Effect of Slope and Aspect on Litter Layer Moisture Content of Lodgepole Pine Stands in the Eastern Slopes of the Rocky Mountains of Alberta

Transcription

1 Effect of Slope and Aspect on Litter Layer Moisture Content of Lodgepole Pine Stands in the Eastern Slopes of the Rocky Mountains of Alberta by Kelsy Ellen Gibos A thesis submitted in conformity with the requirements for the degree of Master of Science in Forestry Faculty of Forestry University of Toronto Copyright by Kelsy Gibos 2010

2 Effect of slope and aspect on litter layer moisture content of lodgepole pine stands in the eastern slopes of the Rocky Mountains of Alberta Kelsy Gibos Master of Science in Forestry Faculty of Forestry University of Toronto 2010 Abstract For two fire seasons in Nordegg, Alberta, a system of in-stand weather stations were arranged along a north and south aligned valley and combined with collection of destructive fine fuel moisture content data in order to quantify variations due to differences in slope and aspect. South-facing sites were found to be slightly warmer (1.5 C), less humid (5%) and received on average 20% more solar radiation than the north-facing sites during the peak burning period of the day. Based on these weather observations a difference of 1 or 2 % moisture content between north and south sites was predicted using existing theoretical relationships. A corresponding difference in observed moisture content was not identified, due to the low transmittance recorded at the in-stand sites (<10% of open solar radiation measurements), variation amongst destructive samples and logistical limits on the number of replicates collected. ii

3 Acknowledgements Primary acknowledgement goes to those directly involved with the creation of this manuscript: B.M. Wotton, D.L. Martell, M. Flannigan, and D. Schroeder. Others with appreciated academic and operational advice include J.Gould, D.Finn and N. Lavoie. I truly appreciated my field squad which was made up of random faces over multiple seasons. Of special note: R. Thompson, R.Brazzoni, F. Hirata, M. Mooseman, R. Hsieh, J. Thomasson, J. Large and L. Gowman. Special thanks for my logistical miracle makers: Y. Choma, K. Anderson and R. Ault. Research support came in multiple forms from the Natural Sciences and Engineering Research Council of Canada (NSERC), FPInnovations (FERIC) Wildland Fire Operations Research Group (WFORG), the Canadian Forest Service (CFS), Alberta Sustainable Resource Development (ASRD), the International Association of Wildland Fire (IAWF), an NSERC Discovery Grant (to D.L. Martell), and the Faculty of Forestry at the University of Toronto. On a personal note, I d like to thank my family, friends and especially T. Johnson for listening to me whine about writing and living in a city for two years. Also, thank you to the MacDonalds whose hospitality was crucial to the success of the final draft. And finally thanks to Mother Nature, for giving me a hard time (twice) and forcing me to be creative. iii

4 Table of Contents Chapter 1: Introduction... 1 Chapter 2: Background information Forest Fire danger rating in Canada Components of the CFFDRS Role of fine fuel moisture Assumptions in the FFMC Moisture content of fine forest fuels Modeling moisture in dead forest fuels The Fine Fuel Moisture Code Variations in the Fine Fuel Moisture Code Fire weather inputs Temperature and relative humidity Wind speed Precipitation Fine fuel moisture dynamics and topography Fire weather Interpolating fire danger across the landscape Solar radiation Influences at the landscape level Influences at the stand level Influences at the fuel level Impacts for fine fuel moisture content Impacts for forest and fire management Prescribed fire planning and operational decision-making Community protection and fuel treatment Private forestry sector Chapter 3: Methodology Study location Site selection Stand characterization methods Forest floor characterization methods Weather station installation In-stand weather station specifications Open fire weather station specifications Sampling intervals for all weather stations Statistical analysis of weather data iv

5 3.3 Fine fuel moisture content destructive sampling The 2008 field season The 2009 field season Sample methods Statistical analysis of moisture data Moisture data from Moisture data from Chapter 4: Results and preliminary discussion Site description Stand characterization Forest floor characterization In-stand micrometeorology Sensor accuracy Temperature and relative humidity Diurnal trends Inter-site differences Solar radiation at the forest floor Sensor adjustment Diurnal trends Transmittance Leaf Area Index Wind speed Diurnal trends Inter-site differences Electronic fuel moisture sensors Diurnal trends Sensor accuracy Destructive moisture content sampling Destructive sampling method variability Intra-site variability Inter-site variability Accuracy of the FFMC Moisture data from the 2008 field season Distribution of sample moisture content Inter-site differences Moisture data from the 2009 field season Distribution of sample moisture content Inter-site differences Drying rate v

6 Chapter 5: Discussion, applications and future research directions Impact of micrometeorological variables on moisture content Ambient air temperature and relative humidity Fuel temperature Wind speed Cumulative impact Study applications and future research directions Stand structure Sample variance Chapter References Appendix A Appendix B vi

