Appendix 3 Climate Change - Open Government Program | … · 2016-09-22 · Suncor Energy Inc. - i - Climate Change Voyageur South Project July 2007 TABLE OF CONTENTS SECTION PAGE

APPENDIX 3

CLIMATE CHANGE IN THE OIL SANDS REGION

Suncor Energy Inc. - i - Climate Change Voyageur South Project July 2007

TABLE OF CONTENTS

SECTION PAGE

1 INTRODUCTION.......................................................................................................1 1.1 GUIDANCE FOR INCORPORATING CLIMATE CHANGE ............................................1 1.2 APPENDIX ORGANIZATION ..........................................................................................2

2 CLIMATE CHANGE...................................................................................................3 2.1 ASSESSMENT APPROACH...........................................................................................3

2.1.1 Climate Forecast Models .................................................................................3 2.1.2 Forecast Scenarios ..........................................................................................4 2.1.3 Baseline Climate ..............................................................................................6

2.2 HISTORIC CLIMATE CHANGE ......................................................................................6 2.3 FUTURE CLIMATE CHANGE .........................................................................................8

2.3.1 Climate Change Relative to the 1961 to 1990 Baseline ..................................9 2.3.2 Climate Change Over the Voyageur South Project Life ................................16

2.4 MODEL SCENARIOS FOR USE IN ENVIRONMENTAL ASSESSMENTS..................24

3 EFFECTS OF CLIMATIC CHANGE ON AIR QUALITY PREDICTIONS ..................29 3.1 ACID DEPOSITION.......................................................................................................29 3.2 ATMOSPHERIC DISPERSION .....................................................................................31 3.3 GROUND LEVEL OZONE.............................................................................................33 3.4 SUMMARY ....................................................................................................................35

4 SUMMARY OF THE CONSIDERATIONS OF CLIMATE CHANGE ON HYDROGEOLOGY..................................................................................................36

5 SUMMARY OF THE CONSIDERATIONS OF CLIMATE CHANGE ON SURFACE WATER HYDROLOGY..........................................................................38 5.1 INTRODUCTION ...........................................................................................................38 5.2 LITERATURE REVIEW .................................................................................................39

5.2.1 General ..........................................................................................................39 5.2.2 Change and Variability in Air Temperature....................................................39 5.2.3 Change and Variability in Precipitation ..........................................................41 5.2.4 Change and Variability in Evaporation and Evapotranspiration ....................43 5.2.5 Change and Variability in Relative Humidity..................................................46 5.2.6 Change and Variability in Streamflows ..........................................................46

5.3 TREND ANALYSES OF AIR TEMPERATURE, PRECIPITATION AND STREAMFLOW .............................................................................................................47 5.3.1 General ..........................................................................................................47 5.3.2 Air Temperature .............................................................................................48 5.3.3 Precipitation ...................................................................................................57 5.3.4 Streamflows ...................................................................................................65 5.3.5 Findings..........................................................................................................79

5.4 SENSITIVITY OF FLOWS IN THE ATHABASCA RIVER TRIBUTARY STREAMS TO POTENTIAL CHANGES IN CLIMATE PARAMETERS ........................80 5.4.1 General ..........................................................................................................80 5.4.2 Beaver River ..................................................................................................83 5.4.3 Muskeg River .................................................................................................85 5.4.4 Findings..........................................................................................................87

5.5 CONCLUSIONS ............................................................................................................87

Suncor Energy Inc. - ii - Climate Change Voyageur South Project July 2007

6 SUMMARY OF THE CONSIDERATIONS OF CLIMATE CHANGE ON WATER QUALITY ...................................................................................................89 6.1 INTRODUCTION ...........................................................................................................89 6.2 LITERATURE REVIEW .................................................................................................89 6.3 MODELLING ANALYSIS...............................................................................................91

6.3.1 Methods .........................................................................................................91 6.3.2 Results ...........................................................................................................92

6.4 CONCLUSIONS ............................................................................................................95

7 SUMMARY OF THE CONSIDERATIONS OF CLIMATE CHANGE ON FISH AND FISH HABITAT................................................................................................96 7.1 INTRODUCTION ...........................................................................................................96 7.2 LITERATURE INFORMATION ......................................................................................96 7.3 IMPACT PREDICTIONS AND CLIMATE CHANGE....................................................101

8 SUMMARY OF THE CONSIDERATIONS OF CLIMATE CHANGE ON TERRESTRIAL RESOURCES ..............................................................................104

9 EFFECTS OF CLIMATE CHANGE ON THE HUMAN AND WILDLIFE HEALTH RISK ASSESSMENT..............................................................................109 9.1 AIR QUALITY PREDICTIONS.....................................................................................111 9.2 WATER QUALITY PREDICTIONS..............................................................................111

10 GLOSSARY AND ABBREVIATIONS.....................................................................112 10.1 GLOSSARY .................................................................................................................112 10.2 ABBREVIATIONS........................................................................................................114

11 REFERENCES......................................................................................................116 11.1 LITERATURE CITED...................................................................................................116 11.2 WEBSITE REFERENCES...........................................................................................129

LIST OF TABLES

Table 2-1 General Circulation Models (GCMs) Included in Assessment ................................4 Table 2-2 Summary of Available Climate Forecasts................................................................5 Table 2-3 Observed Multiple Climate Normals – Fort McMurray.............................................7 Table 2-4 Observed Climate Change – Fort McMurray...........................................................8 Table 2-5 Centre for Climate System Research/National Institute for Environmental

Studies Climate Forecasts for 2040 to 2069 Relative to the 1961 to 1990 Baseline .................................................................................................................10

Table 2-6 Canadian Global Coupled Model (Version 2) Climate Forecasts for 2041 to 2069 Relative to the 1961 to 1990 Baseline......................................................10

Table 2-7 Commonwealth Scientific and Industrial Research Organization Mark 2 Climate Forecasts for 2040 to 2069 Relative to the 1961 to 1990 Baseline .........11

Table 2-8 Max Planck Institute for Meteorology/Deutsches Klimarechenzentrum Climate Forecasts for 2040 to 2069 Relative to the 1961 to 1990 Baseline .........11

Table 2-9 Geophysical Fluid Dynamics Laboratory Climate Forecasts for 2040 to 2069 Relative to the 1961 to 1990 Baseline..........................................................11

Table 2-10 Hadley Centre Coupled Model Climate Forecasts for 2040 to 2069 Relative to the 1961 to 1990 Baseline ...................................................................12

Suncor Energy Inc. - iii - Climate Change Voyageur South Project July 2007

Table 2-11 Comparison of Climate Change Forecasts for 2040 to 2069 Baseline (1961 to 1990)........................................................................................................15

Table 2-12 Summary of Ranked Climate Scenarios................................................................16 Table 2-13 Centre for Climate System Research/National Institute for Environment

Studies Climate Forecasts Over the Voyageur South Project Life........................18 Table 2-14 Canadian Global Coupled Model (Version 2) Climate Forecasts Over the

Voyageur South Project Life ..................................................................................18 Table 2-15 Commonwealth Scientific and Industrial Research Organization Mark 2

Climate Forecasts Over the Voyageur South Project Life .....................................19 Table 2-16 Max Planck Institute for Meteorology/Deutsches Kilmarechenzentrum

Climate Forecasts Over the Voyageur South Project Life .....................................19 Table 2-17 Geophysical Fluid Dynamics Laboratory Climate Forecasts Over the

Voyageur South Project Life ..................................................................................19 Table 2-18 Hadley Centre Coupled Model Climate Forecasts Over the Voyageur

South Project Life...................................................................................................20 Table 2-19 Comparison of Climate Change Values Over the Voyageur South Project

Life .........................................................................................................................23 Table 2-20 Ranked Forecast Scenarios for Climate Change Over the Voyageur

South Project Life...................................................................................................24 Table 2-21 Future Climate Trend Forecasts - Upper Annual Temperature.............................25 Table 2-22 Future Climate Trend Forecasts - Upper Summer Temperature ..........................25 Table 2-23 Future Climate Trend Forecasts - Upper Winter Temperature..............................26 Table 2-24 Future Climate Trend Forecasts - Upper Annual Precipitation..............................26 Table 2-25 Future Climate Trend Forecasts - Upper Summer Precipitation ...........................27 Table 2-26 Future Climate Trend Forecasts - Upper Winter Precipitation ..............................27 Table 2-27 Future Climate Trend Forecasts - Lower Annual Precipitation..............................27 Table 2-28 Future Climate Trend Forecasts - Lower Summer Precipitation ...........................28 Table 2-29 Future Climate Trend Forecasts - Lower Winter Precipitation ..............................28 Table 3-1 Primary Links Between Climate Change and Air Quality ......................................29 Table 3-2 Upper Bound Forecasts for Changes in Summer Precipitation Over the

Voyageur South Project Life ..................................................................................30 Table 3-3 Comparison of 1995 Precipitation to Climate Normals..........................................30 Table 3-4 Summary of Climate Scenarios for Wind Speed ...................................................31 Table 3-5 Comparison of 1995 Average Wind Speeds to Climate Normals..........................32 Table 3-6 Comparison of Wind Speed Categories ................................................................32 Table 3-7 Upper Bound Forecast for Changes in Summer Temperature Over the

Voyageur South Project Life ..................................................................................33 Table 5-1 Statistical Test for Trend Analysis of Oil Sands Region Temperatures.................49 Table 5-2 Statistical T-test and F-test for Two Samples (1944 to 1974 Versus 1975

to 2005) of Temperature Measured at Fort McMurray Airport Station ..................54 Table 5-3 Forecasted Mean Annual Temperature and Annual Total Precipitation to

Year 2050 Based on Observed Trend Lines .........................................................57 Table 5-4 Statistical Test for Trend Analysis of Oil Sands Region Precipitation ...................58 Table 5-5 Statistical T-test and F-test for Two Samples (1944 to 1974 Versus 1975

to 2005) of Precipitation Measured at Fort McMurray Airport Station ...................64 Table 5-6 Location, Drainage Areas and Flow Statistics of Streamflow Stations..................66 Table 5-7 Statistical Test for Trend Analysis of Streamflows ................................................67 Table 5-8 Forecasted Flow Parameters to Year 2050 Based on Observed Trend

Lines ......................................................................................................................77 Table 5-9 Forecasted Air Temperature, Precipitation and Evapotranspiration for

Sensitivity Analysis ................................................................................................82 Table 5-10 Relative Sensitivity Analysis for Beaver River at Environment Canada

Station 07DA018....................................................................................................84 Table 5-11 Relative Sensitivity Analysis for Muskeg River at its Mouth ..................................86

Suncor Energy Inc. - iv - Climate Change Voyageur South Project July 2007

Table 8-1 Boreal Tree Species Ranges of Climatic Tolerance............................................105

LIST OF FIGURES

Figure 2-1 Intergovernmental Panel on Climate Change Emission Scenarios.........................5 Figure 2-2 Determining Change Relative to the 1961 to 1990 Baseline...................................9 Figure 2-3 Forecast Annual Climate Change Relative to the 1961 to 1990 Baseline.............13 Figure 2-4 Forecast Summer and Winter Climate Change Relative to the 1961 to

1990 Baseline ........................................................................................................14 Figure 2-5 Determining Change Over the Voyageur South Project Life.................................17 Figure 2-6 Forecast Annual Climate Change Over the Voyageur South Project Life.............21 Figure 2-7 Forecast Summer and Winter Climate Change Over the Voyageur South

Project Life .............................................................................................................22 Figure 3-1 Comparison of Daily Maximum Temperatures and Daily Maximum 1-Hour

Ozone Concentrations ...........................................................................................34 Figure 5-1 Annual Mean, Maximum and Minimum Temperatures at Fort McMurray or

Fort McMurray Airport Station................................................................................51 Figure 5-2 Spring, Summer, Fall and Winter Temperatures at Fort McMurray or Fort

McMurray Airport Station .......................................................................................52 Figure 5-3 Annual Mean, Maximum and Minimum Temperatures at Whitecourt or

Whitecourt Airport Station......................................................................................55 Figure 5-4 Spring, Summer, Fall and Winter Temperatures at Whitecourt or

Whitecourt Airport Station......................................................................................56 Figure 5-5 Precipitation at Fort McMurray or Fort McMurray Airport Station..........................60 Figure 5-6 Precipitation at Whitecourt or Whitecourt Airport Station ......................................62 Figure 5-7 Trends of Mean Annual Flows for Various Streamflow Stations ...........................71 Figure 5-8 Trends of 7-Day Low Flows for Various Streamflow Stations ...............................74 Figure 6-1 Effect of Climate Change on Molybdenum and Total Dissolved Solids in

the Far Future at the Mouth of Poplar Creek.........................................................93 Figure 6-2 Effect of Climate Change on Labile Naphthenic Acids and Acute and

Chronic Toxicity in the Far Future at the Mouth of Poplar Creek ..........................94

Suncor Energy Inc. - 1 - Climate Change Voyageur South Project July 2007

1.1

1 INTRODUCTION

Evaluations of the potential effects of projects on climate change, or of climate change on projects, are required as part of the Environmental Impact Assessment (EIA) for new projects in Alberta. Guidance on how such evaluations should be made is provided both by the EIA Terms of Reference (TOR) (AENV 2007) as well as in federal guidance documents (FPTCCCEA 2003).

This section has been prepared to summarize the findings with regards to climate change and to demonstrate that the expectations of provincial and federal agencies have been addressed with respect to climate change issues.

GUIDANCE FOR INCORPORATING CLIMATE CHANGE

The Federal-Provincial-Territorial Committee on Climate Change and Environmental Assessment (FPTCCEA) issued a general guidance document in November 2003 for practitioners to use when incorporating climate change issues into environmental assessments (FPTCCCEA 2003). The guidance document sets out the following two approaches for incorporating climate change considerations:

• greenhouse gas (GHG) considerations where the proposed project may contribute to GHG emissions; and

• impact considerations where changing climates may have an impact on the proposed project.

The federal guidance document indicates that projects are typically more closely aligned with one type of consideration or the other, but provides for cases where both considerations could be addressed. A review of oil sands projects suggests that they would be more aligned with the first approach, which is consistent with past oil sands EIAs that have incorporated and documented the climate change issue through considerations of the GHG emissions associated with the project. However, recent Oil Sands Project EIAs (e.g., Suncor 2005; Shell 2005; Imperial Oil 2005) also considered potential impacts of climate change on future temperatures, precipitation and flows in key Oil Sands Region watercourses.

Alberta Environment (AENV), as part of the Voyageur South Project EIA TOR has incorporated specific sections dealing with climate change considering “GHG contributions” and “impact on project” considerations (Volume 1, Appendix 1).


1.2

This report includes a summary of the impact considerations related to climate change as set out in federal guidance and TOR for the EIA. While both the federal guidance and TOR include a requirement for GHG considerations, these are dealt with directly in the air quality section of the EIA.

APPENDIX ORGANIZATION

This appendix is organized as follows: Section 1 provides an introduction. Section 2 provides a listing of the available information regarding changing climate in the Athabasca Oil Sands Region. This includes a review of past climate change as well as the change forecast for the future. A summary of the climate change considerations for air quality are provided in Section 3. Section 4 includes a summary of the considerations of climate change on hydrogeology (groundwater). Section 5 includes a summary of the considerations of climate change and surface water hydrology. The considerations for climate change and water quality are summarized in Section 6, while Section 7 discusses considerations for fish and fish habitat. Sections 8 and 9 summarize climate change considerations for terrestrial resources and human health, respectively.


2.1

2 CLIMATE CHANGE

ASSESSMENT APPROACH

A complete evaluation of the potential effects of climate change on both oil sands projects and impact predictions first requires an understanding of how the climate has been changing and how it might change in the future. Determining historic climate change is relatively straight forward, relying on the long-term climate records available for the city of Fort McMurray (1916 to 2000).

Climate forecasts applied to the Fort McMurray area have been used to determine future climate change. All applicable climate forecast data from the Canadian Climate Impacts Scenarios Project website run by the Canadian Institute for Climate Studies (CICS) have been considered to ensure a thorough evaluation. The data included in this report also ensures consistent presentation of forecasts. For example, when the forecast temperature change for a given model and scenario is presented, the corresponding forecast precipitation change for the same model and scenario is also presented.

2.1.1 Climate Forecast Models

Future climate forecasts require the use of sophisticated mathematical computer programs called General Circulation Models (GCMs). These models simulate the interactions of airborne emissions, the atmosphere, land surfaces and oceans and can take several months to run. The Intergovernmental Panel on Climate Change (IPCC), which has been charged with providing state-of-the-art reviews of climate change science, has made use of a number of different GCMs. The seven models presented in Table 2-1 are recommended for use by the IPCC (IPCC 2005, website). Canadian forecast data from these models were made available by the CICS as part of the Canadian Climate Impacts Scenarios Project.


Table 2-1 General Circulation Models (GCMs) Included in Assessment

Model Name Abbreviation Country Model Resolution(a) [km²]

Centre for Climate System Research / National Institute for Environmental Studies CCSR/NIES Japan 168,000

Canadian Global Coupled Model (Version 2) CGCM2 Canada 74,000 Commonwealth Scientific and Industrial Research Organization Mark 2 CSIRO MK2 Australia 95,000

Max Planck Institute for Meteorology / Deutsches Klimarechenzentrum ECHAM4/OPYC3 Germany 41,000

Geophysical Fluid Dynamics Laboratory GFDL R30 United States 44,000 Hadley Centre Coupled Model HadCM3 United Kingdom 50,000 National Centre for Atmospheric Research Parallel Climate Model(b) NCAR-PCM United States 41,000

(a) The model resolution represents the area of each grid cell used in the respective models. (b) Canadian climate forecasts from the NCAR-PCM model were not available from the CICS web site.

2.1.2 Forecast Scenarios

Given the wide range of inputs available to GCMs, the IPCC has established a series of global GHG emission scenarios based on four potential socio-economic development paths. The Third Assessment Report (IPCC 2001) identifies these scenarios as A1, B1, A2 and B2. The A1 and A2 scenarios represent a focus on economic growth while the B1 and B2 scenarios represent a shift towards more environmentally conscious solutions to growth. Both scenarios A1 and B1 include a shift towards global solutions while the A2 and B2 scenarios include growth based on more localized and regional approaches. Figure 2-1 provides an illustrative summary of the four emission scenarios, which are described more fully in the IPCC Special Report on Emissions Scenarios (IPCC 2000).

Although the IPCC has not stated which of the emission scenarios is most likely to occur, the A2 scenario most closely reflects the current global socio-economic situation, and is closely related to the IS92a scenario that was used by IPCC in its historical climate assessments. In relation to the A2 scenario, scenarios A1, B1 and B2 result in lower long-term GHG emissions over the next century. Of the A1 scenario family, scenario A1FI yields high emissions in the first half of the 21st century due to increasing population and high dependence on fossil fuels for energy. While the IPCC supports all of these scenarios, forecast data from each of them are not available for all seven of the GCMs listed in Table 2-1. A summary of the forecast data available from the CICS web site is provided in Table 2-2. All available models and emissions scenarios were considered in this assessment.


Table 2-2 Summary of Available Climate Forecasts SRES Scenario(a)

Climate Model Forecast Period A1FI A1T A1 A2 B1 B2

CCSR/NIES 2010 to 2069 - A1T A1(1) A2(1) B1(1) B2(1)

CGCM2 2010 to 2069 - - -

A2(1) A2(2) A2(3) A2(x)

- B2(1)

CSIRO MK2 2010 to 2069 - - A1(1) A2(1) B1(1) B2(1) ECHAM4/OPYC3 2010 to 2069 - - - A2(1) - B2(1) GFDL R30 2010 to 2069 - - - A2(1) - B2(1)

HadCM3 2010 to 2069 A1FI - -

A2(1) A2(2) A2(3) A2(x)

B1(1) B2(1) B2(2)

NCAR-PCM(b) 2010 to 2069 - - - - - - (a) The numbers in parenthesis represent the model ensemble number. An ensemble simulation consists of several

modelling runs for the same scenario but with different initial conditions. Each of these runs is referred to by an ensemble number.

(b) Canadian climate forecasts from the NCAR-PCM model were not available from the CICS web site.

SRES = Special Report on Emissions Scenarios. - = Not applicable.

Figure 2-1 Intergovernmental Panel on Climate Change Emission Scenarios

-B: balanced-FI: fossil-intensive-T: non-fossil

A1 A2

B1 B2

MoreRegional

MoreGlobal

MoreEconomic

MoreEnvironmental


2.2

2.1.3 Baseline Climate

An analysis of climate change not only depends on future conditions but also on the baseline climate to which the predictions are compared. Baseline climate information is important for describing average conditions, spatial and temporal variability and anomalous events as well as calibrating and testing climate models (CICS 2005, website).

The IPCC recommends that 1961 to 1990 be adopted as the climatological baseline period in impact assessments (CICS 2005, website). This period has been selected since it is considered to:

• be representative of the present-day or recent average climate;

• be of a sufficient duration to encompass a range of climatic variations, including a number of significant weather anomalies;

• cover a period for which data on all major climatological variables are abundant, adequately distributed over space and readily available;

• include data of sufficiently high quality for use in evaluating impacts; and

• be comparable with baseline climatologies used in other impact assessments.

The scenarios available from CICS are based on the 1961 to 1990 baseline period; therefore, this assessment is also based on the same period.

HISTORIC CLIMATE CHANGE Analyzing historic climate change in the Fort McMurray region involves the review of the current climate normals. Climate normals refer to calculated averages of observed climate values for a given location over a specified time period. The World Meteorological Organization recommends that climate normals be prepared at the end of every decade for a 30-year period (e.g., 1961 to 1990; 1971 to 2000). Table 2-3 provides a summary of the climate normals observed at Fort McMurray. The four seasonal values were determined from three months of data as follows:

• spring – March, April and May;

• summer – June, July and August;

• fall – September, October and November; and

• winter – December, January and February.


Table 2-3 Observed Multiple Climate Normals – Fort McMurray Observed Normals

Climate Data Season Temperature [°C]

Precipitation [mm]

annual -0.8 415.9 spring 0.4 75.3 summer 14.7 189.7 fall 13.2 92.4

Fort McMurray (1921 to 1950)

winter -19.2 58.6 annual -0.5 428.3 spring 0.5 73.9 summer 14.7 194.7 fall 13.3 98.7






winter -18.0 67.3 annual 0.3 464.3 spring 1.6 80.6 summer 15.5 214.8 fall 13.6 109.4


winter -17.3 60.6 annual 0.8 454.9 spring 2.4 74.6 summer 15.6 228.7 fall 13.8 98.1


winter -16.4 53.8

Table 2-4 provides a listing of the observed changes in climate conditions relative to the 1961 to 1990 climate normals. The comparison shows that the 1951 to 1980 period was 0.4°C cooler and received 2% more precipitation annually than the 1961 to 1990 period. The 1971 to 2000 period was 0.5°C warmer and received 2% less precipitation than the 1961 to 1990 period.


Table 2-4 Observed Climate Change – Fort McMurray

Observed Climate Change(a)

Climate Data Season Temperature [°C]

Precipitation [% change]

annual -1.1 -11.6

spring -1.2 -7.1

summer -0.7 -13.2

fall -0.5 -18.3

1921 to 1950 Normals

winter -1.9 -3.4

annual -0.8 -8.4

spring -1.2 -9.1

summer -0.7 -10.3

fall -0.3 -10.8


winter -1.3 -1.6

annual -0.6 -8.0

spring -1.3 -13.6

summer -0.5 -9.1

fall -0.3 -8.8


winter -0.9 1.4

annual -0.4 1.8

spring -0.8 -3.7

summer -0.4 0.7

fall -0.3 2.4


winter -0.7 9.9

annual 0.5 -2.1

spring 0.8 -8.0

summer 0.1 6.1

fall 0.2 -11.5


winter 0.9 -12.6 (a) Observed climate change was determined as the change relative to the 1961 to 1990 normals.

2.3 FUTURE CLIMATE CHANGE

Climate forecast data from various models and emissions scenarios were analyzed to determine potential climate change in region. Since the models are susceptible to inter-decadal variability, the analysis uses the average of 30 years of data, centred on the decade of interest. The future conditions have been represented by the 30-year period between 2040 and 2069, which would be representative of the mid-2050s. This is near the end of the life of Voyageur South Project and incorporates the post operations management and closure period of Voyageur South Project.


Two separate forecasts of climate change have been presented. The first forecast provides the change between the mid-2050s (i.e., the 30-year period from 2040 to 2069) when the Voyageur South Project will be decommissioned and the period from 1961 to 1990. The second forecast represents the climate change expected over the life of the Voyageur South Project. This acknowledges that some of the changes in climate since the 1961 to 1990 period will have already occurred. This change is calculated as the difference between the 30 year average centred on the current conditions and the mid-2050s.

2.3.1 Climate Change Relative to the 1961 to 1990 Baseline

The forecast change in climate relative to the 1961 to 1990 baseline represents the total change forecast between the modelled 30-year average for 1961 to 1990 and the modelled future conditions, as represented by the 30-year period between 2040 and 2069. This 30-year average would be representative of the mid-2050s (i.e., near the end of Voyageur South Project). This is illustrated in Figure 2-2.

Figure 2-2 Determining Change Relative to the 1961 to 1990 Baseline

-8

-6

-4

-2

0

2

4

6

8

1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070

2040 to 2069 average


forecast change over the life of the Voyageur South Project

Annu

al Av

erag

e Tem

pera

ture

[°C]

The forecast changes in temperature and precipitation relative to the 1961 to 1990 baseline, presented in Tables 2-5 through 2-10, were determined for each of the models/scenarios available on the CICS web site for the corresponding model grid cell that covered the Voyageur South Project and the Fort McMurray area. Summer values represent data from June, July and August, and Winter values represent data from December, January and February.


Table 2-5 Centre for Climate System Research/National Institute for Environmental Studies Climate Forecasts for 2040 to 2069 Relative to the 1961 to 1990 Baseline

Change From Baseline (1961 to 1990) Model SRES Scenario Season Temperature

[°C] Precipitation [% Change]

annual 5.3 13.8

summer 3.0 -11.2 CCSR/NIES A1T

winter 6.6 21.0

annual 6.3 -(a)

summer 3.6 -(a)CCSR/NIES A1(1)

winter 8.1 -(a)

annual 4.8 12.0

summer 2.3 -17.6 CCSR/NIES A2(1)

winter 6.2 21.6

annual 4.0 9.6

summer 2.4 -19.0 CCSR/NIES B1(1)

winter 4.9 19.4

annual 5.0 14.4

summer 3.0 -9.2 CCSR/NIES B2(1)

winter 6.1 27.2 (a) Precipitation data are not available for the A1(1) scenario.

