) /2 ; i = 1,2, — I; and j = 2 ,3 ,....... ,n.
The intercept b is calculated by substituting the median time and y and solving for b
in equation (15). The median slope of all elements m k is then taken as the slope of
equation (15) that gives the trend magnitude of the time series.
3.2.6.3 Trend Distribution
The distribution of a given hydroclimatic parameter’s trend magnitude of each grid
cell is plotted with its normal distribution curve.
Let Xi be the series of trend magnitudes for n number of grid cells, then the Gaussian
distribution curve of trend magnitude is obtained as
where x is the mean of the series and cr is the standard deviation of the series (Wilks, 2011).
3.2.6.4 Hydroclimatic trends with elevation
The annual and seasonal trends are plotted with respect to elevation of each grid cell
of the study area to better understand the influence of altitude on the hydroclimatic trends.
The elevations of each grid cell are extracted from the same DEM that is used during data
, -oo < x < 00 (17)
I 3 7 1
interpolation (see Figure 6). The calculated trend magnitudes for each grid cell are plotted
against the elevation. This visually shows the relationship between the trends and elevation in
the CMR.
3.2.6.5 LOESS
Locally weighted regression (LOESS) is a means to estimate a regression through a
multivariate smoothing procedure, fitting a function of the independent variables locally and
in a moving fashion analogous to how a moving average is computed for a time series
(Cleveland & Devlin, 1988). Let x be a point in a data series then the quadratic fit of x using
other points around it, weighted by their distance from the x gives LOESS. In this study,
LOESS is used to evaluate the elevational dependence of hydroclimatic parameters in the
4. Results
The results of the analysis of hydroclimatic data of the CMR are presented in this
chapter. The patterns of air temperature and precipitation in the region based on observed
data from different agencies are shown first. The results of the validation of gridded data with
independent CAMnet data are followed by basic statistics of the hydroclimatology of the
region. The basic hydroclimatic statistics are given in tabular format to provide general
information on the climate of the CMR. Furthermore, annual, seasonal, and monthly air
temperature and precipitation data are analyzed and presented.
The results of the validation of interpolated data are assessed with Pearson’s
correlation coefficient (r) and corresponding /7-values, RMSEs, and NSE coefficients to
ensure the reliability of the data. The temporal and spatial trends and trend plots for annual
and seasonal long-term interpolated air temperature and precipitation data for the region are
then presented and described. The last section of this chapter shows the results of elevational
dependency on hydroclimatic variations and trends in the CMR.
4.1 Observed air temperature/precipitation variations
The CAMnet data are used along with data from Environment Canada and the BC
River Forecast Centre (BCRFC) to enhance the understanding of short-term climatic
variations in the CMR.
Location o f the stations used for preliminary analysis.Zb
zb
com
ob ‘o<0in
oo .COo04in
ooo04in
bS i0T"in
LegendStations
1 1 Study area
Quesnel River Basin
Elevation (m)High : 3496
Low: 382
0 25 50 100 kmL_j i i I i i i I
123°0'0"W 122W W 121W W 120°0'0"W124°0'0’W 119°0'0"W
Figure 7: Location o f the weather stations used for preliminary analysis o f hydroclimatology o f the
CMR.
Analysis of data collected by CAMnet stations as well as from different agencies such
as the BCRFC’s Yanks Peak snow pillow site and Ministry of Forests, Lands and Natural
Resources Operations’ (MLNRO) Likely Airport is performed. This is conducted to obtain
the preliminary information on how annual climate varies near Likely, BC, located on the
western slopes of the CMR (Figure 7).
Table 4 shows the details of the location and elevation of the stations used for this
analysis. These stations cover different elevations ranging from 744 m (Quesnel River
Research Centre (QRRC)) to 2105 m (Upper Castle Creek (UCC)).
Table 4: Details o f the stations used for the preliminary analysis.
Latitude Longitude Elevation
QRRC 52°37’06.4” N 121°35’23.5” W 744 m CAMnet 15 minutes
Browntop Mt. 52°42’28.0” N 121°20’02.4” W 2031 m CAMnet 15 minutes
Spanish Mt. 52°33’48.4” N 121°24’35.4” W 1511 m CAMnet 15 minutes
Yanks Peak
Snow Pillow52°49’00.0” N 1 2 1 °2 r0 0 .0 ” W 1683 m BCRFC 1 hour
Likely Airport 52°36’54.0” N 121°30’48.0” W 1046 m MFLNRO 1 hour
Lower Castle
Creek (LCC)53°03’45.0” N 120°26’04.0” W 1803 m CAMnet 15 minutes
Upper Castle
Creek (UCC)53°02’36.0” N 120°26’18.0” W 2105 m CAMnet 15 minutes
C ross correlation of daily m ean air tem perature among the stations
indicate statistically significant valuesLikely A irpo rt
Y a n k 's P e a k
S p a n is h Mt
B ro w n to p Mt
Figure 8: Cross correlation o f daily mean air temperature among the selected stations analyzed for the
study period o f September 2011 to June 2012.
Cross correlation of daily mean air temperature among the selected stations analyzed
for the study period of September 2011 to June 2012 is shown in Figure 8. There is a
significant (p<0.05) positive correlation among all stations within the region, illustrating that
the closer the stations are to each other, the higher is the correlation in their records. The
correlation is highest between stations located at higher elevations (e.g., Spanish and
Browntop Mountains) or stations at lower elevations (e.g., QRRC and Likely). As the
distance and elevation difference between the stations increases, the relation between
climatic variables decreases.
Daily air temperatures (°C) at QRB Sept2011 - June2012
Likely — Yanks Peak — QRRC — Spanish — Browntop
t --------------------1-------------------- 1--------------------1-------------------- 1--------------------1-------------------- 1-------------------- 1-------------------- 1-------------------1.............. r
Tlme[Day]
Figure 9: Daily air temperature variations in the Quesnel River Basin (QRB), on the western slopes o f
the CMR, September 2011 to June 2012.
Time series plots are constructed for the observational data of air temperature and
precipitation in the region. The combined time series of air temperatures for the five stations
show that, in general, the lower elevation stations (QRRC and Likely) record higher air
temperatures than the high elevation stations (Figure 9). However, in the case of extreme low
air temperatures, such as in the middle of January and February 2012, the high elevation
stations are warmer than lower elevation records (Figure 10). This may be due to air
temperature inversions occurring during extreme winter days.
11-Sap 11-Oct L . . . _______ il-Oec ............ ^2000" • •
■■*
1600-1..-)y i
■~!...k)
"I.
fy]
1200-A A A A
800- ■ ■ ■ ■..... ......7
"T T -8 9
t----- r10 11
1— -2 0
1 ----- r2 4 -8 -6 -4 -2 -7 - 6 - 5 - 4
. 12-Jan _.............: L_ 12-Fab i 12-M»r I ................... ....... .......12000“• • • •
Elev
atio
n (m
)
i 1
i i
fx
- fi
- f - 4 -
A800- ■ ■ ■ ■
-9 -812-May
-7 __ 6̂-7 ^ -5 12-Jun
-4 _ -3 - 6 - 4 - 2 0-2.5 0.0 2.5
2000"
1600"
•
Mi- V_T_
•
, ^
Station♦ Browntop Mt. -j— Spanish Mt.A LikelyRS ! '■ Yanks Peak East
■ QRRC
1200*A A
800- ■ ■■ ...... l—
2.5 5.0 7.5 4 6 8 10 12Monthly Mean Temp(°C)
Figure 10: Elevational dependence o f mean monthly air temperature on the western slopes o f the
CMR (monthly average o f September 2011 - June 2012 observational data).
The average monthly surface lapse rate on the western slopes of the CMR is
calculated to see monthly temperature variations. The monthly average surface temperature
lapse rate of the region is 4.7°C km'1 (Min = 2.4°C km"1 in February, Max = 6.6°C km'1 in
June)1 (Table 5). The atmosphere in the region is generally stable during winter months and
1 July and August excluded
near neutral to unstable during the summer. The unstable atmosphere during the summer,
especially during daytime, causes convective activity such as showers and thunderstorms.
Table 5: Monthly lapse rate in the Cariboo Mountains.
Average monthly 4.1 5.1 5.7 3.2 2.5 2.4 5.9 5,5 6.2 6.6lapse rate (°C km ’)
The monthly total precipitation is calculated for two nearby stations: the QRRC and
Likely Airport stations. These two stations, the Likely RS and QRRC have a 302 m elevation
difference (Table 4). There is a strong correlation in the measurement of monthly
precipitation between the two stations (r = 0. 89,/? < 0.05) (Figure 11).
Total monthly precipitation @ Likely and QRRC
120-
| ioo-'E'o| 80- Q .O<DQ- 6 0 iJSot->« 40-£cO2 20 -
0 -
Station
■ Likely RS
Q R R C
Time[Months]
Figure 11: Monthly total precipitation at the Likely RS and QRRC stations in the QRB that lies on the
western slopes o f the CMR, September 2011 to June 2012.
4.2 Validation of Interpolated (Gridded) Data
The observed data from four CAMnet stations (namely QRRC, Spanish Mountain,
UCC, and Browntop Mountain) are used for the comparison and validation of CCC
interpolated data. These stations are located within the QRB on the western slopes of the
CMR; the UCC station is on the spine of the Cariboo Mountains chain. These stations cover
different elevations ranging from 744 m (QRRC) to 2015 m (UCC). The elevations of the
stations and corresponding gridded data and their elevation difference are given in Table 6.
QRRC and UCC stations, which are at the lowest and highest elevations, respectively, show
negative elevational difference with the gridded data. The Browntop Mountain and Spanish
Mountain stations show positive elevational difference with gridded data.
Table 6: Elevation o f stations used for validation with elevation difference from gridded data.
QRRC 744 921 -177
Browntop Mountain 2031 1706 325
Spanish Mountain 1511 1084 427
UCC 2105 2220 -115
Figure 12 shows the comparison of gridded and observed monthly average daily
minimum air temperature data for the four stations. The CCC minimum air temperature data
are validated with independent observed data at all stations. The NSE coefficient is higher
than 90% for all the stations except at Spanish Mountain (85%), and all correlations (r) are
also higher than 0.9. The daily minimum air temperature comparison shows that NSE
coefficient is higher than 70% for all the stations except Spanish Mountain (67%) and r-
values are higher than 0.8 (p<0.05) for all stations (Appendix A). The NSE and r-values are
high at higher elevation stations such as Browntop Mountain and UCC for minimum air
temperature. Therefore, the minimum air temperature at higher elevation regions of the CMR
is well captured by CCC data.
CCC & CAMnet monthly Tmin. in the CMR
Canadian Climate Coverage — Cariboo Alpine Mesonet
10- NSE= 0.9 , RMSE= 2.19 °C , r= 0.98 (p= 0 )
- 10 -
S—20-I £ 10- .8 °C , r= 0.99 (p= 0 )
NSE= 0.85 , R M S& S5J7 °C , r= 0.97 (p= 0 )
t - 1 0 -f - 2 0 - £ 10- NSE= 0.95 , RMSE= 1.54 "C , r= 0.99 (p= 0 )
- 10 -
-20-I
Time[Months]
Figure 12: Monthly minimum air temperature (CCC and CAMnet) with NSE, RMSE, r, andp values.
Figure 13 shows the monthly average daily maximum gridded and observed air
temperatures for four stations with their NSE, RMSE, and r- values. The CCC maximum air
temperature data correlate well with independent observed data at all stations. The monthly
maximum air temperature NSE is only 0.29 and 0.16 for Browntop and UCC stations
respectively, but they are 0.97 and 0.79 for QRRC and Spanish Mountain, r-values are higher
than 0.9 for all stations. The RMSEs for minimum temperature are low; however, there are
up to about 6°C differences for some stations. This is likely due to the elevation difference
between the station and the elevation of the grid cell nearest to the station whose data are
used for validation.
CCC & CAMnet monthly Tmax. in the CMR
Canadian Climate Coverage — Cariboo Alpine Mesonet
20 -
10-
NSE= 0.29 , RMSE* 6.48 °C , (p= 0
3 20- 5® 10 - Q.
NSE= 0.97 , RMSE= 1.68 "C , r= 0.99 (p= 0 )- 10-1
g 20- S 10-
£ - 1 0 - |NSE= 0.79 , RMSE= 3.92 »C ,_r= 0.99 (p= 0 )
5 20- / Vio- y s Xo-
NSE= 0.16 , RMSE= 6.28 "C ,- IU I I.... . ..'T ..... I ' "
Time[Months]
Figure 13: Monthly maximum air temperature (CCC and CAMnet) with NSE, RMSE, r, and p values.
Figure 14 shows the correlation between CAMnet observed and CCC gridded air
temperature (monthly average minimum and monthly average maximum). For minimum air
temperatures, CCC and CAMnet data show a strong relationship, but interpolated values are
generally underestimated from their observed values. For maximum air temperature, there is
a strong correlation, but CCC data overestimate the daily maximum air temperature than
observed.
The gaps between the interpolated and observed data are not only due to the
interpolation, but also because of elevational difference. The observed stations’ elevations
are different than the elevation of nearest grid cells whose data are extracted for validation
(Table 6).
Correlation: Tmax.Correlation: Tmin
♦ Browntop
* QRRC
• Spanish
•4” UCC
• Browntop
A q r r c
• Spanish
I UCC
■10 -5 0 5 10 15 20 25 30CAMnet Tmax.(Obserwed)(°C)CAMnet Tmin.(Observed)(°C)
Figure 14: Comparison o f mean monthly minimum and maximum air temperature between CAMnet
observed and CCC gridded data for the period o f 2007 - 2010 at four locations in the CMR. The solid
diagonal line is the 1:1 line.
CCC data do not capture precipitation data as well as air temperature data. The
monthly precipitation between observed and gridded data at the QRRC station exhibit a
significant correlation of 0.64 (p <0.05) (Figure 15). There are many gaps in observed
precipitation data for other stations, especially during winter as the remote stations do not
have heated precipitation gauges. Even for QRRC, which has a heated precipitation gauge
and thus more reliable observational data, the gridded data are underestimating the observed
precipitation. Local convective events may be responsible for these differences in the
observed and interpolated precipitation data. For example, for the summer months, the NSE,
RMSE and r-values are -0.48,28.15 mm, and 0.2 (p>0.05) respectively, for QRRC while
they are 0.4, 16.83 mm, and 0.87 (p<0.05) for the winter months.
The comparison of daily minimum and maximum air temperatures and precipitation
data is given in Appendix A. At daily scale both minimum and maximum air temperatures
show high and significant correlations (r>0.88,/?<0.05) for all stations. Precipitation data do
not exhibit good correlation except for QRRC. This is because of missing data for several
days in almost all of these stations.
CCC & CAMnet monthly Precip. in the CMR
■ Canadian Climate CoverageBCariboo Alpine Mesonet
100 -
50-
E E'S'lOOl01 50Q.'O n .(I) u
NSE= -1.44 , RMSE= 44.79 mm , r= 0.2 (p= 0.18 ) CO
3
111111 l l l l l 11 Li.hkllhhLl.LL AhkilLll i u lI k ill.LI *m l o
NSE= 0.35 , RMSE= 18.53 mm , r= 0.64 (p= 0 )DTilO
.2100O|P 50 +* ca OH
NSE= -0.08 , RMSE= 30.96 mm , r= 0.4 (p= 0.01 ) g |
IfaJiillkLlI i jiLllhLili 11 illijlI liNSE= -1.52 , RMSE= 50.98 mm , r= 0.23 (p= (
100 I I I I . | C
* h l i l l i L I L I I I I i . l l l l l u l l . l l i l l i l l l l , ^ J l I i I I i l . 8 1---------1---------1--------- 1--------- 1---------1--------- 1--------- 1--------- 1---------r..........1----------1--------- 1--------- 1---------1--------- !—
^ ^ N.̂ X^ ^ ^ ^^ v9 ^ o ^ ^ o f Vs ^ 0& ' f $ o
Time[Months]
Figure 15: CCC and CAMnet monthly precipitation data with NSE, RMSE, r, andp values. Gaps are
due to missing observational data as remote CAMnet stations record only rainfall.