7 List of Tables Table 2.1 Descriptions of indexes of the Fire Weather Index System and their contributions to fire management and fire behaviour predictions (adapted from Stocks et al. 1989a) 8 Table 2.2 General descriptions of the microclimate of a slope dependent on aspect angle in the northern hemisphere (adapted from Whiteman 2000). 30 Table 2.3 Difference in fire behaviour predictions in a C-3 fuel type based on changes (±2%) in the moisture content of the fine fuels 39 Table 2.4 Hypothetical number of fires per day related to FFMC value from the valley bottom weather station 42 Table 4.1 Stand and forest floor characteristics for each site (standard deviation is reported in italics in brackets) 59 Table 4.2 Mean forest floor fuel loading values for each site (standard deviation is reported in brackets in italic font) 61 Table 4.3 Total and green moss mean depth for each site (standard deviation is reported in brackets in italic font) 61 vii

8 Table 4.4 Differences between temperature and relative humidity sensors from different sites operating under the same conditions. For these comparisons, the valley bottom sensor was chosen as the standard and subtracted from the site sensor (standard deviation is reported in brackets in italic font) 63 Table 4.5 Site temperature and relative humidity compared to that collected in the open at a standard reporting weather station during a drying trend between July 22 and July 25, 2009 (standard deviation is reported in brackets in italic font) 65 Table 4.6 Comparison between north and south-facing sites of hourly temperature and relative humidity data from July 22 to July 25, 2009 using Student s t-tests paired by hour and stratified by time of day 66 Table 4.7 Comparison between sloped sites and valley bottom of hourly temperature and relative humidity data from July 22 to July 25, 2009 using Student s t-tests paired by hour and stratified by time of day 67 Table 4.8 Theoretical slope adjustment factor for sensors measuring solar radiation on slopes as determined by a basic model to predict open solar radiation. For the 2008 data, solar radiation was predicted for between June 1 and August 31 and for the 2009 data predicted between July 22 and August 2 (standard deviation is shown in bracket in italics) 70 Table 4.9 Ratio between solar radiation received in-stand and in the open stratified by the amount of solar radiation received at the valley bottom open station 73 viii

9 Table 4.10 Total incident solar radiation (kw/m 2 ) received at each site by month as determined by the basic solar radiation model (Appendix B) 74 Table 4.11 Difference in transmittance between north and south-facing sites on sunny days in 2008 and 2009 (valley bottom open solar radiation greater than 0.8 kw/m 2 ) 75 Table 4.12 Estimated LAI values for each site based on data from days with valley bottom open solar radiation > 0.8 kw/m 2 from 2009 (n=60) 76 Table 4.13 Leaf Area Index (LAI) for a variety of Pinus species, environments and stand characteristics (adapted from Pearson et. al 1984 and Vose et al. 1994) 77 Table 4.14 Ratios between in-stand and open wind speeds for all sites for 2009 field season 78 Table 4.15 Wind speed at 1.5 m height stratified by time and compared between sites using paired Student s t-tests 79 Table 4.16 Wind speed ratio between in-stand and 10 m open compared between sites using paired Student s t-tests 79 Table 4.17 Electronic moisture content compared to observed litter moisture content using paired Student s t-tests on all the data collected in the 2009 field season 83 ix

10 Table 4.18 Electronic moisture content compared to observed litter moisture content using paired Student s t-tests on data collected during the drying trend between July 22 and July 25, Table 4.19 Mean standard deviation for moss and litter samples for ranges of observed mean moisture content 85 Table 4.20 Comparison of mean moisture content and standard deviation between Hylocomium splendens and Pleurozium schreberi samples collected under similar conditions (n=4) 87 Table 4.21 Differences in litter moisture content between sampling locations at each site using paired Student s t-tests 88 Table 4.22 Standard deviations of the litter destructive moisture content samples from Table 4.23 Mean difference between the observed moisture content and that predicted by either the hourly or diurnal FFMC model for the 2008 dataset 94 Table 4.24 Inter-site differences in observed moisture content adjusted to 1700 hrs MST as determined by a paired Student s t-test 96 Table 4.25 Comparison between moisture content of all 2009 samples using a Student s t-test paired by time and day collected 100 x

11 Table 4.26 Student s paired t-test comparing samples collected at the same time on the same day from the drying trend between July 22 and July 25, Table 4.27 Difference in percent moisture content change per hour between sites for the drying trend data (July 22 to July 25) determined by a Student s t-test paired by sample time 102 Table 4.28 Difference in the fraction of total evaporable moisture remaining in needle litter at time t during the drying trend determined by a Student s t-test paired by sample time 103 Table 5.1 Difference in equilibrium moisture content between sites using dry weather evaluated by a Student s t-test paired by hour and stratified by time of day 108 xi