Table 2-6 Canadian Global Coupled Model (Version 2) Climate Forecasts for 2041 to 2069 Relative to the 1961 to 1990 Baseline



annual 2.8 4.9

summer 2.4 11.2 CGCM2 A2(1)

winter 4.2 -4.6

annual 2.6 3.9


winter 4.3 -9.7

annual 2.6 5.7


winter 3.6 2.9

annual 2.7 4.3

summer 2.3 6.7 CGCM2 A2(x)

winter 4.0 -4.1

annual 2.0 3.4

summer 1.9 2.8 CGCM2 B2(1)

winter 2.8 1.7


Table 2-7 Commonwealth Scientific and Industrial Research Organization Mark 2 Climate Forecasts for 2040 to 2069 Relative to the 1961 to 1990 Baseline



annual 3.7 14.1 summer 1.8 6.2 CSIRO Mk2b A1(1) winter 4.2 24.3 annual 3.3 14.0 summer 1.6 3.7 CSIRO Mk2b A2(1) winter 4.1 24.0 annual 3.2 10.8 summer 1.7 6.7 CSIRO Mk2b B1(1) winter 3.6 22.7 annual 3.4 11.9 summer 1.8 0.9 CSIRO Mk2b B2(1) winter 3.8 22.2

Table 2-8 Max Planck Institute for Meteorology/Deutsches Klimarechenzentrum Climate Forecasts for 2040 to 2069 Relative to the 1961 to 1990 Baseline



annual 3.4 -5.4 summer 2.8 -21.1 ECHAM4/OPYC3 A2(1) winter 5.5 7.9 annual 3.6 -5.1 summer 2.9 -8.7 ECHAM4/OPYC3 B2(1) winter 5.9 5.0

Table 2-9 Geophysical Fluid Dynamics Laboratory Climate Forecasts for 2040 to 2069 Relative to the 1961 to 1990 Baseline



annual 3.0 11.6 summer 2.7 8.3 GFDL R30 A2(1) winter 3.3 12.3 annual 2.6 6.7 summer 2.9 -10.1 GFDL R30 B2(1) winter 3.1 12.9


Table 2-10 Hadley Centre Coupled Model Climate Forecasts for 2040 to 2069 Relative to the 1961 to 1990 Baseline



annual 3.2 18.4

summer 3.4 16.4 HadCM3 A1FI

winter 3.6 30.6

annual 1.9 13.5

summer 2.9 15.8 HadCM3 A2(1)

winter 0.9 21.8

annual 2.7 9.4


winter 2.5 16.3

annual 2.0 17.3


winter 2.5 28.7

annual 2.2 13.1

summer 2.9 7.6 HadCM3 A2(x)

winter 2.0 22.4

annual 2.0 14.3

summer 2.3 9.4 HadCM3 B1(1)

winter 1.8 23.0

annual 1.6 16.1


winter 0.8 29.7

annual 2.3 16.1


winter 2.8 29.7

Figure 2-3 illustrates the annual climate change forecasts relative to the 1961 to 1990 baseline period, while the summer and winter changes are illustrated in Figure 2-4.

SuncVo

Figure 2-3 Forecast Annual Climate Change Relative to the 1961 to 1990 Baseline

or Energy Inc. - 13 - Climate Change yageur South Project July 2007

-10

-8

-6

-4

-2

0

2

-50% -40% -30% -20% -10% +0% +10% +20% +30% +40% +50%

Precipitation Change [%]

4

6

8

10

Tem

pera

ture

Cha

nge [

°C]

CCSR/NIES

CGCM2 CSIRO Mk2b

ECHAM4/OPYC3 GFDAL R30 HadCM3


-10

-8

-6

-4

-2

0

2

4

6

8

10

-50% -40% -30% -20% -10% +0% +10% +20% +30% +40% +50%


Tem

pera

ture

Cha

nge [

°C]

CCSR/NIES CGCM2 CSIRO Mk2b ECHAM4/OPYC3 GFDAL R30 HadCM3

-10

-8

-6

-4

-2

0

2

4

6

8

10

-50% -40% -30% -20% -10% +0% +10% +20% +30% +40% +50%

Precipitation Change [%]Te

mpe

ratu

re C

hang

e [°C

]


Figure 2-4 Forecast Summer and Winter Climate Change Relative to the 1961 to 1990 Baseline

Summer Winter


Table 2-11 provides a summary of the range of changes in temperature and precipitation forecasts relative to the 1961 to 1990 baseline for each of the 26 model and climate forecast scenario combinations available on the CICS website. Annual forecast changes in temperature range from 1.6 to 6.3°C while annual forecast changes in precipitation range from -5 to 18%.

Table 2-11 Comparison of Climate Change Forecasts for 2040 to 2069 Baseline (1961 to 1990)

Change From Baseline (1961 to 1990) Climate Model Period Temperature


annual 4.0 to 6.3 9.6 to 14.4

summer 2.3 to 3.6 -19.0 to -9.2 CCSR/NIES

winter 4.9 to 8.1 19.4 to 27.2

annual 2.0 to 2.8 3.4 to 5.7

summer 1.9 to 2.4 2.2 to 11.2 CGCM2

winter 2.8 to 4.3 -9.7 to 2.9

annual 3.2 to 3.7 10.8 to 14.1

summer 1.6 to 1.8 0.9 to 6.7 CSIRO MK2

winter 3.6 to 4.2 22.2 to 24.3

annual 3.4 to 3.6 -5.4 to -5.1

summer 2.8 to 2.9 -21.1 to -8.7 ECHAM4/OPYC3

winter 5.5 to 5.9 5.0 to 7.9

annual 2.6 to 3.0 6.7 to 11.6

summer 2.7 to 2.9 -10.1 to 8.3 GFDL R30

winter 3.1 to 3.3 12.3 to 12.9

annual 1.6 to 3.2 9.4 to 18.4

summer 2.3 to 3.4 1.6 to 16.4 HadCM3

winter 0.8 to 3.6 16.3 to 30.6

While all of the forecast information is valuable, it is not practical to evaluate the potential impacts for every possible scenario. The challenge of selecting the appropriate scenarios to be evaluated can be addressed by using the approach of Burn (2003). Specifically, model forecasts are ranked by annual average temperature, summer (i.e., June, July and August) average temperature, winter (i.e., December, January and February) average temperature, annual precipitation, summer precipitation and winter precipitation. Within each of the six ranking methods, the combinations of models and scenarios are ranked and the temperature and precipitation changes for the 3rd highest (88th percentile), 12th highest (approximately the median) and 23rd highest (12th percentile) scenarios are determined. Burn (2003) recommended using the 86th percentile forecasts in


environmental assessments in the Mackenzie Valley, which are approximated by the 3rd highest ranked values in Table 2-12.

Table 2-12 Summary of Ranked Climate Scenarios

Change From Baseline (1961 to 1990) Ranking Method Rank Model and

SRES Scenario Temperature [°C]

Precipitation [% Change]

3rd highest CCSR/NIES-B2(1) 5.0 14.4

12th highest HadCM3-A1FI 3.2 18.4 annual temperature

23rd highest CGCM2-B2(1) 2.0 3.4

3rd highest CCSR/NIES-A1T 3.0 -11.2

12th highest GFDL R30-A2(1) 2.7 8.3 summer temperature

23rd highest CSIRO Mk2-B2(1) 1.8 0.9

3rd highest CCSR/NIES-A2(1) 6.2 21.6

12th highest CGCM2-A2(x) 4.0 -4.1 winter temperature

23rd highest HadCM3-A2(x) 2.0 22.4

3rd highest HadCM3-B2(1) 1.6 16.1

12th highest CCSR/NIES-A2(1) 4.8 12.0 annual precipitation


3rd highest CGCM2-A2(1) 2.4 11.2

12th highest CSIRO Mk2-A1(1) 1.8 6.2 summer precipitation

23rd highest CCSR/NIES-A2(1) 2.3 -17.6


12th highest HadCM3-A2(1) 0.9 21.8 winter precipitation

23rd highest CGCM2-A2(x) 4.0 -4.1

Note: SRES = Special Report on Emissions Scenarios.

2.3.2 Climate Change Over the Voyageur South Project Life

While the forecast climate change relative to the baseline presented in Section 2.3.1 is interesting from both an academic perspective and for comparison to historic observations, these predictions do not indicate how the climate might change over the life of the Voyageur South Project. To determine how climate might change over the project life of the Voyageur South Project it is necessary to determine the difference between the climate near the end of the Voyageur South Project life, represented by the 30-year average of 2040 to 2069, and the 30-year average centred on the current conditions. This acknowledges that some of the changes in climate since the 1961 to 1990 period will have already occurred. Therefore, the current period is represented by the 30-year period from 1990 to 2029, which was scaled for each model/scenario combination using the 1961 to 1990 baseline and 2010 to 2039 forecasts, as illustrated in Figure 2-5.


Figure 2-5 Determining Change Over the Voyageur South Project Life

-10

-8

-6

-4

-2

0

2

4

6

8

10

1960 1970 1980 1990 2000 2010 2020 2030

2040 2050 2060 2070




-8

-6

-4

-2

0

2

4

6

8

1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070



forecast change over the life of the Voyageur South Project

Step 1 – Determine the 1990 to 2019 average

Step 2 – Determine the change from the 1990 to 2019 average

Annu

al Av

erag

e Tem

pera

ture

[°C]

An

nual

Aver

age T

empe

ratu

re [°

C]

Future changes in temperature and precipitation have been determined for each of the models and scenarios available on the CICS web site. Tables 2-13 to 2-18 provide a summary of the forecast change over the life of Voyageur South Project (i.e., difference between 2040 to 2069 average) for the Fort McMurray area.


Table 2-13 Centre for Climate System Research/National Institute for Environment Studies Climate Forecasts Over the Voyageur South Project Life

Change Over Voyageur South Project Life Model SRES Scenario Season Temperature


annual 4.5 8.6 summer 2.5 -7.0 CCSR/NIES A1T winter 6.5 13.1 annual 5.0 -

summer 3.0 - CCSR/NIES A1(1) winter 6.9 - annual 4.1 7.5 summer 1.8 -11.0 CCSR/NIES A2(1) winter 5.5 13.5 annual 3.1 6.0 summer 1.7 -11.9 CCSR/NIES B1(1) winter 4.2 12.1 annual 3.7 9.0 summer 2.1 -5.7 CCSR/NIES B2(1) winter 5.1 17.0

SRES = Special Report on Emissions Scenarios. - = Precipitation data are not available for the A1(1) scenario.

Table 2-14 Canadian Global Coupled Model (Version 2) Climate Forecasts Over the Voyageur South Project Life



annual 2.2 3.1 summer 1.7 7.0 CGCM2 A2(1) winter 3.7 -2.9 annual 1.7 2.4 summer 1.5 1.4 CGCM2 A2(2) winter 3.0 -6.1 annual 1.7 3.5 summer 1.7 4.7 CGCM2 A2(3) winter 2.1 1.8 annual 1.9 2.7 summer 1.6 4.2 CGCM2 A2(x) winter 2.9 -2.6 annual 1.3 2.1 summer 0.9 1.8 CGCM2 B2(1) winter 1.8 1.0

SRES = Special Report on Emissions Scenarios.


Table 2-15 Commonwealth Scientific and Industrial Research Organization Mark 2 Climate Forecasts Over the Voyageur South Project Life



annual 2.6 8.8 summer 1.0 3.9 CSIRO Mk2b A1(1) winter 3.0 15.2 annual 2.3 8.7 summer 1.1 2.3 CSIRO Mk2b A2(1) winter 2.7 15.0 annual 1.9 6.7 summer 1.1 4.2 CSIRO Mk2b B1(1) winter 2.1 14.2 annual 1.9 7.4 summer 1.1 0.6 CSIRO Mk2b B2(1) winter 2.0 13.8


Table 2-16 Max Planck Institute for Meteorology/Deutsches Kilmarechenzentrum Climate Forecasts Over the Voyageur South Project Life



annual 2.1 -3.4 summer 1.8 -13.2 ECHAM4/OPYC3 A2(1) winter 3.5 4.9 annual 2.4 -3.2 summer 2.1 -5.5 ECHAM4/OPYC3 B2(1) winter 4.0 3.1


Table 2-17 Geophysical Fluid Dynamics Laboratory Climate Forecasts Over the Voyageur South Project Life



annual 2.1 7.3 summer 2.0 5.2 GFDL R30 A2(1) winter 2.2 7.7 annual 1.8 4.2 summer 1.6 -6.3 GFDL R30 B2(1) winter 2.6 8.1



Table 2-18 Hadley Centre Coupled Model Climate Forecasts Over the Voyageur South Project Life



annual 2.4 11.5

summer 2.7 10.2 HadCM3 A1FI

winter 2.7 19.1

annual 1.4 8.5


winter 0.8 13.6

annual 1.9 5.8


winter 1.3 10.2

annual 1.4 10.8


winter 1.8 17.9

annual 1.6 8.2

summer 2.1 4.7 HadCM3 A2(x)

winter 1.3 14.0

annual 1.2 8.9


winter 1.0 14.4

annual 1.0 10.1


winter 0.6 18.5

annual 1.7 10.1


winter 2.3 18.5


Figure 2-6 illustrates the forecast changes in annual precipitation and temperature over the life of Voyageur South Project. The changes in the summer and winter temperature and precipitation are illustrated in Figure 2-7.

SuncVo

Figure 2-6 Forecast Annual Climate Change Over the Voyageur South Project Life


-10

-8

-6

-4

-2

0

2

-50% -40% -30% -20% -10% +0% +10% +20% +30% +40% +50%


Tem

pera

ture

Cha

nge [

°C]

4

6

8

10



-10

-8

-6

-4

-2

0

2

4

6

8

10

-50% -40% -30% -20% -10% +0% +10% +20% +30% +40% +50%


Tem

pera

ture

Cha

nge [

°C]


-10

-8

-6

-4

-2

0

2

4

6

8

10

-50% -40% -30% -20% -10% +0% +10% +20% +30% +40% +50%

Precipitation Change [%]Te

mpe

ratu

re C

hang

e [°C

]


Figure 2-7 Forecast Summer and Winter Climate Change Over the Voyageur South Project Life

Summer Winter


Table 2-19 provides a summary of the forecast changes in temperature and precipitation over the life of Voyageur South Project. Annual forecast changes in temperature range from 1.0 to 5.0°C. Annual forecast changes in precipitation range from -3 to 12%.

Table 2-19 Comparison of Climate Change Values Over the Voyageur South Project Life

Change Over Voyageur South Project Life Climate Model Period Temperature


annual 3.1 to 5.0 6.0 to 9.0

summer 1.7 to 3.0 -11.9 to -5.7 CCSR/NIES

winter 4.2 to 6.9 12.1 to 17.0

annual 1.3 to 2.2 2.1 to 3.5

summer 0.9 to 1.7 1.4 to 7.0 CGCM2

winter 1.8 to 3.7 -6.1 to 1.8

annual 1.9 to 2.6 6.7 to 8.8

summer 1.0 to 1.1 0.6 to 4.2 CSIRO MK2

winter 2.0 to 3.0 3.8 to 15.2

annual 2.1 to 2.4 -3.4 to -3.2

summer 1.8 to 2.1 -13.2 to -5.5 ECHAM4/OPYC3

winter 3.5 to 4.0 3.1 to 4.9

annual 1.8 to 2.1 4.2 to 7.3

summer 1.6 to 2.0 -6.3 to 5.2 GFDL R30

winter 2.2 to 2.6 7.7 to 8.1

annual 1.0 to 2.4 5.8 to 11.5

summer 1.5 to 2.7 1.0 to 10.2 HadCM3

winter 0.6 to 2.7 10.2 to 19.1

As discussed in the previous section, the approach from Burn (2003) was for choosing scenarios to evaluate climate change in northern Canada. The model forecasts were ranked by annual, summer and winter average temperature, as well as the annual, summer and winter precipitation. For each of the six ranking methods, the combinations of models and scenarios have been ranked and the temperature and precipitation changes for the 3rd highest (88th percentile), 12th highest (approximately the median) and 23rd highest (12th percentile) scenarios determined. The ranked model scenarios are provided in Table 2-20.


Table 2-20 Ranked Forecast Scenarios for Climate Change Over the Voyageur South Project Life

Change Over Voyageur South Project Life

Ranking Method Rank Model and SRES Scenario Temperature



12th highest GFDL R30-A2(1) 2.1 7.3 annual temperature

23rd highest HadCM3-A2(1) 1.4 8.5


12th highest ECHAM4/OPYC3-A2(1) 1.8 -13.2 summer temperature

23rd highest CSIRO Mk2-B2(1) 1.1 0.6


12th highest HadCM3-A1FI 2.7 19.1 winter temperature



12th highest CCSR/NIES-A2(1) 4.1 7.5 annual precipitation


3rd highest CGCM2-A2(1) 1.7 7.0

12th highest CSIRO Mk2-A1(1) 1.0 3.9 summer precipitation

23rd highest CCSR/NIES-A2(1) 1.8 -11.0



23rd highest CGCM2-A2(x) 2.9 -2.6


2.4 MODEL SCENARIOS FOR USE IN ENVIRONMENTAL ASSESSMENTS

As outlined in Sections 2.3.1 and 2.3.2, the climate models and scenarios were ranked by annual, summer and winter average temperature, as well as the annual, summer and winter precipitation. For each ranking methods, the 3rd highest (88th percentile), 12th highest (approximately the median) and 23rd highest (12th percentile) scenarios determined. For the purposes of the environmental assessment, the combinations of models and scenarios that yielded the 3rd highest changes in annual, summer and winter temperatures along with the 3rd and 23rd highest changes in annual, summer and winter precipitation over the Voyageur South Project life will be carried forward into the assessment. These nine combinations of models and scenarios are consistent with the INAC recommendations for representing the upper bounds for changes in temperature and upper and lower bounds for changes in precipitation.


Table 2-21 provides the climate change for the upper annual temperature scenario, corresponding with the CCSR/NIES–A2(1) model forecast. This scenario and model combination yielded the 3rd highest forecast of annual temperature change. The 3rd highest forecast corresponds with the 88th percentile prediction and is consistent with the approach suggested by INAC for selecting climate forecast scenarios.

Table 2-21 Future Climate Trend Forecasts - Upper Annual Temperature

Change From the 1961 to 1990 Baseline

Change Over Voyageur South Project Life Model

Scenario Season Temperature


Temperature [°C]


annual 4.8 12.0 4.1 7.5

spring 6.6 25.0 5.7 15.6

summer 2.3 -17.6 1.8 -11.0

fall 4.3 19.0 3.6 11.9

CCSR/NIES A2(1)

winter 6.2 21.6 5.5 13.5

Table 2-22 provides the climate change for the upper summer temperature scenario, corresponding with the CCSR/NIES–A1T model forecast. This scenario and model combination yielded the 3rd highest forecast of summer temperature change, which corresponds with the 88th percentile prediction.

Table 2-22 Future Climate Trend Forecasts - Upper Summer Temperature





Temperature [°C]


annual 5.3 13.8 4.5 8.6

spring 6.8 28.8 4.9 18.0

summer 3.0 -11.2 2.5 -7.0

fall 4.8 16.6 4.1 10.4

CCSR/NIES A1T

winter 6.6 21.0 6.5 13.1

Table 2-23 provides the climate change for the upper winter temperature scenario, corresponding with the CCSR/NIES–A2(1) model forecast. This scenario and model combination yields the 3rd highest forecast (i.e., 88th percentile prediction) of winter temperature change.


Table 2-23 Future Climate Trend Forecasts - Upper Winter Temperature




[°C] Precipitation [%Change]

Temperature [°C]


annual 4.8 12.0 4.1 7.5

spring 6.6 25.0 5.7 15.6

summer 2.3 -17.6 1.8 -11.0

fall 4.3 19.0 3.6 11.9

CCSR/NIES A2(1)

winter 6.2 21.6 5.5 13.5

Table 2-24 provides the climate change for the upper annual precipitation scenario that corresponds with the HadCM3–B2(1) model forecast. This scenario and model combination yielded the 3rd highest forecast of annual precipitation change (i.e., 88th percentile prediction).

Table 2-24 Future Climate Trend Forecasts - Upper Annual Precipitation





Temperature [°C]


annual 1.6 16.1 1.0 10.1

spring 0.6 6.3 0.3 3.9

summer 2.5 8.1 1.5 5.0

fall 2.5 20.4 1.6 12.8

HadCM3 B2(1)

winter 0.8 29.7 0.6 18.5

Table 2-25 provides the climate change for the upper summer precipitation scenario that corresponds with the CGCM2–A2(1) model forecast. This scenario and model combination yielded the 3rd highest (i.e., 88th percentile) forecast of summer precipitation change.


Table 2-25 Future Climate Trend Forecasts - Upper Summer Precipitation





Temperature [°C]


annual 2.8 4.9 2.2 3.1

spring 3.4 5.2 2.6 3.2

summer 2.4 11.2 1.7 7.0

fall 1.2 7.9 0.8 4.9

CGCM2 A2(1)

winter 4.2 -4.6 3.7 -2.9

Table 2-26 provides the climate change for the upper winter precipitation scenario that corresponds with the HadCM3–B2(1) model forecast. This scenario and model combination yielded the 3rd highest forecast of winter precipitation change (i.e., 88th percentile prediction).

Table 2-26 Future Climate Trend Forecasts - Upper Winter Precipitation





Temperature [°C]


annual 1.6 16.1 1.0 10.1

spring 0.6 6.3 0.3 3.9

summer 2.5 8.1 1.5 5.0

fall 2.5 20.4 1.6 12.8

HadCM3 B2(1)

winter 0.8 29.7 0.6 18.5

Table 2-27 provides the climate change for the lower annual precipitation scenario that corresponds with the CGCM2-B2(1) model forecast. This scenario and model combination yielded the 23rd highest (12th percentile) forecast of annual precipitation change.

Table 2-27 Future Climate Trend Forecasts - Lower Annual Precipitation





Temperature [°C]


annual 2.0 3.4 1.3 2.1

spring 2.4 0.1 1.7 0.1

summer 1.9 2.8 0.9 1.8

fall 1.0 9.1 0.7 5.7

CGCM2 B2(1)

winter 2.8 1.7 1.8 1.0


Table 2-28 provides the climate change for the lower summer precipitation scenarios that corresponds with the CCSR/NIES–A2(1) model forecast. This scenario and model combination yielded the 23rd highest forecast (12th percentile) change for annual precipitation.

Table 2-28 Future Climate Trend Forecasts - Lower Summer Precipitation





Temperature [°C]


annual 4.8 12.0 4.1 7.5

spring 6.6 25.0 5.7 15.6

summer 2.3 -17.6 1.8 -11.0

fall 4.3 19.0 3.6 11.9

CCSR/NIES A2(1)

winter 6.2 21.6 5.5 13.5

Finally, Table 2-29 provides the climate change for the lower winter precipitation scenario that corresponds with the CGCM2-A2(x) model forecast. This scenario and model combination yielded the 23rd highest (i.e., 12th percentile) forecast of winter precipitation change.

Table 2-29 Future Climate Trend Forecasts - Lower Winter Precipitation





Temperature [°C]


annual 2.7 4.3 1.9 2.7

spring 2.9 7.0 2.0 4.4

summer 2.3 6.7 1.6 4.2

fall 1.4 7.8 0.9 4.9

CGCM2 A2(x)

winter 4.0 -4.1 2.9 -2.6


3 EFFECTS OF CLIMATIC CHANGE ON AIR QUALITY PREDICTIONS

While climate change should not have a direct effect on air quality or the air quality predictions presented in the assessment, there are several indirect effects that might impact the predictions presented in the EIA. Changing climate could alter a number of the meteorological parameters that could, in turn, affect the EIA air predictions. A summary of the primary linkages between climate change and air quality are set out in Table 3-1. Each of the linkages listed in the table will be discussed separately below.

Table 3-1 Primary Links Between Climate Change and Air Quality

Precipitation Temperature Wind Speed

Acid Deposition – Potential Acid Input (PAI)

Higher rainfall rates would result in higher wet deposition and PAI. Lower rainfall rates would result in lower wet deposition and PAI.

Increased temperatures during the spring could result in more of the precipitation falling in the form of rain, which would result in higher wet deposition and PAI.

no linkage

Atmospheric Dispersion

no linkage no linkage

Higher wind speeds tend to enhance dispersion resulting in lower short-term concentrations. Lower wind speeds tend to hinder dispersion resulting in higher short-term concentrations.

Ground Level Ozone

no linkage Increased temperatures could result in an enhanced potential for ozone formation of ground level ozone.

no linkage

3.1 ACID DEPOSITION

Climate change should not directly affect the predictions of Potential Acid Input (PAI) presented in the EIA; however, increased rainfall could lead to higher wet deposition and higher predictions of PAI. Warming temperatures that could cause a shift from snowfall to rainfall could be an incremental contributor to PAI. Of the identified scenarios, the greatest effect on the PAI predictions is likely to occur with the upper summer precipitation case. As shown in Table 2-20 and detailed in Table 2-25, the Canadian Global Coupled Model – Version 2 (CGCM2) with the A2(1) scenario yielded the 3rd highest or 88th percentile estimates for changes in summer precipitation. The forecasts associated with this scenario and model are reproduced in Table 3-2.


Table 3-2 Upper Bound Forecasts for Changes in Summer Precipitation Over the Voyageur South Project Life

Precipitation Change

Model Scenario Season Change From the

1961 to 1990 Baseline[%]

Change Over Voyageur South

Project Life [%]

annual 4.9 3.1

spring 5.2 3.2

summer 11.2 7.0

fall 7.9 4.9

CGCM2 A2(1)

winter -4.6 -2.9

Since the current GCMs do not have the resolution necessary to simulate all of the parameters necessary to model PAI, it is not feasible to model this specific scenario. However, it is possible to compare the 1995 meteorological data set used to model PAI in the Fort McMurray region with the observed climate normals to see whether the current predictions can offer an indication of how changing climate may affect the PAI.

Table 3-3 compares the 1995 meteorological data set that was used to model PAI to the 1961 to 1990 Fort McMurray climate normals. The annual precipitation during 1995 was 9.7% higher than normal, while rainfall was 56.9% higher during the summer months.

Table 3-3 Comparison of 1995 Precipitation to Climate Normals

Precipitation [mm]

Season 1961 to 1990

Normals 1995 Observation

Difference From Normals

[%]

annual 464.3 509.4 9.7

spring 80.6 56.8 -29.5

summer 214.8 337.0 56.9

fall 109.4 70.1 -35.9

winter 60.6 30.1 -50.3

In contrast, the upper bound summer precipitation forecast from scenario A2(1) of the CGCM2 model predicted an increase in summer precipitation of 11.2% from the 1961 to 1990 baseline combined with a 7.0% change in annual precipitation. Therefore, the 1995 meteorological data used in the dispersion modelling not only provides conservative estimates of the current deposition


rates but also conservative estimates of the rates that could be expected in the future.

3.2 ATMOSPHERIC DISPERSION

The parameter likely to have the greatest effect on the dispersion predictions is the change to wind speed. Table 3-4 shows the forecast change in wind speed for the ranked scenarios over the Voyageur South Project life (as shown in Table 2-20).

Table 3-4 Summary of Climate Scenarios for Wind Speed

Wind Speed Change

Ranking Method Rank Model and SRES Scenario

Change From 1961 to 1990

Baseline [%]

Change Over Voyageur South

Project Life [%]

3rd highest CCSR/NIES-A2(1) -1.4 -0.9

12th highest GFDL R30-A2(1) n/a n/a annual temperature

23rd highest HadCM3-A2(1) -1.2 -0.7


12th highest ECHAM4/OPYC3-A2(1) -1.1 -0.7 summer temperature

23rd highest CSIRO Mk2-B2(1) -5.2 -3.3

3rd highest CCSR/NIES-A2(1) -3.2 -2.0

12th highest HadCM3-A1FI 9.5 5.9 winter temperature



12th highest CCSR/NIES-A2(1) -1.4 -0.9 annual precipitation

23rd highest CGCM2-B2(1) n/a n/a

3rd highest CGCM2-A2(1) n/a n/a

12th highest CSIRO Mk2-A1(1) -3.9 -2.5 summer precipitation




23rd highest CGCM2-A2(x) 8.5 5.3

n/a = Not available.

Generally, lower wind speeds are associated with increased ground-level concentrations. Therefore, the lower bound predictions from Table 3-4 represents the conditions likely to most affect the predictions presented in this assessment. While the current crop of GCMs do not have the resolution necessary to simulate all of the parameters necessary to complete dispersion modelling for the Oil Sands Region, it is possible to compare the 1995


meteorological data set used in the modelling with the observed Fort McMurray climate normals and forecast trends.