4.3 General hydroclimatology of the region
4.3.1 General statistics
Table 7 shows the basic hydroclimatological statistics of the CMR, summarized
seasonally and annually for the period of 1950-2010 using CCC data. The minimum,
maximum, and mean values of each hydroclimatic parameters (namely minimum air
temperature, maximum air temperature, and precipitation) averaged over the region is
calculated and shown in the table. The annual and seasonal values are given with their
standard deviations (SD) and coefficient of variations (CV) for precipitation and only SD for
air temperature. Mean annual minimum air temperature over the region is below freezing
while maximum air temperature is above freezing. During winter, both maximum and
minimum air temperatures are below freezing while during summer both are above freezing.
The total mean annual precipitation over the area is about 738 mm, with wet years receiving
more than 1000 mm precipitation and dry years receiving less than 500 mm on average.
Table 7: Basic statistics of hydroclimatic parameters in the CMR, 1950-2010.
Annual -7.4°C -0.9°C -3.7°C 1.3°C NA
Winter -16.3°C -9.8°C -12.8°C 1.2°C NA
Tm in. Spring -8.8°C -1.2°C -4.4°C 1.5°C NA
Summer 2.0°C 8.0°C 5.2°C 1.2°C NA
Fall -6.6°C -0.1 °C -2.8°C 1.3°C NA
521
Annual 1.1°C 11.1°C 7.3°C 1.9°C NA
Winter -7.5°C -1.5°C -3.8°C 1.3°C NA
Tmax. Spring -0.2°C 12.3°C 7.4°C 2.4°C NA
Summer 10.8°C 23.4°C 18.4°C 2.3°C NA
Fall 1.0°C 10.7°C 6.9°C 1.9°C NA
Annual -3.2°C 5.8°C 1.8°C 1.6°C NA
Winter -11.9°C -5.5°C -8.3°C 1.2°C NA
Tmean Spring -4.6°C 6.4°C 1.6°C 1.9°C NA
Summer 6.4°C 16.7°C 11.8°C 1.8°C NA
Fall -2.9°C 5.8°C 2.1 °C 1.6°C NA
Annual 425 mm 1008 mm 739 mm 140 mm 19%
Winter 98 mm 294 mm 191 mm 46 mm 25%
Prep. Spring 70 mm 213 mm 140 mm 32 mm 23%
Summer 132 mm 277 mm 209 mm 31 mm 15%
Fall 99 mm 283 mm 196 mm 44 mm 23%
The minimum, maximum, mean values, and standard deviations of average
hydroclimatic data are calculated for the CMR to capture the average annual and seasonal
variability. The SD of mean annual, summer, fall, and spring air temperature is 2°C but for
winter, it is 1°C. The SD of minimum air temperature is less than the maximum air
temperature, indicating that minimum temperatures are less dispersed than the maximum
temperatures in the region. The annual precipitation CV is 19%, during winter it is above
25%, while it is only 15% during summer. The precipitation in the region is less dispersed
from its mean value during the summer months while it is highly dispersed during winter
months.
4.3.2 Average Hydroclimatic variation
Annual, seasonal, and mean monthly hydroclimatic variations of the CMR are
analyzed. Average minimum air temperature in the region is below -15°C during winter
months while the highest minimum air temperature is >5°C in July (Figure 16). The
maximum average monthly air temperature in the region is below -5°C during winter and is
nearly 20°C during summer months. Generally, for winter and spring the air temperature
range is almost equal, but for fall months it is narrower. On average, there is almost a 40°C
difference in air temperature between the warmest and coldest time of a year. This indicates
that the region experiences extremes in air temperatures, reflecting its continentality.
M onthly T em pera ture ove r the CM R
O-
O-
O-
Jan F eb Mar Apr May Jun JulT im e [Month]
Figure 16: Box plots o f monthly maximum (top) and minimum (bottom) air temperatures over the
CMR, 1950-2010. The small white diamond in the centre shows the mean, the black line in the centre
shows the median, the notch shows the 95% confidence interval o f the median, the vertical line shows
range o f minimum and maximum values excluding outliers, and the black dot shows the outliers
defined as the values greater than 1.5 times the interquartile range o f corresponding air temperature.
There are lower minimum air temperature variations during the summer, but higher
variations during winter, whereas maximum air temperatures do not show much variation.
Average annual spatial variation of minimum and maximum air temperatures show that
higher elevation areas are below freezing or near to 0°C year-round with up to 2°C of
standard deviation (Figure 17). The SD of average annual minimum air temperature
(minimum = 0.94°C, maximum = 1.81°C, and mean = 1.20°C) is greater than that of average
annual maximum air temperature (minimum = 0.86°C, maximum - 1.04°C, and mean =
0.93°C). Therefore, average annual minimum air temperature variation in the region is higher
than average annual maximum air temperature variation.
T m in : M ea n
»'
•121 •120L o n g i t u d e ( ”W )
T m in : S D
-1 2 2 -121 -1 2 0 L o n g i t u d e ( ° W )
T m ax : M ea nX '
•122 -121L o n g i t u d e (* W )
T m ax : S D
- 1 2 2 -1 2 1 - 1 2 0 - 1 1 8 L o n g i t u d e ( ° W )
Figure 17: Mean annual minimum and maximum air temperatures and standard deviations (SD) overthe CMR, 1950-2010.
Mean Tmin: Seasonal
<UsI s
Winter ( • S***
\II/
Jl[
i I -...
......
..."1
e/
Summer [ Fat
%
~ l r --122 -121 -120 -119 -122 -121 -120 -119
L ongitude (°W )
Temp. [‘‘Cl
M ean Tmax: S easonal! = :
r \
o
-
Summar 1 Fat
%
0
-122 -121 -120 -119 -122 -121 -120 -119L ongitude (°W )
Temp.[°CJ
SD Tmin: S easonal SD Tmax: S easonal
■as
- - |v w n r vp>**9
% ''/
r1L
—y
S u m m e r ; [ F a t
p )
\ J-122 -121 -120 -119 -122 -121 -120 -119
L ongitude (°W )
Temp.f’C]
Kto «S
W in ter I ’
\ /9wmm | F a t
P \
-122 -121 -120 -119 -122 -121 -120 -119Longitude (aW )
Temp.[e,C]
Figure 18: Seasonal mean minimum and maximum air temperatures and their SD over the CMR,1950-2010.
571
Monthly P rec ip itation ove r the CM R
o-1Jan Feb Mar Apr May Jun Jul
Time[Month]
Figure 19: Boxplot o f monthly precipitation in the CMR, 1950-2010. The small white diamond in the
centre shows the mean, the black line in the centre shows the median, the notch shows 95%
confidence interval o f the median, the vertical line shows range o f minimum and maximum values
excluding outliers, and the black dot shows the outliers defined as the values greater than 1.5 times
the interquartile range o f the precipitation.
The seasonal variation of mean seasonal minimum and maximum air temperatures
show that winters are below freezing and summers are above freezing over the entire region.
The air temperature variation in the region is highest during winter and lowest during
summer (Figure 18).
Figure 19 shows the mean monthly total precipitation over the CMR. Generally,
winter and summer months receive higher precipitation amounts than spring and fall. For
example, monthly mean precipitation is about 72 mm in January, while that in April is only
40 mm.
Mean annual precipitation is low at lower elevations compared to higher elevations of the
Cariboo Mountains; the southeastern part of the region receives more precipitation. Mean
seasonal precipitation variations show that the spring and fall months receive less
precipitation than winter and summer, with higher elevation regions receiving more
precipitation (Figure 21). Seasonal total precipitation is highly varying during winter and
summer followed by fall and spring, respectively (Figure 21). The wettest three months are
June, January, and December. Most of the precipitation should be in the form of snow
because winter months show the highest amounts of precipitation.
Precipitation: CVPrecipitation: Mean
CV[%]
-121 -120 Longitude (°W) -121 -120
Longitude (°W)
Figure 20: Mean annual precipitation and variation over the CMR, 1950-2010.
aj 3
Mean Prep.: Seasonal
Summar |1 Fal
-122 -121 -120 -119 -122 -121 -120 -119Longitude (°W)
Prcp.|mm]
%2
CV Prop : SeasonalviUMar
r \ . f x
i \X i . i
UtammraiFwaaaaaawa | Frt
0-122 -121 -120 -119 -122 -121 -120 -119
Longitude (°W)
cvr%i
100 150 200 250 25 30 35 40
Figure 21: Mean seasonal precipitation with seasonal variation expressed by the CV (%), 1950-2010.
4.4 Hydroclimatic trends in the Cariboo Mountains Region
4.4.1 Temporal hydroclimatic trends in the CMR
4.4.1.1 Annual Trends
Average annual air temperature anomalies and trends are calculated and plotted for
the CMR based on the CCC dataset. The annual minimum air temperature anomaly with the
trend is shown in Figure 22 (the trends are given in Appendix D).
Annual minimum temp, anomaly in the CMR
Trend= 0.32 °C/decade
p = 8. le-062 .0 -
1.0 -
s- 0.5-
1 o.o-O£-0.5-
3-1.0
-1.5-
-2.5- - 5 yrs moving average — Median based Trend
^Negative Anomaly! Positive Anomaly-3.0-
-3.5
Time [Year]
Figure 22: Annual minimum air temperature anomalies and trend for the CMR, 1950-2010.
A strong positive trend of 0.32°C decade'1 (p<0.05) is observed in annual minimum
air temperature over the 61-year period in the CMR. Although there is high fluctuation in
these temperatures, the 5-year moving average shows a continuously increasing minimum air
temperature in recent decades. The annual minimum air temperature anomaly shows positive
anomalies for many recent years (24 out of 30 recent years) (Figure 22). The 1950s and
1960s show negative anomalies with low values (anomalies <3.0°C). High values (anomalies
>1.5°C) occur in recent decades such as the 1990s and 2000s. After 2000 there are
continuous positive anomalies except in 2008 and 2009 (cf. discussion in section 5.1).
Annual maximum temp, anomaly in the CMR
Trends 0.19 °C/decade p = 0.0073
2.0
O 1 .0 -
>>™ 0.5-
jo -0.5 -<uo .E-1.0-
-1.5-5 yrs moving average ” Median based Trend
| Negative A nom alyH Positive Anomaly- 2 .0 -
Time [Year]
Figure 23: Annual maximum air temperature anomalies and trend with moving average for the CMR,
1950-2010.
A significant positive trend of 0.19°C decade'1 (p<0.05) is observed in annual
maximum air temperature anomalies over the 61-year period in the CMR, a trend 0.13°C
decade'1 less than for the minimum air temperature trend. Year to year maximum air
temperature variations are high. The annual maximum air temperature anomalies show
positive anomalies for many recent years (22 out of 30 recent years) (Figure 23). After 1986,
most of the years show positive anomalies (as high as 2.0°C). The minimum and maximum
air temperature anomalies coincided for 25 years (positive anomalies coincided 21 years and
negative anomalies coincided 4 years) during the most recent 30 years. The mean air
temperature trend and anomalies are given in Appendix E (cf. discussion in section 5.1).
There is no specific trend in precipitation during the past six decades over the CMR.
This is interesting given the significant changes in temperature in the CMR. Maximum total
annual precipitation is >900 mm per year, while the minimum value is <500 mm.
CO
EocCOco5 - 100-
Q .o0)
Annual total precipitation anomaly in the CMR200 -
_ 100 -
EE
o-
- 200 -
-300-
Trend= -0.92 mm/decade
p = 0.93
5 yrs moving average— Median based Trend
| Negative Anomaly | Positive Anomaly 1------- 1—
\ NVb
Time [Year]
Figure 24: Annual precipitation anomalies and trend with 5-year moving average in the CMR, 1950-
2010 .
63 |
4.4.1.2 Seasonal Trends
Seasonal Minimum Temp, anomalies and trend in the CMR
Trend= 0.62 °C /decadep = 0
Trend= 0.35 °C /decade
P“ 0
Trend= 0.1 ’C /decade p= 0.44
i------------ 1------------ 1------------ 1------------1------------ 1------------1------------ 1------------1------------ 1------------ 1------------ 1------------ r
Time [Year]
— 5 yrs moving average — Median based Trend | Negative Anomaly f l Positive Anomaly
Figure 25: Seasonal minimum air temperature anomalies and trends in the CMR, 1950-2010.
Figure 25 shows seasonal average daily minimum air temperature anomalies and
trends. The minimum air temperature trends in winter (0.62°C decade'1,/><0.05) and spring
(0.35°C decade'1, p<0.05) are significantly higher compared to summer (0.12°C decade'1,
p<0.05) and fall (0.10°C decade'1, p>0.05). The seasonal temporal trends are statistically
significant for all seasons except for fall. Furthermore, seasonal anomalies of minimum air
temperatures show that, in recent years, seasons are much warmer (especially after the
1990s) compared to previous years. For example, out of the most recent 30 years, 21 years
show positive winter anomalies and 26 years show positive spring anomalies. Winter has the
highest positive anomaly of up to about 5°C in some years while summer has the lowest.
2.5-
o.o-
-2.5-
-5.0-
2.5-
o.o-00^ - 2 .5 -(0Eg -5 .0 -(0
1
§5 2.5-Ol
| 0 .0-
-2.5-
-5.0-
2.5-
o.o-
-2.5-
-5.0-
Seasonal Maximum Temp, anomalies and trend in the CMR
| I I 1 Trend= 0.39 °C /decade| | p=0.01
Winter
w “ i 1*
■ T rend= 0 .15°C /decadep= 0.15
«*»
Trend=0.11 °C /decadep= 0.28
j
1 Trend= -0.04 °C /decade 1 p - 0.74
~
« # d ^ d S ^ c # d?>S # c£ S
Time [Year]
— 5y rs moving average — Median based Trend H Negative Anomaly J | Positive Anomaly
Figure 26: Seasonal maximum air temperature anomalies and trends in the CMR, 1950-2010.
Figure 26 shows seasonal daily average maximum air temperature anomalies and
their trends. Maximum seasonal air temperature trends are lower than minimum seasonal air
temperature trends. Winter shows the highest significant maximum air temperature trends
(0.39°C decade'1, /?<0.05) among all the seasons followed by spring (0.15°C decade'1,
jC>>0.05). Unlike minimum air temperature trends (where only the fall temperature trend is not
statistically-significant), maximum air temperature trends are only significant in winter
(p<0.05). Furthermore, seasonal anomalies of maximum air temperatures show that winters
in recent decades are warmer than before (20 years out of recent 30 years show positive
anomalies). The mean seasonal air temperature anomalies and trends also show anomalies
and trends similar to minimum air temperatures.
Each year’s seasonal total precipitation shows varying trends among the different
seasons (Figure 27). Winter precipitation decreases by about 6.9 mm decade'1 (p>0.05) even
though temperature is increasing while spring (1.4 mm decade'1, p>0.05) and fall (5.1 mm
decade'1, /?>0.05) show increasing trends, but none of these trends are statistically significant.
Seasonal anomalies of total precipitation show that winters in recent years experience less
abundant precipitation.