Table 3-5 shows how the average wind speeds in 1995 compared to the long-term normals for the region. During 1995, the annual wind speeds were 7.8% below the climate normals. This change is greater than the annual forecast change from the 1961 to 1990 baseline of -3.3% from the B2(1) scenario of the CSIRO Mk2 model.

Table 3-5 Comparison of 1995 Average Wind Speeds to Climate Normals

Average Wind Speed [km/hr]

Season 1961 to 1990



[%]

annual 9.6 8.8 -7.8

spring 10.7 9.7 -8.8

summer 9.2 8.2 -11.5

fall 9.7 8.6 -10.9

winter 8.8 8.8 0.7

Table 3-6 shows the frequency of occurrence of different wind speed categories for the 1961 to 1990 normals and 1995. Overall, 1995 had 5% more calm hours and about 5% fewer hours with wind speeds greater than 10 km/hr than the 1961 to 1990 normals.

Table 3-6 Comparison of Wind Speed Categories

Frequency of Occurrence [%]

Wind Speed Category 1961 to 1990



[%]

calm 15.7 20.4 4.8

1 to 5 km/h 11.6 10.7 -0.9

6 to 10 km/h 30.9 32.5 1.6

11 to 15 km/h 23.1 20.1 -3.1

16 to 20 km/h 11.5 9.9 -1.6

> 20 km/h 7.2 6.4 -0.8

Depending on the models considered, the average wind speeds in the Oil Sands Region are predicted to either increase (i.e., enhanced dispersion) or decrease (i.e., reduced dispersion). However, the 1995 data used to model concentrations in the region had average wind speeds well below historic observation and had a


greater number of hours with calm winds. Therefore the 1995 data used in the assessment represents a conservative estimate of current and future conditions.

3.3 GROUND LEVEL OZONE

Ozone is an essential part of the upper atmosphere that protects us from most of the sun’s harmful ultra-violet radiation. Ozone can also be present at the earth’s surface. Ground level ozone can be attributed to three causes in Canada: photochemical ozone formation, stratospheric intrusion and long-range transport.

The meteorological conditions ideally suited to the formation of ground level ozone are rare in northern Alberta. This has led to suggestions that photochemical ozone formation is not possible in northeastern Alberta because the region does not experience the necessary weather conditions. However, monitoring data from the Oil Sands Region has shown patterns of ozone concentrations that are consistent with photochemical ozone formation (i.e., hourly ozone concentrations that rise to peak levels near the middle of the day and then fall off rapidly at night). The low number of hours when the observed ozone readings were above the Alberta Ambient Air Quality Objectives suggests that photochemical reactions are relatively weak in the Oil Sands Region. This is likely due to the relatively cool regional temperatures compared to the optimal conditions for ozone formation (i.e., greater than 25°C). However, changing climate may result in higher temperatures and enhance the potential for photochemical ozone formation in the region.

The climate parameter most likely to affect ground level ozone concentrations is the summer temperature. The forecasts from the CCSR/NIES model for scenario A1T yielded the upper summer temperatures over the life of Voyageur South Project. Table 3-7 summarizes the climate trends forecast for that model and scenario combination (reproduced from Table 2-22).

Table 3-7 Upper Bound Forecast for Changes in Summer Temperature Over the Voyageur South Project Life




[°C] Precipitation[% Change]

Temperature [°C]


annual 5.3 13.8 4.5 8.6

spring 6.8 28.8 4.9 18.0

summer 3.0 -11.2 2.5 -7.0

fall 4.8 16.6 4.1 10.4

CCSR/NIES A1T

winter 6.6 21.0 6.5 13.1


While higher summer temperatures could result in an increased potential for ground level ozone formation in the region, this relationship is not evident from the monitoring results from stations operated by the Wood Buffalo Environmental Association (WBEA). The WBEA monitoring stations are the closest continuous ozone monitoring stations to Voyageur South Project. Figure 3-1 presents a comparison of daily maximum temperatures and the corresponding 1-hour maximum ozone concentration. This data was collected at the Athabasca Valley Station from 1998 through 2004. Monitoring results at the Patricia McInnes, Fort McKay and Fort Chipewyan Stations all demonstrate similar patterns as those shown in Figure 3-1.

If a strong correlation were present between the maximum temperatures and the peak ozone concentrations in the region, this should be evident in the monitoring data. As illustrated in Figure 3-1, the peak ozone concentrations do not always correspond to the highest temperatures. On days when temperatures are greater than 30°C, ozone concentrations range from approximately 24 to 71 ppb, which indicates that higher temperatures do not always correspond to high ozone concentrations. There are also high ozone concentrations occurring during periods when the daily maximum temperature is below 0°C. Although the upper summer temperature forecast change of 2.5°C over the life of Voyageur South Project may result in increased daily maximum temperatures, this may not correspond to increased peak ozone concentrations.

Figure 3-1 Comparison of Daily Maximum Temperatures and Daily Maximum 1-Hour Ozone Concentrations

0

10

20

30

40

50

60

70

80

90

-40 -30 -20 -10 0 10 20 30 40Daily Maximum Temperature [°C]

Dai

ly M

axim

um 1

-Hou

r Ozo

ne C

once

ntra

tion

[ppb

]


The Ozone Management Framework for the Regional Municipality of Wood Buffalo (CEMA 2006) has developed ozone management strategies based on four trigger levels that will manage ozone levels in the future. Additional research and modelling for the Oil Sands Region is currently being conducted by Environment Canada.

3.4 SUMMARY

In conclusion, the air quality predictions in the assessment are considered representative of conditions over the life of the Voyageur South Project since the 1995 meteorological data (temperature, wind speed and precipitation) cover the range of climate forecast values.


4 SUMMARY OF THE CONSIDERATIONS OF CLIMATE CHANGE ON HYDROGEOLOGY

Climate change could potentially impact elements of the hydrologic cycle, including precipitation, evaporation, and thus groundwater recharge in the area of the site. Hydrogeologic predictions based on such parameters could, therefore, be affected. Global climate models have been used to simulate future climatic conditions, with some results suggesting that there may be a global increase in precipitation and evaporation. In general, most models predict that an increase in temperature could shift the present day mid-latitude rain belt northward, snowmelt and spring runoff would occur earlier than at present, and evapotranspiration would likely increase, start earlier and have an extended duration. Furthermore, some areas may experience drier summers. This could potentially reduce recharge to the regional groundwater system (Environment Canada 2006).

Based on the analysis of climatic and hydrologic data for the Oil Sands Region and elsewhere in Alberta (see Section 5 of this Appendix):

• There has been a warming trend in the past three decades in the Oil Sands Region.

• Recorded annual total precipitation, particularly in spring and early summer, show increasing trends, while precipitation in early fall and winter show decreasing trends. To date, these trends are not statistically significant.

• Based on 90 years of data for the Bow River at Banff, 7-day low flows show an increasing trend, implying higher baseflows (groundwater discharge). The data also indicate that warmer winter temperatures have resulted in snowmelt runoff in October, November and early March.

• The linkage between changes in air temperature and precipitation (and correspondingly to groundwater recharge) in the Oil Sands Region cannot be reliably established on the basis of available data.

Evaluating the effects of climatic change on a groundwater system not only requires knowledge of the changes within that system, but also knowledge of the groundwater system itself and, more specifically, the factors controlling recharge. The factors that control recharge are related to the hydrologic landscape of the aquifer system (Sanford 2002). Three primary factors that control water flow are climate, topography and the geologic framework. Precipitation supplies the land surface with water, the soil characteristics allow the water to infiltrate to the groundwater system and the geologic framework provides the permeability necessary for groundwater flow (Sanford 2002). The surface and subsurface


factors controlling recharge can be correlated with a region’s precipitation and topographic relief. The geologic framework tends to control the rate of recharge in regions of typically humid climates or low topographic relief (Sanford 2002). In a study completed by Chen (2005), model simulations suggested that thousands of years were required for water levels to fully respond to changes in climate.

When considering the development of a numerical groundwater model, the model parameter most likely to be affected by climate change is recharge to an aquifer. However, it should be noted that recharge is only one of the many factors involved in adequately modelling a groundwater system. Other factors that must be considered include the hydraulic properties of the aquifer and any overlying strata and appropriately assigning those properties to the hydrostratigraphic framework of the model. Based on the physiographic conditions of the site, the geologic framework is considered the controlling factor for recharge. Thus, recharge within the LSA is considered to be controlled by lithology not climate.

Considering that the residence time of groundwater for a regional system is on the order of tens of thousands of years, it will likely take a similar period for a newly disturbed system to come to a new equilibrium in response to long-term climatic changes. In addition, due to the large volume of water stored in groundwater flow systems and the generally slow rate of movement, they are not sensitive to short-term fluctuations in climate on the order of decades or even hundreds of years. They are, however, sensitive to long-term changes, such as those that caused the transgression or regression of the continental ice sheet that covered northern Alberta. Even these dramatic changes are believed, from one study, to have caused changes in water tables of only a few metres (SRC 2000). The uncertainty in model output associated with climate change is not, therefore, significant in comparison to output uncertainty associated with parameters such as large scale hydraulic conductivity. In other words, the model prediction is sensitive to the modification of hydraulic parameters, but not to short term variations in climate.

In light of the above discussion, climate change is expected to have a negligible effect on the predictions provided in the EIA with respect to groundwater flow and quality.


5 SUMMARY OF THE CONSIDERATIONS OF CLIMATE CHANGE ON SURFACE WATER HYDROLOGY

5.1 INTRODUCTION

Hydrologic issues associated with climate change that have been raised in the past include the potential effects on the Athabasca River flows, particularly the winter low flows, as well as effects on flows in the tributaries to the Athabasca River in the Oil Sands Region. An analysis of apparent or significant trends in climatic parameters and streamflows is necessary to assist in defining potential scenarios of future climatic and hydrologic regimes for consideration in the EIA.

The objective of the analyses presented herein is to generate information for a discussion of relevant hydrologic issues associated with climate change. The analyses involved the following tasks:

• A review of the literature on trends in climate parameters and streamflows, specifically as they pertain to effects on hydrology in Alberta and the Oil Sands Region. The purpose of the literature review was to document the existing information on climate change or variability and its effects on hydrology at regional and local scales, particularly in the Oil Sands Region. This review provided a basis for summarizing the main findings from the existing literature, identifying the gaps in the current understanding of the hydrologic processes that may be affected by climate change, and assessing potential implications to environmental assessments of proposed mining developments in the Oil Sands Region.

• Statistical and trend analyses of recorded precipitation, temperature and streamflow data at regional and local scales, particularly in the Oil Sands Region. The trend analyses were conducted to determine the presence or absence of statistically significant trends in pertinent climate and streamflow variables and to establish any linkages or correlations between trends in climate and trends in streamflow variables, if trends were detected from the recorded data. The analysis included an assessment of trends in the Athabasca River flow regime based on the currently available recorded flow data.

• A sensitivity analysis of potential changes in air temperature, precipitation and potential evapotranspiration (associated with climate change) on the hydrology of tributary streams to the Athabasca River in the Oil Sands Region. The analysis involved varying the recorded daily precipitation and air temperature at Fort McMurray using forecasted seasonal changes, increasing the potential evapotranspiration


corresponding to forecasted air temperature, and assessing the sensitivity of the simulated flows to these potential changes in the climatic variables using the Hydrological Simulation Program - Fortran (HSPF) model, which has been calibrated and implemented for the Oil Sands Region.

5.2 LITERATURE REVIEW

5.2.1 General

There is a general agreement in the existing literature on the evidence showing that global surface air temperatures have been increasing during the past decades. The increase in air temperature is postulated to be the result of either an increase in greenhouse gases emission (GHG) or climate variability (i.e., due to variations in sun or volcanic activity or El Niño and La Niña events) during past decades, or both. Global Climate Model (GCM) simulations suggest that air temperature may continue to increase in the future.

The GCM simulations for the Voyageur South Project predict warming of 1 to 5°C by the mid 2050s, with the most pronounced changes taking place in northern latitudes (Nicholls et al. 1996). However, predictions of changes in climate at a watershed scale or even a larger regional scale using GCMs are less reliable than global predictions (Arnell et al. 1996; Georgievskii et al. 1996; Zhang et al. 2000a). Therefore, the assessment of climate change effects on surface water quantity in the local watersheds used a combination of temperature predictions from GCMs and observed trends in the Fort McMurray area.

The predicted rise in the Earth’s surface air temperature could cause an increase in average global evaporation and an increase or decrease in precipitation (Bloomfield 1992; Mann et al. 1998; Vinnikov et al. 1990; Gan 1995; Zhang et al. 2000a). Detection of historic trends, changes and variability in climatic variables is essential for understanding or estimating potential future hydrologic changes associated with climate change.

5.2.2 Change and Variability in Air Temperature

Global Climate Models, such as the Canadian Climate Center (CCC) model, predict warming trends of 1.0 to 1.5°C from 2001 to 2050 over the Canadian Prairies under a 2×CO2 (doubling of atmospheric carbon dioxide concentration) level scenario, with the largest seasonal increase in temperature occurring in winter. The 2×CO2 scenario used previously by the Intergovernmental Panel on Climate Change (IPCC) has been replaced by the new emissions scenarios (IPCC 2000). Increases in the near surface air temperature could change


precipitation amounts and storm patterns. In turn, changes in air temperature and precipitation could affect the hydrology of Canadian rivers, including changes to the volume and timing of streamflow and river ice conditions.

Using proxy data, Mann et al. (1998) and McIntyre and McKitrick (2003) showed that the air temperature index (i.e., mean annual air temperature) in the late 20th century was higher than from 1500 to 1980 for the northern hemisphere. Many researchers have also shown that the 1980s and 1990s were the warmest years on record. These findings corroborate reports by many northern residents that winters are getting warmer in the Northwest Territories. However, the increase in surface air temperatures has not been continuous. In fact, the recorded data indicate a cooling period from the 1940s to 1970s in the middle and high latitudes of the northern hemisphere (Moran and Morgan 1997).

Using rehabilitated historical air temperature data from the Canadian Historical Temperature Database (CHTD), Chaikowsky (2000) investigated trends of mean, minimum and maximum air temperatures based on the data from 25 stations in Alberta including the Fort McMurray Airport climate station. In addition, trends based on historical data were compared to those estimated from the Canadian Global Coupled Model (CGCMI). Air temperature trends within Alberta were summarized for two periods, 1938 to 1995 and 1960 to 1995, for investigating the effect of time period on the magnitude of air temperature trends in the province.

The trend in annual mean air temperature for the Fort McMurray area was found to be an approximately 0.09°C increase per decade for the period of 1938 to 1995 and an 0.46°C increase per decade for the time period of 1960 to 1995 (Chaikowsky 2000). The rate of mean temperature increase over the shorter, more recent time period was about four times the rate of increase over the longer time period. Warming generally occurred during the period of January to June, with the greatest warming occurring in March. Typically, the months from July to December show a general cooling trend, with the greatest monthly cooling taking place in November. Although the trend in annual mean air temperature over the last 58 years was generally upward, some months actually exhibited decreasing trends. The monthly air temperature trends were similar at the Alberta stations included in the study.

The CGCMI outputs show a warming trend of about 0.3°C per decade for Alberta over the 1900 to 2001 period, with greater increases in the minimum air temperature than in the maximum air temperature (Chaikowsky 2000). The amount of warming estimated using the CGCMI outputs over the 1938 to 1995 and 1960 to 1995 periods were less than the warming observed in Alberta over these periods. The most rapid warming was estimated for the period following


the year 2000. Over the 2000 to 2100 period, the CGCMI run, which included only greenhouse gas forcing, estimated a mean increase of 5°C. Chaikowsky (2000) concluded that the CGCMI results differed greatly from observations (i.e., about 0.5 to 1.0°C) and hence were not likely useful in estimating temperature variations at the provincial scale (Alberta).

Van Wijngaarden and Vincent (2003) analyzed the historical average hourly winter air temperatures for the period 1954 to 2003 and found an increasing air temperature trend of about 4°C for 50 years (or 0.8°C per decade) for the Oil Sands Region. Similar trends were reported for spring air temperatures. Summer and fall air temperatures showed relatively smaller rates of increase.

Using combined land-surface and sea-surface temperature data, Jones et al. (2001) determined that the annual surface air temperature trends for the period 1976 to 2000 for the Oil Sands Region is about 0.2°C increase per decade (IPCC 2001). In addition, seasonal surface air temperatures for spring, summer, and winter indicated warming or increasing trends for the period 1976 to 2000, while a cooling trend or decreasing air temperature was observed for the fall season.

Shen (1999) performed a trend analysis of annual maximum and minimum air temperature using data at 38 long-term climate stations (1884 to 1996) in Alberta. The weighted average of individual station values was used to obtain an Alberta average of climate variables for each month. The Alberta averages were smoothed using an eleven-year running mean to identify the pattern of variation in time. Based on the running means, it was observed that maximum temperatures did not have an upward trend, whereas minimum temperatures had a clear upward trend of about 0.8°C for the period 1920 to 1996.

Gan (1998) applied Kendall’s trend analysis method to the maximum, minimum and average air temperature data from 37 weather stations (14 in Alberta, 14 in Saskatchewan, eight in Manitoba and one in Ontario). The results indicate that between 1949 and 1989 the Canadian prairies have experienced warming, especially in January, March, April and June. In March and June more than 60% of the stations exhibited statistically significant warming at the 5% level of significance. Zhang et al. (2000a) and Hengeveld (1991) observed similar trends for the prairies in winter and spring.

5.2.3 Change and Variability in Precipitation

Predictions of changes in precipitation using GCMs are less definite than predictions in air temperature (Schlesinger and Mitchell 1985; Hennessy et al. 1997; Gregory et al. 1997). Cohen (1991) found no consensus in the projected


changes in the net water supply of the Saskatchewan River basin under global warming from five GCMs. GCMs are based on simple land phase hydrology processes and coarse grid resolutions. Their simulations of possible changes in hydrologic processes are not expected to be reliable, particularly at regional and local scales.

Results reported in IPCC (2001) suggest that winter, spring, summer and fall average precipitation amounts have increased by about 30, 0, 15 to 20 and 20%, respectively, in the Oil Sands Region over the time period of 1900 to 1999 (100 years) compared to the 1961 to 1990 normal. The study concluded that an increase in mean annual precipitation had occurred over the last century, with an approximately 20% rise for the period 1900 to 1999. The segment of that time period with the largest rising precipitation trend appeared to be from 1946 to 1975 (IPCC 2001).

Studies of trends in precipitation based on recorded historical long-term data show a range in the magnitude of change, and sometimes differing directions of change. Zhang et al. (2000a) analyzed precipitation totals and the ratio of snowfall to total precipitation using climate data from 1900 to 1998 across Canada. Their analysis shows that annual precipitation totals have changed by -10 to +35%, with the strongest increases occurring in the northern regions of the country. The ratio of snowfall to total precipitation has also increased as a result of an increased winter precipitation, which generally falls as snow. Negative trends were identified in some southern regions during spring. However, there was no evidence of changes in the frequency of heavy precipitation events across Canada (Zhang et al. 2000b).

An analysis of daily precipitation time series based on rehabilitation time series (1938 to 1996) from 69 locations across Canada by Mekis and Hogg (1999) indicated an increase of about 1.9% per decade for the Oil Sands Region. Analysis of annual total snow and rain data indicates an increase of about 0.8 and 2.4%, respectively, for the Oil Sands Region. The seasonal precipitation (spring, summer and fall) increased by about 0.6, 3.0 and 3.5%, respectively, for the period from 1938 to 1996. Winter total precipitation decreased by about 2.7%. These values are comparable to those reported by IPCC (2001), assuming an even distribution of the changes for the period of 1900 to 1999 for each decade in this century. For example, the reported 20% increase in precipitation over a century is assumed to be equivalent to a 2% rise per decade. A notable exception is for the winter season which is different in the direction of trend.

Gan (1998) analyzed monthly precipitation data at 37 stations in the Canadian prairies from 1949 to 1989 and showed that, between November and February, 8 to 18% of the stations experienced decreases in precipitation. The remaining


stations showed no trend at the 5% level of significance. Other studies indicate less confidence in precipitation trends in Canada for climate warming scenarios. There is no consensus on whether precipitation will increase or decrease or how climate change may affect severe weather events in the Canadian prairies (Gan 1995).

Van Wijngaarden and Vincent (2003) examined daily precipitation data for the time period 1953 to 2003 for 75 stations across Canada including the Fort McMurray Airport Station. The total precipitation for each season was computed along with the percentage change compared to the average seasonal amount received during 1961 to 1990. The results indicate that precipitation appears to increase slightly for the spring, summer and fall but decrease significantly in winter (more than 50% for the Oil Sands Region).

Differing opinions also exist concerning the historical trends in extreme rainfall events. Frich et al. (2001) showed that the maximum annual five-day total precipitation data for the Oil Sands Region show a positive trend of greater than 15% for the period of 1961 to 1990. Other researchers have also reported increases in heavy precipitation, and snowfall amounts north of 55°N (IPCC 2001; Zhang et al. 2000a,b). However, Hogg and Carr (1985) found that there is a slight but insignificant increase in extreme rainfall across Canada.

Snow cover is considered to be a useful indicator of climate change because of its sensitivity to air temperature (Karl et al. 1993). Myeni et al. (1997) reported an earlier disappearance of spring snow cover in response to the recent trend toward warmer spring air temperatures. Other researchers have reported similar findings over much of North America (Foster 1989; Stuart et al. 1991; Robinson et al. 1991; Brown and Goodison 1996). Linear regression analysis has been used to assess Canadian monthly snow depth and seasonal snow cover duration changes between 1946 and 1995 (Brown and Braaten 1998). The trends over this time period in the average inter-annual change in mean monthly snow depth were determined to be decreasing in nature in nearly all months for the Oil Sands Region. This study approximated maximum snow depth changes in the Mackenzie Basin to be 1.0 to 1.5 cm/y (centimetres per year). Decreases in the average inter-annual change in spring snow cover duration over the period of 1946 to 1995 were observed for the Fort McMurray region.

5.2.4 Change and Variability in Evaporation and Evapotranspiration

Potential Evapotranspiration (PET) has been incorporated in GCMs and climate impact models in various ways. Rind et al. (1997) discussed four methods by which PET has been formulated in various climate change related applications.


Future projections using these PET formulations often disagree, even though they use the same temperature and precipitation forecasts from GCMs. For example, the aerodynamic formulation, which is used in most GCMs, resulted in a relatively large increase in PET values compared to observed changes (i.e., almost four times the observed values).

Actual Evapotranspiration (AET) was determined by McGinn et al. (2001, website) using a coupled Canadian Climate Center Global Circulation Models (GCMII and GCMI-A) and a modified Versatile Soil Moisture Budget model (mVSMB) (Akinremi et al. 1996). The GCMII model predicted an increase in AET of about 7 to 18% in Canadian Prairies, with the greatest increase in Alberta. A GCMI-A model that included influences from oceans coupling and the effects of aerosols, predicted a 6% increase in Alberta prairies although other prairie provinces show a decrease in evapotranspiration of about 5% (Saskatchewan and Manitoba). Model scenario that combined historic precipitation (less precipitation) with the GCM warming scenarios (CGCMI-HP) indicated an increase of only about 2% for AET in Alberta. The GCMI-A model seemed to give more consistent results relative to historic patters and may better reflect future climate patterns.

Roderick and Farquhar (2002) argue that pan evaporation, which is normally used as surrogate for estimating PET, has shown a decreasing trend over the past 50 years. The decrease in pan evaporation is attributed to a decrease in solar radiation and associated observed changes in the diurnal temperature range and vapor pressure deficit. An increase in cloud cover and the concentration of atmospheric aerosols result in a decrease in sunlight and reduce loss of long-wave irradiance from the surface at night.

On the other hand, using parallel observations of actual and pan evaporation at six Russian, one Latvian and one U.S. experimental sites, Golubev et al. (2001) showed that pan evaporation time series increased in eastern United States and USA west of 122°W; north of 45°N, but decreased in most regions of southern Russia and the Midwest, Great Plains, Mountainous West and Great Lakes zones of the USA in the past 40 years. The increase in pan evaporation varies from 0.2 to 3.0% over a period of 10 years while the decrease in pan evaporation varies from 1.6 to 8.9% over a period of 10 years. Golubev et al. (2001) also show that an observed increase or decrease in pan evaporation will not necessarily translate into an observed increase or decrease in actual evaporation. For example, for Midwest USA, pan evaporation decreased by about 3.4% over a period of 10 years while actual evaporation increased by 1 to 2% over the same period (i.e., an inverse relationship).


Huntington (2006) has synthesized the inconsistencies between decreasing pan evaporation and increasing surface warming. Huntington (2006) suggests that for northern temperate humid climates, that is, where moisture is not limiting, lengthening of the growing season due to warming trends would lead to an overall increase in evapotranspiration.

Martin (2002) conducted estimates of mean annual gross evaporation from a free water surface of small to moderate-sized waterbodies in Canada over a 30-year period (1971 to 2000) at 55 locations. The station locations were in the Prairie Provinces in Canada, including British Columbia (east of the Rocky Mountains), Alberta, Saskatchewan and Manitoba. Indirect measurements using the Meyer formula was used to calculate gross evaporation in this study. Data on monthly mean air temperature, dew point temperature, relative humidity and wind speed data were obtained from Environment Canada archives. The 1971 to 2000 normal are low compared to the 1961 to 1990 normals. The mean decrease in gross evaporation over a period of 30 years (i.e., 1971 to 2000) is about 2.7% compared to the mean over a period of 30 years from 1961 to 1990. The change varies from a decrease of about 6.5% at Yorkton to an increase of about 1.3% at Fort McMurray.

Burn and Hesch (2006) compared trends in recorded monthly pan evaporation and monthly Potential Evaporation (PE) estimated using Meyer’s formula for 11 sites located on Canadian Prairies for the months of May to September. Trends were identified using the Mann-Kendall test with a significance level of 10%. About 36% of the PE data shows significant decreasing trends and only 15% of the data shows significant decreasing trends for PE. Less than 10% of the data shows increasing trends for both pan and potential evaporation. About 58% of the potential evaporation data and 77% of the PE have no trends or trends that are not significant at 10% level. Seven trends (out of 53 cases) agree for both PE and pan evaporation, with six trends showing a decrease in trends and one showing an increase in trend. The largest disagreement is when PE shows a decreasing trend and pan evaporation results in no trend. In general, trends in PE were attributed to wind speed, Vapour Pressure Deficit (VPD), or a combination of the two variables.

Ramirez et al. (2005) observed evidence of the complementary relationship in evapotranspiration on a large-scale. This study analyzed the relationship between pan evaporation, potential evapotranspiration and actual evapotranspiration from 25 basins across the United States. The observation consisted of annual measured PE (a surrogate for potential evapotranspiration) at 192 stations and computed actual evapotranspiration from an annual water-budget (as a difference between precipitation and runoff) from 1953 to 1994. The complementary relationship indicates that an increase in potential


evapotranspiration will be offset with a corresponding decrease in actual evapotranspiration or vice versa.

Schindler and Donahue (2006, website) states that regional general circulation models coupled with a modified method of calculation of PET (Thornthwaite 1948) indicate that the predicted warming could increase evaporation by up to 55% in some regions of the western prairie provinces in the 21st century.

Analysis of trend in historical evapotranspiration has resulted in mixed conclusions as to whether actual evapotranspiration or PET is showing an increasing or decreasing trend. Hence, the uncertainty associated with predicting future changes in evapotranspiration is even higher. Barnett et al. (2005) argued that in snowmelt dominated regions, these uncertainties are reduced since changes in the timing of snowmelt runoff induce a negative feedback on changes in evapotranspiration. Earlier snow melt results in increased soil moisture (and so also the water available for evapotranspiration) earlier in the season, a time when potential evaporation (dominated by net radiation) is low. Later in the year, when potential evaporation is higher, the shift in snowmelt timing reduces soil moisture, and hence evaporative resistance is increased, again reducing the effect of evaporation changes.

Given that the various studies provide contradictory conclusions about changes in solar radiation, vapor pressure deficit, potential and actual evapotranspiration due to potential climate change or variability, PET derived based on changes in air temperature was used to determine the sensitivity of flows in the Athabasca River tributary streams to potential climate change.

5.2.5 Change and Variability in Relative Humidity

Relatively little work has been undertaken to quantify long-term changes in relative humidity in Canada and the Oil Sands Region. Van Wijngaarden and Vincent (2003) assessed changes in relative humidity over the period 1953 to 2003 based on hourly data recorded at 75 airport stations located throughout Canada. Linear trends were computed and statistical t-tests were performed to determine whether the linear trends were significant at the 5% level. The results of analysis showed a decrease of about 10 to 12% over the time period of 1953 to 2003 for the winter and spring seasons, and a decrease of about 2 to 4% over the same period for the summer and fall seasons in the Oil Sands Region.

5.2.6 Change and Variability in Streamflows

The Canadian prairies (Alberta, Saskatchewan and Manitoba) have experienced about 20 serious droughts in the nineteenth century and over 10 serious droughts


in the twentieth century (Godwin et al. 1986). While it is certain that droughts will continue to occur in the prairies, it is not certain if future droughts will be more severe, more frequent, or both.

Based on an analysis of 50 sets of natural streamflow data, Gan (1998) showed that negative trends are much more prevalent than positive trends. Most of the positive trends occur in March and might be attributed to an earlier onset of spring melt caused by climatic warming. Higher flows in March could result in lower flows later in May and June. It seems that the Canadian prairies have experienced a warmer and somewhat drier climate in the last four to five decades. However, it is not clear that the drier climate has increased the frequency and severity of prairie droughts.

Yue et al. (2001) performed a trend analysis on annual mean, maximum and minimum Streamflow data for 213 stations in the Canadian Reference Hydrometric Basin Network (RHBN) using data from 1957 to 1997. The results of their analysis show a band of downward trends stretching from the Pacific to the Atlantic between about 50 and 58 latitudes, specifically for mean and maximum flows. However, a band of upward trend occurs in northerly latitudes above 58 latitude for mean, maximum and minimum flows. A complex pattern with clustering of direction was noted for the annual minimum flow.

Zhang et al. (2001) also presented trends computed using RHBN data from 1947 to 1996. Systematic analysis of 30-, 40- and 50-year study periods provided a significant trend of decreasing annual mean streamflow at the 10% level of significance across southern Canada. The monthly mean streamflow has decreased for most calendar months (except March and April) with the strongest decrease in summer and autumn months. However, significant increasing trends have been observed for the months of March and April. This might be attributed to an earlier snowmelt due to warmer spring air temperatures. The minimum annual flow and various percentiles of daily flows (below 40th percentile and above 90th percentile) indicate significant decreasing trends (i.e., at the 10% level of significance) in southern Canada and increasing trends in northern British Columbia and Yukon Territory.

5.3 TREND ANALYSES OF AIR TEMPERATURE, PRECIPITATION AND STREAMFLOW

5.3.1 General

Trend analyses of recorded near surface air temperature, precipitation and streamflows in the Oil Sands Region and in Alberta were conducted to determine:

SuncVo


• the presence of statistically significant trends in pertinent climate and streamflow variables; and

• any linkages or correlations between trends in climate and trends in streamflow variables, if such trends were detected from the previous analyses.

The following sections present the methodology and results of the analyses.

5.3.2 Air Temperature

The analysis of trends in air temperature in Alberta generally and in the Oil Sands Region specifically, as a result of climate change, climate variability, or both, was based on the air temperature data recorded at the Fort McMurray Airport (1919 to 2005) and Whitecourt Airport stations (1943 to 2005). Air temperature trends were computed for eight statistical parameters at each station, including monthly mean, seasonal average (spring, summer, fall, and winter), annual mean, and annual maximum and minimum air temperatures. The results indicate an increasing trend in air temperature at both stations for all seasons (Table 5-1) except in the month of October for data recorded at Fort McMurray Airport station, which shows a decreasing trend.

5.3.2.1 Fort McMurray Airport Station

An analysis of mean annual, minimum and maximum daily air temperatures at the Fort McMurray Airport station indicates an increasing trend (i.e., significant at the 5% level) since the 1970s (Figures 5-1 (a) to (c)). Average daily air temperatures in spring, summer and winter also have increasing trends that are significant at the 5% level (Figures 5-2 (a), (b) and (d)). However, the apparent increasing trend in average fall daily air temperature is not statistically significant at the 5% level (Figure 5-2 (c)).


Table 5-1 Statistical Test for Trend Analysis of Oil Sands Region Temperatures

Mann-Kendall Test Spearman Rank Order Correlation Coefficient Test for Trend

Station Month No

Data TestStat

Std. Dev.

MK- Stat

p- value

Sen- slope rs t t(α=0.05) t(α=0.01)

Trend

January 62 320 164.63 1.944 0.0260 0.063 -0.2475 -1.9783 -2.0003 -2.6603 (+)NS

February 62 561 164.63 3.408 0.0003 0.110 -0.4156 -3.5397 -2.0003 -2.6603 (+)S

March 62 423 164.63 2.569 0.0051 0.069 -0.3121 -2.5449 -2.0003 -2.6603 (+)S

April 62 349 164.63 2.120 0.0170 0.043 -0.2649 -2.1283 -2.0003 -2.6603 (+)S

May 62 161 164.63 0.978 0.1641 0.015 -0.1405 -1.0991 -2.0003 -2.6603 (+)NS

June 62 436 164.63 2.648 0.0040 0.029 -0.3570 -2.9602 -2.0003 -2.6603 (+)S

July 62 496 164.63 3.013 0.0013 0.022 -0.3753 -3.1362 -2.0003 -2.6603 (+)S

August 62 271 164.63 1.646 0.0499 0.021 -0.1975 -1.5606 -2.0003 -2.6603 (+)NS

September 62 123 164.63 0.747 0.2275 0.009 -0.1091 -0.8503 -2.0003 -2.6603 (+)NS

October 62 -191 164.63 -1.160 0.1230 -0.016 0.1477 1.1567 2.0003 2.6603 (-)NS

November 62 101 164.63 0.613 0.2698 0.018 -0.0779 -0.6052 -2.0003 -2.6603 (+)NS

December 62 225 164.63 1.367 0.0859 0.040 -0.1768 -1.3910 -2.0003 -2.6603 (+)NS

Annual 62 697 164.63 4.234 0.0000 0.035 -0.5120 -4.6167 -2.0003 -2.6603 (+)S

Spring 62 503 164.63 3.055 0.0011 0.047 -0.3990 -3.3706 -2.0003 -2.6603 (+)S

Summer 62 563 164.63 3.420 0.0003 0.023 -0.4424 -3.8208 -2.0003 -2.6603 (+)S

Fall 62 41 164.63 0.249 0.4017 0.002 -0.0317 -0.2457 -2.0003 -2.6603 (+)NS

Winter 62 439 164.63 2.667 0.0038 0.060 -0.3471 -2.8667 -2.0003 -2.6603 (+)S

Annual Maximum 62 512 164.43 3.114 0.0009 0.033 -0.3900 -3.2808 -2.0003 -2.6603 (+)S

Air Temperature at Fort McMurray Airport Station (1944 to 2005)

Annual Minimum 62 373 164.54 2.267 0.012 0.052 -0.2793 -2.2534 -2.0003 -2.6603 (+)S

January 63 303 168.60 1.797 0.0362 0.078 -0.2376 -1.9106 -1.9996 -2.6589 (+)NS

February 63 571 168.60 3.387 0.0004 0.102 -0.4301 -3.7210 -1.9996 -2.6589 (+)S

Marchj 63 570 168.60 3.381 0.0004 0.086 -0.4020 -3.4290 -1.9996 -2.6589 (+)S

April 62 485 164.63 2.946 0.0016 0.050 -0.3740 -3.1239 -2.0003 -2.6603 (+)S

Air Temperature at Whitcourt Airport Station (1943 to 2005)

May 62 313 164.63 1.901 0.0286 0.021 -0.2224 -1.7668 -2.0003 -2.6603 (+)NS


Table 5-1 Statistical Test for Trend Analysis of Oil Sands Region Temperatures (continued)

Mann-Kendall Test Spearman Rank Order Correlation Coefficient Test for Trend

Station Month No

Data TestStat

Std. Dev.

MK- Stat

p- value

Sen- slope rs t t(α=0.05) t(α=0.01)

Trend

June 62 653 164.63 3.967 0.0000 0.034 -0.5038 -4.5178 -2.0003 -2.6603 (+)S

July 61 474 160.70 2.950 0.0016 0.021 -0.3810 -3.1649 -2.0010 -2.6618 (+)S

August 61 410 160.70 2.551 0.0054 0.029 -0.3487 -2.8578 -2.0010 -2.6618 (+)S

September 61 186 160.70 1.157 0.1235 0.015 -0.1933 -1.5136 -2.0010 -2.6618 (+)NS

October 61 63 160.69 0.392 0.3475 0.005 -0.0496 -0.3814 -2.0010 -2.6618 (+)NS

November 61 154 160.70 0.958 0.1689 0.028 -0.1218 -0.9425 -2.0010 -2.6618 (+)NS

December 63 381 168.60 2.260 0.0119 0.065 -0.2771 -2.2522 -1.9996 -2.6589 (+)S

Annual 60 812 156.79 5.179 0.0000 0.046 -0.6458 -6.4415 -2.0017 -2.6633 (+)S

Spring 62 681 164.63 4.136 0.0000 0.057 -0.4979 -4.4474 -2.0003 -2.6603 (+)S

Summer 61 782 160.70 4.866 0.0000 0.028 -0.6012 -5.7791 -2.0010 -2.6618 (+)S

Fall 61 244 160.70 1.518 0.0645 0.020 -0.1999 -1.5670 -2.0010 -2.6618 (+)NS

Winter 63 557 168.60 3.304 0.0005 0.075 -0.4365 -3.7891 -1.9996 -2.6589 (+)S

Annual Maximum 62 189 164.43 1.149 0.1252 0.011 -0.1536 -1.2041 -2.0003 -2.6603 (+)NS

Air Temperature at Whitcourt Airport Station (1943 to 2005) (continued)

Annual Minimum 63 380 168.55 2.254 0.012 0.071 -0.2801 -2.2785 -1.9996 -2.6589 (+)S

Note: S = Significant; NS = Not Significant; rs = Spearman Rank Order Correlation Coefficient; t = Student’s t.


Figure 5-1 Annual Mean, Maximum and Minimum Temperatures at Fort McMurray or Fort McMurray Airport Station

(b) Maximum Daily Temperature

202122232425262728

1915 1925 1935 1945 1955 1965 1975 1985 1995 2005

Tem

pera

ture

[°C

]

(a) Mean Annual Temperature

-4-3-2-101234

1915 1925 1935 1945 1955 1965 1975 1985 1995 2005

Tem

pera

ture

[°C

]

(c) Minimum Daily Temperature

-50

-45

-40

-35

-30

-25

-20

1915 1925 1935 1945 1955 1965 1975 1985 1995 2005

Tem

pera

ture

[°C

]

NOTES: Data from 1922 to 1943 are at Fort McMurray Station. Data from 1944 to 2005 are at Fort McMurray Airport Station.


Figure 5-2 Spring, Summer, Fall and Winter Temperatures at Fort McMurray or Fort McMurray Airport Station

(a) Spring (March, April and May)

-4.0-3.0-2.0-1.00.01.02.03.04.05.06.0

1915 1925 1935 1945 1955 1965 1975 1985 1995 2005

Mea

n T

empe

ratu

re [o C

]

(b) Summer (June, July and August)

12.0

13.0

14.0

15.0

16.0

17.0

18.0

1915 1925 1935 1945 1955 1965 1975 1985 1995 2005

Mea

n T

empe

ratu

re [o C

]

(c ) Fall (September, October and November)

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

1915 1925 1935 1945 1955 1965 1975 1985 1995 2005

Mea

n Te

mpe

ratu

re [o C

]

(d) Winter (December, January and February)

-30.0

-25.0

-20.0

-15.0

-10.0

-5.0

0.0

1915 1925 1935 1945 1955 1965 1975 1985 1995 2005

Mea

n T

empe

ratu

re [o C

]

SuncVo


Two-sample statistical t-tests of annual and seasonal mean air temperature corresponding to the periods 1944 to 1974 and 1975 to 2005 indicate the two samples have different mean spring, summer, winter and annual air temperatures (Table 5-2). For the fall season, the mean air temperatures for the two samples are statistically similar based on the t-test. The results of the t-test suggest that there has been a significant change (increase) in seasonal and annual mean air temperatures during the period 1975 to 2005 compared to the period 1944 to 1974. However, the variances in daily temperature for both periods are statistically similar based on the F-test.

5.3.2.2 Whitecourt Airport Station

The annual mean and minimum air temperatures based on the data recorded at the Whitecourt Airport Station show an increasing trend, similar to the Fort McMurray station data, that is significant at the 5% level (Table 5-1 and Figure 5-3 (a) and (c)). However, the apparent increasing trend in maximum daily air temperature (Figure 5-3 (b) is not statistically significant at the 5% level. The monthly and seasonal surface air temperatures have trends similar to those for the Fort McMurray Airport Station (Table 5-2 and Figures 5-4 (a) to (d)) except for the month of October.

Based on a trend line fitted to recorded data, the forecasted mean annual air temperature at the Fort McMurray Airport and Whitecourt Airport stations will increase by 2.7 and 3.7°C, respectively, by the year 2050, compared to the 1961 to 1990 temperature normal (Table 5-3).


Table 5-2 Statistical T-test and F-test for Two Samples (1944 to 1974 Versus 1975 to 2005) of Temperature Measured at Fort McMurray Airport Station

Spring Summer Fall Winter Annual Test Parameter

1944 to 1974 1975 to 2005 1944 to 1974 1975 to 2005 1944 to 1974 1975 to 2005 1944 to 1974 1975 to 2005 1944 to 1974 1975 to 2005

annual mean [oC] 0.485 2.242 14.898 15.555 1.089 1.390 -18.498 -15.750 -0.421 0.927

variance 3.382 4.296 0.733 0.771 2.987 2.747 9.638 7.753 0.997 1.312

observations 31 31 31 31 31 31 31 31 31 31

hypothesized mean difference 0 - 0 - 0 - 0 - 0 -

df 59 - 60 - 60 - 59 - 59 -

t Stat -3.530 - -2.982 - -0.701 - -3.669 - -4.938 -

P(T<=t) one-tail 0.000 - 0.002 - 0.243 - 0.000 - 0.000 -

t critical one-tail 1.671 - 1.671 - 1.671 - 1.671 - 1.671 -

P(T<=t) two-tail 0.001 - 0.004 - 0.486 - 0.001 - 0.000 -

t critical two-tail 2.001 - 2.000 - 2.000 - 2.001 - 2.001 -

t-test

remark rejected rejected accepted rejected rejected df 30 - 30 - 30 - 30 - 30 30

F 0.787 - 0.951 - 1.088 - 1.243 - 0.760 0.787

P(F<=f) one-tail 0.258 - 0.445 - 0.410 - 0.277 - 0.228 0.258

F critical one-tail 0.543 - 0.543 - 1.841 - 1.841 - 0.543 0.543

F-test

remark accepted accepted accepted accepted accepted Note: - = Not required; Df = Degree of freedom; F = F-statistic; P = probability; T= t-statistic.


Figure 5-3 Annual Mean, Maximum and Minimum Temperatures at Whitecourt or Whitecourt Airport Station

(b) Maximum Daily Temperature

1516171819202122232425

1940 1950 1960 1970 1980 1990 2000

Tem

pera

ture

[°C

](a) Mean Annual Temperature

-2

-1

0

1

2

3

4

5

1940 1950 1960 1970 1980 1990 2000

Tem

pera

ture

[°C

]

(c) Minimum Daily Temperature

-45

-40

-35

-30

-25

-20

-15

1940 1950 1960 1970 1980 1990 2000

Tem

pera

ture

[°C

]

NOTES: Data from December 1, 1942 to July 26, 1978 are at Whitecourt Station. Data from July 27, 1978 to December, 31, 2005 are at Whitecourt Airport Station.


Figure 5-4 Spring, Summer, Fall and Winter Temperatures at Whitecourt or Whitecourt Airport Station

(a) Spring (March, April and May)

-2.0-1.00.01.02.03.04.05.06.07.0

1940 1950 1960 1970 1980 1990 2000

Mea

n T

empe

ratu

re [o C

]

(b) Summer (June, July and August)

10.0

11.0

12.0

13.0

14.0

15.0

16.0

17.0

1940 1950 1960 1970 1980 1990 2000

Mea

n T

empe

ratu

re [o C

]

(c) Fall (September, October and November)

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

1940 1950 1960 1970 1980 1990 2000

Mea

n T

empe

ratu

re [o C

]

(d) Winter (December, January, and February)

-25.0

-20.0

-15.0

-10.0

-5.0

0.0

1940 1950 1960 1970 1980 1990 2000

Mea

n T

empe

ratu

re [o C

]

SuncVo


Table 5-3 Forecasted Mean Annual Temperature and Annual Total Precipitation to Year 2050 Based on Observed Trend Lines

Annual Mean Air Temperature [oC] Total Precipitation [mm] Station Average

(1961 to 1990) Forecasted

to 2050 Difference Average (1961 to 1990)

Forecasted to 2050

Difference [%]

Fort McMurray Airport Station (1944 to 2005)

0.27 2.98 2.71 464 449 -3.2%

Whitecourt Airport Station (1945 to 2005)

1.88 5.55 3.67 578 614 6.3%

5.3.3 Precipitation

An analysis of monthly, seasonal and annual precipitation based on the data recorded at the Fort McMurray and Whitecourt Airport stations (Table 5-4, Figures 5-5 (a) to (e) and 5-6 (a) to (e)) does not suggest any significant trend over the last 50 to 60 years.

The analysis of the monthly precipitation data and seasonal total precipitation at the Fort McMurray station shows that there has been an apparent decrease in precipitation in winter (i.e., November to February) and the beginning of autumn (i.e., August and September). Monthly precipitation apparently has increased in the spring and the beginning of summer (i.e., from April to July). However, the increasing and decreasing trends in monthly precipitation are not statistically significant at the 5% level. Similar results were obtained using the data recorded at the Whitecourt Airport Station, with the exception that winter precipitation has a decreasing trend that is significant at the 5% level (Table 5-4).

For the results of the t-tests and F-tests on annual and seasonal totals and variances of precipitation values recorded at the Fort McMurray Airport Station for two samples corresponding to the periods 1944 to 1974 and 1975 to 2005, (Table 5-5). The t-test indicates that the two samples have comparable total precipitation in spring, summer, fall and annually. For the winter season, the two samples have different total precipitation.

The recorded data at the two stations indicate an apparent, though not statistically significant, increase in annual total precipitation. The forecast of total precipitation to the year 2050, based on the apparent annual trend, suggests a decrease of about 3.2% and an increase of about 6.3% at the Fort McMurray Airport and Whitecourt Airport stations, respectively, relative to the 1961 to 1990 precipitation normal.


Table 5-4 Statistical Test for Trend Analysis of Oil Sands Region Precipitation

Mann-Kendall Test Spearman Rank Order Correlation Coefficient Test for Trend Location Period

No. Data

Test Stat

StandardDeviation MK-Stat p-value Sen-slope rs t t

(α=0.05) t

(α=0.01)

Trend

January 62 -91 164.62 -0.553 0.2902 -0.036 0.0785 0.6100 2.0003 2.6603 (-)NS February 62 -68 164.61 -0.413 0.3398 -0.028 0.0381 0.2955 2.0003 2.6603 (-)NS March 62 53 164.62 0.322 0.3737 0.022 -0.0513 -0.3977 -2.0003 -2.6603 (+)NS April 62 1 164.63 0.006 0.4976 0.000 -0.0023 -0.0181 -2.0003 -2.6603 (-)NS May 62 176 164.63 1.069 0.1425 0.159 -0.1339 -1.0463 -2.0003 -2.6603 (+)NS June 62 238 164.62 1.446 0.0741 0.335 -0.1908 -1.5053 -2.0003 -2.6603 (+)NS July 62 117 164.63 0.711 0.2386 0.171 -0.1005 -0.7825 -2.0003 -2.6603 (+)NS August 62 -112 164.63 -0.680 0.2482 -0.127 0.0952 0.7410 2.0003 2.6603 (-)NS September 62 -109 164.63 -0.662 0.2540 -0.094 0.0982 0.7641 2.0003 2.6603 (-)NS October 62 74 164.62 0.450 0.3265 0.041 -0.0814 -0.6327 -2.0003 -2.6603 (+)NS November 62 -234 164.61 -1.422 0.0776 -0.102 0.1646 1.2923 2.0003 2.6603 (-)NS December 62 -328 164.62 -1.992 0.0232 -0.143 0.2219 1.7631 2.0003 2.6603 (-)NS annual 62 45 164.63 0.273 0.3923 0.238 -0.0273 -0.2117 -2.0003 -2.6603 (+)NS spring 62 153 164.63 0.929 0.1763 0.156 -0.1390 -1.0875 -2.0003 -2.6603 (+)NS summer 62 199 164.63 1.209 0.1134 0.446 -0.1589 -1.2463 -2.0003 -2.6603 (+)NS fall 62 -70 164.63 -0.425 0.3353 -0.138 0.0596 0.4622 2.0003 2.6603 (-)NS

Precipitation at Fort McMurray Airport Station (1944 to 2005)

winter 62 -227 164.63 -1.379 0.084 -0.208 0.1575 1.2356 2.0003 2.6603 (-)NS January 63 -107 168.60 -0.635 0.2628 -0.073 0.0661 0.5172 1.9996 2.6589 (-)NS February 63 -627 168.59 -3.719 0.0001 -0.306 0.4455 3.8860 1.9996 2.6589 (-)S March 63 49 168.59 0.291 0.3857 0.027 -0.0264 -0.2059 -1.9996 -2.6589 (+)NS April 63 66 168.59 0.391 0.3477 0.042 -0.0568 -0.4444 -1.9996 -2.6589 (+)NS May 61 140 160.69 0.871 0.1918 0.163 -0.1181 -0.9139 -2.0010 -2.6618 (+)NS June 62 236 164.63 1.434 0.0759 0.504 -0.2059 -1.6294 -2.0003 -2.6603 (+)NS July 61 -23 160.69 -0.143 0.4431 -0.047 -0.0034 -0.0263 -2.0010 -2.6618 (-)NS August 61 -171 160.69 -1.064 0.1436 -0.333 0.1417 1.0998 2.0010 2.6618 (-)NS September 61 234 160.68 1.456 0.0727 0.264 -0.1939 -1.5183 -2.0010 -2.6618 (+)NS October 61 149 160.68 0.927 0.1769 0.080 -0.1246 -0.9643 -2.0010 -2.6618 (+)NS November 61 -26 160.68 -0.162 0.4357 -0.009 0.0332 0.2553 2.0010 2.6618 (-)NS

Precipitation at Whitecourt Airport Station (1943 to 2005)

December 63 -271 168.60 -1.607 0.0540 -0.131 0.2023 1.6137 1.9996 2.6589 (-)NS


Table 5-4 Statistical Test for Trend Analysis of Oil Sands Region Temperatures (continued)


No. Data

Test Stat

StandardDeviation MK-Stat p-value Sen-slope rs t t

(α=0.05) t

(α=0.01)

Trend

annual 60 22 156.79 0.140 0.4442 0.097 -0.0191 -0.1452 -2.0017 -2.6633 (+)NS spring 61 158 160.70 0.983 0.1627 0.218 -0.1206 -0.9329 -2.0010 -2.6618 (+)NS summer 61 78 160.70 0.485 0.3137 0.312 -0.0641 -0.4933 -2.0010 -2.6618 (+)NS fall 60 203 156.79 1.295 0.0977 0.315 -0.1604 -1.2376 -2.0017 -2.6633 (+)NS

Precipitation at Whitecourt Airport Station (1943 to 2005) (continued)

winter 63 -457 168.59 -2.711 0.003 -0.528 0.3532 2.9483 1.9996 2.6589 (-)S



Figure 5-5 Precipitation at Fort McMurray or Fort McMurray Airport Station

(a) Annual Precipitation

0

100

200

300

400

500

600

700

1923 1933 1943 1953 1963 1973 1983 1993 2003

Prec

ipita

tion

[mm

]

(b) Spring Precipitation (March, April and May)

0

20

40

60

80

100

120

140

160

180

1923 1933 1943 1953 1963 1973 1983 1993 2003

Tot

al P

reci

pita

tion

[mm

]

NOTES: Data from 1922 to 1943 are at Fort McMurray Station. Data from 1944 to 2005 are at Fort McMurray Airport Station. Annual total precipitation is not calculated for 1922 due to insufficient data.


Figure 5-5 Precipitation at Fort McMurray or Fort McMurray Airport Station (continued)

(c) Summer Precipitation (June, July and August)

050

100150200250300350400

1923 1933 1943 1953 1963 1973 1983 1993 2003

Tot

al P

reci

pita

tion

[mm

]

(d) Fall Precipitation (September, October and November)

0

50

100

150

200

250

1923 1933 1943 1953 1963 1973 1983 1993 2003

Prec

ipita

tion

[mm

]

(e) Winter Precipitation (December, January and February)

0

20

40

60

80

100

120

1923 1933 1943 1953 1963 1973 1983 1993 2003

Prec

ipita

tion

[mm

]


Figure 5-6 Precipitation at Whitecourt or Whitecourt Airport Station

(a) Anuual Precipitation

0

100

200

300

400

500

600

700

800

1943 1953 1963 1973 1983 1993 2003

Prec

ipita

tion

[mm

]

(b) Spring Precipitation (March, April and May)

0

50

100

150

200

250

1943 1953 1963 1973 1983 1993 2003

Tot

al P

reci

pita

tion

[mm

]

NOTES: Data from Dec. 1, 1942 to Jul. 26, 1978 are at Whitecourt Station. Data from Jul. 27, 1978 to Dec. 31, 2003 are at Whitecourt Airport Station. Annual total precipitation is not calculated for years 1942, 1944, 1978 and 1997 due to insufficient data.