Seasonal Precipitation anomalies and trend in the CMR
Trend= -6.88 mtn/decadep=0.08
Trend= 1.38 mm/decade
Trend=-1.31 mm/decade p= 0 .7 |
Trend=5.11 mm/decadep= 0.18 ■ ■
t------------1------------1------------1------------1------------1------------1------------1------------1------------1------------ 1------------1------------r
Time [Year]
— 5 yrs moving average — Median based Trend ^Negative Anomaly ( | Positive Anomaly
Figure 27: Seasonal total precipitation anomalies and trends over the CMR, 1950-2010.
4.4.2 Spatial hydroclimatological trends in the CMR
Annual and seasonal trend analyses for minimum and maximum air temperatures and
precipitation are performed for each grid point (~ 10 km x 10 km) of the CMR.
4.4.2.1 Annual Trends
Annual Tmin. trend in the CMR
■'*m
com
0"OD
CMm
Trend (°Cdecade )
SignificanceSig. • N ot sig.++++♦♦
+ ♦♦ + ♦* + ♦♦ +V 4- * ♦+ + + + ♦ + ♦ + ♦ + *♦♦ +B*
+ +♦♦ + ♦ ♦ ♦ +♦ ♦ • ♦ ++♦ + ♦ ++♦ +♦ ♦ ♦ ♦ ♦ +* ♦ ♦ ♦ +++
♦*++♦♦+♦♦++++♦♦+♦++♦
1
-122 -121 Longitude (°W)
-120 -119
Figure 28: Minimum air temperature trends and their significance over the CMR, 1950-2010.
Annual Tmax. trend in the CMR
■'f10 Trend (°Cdecade 1)
Significance
*■ Sig. • Not sig.
com
a)TJ3ro
CMin
0.3
-122 -121 -120 Longitude (°W )
-119
Figure 29: Maximum air temperature trends and their significance over the CMR, 1950-2010.
Figure 28 shows the annual spatial trend of minimum air temperatures while Figure
29 shows the spatial trend of maximum air temperature. For minimum air temperature, the
minimum trend over the region is 0.08°C decade'1 and the maximum trend is 0.69°C
decade1. All grid cells show a significant increase of minimum air temperature except those
grid cells represented by dots in the southwest, lower elevations of the CMR (Figure 28).
Maximum air temperature trends range from 0.06°C decade'1 to 0.30°C decade'1 (cf.
discussion in section 5.1.2). All grid cells show a significant increase of maximum air
temperature except points represented by dots in the southeastern part of the domain (Figure
29).
Spatial minimum air temperature trend magnitudes are higher than the maximum air
temperature trend magnitudes. Air temperature is rising throughout the CMR with higher
spatial variability in minimum air temperature trend than in the maximum air temperature
trend. Furthermore, spatial trends of minimum and maximum air temperature show an almost
opposite pattern of increase with elevation. The minimum air temperature trends are stronger
at higher elevations and maximum air temperature trends are stronger in lower elevations.
Further details of elevational dependency of air temperature trends are discussed in Results
section 4.5 and Discussion section 5.3.
Figure 30 shows the varying pattern of annual precipitation trends: it is increasing in
some parts and decreasing in other parts of the CMR. The extreme values of annual
precipitation trends range from -3 mm year'1 to +3 mm year'1 but most of the grid cells are
not significant ip >0.05) as represented by dots in Figure 30 (cf. discussion in section 5.1.2).
Annual total precipitation trend in the CMR
10
COW
a;■o34-»CD
CNm
Trend (mmdecade 1)
Significance + Sig. • Not sig.
♦+ ♦ +
* ♦ + m.
-12l' -120Longitude (°W)
-122 -1 1 9
Figure 30: Annual total precipitation trends and their significance over the CMR, 1950-2010.
4.4.2.2 Seasonal trends
Spatial trends of seasonal minimum temperature
Winter
co10
CMlO
<DTJ3
Spring
^XXXXXXKXXX**ftXXMKft**(* pxXXKX»ttXXKX*KXXKtt K X X X X K X K X X X M M X X X
j N I IKNNXIX IIt ia i l t l l l1 IX IK II«M XKKKI«#t»t\*im**
*******VKRIM«
Fall
to 3
COm
CMLO
■ ■ « « « ■ ■ » « x x x x x x x■■MXKXMNXXXXXKXXXXXX
X X X X
-122 -121 -120 -119 -122Longitude (°W)
-121 -120 -119
Trend (°Cdecade )
0.0 0.4 0.8 1.2
Significance « Sig. • Not sig.
Figure 31: Spatial variation o f seasonal minimum air temperature trends over the CMR, 1950-2010.
F ig u re 31 sh o w s th e m in im u m a ir tem p e ra tu re tre n d s fo r fo u r sea so n s in th e C M R .
All grid cells for all seasons show positive and significant trends in minimum air temperature
except for a few grid cells during fall that are not significant (Figure 31). There is a strong
seasonal variation in the trend magnitude: winter (minimum 0.24°C decade'1, maximum
1.12°C decade1), spring (minimum 0.09°C decade1, maximum 0.72°C decade'1), summer
(minimum -0.18°C decade'1, maximum 0.42°C decade'1), and fall (minimum -0.12°C
decade'1, maximum 0.29°C decade'1). Winter and spring trends are stronger in their
magnitude and are highly significant.
Figure 32 shows the maximum air temperature trends for four seasons in the CMR.
During winter and spring, the maximum air temperature trends are positive for all grid cells,
and for the most part, significant. The summer and fall trends are positive as well as negative
based on the spatial location (Figure 32). The summer trends are not significant for most of
the grid cells while fall trends are significant for all grid cells. There is a strong seasonal
variation in the trend magnitude: winter (minimum 0.21°C decade"1, maximum 0.59°C
decade'1), spring (minimum 0.03°C decade'1, maximum 0.36°C decade'1), summer
(minimum -0.13°C decade'1, maximum 0.32°C decade'1), and fall
(minimum -0.14°C decade’1, maximum 0.13°C decade'1) (cf. discussion in section 5.1.2).
Unlike the opposite pattern of elevational dependence of annual minimum and maximum air
temperature trends, seasonal air temperature trends show different patterns with elevation.
This is further explained in Results section 4.5 and Discussion section 5.3.
Spatial trends of seasonal maximum temperature
Winter
S '
COin
CMm
HRlMVlilh
Q>“ODCD
Summer
s '
CO10
CMIf)
-122 -121 -120 -119 -122Longitude (°W )
-121 -120 -119
Trend (°C decade1) Significance* Sig. • Not sig.
0.0 0.2 0.4 0.6
Figure 32: Spatial variation o f seasonal maximum air temperature trends over the CMR, 1950-2010.
Spatial variation of seasonal precipitation trends
Winter
coif)
egif)
CDu3is ^ " cu »n
KVt t K « V H P
K K » * X S X K X R * K * f t K X X x>«x«xxirx itexi<m«x
K x x x M x x M K x x a a a K x x a x x x a a a a a a a a a K a w i x i m i i i K i R K i i i a i i f i i t M M f * • ♦ ♦ • • ♦ X x xKKM
Spring
• e e e a « e a e x e e x e x i t K R X R * K a a a a x axxkxxxx x;xxkxxk'kmm|II
• XKXKXK XWtt XttX**KKXK*lf Ktf X X * K M K X tt X X #* * « S§K$lt* iix ixx ttixn i
Summer Fal
oo"tn
CMtn
-122 -121 -120 -119 -122Longitude (°W )
-121 -120 -119
Trend (m m decade 1)
■ P WM-10 0 10
Significance Sig. " Not sig.
Figure 33: Spatial variation o f seasonal precipitation trends over the CMR, 1950-2010.
Seasonal precipitation trends show higher variation both spatially and in terms of
their significance (Figure 33). Only the spring precipitation is increasing over the whole
region. For all other seasons, precipitation is increasing in some parts and decreasing in other
parts of the CMR. For example, there is significantly decreasing precipitation trends in the
northern part of the CMR during winter while it is increasing in the southern part during
spring, summer, and fall. Summer precipitation trends are significant for the entire region.
For spring and fall only the higher precipitation region in the south is significant. Seasonal
precipitation trends show that the precipitation is increasing in the lower elevation region
than the higher elevation region (cf. discussion in section 5.1.2).
4.5 Elevation dependence o f hydroclim atic trends
The temporal and spatial trends presented in Section 4.4 show that hydroclimatic
parameters, especially minimum and maximum air temperatures in the CMR, are influenced
considerably by seasonal variability as well as different spatial patterns associated with
elevation. In this section, the results of how the air temperature trends are associated with
elevation in the CMR are presented.
4.5.1 Elevational dependence of annual trends
Annual minimum Temp, trend vs elevation in the CMR
ooinCM
♦ Sig.trend
• Not sig. trend
ooo0 .42844=
o "
• y 8 \* : • *o
0.70.4Trend ( Cdecade” ')
0.5 0.6 0.80.2 0.30.0
Figure 34: Minimum air temperature trend versus elevation, 1950-2010. Each point represents the
elevation of a grid cell while the solid blue line represents the LOESS fit.
Figures 34 and 35 show the vertical profile of annual minimum and maximum air
temperature trends with elevation over 1950-2010. There is higher variability of minimum air
temperature trend ranging from about 0.0°C decade'1 to 0.8°C decade'1 at different elevations
(-500 m to >2500 m). Higher and significant minimum air temperature trends arise clearly at
higher elevations, with grid cells >2000 m a.s.l. exhibiting trends >0.5°C decade'1 (cf.
discussion in section 5.3).
Annual maximum Temp, trend vs elevation in the CMR
♦
♦| 1 , , , 1 , r0.05 0.10 0.15 0.20 0.25 0.30 0.35
Trend fC decade-1)
Figure 35: Maximum air temperature trend versus elevation, 1950-2010. Each point represents the
elevation o f a grid cell while the solid blue line represents the LOESS fit.
Maximum air temperature trends range from about 0.05 °C decade'1 to
0.30°C decade'1 at different elevations. Higher and significant maximum air temperature
trends are found at lower elevations. Those grid cells that are above 2000 m a.s.l. show
mostly non-significant maximum air temperature trends with a magnitude of about 0.1 °C
decade*1 (Figure 35 and discussion in section 5.3). Generally, the areas below 1500 m a.s.l.
show significant maximum air temperature trends >0.15°C decade'1.
4.5.2 Elevational dependence of seasonal trends
To obtain further insights on the air temperature trends’ elevational dependency in the
CMR, analyses are now conducted on a seasonal basis.
Seasonal Tmin. trend vs elevation in the CMR
R - 0 . 6 1 n= 844
♦ S ig . t r e n d♦ N o t s ig . tr e n d
Figure 36: Elevational dependence o f seasonal minimum air temperature trends, 1950-2010. The solid
blue line shows the LOESS fit o f trends with elevation.
Seasonal Tmax. trend vs elevation in the CMR
♦ Sig.trend• Not sig. trend
Trend [“Cdecade ]
Figure 37: Elevational dependence o f seasonal maximum air temperature trends, 1950-2010. Thesolid blue line shows the LOESS fit o f trends with elevation.
Figures 36 and 37 show the elevational dependency of seasonal minimum and
maximum air temperature trends in the CMR over 1950-2010. The blue solid lines are
LOESS fits that show the general relation between elevation and air temperature trend
magnitude.
For minimum air temperature, winter, spring, and summer trends are greater at higher
elevations. Minimum air temperature trend magnitude above 2500 m a.s.l. reaches up to
1.3°C decade'1 during winter, while spring exhibits trends above 0.7°C decade'1 and summer
near 0.5°C decade1. For most of the grid cells, the minimum air temperature trend is not
significant for fall; nevertheless three of the grid cells that lie above 2000 m a.s.l. show a
significant air temperature trend of about 0.3-0.4°C decade'1. For maximum air temperature,
winter trends are mostly significant but do not show a relationship with elevation. The spring,
summer, and fall trends decrease with elevation, but only part of spring and summer trends
are statistically significant. The mean seasonal air temperature versus elevation plots are
given in Appendix I (cf. discussion in section 5.3).
4.5.3 Elevational dependence of precipitation trends
Total annual precipitation trends do not show a relationship with elevation (Figure
38). The significant precipitation trends are either low or high; the higher elevation (>2000
m) grid cells do not show significant trends. Seasonal precipitation trends also do not show
specific patterns with elevation (Figure 39). The significant precipitation trends grid cells are
mostly in the lower elevations of the region.
Total annual precipitation trend vs elevation in the CMR
co00>.0)Ul
oolOCM
OOOCM
OOin
ooo
w* Sig.trend #
♦ Not sig. trend ♦ \ # R2 = 0 02
***•/*'.*. . / . / * * n = 844•4 • . . •
♦ ♦ ♦ .
~f • » . » : ' V * **v* •.
4
Trend (mmyear )
Figure 38: Annual precipitation trend versus elevation, 1950-2010. The solid blue line shows the
LOESS fit of trends with elevation.
Seasonal precipitation trend vs elevation in the CMR
* Sig.trend• Not sig. trend
Trend [mmdecade ]
Figure 39: Elevational dependence o f seasonal precipitation trends, 1950-2010. The solid blue lineshows the LOESS fit o f trends with elevation.
5. Discussion
The linear and temporal hydroclimatic trends in the CMR, elevational dependence of
these trends, and impacts of such trends in the region are discussed in this chapter. The
spatial trend magnitude distribution is also described. Furthermore, findings of this study are
used to discuss some of the physical mechanisms in the CMR that are possibly associated
with the elevational dependency of the air temperature trends. Finally, possible impacts of
climate change to the region’s hydrology and ecosystems are explored.
5.1 Hydroclimatic trends
5.1.1 Magnitude and significance of linear trends
Annual and seasonal minimum and maximum air temperatures show significant
increasing trends over the period of 1950 to 2010 in the CMR (Figures 22 and 25) providing
clear evidence that the region is warming in recent decades. The minimum and maximum air
temperatures of the region rose by 1.93°C and 1.16°C respectively in the last six decades.
These warming rates are consistent with reported increased air temperatures for BC
(Dawson, Werner, & Murdock, 2008; MoE, 2014; PCIC, 2014; Picketts et al., 2012). A
report on BC’s climate states that its annual air temperature has warmed by 0.5-1.7°C during
the 20th century (MoE, 2014). The 1PCC AR5 (2013) report suggests that warming trends
over the past six decades are very likely due to positive radiative forcing. Increases in
greenhouse gases (GHGs) emissions, particularly from anthropogenic sources, have been
causing the positive radiative forcing and therefore, surface warming globally (IPCC, 2013).
Therefore, the trends observed in the CMR during the last six decades are consistent with
warming temperatures globally, and are associated with anthropogenic influences on climate.
The annual total precipitation does not show a specific pattern of increasing or
decreasing trend. Total annual precipitation anomalies show multiple continuous years of
positive anomalies during the 1990s and recent negative anomalies (Figure 24). Strong
negative anomalies in 2009 (about 150 mm) and 2010 (about 300 mm) may be associated
with the moderate El Nino events of these years.
S. 1.2 Magnitude and distribution of spatial trends
Trend Magnitude
The average annual spatial warming rates over the CMR are 0.33°C decade'1 and 0.18°C
decade'1 for minimum and maximum air temperatures, respectively (Figures 28 and 29). The
warming rates and spatial patterns are different across the seasons in the CMR. For example,
Table 8 shows the average air temperature increases over the CMR. The minimum air
temperature trends are higher than the maximum air temperature trends, but with large spatial
variability.