SuncVo


Figure 5-6 Precipitation at Whitecourt or Whitecourt Airport Station (continued)

01943 1953 1963 1973 1983 1993 2003

(d) Fall Precipitation (September, October and November)

0

50

100

150

200

250

1943 1953 1963 1973 1983 1993 2003

Tot

al P

reci

pita

tion

[mm

]

(e) Winter Precipitation (December, January and February)

0

20

40

60

80

100

120

140

1943 1953 1963 1973 1983 1993 2003

Tot

al P

reci

pita

tion

[mm

]

(c) Summer Precipitation (June, July and August)

100

200

300

400

500

600

Tot

al P

reci

pita

tion

[mm

]


Table 5-5 Statistical T-test and F-test for Two Samples (1944 to 1974 Versus 1975 to 2005) of Precipitation Measured at Fort McMurray Airport Station

Spring Summer Fall Winter Annual Test and Parameter 1944 to

1974 1975 to

2005 1944 to

1974 1975 to

2005 1944 to

1974 1975 to

2005 1944 to

1974 1975 to

2005 1944 to

1974 1975 to

2005

total precipitation [mm] 71.474 75.516 204.655 220.735 104.410 93.035 65.016 49.226 445.781 438.106

variance [mm2] 993.261 673.065 3,208.180 4,566.470 1,579.870 1,337.509 684.615 182.271 9,230.930 7,267.757

observations 31 31 31 31 31 31 31 31 31 31

hypothesized total difference 0 - 0 - 0 - 0 - 0 -

df 58 - 58 - 60 - 45 - 59 -

t stat -0.551 - -1.015 - 1.172 - 2.986 - 0.333 -

P(T<=t) one-tail 0.292 - 0.157 - 0.123 - 0.002 - 0.370 -

t critical one-tail 1.672 - 1.672 - 1.671 - 1.679 - 1.671 -

P(T<=t) two-tail 0.584 - 0.314 - 0.246 - 0.005 - 0.741 -

t critical two-tail 2.002 - 2.002 - 2.000 - 2.014 - 2.001 -

t-test

remark accepted accepted accepted rejected accepted

df 30 - 30 - 30 - 30 - 30 -

F 1.476 - 0.703 - 1.181 - 3.756 - 1.270 -

P(F<=f) one-tail 0.146 - 0.169 - 0.326 - 0.000 - 0.258 -

F critical one-tail 1.841 - 0.543 - 1.841 - 1.841 - 1.841 -

F-test

remark accepted accepted accepted rejected accepted - = Not required; Df = Degree of freedom; F = F-statistic; P = probability; T= t-statistic.

SuncVo


5.3.4 Streamflows

The recorded flow data at Environment Canada Stations 07DA001 (Athabasca River below Fort McMurray), 07BE001 (Athabasca River at Athabasca), 07AD002 (Athabasca River at Hinton), 07CD001 (Clearwater River at Draper) and 05BB001 (Bow River at Banff) were analyzed using Spearman and Mann-Kendall tests for trend to identify possible trends in maximum, mean, minimum, monthly mean and seasonal mean flows. The location, drainage area and number of years of records at each station are given in Table 5-6.

5.3.4.1 Athabasca River Below Fort McMurray (07DA001)

The results of the analysis suggest a statistically significant negative trend at the 5% level for annual mean flow, and the mean flow in the spring, fall and winter seasons for the Athabasca River below Fort McMurray. The results also indicate a negative trend in the summer, annual daily maximum and 7-day low flows. However, these trends are not statistically significant at the 5% level (Table 5-7 and Figures 5-7 and 5-8).

Based on the trend line fitted to the recorded data, (Figures 5-7 (a) and 5-8 (a)), the forecasted annual mean flow would decrease by about 38.5% and the 7-day low flow would decrease by 19% by the year 2050 compared to the 1961 to 1990 statistics (Table 5-8).

5.3.4.2 Athabasca River at Athabasca (07BE001)

The recorded flows in Athabasca River at Athabasca (Station 07DE001) from 1952 to 2005 were analyzed for trend. The results indicate an apparent negative trend for annual mean, spring and fall flows and a positive trend for annual maximum flow, mean summer and winter flows and 7-day minimum flows (Table 5-7). The mean monthly flows in winter (December to May) and summer (July and August) indicate an apparent positive trend while the mean monthly flows in spring (June) and fall (September to November) indicate negative trend. However, none of either the positive or negative trends is statistically significant at the 5% level.

Based on the trend line fitted to the recorded data (Figures 5-7 (c) and IV-5-8 (c)), the forecasted annual mean flow will decrease by about 14% and the 7-day low flow will increase by about 7% by the year 2050 compared to the 1961 to 1990 statistics (Table 5-8).

Suncor Energy Inc. - 66 Climate Change Voyageur South Project July 2007

Table 5-6 Location, Drainage Areas and Flow Statistics of Streamflow Stations Flow [m3/s]

No. Station Name Station Number Latitude Longitude

Drainage Area [km2]

Period No. of

Complete Years of

Data

Mean Annual

Flow 2-Year Peak

10-Year Peak

100-Year Peak

7-Day, 10-Year

Low

1 Athabasca River below Fort McMurray 07DA001 56°46'50¨N 111°24'0¨W 133,000(G),

131,000(E) 1957 to 2006 45 630 2,366 3,685 5,414 100.5

2 Athabasca River at Athabasca 07BE001 54°43'20¨N 113°17'10¨W 74,600(G),

73,500(E) 1913 to 2005 73 426.0 1,838 3,205 5,342 51.2

3 Athabasca River at Hinton 07AD002 53°25'23¨N 117°34'14¨W 9,780(G),

9,780(E) 1961 to 2005 46 172.3 787 1,038 1,271 18.4

5 Athabasca River near Windfall 07AE001 54°12'25¨N 116°3'45¨W 19,600(G),

19,600(E) 1960 to 2005 19 253.1 1,124 1,652 2,276 28.7

4 Clearwater River at Draper 07CD001 56°41'7¨N 111°15'15¨W 30,800(G),

30,800(E) 1930 to 2006 48 119.3 375 606 835 32.8

6 Bow River at Banff 05BB001 51°10'30¨N 115°34'10¨W 2,210(G), 2,210(E) 1909 to 2005 97 39.3 204 286 373 5.5

7 Bow River at Calgary 05BH004 51°03'00¨N 114°03'00¨W 7,860(G), 7,820(E) 1911 to 2005 92 90.2 324 573 1,030 17.1

Note: G = gross area; E = effective area (contributing to runoff).


Table 5-7 Statistical Test for Trend Analysis of Streamflows


No. Data Test Stat Std.Dev. MK-Stat p-value Sen-slope rs t t(α=0.05) t(α=0.01) Trend

January 47 -229 109.05 -2.100 0.0179 -1.305 0.3237 2.2947 2.0141 2.6896 (-)S February 47 -246 109.04 -2.256 0.0120 -0.947 0.3053 2.1508 2.0141 2.6896 (-)S March 48 -44 112.51 -0.391 0.3479 -0.255 0.0850 0.5786 2.0129 2.6870 (-)NS April 46 -65 105.62 -0.615 0.2691 -1.820 0.0948 0.6316 2.0154 2.6923 (-)NS May 48 -300 112.51 -2.666 0.0038 -10.119 0.3881 2.8559 2.0129 2.6870 (-)S June 48 -153 112.51 -1.360 0.0869 -5.967 0.2214 1.5397 2.0129 2.6870 (-)NS July 48 -62 112.51 -0.551 0.2908 -1.797 0.0687 0.4672 2.0129 2.6870 (-)NS August 48 -32 112.51 -0.284 0.3880 -0.903 0.0560 0.3805 2.0129 2.6870 (-)NS September 48 -218 112.51 -1.938 0.0263 -4.953 0.2728 1.9231 2.0129 2.6870 (-)S October 49 -244 116.01 -2.103 0.0177 -3.835 0.3342 2.4308 2.0117 2.6846 (-)S November 48 -238 112.51 -2.115 0.0172 -2.116 0.3582 2.6023 2.0129 2.6870 (-)S December 47 -187 109.05 -1.715 0.0432 -1.144 0.2628 1.8274 2.0141 2.6896 (-)NS annual 44 -310 98.87 -3.135 0.0009 -4.953 0.4421 3.1946 2.0181 2.6981 (-)S spring 48 -268 112.51 -2.382 0.0086 -4.489 0.3270 2.3466 2.0129 2.6870 (-)S summer 48 -106 112.51 -0.942 0.1731 -2.635 0.1646 1.1316 2.0129 2.6870 (-)NS fall 48 -252 112.51 -2.240 0.0126 -4.134 0.3108 2.2177 2.0129 2.6870 (-)S winter 47 -249 109.05 -2.283 0.0112 -1.123 0.3261 2.3139 2.0141 2.6896 (-)S annual max 48 -130 112.48 -1.156 0.1239 -9.223 0.1551 1.0646 2.0129 2.6870 (-)NS

Athabasca River below Fort McMurray (1958 to 2005)

7Q 48 -193 112.49 -1.716 0.0431 -0.643 0.2676 1.8834 2.0129 2.6870 (-)NS January 72 206 205.71 1.001 0.1583 0.099 -0.1316 -1.1108 -1.9944 -2.6479 (+)NS February 72 188 205.71 0.914 0.1804 0.080 -0.1120 -0.9430 -1.9944 -2.6479 (+)NS March 72 322 205.71 1.565 0.0588 0.192 -0.1646 -1.3960 -1.9944 -2.6479 (+)NS April 71 251 201.47 1.246 0.1064 0.737 -0.1662 -1.3997 -1.9949 -2.6490 (+)NS May 85 2 263.47 0.008 0.4970 0.003 -0.0108 -0.0981 -1.9890 -2.6364 (+)NS June 85 -256 263.47 -0.972 0.1656 -1.070 0.1139 1.0442 1.9890 2.6364 (-)NS July 86 361 268.11 1.346 0.0891 1.502 -0.1429 -1.3233 -1.9886 -2.6356 (+)NS August 86 85 268.11 0.317 0.3756 0.232 -0.0361 -0.3311 -1.9886 -2.6356 (+)NS

Athabasca River at Athabasca (1913 to 2005)

September 86 -139 268.11 -0.518 0.3021 -0.354 0.0445 0.4079 1.9886 2.6356 (-)NS


Table 5-7 Statistical Test for Trend Analysis of Streamflows (continued)



October 86 -63 268.11 -0.235 0.4071 -0.065 0.0311 0.2856 1.9886 2.6356 (-)NS November 72 -198 205.71 -0.963 0.1679 -0.225 0.1213 1.0221 1.9944 2.6479 (-)NS December 72 53 205.71 0.258 0.3983 0.035 -0.0292 -0.2446 -1.9944 -2.6479 (+)NS annual 71 -137 201.47 -0.680 0.2482 -0.258 0.0581 0.4836 1.9949 2.6490 (-)NS spring 71 -61 201.47 -0.303 0.3810 -0.110 0.0347 0.2882 1.9949 2.6490 (-)NS summer 86 95 268.11 0.354 0.3615 0.368 -0.0412 -0.3776 -1.9886 -2.6356 (+)NS fall 72 -220 205.71 -1.069 0.1424 -0.379 0.1282 1.0813 1.9944 2.6479 (-)NS winter 71 111 201.47 0.551 0.2908 0.073 -0.0719 -0.5991 -1.9949 -2.6490 (+)NS annual max 86 135 268.07 0.504 0.3073 1.375 -0.0577 -0.5297 -1.9886 -2.6356 (+)NS

Athabasca River at Athabasca (1913 to 2005) (continued)

7Q 71 191 201.47 0.948 0.1716 0.083 -0.1237 -1.0358 -1.9949 -2.6490 (+)NS January 44 130 98.87 1.315 0.0943 0.123 -0.2069 -1.3706 -2.0181 -2.6981 (+)NS February 44 98 98.87 0.991 0.1608 0.065 -0.1632 -1.0721 -2.0181 -2.6981 (+)NS March 44 224 98.87 2.266 0.0117 0.171 -0.3315 -2.2771 -2.0181 -2.6981 (+)S April 45 98 102.23 0.959 0.1689 0.104 -0.1564 -1.0383 -2.0167 -2.6951 (+)NS May 45 10 102.23 0.098 0.4610 0.059 -0.0242 -0.1590 -2.0167 -2.6951 (+)NS June 45 -148 102.23 -1.448 0.0738 -1.569 0.2350 1.5857 2.0167 2.6951 (-)NS July 45 -74 102.23 -0.724 0.2346 -0.620 0.1236 0.8167 2.0167 2.6951 (-)NS August 45 -170 102.23 -1.663 0.0482 -1.002 0.2489 1.6850 2.0167 2.6951 (-)NS September 45 -80 102.23 -0.783 0.2169 -0.407 0.1152 0.7602 2.0167 2.6951 (-)NS October 45 -70 102.23 -0.685 0.2467 -0.192 0.1075 0.7091 2.0167 2.6951 (-)NS November 45 64 102.23 0.626 0.2656 0.075 -0.0980 -0.6459 -2.0167 -2.6951 (+)NS December 45 122 102.23 1.193 0.1163 0.110 -0.2036 -1.3634 -2.0167 -2.6951 (+)NS annual 44 -114 98.87 -1.153 0.1244 -0.315 0.1884 1.2435 2.0181 2.6981 (-)NS spring 44 58 98.87 0.587 0.2787 0.137 -0.0911 -0.5925 -2.0181 -2.6981 (+)NS summer 45 -172 102.23 -1.683 0.0462 -1.280 0.2601 1.7662 2.0167 2.6951 (-)NS fall 45 -108 102.23 -1.056 0.1454 -0.341 0.1487 0.9864 2.0167 2.6951 (-)NS winter 44 116 98.87 1.173 0.1203 0.098 -0.1652 -1.0854 -2.0181 -2.6981 (+)NS annual max 45 -202 102.21 -1.976 0.0241 -3.768 0.3307 2.2980 2.0167 2.6951 (-)S

Athabasca River at Hinton (1961 to 2003)

7Q 44 350 98.87 3.540 0.0002 0.206 -0.4879 -3.6228 -2.0181 -2.6981 (+)S





January 48 -172 112.51 -1.529 0.0632 -0.234 0.2187 1.5204 2.0129 2.6870 (-)NS February 48 -140 112.51 -1.244 0.1067 -0.184 0.1585 1.0887 2.0129 2.6870 (-)NS March 48 -98 112.51 -0.871 0.1919 -0.091 0.1069 0.7294 2.0129 2.6870 (-)NS April 47 -53 109.05 -0.486 0.3135 -0.228 0.0605 0.4064 2.0141 2.6896 (-)NS May 48 -376 112.51 -3.342 0.0004 -2.923 0.4704 3.6150 2.0129 2.6870 (-)S June 48 -152 112.51 -1.351 0.0884 -1.066 0.2015 1.3951 2.0129 2.6870 (-)NS July 48 2 112.51 0.018 0.4929 0.042 0.0123 0.0832 2.0129 2.6870 (+)NS August 48 -45 112.51 -0.400 0.3446 -0.227 0.0461 0.3131 2.0129 2.6870 (-)NS September 49 -138 116.01 -1.190 0.1171 -0.688 0.1696 1.1798 2.0117 2.6846 (-)NS October 49 -186 116.01 -1.603 0.0544 -0.914 0.2370 1.6727 2.0117 2.6846 (-)NS November 49 -178 116.01 -1.534 0.0625 -0.410 0.2116 1.4845 2.0117 2.6846 (-)NS December 49 -182 116.01 -1.569 0.0583 -0.281 0.2353 1.6598 2.0117 2.6846 (-)NS annual 47 -269 109.05 -2.467 0.0068 -0.727 0.3430 2.4493 2.0141 2.6896 (-)S spring 47 -355 109.05 -3.256 0.0006 -1.402 0.4334 3.2260 2.0141 2.6896 (-)S summer 48 -46 112.51 -0.409 0.3413 -0.362 0.0665 0.4523 2.0129 2.6870 (-)NS fall 49 -178 116.01 -1.534 0.0625 -0.699 0.2235 1.5718 2.0117 2.6846 (-)NS winter 48 -172 112.51 -1.529 0.0632 -0.235 0.2319 1.6167 2.0129 2.6870 (-)NS annual max 47 -346 109.02 -3.174 0.0008 -5.000 0.4795 3.6651 2.0141 2.6896 (-)S

Clear Water River at Draper (1930 to 2004)

7Q 47 -165 109.05 -1.513 0.0651 -0.145 0.1852 1.2645 2.0141 2.6896 (-)NS January 95 621 311.03 1.997 0.0229 0.009 -0.2066 -2.0358 -1.9858 -2.6297 (+)S February 95 511 311.03 1.643 0.0502 0.007 -0.1790 -1.7543 -1.9858 -2.6297 (+)NS March 95 861 311.03 2.768 0.0028 0.010 -0.2880 -2.9000 -1.9858 -2.6297 (+)S April 95 89 311.03 0.286 0.3874 0.002 -0.0339 -0.3270 -1.9858 -2.6297 (+)NS May 95 -243 311.03 -0.781 0.2173 -0.060 0.0824 0.7977 1.9858 2.6297 (-)NS June 95 -209 311.03 -0.672 0.2508 -0.083 0.0709 0.6853 1.9858 2.6297 (-)NS July 95 -571 311.03 -1.836 0.0332 -0.163 0.1967 1.9344 1.9858 2.6297 (-)S August 95 -1161 311.03 -3.733 0.0001 -0.167 0.3623 3.7481 1.9858 2.6297 (-)S September 95 -973 311.03 -3.128 0.0009 -0.080 0.3218 3.2779 1.9858 2.6297 (-)S

Bow River at Banff (1909 to 2003)

October 95 -229 311.03 -0.736 0.2308 -0.012 0.0740 0.7151 1.9858 2.6297 (-)NS





November 95 145 311.03 0.466 0.3205 0.005 -0.0525 -0.5073 -1.9858 -2.6297 (+)NS December 95 651 311.03 2.093 0.0182 0.013 -0.2116 -2.0881 -1.9858 -2.6297 (+)S annual 95 -713 311.03 -2.292 0.0109 -0.046 0.2485 2.4738 1.9858 2.6297 (-)S spring 95 -197 311.03 -0.633 0.2632 -0.016 0.0688 0.6650 1.9858 2.6297 (-)NS summer 95 -649 311.03 -2.087 0.0185 -0.144 0.2213 2.1887 1.9858 2.6297 (-)S fall 95 -537 311.03 -1.727 0.0421 -0.025 0.1798 1.7625 1.9858 2.6297 (-)NS winter 94 575 306.16 1.878 0.0302 0.009 -0.1886 -1.8425 -1.9861 -2.6303 (+)NS annual max 95 -656 311.00 -2.109 0.0175 -0.459 0.2228 2.2041 1.9858 2.6297 (-)S

Bow River at Banff (1909 to 2003) (continued)

7Q 95 707 311.03 2.273 0.0115 0.008 -0.2368 -2.3502 -1.9858 -2.6297 (+)S



Figure 5-7 Trends of Mean Annual Flows for Various Streamflow Stations

(a) Athabasca River Flow Below Fort McMurray

y = -3.697x + 7989.8R2 = 0.5998

y = 5E+40x-11.494

R2 = 0.6137

0

200

400

600

800

1000

1200

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Ann

ual M

ean

Flow

s [m

3 /s]

(b) Bow River at Banff

y = -0.0303x + 98.671R2 = 0.1639 y = -0.1216x + 280.4

R2 = 0.6203

y = 6E+21x-6.1192

R2 = 0.6213

0

10

20

30

40

50

60

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Ann

ual M

ean

Flow

s [m

3 /s]


Figure 5-7 Trends of Mean Annual Flows for Various Streamflow Stations (continued)

(c) Athabasca River at Athabasca

y = -0.7323x + 1883.4R2 = 0.1269

y = 7E+13x-3.4061

R2 = 0.1332

0

100

200

300

400

500

600

700

800

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Ann

ual M

ean

Flow

s [m

3 /s]

(d) Athabasca River at Hinton

y = -0.2616x + 692.62R2 = 0.1884

y = 9E+11x-2.9485

R2 = 0.1837

0

50

100

150

200

250

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Annu

al M

ean

Flow

s [m

3 /s]


Figure 5-7 Trends of Mean Annual Flows for Various Streamflow Stations (continued)

(e) Clearwater River at Draper

y = -0.9236x + 1952.2R2 = 0.4975

y = 2E+53x-15.517

R2 = 0.5063

0

50

100

150

200

250

1935 1945 1955 1965 1975 1985 1995 2005

Annu

al M

ean

Flow

s [m

3 /s]

(f) Bow River at Calgary

y = -0.0855x + 257.38R2 = 0.0825

y = -0.343x + 769.81R2 = 0.4213

y = 4E+26x-7.486

R2 = 0.4092

0

20

40

60

80

100

120

140

160

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Annu

al M

ean

Flow

s [m

3 /s]


Figure 5-8 Trends of 7-Day Low Flows for Various Streamflow Stations

(a) Athabasca River Below Fort McMurray

y = -0.3775x + 894.49R2 = 0.2829

y = 6E+19x-5.3426

R2 = 0.2927

0

50

100

150

200

250

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

7Q F

low

s [m

3 /s]

(b) Bow River at Banff

y = 0.0094x - 11.658R2 = 0.283

y = -0.0071x + 20.981R2 = 0.0502

y = 1E+07x-1.8973

R2 = 0.044

0

1

2

3

4

5

6

7

8

9

10

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

7Q F

low

s [m

3 /s]


Figure 5-8 Trends of 7-Day Low Flows for Various Streamflow Stations (continued)

(c) Athabasca River Flow at Athabasca

y = 0.138x - 193.34R2 = 0.0563

y = 6E-11x3.6726

R2 = 0.0636

0

20

40

60

80

100

120

140

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

7Q F

low

[m3 /s

]

(d) Athabasca River at Hinton

y = 0.1814x - 335.19R2 = 0.6104

y = 2E-47x14.58

R2 = 0.5987

0

5

10

15

20

25

30

35

40

45

50

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

7Q F

low

s [m

3 /s]

SuncVo


Figure 5-8 Trends of 7-Day Low Flows for Various Streamflow Stations (continued)

(e) Clearwater at Draper

y = 4E+08x-2.100540

50

60

70

80

85 1995 2005

7Q F

low

[m3 /s

]

y = -0.0492x + 143.72R2 = 0.0865

R2 = 0.0856

0

10

20

30

1935 1945 1955 1965 1975 19


Table 5-8 Forecasted Flow Parameters to Year 2050 Based on Observed Trend Lines Annual Mean Flow

[m3/s] 7-day Low flow

[m3/s] Station Name Station

Number Latitude Longitude Drainage

Area [km2]

Period Average (1961 to

1990) Forecasted

to 2050 PercentChange

[%]

Average(1961 to

1990) Forecasted

to 2050 Percent Change

[%]

Athabasca River below Fort McMurray 07DA001 56°46'50¨N 111°24'0¨W 133,000 1957 to 2006 667.8 430.0 -35.6 149.2 121.6 -18.5

Athabasca River at Athabasca 07BE001 54°43'20¨N 113°17'10¨W 74,600 1952 to 2005 444.1 382.2 -13.9 83.9 89.6 6.8

Athabasca River at Hinton 07AD002 53°25'23¨N 117°34'14¨W 9,780 1961 to 2005 172.7 156.3 -9.5 23.5 36.7 56.3

Clearwater River at Draper 07CD001 56°41'7¨N 111°15'15¨W 30,800 1930 to 2005 121.6 81.8 -32.7 46.8 44.2 -5.5

Bow River at Banff 05BB001 51°10'30¨N 115°34'10¨W 2,210 1909 to 2005 39.5 36.6 -7.4 6.9 7.6 10.1

Bow River at Calgary 05BH004 51°03'00¨N 114°03'00¨W 7,860 1911 to 2005 89.6 82.1 -8.3 - - -

- = Not Applicable.

Suncor Energy Inc. - 78 Climate Change Voyageur South Project July 2007

5.3.4.3 Athabasca River at Hinton (07AD002)

The Athabasca River at Hinton represents a high altitude watershed with glacial melt runoff. Recorded annual mean and summer and fall flows at this station indicate decreasing trends that are not significant at the 5% level. The annual maximum flows indicate a decreasing trend that is significant at the 5% level. Spring and winter flows show increasing trends that are not significant at the 5% level. However, the annual maximum and 7-day low flows indicate an increasing trend that is significant at the 5% level.

The forecasted annual mean flow at this station would decrease by about 9.5% by the year 2050 and the 7-day low flow would increase by about 56.3% by the year 2050 compared to 1961 to 1990 statistics (Table 5-8).

5.3.4.4 Clearwater River at Draper (07CD001)

The Clearwater River is a tributary to the Athabasca River at Fort McMurray. An analysis of the flow data recorded at this station indicates trends similar to those at the Athabasca River below Fort McMurray station (Table 5-7). An analysis of annual mean, maximum and spring flows indicates a decreasing trend that is significant at the 5% level. Summer, fall, winter and 7-day low flows have decreasing trends that are not significant. Forecasts for 2050, based on the trend line fitted to recorded data, indicate that the annual mean flow would decrease by about 32.7% and the 7-day low flow would decrease by about 5.5% compared to the 1961 to 1990 statistics.

5.3.4.5 Bow River at Banff (05BB001)

The Bow River at Banff station is not located in the Oil Sands Region. This station has been included in the trend analysis because its record of natural flows goes back to 1909. In addition, the flows at this station may be more directly affected by climate change because the main source of runoff is glacial melt from the Rocky Mountains. The station also exhibits trends similar to the Athabasca River below Fort McMurray for the concurrent periods of record (Figures 5-7 (a) and 5-8 (b)).

An analysis of the 95 years of data indicates that the annual mean, maximum and summer mean flows have decreasing trends that are significant at the 5% level (Table 5-7). The mean flows in winter indicate an increasing trend that is not significant, while the 7-day low flows indicates an increasing trend that is significant at the 5% level.


The relatively long series of data on the Bow River indicates that there are distinct cycles of wet and dry years, that is, the data exhibit cyclic patterns. These cycles appear to also be present in the flow series for the Athabasca River, however, the relative shortness of the latter series prevents more detailed analysis. Cycles in hydrologic series will tend to magnify trends when only partial segments of the cycles are analyzed. It appears that the long series of flow data on the Bow River provides a more realistic assessment of possible trends in flows.

Based on the trend line fitted to the recorded data, the forecast by the year 2050 suggests a decrease of about 7.4% for mean annual flows and an increase of about 10.1% in 7-day low flows compared to the 1961 to 1990 statistics (Table 5-8).

5.3.5 Findings

The results of an analysis of regional and local climatic and hydrologic variables in the Oil Sands Region and in Alberta indicate a warming trend in the past three to four decades. The increasing trend of near surface air temperature is significant. The analysis shows that though there is a decrease in monthly precipitation in winter and at the beginning of fall, there is an increasing trend in spring and beginning of the summer season. The annual total precipitation indicates a decreasing trend for recorded data at Fort McMurray Airport Station and an increasing trend using recorded data at Whitecourt station. However, the increasing and decreasing trends in monthly and annual total precipitation are not significant at the 5% level.

The Athabasca River flow records at various stations including tributary rivers show a decreasing trend in recent years due to climate change or variability. Similar trends are also observed at the Bow River at Banff and at Calgary stations using data over the same period as the recorded Athabasca River flows (i.e., 1958 to 2005). However, using a longer period of record (i.e., 1909 to 2005), the analysis of the recorded flows for the Bow River shows trends that are less exaggerated compared to the 40 to 50 year flow series for the Athabasca River. It appears that cycles present in the hydrologic series tend to exaggerate trends when only partial segments of the cycles (due to short periods of record) are analyzed. Based on trends established using longer period of record on the Bow River, the mean annual flows would likely decrease by about 7.4% compared to about 21.1% using the shorter period (1958 to 2005). In addition, the analysis using the longer period of record indicates that the 7-day low flow would increase instead of the decreasing trend that is observed based on the shorter period of record. Hence, the recent 40 to 50 year period of records may


not properly reflect the long-term trend due to climate variability, climate change or both.

Based on the trend analysis using recorded climate and hydrologic data in the Oil Sands Region, it is difficult to establish a definite link between change in near surface air temperature, precipitation and flows in the Athabasca River. Therefore, there is a large degree of uncertainty in carrying the hydrologic effects of climate change or variability forward in time for any impact assessment. The GCMs suggest that an increase in near surface air temperature would result in increased precipitation. For the Oil Sands Region, there is a clear increasing trend in near surface air temperature over the last three to four decades. However, the increase in air temperature did not result in corresponding increases or decreases in precipitation and streamflow.

There is a statistically insignificant increase in recorded precipitation in the last three to four decades. Recorded streamflows appear to follow a significant decreasing trend for the same period. This suggests that there may be an increase in the magnitude of actual evapotranspiration from a watershed. The only link that can be established between near surface air temperature and streamflows is the winter runoff for the Athabasca River at Hinton and the Bow River at Banff stations. An increase in near surface air temperature resulted in increased winter runoff as a result of early glacial melt in March and snow melt runoff in the months of October and November.

5.4 SENSITIVITY OF FLOWS IN THE ATHABASCA RIVER TRIBUTARY STREAMS TO POTENTIAL CHANGES IN CLIMATE PARAMETERS

5.4.1 General

The following sections present the results of a sensitivity analysis of imposed changes in air temperature, precipitation and potential evapotranspiration on flows in the Athabasca River tributary streams.

The calibrated HSPF model was used to carry out the sensitivity analysis. Two combinations of precipitation, air temperature scenarios and potential evapotranspiration scenarios were simulated. The simulation runs were done for the Beaver and Muskeg rivers in the Oil Sands Region. Flow statistics (annual mean flow, winter mean flow, 10-year flood peak flow, 100-year flood peak flow and 7-day, 10-year return period low flow or 7Q10) were derived from the simulated series and compared with the baseline statistics without accounting for the potential effects of climate change.

SuncVo


The results of the sensitivity analysis are presented in the following sections.

The two scenarios were developed based on forecasted changes in precipitation and air temperature by GCMs, recorded data at Fort McMurray station and potential evapotranspiration derived based on forecasted air temperature using Morton’s evaporation model.

The sensitivity analysis was conducted by changing the HSPF model input (i.e., air temperature, precipitation and potential evapotranspiration) as follows:

• Potential evapotranspiration determined using Morton’s model based on changes in air temperature.

• change in air temperature for both scenarios is the same. The seasonal changes air temperatures are determined using a combination of forecasted values by GCM model (i.e., CGCM2 – A2(1)) and trends developed based on recorded air temperature at Fort McMurray Airport station as shown in Table 5-9; and

• Scenario 2: a decrease in mean annual precipitation forecasted by ECHAM4 B21 model as shown in Table 5-9;

• Scenario 1: an increase in mean annual precipitation forecasted by CGCM2 – A2(1) model as given in Table 5-9;


Air Temperature -

Forecasted Changes to Year 2050

Increasing Precipitation -

Forecasted Changes to Year 2050 - CGCM2 -

A2(1)

Decreasing Precipitation -

Forecasted Changes to Year 2050 - ECHAM4 B21

Potential Evapotranspiration - Forecasted Changes

to Year 2050 - Morton's Model

Actual Evapotranspiration-

Forecasted Changes to Year 2050 - Scenario 1 -

HSPF Model

Actual Evapotranspiration-

Forecasted Changes to Year 2050 - Scenario 2 -

HSPF Model Period

[oC] [mm] [%] [mm] [%] [mm] [%] [mm] [%] [mm] [%]

spring 2.0 2.6 3.2 -8.2 -10.2 48 18 1 1 -3 -3

summer 2.0 15.0 7.0 -11.8 -5.5 71 16 13 7 -4 -2

fall 1.5 5.4 4.9 -0.1 -0.1 17 22 6 16 5 13

winter 3.5 -1.8 -2.9 1.9 3.1 17 0 1 0 1 0

annual 2.2 21.4 4.6 -18.1 -3.9 152 19 21 6 -1 0

Note: Scenario 1 corresponds to increase in precipitation (i.e., CGCM2-A2(1) model) and Scenario 2 corresponds to decrease in precipitation (i.e., ECHAM4-B21 Model).

Table 5-9 Forecasted Air Temperature, Precipitation and Evapotranspiration for Sensitivity Analysis

SuncVo

SuncVo


5.4.2 Beaver River

The Beaver River has a drainage area of about 165 km2 at the Environment Canada Station 07DA018. The basin is well vegetated and consists mainly of upland areas (ground slopes greater than 0.5%) covered by peat soils that vary in thickness from 0.3 to 1 m.

The results of the sensitivity analysis for the two combinations of changes in air temperature, precipitation and potential evapotranspiration are provided (Table 5-10). The maximum decrease of about 26.8% in annual mean flow occurs as a result of the combination of Scenario 2, which is a decrease in precipitation, and an increase in daily air temperature and potential evapotranspiration. This combination also results in a decrease of about 35.8% in the 10-year peak flow. However, the mean winter flow increases by more than three times the baseline condition.


Table 5-10 Relative Sensitivity Analysis for Beaver River at Environment Canada Station 07DA018

Discharge Change to Discharge

Scenario Parameter Description Parameter Change

Winter Mean [m³/s]

Annual Mean[m³/s]

10-Year peak [m³/s]


Winter Mean[%]

Annual Mean [%]

10-Year peak [%]

100-Year peak [%]

0 none baseline condition none 0.062 0.571 18.5 39.5 none none none none

1 PREC, ATMP, PET

increase in precipitation, temperature and potential evapotranspiration

seasonal 0.114 0.519 15.4 41.9 83.9 -9.1 -17.0 6.1

2 PREC, ATMP, PET

decrease in precipitation, and increase in temperature and potential evapotranspiration

seasonal 0.103 0.418 11.9 31.0 66.1 -26.8 -35.8 -21.5

PREC = Precipitation; PET = Potential Evapotranspiration; ATMP = Air Temperature.


5.4.3 Muskeg River

The Muskeg River has a drainage area of about 1,485 km2 at its mouth. The basin is well vegetated and consists of about 53% upland areas (ground slopes greater than 0.5%) and 47% lowland areas (ground slopes less than 0.5% and extensive muskeg terrain).

The results of the sensitivity analysis of the model’s output to changes in precipitation, air temperature and PET is provided in Table 5-11). A sensitivity analysis of two combinations of precipitation, air temperature and potential evaporation was performed for the Muskeg River watershed (Table 5-11). Theoretically, an increase in air temperature could result in an increase in potential evapotranspiration if all other parameters that govern potential evapotranspiration remain constant. The combination of a decrease in precipitation, an increase in air temperature and an increase in potential evapotranspiration (i.e., Scenario 2), results in a decrease of annual mean runoff, 7Q10 low flow and 10-year peak flow by about 24.4, 15.2 and 12.1%, respectively. However, the winter mean flow will increase by 55.6%.

or Energy Inc. - 86 - Climate Changyageur South Project July 200

e 7

Discharge Change to Discharge

Scenario Parameter Description Parameter Change

Winter Mean [m³/s]

Annual Mean [m³/s]



7Q10 [L/s]

Winter Mean [%]

Annual Mean [%]

10-year Peak [%]

100-year Peak [%]

7Q10[%]

0 none baseline condition none 0.464 3.883 54.283 83.72 22.46 none none none none none

1 PREC, ATMP, PET

increase in precipitation, temperature and potential evapotranspiration

seasonal 0.790 3.597 52.0 83.4 22.71 70.2 -7.4 -4.1 -0.3 1.1

2 PREC, ATMP, PET

decrease in precipitation, and increase in temperature and potential evapotranspiration

seasonal 0.722 2.933 47.7 70.7 19.05 55.6 -24.4 -12.1 -15.5 -15.2

Table 5-11 Relative Sensitivity Analysis for Muskeg River at its Mouth

PREC = Precipitation; PET = Potential Evapotranspiration; ATMP = Air Temperature.

SuncVo


5.5

5.4.4 Findings

The sensitivity of the HSPF model outputs, which are used in impact assessments was performed using imposed changes on parameters that may represent potential climate change or variability such as precipitation, air temperature and potential evapotranspiration. The results of the modelled sensitivity analysis indicate the following:

• A combination of increase in precipitation, air temperature and potential evapotranspiration will result in an increase in mean winter runoff (i.e., by about 70%) and marginal increase in 7Q10 low flow (i.e., less than 1%). However, the annual mean runoff and 10-year flood flow will decease by about 7 and 5%, respectively.

• A combination of decrease in precipitation, an increase in air temperature and potential evapotranspiration will result in an increase in mean winter runoff (i.e., by about 56%). However, the annual mean runoff, 7Q10 low flow and 10-year flood flow will decease by about 24, 15 and 16%, respectively.

CONCLUSIONS

The results of the literature review on climate change, climate variability or both, and the analysis of local and regional climatic and hydrologic variables in the Oil Sands Region support the following conclusions:

• There has been a warming trend (increasing trend in near surface air temperature) in the past three to four decades in the Oil Sands Region.

• The recorded annual total precipitation, and precipitation in spring and at the beginning of summer show increasing trends while precipitation in winter and beginning of fall shows a decreasing trend. However, the increasing and decreasing trends in precipitation are not significant.

• The analysis of recorded annual mean flows in the Oil Sands Region and other parts of Alberta indicates a significant decreasing trend in recent years as a result of climate change or variability, or both. However, the results of an analysis of longer period of flow record (about 90 years) for the Bow River at Banff show less exaggerated trends compared to the 40 to 50 years of records available for the Athabasca River. Hence, data in the last 40- to 50-year period may not properly reflect the longer term trend.

• Based on the longer (about 90 years) period of record data for the Bow River at Banff, 7-day low flows show an increasing trend. Warmer


winter temperatures have resulted in snowmelt runoff in October and November and early snowmelt in March.

• It is not possible to reliably predict any future hydrologic effects due to climate change or variability forward in time for any environmental impact assessment, since the linkage between changes in air temperature and precipitation, and changes in streamflows in the Oil Sands Region cannot be established on the basis of available data.

The results of the streamflow analysis and the sensitivity analysis of the Athabasca River tributary streams in the Oil Sands Region due to potential climate change or variability, support the following conclusions, which are used for assessing the effects of climate change on impact assessment sections:

• The mean annual flow for the Athabasca River could potentially decrease by about 10% over the next 60 years.

• The 7-day low flow for the Athabasca River would remain unchanged. This is a conservative prediction, because model simulation as well as an analysis of both regional and local data indicates an increasing trend.

• The sensitivity of flows in small tributary streams to Athabasca River to potential climate change can be analyzed for effect assessment by assuming an increase in air temperature, an increase and a decrease in annual precipitation (i.e., two scenarios), and an increase potential evapotranspiration over the next 50 years.


6.1

6.2

6 SUMMARY OF THE CONSIDERATIONS OF CLIMATE CHANGE ON WATER QUALITY

INTRODUCTION

One objective of the climate change analysis presented in this section was to review and discuss existing studies and information on climate change with reference to surface water quality in the Oil Sand Region. A second and more specific objective was to investigate the potential effects of climate change on water quality predictions for receiving waterbodies in the Voyageur South Project Aquatic Study Area (ASA). A literature review was completed to address the first objective, with a summary of the main findings provided in Section 6.2. The second objective was evaluated using the modelling exercise described in Section 6.3. Conclusions regarding the effect of climate change on predicted concentrations are also presented in Section 6.3.

LITERATURE REVIEW

The majority of the existing literature is focused on effects of climate change on meteorological parameters, such as air temperature and precipitation, and not on water quality. However, changes to air temperature and precipitation can lead to changes in infiltration, snow cover, evapotranspiration and, ultimately, streamflow, which could affect water quality (Chalecki and Gleick 1999; Murdoch et al. 2000). Of the literature that does describe potential effects of climate change on water quality, the majority focuses on water temperature and dissolved oxygen. Nutrients are also discussed, but metals and organic parameters are rarely mentioned.

Anthropogenic effects, such as changes in land use related to climate change, may have similar or greater impacts on water quality than climate change itself, depending on the region (Cruise et al. 1999; Murdoch et al. 2000; Hutjes et al. 1998). Many studies have focused on differentiating these effects (Worrall et al. 2003; Cruise et al. 1999; Moore et al. 1997; Walker et al. 2000; Interlandi and Crockett 2003; Ramstack et al. 2004). However, anthropogenic sources are not always considered in the literature, so conclusions regarding the effects of climate change on water quality must be carefully evaluated. A summary of key findings is provided below.

There is general agreement that climate change has and will continue to lead to increased air temperatures in northern Alberta (Mann et al. 1998; McIntyre and McKitrick 2003; Chaikowsky 2000; Van Wijngaarden and Vincent 2003).


Opinions are mixed about how changes in air temperature may affect precipitation rates. For example, Zhang et al. (2000a) reported a large upward trend in annual precipitation in Canadian northern regions. Mekis and Hogg (1999) indicated only a slight increase in annual precipitation, while Van Wijngaarden and Vincent (2003) reported slight increases in spring, summer and fall, with significant decreases in winter. Potential effects to streamflow are also uncertain. Yue et al. (2001) identified a downward trend in regions between latitude 50° and 58° N, and upward trends above latitude 58°N. Fort McMurray and Embarras are located at approximately 53° and 56° N, respectively.

General warming trends are expected to increase water temperature as a result of shorter ice-covered periods in rivers and lakes (Beltaos 2000; Stefan et al. 1993; Fang and Stefan 1997, 2000; Fang et al. 1999; Prowse and Beltaos 2002; Ozaki et al. 2003; Jansen and Hesslein 2004; Magnuson et al. 1997; Cohen 1995, 1997). Shorter ice-covered periods should allow biochemical reactions to occur that normally cease during anoxic conditions (e.g., ice cover) because of increased aeration. Increased biological activity could lead to increased oxygen demands, with a net result of lower overall Dissolved Oxygen (DO) concentrations in the water column. In addition, DO saturation levels decrease with rising water temperature, limiting the volume of oxygen in the water column (Thomann and Mueller 1987).

Climate change may also lead to changes in lake hydrodynamics. Warmer water temperatures could lead to deepened thermoclines and disrupt the ratio of water present in the epilimnion and hypolimnion. Stefan et al. (1993) expect several types of lakes to experience longer stratification periods, which may prevent lake mixing and thereby limit the influx of oxygen from the surface to the hypolimnion. Temperate dimictic lakes (i.e., those that mix twice a year) may become monomictic (i.e., mix once a year), and cold monomictic lakes may become stratified (Schindler 1997; Hostetler and Small 1999; Magnuson et al. 1997). Maxwell et al. (1997) and Schindler (2001) also concluded that warmer air temperatures and lower streamflows could lead to the reduction, if not the disappearance, of many wetlands. Since some wetlands act as purification facilities, water chemistry in some receiving streams may also change.

Some studies have been completed on the effect of climate and anthropogenic changes on targeted water quality parameters (Moore et al. 1997; Cruise et al. 1999; Walker et al. 2000; Interlandi and Crockett 2003; Ramstack et al. 2004; Boesch et al. 2001; Boorman 2003; Worrall et al. 2003; Struyf et al. 2004). The majority of the studies focus on nutrients, which is generally an issue in densely populated areas with heavy agricultural activities. Limited attention has so far been given to metals or organics, which are often of concern in the Oil Sands Region. The nutrient-focused studies do, however, give insight into the evolution


6.3

of climate change effects on water quality. It appears that the main pathway for water quality to be affected would be through changes in surface flow. For example, warmer air temperatures may gradually increase evaporation, which could lead to a reduction in water levels and flows in lake and rivers. This reduction in assimilative capacity could, subsequently, lead to increased in-stream concentrations. The linkage between warmer air temperature and reduced streamflow has not, however, been clearly established. As previously stated, some studies (e.g., Yue et al. 2001) indicate that streamflows could increase in some areas and decrease in others.

A modelling exercise was completed to determine what effects, if any, climate change could have on the water quality predictions produced for the Voyageur South Project EIA. Modelling methods, assumptions and results are presented in Section 6.3.

MODELLING ANALYSIS

6.3.1 Methods

A sensitivity analysis was completed to evaluate the potential effects of climate change on EIA water quality predictions. This task involved simulating in-stream concentrations for selected parameters using Poplar Creek water quality model for two new flow scenarios. The new flow scenarios characterize the likely range of key hydrological conditions associated with climate change (Section 5), and include the following adjustments:



The daily flow series generated by the HSPF flow model were used in Poplar Creek water quality model to re-simulate water quality at the mouth of Poplar Creek in the far future. Although parameter concentrations were predicted for both Unnamed Waterbody and Poplar Creek at various time snapshots for the EIA assessment, the climate change analysis focused on the mouth of Poplar Creek in the far future, because water quality in Unnamed Waterbody is largely unaffected by project activities. In contrast, water quality in Poplar Creek is expected to change as a result of project activities, as discussed in Volume 3, Section 6.7. of the Voyageur South Project EIA.


No adjustments were made to seepage rates, decay rates or the values assigned to other parameters considered in the assessment. They remained consistent with those used to complete the EIA.

Labile naphthenic acids (i.e., those capable of changing state; readily undergoing change or breakdown), Total Dissolved Solids (TDS), molybdenum, acute toxicity and chronic toxicity were selected as assessment parameters for the climate change analysis. Labile naphthenic acids, TDS and molybdenum were chosen because they have been identified as signatures of process-affected waters and were identified as parameters of potential concern to aquatic health. Acute and chronic toxicity were selected, because they are considered to be the most important of the 48 parameters included in the water quality assessment.

6.3.2 Results

As illustrated in Figure 6-1, the EIA predictions for molybdenum and TDS were higher than those predicted for the two climate change scenarios. In contrast, the EIA predictions for labile naphthenic acids, acute toxicity and chronic toxicity were slightly lower than those predicted for the two climate change scenarios, although predicted peak concentrations were virtually identical across the three scenarios for all three parameters (Figure 6-2). An examination of the results revealed that the higher air temperatures assumed in the climate change simulations led to reduced surface water flows from both pit lakes, with greater reductions from the North Pit Lake. Predicted far future concentrations of molybdenum and TDS are lower in the South Pit Lake than in the North Pit Lake. Consequently, predicted concentrations of molybdenum and TDS were lower for the climate change scenarios, because a larger proportion of the flow in Poplar Creek originated from the South Pit Lake in comparison to that occurring in the absence of climate change.

A similar pattern was not observed for labile naphthenic acids, chronic toxicity and acute toxicity, because process-affected seepage contains far higher levels of these variables than either pit lake. With climate change resulting in reduced outflows from both pit lakes, the assimilative capacity of Poplar Creek was reduced, and the process-affected seepage entering Poplar Creek represented a greater proportion of the overall flow. However, waters in Poplar Creek were non-toxic under both the climate change scenarios, consistent with the EIA predictions.

Molybdenum

Total dissolved solids

Median and peak concentrations are presented as: EIA Case (Scenario 1, Scenario 2)

0

0.2

0.4

0.6

0.8

1

1.2

0 100 200 300 400 500 600 700 800Concentration (mg/L)

Pro

port

ion

of o

bser

vatio

ns le

ss th

anst

ated

val

ue

Scenario 1 EIA Case Scenario 2

Median = 556 (542, 498)Peak = 697 (695, 693)

0

0.2

0.4

0.6

0.8

1

1.2

0 0.05 0.1 0.15 0.2 0.25Concentration (mg/L)

Pro

port

ion

of o

bser

vatio

ns le

ss th

anst

ated

val

ueScenario 1 EIA Case Scenario 2

Median = 0.15 (0.14, 0.12)Peak = 0.23 (0.23, 0.23)

Labile naphthenic acids

Acute toxicity

Chronic toxicity

Median and peak concentrations are presented as: EIA Case (Scenario 1, Scenario 2)

0

0.2

0.4

0.6

0.8

1

1.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7Concentration (TUc)

Prop

ortio

n of

obs

erva

tions

less

than

stat

ed v

alue


Median = 0.09 (0.1, 0.1)Peak = 0.5 (0.5, 0.5)

0

0.2

0.4

0.6

0.8

1

1.2

0 0.05 0.1 0.15 0.2 0.25 0.3Concentration (TUa)

Prop

ortio

n of

obs

erva

tions

less

than

stat

ed v

alue


Median = 0.07 (0.07, 0.08)Peak = 0.27 (0.27, 0.28)

0

0.2

0.4

0.6

0.8

1

1.2

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5Concentration (mg/L)

Prop

ortio

n of

obs

erva

tions

less

than

stat

ed v

alue


Median = 0.6 (0.6, 0.7)Peak = 4.3 (4.3, 4.4)


6.4 CONCLUSIONS

Climate change may have an effect on water quality at the mouth of Poplar Creek in the far future. However, based on the simulations described herein, the effect is not sufficient to alter the conclusions of the EIA, with respect to water quality. Toxicity levels at the mouth of Poplar Creek are projected to remain below levels of concern.


7.1

7.2

7 SUMMARY OF THE CONSIDERATIONS OF CLIMATE CHANGE ON FISH AND FISH HABITAT

INTRODUCTION

The potential effects of climate change on the conclusions of the EIA were evaluated through the consideration of the aspects of the assessment that could be influenced, in a cumulative manner, by climate change. These aspects include stream flow, water level and water quality, as influenced by possible changes in air temperature, evaporation and precipitation. Such changes may affect the availability, quality or quantity of fish habitat, as well as potentially affecting fish abundance and fish habitat diversity.

The climate change analysis included an examination of the relevant literature and a specific assessment of the results reported by Hydrology and Water Quality in Sections 5 and 6. The climate change literature was examined for existing information concerning the effects of climate change on freshwater fish populations and fish habitats, with emphasis on northern Alberta. This information was used as a basis for a general evaluation of the potential cumulative effects of the Voyageur South Project under climate change. The specific assessment was based on the outcome of the analyses conducted by the hydrology and water quality components to quantify the potential effects of climate change on watercourses and waterbodies in the ASA. The analyses completed involved an examination of potential changes to stream flow and the concentrations of selected water quality parameters at selected locations within the ASA. Specific predictions regarding changes in water temperature and thermal regime due to climate change were not completed as there are no predicted effects of the Voyageur South Project on thermal regime in any watercourse or waterbody in the ASA and, therefore no predicted cumulative effects under climate change.

LITERATURE INFORMATION

Potential Changes to Physical Habitat

Recent trends in climate change indicate global average surface temperatures have increased by 0.6°C during the 20th Century. This has been accompanied by retreat of glaciers and a reduction in the duration of lake and river ice cover by two weeks in the mid- and high latitudes of the northern hemisphere (Shuter et al. 2002a). It is predicted that global surface temperatures will continue to increase with the most pronounced effects occurring at high latitudes and during


the winter. Greater variation in precipitation and increased frequency of droughts and floods are also predicted (Shuter et al. 2002a).

Human induced climate change scenarios for northern Canada include further temperature increases (Reist 1994). Climate change is expected to be accompanied by more extreme variation in precipitation as well as continued reductions in periods of ice cover for lakes and rivers. Climate change is expected to have both indirect and direct physical effects on aquatic environments in northern parts of Canada (von Finster 2001). Many of these physical changes are interrelated but for practical purposes can be placed into five categories:

1. Changes to water budget.

2. Changes to aquatic thermal regimes.

3. Changes to water quality.

4. Reduced system stability.

5. Changes to aquatic connectivity.

Potential physical effects to aquatic systems within each of these five categories may include, but are not limited to, the effects described below.

1) Changes to Water Budget

Potential physical effects include:

• changes to total inflow to surface waters;

• increased evaporation from the surface of lakes and rivers;

• reduced outflow from lakes to outlet streams and rivers, resulting in a reduction in downstream stream flows;

• reduced recharge of aquifers located upslope of the lakes;

• modifications to river flow;

• modifications to water level and volume of lakes;

• modifications to size and location of marginal habitats, such as the littoral zones, wetlands and stream banks; and

• changes to lake / reservoir residence times.


2) Changes to Aquatic Thermal Regimes


• warmer average water temperatures;

• earlier onset of stratification in lakes; and

• reduced ice cover periods.

3) Changes to Water Quality


• changes to oxygen availability as saturation levels decline due to increasing water temperatures;

• changes to the availability of nutrients due to changes in lake residence times and loading rates; and

• changes to parameter concentrations in relation to reduced stream flows (i.e., potential for reduced assimilative capacities).

4) Reduced System Stability


• more frequent flooding events;

• more frequent drought events;

• increased deposition of organic or inorganic sediments into streams; and

• fluctuating water levels.

5) Changes to Aquatic Connectivity


• reduced connectivity between waterbodies in areas where permanent streams transform into ephemeral ones.

Potential Changes to Fish and Fish Habitat

Fish are sensitive to changes within their aquatic environment. An understanding of fish habitat, life history and physiology provides insight into their potential response to the physical effects of climate change. Possible linkages between


climate change and potential effects to fish and fish habitat are provided below. Some of these linkages have been supported by research and some are based on an understanding of established relationships between fish and their aquatic environment.

1) Changes to Growth, Recruitment and Abundance

Water temperature influences physiological processes in fish such that changes in water temperature have the potential to affect fish growth, recruitment and abundance. The optimization of physiological processes is temperature dependent and is linked to growth rates in fish, which in turn is directly related to recruitment rates and the abundance of fish.

Among populations along the northern extent of their range, it is hypothesized that length of the growing season and the rate of growth over the first growing season will increase in warmer years (Shuter et al. 2002b). Recruitment is dependent on growth rate, because young-of-the-year must reach a critical size in the fall to survive the winter and potentially be recruited into the adult population. Several studies support the dependence of growth and recruitment of fish on temperature. A study by Murchie and Power (2005) found a temperature-based growth model for young-of-the-year yellow perch explained up to 99.6% of the variability in first year growth for populations living in three northern Alberta lakes. The results of the study suggest that temperature markedly influences young-of-the-year fish growth for species at the fringe of their distribution, with potential reductions in growth at the southern distribution extreme and potential increases in growth at the northern distribution extreme. Other studies find that warmer summers have positive effects on recruitment rates for northern pike while warmer falls/winters have negative effects on recruitment rates for lake trout in Ontario (Casselman 2002; Shuter et al. 2002a).

2) Changes to Fisheries Yields

Predictive models suggest that climate change can increase the yields of some species and reduce the yields of others (Reist 1994). The response would likely be population specific and dependent upon the geographic location, the relationship to climate gradient, and the likely temperature increase associated with climate change (Minns and Moore 1992). The most relevant study to northern Alberta comes from Minns and Moore (1992) who modelled the local spatial consequences of fish yields for three cool water species in eastern Canada. Substantial spatial redistribution of fisheries yields were predicted with areas presently supporting high yields becoming marginal and currently marginal areas becoming optimal.


3) Shifts in Geographical Distribution

As a response to changing habitat conditions brought on by forecasted climate change, species adapted to warmer aquatic conditions are predicted to extend their distribution limits north as their physiological needs become more optimized in these previously unsuitable areas. Northerly distributed cold water and arctic fishes are expected to experience a reduction in distribution in currently occupied southern-most areas and become extirpated from areas that become physiologically intolerable (Reist 1994).

Some implications of climate change in Alberta can be considered by understanding the thermal guilds of species that live there. Fish in Northern Alberta can be categorized into three thermal guilds based on their habitat distributions. Northern pike, walleye and yellow perch are members of the “cool water” guild. Fish species within this guild tend to have southern centers of distribution and northern distribution edges (Schlesinger and Regier 1982). Lake whitefish and cisco are part of the “cold water guild”, which typically have a wide distribution but a narrow thermal tolerance. Arctic grayling is a member of the “Arctic guild” of fish that have centers of distribution in high latitudes (Schlesinger and Regier 1982). Therefore, if water temperatures increase as a result of climate change, cool water species may find more suitable temperatures at their northern distribution limit, whereas effects on their southern distribution centres would depend on the extent of the temperature change. Cold water species could potentially be adversely affected throughout their range, depending on the level of the temperature increase. Arctic species may find conditions unsuitable at the southern limit of their distribution and may not have the potential to expand beyond their current northern distribution limit.

4) Changes to Species Diversity and Community Composition

The combined ecological effects of climate change are not easily predicted; however, it is expected that the culmination of effects to fish and fish habitat brought about by climate change will have widespread ecological outcomes that can influence local diversity and community composition. Using a climate change scenario, Jackson and Mandrak (2002) demonstrated how the predicted expansion of one piscivorous species beyond its current distribution could lead to the extirpation of four cyprinid prey species in lakes within Ontario.