Table 8: Average minimum and maximum air temperatures trends over the CMR, 1950-2010. In
parenthesis are percentage of the significant values across the domain of the CMR.
Trnin. Tmax.
W inter 0.63 (89 %) 0.40 (93%)
Spring 0.39 (94%) 0.20 (33%)
Summer 0.15(78%) 0.10(5%)
Fall 0.13 (1%) -0.01 (<1%)
There is a clear difference of air temperature trends with elevation over the CMR but
the trends are not distinctly different along the western and eastern aspects of the study area.
Both the western and eastern slopes of the Cariboo Mountains show similar trends with
elevation. The northwestern and the southeastern regions of the CMR show significant
decreasing and increasing precipitation trends, respectively, but the central part o f the CMR
does not show significant trends (Figure 30).
Trend Distribution
The distribution of hydroclimatic trends over the CMR is analyzed to understand
which trend magnitudes are dominant, how they are dispersed from the mean trend, and how
they are related to elevation.
Distribution of minimum air temperature trend magnitudeMean=0.33
SD=0,12
OO 0 4 0 2 0 3 0 4 0 5 0 6 0 7 O ?Trend m agnitude [ ° C d e c a d e -1 ]
Distribution of maximum air tem perature trend magnitude
Mean=0.18SD=0.06
0 0 4 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34Trend m agnitude [ ° C d e ca d e -1 ]
Figure 40: Histogram o f the distribution o f air temperature trend magnitudes with mean trend (°C
decad e1) and its standard deviation (SD) (°C decade'1) over the CMR, 1950-2010. The solid red line
shows the mean trend magnitude and the solid black curve shows the Gaussian distribution o f the
trend magnitudes.
Figure 40 shows the distribution of annual minimum and maximum air temperature
trends over the CMR. The mean and standard deviation of trends are fitted with the Gaussian
distribution. The mean trend over the area is 0.33°C decade1 for minimum air temperature
with a standard deviation of 0.12°C decade'1. Similarly, the mean trend is 0.18°C decade"1 for
maximum air temperature with a standard deviation of 0.06°C decade'1. All trend magnitudes
for both minimum and maximum air temperatures in the CMR are positive. The highest
frequency of the trend magnitude for minimum air temperature trends is 0.35-0.40°C
decade'1, which is greater than the mean minimum air temperature trend (0.33°C decade'1).
For maximum air temperature, the highest frequency of trends is 0.14-0.16°C decade'1,
which is less than the mean maximum air temperature trend (0.18°C decade'1).
Distribution of precipitation trend magnitude
i-------- ,-----------------------j---------------------- 1 ~ |-----------------------1-----------------------1---------------------- \ ------------------ I-----------------------1---------
-40 -30 -20 -10 0 10 20 30 40
Trend magnitude [mm decade' 1 ]
Figure 41: Histogram o f the distribution o f annual total precipitation trend magnitudes with mean
trend (mm decade'1) and its standard deviation (SD) (mm decade'1) over the CMR, 1950-2010. The
solid red line shows the mean trend magnitude and the solid black curve shows the Gaussian
distribution o f the trend magnitudes.
The precipitation trend magnitude also follows the Gaussian distribution with mean
trend of nearly zero (-0.49 mm year"1). The distribution of precipitation trends is highly
dispersed (SD = 13.79 mm year'1) ( Figure 41).
S'
S'<0TJ•e0) 0 *
ooz 8"
CM
Seasonal temperatiTtnm
Mean= 0.64 , SD=0.02
jre trend distributionTmax
, SD= 0 ^ ^ ^ ^ ^ ^
Mean= 0 .39 , SD= 0.01
A
^ ^ ^ ^ ^ l e a n = 0.1 B , SD= 0.01
A
I Mean= 0.07 , SD= 0.01
A 1
Mean= 0.14 , SD= 0.01 Mean= -0.03 , SD= 0.01
iS'
Trend magnitude [ ° C decade ]
Figure 42: Seasonal trend distributions for minimum and maximum air temperatures in the CMR,
1950-2010. The solid red lines show seasonal mean trends for each season. The mean (°C decade"1)
and SD (°C decade'1) o f each season is given. Relative magnitudes o f seasonal trends are also
presented.
The seasonal trend distributions show large variation in terms of trend magnitude and
distribution among the seasons as well as between minimum and maximum air temperatures.
Most notably, increasing winter and spring air temperature trends show nearly Gaussian
distributions. The summer and fall air temperature trends are skewed; the minimum air
temperature trends are positively skewed and maximum air temperature trends are negatively
skewed (Figure 42).
Comparison with similar studies
The warming trends found in this study, especially for minimum air temperature over
the CMR are not only consistent with, but also stronger than most of the regional, Northern
Hemisphere, and global warming trends. The findings on hydroclimatic trends, especially air
temperatures, are consistent with the findings of Bonsai & Prowse (2003), Ceppi et al.
(2012), Dery & Wood (2005), Easterling (2000), Mann, Bradley, & Hughes (1999), Picketts
et al. (2012), Pike et al. (2008), MWLA (2002), Rangwala et al. (2013), Trenberth (1990),
Stocker et al. (2013), and X. Zhang, Vincent, Hogg, & Niitsoo (2000).
Of note, the results for minimum air temperature trends in the Cariboo Mountains
follow those of Liu et al. (2009), Diaz & Bradley (1997), and Fyfe & Flato (1999). In a
similar study for the Tibetan Plateau, Liu et al. (2009) examined the elevational dependency
of minimum surface air temperatures using station data. They found more prominent
warming at higher elevations than at lower elevations, especially during winter and spring.
Diaz & Bradley (1997) revealed higher increases in daily minimum air temperature than
maximum air temperature with elevation in the regional high elevation stations analysis of
the American and Canadian Rockies. Fyfe & Flato (1999) found enhanced warming signals
in the higher elevation region over the Rocky Mountains. They used the Canadian Centre for
Climate Modelling and Analysis (CCCma) Coupled Climate Model, and found marked
elevational dependency of increased surface air temperature at higher elevation in winter and
spring.
These findings are not consistent with McGuire et al. (2012) who reported strong
warming signals at mid-elevations of the Rocky Mountain Front Range for both 56 and 20
year periods. Further, the results of this study contradict those of Vuille & Bradley (2000)
who found reduced air temperature trends with increasing elevation in the tropical Andes.
These contradictions are more likely due to differences in study areas or in the approaches of
analysis.
There are many different factors and processes responsible for complex mountain
region warming. There are certain mechanisms associated with elevation that provide
specific responses to daily minimum and maximum air temperatures. Processes and
mechanisms possibly responsible for different air temperature responses with elevation and
their role in context of the CMR elevational dependency of warming are now discussed.
5.2 Physical mechanisms associated with trends
Seasonal breakdowns of air temperature trends over the CMR show spatial and
seasonal variations. Therefore, there should be some physical mechanisms related to
elevation of the area as well as the seasons that influence these patterns of air temperature
trends. Some of the physical mechanisms at local scales driven by climatic control of
mountains are presented in Tables 1 and 2. The role of SAF, clouds, soil moisture, specific
humidity, and other factors are discussed as possible factors responsible for such
hydroclimatic trends over the Cariboo Mountains. However, a detailed analysis and role of
impacts of each of these components is beyond the scope of this study. The summary of the
possible factors responsible for elevational warming in the CMR and their seasonal relevance
are presented in Table 9.
Table 9: Possible factors responsible for elevational warming in the CMR.
SAF (Decreases in Snow/Ice
Albedo)
Primarily spring;
Important in winter (Lower
elevation) and summer (Higher
elevation)
Increases Tmm
Increases Tmax
Increases in Cloud Cover
(Daytime)All seasons Decreases Tmax
Increases in Cloud
Cover (Nighttime)
All seasons (greater
effects in winter)Increases Tmm
Increases in Soil
Moisture
Snowmelt effects are strongest
in spring and winter; rainfall
effects are strongest in summer
Decreases diurnal
temperature range
(DTR); Decreases
Tmax
Increases in Specific
HumidityPrimarily winter Increases Tmjn
Local factors (Forest die back
because of mountain pine
beetle attack and impacts on
local energy balance)
All seasons Increases surface
temperature (Tmm
and Tmax)
5.2.1 Snow-albedo feedback (SAF)
Sharp increases in the minimum air temperature during spring in the CMR may be
due to the SAF. The SAF mechanism is one of the strongest climate drivers in the natural
climate system. It causes elevational gradients of warming in snow-covered regions,
particularly near the 0°C isotherm in relation to the surface energy budget (Flanner, Shell,
Barlage, Perovich, & Tschudi, 2011). Indeed, many studies have reported amplified warming
during spring days due to the SAF. Dery and Brown (2007) find amplified trends of negative
snow cover extent (SCE) in the Northern Hemisphere, North America, and Eurasia during
spring and argue that it is a possible driver contributing to recent changes in observed SCE.
Analysis of more than 1000 high elevation homogenized surface air temperature records
across the globe show strongest warming rates near the annual 0°C isotherm due to the SAF
(Pepin & Lundquist, 2008). Scherrer et al. (2012) quantify the effect of the SAF on Swiss
spring air temperature trends using daily air temperature and snow depth measurements.
They found that the daily mean 2-m temperature of a spring day without snow cover is on
average 0.4°C warmer than the one with snow cover at the same location. In the context of
these findings, the SAF may have played a significant role for the steep minimum spring air
temperature increase with elevation in the CMR. Furthermore, Fyfe & Flato (1999) have
shown that surface albedo changes are responsible for net solar radiation change, that in turn
has dominated the surface energy balance change. The effects of such changes are
responsible for surface warming during winters in the Rockies. The location of the Rockies is
near to this study area, lying in western North America, with similar global circulation
influences. Therefore, winter air temperature increases in the CMR may be related to the
surface albedo changes.
5.2.2 Clouds
The amplified warming in the CMR may relate to cloud-radiation feedback
mechanisms. Clouds have impacts both on short and long wave radiation, and thus affect
surface air temperatures (Warren, Eastman, & Hahn, 2007). Most notably they affect the
DTR and precipitation directly (Free & Sun, 2014; Ramanathan et al., 1989).
Milewska (2004) finds positive trends of cloud cover especially in night time in BC.
Dai, Karl, Sun, & Trenberth (2006) and Free and Sun (2014) show increasing trends in cloud
cover over the northwest United States and the Northern Hemisphere in recent decades.
Although their findings are not specific to the CMR region, their analyses cover northwestern
North America where the CMR lies. The increase of cloud concentration increases the
downwelling longwave radiation that results in increased minimum air temperature over the
CMR. Similarly, increased cloud concentration decreases surface insolation during daytime,
especially during summer, that decreases daily maximum temperature of the CMR.
Therefore, the increased cloud over the CMR in recent decades could be related with the air
temperature trend patterns observed over the CMR.
Furthermore, Beniston & Rebetez (1996) find that there is an increase of night time
air temperatures in winters in the lower valleys of the Alps than the regions exposed to
stratus clouds at higher elevations. The clouds trap outgoing infrared radiation and warm the
region. Most notably in winter, it is observed that the atmosphere near the CMR is calm and
there is often a temperature inversion layer that may be associated with trapped clouds
(section 4.1). Liu et al. (2009) also find increasing monthly minimum air temperatures with
elevation and suggest that this dependency may be caused partially by cloud/radiation
feedback effects. Similar conditions may have occurred in the CMR exhibiting higher
temperature trends in higher elevations.
5.2.3 Soil moisture and specific humidity
Soil moisture is considered as another important factor for air temperature increases
(Seneviratne, Liithi, Litschi, & Schar, 2006). Soil moisture conditions affect the energy
balance resulting in a change in surface air temperatures (Meng & Shen, 2014). Soil moisture
influences all regions except water bodies. Daily maximum air temperature (Tmax) increases
with decreasing soil moisture whereas an increase in soil moisture decreases DTR (Durre,
Wallace, & Lettenmaier, 2000). The soil moisture content of the CMR may be decreasing
with decreasing precipitation during summer. This may have contributed to increased
maximum air temperature during summer in the CMR.
There are studies that show an increase in minimum air temperature with an increase
in specific humidity, primarily in winter with smaller effects during spring and fall
(Rangwala et al., 2009; Rangwala & Miller, 2012). With increasing cloud concentration and
precipitation in recent decades, there should be an increase in specific humidity over the
CMR. This may have contributed to increasing air temperatures, especially minimum air
temperature.
The role of soil moisture and specific humidity for the air temperature trends over the
CMR should not be as significant as SAF and clouds because of precipitation as snow for
winter months and insignificant precipitation trends over the period of study.
5.2.4 Other Factors
Local and regional disturbances and changes may have also influenced the air
temperature of the Cariboo Region. Maness, Kushner, & Fung (2012) find that about a 1°C
increase in summertime surface air temperature is associated with shifting evapotranspiration
and albedo due to forest dieback following the mountain pine beetle infestation in BC. The
wildfires and modifications in climatic circulation due to teleconnections such as the Pacific
Decadal Oscillation (PDO) and El Nino-Southern Oscillation (ENSO) may have also
influenced the air temperature pattern in the CMR (Ropelewski & Halpert, 1986; Stewart,
Cayan, & Dettinger, 2005).
Furthermore, other factors such as atmospheric brown clouds, industrial pollutants,
and aerosol concentrations may be considered as additional factors responsible for
elevational warming because of their role in absorbing the longwave radiation and radiating
back to the earth’s surface, making it warmer. For the CMR, the effect of pollutants, aerosols,
and black carbon should be minimal because of the location of the study area. It is relatively
less influenced by anthropogenic factors owing to the lack of large industries nearby.
Precipitation variation is associated with the mountainous topography of the CMR
that directly influences the precipitation pattern between the leeward and windward sides.
Local convective activity and wind activity are also responsible for precipitation variation in
the region.
| 97 |
5.3 Elevational dependency of hydroclimatic trends
General
Most of the grid cells of the study domain lie at mid-elevations (900-1500 m a.s.l.),
with the range being 631 m to 2365 m (Figure 6). There are fewer points with climatic
records at the elevation extremes, i.e. below 800 m and above 2000 m (Figure 6). Thus,
findings of this study generally report the hydroclimatic trends that occurred in the mean
elevations of the CMR.
If it would be only global warming and not the influence of elevation, then either
uniform warming or higher warming in lower elevation throughout the region is expected.
However, the results indicate that there is a spatial difference across elevations in the
seasonal trend magnitudes over the study area. Therefore, there is an influence of altitude to
the variation of long-term hydroclimatic trends. There may be different physical phenomena
associated with this variation either combined or individually. The quantification of the
contribution of each of the physical processes to test the elevational dependency of air
temperature trends is important but beyond the scope of this work.
Elevational Dependency
Elevational dependency of warming in the mountains is important because it has
potential to provide early and detectable climate change signals and assess environmental
impacts of global warming in these regions (Fyfe & Flato, 1999; Liu et al., 2009). There is
clear evidence of elevational dependence of air temperature with elevation in the CMR.
For the minimum air temperature, generally, the grid point’s trends, i.e. LOESS fits, form
almost a J-shaped curve showing almost flat line until a 0.2°C decade'1 trend at about 1200
m a.s.l. and moving straight up (Figure 34). Therefore, the higher the elevations of grid cells,
the stronger are the trends. However, the reliability of the fits is low above 2500 m a.s.l.
because of the fewer grid cells. For the maximum air temperature, generally, the annual
trends are decreasing with elevation except during winter. Winter maximum air temperature
trends do not show any specific pattern with elevation.