5) Changes to Water Quality

Physical effects of climate change on water quality are very complex but some generalizations can be made based on current understanding of climate change and water. Water quality changes associated with climate change include changes in DO concentrations and changes in TDS. Certain life stages of fish,


7.3

such as egg and larval stages, are sensitive to low DO and high turbidity. These stages may be affected by changes that may occur as a result of climate change. Similarly, fish species may be affected by changes in the concentrations of other parameters that results from reductions in stream flows and, consequently, assimilative capacity.

6) Changes to Access and Migration

Physical effects of climate change on fish habitat include modifications to flow regimes through changes in seasonal precipitation, increased evapotranspiration and the stabilization or destabilization of waterways. Migratory species are dependent on seasonal flow regimes for access to and from spawning grounds. Changes to flow regimes brought about by climate change could influence the timing of migration for fish or change established routes and spawning grounds. Friedland (2002), for instance, found changes to migration routes, timing and performance in salmon stocks as a response to short- and medium-term climate variation.

The capability of non-migratory species to exploit ephemeral lakes or streams hinges on seasonal connectivity of surface waterbodies. It is hypothesized that under models of decreased precipitation, reduced flow and higher frequency of drought, fragmentation would be expected to increase and seasonal access to ephemeral waterbodies would be hindered by reduced aquatic connectivity. Under models of higher precipitation, increasing flow and flooding, the opposite effect would be expected to result.

IMPACT PREDICTIONS AND CLIMATE CHANGE

This section provides an assessment of the possible cumulative effect of climate change on the specific predictions for the Voyageur South Project that are sensitive to the possible linkages identified for the assessment, including fish habitat, fish abundance and diversity. Two climate change scenarios were developed based on forecasted changes in precipitation and air temperature, recorded data at Fort McMurray station and potential evapotranspiration. The first scenario assumed an increase in mean annual precipitation, the second scenario assumed a decrease in mean annual precipitation.

Due to predictions of a warming climate, both climate change scenarios are predicted to result in higher air temperatures, leading to increases in average water temperatures and increases in winter flows due to warmer winters. Climate Change Scenario 1 is expected to result in small decreases in flows during the open-water period, but a small increase in winter flows as depicted by the minimum seven day average low flow over 10 years (7Q10). The Scenario 2


prediction is for a dryer climate, with larger decreases in open-water flows, as well as a decrease in the 7Q10.

Fish Habitat and Fish Abundance

The predicted effects of the Voyageur South Project were assessed under the predicted effects of the two climate change scenarios. Comparisons are provided for the portions of the two watercourses for which there are predicted effects to fish habitat or fish abundance that are sensitive to climate change: lower Poplar Creek and the Athabasca River.

The fish habitat assessment results predicted a residual effect on habitat in Poplar Creek due to reduced flows in the open-water period. Changes in mean annual flow were predicted to result in about a 70% flow reduction from current conditions at the mouth of Poplar Creek through operations and until closure. However, the mean annual flow is expected to increase upon mine closure (i.e., initial pit lake discharge and far future snapshots) to levels between pre-development and current conditions. Climate change effects on Poplar Creek would be similar to the Athabasca River tributary streams that were used in the sensitivity analysis. As a result, there would be an increase in winter flows in lower Poplar Creek under both climate change scenarios. Increased winter flows could provide increased water depth, increased wetted channel area and increased accessibility for fish, potentially improving the suitability of the habitat for overwintering. Waters in Poplar Creek were predicted to be non-toxic under both climate change scenarios used in water quality modelling.

Cumulative effects during the open-water period under both climate change scenarios may result in an increase in the frequency of periods when habitats in lower Poplar Creek may be less suitable or entirely unusable by fish due to poor access or extremely low flows. However, due to the habitat alterations predicted for lower Poplar Creek, full compensation for this habitat will be provided in the habitat compensation plan associated with the Voyageur South Project (see Fish Habitat Conceptual Compensation Plan, Appendix 18). Project development is assumed to result in the loss of habitats in lower Poplar Creek and full compensation for these habitats is included in the compensation plan. Therefore, as a result of the proposed compensation, there are no predicted residual adverse effects on fish habitat in lower Poplar Creek due to cumulative changes in streamflow under climate change.

The Voyageur South Project is expected to contribute to cumulative changes in Athabasca River flows, due to water diversion activities. Based on observed trends between Athabasca River flows and climate conditions, mean annual flows are estimated to decrease by about 10% due to climate change


(see Section 5 of the Appendix). However, as Suncor has committed to meeting the Water Management Framework established for the Athabasca River (AENV and DFO 2007) throughout its operations, the cumulative effects of the Voyageur South Project, even in consideration of climate change, are not considered to result in residual adverse effects on fish habitat in the Athabasca River.

Fish and Fish Habitat Diversity

Fish and fish habitat diversity can be affected by changes in fish habitat or fish abundance (including effects on fish abundance via changes in aquatic health). The assessment of cumulative effects of the Voyageur South Project and climate change indicated negligible net effects on aquatic health, fish habitat and fish abundance. Therefore, negligible adverse effects on fish and fish habitat diversity are expected due to cumulative changes under climate change.


8 SUMMARY OF THE CONSIDERATIONS OF CLIMATE CHANGE ON TERRESTRIAL RESOURCES

An evaluation of the historic changes in temperature and precipitation as well as the predicted changes in the future was completed for the Voyageur South Project. The possible changes in temperature and precipitation were then considered in the evaluation of effects to the Voyageur South Project for the success of the reclaimed landscape.

The reclaimed landscape for the Voyageur South Project will be planted with typical boreal forest vegetation communities. These vegetation communities are found at various latitudes and elevations throughout the boreal forest and are exposed to a wide range of climatic conditions. An analysis of temperature data from the southern edge of the boreal forest (Bisset, Manitoba) and the northern edge (Yellowknife, NWT) was performed to evaluate whether predicted future temperatures in the Fort McMurray area will be within the range of temperatures currently experienced in the boreal forest region. Climate normal data is taken from the Canadian Climate Normals 1971 to 2000 (Environment Canada 2006).

The average annual temperature in the boreal forest range from 2.1°C in the south (Athabasca) to -4.6°C in the north (Yellowknife) (Environment Canada 2006). The average annual temperature in Fort McMurray is 0.3°C. The predicted future climate trends indicate that the average annual temperature is expected to rise between 1.4 and 4.1°C in the Fort McMurray area over the life of the Voyageur South Project. Based on these predicted trends, annual average temperatures in the Fort McMurray area will be comparable to temperatures currently experienced at the southern edge of the boreal forest.

The minimum monthly temperatures observed in Athabasca and Yellowknife are -19.9 and -30.9°C, respectively (Environment Canada 2006). The minimum monthly temperature in Fort McMurray is -24.0°C, with future climate trends predicting an increase of between 1.3 to 5.5°C in minimum monthly temperatures over the life of the Voyageur South Project. This predicted trend indicates that minimum monthly temperature in the Fort McMurray area will be within the temperature range already experienced within the boreal forest region.

The maximum monthly temperatures observed in Athabasca and Yellowknife are 22.2 and 21.1°C, respectively (Environment Canada 2006). It is interesting to note that the maximum monthly temperature in Fort McMurray (23.2°C) is already warmer than the maximums observed at either the southern limit or the northern limits of the boreal forest. This suggests that maximum monthly


temperatures currently recorded in the boreal forest are more localized phenomena. The future climate trends for maximum monthly temperatures in Fort McMurray are predicted to increase between 1.1 and 5.5°C over the life of the Voyageur South Project (Table 2-20). Although the future monthly maximum temperature for Fort McMurray is predicted to be higher than other boreal forest regions in Alberta or the NWT, it is still within the temperature range experienced by other boreal forest regions in Canada. For example, the monthly maximum temperature at Bisset, Manitoba is 24.9°C.

Beyond the life of the Voyageur South Project, the results of modeling of climatic variables applicable to vegetation growth and the predicted future normals are provided in tables 2-21 to 2-29 in Section 2.4. Upper summer temperature and upper and lower precipitation account for the growing season and moisture availability required for vegetation development. An average summer temperature between 17.4 and 18.5°C is predicted for the Fort McMurray region. Average winter temperatures are expected to rise to between -10.7 and -14.5°C. Annual rainfall is predicted to vary from 480 to 528 mm per year.

Table 8-1 lists the climate ranges for all tree species found in the Regional Study Area (RSA). As a major component of boreal vegetation communities, tree species show the range of climate variation for which boreal species are adapted. The forecasted Fort McMurray normals for between 2040 and 2069 are well within these species ranges of tolerances.

Table 8-1 Boreal Tree Species Ranges of Climatic Tolerance

Tree Species Summer (July) Mean Temp. [oC]

Lowest Mean Temp. [oC]

Highest Mean Temp. [oC]

Mean Annual Precipitation [mm]

Trembling Aspen 16 to 23 -34 to -61 32 to 41 180 to 1,020

Balsam Poplar 12 to 24 -18 to -62 30 to 44 150 to 1,400

Paper Birch 13 to 21 - - 300 to 1,520

Jack Pine 13 to 22 -21 to -46 29 to 38 250 to 1,400

White Spruce 13 to 21 -29 to -54 34 to 43 250 to 1,270

Black Spruce 16 to 24 -34 to -62 27 to 41 380 to 760

Tamarack 13 to 24 -29 to -62 29 to 43 180 to 1,400

Balsam Fir 16 to 18 - - 390 to 1,400

Table adapted from Burns and Honkala (1990). - = No data.


Soil Responses to Climate Change

Soil is a part of the natural world that is both affected by and contributing to global warming. Research indicates that climate change threatens to affect soil in a variety of ways. The primary result of increased air temperatures are subsequent increases in soil temperatures (Golder 2005; Gundersen et al. 2006; Nakawatase and Peterson 2006). Increased winter air temperatures could also affect snowpack depth (Nakawatase and Peterson 2006). Snowpack depth affects soil temperature and both the start and length of the growing season (Körner 1995). Furthermore, a reduced snowpack would reduce soil moisture (Nakawatase and Peterson 2006). This in combination with higher summer temperatures may lead to an increase in summer soil moisture stress for vegetation.

Changes in air temperature are expected to result in chemical, hydrological and biological changes in the soil environment (Golder 2005). Changes to the structure (e.g., horizon development), productivity, nutrient status and quality may be a result of warming soils. A variety of research predicts changes in the rates of soil/litter decomposition and nutrient cycling (Jamieson et al. 1999; Price et al. 1999; Gundersen et al. 2006). Changes in soil decomposition rates/litter decay rates are predicted to increase between 4 to 7% in northern Alberta (Golder 2005). Many researchers have also suggested that increased precipitation would lead to increased leaching of soil nutrients in some soils, especially if temperature is increasing decomposition. Jamieson et al. (1999) predicted short term positive increases in gross nitrogen (N) mineralization and hence nutrient availability. Gundersen et al. (2006) also predicted sustained high mineralization and nitrification rates. Another report found that the response to warming was an increase of 46% in net N mineralization (Rustad et al. 2001). Boreal forest growth is strongly limited by the availability of nitrogen in the soils (Jerabkova et al. 2006). Changes to soil biogeochemistry resulting in increases in N mineralization levels could result in short-term increases in vegetation productivity.

Lastly, greenhouse gases are increasing levels of carbon dioxide (CO2) and N deposition to the soils. While both may act as a fertilizer, N deposition is also speculated to acidify soils and reduce tree growth in some circumstances (Loehle 2003). Soil is one of the largest sources of carbon in the world (Soil-Net 2006, website). It is primarily accumulated through plants that “fix” the carbon from CO2; the soil then directly absorbs the carbon as the plants decay. Gundersen et al. (2006) found that increased atmospheric CO2 initially results in increased storage of C in the upper soil layers and biomass. However, carbon is naturally broken down in the soil and released to the atmosphere as CO2 gas. As the air temperature increases, decomposition occurs more rapidly, which may potentially contribute to global warming (Jamieson et al. 1999; Zhou et al. 2005).


Complex interactions exist among variables such as temperature, moisture, decomposition and nutrient cycling. Thus, medium- to long-term effects of climate change to soil biogeochemistry have been more difficult to predict (Jamieson et al. 1999).

Vegetation Responses to Climate Change

Spatial distribution and species composition of the boreal forests are expected to change with the anticipated change in climate (Jamieson et al. 1999; Loehle 2003; Zhou et al. 2005). Biogeographic models predict widespread species migration (i.e., southern communities migrating northward) (Nakawatase and Peterson 2006). Some research predicts that many important species, particularly northern pines (Pinus spp.) and spruces (Picea spp.) may be extirpated from some areas because of climate change (Walker et al. 2002; Scheller and Mladenoff 2005). Loehle (2003) states that the rate at which a forest can be invaded, even by a much superior competitor, is limited by the rate at which openings become available (i.e., by disturbance). Intact forests are resistant to invasion and their response to moderate climate change should be slow with a prolonged transition on the order of 500 to 3,000 years. It will take hundreds to thousands of years for the forest population to come to a new equilibrium. Reclaimed ecosystems may be less resistant to invasion than established ecosystems.

Recent observations have strengthened the concept that species respond individually to climate change and not as a cohesive unit (Brooks et al. 1998; Loehle 2003; Nakawatase and Peterson 2006). Qinfeng et al. (2004) report that growth trajectories and responses of species under the same climate regimes were clearly highly individualistic, and even the same species performed differently under different climate conditions or when planted with different species. Because forest growth responds differently to climactic variability in different environments, management of forest ecosystems will need to consider growth response at local to watershed scales (Nakawatase and Peterson 2006).

Research indicates that the southern boundary of the central Canadian boreal forest is controlled by water limitations and fire frequency, while the northern boundary is controlled by temperature limitations (Brooks et al. 1998). As temperature and precipitation are two of the dominant controlling factors in the central Canadian boreal forest boundary, they are two of the most important factors to look at when considering vegetation response to climate change in the boreal forest. Temperature affects many processes in plants including photosynthesis, respiration and growth, as well as the flux of pollutants to the plant (Brooks et al. 1998). Warm and dry summer conditions increase respiration rates, reduce photosynthetic production, reduce leaf area and reduce energy reserves (Nakawatase and Peterson 2006). Furthermore, in areas that become


drier, fire return intervals are expected to become shorter and fire intensities are expected to increase (Golder 2005; Nakawatase and Peterson 2006). Warmer spring temperatures lengthen the growing season by accelerating snowmelt. Theurillat and Guisan (2001) concludes that since the early 1960s the average annual growing season in a European study area has lengthened 11 days, and is the result of an increase in mean annual air temperature.

Precipitation also has many effects on vegetation, with the most prominent being on soil properties including moisture and temperature (Brooks et al. 1998). An important factor regarding changes in climate is that seasonal distribution of increased precipitation and temperature are usually more important than annual amounts (Brooks et al. 1998). Bell and Threshow (2002) also indicates that changes in the climate could also lead to changes in the development pattern of species, thus affecting inter-specific and dependent relationships within natural communities.

Potential responses to climate change include persistence in the modified climate, migration to more suitable climates, or extinction. Potential persistence outcomes include gradual genetic adaptation of populations, phenotypic plasticity (individual variations in properties produced by given genotypes in conjunction with the environment), or ecological buffering (edaphic climax as opposed to climatic climax) (Theurillat and Guisan 2001). Evidence gleaned from past climate change has indicated that species are more likely to respond by migration as opposed to adapting genetically. Thus, increased temperature could result in migration of species to traditionally cooler areas, including migration further north and higher in elevation (Theurillat and Guisan 2001).

It is important to note that disturbance plays an important role in a community’s response to climate change. Active competition among trees is largely confined to the seedling and sapling stage, with the duration of canopy occupancy also playing a competitive role (Loehle 2003). Forest invasion is limited by open spaces that are created via disturbance. It has been found that increased disturbance speeds up competitive displacement and clearly speeds up the invasion process. Disturbance may accelerate the shift toward more southern species, although the effect is variable across the landscape (Scheller and Mladenoff 2005).

Regardless of which response vegetation has to climate change, it is important to note that each species will adapt based on their most limiting factors, thus complete communities may not respond the same way, or at all, to changes in the climate.


9 EFFECTS OF CLIMATE CHANGE ON THE HUMAN AND WILDLIFE HEALTH RISK ASSESSMENT

Recent literature sources summarize the potential linkages between climate change and effects on human and wildlife health (Frumkin 2005; Suter 2007; Peterson and Johnson 1995; IPCC 2001). This literature review is not meant to be exhaustive, but it does highlight the key human health concerns related to climate change and provides a basis for interpretation of climate change predictions associated with the Voyageur South Project.

Potential direct effects on human and wildlife health include:

• temperature changes and heat waves that contribute directly to health effects and mortality; and

• ultraviolet light that can contribute to long-term human health effects.

Potential indirect effects on human health include:

• air pollution;

• communicable and vector borne diseases;

• changes in species diversity and habitat; and

• flooding or drought.

There is uncertainty in the relationship between climate change and potential health effects. Much of the research has been focused on infectious disease transmission and the relationship between daily weather and mortality. Studies of long-term trends in health impact as a result of climate are limited. Some of the key challenges include (IPCC 2001):

• isolating the effects of climate change from the many other factors that affect human health (i.e., determining causality);

• recognizing variations in vulnerability of different human populations and the interactions between climate change effects and vulnerability variables;

• characterizing the uncertainty in the models used for estimating potential health impacts;


• understanding the interaction of climate change variables with other large-scale environmental changes (e.g., forest clearance, human population density, changing land use patterns, population movement);

• estimating exposure-response relationships for climate variables; and

• understanding the capacity of human populations to adapt to new climate conditions, either through physical adaptations in individuals or adaptation of public health systems.

Temperature Change

The increased ambient temperatures as a result of climate change may have direct effects on human health due to increases in the frequency and intensity of heat waves, as well as warmer summers and milder winters (IPCC 2001). Populations in warm regions are typically sensitive to low temperatures and populations in cooler regions are typically sensitive to heat (Patz and Kovats 2002). Predicted changes in temperatures for the Oil Sands Region, although higher in general, are not such that high ambient temperatures are expected to have direct health impacts.

Air Pollution

Climate change can influence the transport and/or formation of various airborne chemicals. Climate change may increase the concentration of ground-level ozone, but the magnitude is uncertain (Patz et al. 2001). However, there are few studies on the effects of climate change on other airborne chemicals and results are often conflicting (IPCC 2001).

Changes in Species Diversity and Habitat

Climate change is likely to affect habitat first (soil, water, nutrients), which subsequently can result in flora and fauna effects. If, for example, tree species shift (Bolliger et al. 2000) the landscape level ecosystem changes, which could alter wildlife food chains and may affect access to traditional game and vegetation.

Flood and Drought

Changing habitat and population displacement can occur through flood, drought and fire (fire becoming increasingly more frequent with drought). Whether or not annual temperature changes or average precipitation changes result in measurable differences to a regional landscape, extreme weather events brought about by climate change could result in displacement of valued species through habitat loss, increased stress on ecosystems and increased physical stress on populations (human and wildlife) (Patz 2005).


9.1

9.2

While any of these parameters could affect the Oil Sands Region, it is unlikely that such changing parameters on the Voyageur South Project would directly result in significant differences in the health risk assessment for the Voyageur South Project. This is described in the following sections.

AIR QUALITY PREDICTIONS

The effects of climate change on air quality predictions are evaluated in Section 3 of this Appendix. The assessment indicates that the EIA process and model predictions for air emissions based on 1995 metrological data represent conservative estimates of current and future conditions. Therefore, the health assessment results for the air quality pathway, as presented in the Voyageur South Project EIA, should apply even under the considered climate change scenarios.

WATER QUALITY PREDICTIONS

The effects of climate change on water quality predictions were evaluated in Section 6 of this Appendix. Two climate change scenarios were assessed for total dissolved solids, naphthenic acids, molybdenum, acute toxicity (aquatic) and chronic toxicity (aquatic) at the mouth of the Poplar Creek in the far future. These scenarios differed from the EIA assessment as follows:



The results of the evaluation of these water quality surrogates indicated that predicted 90th percentile concentrations of all parameters evaluated under the climate change scenario were equal to not significantly different than predicted 90th percentile concentrations evaluated in the health risk assessment (e.g., naphthenic acid 90th percentile: EIA = 3.3 mg/L; scenario 1 = 3.5mg/L; scenario 2 = 3.7 mg/L; and molybdenum 90th percentile: EIA = 0.19 mg/L; scenario 1 = 0.19 mg/L; scenario 2 = 0.19 mg/L). Based on these surrogates for water quality, the multimedia risk assessment results, as presented in Volume 3, Section 4.2.4 and 4.3.4, would apply even under these two climate change scenarios.


10.1

10 GLOSSARY AND ABBREVIATIONS

GLOSSARY

Acute A stimulus severe enough to rapidly induce an effect; in aquatic toxicity tests, an effect observed in 96 hours or less is typically considered acute. When referring to aquatic toxicology or human health, an acute effect is not always measured in terms of lethality.

Anoxia Little to no dissolved oxygen in the water sample. Waters with <2 mg/L of dissolved oxygen experience anoxia.

Anthropogenic Pertaining to the influence of human activities.

Baseline A surveyed or predicted condition that serves as a reference point to which later surveys are coordinated or correlated.

Chronic The development of adverse effects after extended exposure to a given substance. In chronic toxicity tests, the measurement of a chronic effect can be reduced growth, reduced reproduction or other non-lethal effects, in addition to lethality. Chronic should be considered a relative term depending on the life span of the organism.

Dissolved Oxygen (DO)

Measurement of the concentration of dissolved (gaseous) oxygen in the water, usually expressed in milligrams per litre (mg/L).

Evapotranspiration A measure of the ability of the atmosphere to remove water from a location through the processes of evaporation and water loss from plants (transpiration).

Groundwater That part of the subsurface water that occurs beneath the water table, in soils and geologic formations that are fully saturated.

Hydrogeology The study of the factors that deal with subsurface water (groundwater) and the related geologic aspects of surface water. Groundwater as used here includes all water in the zone of saturation beneath the earth’s surface, except water chemically combined in minerals.

Hydrology The science of waters of the earth, their occurrence, distribution, and circulation; their physical and chemical properties; and their reaction with the environment, including living beings.


Littoral Zone The zone in a lake that is closest to the shore. It includes the part of the lake bottom, and its overlying water, between the highest water level and the depth where there is enough light (about 1% of the surface light) for rooted aquatic plants and algae to colonize the bottom sediments.

Naphthenic Acids Generic name used for all the organic acids present in crude oils.

Oil Sands Region The Oil Sands Region includes the Fort McMurray – Athabasca Oil Sands Subregional Integrated Resource Plan (IRP), the Lakeland Subregional IRP and the Cold Lake – Beaver River subregional IRP.

Ozone (O3) Ozone is a gas that occurs both in the Earth's upper atmosphere and at ground level. Ozone in the upper atmosphere protects living organisms by preventing damaging ultraviolet light from reaching the Earth’s surface. Ground-level ozone is an air pollutant with harmful effects on the respiratory systems of animals.

Potential Acid Input (PAI) A composite measure of acidification determined from the relative quantities of deposition from background and industrial emissions of sulphur, nitrogen and base cations.

Total Dissolved Solids (TDS)

The total concentration of all dissolved compounds solids found in a water sample. See filterable residue.

Toxicity The inherent potential or capacity of a material to cause adverse effects in a living organism.

Wetlands Wetlands are land where the water table is at, near or above the surface or which is saturated for a long enough period to promote such features as wet-altered soils and water tolerant vegetation. Wetlands include organic wetlands or “peatlands,” and mineral wetlands or mineral soil areas that are influenced by excess water but produce little or no peat.


10.2 ABBREVIATIONS

°C Temperature in degrees Celsius % Percent < Less than > More than 7Q10 Lowest 7-day consecutive flow that occurs, on average, once every 10

years AENV Alberta Environment AET Actual Evapotranspiration ASA Aquatic Study Area CCC Canadian Climate Centre CCSR/NIES Centre for Climate System Research/National Institute for Environmental

Studies CEMA Cumulative Environmental Management Association CGCMI Canadian Global Coupled Model CGCM2 Canadian Global Coupled Model (Version 2) CHTD Canadian Historical Temperature Database CICS Canadian Institute for Climate Studies CO2 Carbon dioxide CSIRO MK2 Commonwealth Scientific and Industrial Research Organization Mark 2 DO Dissolved Oxygen ECHAM4/OPYC3 Max Planck Institute for Meteorology/Deutsches Kilmarechenzentrum e.g. For example EIA Environmental Impact Assessment et al. Group of authors FPTCCEA Federal-Provincial-Territorial Committee on Climate Change and

Environmental Assessment GCM General Circulation Model GFDL R30 Geophysical Fluid Dynamics Laboratory GHG Greenhouse gas HadCM3 Hadley Centre Coupled Model HSPF Hydrological Simulation Program-Fortran i.e. That is INAC Indian and Northern Affairs Canada IPCC Intergovernmental Panel on Climate Change km2 Square kilometre km/h Kilometres per hour L/s Litres per second


mm Millimetre m3/s Cubic metres per second N Nitrogen NCAR-PCM National Centre for Atmospheric Research Parallel Climate Model PAI Potential Acid Input PE Potential Evaporation PET Potential Evapotranspiration RHBN Reference Hydrometric Basin Network RSA Regional Study Area spp. Multiple Species SRES Special Report on Emissions Scenarios TDS Total Dissolved Solids TOR Terms of Reference VPD Vapour Pressure Deficit WBEA Wood Buffalo Environmental Assocation


11.1

11 REFERENCES

LITERATURE CITED

AENV (Alberta Environment). 2007. Final Terms of Reference for Environmental Impact Assessment Report for the Suncor Voyageur South Project. Issued by Alberta Environment. Edmonton, AB.

AENV and DFO (Alberta Environment and Fisheries and Oceans Canada). 2007. Water Management Framework: Instream Flow Needs and Water Management System for the Lower Athabasca River. Edmonton, AB. 37 pp.

Akinremi, O.O., S.M. McGinn and A.G. Barr. 1996. Simulation of soil moisture and components of the hydrological cycle using a water budget approach. Canadian Journal of Soil Science 76:133-142.

Arnell N., B. Bates, H. Lang, J.J. Magnuson and P. Mulholland. 1996. Hydrology and Freshwater ecology, Climate Change 1995. Impacts, Adaptations and Mitigation of Climate Change: Scientific-Technical Analysis. Edited by R.T. Watson, M.C. Zinyowera and R.H. Moss. Cambridge Univ. Press, New York. 325-364 pp.

Barnett, T.P., J.C. Adams and D.P. Letternmaier. 2005. Potential Impacts of a Warming Climate on Water Availability in Snow-dominated Regions. Nature Vol 438 doi:101038/nature04141.

Bell, J. and M. Threshow 2002. Air Pollution and Plant Life. Second Edition. John Wiley & Sons Ltd. West Sussex, England.

Beltaos, S. 2000. Advances in River Ice Hydrology. Hydrological Processes. 14: 1,613-1,625.

Bloomfield, P. 1992. Trends in Global Temperatures, Climate Change. 21: 1-16.

Boesch, D.F., R.B. Brinsfield and R.E. Magnien 2001. Chesapeake Bay Eutrophication: Scientific Understanding, Ecosystem Restoration, and Challenges for Agriculture. Journal of Environmental Quality. 30:303-320.


Bolliger, J., F. Kienast and N.E. Zimmerman. 2000. Risks of global warming on montane and subalpine forests in Switzerland – a modeling study. Reg. Environmental Change. 1:99-111.