Not only the annual but also seasonal minimum and maximum air temperature trends are
elevation dependent. For every season, the minimum air temperature trend increases with
elevation, following a J-shaped curve except for fall (Figure 36). The maximum air
temperature trend decreases with elevation almost linearly for all seasons except winter.
Winter maximum air temperature does not indicate a specific pattern with elevation (Figure
37). Likewise, the precipitation trend does not follow a specific pattern with elevation
(Figure 39).
Positive minimum air temperature trends with greater magnitude in winter and spring
may be linked to a specific phenomenon occurring during these periods in the region. There
may be temperature inversions and fog formation in the lower valleys but not in high
elevations especially during winters. This causes a higher minimum temperature response in
the higher elevations. For spring, one important phenomenon that occurs in the CMR is the
SAF that may have contributed to sharp increases in minimum air temperature trends with
elevation. Furthermore, generally higher elevations experience higher cloud
concentration/formation, recent decades show that the rate of cloudiness is increasing in the
United States and Canada; specifically during night time in BC (Dai et al., 2006; Free & Sun,
2014; Milewska, 2004). This may be happening over the CMR and have contributed to
increased minimum and decreased maximum air temperature trend magnitudes with
elevation.
The results do not show a clear relationship between the precipitation trends and
elevation in the CMR.
5.4 Implications of trends
5.4.1 Seasonal shifting
With increasing warming trends in cold regions such as the CMR, one of the most
noted impacts would be on snowmelt initiation in spring and start of the freezing period in
fall. For many practical purposes, freezing and melting temperatures are of special interest in
cold regions. For example, winters are dominated by the cryospheric components, bringing
changes to ecosystems, water resources as well as society and economy (Bonsai & Prowse,
2003). This study shows a substantial increase in air temperatures similar to global warming.
It is expected that there is a substantial change over the m elting and freezing tim e in the
CMR.
I ioo|
Trend of Spring days [Julian] with Temp, threshold of 0°C
Figure 43: Linear trends o f start o f days with greater than 0°C temperature threshold for minimum,
maximum, and mean air temperature (areal average) in the CMR, 1950-2010.
Trend of fall days [Julian] with Tem p , threshold of 0°C330 T
320-
310-
300-
290-y =261 +0.021 x, /* = 0.00987
300-
290-
280-
y S174+U.055-X, r =0.0075
y =*163+0.045/y, ^ = 0.013270-
260-
250-
Time [Year]
Figure 44: Linear trend o f start o f freezing days with temperature threshold o f less than 0°C for
minimum, maximum, and mean air temperature (areal average) over the CMR, 1950-2010.
A trend analysis is conducted to observe changes in the timing of the transition
between subfreezing and above freezing conditions and vice versa. The daily minimum,
maximum, and mean air temperatures are filtered using a 31-day running mean for spring
and fall. A 0°C air temperature is considered as the threshold value for melting (freezing)
| 102 |
start days of spring (fall). The changes in the number of threshold days with year is
calculated to measure the seasonal shifting.
Figures 43 and 44 show the trend of days with a 0°C air temperature threshold for
spring and fall respectively. For spring, the number of days is decreasing in the CMR (-4.1
days decade'1) for minimum air temperature while the number of days for fall 0°C is
increasing (0.45 days decade'1) for minimum air temperature. Generally, minimum air
temperature trends are considered to refer to changes of melting (freezing) periods.
The numbers of days with 0°C air temperature threshold are decreasing for spring
while increasing for fall. The trends towards earlier spring and later fall days over the CMR
are consistent with the hypothesis that with increasing warming there may be shorter snow
seasons (Choi et al. 2010).
Timing of snowmelt is affected by both the air temperature and precipitation. Early
snowmelt leads to the early runoff, in turn influencing the overall hydrology of the area
(Lundquist, Dettinger, Stewart, & Cayan, 2009; Pederson et al., 2011). There are other
potential associated impacts such as decreases in reservoir efficiencies (Stewart, Cayan, &
Dettinger, 2004), increases in the number of forest fire events (Westerling, Hidalgo, Cayan,
& Swetnam, 2006), increases in river flooding, and disturbances in the aquatic habitat, etc.
(Tohver, Hamlet, & Lee, 2014; Zwiers, Schnorbus, & Maruszeczka, 2011). It will also
influence water rights and management issues among provinces and states in the future
(Kenney, Klein, Goemans, Alvord, & Shapiro, 2008).
5.4.2 Implications to the ecohydrology
This study reports almost a 2°C of minimum air temperature increase in the CMR during
the last six decades with the rate of change increasing. Therefore, the region is warming and
is likely to warm in many years to come. Such warming has and will have broader
implications towards the local ecohydrology of the CMR and overall northern BC.
There is a strong relation between climate and the overall ecosystem of a particular
region (Mooney et al., 2009). There are many examples showing ecological impacts of
climate change at local, regional, and global level ecosystems (Grimm et al., 2013; Mooney
et al., 2009; Walther et al., 2002; Weed, Matthew, & Hicke, 2013). Increased frequency and
intensity of weather events are the significant driving factors for population dynamics of
different species of any region. In the case of the CMR, the mountain caribou, salmon, and
other species are directly impacted by climate changes over the region (Dery et al., 2012;
Vors & Boyce, 2009). Nonetheless, the consequences of short-term and long-term climatic
variations on complex ecological processes are not clear. In the following sections, some of
the impacts of such changes on selected animal species of the study area and water resources
of the region are discussed.
5.4.2.1 Impacts on water resources
Increased air temperatures and changes in the precipitation regime over western North
America will affect the hydrology of the region. This will have impacts on hydropower
generation, potentially cause disasters such as floods and droughts, affect water demand for
irrigation and household consumption, and disturb aquatic habitats (Zwiers et al., 2011).
I 104|
Glaciers cover about 3% of the BC landmass and play a significant role in the river runoff
as well as hydropower generation in BC. There are recent studies that show the accelerated
retreat of glaciers in the region (Beedle, 2014; Bolch et al., 2010; Moore & Demuth, 2001).
Although there are very few findings to relate these recessions with increasing air
temperature and changes in precipitation patterns, it may be accelerating due to the warming
in the region. The faster retreat of glaciers in the CMR will also affect the flow in the Fraser
River and its tributaries that drain the region.
Most of the Cariboo Mountains drain into the Fraser River, a major habitat for keystone
salmon species. Many studies and model simulations show significant changes over the
hydrological regime of the Fraser River Basin with increasing temperature (Dery et al., 2012;
Kang, Shi, Gao, & Dery, 2014; Shrestha, Schnorbus, Werner, & Berland, 2012). Rising air
temperature is one of the causes of a greater range of annual runoff fluctuations in the Fraser
River (Dery et al., 2012). Such fluctuations have detrimental effects on migrating and
spawning salmon.
In addition to the study area’s specific impacts, there will be implications of
hydroclimatic changes over the CMR on many sectors of the CMR as well as northern and
central BC as a whole. Some of the implications of increasing air temperatures over the
region include the mountain pine beetle epidemic, increase in frequency and occurrence of
forest fire events, extreme weather events, etc. (Kurz et al., 2008). These may cause much
environmental and economic losses. Therefore, significantly increasing air temperatures, as
shown in the CMR, have broader implications towards the hydrology, ecosystems and
ecosystem services, and overall environmental health of the CMR and northern BC.
I 105|
5.4.2.2 Impacts on animal species
The endangered mountain caribou (mountain ecotype of woodland caribou) population
that is found in relatively wet and mountainous terrain of BC is declining. One of the causes
for population decline may be the milder winters due to climate change along with human
influences (Opdam & Wascher, 2004; Parks Canada, 2011; Vors & Boyce, 2009). Mountain
caribou population distribution has declined over the past 50 to 100 years and now they are
limited to only 12 recognized sub-populations (Wittmer et al., 2005). Broadly, the mountain
caribou population has been stratified into four different geographic regions in BC with one
of them being the Cariboo Mountains where there are now only 850 caribous or so
(Mountain Caribou Science Team, 2005). In the Cariboo Mountains, seasonal migration of
the caribou to lower elevations is further limited because of shallower snowpacks. This
migration will be impacted even further should winter air temperatures continue to warm in
the region.
The primary winter food for caribou is arboreal lichen that is abundantly found in the
coniferous forests of the CMR. They walk on top of the deep snowpack (generally >2 m) in
the mountains (Apps, McLellan, Kinley, & Flaa, 2001; Wittmer et al., 2005). With a late
start of freezing conditions during fall and milder winters, there will be reduction in the
snowpack thickness making it difficult for caribou to access the arboreal lichen. In addition,
the warmer and drier winter conditions are favorable for deer, elk, and moose to move to the
Cariboo Mountains. This will increase the competition for food amongst animals (Mountain
Caribou Science Team, 2005). Furthermore, there are studies that show the change in nutrient
cycling with warming that would affect caribou populations adversely by creating
competition with browsing ungulates and eliminating food sources, especially in winters
| 106|
(Lenart, Bowyer, Hoef, & Ruess, 2002). Complex interactions among seasonal hydroclimatic
variations, snowfall patterns, wildfire events, forest insect disease outbreaks, etc., have the
potential to increase and compound the impacts on the mountain caribou. This is expected to
increase with rising air temperature in the future.
5.5 Limitations of this study
There are limitations to this study that require some attention. First are the data gaps
in the CAMnet data used for the general climatology of the region and validation of
interpolated data. Further, the accuracy of the interpolated data, ANUSPLIN, is another
limitation of this study. Unfortunately, there are limited weather stations generating long
term data to conduct a detailed and systematic analysis over the region.
The nature of data sets such as their quality, time of availability, etc. influence the
results based on these data. Observation-based interpolated data are used in this study, but
inaccuracies arise from the fact that there are fewer stations in the higher elevations for
interpolation. The data sets are crosschecked to corroborate the quality of data in representing
the study area, yet the interpolated data may not represent the real scenario exactly. This is
also because the interpolated data used are not specific to the study area, but are extracted
from the entire Canadian domain. Data resolution remains coarse to represent the details of
complex mountainous topography of the region. In addition, the gridding process may have
smoothed the data spatially. These gridded data may have some errors too. The precipitation
analysis is based on interpolated total data only. It is not clear how much of the precipitation
is contributed by snow and how much is from rainfall because of unavailability of snowfall
data.
| 107 |
There is limited literature that examines the effects of glacier retreat in the CMR in
relation to global warming. Further, there are limited publications emphasizing impacts of
climate change on the water resources of the CMR. Therefore, the discussions in this study
are limited to available studies.
| 108 |
6. Conclusions, Recommendations and Future Work
The conclusions, recommendations, and future work based on the analyses are
presented in this chapter. The first part summarizes the main findings of this study. The
hydroclimatic state in the CMR is summarized based on linear and spatial trends, with their
magnitude and significance at annual and seasonal scales. Furthermore, the elevational
dependency of the hydroclimatic trends in the CMR and impacts due to these changes are
presented. The second part of this chapter consists of recommendations for further research
and proposes some future work based on the scope and limitations of this study.
6.1 Conclusion
6.1.1 General Hydroclimatoiogy
Average annual minimum daily air temperature in the CMR is -3.7°C (minimum -16.3°C
in winter and maximum 8.0°C in summer) during 1950-2010. Similarly, the average annual
maximum daily air temperature is 7.3°C (minimum -7.4°C in winter and maximum 23.4°C in
summer) for the same period. The total mean annual precipitation over the region is 738 mm.
An analysis of observed climate data along the western slopes of the Cariboo Mountains
shows that higher elevation regions have lower air temperatures than lower elevations. The
monthly average surface temperature lapse rate of the region is 4.7°C km'1 during the period
of September 2011 June 2012. Air temperature is found to be inversely related to elevation in
all months, except during extreme weather events in January and February. For these days, it
may be due to air temperature inversions occurring in the region.
6.1.2 Linear and spatial trends
A hydroclimatic trend analysis of the region was performed using the daily frequency
Canadian Climate Coverage (CCC)’s interpolated data from 1950 to 2010. The minimum and
maximum air temperatures and precipitation were summarized for monthly, seasonal, and
annual frequency. The nonparametric Mann-Kendall trend test was performed to detect the
trends and their significance. The Theil-Sen estimate was used to evaluate the magnitude of
the trends over the region at different temporal and spatial resolutions.
The minimum and maximum air temperatures of the region have risen by 1.9°C and
1.2°C, respectively, in the last six decades. Area averaged annual minimum and maximum
air temperatures show significant positive trends over the period of study. The minimum air
temperature trend over the region is 0.32°C decade'1 while maximum air temperature is
0.20°C decade"1. Although the total annual precipitation does not show any significant trend,
there is year-to-year variation of total precipitation by ±30% from its long-term mean.
Anomalies in air temperatures showed that recent years, especially after 1996, are warmer
across the CMR. There is a greater seasonal variation in the hydroclimatic trends in the
CMR. The winter and spring minimum air temperature trends are 0.62°C decade'1 and
0.35°C decade'1 respectively followed by the summer trend of 0.12°C decade'1. All of these
trends are highly significant ip <0.05) except for the fall minimum air temperature trend. For
maximum air temperature, only winter trends are significant. The seasonal precipitation does
not show significant trends during last six decades.
The spatial trends of the region show mostly significant positive annual trends for air
temperatures, but both positive and negative trends for precipitation. The average minimum
air temperature warming rate is 0.33°C decade'1. The spatial trend shows greater variability
I 1101
with magnitudes ranging from 0.07-0.67°C decade'1. The mean maximum air temperature
trend over the region is 0.18°C decade'1 (range of 0.03-0.34°C decade'1) with less spatial
variation compared to the minimum air temperature.
Contrary to yearly trends, there is a clear and strong spatial variation of seasonal trends.
On the seasonal scale, the analysis reveals that there are all positive trends of minimum air
temperature in the region except some grid cells showing insignificant negative trends during
the fall. Winter and spring minimum air temperature trends are strong and positive. They
show greater spatial variability. For example, winter minimum air temperature trends range
from 0.24°C decade'1 to 1.18°C decade'1 (mean = 0.63°C decade'1) and spring minimum air
temperature trends range from 0.09°C decade'1 to 0.72°C decade'1 (mean = 0. 39°C decade'1).
The summer and fall show little spatial variability. Maximum air temperature trends for
winter and spring are all positive and mostly significant. The summer and fall trends range
from negative to positive values and generally are not significant, showing little spatial
variability. For example, winter trends range from 0.21°C decade'1 to 0.59°C decade'1 (mean
= 0.39°C decade’1) while fall trends range from -0.12°C decade'1 to 0.13°C decade'1 (mean =
-0.03°C decade'1).
The precipitation trends show greater spatial variability over the study area. Most o f the
grid cells do not show significant trends; in addition, there are positive trends in some areas
of the CMR and negative ones in other areas. Winter precipitation trends range from -18.5
mm decade'1 to 6.2 mm decade'1 (mean = -6.8 mm decade'1), spring -2.2 mm decade'1 to 9.3
mm decade'1 (mean =1.9 mm decade'1), summer -4.2 mm decade'1 to 8.5 mm decade'1 (mean
= -0.2 mm decade'1), and fall -6.6 mm decade'1 to 15.4 mm decade'1 (mean = 5.3 mm
decade'1).
I 1 1 1 i
6.1.3 Elevational dependency of hydroclimate
There is a clear difference in the trends with respect to elevation in the CMR. Both the
western and eastern slopes of the Cariboo Mountains show similar patterns in the air
temperature trend with respect to elevation.