Boorman, D.B. 2003. Climate, Hydrochemistry and Economics of Surface-water Systems (CHESS): Adding a European Dimension to the Catchment Modelling Experience Developed Under LOIS. The Science of the Total Environment. 314-316: 411-437.

Brooks, J., L. Flanagan and J. Ehleringer 1998. Responses of Boreal Conifers to Climate Fluctuations: Indicators from Tree-ring Widths and Carbon Isotope Analysis. Canadian Journal of Forest Research. 28:524-533.

Brown, R.D. and B.E. Goodison.1996. Interannual Variability in Reconstructed Canadian Snow Cover 1915-1992. J. Clim. 9: 1299-1318 pp.

Brown, R.D. and R.O. Braaten. 1998. Spatial and Temporal Variability of Canadian Monthly Snow Depths, 1946-1995. Atmosphere-Ocean. 36 (1): 37–54.

Brutsaert, W. and M. Parlange. 1998. Hydrologic Cycle Explains the Evaporation Paradox. Nature. 396:30.

Burn, D. 1994. Hydrologic Effects of Climate Change in West-Central Canada. Journal of Hydrology. 160: 53-70.

Burn, C.R. 2003. Climate Change Scenarios for the Mackenzie Gas Project. Internal paper, Indian and Northern Affairs Canada. 11 pp.

Burn, D.H. and N.M. Hesch. 2006. A Comparison of Trends in Potential and Pan Evaporation for the Canadian Prairies. Canadian Water Resources Journal. 31(3): 173-184.

Burns, R. M. and B. H. Honkala 1990. 1: Silvics of North America; 2: Conifers. In Hardwoods. Agriculture Handbook 654. U.S. Department of Agriculture, Forest Service. Washington DC. 887 pp.

Casselman, J.M. 2002. Effects of Temperature, Global Extremes, and Climate Change on Year-Class Production of Warmwater, Coolwater, and Coldwater Fishes in the Great Lakes Basin. Pages xiii-xix in Fisheries in a Changing Climate, American Fisheries Society Symposium 32. Maryland, Bethesda.


CEMA. 2006. Ozone Management Framework for the Regional Municipality of Wood Buffalo Area. April 2006.

Chaikowsky, C.L.A. 2000. Analysis of Alberta Temperature Observations and Estimates by Global Climate Models. Report for Science and Technology Branch, Environmental Sciences Division, Alberta Environment. Pub. No: T/562. October 2000.

Chalecki, E.L. and P.H. Gleick 1999. A Framework of Ordered Climate Effects on Water Resources: A Comprehensive Bibliography. Journal of the American Water Resources Association. 35: 1657-1665.

Cohen, S.J. 1991. Possible Impacts of Climate Warming Scenarios on Water Resources in the Saskatchewan River Sub-basin, Canada. Climate Change. 19: 291-317.

Cohen, S.J. 1995. The Potential Effects of Climate Change in the Peace, Athabasca and Slave River Basins: A Discussion Paper. Northern River Basins Study. Project Report No. 65. Edmonton, AB.

Cohen, S.J. 1997. Mackenzie Basin Impact Study – Final Report – Summary of the Results. Issued under the authority of the Minister of Environment, Minister of Supply and Services. Catalogue No. En 50-118/1997-1E. Ottawa, ON.

Cruise, J.F., A.S. Limaye and N. Al-Abed 1999. Assessment of Impacts of Climate Change on Water Quality in the Southeastern United States. Journal of the American Water Resources Association. 35:1,539-1,550.

Environment Canada. 2006. Canada's fourth national report on climate change - actions to meet commitments under the United Nations Framework Convention on Climate Change.

Fang, X. and H.G. Stefan 1997. Simulated Climate Change Effects on Dissolved Oxygen Characteristics in Ice-covered Lakes. Ecological Modelling. 103: 209-229.

Fang, X. and H.G. Stefan 2000. Projected Climate Change Effects on Winterkill in Shallow Lakes in the Northern United States. Environmental Management. 25: 291-304.


Fang, X., H. G. Stefan and S. R. Alam 1999. Simulation and Validation of Fish Thermal DO Habitat in North-central US Lakes Under Different Climate Scenarios. Ecological Modelling. 118: 167-191.

Foster, J.L. 1989. The Significance of the Date of Snow Disappearance on the Arctic Tundra as a Possible Indicator of Climate Change. Arctic Alpine Res. 21(1): 60-70.

FPTCCCEA (Federal-Provincial-Territorial Committee on Climate Change and Environmental Assessment). 2003. Incorporating Climate Change Considerations in Environmental Assessment: General Guidance for Practitioners. November 2003. 48 pp.

Frich, P., L.V. Alexander, P. Della-Marta, B. Gleason, M. Haylock, A. Klein-Tank and T. Peterson. 2001. Observed Coherent Changes in Climatic Extremes During the Second Half of the 20th Century. Climate Research. 19: 193-212.

Friedland, K.D. 2002. Forcasts of Atlantic Salmon Transoceanic Migration: Climate Change Scenarios and Anadromy in the North Atlantic. Abstract presented at Fisheries in a Changing Climate, American Fisheries Society Symposium 32. Maryland, Bethesda.

Frumkin, H. ed. 2005. Environmental Health: From Global to Local. Jossey-Bass (John Wiley & Sons), San Francisco, CA. 1108 pp.

Gan, T.Y. 1995. Trends in Air Temperature and Precipitation for Canada and Northeastern United States, Int. J. Climatol., Royal Metearological Society 15: 1115-1134.

Gan, T.Y. 1998. Hydroclimatic trends and possible climatic warming in the Canadian Prairies, Water Resources Research, Vol. 34, No. 11: 3009-3015.

Georgievskii, V.Y., A.V. Ezhov, A.L. Shalygin and A.I. Shiklomanov. 1996. Assessment of the Effect of Possible Climate Changes on Hydrological Regime and Water Resources of Rivers in the Former USSR, Russ, Meteorol. Hydrol., 11: 66-74.

Godwin, R.B., 1986. Drought: A Surface Water Perspective, in Drought: The impending Crisis? Proceedings of the 16th Canadian Hydrology Symposium, Natl. Res. Counc., Ottawa, ON, 27-43 pp


Golder (Golder Associates Ltd.). 2005. Monitoring Climate and Climate Change Impacts in the National Parks and Northern Bioregion. Final Report. Calgary, AB. pp 3-11.

Golubev V.S., J.H. Lawrimore, P.A. Groisman, N.A. Speranskaya, S.A. Zhuravin, M.J. Menne, T.C. Peterson and R.W. Malone. 2001. Evaporation Changes over the Contiguous United States and Former USSR: A Reassessment. Geophysical Research Letters. 28(13): 2665-2668.

Gregory, J.M., J.F.B Mitchell and A. J. Brady. 1997. Summer Drought in Northern Mid-Latitudes in a Time-dependent CO2 Climate Experiment. Journal of Climate. 10: 2453-66.

Gundersen, P., Schmidt, I.K. and K. Raulund-Rasmussen. 2006. Leaching of Nitrate from Temperate Forests — Effects of Air Pollution and Forest Management. Environmental Reviews 14: 1–57.

Hengeveld, H. 1991. Understanding Atmospheric Change. A Survey of the Background Science and Implications of Climate Change and Ozone Depletion, SOE Rep. 91-2, Atmos. Environ. Serv., Environ. Can., Downsview, ON.

Hennessy, K.J., J.M. Gregory and J.F.B Mitchell. 1997. Changes in Daily Precipitation Under Enhanced Greenhouse Conditions. Climate Dynamics. 13: 667-80.

Hobbins, M.T., J.A. Ramirez and T.C. Brown. 2004. Trends in pan evaporation and actual evapotranspiration across the conterminous U.S.: Paradoxical or complementary? Geophysical Research Letters. Vol. 32, L13503. doi:10.1029/2004GL019846.

Hogg, W.D. and D.A. Carr. 1985. Rainfall Frequency Atlas for Canada. Canadian Government Publishing Centre. Ottawa, ON.

Hostetler, S. and E. Small 1999. Response of North American Freshwater Lakes to Simulated Future Climates. Journal of the American Water Works Association. 35: 1,625-1,638.


Hutjes, R.W.A., P. Kabat, S.W. Running, W.J. Shuttleworth, C. Field, B. Bass, M.A.F. da Silva Dias, R. Avissar, A. Becker, M. Claussen, A.J. Dolman, R.A. Feddes, M. Fosberg, Y. Fukushima, J.H.C. Gash, L. Guenni, H. Hoff, P.G. Jarvis, I. Kayane, A.N. Krenke, C. Liu, M. Meybeck, C.A. Nobre, L. Oyeband, A. Pitman, R.A Pielke, M Raupach, B Saugier, E.D Schulze, P.J Sellers, J.D. Tenhunen, R. Valentini, R. Victoria and C.J. Vorosmarty 1998. Biospheric Aspect of the Hydrological Cycle. Journal of Hydrology. 212-213:1-21.

Huntington T.G. 2006. Evidence for Intensification of the Global Water Cycle: Review and Synthesis. Journal of Hydrology. Vol. 319. 83-95 pp.

Imperial Oil (Imperial Oil Resources Ventures Limited). 2005. Kearl Oil Sands Project - Mine Development. Volume 1 to 9. Submitted to Alberta Energy and Utilities Board and Alberta Environment. Prepared by Imperial Oil Resources Ventures Limited in association with Golder Associates Ltd., AXYS Environmental Consulting Ltd., Komex International Inc. and Nichols Applied Management. Calgary, AB. Submitted July, 2005.

Interlandi, S.J. and C.S. Crockett 2003. Recent Water Quality Trends in the Schuylkill River, Pennsylvania, USA: A Preliminary Assessment of the Relative Influences of Climate, River Discharge and Suburban Development. Water Research. 37: 1,737-1,748.

IPCC (Intergovernmental Panel on Climate Change). 2000. Special Report on Emissions Scenarios. N. Nakicenovic and R. Swart (ed.). Cambridge University Press. Cambridge, United Kingdom. 612 pp.

IPCC. 2001. Climate Change 2001: The Scientific Basis. J.T. Houghton, Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. Maskell, C.A. Johnson (eds.). Cambridge University Press, New York.

Jackson, D.A. and N.E. Mandrak. 2002. Changing Fish Biodiversity: Predicting the Loss of Cyprinid Biodiversity Due to Global Climate Change. Pages 89-98 in N.A. McGinn (editor). Fisheries in a Changing Climate, American Fisheries Society Symposium 32. Maryland, Bethesda.

Jamieson, N., R. Monoghan and D. Barraclough. 1999. Seasonal Trends of Gross N Mineralization in a Natural Calcareous Grassland. Global Change Biology 5: 423-431.


Jansen, W. and R.H. Hesslein. 2004. Potential Effects of Climate Warming on Fish Habitats in Temperate Zone Lakes with Special Reference to Lake 239 of the Experimental Lakes Area (ELA), North-Western Ontario. Environmental Biology of Fishes. 70:1-22.

Jerabkova, L., C. Prescott and B. Kiskchuck. 2006. Nitrogen Availability in Soil and Forest Floor of Contrasting Types of Boreal Mixedwood Forests. Canadian Journal of Forest Research 36 (1): 112-122.

Jones, P.D., T.J. Osborn, K.R. Briffa, C.K. Folland, E.B. Horton, L.V. Alexander, D.E. Parker and N.A. Rayner. 2001. Adjusting for Sampling Density in Grid Box Land and Ocean Surface Temperature Time Series. J. Geophys. Res. 106: 3371-3380.

Karl, T.R., P.Ya. Groisman, R.W. Knight and R.R. Heim Jr. 1993. Recent Variations of Snow Cover and Snowfall in North America and Their Relation to Precipitation and Temperature Variations. J. Clim. 6: 1327-1344.

Körner, C. 1995. Arctic and Alpine Biodiversity: Patterns, Causes, and Ecosystem Consequences. Springer-Verlag, Berlin, Germany.

Lawrimore, J.H. and T.C. Peterson. 2000. Pan Evaporation Trends in Dry and Humid Regions of the United States. Journal of Hydrometeorology. 1(6):543-546.

Loehle, C. 2003. Competitive Displacement of Trees in Response to Environmental Change or Introduction of Exotics. Environmental Management 32 (1): 106-115.

Magnuson, J.J., K.E. Webster, R.A. Assel, C.J. Bowser, P.J. Dillon, J.G. Eaton, H.E. Evans, E.J. Fee, R.I. Hall, L.R. Mortsch, D.W. Schindler and F.H. Quinn 1997. Potential Effects of Climate Changes on Aquatic Systems: Laurentian Great Lakes and Precambrian Shield Region. Hydrological Processes. 11: 825-871.

Mann, M.E., R.S. Bradley and M.K. Hughes. 1998. Global-Scale Temperature Patterns and Climate Forcing Over the Past Six Centuries, Nature. 392. 779-787.

http://www.springerlink.com/content/102877/

http://www.ingentaconnect.com/content/nrc/cjfr


Martin, F.R.J. 2002. Gross Evaporation for the 30-Year Period 1971-2000 in the Canadian Prairies. Hydrology Report #143. Agriculture and Agri-Food Canada, Prairie Farm Rehabilitation Administration. Regina, SK.

Maxwell, B., N. Mayer and R. Street. 1997. The Canada Country Study: Climate Impacts and Adaptation – National Summary for Policy Makers. Environment Canada. Catalogue No. En 56-119/8-1997-1. Ottawa, ON.

McIntyre, S. and R. McKitrick. 2003. Corrections to the Mann et al. (1998) Proxy Data Base and Northern Hemispheric Average Temperature Series. Energy and Environment, 14: (6).

Mekis, E and W.D. Hogg. 1999. Rehabilitation and Analysis of Canadian Daily Precipitation Time Series. Atmosphere-Ocean 37 (1): 53–85.

Minns, C.K. and J.E. Moore. 1995. Factors Limiting the Distribution of Ontario’s Fresh Water Fishes: The Role of Climate and Other Variables, and the Potential Impacts of Climate Change. Pages 137-160 in R.J. Beamish (editor). Climate Change and Northern Fish Populations. Canadian Special Publication Fisheries and Aquatic Sciences 121.

Moore, M.V., M.L. Pace, J.R. Mather, P.S. Murdoch, R.W. Howarth, C.L. Folt, C.Y. Chen, H.F. Hemond, P.A. Flebbe, and C.T. Driscoll. 1997. Potential effects of climate change on freshwater ecosystems of the New England/Mid-Atlantic region. Hydrologic Processes. 11: 925-947.

Moran, J.M. and M.D. Morgan. 1997. Meteorology: The Atmosphere and the Science of Weather, 5th ed. Prentice-Hall, Inc., Upper Saddle River, New Jersey. 530 pp.

Murchie, K. and M. Power. 2005. Influence of Temperature on Young-of-the Year Yellow Perch Growth. Department of Biology, University of Waterloo. Waterloo, ON.

Murdoch, P.S., J.S. Baron and T.L. Miller 2000. Potential Effects of Climate Change on Surface-Water Quality in North America. Journal of the American Water Resources Association. 36: 347-366.

Myeni, R.B., C.D. Keeling, C.J. Tucker, G. Aarar and R.R. Neuman. 1997. Increased Plant Growth in Northern High Latitudes From 1981 to 1991. Nature. 386: 698-701.


Nakawatase, J. and D. Peterson. 2006. Spatial Variability in Forest Growth – Climate Relationships in the Olympic Mountains, Washington. Canadian Journal of Forestry Research. 36: 77–91.

Nicholls, N., G.V. Gruza, J. Jouzel, T.R. Karl, L.A. Ogallon and D.E. Parker. 1996. Observed Climate Variability and Change, in Climate Change 1995, The Science of Climate Change, edited by J.T. Houghton. Cambridge Univ. Press, New York. 132-192 pp.

Ozaki, N., T. Fukushima, H. Harasawa, T. Kojiri, K. Kawashima and M. Ono. 2003. Statistical Analyses on the Effects of Air Temperature Fluctuations on River Water Qualities. Hydrological Processes. 17: 2,837-2,853.

Price, D.T., C.H. Peng,, M.J. Apps and D.H. Halliwell. 1999. Simulating Effects of Climate Change on Boreal Ecosystem Carbon Pools in Central Canada. Journal of Biogeography. 26: 1237-1248.

Patz, J.A., M.A. McGeehin and S.M. Bernard. 2001. The Potential Health Impacts of Climate Variability and Change for the United States. Journal of Environmental Health. 64(2): 20-28.

Patz, J.A. and R.S. Kovats. 2002. Hotspots in Climate Change and Human Health. British Medical Journal. 325:1094-1098.

Patz, J.A. 2005. Climate Change, Chapter 11 in: Frumkin, H. ed. 2005. Environmental Health: From Global to Local. Jossey-Bass (John Wiley & Sons). San Francisco, CA. 238-266 pp.

Peterson, D.L. and D.R. Johnson, ed. 1995. Human Ecology and Climate Change: People and Resources in the Far North. Taylor & Francis. Washington, DC. 37 pp.

Peterson, T.C., V.S. Golubev, P.Ya. Grolsman. 1995. Evaporation losing its strength. Nature. 377: 687-688.

Prowse, T.D. and S. Beltaos 2002. Climatic Control of River-ice Hydrology: A Review. Hydrological Processes. 16: 805-822.

Qinfeng, G., J. Brandle, M. Schoeneberger and D. Buettner. 2004. Simulating the Dynamics of Linear Forests in Great Plains Agroecosystems Under


Changing Climates. Canadian Journal of Forestry Research. 34: 2564–2572.

Ramirez, J.A., M.T. Hobbins and T.C. Brown. 2005. Observational evidence of the complementary relationship in regional evaporation lends strong support for Bouchet’s hypothesis. Geophysical Research Letter Vol. 32, L15401. doi:10.1029/2005GL023549.

Ramstack, J.M., S.C. Fritz and D.R. Engstrom. 2004. Twentieth Century Water Quality Trends in Minnesota Lakes Compared with Presettlement Variability. Canadian Journal of Fisheries and Aquatic Science. 61: 561-576.

Reist, J.D. 1994. An Overview of the Possible Effects of Climate Change on Northern Freshwater and Anadromous Fishes. In S. J. Cohen (ed.). Mackenzie Basin Impact Study Interim Report #2. Environment Canada. Ottawa, ON.

Rind, D., C. Rosenzweig and M. Stieglitz 1997. The Role of Moisture Transport between Ground and Atmosphere in Global Change. Ann. Rev. Energy Environ. 22: 47-74.

Robinson, D.A., F.T. Keimig and K.F. Dewey. 1991. Recent Variations in Northern Hemisphere Snow Cover. In: Proc. 15th Ann. Climate Diagnostics Workshop. Asheville, NC. 219-224 pp.

Roderick M.L. and G.D. Farquhar. 2002. The Cause of Decreased Pan Evaporation over the Past 50 Years. Science. 298:1410-1411.

Rustad, L.E., J.L. Campbell, G.M. Marion, R.J. Norby, M.J. Mitchell, A.E. Hartley, J.H.C. Cornelissen, J. Gurevitch and GCTE-NEWS. 2001. A Meta-Analysis of the Response of Soil Respiration, Net Nitrogen Mineralization, and Aboveground Plant Growth to Experimental Ecosystem Warming. Oecologia. 126: 543–562.

Sanford, S. 2002. Recharge and groundwater models: an overview. Hydrogeology Journal. 10:110-120.

Scheller, R.M. and D.J. Mladenoff. 2005. A Spatially Interactive Simulation of Climate Change, Harvesting, Wind, and Tree Species Migration and


Projected Changes to Forest Composition and Biomass in Northern Wisconsin, USA. Global Change Biology 11: 307–321.

Schindler, D.W. 1997. Widespread Effects of Climatic Warming on Freshwater Ecosystems in North America. Hydrological Processes. 11:1042-1067.

Schindler, D.W. 2001. The Cumulative Effects of Climate Warming and Other Human Stresses on Canadian Freshwaters in the New Millennium. Canadian Journal of Fisheries and Aquatic Science. 58: 18-29.

Schlesinger, D.A. and H.A. Regier. 1982. Climate and Morphoedaphic Indices of Fish Yields from Natural Lakes. Transactions, American Fisheries Society 111: 141-150.

Schlesinger, M.E. and J.F.B. Mitchell. 1985. Model Kearl Projection of the Equilibrium Climatic Response to Increased Carbon Dioxide in Kearl Projecting the Climate Effects of Increasing Carbon Dioxide, Rep. DOE/ER-0237, edited by M.C. McCracken and F.M. Luther, U.S. Dep. of Energy. Washington, D.C.

Shell (Shell Canada Limited). 2005. Muskeg River Mine Expansion Project Application and Environmental Impact Assessment. Submitted to Alberta Energy and Utilities Board and Alberta Environment. Prepared by Golder Associates Ltd. and Nichols Applied Management. Volume 1,2, 3 and 4. Fort McMurray, AB. Submitted April, 2005.

Shen, S.S. 1999. An Assessment of the Change in Temperature and Precipitation in Alberta. Report prepared for Science and Technology Branch, Environmental Sciences Division, Alberta Environment.

Shuter, J.B., R.F. Carline and N.A. McGinn. 2002a. An Overview of the Symposium. Pages xiii-xix in N A. McGinn editor. Fisheries in a Changing Climate, American Fisheries Society Symposium 32. Maryland, Bethesda.

Shuter, J.B., C.K. Minns and N. Lester. 2002b. Climate Change, Freshwater Fish, and Fisheries: Case Studies from Ontario and Their Use in Assessing Potential Impacts. Pages 77-88 in N.A. McGinn editor. Fisheries in a Changing Climate, American Fisheries Society Symposium 32. Maryland, Bethesda.


Stefan, H.G., M. Hondzo and X. Fang 1993. Lake Water Quality Modeling for Projected Future Climate Scenarios. Journal of Environmental Quality. 22: 417-431.

Stuart, R.A., D.A. Etkin and A.S. Judge. 1991. Recent Observations of Air Temperature and Snow Depth in the Mackenzie Valley Area and Their Implications on the Stability of Permafrost Layers. Canadian Climate Centre Report 91-2, Atmospheric Environment Service, Downsview, ON. 178 pp.

Struyf, E., S. Van Damme and P. Meire 2004. Possible Effects of Climate Change on Estuarine Nutrient Fluxes: A Case Study in the Highly Nutrified Schelde Estuary (Belgium, The Netherlands). Estuarine, Coastal and Shelf Science. 60: 649-661.

Suncor (Suncor Energy Inc.). 2005. Voyageur Project Application and Environmental Impact Assessment. Submitted to Alberta Energy and Utilities Board and Alberta Environment. Volumes 1A, 1B, 2, 3, 4, 5 and 6. Fort McMurray, AB. Submitted March, 2005.

Suter, G.W. 2007. Ecological Risk Assessment, 2nd Edition. CRC Press, Taylor & Francis Group. Boca Raton, FL. 643 pp.

Theurillat, J.P. and A. Guisan. 2001. Potential Impact of Climate Change on Vegetation in the European Alps: A Review. Climatic Change 50: 77-109. Kluwer Academic Publishers. Netherlands.

Thomann, R.V. and J.A. Mueller 1987. Principles of Surface Water Quality Modeling and Control. Harper Collins Publisher. New York, NY.

Thornthwaite, C.W. 1948. An approach toward a rational classification of climate. Geographic Review. 38: 55-94

van Wijngaarden W.A. and L.A. Vincent. 2003. Trends in Relative Humidity in Canada from 1953-2003. Report prepared for Climate Research Branch. Meteorological Service of Canada.

Vinnikov, K.Y., P.Y. Gorisman and K.M. Lugina. 1990. Empirical Data on Contemporary Global Climate Changes (temperature and precipitation), J. Climate. 3: 662-667.


von Finster, A. 2001. Draft Discussion Paper, Possible Effects of Climate Change on the Physical Characteristics of Fish Habitats in the Yukon Basin in Canada. Habitat and Enhancement Branch, Department of Fisheries and Oceans, Whitehorse Yukon.

Walker, K.V., M.B. Davis and S. Sugita. 2002. Climate Change and Shifts in Potential Tree Species Range Limits in the Great Lakes Region. Journal of Great Lakes Research. 28: 555–567.

Walter, M.T., D.S. Wilks, J.Y. Parlange and R.L. Schneider. 2004. Increasing Evapotranspiration from the Conterminous United States. Journal of Hydrometeorology. 5(3): 405-408.

Worrall, F., W.T. Swank and T.P. Burt 2003. Changes in Stream Nitrate Concentrations Due to Land Management Practices, Ecological Succession, and Climate: Developing a Systems Approach to Integrated Catchment Response. Water Resources Research. 39(7). doi: 10.1029/2000WR000130.

Yue, S., P. Pilon, B. Phinney and G. Cavadias. 2001. Pattern of Trend in Canadian Streamflow. 58th Eastern Snow Conference. Ottawa, ON.

Zhang, X., L.A. Vincent, W.D. Hogg and A. Niitsoo. 2000a. Temperature and Precipitation Trends in Canada During the 20th Century. Atmosphere Ocean. 38(3): 395-429.

Zhang, X., W.D. Hogg and E. Mekis. 2000b. Spatial and Temporal Characteristics of Heavy Precipitation Events Over Canada, J. Clim.

Zhang, X., K.D. Harvey, W.D. Hogg and T.R. Yuzyk. 2001. Trends in Canadian Streamflow. Water Resources Research. 37 (4): 987-998.

Zhou, X., C. Peng, Q. Dang, J. Chen and S. Parton. 2005. Predicting Forest Growth and Yield in Northeastern Ontario Using the Process-Based Model of TRIPLEX1.0. Canadian Journal of Forestry Research. 35: 2668-2280.


11.2 WEBSITE REFERENCES

CICS (Canadian Institute for Climate Studies). 2005. Available on-line at: http://www.cics.uvic.ca/ Accessed November 2005.

IPCC (Intergovernmental Panel on Climate Change). 2005. The IPCC Data Distribution Centre. Available on-line at: http://ipcc-ddc.cru.uea.ac.uk/. Accessed November 2005.

MacCracken, M., E. Barron, D. Easterling, B. Felzer. and T. Karl. 2001. Scenarios for Climate Variability and Change. National Assessment Synthesis Team, US Global Change Research. Available on-line at: http://www.usgcrp.gov/usgcrp/Library/nationalassessment/01Climate.pdf.

McGinn, S.M., A Shepherd and O.O. Akinremi. 2001. Assessment of Climate Change and Impacts on Soil Moisture and Drought on the Prairies. Final Report to the Climate Change Action Fund (CCAF). Available on-line at: http://www.adaptation.nrcan.gc.ca/projdb/pdf/agri10_e.pdf.

Shindler, D.W. and W.F. Donahue. 2006. An Impending Water Crisis in Canada’s Western Prairie Provinces. Proceedings of the National Academy of Sciences. Available on-line at: http://www.pnas.org/cgi/content/abstract/103/19/7210.

Soil-Net. 2006. Presented by the National Soil Resources Institute (NSRI) of Cranfield University at Silsoe, UK. Available at: http://www.soil-net.com/schools/soil_climate2.htm, Accessed: March 13, 2007.

http://www.cics.uvic.ca/

http://ipcc-ddc.cru.uea.ac.uk/

http://www.usgcrp.gov/usgcrp/Library/nationalassessment/01Climate.pdf

http://www.pnas.org/cgi/content/abstract/103/19/7210

http://www.cranfield.ac.uk/nsri/

http://www.soil-net.com/schools/soil_climate2.htm

http://www.soil-net.com/schools/soil_climate2.htm

Documents

Appendix 3 Climate Change - Open Government Program | … · 2016-09-22 · Suncor Energy Inc. - i - Climate Change Voyageur South Project July 2007 TABLE OF CONTENTS SECTION PAGE