The annual minimum air temperature trends are stronger and significant at higher
elevations with magnitude of more than 0.5°C decade'1 above 2000 m a.s.l. The annual
maximum air temperature trends show the opposite pattern of increase with elevation, i.e.
stronger and significant maximum air temperature trends are observed at lower elevations,
mostly below 1500 m a.s.l. There is no specific annual precipitation trend with elevation.
The patterns of air temperature trends variability are related with the elevation of the
region. The seasonal breakdown of minimum air temperature trends with elevation show
stronger, significant trends with increasing elevations. Winter and spring trends are >0.50°C
decade'1 above 2000 m a.s.l. (extreme values of winter trends above 2000 m a.s.l. are 0.80°C
decade'1 to 1.30°C decade'1 and for spring 0.60°C decade'1 to about 0.80°C decade'1). For
maximum air temperature, most significant trends are observed below 1500 m a.s.l. for all
seasons showing decreasing trends with increasing elevation except for winter. During
winter, there is no specific pattern of trend with elevation. The seasonal precipitation trends
also do not show any pattern with elevation.
The SAF and clouds along with other factors in the region may be responsible for sharp
gradients in air temperature trends with elevation in the CMR. The role of the SAF is
significant during spring; summer elevational dependency of the air temperature may be
influenced by soil moisture, aerosols, and change in local surface energy balance because of
mountain pine beetle disturbances. SAF and increasing cloud concentration may be
responsible for winter time air temperature trend variations in the CMR.
6.1.4 Impacts of hydroclimatic variations
There have been direct and indirect impacts of hydroclimatic variations over the local and
regional ecohydrology and economy of BC. Such impacts are expected to continue into the
21st century. Spring melt is starting earlier (0.41 day decade'1) and fall freezing is delayed
(0.45 day decade'1). Such timing of snowmelt change will affect runoff generation and the
overall hydrology/water resources of the region.
Milder winters will affect the endangered mountain caribou populations in the CMR
through increases in competition with other species such as elk, moose, and deer. Further,
there will be shallow snow depths that make the caribou’s major winter food, arboreal lichen,
inaccessible. With a warming environment, there is increased runoff variability in the Fraser
and North Thompson Rivers, major habitats for salmon. Increased mountain pine beetle
epidemic frequency, forest fires, sedimentation in reservoirs, effects on hydroelectricity
generation, and glacier retreat are other impacts expected to occur due to hydroclimatic
changes in the CMR.
The CMR is warming; the minimum air temperature is increasing at a faster rate than the
lowland counter parts. There are stronger warming signals at higher elevations; minimum air
temperature is increasing at a faster rate, especially in winter and spring. Such changes have
impacts over the ecohydrology and overall environmental health of northern BC and region.
1113|
6.2 Recom m endations and Future W ork
To reduce the uncertainties associated with climatic variations, it is recommended to have
a dense network of continuously operating weather stations in the region, especially at high
elevations. It is recommended to establish and operate new weather stations at higher
elevations, especially above 2000 m for the long term monitoring of mountain climate.
The interpolated data were extracted from the large Canadian domain for the trend
analyses. The use of a large domain may have smoothed the data, especially for the complex
mountain environment of the CMR. Therefore, it is recommended and proposed as a future
effort to develop specific gridded hydroclimatic datasets for the CMR. The specific gridded
data should incorporate any observed data available in the region as well as model output.
Results from such data would be more specific, provide more precise estimates, and reduce
uncertainties.
The hydroclimatology of the CMR is influenced by large scale atmospheric circulation
patterns and mountain specific phenomena. Therefore, there is a need to develop multiple
regression models using atmospheric circulation patterns such as 500 hPa geopotential height
fields, radiation/energy distribution data of the region, etc. Further, this study showed
opposite warming patterns of minimum and maximum air temperatures, with the minimum
air temperature trend increasing with elevation and maximum air temperature trend
decreasing with elevation. Therefore, there are still uncertainties on how mountain regions
will respond to global warming.
This study is a special case of a relatively small mountainous region. It is difficult to
estimate the contribution of specific physical mechanisms such as the cloud concentration or
SAF on air temperature changes. Therefore, further investigation of relevant physical
I H4 |
mechanisms and accurate quantification of processes and their role for elevational
dependency of warming in large mountain ranges is suggested. This could be achieved by
developing proper regression models and performing Principal Component Analysis (PCA)
analysis of 500 hPa geopotential height fields. Furthermore, development and use of high
resolution climate models is recommended.
There are many complexities associated with such analysis in mountain regions. The
proper methods of investigation to identify the effect of each parameter to the
hydroclimatology of the CMR are important. An extended analysis of daily maximum and
minimum air temperature throughout many mountain chains of the globe would improve the
understanding of elevational sensitivity of the mountains in the context of global warming.
In addition, large-scale hydroclimatic variation analysis is recommended for a better
understanding of the impacts of climate change. Similarly, case studies from large mountains
or mountain ranges are recommended for improved understanding of elevational dependency
of hydroclimatic parameters. For example, an analysis of elevational dependency of trends in
the Canadian Rockies or the Columbia Mountains would enhance the understanding of recent
changes observed in East-Central BC.
This study has explored the dependence of hydroclimatic variables in the CMR and
contributed to enhance our understanding of mountain climate and their responses to the
climate change. Furthermore, the roles of different physical mechanisms responsible for
elevational dependency of hydroclimate in the CMR are explained. The implications of
hydroclimatic changes in the ecohydrology of the CMR are discussed. This work is expected
to fill the gaps of our understanding about the pristine mountain environment and provide
[115|
scientific information to policy makers and communities to formulate better policies and
make informed decisions.
S H 6 j
7. References
Apps, C. D., McLellan, B. N ., Kinley, T. A., & Flaa, J. P. (2001). Scale-dependent habitat selection by Mountain Caribou, Columbia Mountains, British Columbia. Journal o f Wildlife Management, 65(1), 65-77 . Retrieved from http://cat.inist.fr/?aModele=afficheN&cpsidt=938318
Barry, R. G. (1992). Mountain climatology and past and potential future changes in Mountain Regions: A Review. Mountain Research and Development, 72(1), 71-86 .
Barry, R. G. (2008). Mountain Weather and Climate (3rd ed.). Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.
Barry, R. G. (2012). Recent advances in mountain climate research. Theoretical and A pplied Climatology, 7 /0 (4 ), 549-553. doi:10.1007/s00704-012-0695-x
BC Parks. (2014). Cariboo Mountains Provincial Park. Cariboo Mountains Provincial Park.Retrieved March 29, 2014, fromhttp://www.env.gov.bc.ca/bcparks/explore/parkpgs/cariboo_mt/
BCRFC. (2014). Automated Snow Pillow Data Archive. Autom ated Snow Pillow D ata Archive. Retrieved January 10, 2014, from http://bcrfc.env.gov.bc.ca/data/asp/archive.htm
Beacham, T. D., Lapointe, M., Candy, J. R., McIntosh, B., MacConnachie, C., Tabata, A., ... Withler, R. E. (2004). Stock identification o f Fraser River Sockeye Salmon using Microsatellites and Major Histocompatibility complex variation. Transactions o f the American Fisheries Society, 133(5), 1117-1137. doi: 10.1577/T04-001.1
Beacham, T. D., & Murray, C. B. (1989). Variation in developmental biology o f sockeye salmon (Oncorhynchus nerka) and chinook salmon (O. tshawytscha) in British Columbia. Canadian Journal o f Zoology, 67, 2081—2089.
Beedle, M. J. (2014). Glacier change in the Cariboo Mountains o f British Columbia,Canada (1946- 2011). University o f Northern British Columbia.
Beedle, M. J., Menounos, B., & Wheate, R. (2014). Glacier change in the Cariboo Mountains, British Columbia, Canada (1952-2005). The Cryosphere Discussions, 8(3), 3367-3411. doi: 10.5194/tcd-8-3 367-2014
Beniston, M. (2003). Climatic change in Mountain Regions: A Review o f possible impacts. Climatic Change, 59, 5-31.
Beniston, M. (2006). Mountain Weather and Climate: A general overview and a focus on Climatic Change in the Alps. H ydrobiologia, 562(1), 3 -16 . doi: 10.1007/s 10750-005-1802-0
Beniston, M., Diaz, H. F., & Bradley, R. S. (1997). Climatic change at high elevation sites: an overview. Climatic Change, 36, 233-251.
I U7 |
Beniston, M., & Fox, D. G. (1995). Impacts o f Climate Change on Mountain Regions. In R. T. Watson, M. C. Zinyowera, & R. H. Moss (Eds.), Working Group IIR eport "Impacts, Adaptations and M itigation o f Climate Change: Scientific-Technical Analyses ” Contribution o f Working Group II to the Second Assessment Report o f the Intergovernmental Panel on Climate Change. IPCC.
Beniston, M., & Rebetez, M. (1996). Regional Behaviour o f Minimum Temperatures in Switzerland for the Period 1979-1993. Theoretical and A pplied Climatology, 243, 231-243.
Bolch, T., Menounos, B., & Wheate, R. (2010). Landsat-based inventory o f glaciers in western Canada, 1985-2005. Remote Sensing o f Environment, 114( 1), 127-137. doi: 10.1016/j.rse.2009.08.015
Bonsai, B. R., & Prowse, T. D. (2003). Trends and variability in Spring and Autumn 0°C-isotherm dates over Canada. Climatic Change, 57, 341-358.
Bradley, R. S. S., Keimig, F. T., & Diaz, H. F. (2004). Projected temperature changes along the American Cordillera and the planned GCOS network. Geophysical Research Letters, 37(16),L 16210. doi: 10.1029/2004GL020229
Burford, J. E., Dery, S. J., & Holmes, R. D. (2009). Some aspects o f the hydroclimatology o f the Quesnel River Basin, British Columbia, Canada. H ydrological Processes, 23, 1529-1536. doi: 10.1002/hyp
Bum, D. H., & Elnur, M. A. H. (2002). Detection o f hydrologic trends and variability. Journal o f H ydrology, 255(1-4), 107-122. doi: 10.1016/S0022-1694(01)00514-5
Ceppi, P., Scherrer, S. C., Fischer, A. M., & Appenzeller, C. (2012). Revisiting Swiss temperature trends 1959-2008. International Journal o f Climatology, 32(2), 203-213. doi:10.1002/joc.2260
Choi, G., Robinson, D. a., & Kang, S. (2010). Changing Northern Hemisphere snow seasons. Journal o f Climate, 23(19), 5305-5310. doi:10.1175/2010JCLI3644.1
Cleveland, W. S., & Devlin, S. J. (1988). Locally weighted regression: An approach to regression analysis by local fitting. Journal o f American Statistical Association, 53(403), 596-610.
Dai, A., Karl, T. R., Sun, B., & Trenberth, K. E. (2006). Recent trends in cloudiness over the United States: A tale o f monitoring inadequacies. Bulletin o f the American M eteorological Society, 57(5), 597-606. doi: 10.1175/BAM S-87-5-597
Dalgaard, P. (2008). Introductory Statistics with R (2nd ed.). Springer.
Daly, C. (2006). Guidelines for assessing the suitability o f spatial climate data sets. International Journal o f Climatology, 26(6), 707-721. doi: 10.1002/joc. 1322
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., & Pasteris, P. P. (2008). Physiographically sensitive mapping o f climatological temperature and precipitation across the conterminous United States. International Journal o f Climatology, 28, 2031-2064. doi: 10.1002/joc
i U S I
Davis, E. J., & Reed, M. G. (2013). Multi-level governance o f British Columbia’s mountain pine beetle crisis: The roles o f memory and identity. Geoforum, 47, 32-41. doi: 10.1016/j .geoforum.2013.02.005
Dawson, R. J., Werner, A. T., & Murdock, T. Q. (2008). Prelim inary analysis o f Climate Change in the Cariboo-Chilcotin area o f British Columbia (p. 49). Pacific Climate Impacts Consortium, University o f Victoria, Victoria BC: Pacific Climate Impacts Consortium, University o f Victoria, Victoria BC.
Dery, S. J., & Brown, R. D. (2007). Recent Northern Hemisphere snow cover extent trends and implications for the snow-albedo feedback. Geophysical Research Letters, 34(22), L22504. doi :10.1029/2007GL031474
Dery, S. J., Clifton, A., MacLeod, S., & Beedle, M. J. (2010). Blowing Snow Fluxes in the Cariboo Mountains o f British Columbia, Canada. Arctic, Antarctic, and Alpine Research, 42(2), 188—197. doi: 10.1657/1938-4246-42.2.188
Dery, S. J., Hemandez-Henriquez, M. A., Owens, P. N., Parkes, M. W., & Petticrew, E. L. (2012). A century o f hydrological variability and trends in the Fraser River Basin. Environmental Research Letters, 7(2), 024019. doi: 10.1088/1748-9326/7/2/024019
Dery, S. J., Stieglitz, M., McKenna, E. C., & Wood, E. F. (2005). Characteristics and Trends o f River Discharge into Hudson, James, and Ungava Bays, 1964 - 2000. Journal o f Climate, 18, 2 5 4 0 - 2557.
D eiy, S. J., & Wood, E. F. (2005). Observed twentieth century land surface air temperature and precipitation covariability. Geophysical Research Letters, 32(21), L21414. doi: 10.1029/2005GL024234
Diaz, H. F., & Bradley, R. S. (1997). Temperature variations during the last century at high elevation sites. Climatic Change, 36, 253-279.
Dong, D., Huang, G., Qu, X., Tao, W., & Fan, G. (2014). Temperature trend-altitude relationship in China during 1963-2012. Theoretical and A pplied Climatology, doi: 10.1007/s00704-014-1286- 9
Durre, I., Wallace, J. M., & Lettenmaier, D. P. (2000). Dependence o f extreme daily Maximum temperatures on antecedent soil moisture in the contiguous United States during summer. Journal o f Climate, 13, 2641-2651.
Easterling, D. R. (2000). Climate extremes: Observations, modeling, and impacts. Science,259(5487), 2068-2074. doi: 10.1126/science.289.5487.2068
English, K. K., Koski, W. R., Sliwinski, C., Blakley, A., Cass, A., & Woodey, J. C. (2005). Migration timing and river survival o f late-run Fraser river Sockeye Salmon estimated using Radiotelemetry techniques. Transactions o f the American Fisheries Society, 134(5), 1342-1365. doi: 10.1577/T04-119.1
I 119 I
Environment Canada. (2014). Historical Climate Data. H istorical Climate Data. Retrieved April 04, 2014, from http://climate.weather.gc.ca/data_index_e.html
ESRI. (2011). ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute.
Eyles, N. (1995). Characteristics and origin o f coarse gold in Late Pleistocene sediments o f the Cariboo placer mining district, British Columbia, Canada. Sedimentary Geology, 95(1-2), 6 9 - 95. doi: 10.1016/0037-0738(94)00101 -Y
Flanner, M. G., Shell, K. M., Barlage, M., Perovich, D. K., & Tschudi, M. A. (2011). Radiativeforcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008. Nature Geoscience, 4(3), 151-155. doi:10.1038/ngeo 1062
ForestEthics. (2014). Inland Temperate Rainforest: Saving the endangered Mountain Caribou. Inland Temperate Rainforest: Saving the endangered mountain caribou. Retrieved September 17,2014, from http://www.forestethics.org/inland-temperate-rainforest
Free, M., & Sun, B. (2014). Trends in U.S. total cloud cover from a Homogeneity-Adjusted Dataset. Journal o f Climate, 27(13), 4959-4969. doi: 10.1175/JCLI-D-l 3-00722.1
Fyfe, J. C., & Flato, G. M. (1999). Enhanced Climate Change and its detection over the Rocky Mountains. Journal o f Climate, 12, 230-243.
Gan, T. Y., & Kwong, Y. T. J. (1992). Identification o f warming trends in Northern Alberta and Southern Northwest territories by the non-parametric Kendall Test Using hydrometric data to detect and moitor climate channge. In G. E. Kite & K. D. Harvey (Eds.), Proceedings o f NHRI Symposium 8 (pp. 43-56). Saskatoon.
GeoBC. (2014). Barkerville. The Province o f British Columbia - GeoBC. Retrieved September 17, 2014, from http://apps.gov.bc.ca/pub/bcgnws/names/l 1124.html
Ghatak, D., Sinsky, E., & Miller, J. (2014). Role o f snow-albedo feedback in higher elevation warming over the Himalayas, Tibetan Plateau and Central Asia. Environmental Research Letters, 9(11), 114008. doi: 10.1088/1748-9326/9/11/114008
Gocic, M., & Trajkovic, S. (2013). Analysis o f changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. G lobal and Planetary Change, 100, 172-182. doi: 10.1016/j.gloplacha.2012.10.014
Goosse, H., Barriat, P. Y., Lefebvre, W., Loutre, M. F., & Zunz, V. (2010). Introduction to climate dynamics and climate modelling. Online textbook available at http://www.climate.be/textbook. Retrieved from http://www.climate.be/textbook
Grimm, N. B., Chapin, F. S., Bierwagen, B., Gonzalez, P., Groffman, P. M., Luo, Y., ... Williamson,C. E. (2013). The impacts o f climate change on ecosystem structure and function. Frontiers in Ecology and the Environment, 11(9), 474-482. doi: 10.1890/120282
|1 2 0 |
Hageli, P., & McClung, D. M. (2003). Avalanche characteristics o f a transitional snow climate— Columbia Mountains, British Columbia, Canada. C old Regions Science and Technology, 57(3), 255-276. doi: 10.1016/SO 165-232X(03)00069-7
Hijmans, R. J. (2014). Raster: Geographic data analysis and modeling. R package version 2.2-12. http://CRAN.R-project.org/package=raster.
Hopkinson, R. F., McKenney, D. W., Milewska, E. J., Hutchinson, M. F., Papadopol, P., & Vincent, L. A. (2011). Impact o f Aligning Climatological Day on Gridding Daily Maximum-Minimum Temperature and Precipitation over Canada. Journal o f A pplied M eteorology and Climatology, 50(8), 1654-1665. doi: 10.1175/2011JAM C2684.1
Hutchinson, M. F., McKenney, D. W., Lawrence, K., Pedlar, J. H., Hopkinson, R. F., Milewska, E.,& Papadopol, P. (2009). Development and Testing o f Canada-Wide Interpolated Spatial Models o f Daily Minimum-Maximum Temperature and Precipitation for 1961-2003. Journal o f A pplied M eteorology and Climatology, 48(4), 725-741. doi: 10.1175/2008JAM C1979.1
IPCC. (2013). Climate Change 2013: The Physical Science Basics. Contibution o f Working Group I to the Fifth Assessment Report o f the Intergovernmental Panel on Cliamte Change. (T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. B oschung,... P. M. Midgley, Eds.)(p. 1535). Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.
Kang, D. H., Shi, X., Gao, H., & Dery, S. J. (2014). On the changing contribution o f snow to the hydrology o f the Fraser River Basin, Canada. Journal o f H ydrometeorology, 15, 1344—1365. doi: 10.1175/JHM-D-13-0120.1 [In Press]
Kendall, M. G. (1975). Rank Correlation Methods. (4th ed.). London: Charles Griffin.
Kenney, D., Klein, R., Goemans, C., Alvord, C., & Shapiro, J. (2008). The Impact o f Earlier Spring Snowmelt on Water Rights and Adm inistration: A Preliminary Overview o f Issues and Circumstances in the Western States (Final Project Report).
Krause, P., Boyle, D. P., & Base, F. (2005). Comparison o f different efficiency criteria for hydrological model assessment. Advances in Geosciences, 5, 89-97.
Kurz, W. A., Dymond, C. C., Stinson, G., Rampley, G. J., Neilson, E. T., Carroll, A. L., ... Safranyik, L. (2008). Mountain pine beetle and forest carbon feedback to climate change. Nature, 452(7190), 987-90 . doi:10.1038/nature06777
Lenart, E. A., Bowyer, R. T., Hoef, J. Ver, & Ruess, R. W. (2002). Climate change and Caribou: Effects o f summer weather on forage. Canadian Journal o f Zoology, 80, 664-678. doi:10.1139/Z02-034
Liu, X., & Chen, B. (2000). Climatic warming in the Tibetan Plateau during recent decades. International Journal o f Biometeorology, 1742, 1729-1742.
Liu, X., Cheng, Z., Yan, L., & Yin, Z.-Y. (2009). Elevation dependency o f recent and futureminimum surface air temperature trends in the Tibetan Plateau and its surroundings. G lobal and Planetary Change, 68(3), 164-174. doi:10.1016/j.gloplacha.2009.03.017
Lu, A., Kang, S., Li, Z., & Theakstone, W. H. (2010). Altitude effects o f climatic variation on Tibetan Plateau and its vicinities. Journal o f Earth Science, 21(2), 189-198. doi:10.1007/sl2583-010- 0017-0
Luce, C. H., Abatzoglou, J. T., & Holden, Z. a. (2013). The missing mountain water: slowerwesterlies decrease orographic enhancement in the Pacific Northwest USA. Science (New York, N.Y.), 342(1360), 1360-1363. doi: 10.1126/science. 1242335
Lundquist, J. D., Dettinger, M. D., Stewart, I. T., & Cayan, D. R. (2009). Variability and Trends in Spring Runoff in the Western United States. In F. Wagner (Ed.), Climate warming in western North America-Evidence and environmental effects (pp. 63-76). University o f Utah Press.
Macleod, S., & Dery, S. J. (2007). The Cariboo Alpine Mesonet. CMOS Bulletin SCMO, 35(2), 4 5 - 51.
Maness, H., Kushner, P. J., & Fung, I. (2012). Summertime climate response to mountain pine beetle disturbance in British Columbia. Nature Geoscience, 5(12), 1-6. doi:10.1038/ngeol642
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245-259.
Mann, M. E., Bradley, R. S., & Hughes, M. K. (1999). Northern Hemisphere Temperatures during the Past Millennium: Inferences, uncertainties, and limitations. Geophysical Research Letters,26(6), 759-762.
Martinec, J., & Rango, A. (1989). Merits o f Statistical criteria for the performance o f Hydrological Models. Journal o f the American Water Resources Association, 25(2), 421 —432. doi: 10.1111 /j. 1752-1688.1989.tb03079.x
Maurer, M. K., Menounos, B., Luckman, B. H., Osborn, G., Clague, J. J., Beedle, M. J., ... Atkinson, N. (2012). Late Holocene glacier expansion in the Cariboo and northern Rocky Mountains, British Columbia, Canada. Quaternary Science Reviews, 51, 71-80. doi: 10.1016/j .quascirev.2012.07.023
McCuen, R. H. (2003). M odeling H ydrologic Change: Statistical Methods. Lewis Publishers.
McGuire, C. R., Nufio, C. R., Bowers, M. D., & Guralnick, R. P. (2012). Elevation-dependenttemperature trends in the Rocky Mountain Front Range: changes over a 56- and 20-year record. P loSO ne, 7(9), e44370. doi:10.1371/journal.pone.0044370
McKenney, D. W., Hutchinson, M. F., Papadopol, P., Lawrence, K., Pedlar, J., Campbell, K., ... Owen, T. (2011). Customized Spatial Climate Models for North America. Bulletin o f the American M eteorological Society, 92(12), 1611-1622. doi:10.1175/2011BAMS3132.1
Mcleod, A. A. I. (2013). Kendall: Kendall rank correlation and Mann-Kendall trend test.
Meng, L., & Shen, Y. (2014). On the Relationship o f Soil Moisture and Extreme Temperatures in East China. Earth Interactions, 18( 1), 1-20. doi: 10.1175/2013EI000551.1
Milewska, E. J. (2004). Baseline cloudiness trends in Canada 1953-2002. Atmosphere-Ocean, 42(4), 267-280. doi: 10.3137/ao.420404
Modarres, R., & Sarhadi, A. (2009). Rainfall trends analysis o f Iran in the last half o f the twentieth century. Journal o f Geophysical Research, 114, D03101. doi:10.1029/2008JD010707
MoE. (2014). Effects o f Climate Change. Effects o f Climate Change in British Columbia. Retrieved from Ministry o f Environmet, Government o f British Columbia. http://www.livesmartbc.ca/learn/effects.html
MoF. (1979). Biogeoclimatic Zones and Subzones o f the Cariboo Forest Region. (M. Annas, R & R. Coup6, Eds.). Ministiy o f Forest,Provience o f British Columbia.
Mondal, A ., Kundu, S., & Mukhopadhyay, A. (2012). Rainfall trend analysis by Mann-Kendall Test: A case study o f North-Eastern part o f Cuttack District, Orissa. International Journal o f Geology, 2(1), 70-78 .
M ooney, H., Larigauderie, A., Cesario, M., Elmquist, T., Hoegh-Guldberg, O., Lavorel, S., ...Yahara, T. (2009). Biodiversity, climate change, and ecosystem services. Current Opinion in Environmental Sustainability, 1(1), 46-54 . doi:10.1016/j.cosust.2009.07.006
Moore, R. D., & Demuth, M. N. (2001). Mass balance and streamflow variability at Place Glacier, Canada, in relation to recent climate fluctuations. H ydrological Processes, 75(18), 3473-3486. doi: 10.1002/hyp. 1030
Mountain Caribou Science Team. (2005). Mountain Caribou in British C olum bia: A Situation Analysis (pp. 1-9).
MWLA. (2002). Indicators o f Climate Change fo r British Columbia 2002. Ministry o f Water,Land and Air Protection,British Columbia.
Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I - A discussion o f principles. Journal o f Hydrology, 10, 282-290.
NRCan. (2014). Regional, national and international climate modeling. Regional, national and international climate modeling. Retrieved April 07, 2014, from http://cfs.nrcan.gc.ca/projects/3?lang=en_CA
Opdam, P., & Wascher, D. (2004). Climate change meets habitat fragmentation: linking landscape and biogeographical scale levels in research and conservation. Biological Conservation, 777(3), 285-297. doi: 10.1016/j.biocon.2003.12.008
Parks Canada. (2011). Conservation Strategy fo r Southern Mountain Caribou in C an ada’s National Parks. Parks Canada.
PCIC. (2014). Climate Summary fo r Cariboo Region. Retrieved fromhttp://www.pacificclimate.org/sites/default/files/publications/Climate_Summary-Cariboo.pdf
| 123 |
Pederson, G. T., Gray, S. T., Ault, T., Marsh, W., Fagre, D. B., Bunn, A. G., ... Graumlich, L. J.(2011). Climatic controls on the Snowmelt Hydrology o f the Northern Rocky Mountains. Journal o f Climate, 24(6), 1666-1687. doi:10.1175/2010JCLI3729.1
Pepin, N., & Losleben, M. (2002). Climate change in the Colorado Rocky Mountains: Free air versus surface Temperature Trends. International Journal o f Climatology, 22, 311-329. doi: 10.1002/joc.740
Pepin, N., & Lundquist, J. D. (2008). Temperature trends at high elevations : Patterns across the globe. Geophysical Research Letters, 35, 1-6. doi:10.1029/2008GL034026
Picketts, I. M., Werner, A. T., Murdock, T. Q., Curry, J., Dery, S. J., & Dyer, D. (2012). Planning for climate change adaptation: lessons learned from a community-based workshop. Environmental Science & Policy, 17, 82-93. doi:10.1016/j.envsci.2011.12.011
Pike, R. G., Bennett, K. E., Redding, T. E., Werner, A. T., Spittlehouse, D. L., Moore, R. D. D., ... Tschaplinski, P. J. (2008). Climate Change Effects on Watershed Processes in British Columbia (Vol. 3). Ministry o f Forests, Lands and Natural Resources Operations.
R Core Team. (2014). R: A language and environment for statistical computing. R Foundation fo r Statistical Computing, Vienna, Austria. Vienna, Austria.: R Foundation for Statistical Computing. Retrieved from http://www.r-project.org/.
Ramanathan, V., Cess, R. D., Harrison, E. F., Minnis, P., Barkstrom, B. R., Ahmad, E., & Hartmann, D. (1989). Cloud-radiative forcing and climate: results from the Earth radiation budget experiment. Science (New York, N.Y.), 243(4887), 57-63. doi: 10.1126/science.243.4887.57
Rangwala, I., & Miller, J. R. (2012). Climate change in mountains: a review o f elevation-dependent warming and its possible causes. Climatic Change, 114(2-4), 527-547. doi: 10.1007/s 10584- 012-0419-3
Rangwala, I., Miller, J. R., & Xu, M. (2009). Warming in the Tibetan Plateau: Possible influences o f the changes in surface water vapor. G eophysical Research Letters, 36(6), L06703. doi: 10.1029/2009GL037245
Rangwala, I., Sinsky, E,, & Miller, J. R. (2013). Amplified warming projections for high altituderegions o f the northern hemisphere mid-latitudes from CMIP5 models. Environmental Research Letters, 8(2), 024040. doi: 10.1088/1748-9326/8/2/024040
Ropelewski, C. F., & Halpert, M. S. (1986). North American Preciptation and Temperature patterns associated with the El Nino/Southern Oscillation (ENSO). Monthly Weather Review, 114, 2 3 5 2 - 2362.
Scherrer, S. C., Ceppi, P., Croci-Maspoli, M., & Appenzeller, C. (2012). Snow-albedo feedback and Swiss spring temperature trends. Theoretical and A pplied Climatology, 110(4), 509-516. doi: 10.1007/s00704-012-0712-0
I 124|
Sen, P. K. (1968). Estimates o f the Regression Coefficient based on Kendall’s Tau. Journal o f the American Statistical Association, 63(324), 1379-1389. Retrieved from http://www.jstor.org/stable/2285891
Seneviratne, S. I., Liithi, D., Litschi, M., & Schar, C. (2006). Land-atmosphere coupling and climate change in Europe. Nature, 443(7108), 205-209. doi:10.1038/nature05095
Shi, X., Dery, S. J., Groisman, P. Y., & Lettenmaier, D. P. (2013). Relationships between recent Pan- Arctic snow cover and Hydroclimate Trends. Journal o f Climate, 26(6), 2048-2064. doi:10.1175/JCLI-D-12-00044.1
Shrestha, R. R., Schnorbus, M. A., Werner, A. T., & Berland, A. J. (2012). Modelling spatial and temporal variability o f hydrologic impacts o f climate change in the Fraser River basin, British Columbia, Canada. H ydrological Processes, 26(12), 1840-1860. doi:10.1002/hyp.9283
Statistics Canada. (2014). Cariboo Regional District 2011 population census. Retrieved from http://wwwl2.statcan.gc.ca/census-recensement/2011/dp-pd/map-carte/index-eng.cfm
Stevenson, S. K., Armleder, H. M., Arsenault, A., Coxson, D., DeLong, S. C., & Jull, M. (2011). British C olum bia’s Inland Rainforest. UBC Press. Vancouver.Toronto.
Stewart, I. T., Cayan, D. R., & Dettinger, M. D. (2004). Changes in snowmelt runoff timing inwestern North America under a “Business as usual” Climate change scenario. Climatic Change, 62, 217-232.
Stewart, I. T., Cayan, D. R., & Dettinger, M. D. (2005). Changes toward earlier Streamflow Timing across Western North America. Journal o f Climate, 18, 1136-1155.
Stocker, T. F., D.Qin, Plattner, G.-K., Alexander, L. V., Allen, S. K., Bindoff, N. L., ... Gregory, J.M. (2013). Technical summary. In T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, ... P. M. M idgley (Eds.), Climate Change 2013: The Physical Science Basis. Contribution o f Working Group I to the Fifth Assessment Report o f the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and N ew York, N Y , USA: Cambridge University Press. Retrieved fromhttp://www.climatechange2013.org/images/report/W GlAR5_TS_FlNAL.pdf
Storch, H. Von, & Zwiers, F. W. (1999). Statistical Analysis in Climate Research. Cambridge University Press.
Tohver, I. M., Hamlet, A. F., & Lee, S.-Y. (2014). Impacts o f 21 st-Century Climate Change onHydrologic extreme in the Pacific Northwest region o f North America. Journal o f the American Water Resources Association(JAWRA), 1-16. doi:10.111 l/jawr.12199
Tong, J., Dery, S. J., & Jackson, P. L. (2009a). Interrelationships between MODIS / Terra remotely sensed snow cover and the hydrometeorology o f the Quesnel River Basin, British Columbia, Canada. H ydrology and Earth System Sciences, 13, 1439-1452.
| 125 |
Tong, J., Dery, S. J., & Jackson, P. L. (2009b). Topographic control o f snow distribution in an alpine watershed o f western Canada inferred from spatially-filtered MODIS snow products. Hydrology and Earth System Sciences, 13(3), 319-326. doi: 10.5194/hess-13-319-2009
Trenberth, K. E. (1990). Recent Observed Interdecadal Climate Changes in the Northern Hemisphere. Bulletin o f the American M eteorological Society, 71(7), 988-993.
Urrutia, R., & Vuille, M. (2009). Climate change projections for the tropical Andes using a regional climate model: Temperature and precipitation simulations for the end o f the 21st century. Journal o f Geophysical Research, 114( D2), D02108. doi: 10.1029/2008JD011021
Vors, L. S., & Boyce, M. S. (2009). Global declines o f caribou and reindeer. G lobal Change Biology, 75(1 1), 2626-2633. doi: 10.1111/j. 1365-2486.2009.01974.x
Vuille, M., & Bradley, R. S. (2000). Mean annual temperature trends and their vertical structure in the tropical Andes. Geophysical Research Letters, 27(23), 3885-3888.
Vuille, M., Bradley, R. S., Werner, M., & Keiming, F. (2003). 20th Century Climate change in the Tropical Andes : Observations and Model Results. Climatic Change, 59, 75-99 .
Vuille, M., Francou, B., Wagnon, P., Juen, I., Kaser, G., Mark, B. G., & Bradley, R. S. (2008).Climate change and tropical Andean glaciers: Past, present and future. Earth-Science Reviews, 59(3-4), 79-96. doi:10.1016/j.earscirev.2008.04.002
Walther, G., Post, E., Convey, P., Menzel, A ., Parmesan, C., Beebee, T. J. C., ... Bairlein, F. (2002). Ecological responses to recent climate change. Nature, 416, 389-395.
Wang, Q., Fan, X., & Wang, M. (2013). Recent warming amplification over high elevation regions across the globe. Climate Dynamics, 43, 87—101. doi:10.1007/s00382-013-1889-3
Wang, T., Hamann, A ., Spittlehouse, D. L., & Murdock, T. Q. (2012). ClimateWNA— High-Resolution Spatial Climate Data for Western North America. Journal o f A pplied M eteorology and Climatology, 57(1), 16-29. doi:10.1175/JAM C-D-l 1-043.1
Warren, S. G., Eastman, R. M., & Hahn, C. J. (2007). A Survey o f changes in cloud cover and cloud types over land from surface observations, 1971—96. Journal o f Climate, 20(4), 717—738. doi: 10.1175/JCL14031.1
Weed, A. S., Matthew, P. A., & Hicke, J. A. (2013). Consequences o f climate change for biotic disturbances in North American forests. Ecological M onographs, 55(4), 441—470.
Westerling, A. L., Hidalgo, H. G., Cayan, D. R., & Swetnam, T. W. (2006). Warming and earlier spring increase western U.S. forest wildfire activity. Science (New York, N.Y.), 575(5789), 9 4 0 - 3. doi: 10.1126/science. 1128834
Whiteman, C. D. (2000). Mountain M eteorology: Fundamentals and Applications. Oxford University Press.
Wickham, H. (2009). ggplot2 Elegant Graphics fo r D ata Analysis Use R!. Springer.
I 126|
Wickham, H. (2011). The Split-Apply-Combine strategy for data analysis. Journal o f Statistical Software, 40( 1), 1-29. Retrieved from http://www.jstatsoft.org/v40/i01/
Wikipedia. (2013). Cariboo Mountains Sub-ranges. Wikipedia,the free encyclopedia. Retrieved December 11, 2013, from http://en.wikipedia.org/wiki/Cariboo_Mountains
Wikipedia. (2014). Barkerville,BC. Barkerville, B C. Retrieved September 17, 2014, from http://www.barkerville.ca/pages/HistoryO 1 .html
Wilks, D. S. (2011). Statistical M ethods in the Atmospheric Sciences (3rd ed.). Elsevier Inc.
Williams, M. W., Losleben, M., Caine, N., & Greenland, D. (1996). Changes in climate and hydrochemical responses in a high-elevation catchment in the Rocky Mountains, USA. Limnology and Oceanography, 41(5), 939-946. doi: 10.4319/lo. 1996.41.5.0939
Wittmer, H. U., Mclellan, B. N ., Seip, D. R., Young, J. A., Kinley, T. A ., Watts, G. S., & Hamilton,D. (2005). Population dynamics o f the endangered mountain ecotype o f woodland caribou ( Rangifer tarandus caribou) in British Colum bia, Canada. Canadian Journal o f Zoology, 83, 407-418. doi:10.1139/Z05-034
WMB. (2014). Weather Stations. M inistry o f Forests , Lands and Natural Resource Operations - Wildfire Management Branch Weather Stations. Retrieved January 10, 2014, from http://bcwildfire.ca/Weather/stations.htm
You, Q., Kang, S., Pepin, N., Fliigel, W.-A., Yan, Y ., Behrawan, H., & Huang, J. (2010).Relationship between temperature trend magnitude, elevation and mean temperature in the Tibetan Plateau from homogenized surface stations and reanalysis data. G lobal and Planetary Change, 77(1-2), 124-133. doi:10.1016/j.gloplacha.2010.01.020
Zhang, T., Osterkamp, T. E., & Stamnes, K. (1996). Some characteristics o f the Climate in Northern Alaska, U.S.A. Arctic and Alpine Reserach, 28(4), 509-518.
Zhang, X., Vincent, L. a., Hogg, W. D., & Niitsoo, A. (2000). Temperature and precipitation trends in Canada during the 20th century. Atmosphere-Ocean, 38(3), 395^429. doi: 10.1080/07055900.2000.9649654
Zwiers, F. W., Schnorbus, M. A., & Maruszeczka, G. D. (2011). Hydrologic Impacts o f Climate Change on BC Water Resources. Pacific Climate Impacts Consortium, University o f Victoria, Victoria BC.
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8. Appendices
Appendix A Validation of gridded data with observed data (daily frequency).
C C C & C A M n e t d a i l y T m i n . i n t h e C M R
2 0 -
0 -
- 2 0 -
- 4 0 -
.............. NSE- 0.71 . RMSEb 4 27 'C . r« 0.89 (p - 0 ) .......................................................................................... ....................................................................................................... Browntop
E 20'^ o-| - 2 0 -
£® -40~
NSE- 0.87 , RMSE*2.87 *C , r» 0.96 (p« 0 )
|e 2 0 '
i o -
| . 2 0 -re
q -40 -
NSE. 0 6 7 ,R y S E U » 2 X , r. 0 8 » ( P - ° ) t . I t . . 1 j 4 j A A i l . i J j , . w
2 0 -
o--20 -
-4 0 -
NSE- 0.79 . RMSE- 3.71 *C . r* 0.92 (p«6 )
8o
£ & <£> .<?> c ? S ’ c ? S>^ # o ^ & O ^ ^ ^ 0 / ■ ^ ^ 0
T im e [D a y s ]
20-
o-
-20-
CCC & CAMnet daily Tmax. in the CMR
— Canadian Climate Coverage — Cariboo Alpine Mesonet
y fNSE* 0.36 ,RMSEa 6.96 ®C, r- 0.95 (p- 0 ) ^ i l l
O ^ 20-| ° |- 2 0 -
iNSE- 0 .9 4 . RMSE- 2.59 "C, J 0.97 (p* 0 ) 4 WtH
E1 20- is o- >»« -2 0 -
i|NSE- 0.79 . RMSE* 4.5 'C , rX .9 6 ( p - 0 ) | | |
20-
o-
-20-
l|NSE* 0 .3 3 . RMSE" 6.86 *C , r* 0.94 (p * 0 1 * 1
^ ^ ^ ^ ^^ ^ o Vs ^ o ^ $ O ^ ^ ^ O'5-Time[Days]
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CCC & CAMnet daily Precip. in the CMR
^ C a n a d ia n Climate C o v e ra g e C a r ib o o Alpine Mesonet
30- NSE- -0.94 . RMSE- 3 22 mm , r- 0 27 (p« 0 )
20 *
10 -
30* NSE- 0 2 3 , RMSE- 2.67 mm . r- 0.55 (p - 0 )
20 -
30- NSE- -0.16 . RMSE- 3.69 mr . r - 0 2 9 (p - 0 )
30- MSE- -0.98 , RMSE- 3.58 m n . r» 0 2 2 (p* 0 )
10 -
TimeJDays]
I 129 |
A ppendix B Spatial plots o f m onthly minimum and m axim um air temperatures.
Monthly Tmin. (average 950-2010) over the CMR
Lat
itude
52 53
54 52
53 54
52 53
54I
II
I
II
I
II
Jan Feb Mar Apr
'W ;'’
: \ i 0
Temp.{°C]
-5
| :
May Jun Jut Aug
I \
r V - - Nw ......Sap ......... Oet Dm
-122 -121 -120 -119 -122 -121 -120 -119 -122 -121 -120 -119 -122 -121 -120 -119Longitude
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Monthly Tmax. (average 1950-2010) over the CMR
122 -121 -120 -119 -122 -121 -120 -119 -122 -121 -120 -119 -122 -121 -120 -119Longitude
A ppendix C Spatial plots o f m onthly precipitation.
Monthly Prep, (average 1950-2010) over the CMR
Prep.[mm] ■ 600
122 -121 -120 -119 -122 -121 -120 -119 -122 -121 -120 -119Longitude
I 132 |
A ppendix D M inim um and m axim um annual temperature and trend.
Annual Tmin. trend in the CMR,1950-2010Trend= 0.322 °C/decade
p-value= 8.13e-06- 2 -
-3-
M5 yrs moving average ~ Median B ased Trend- 6 -
^ YriyTmin.-7-\
Time[Year]
Annual Tmax. trend in the CMR, 1950-2010Trend= 0.193 °C/decade
p-value=y.007329
7H>»—>-
6 YrlyTmax.
5 yrs moving a \erage “ Median B ased Trend5
&
Time[Year]
| 133|
A ppendix E M ean annual air temperature anom aly and trend.
Annual mean temp, anomaly in the CMR, 1950-2010
2.0 - Trend- 0.27 °C/decade
p = 5.4e-05
>. 0.5-
o 0 .0 -
£-0.5-
- 1. 0 -
-1.5-
- 2 . 0 - 5 yrs moving average “ Median based Trend-2.5-
|N egative Anomaly! Positive Anomaly
Time [Year]
Annual mean temp, trend in the CMR, 1950-2010Trend= 0.2696 °C/decade ± ^
p-value= 5.38e-05 A » ]'
>»
” 5 yrs moving average “ Median Based Trend
A YrlyTmean
Time[Year]
I 134 |
A ppendix F Seasonal minimum , m axim um , and m ean temperatures and trends.
Seasonal Minimum
Trend= 0.62 "C/Decade , pval= 0^
trend in the CMR, 1950-2010
- 1 0 -
-15-
- 20 -
-21Trend= 0.35 °C /decade , pval= 0 *
-4-
- 6 -
5 yrs moMng average — Median Based Trend
Trend= 0.12 °C /d e cad e , pval= 0.$\ Temp.
- 2 -
* A *-4-
- 6 -Trend= 0.1‘‘C /decade , pval= 0.44
Time[Year]
I 135 |
Seasonal maximum Temp, trend in the CMR, 1950-2010 ♦........................................
A ♦ ,4>Trend= 0.39 ’C/decade , pval= 0.01
4
is
> W 'T .o
E 22 0)H 20
Irend= 0.15 ‘Q/decade .BAal=a.t5. ■ 5 yrs moving average — Median Based Trend
v3.
18-
16
10.0
7.5-
5.0-
2.5
i !l A Temp.\ A A A
Trand= 0.11 ’C/decade . pval= 0.28
o>ie■33:
* 4 N * ; . * *
uTrend= -0.04 °C/decade, pval= 0.74
‘Hi»
nfbN ctf3 r»NTlme[Year]
| 136 |
Winter Spring. Summer Fail
O!CM;I ^omO)a:o
C ;
o>
CL
a
* - ■
o <-11 ✓ ^ - <(
m in o o oo o in o in oin csi o cvi in
o
t-*
A ppendix G Spatial trend o f mean annual air temperature.
Annual Tmean trend in the CMR, 1950-2010
■'3-to
Significance
* Sig.
com
a)T33co
CMin
Trend (°Cdecade 1)
-122 -121 -120 Longitude (°W )
-119
I 138 |
A ppendix H Spatial trend o f m ean seasonal temperature in the CM R, 1950-2010.
W inter.Tmean
-122 -121 -120 Longitude
-119
T r e n d (“C d e c a d e 1) S ig n i f ic a n c e
S ig . * N o t s ig .
0.3 0.4 0.5 0.6 0 7 0.8
S p rin g jm e a n
Longitude
T r e n d T C d e c a d e )
0.20 0.25 0.30 0.35
Sum m er.! m ean
Longitude
S ig n if ic a n c eT r e n d ( X d e c a d e )
Sig. * Not stg
Fall.Tmean
« ■ » « * * * * # * ■ a a r
Longitude
Trend ( " C d e c a d e S i g n i f i c a n c e
0.00 0.05 0.10 0.15
| 139 |
A ppendix I Annual and seasonal mean temperature trends vs elevation.
Annual mean Temp, trend vs elevation in the CMREl
evat
ion
(m)
1000
15
00
2000
25
00I
II
!
♦♦
♦ Sig.trend
R2 0 56 ------------------------------------------------------ — ^«*** •*—-------- ---------------------n u . ; h j --------- ” - "A J E * ♦ ♦ ♦ *n & A - A ------------------------------------------ + * + J m w Z * -----------*— >— -------------------------------11 U TT------------------------------------------ W L a. AP » » — ^ + » » »-------------------------- -
. * • j g S f r f c • ? * . * * ' * .i . . . f l y * # ♦♦ ♦ a • • r .* *«* ♦
% ? * + * * * * *
* * • ' ' V A 't ; V f f c & V ?# ^ * ♦ ♦
♦1 1 1 1 1 1 1
3.10 0.15 0.20 0.25 0.30 0.35 0.40Trend ( Cdecade )
| 140 |
• Sig.trend♦ Not sig. trend
Trend [°Cdecade ]