12 List of Figures Figure 2.1 Structure of the Canadian Forest Fire Danger Rating System (adapted from Taylor and Alexander 2006) 7 Figure 2.2 The difference between hourly FFMC and diurnal FFMC values from the valley bottom open station from August 16 to August 20, Figure 2.3 (a) Simplified graph showing temperature versus log drying rate with constant relative humidity and wind within the FFMC. (b) Simplified graph showing relative humidity plotted against log drying rate, keeping temperature and wind inputs constant within the FFMC 19 Figure 2.4 Diagram showing the log drying rate (log moisture content per day) plotted against wind speed with constant temperature and humidity values within the FFMC 20 Figure 2.5 The difference in ISI using the standard FFMC relationship versus the FFMC adjusted by ±2% moisture content (using constant wind speed of 10 km/h in C-3 fuel type) 38 Figure 3.1 Sampling protocol for forest floor characterization 47 Figure 3.2 Installation of weather stations at the standard sites (FireSmart stand not shown) 48 Figure 3.3 Configuration of in-stand Campbell Scientific weather station (not showing wind sensor) 49 xii

13 Figure 3.4 Schematic of in-stand weather station set-up and destructive sampling routine 54 Figure 3.5 Visual representation of estimating moisture content along a diurnal drying curve 56 Figure 4.1 Temperature and relative humidity sensor test under similar environmental conditions. The FireSmart and valley bottom open sensors were also tested separately and displayed similar results. 62 Figure 4.2 Temperature and relative humidity time series during optimal drying conditions on July 25, Figure 4.3 Solar radiation received at all sites on July 25, Figure 4.4 Mean difference in solar radiation by hour during peak solar radiation conditions (n=345) 72 Figure 4.5 Wind speed time series during optimal drying conditions on July 25, Figure 4.6 Time series from July 22 to July 25 showing electronic moisture content for two sensors installed at the south-facing low site 80 xiii

14 Figure 4.7 Average electronic moisture content from July 24 to July 26, On July 26, a rain event occurred with more precipitation occurring at the south-facing sites (7.3 mm) than at the north-facing sites (1.4 mm). The data for the north-facing low site on July 27, 2009 was accidentally truncated by a battery failure. 81 Figure 4.8 Observed litter moisture content compared to moisture content estimated by an electronic sensor for two sites during drying conditions on July 24 and 25 th, Figure 4.9 Standard deviation for observed moisture content of moss tip and needle litter samples collected in both 2008 and Figure 4.10 Litter and moss-tip moisture content data from the south-facing low site from July 25 to July 26 showing the difference after a rainfall event 86 Figure 4.11 Pleurozium schreberi (top left) and Hylocomium splendens (bottom left) feathermoss species found at research location. Density of the Hylocomium splendens at the north-facing high site (right). 87 Figure 4.12 Herbaceous vegetation cover at the valley bottom site was higher than that of the other sites 89 xiv

15 Figure 4.13 The predicted moisture content from the daily FFMC from July 15 to September 4, 2008 plotted against the afternoon (peak burning period) observed litter moisture content from the valley bottom and south-facing low sites. The daily FFMC was calculated from the open weather station in the valley bottom. 91 Figure 4.14 The predicted moisture content from the daily FFMC from July 23 to August 10, 2009 plotted against the afternoon (peak burning period) observed litter moisture content from the valley bottom and south-facing low sites. The daily FFMC was calculated from the open weather station in the valley bottom. The observed values are from 1600 MDT. 92 Figure 4.15 The diurnally adjusted FFMC (left) and hourly FFMC (right) calculated from hourly weather at the valley bottom open station compared to the observed litter moisture content from the south-facing low site for the 2009 dataset 93 Figure 4.16 Histograms showing distribution of litter and moss-tip samples collected in Figure 4.17 Observed moisture content for destructive litter samples collected during A period of rain extended from August 3 to August 9. Moss moisture data showed a similar pattern but with greater response to rainfall. 98 Figure 4.18 Observed litter moisture for destructive litter samples collected during a drying trend from July 22 to July 25, xv

16 Figure 5.1 Mean difference in estimated equilibrium moisture content (%) between the sloped and FireSmart sites and the valley bottom in-stand sites for dry days (July 22 to July 25, 2009) 107 Figure 5.2 Model showing the effect of transmittance on predicted moisture content for conditions on July 25, Figure 5.3 Model showing the effect of wind speed on moisture content in a stand with 30% transmission using temperature, relative humidity and solar radiation data from July 25, Figure 5.4 Predicted moisture content using the hourly FFMC model and Wotton s (2009a) model adjusted to represent maximum observed differences in weather parameters at the southfacing and north-facing high sites 113 Figure 5.5 Predicted moisture content using the hourly FFMC and Wotton s (2009) model adjusted to represent maximum observed differences in weather parameters at the FireSmart stand 114 Figure 5.6 Time series of observed litter moisture sampled in the FireSmart and south-facing low stands from July 29 to August 2, xvi

17 List of Appendices Appendix A. Field protocols Appendix B. Solar radiation model xvii

18 1 2 Chapter 1 Introduction Previous to the adoption of this scale, forest managers had no standard terminology whatsoever for describing fire danger. Usually, the more expletives used, the greater the danger, but when expletives had to be eliminated the reporting officer often had difficulty in specifying extreme and critical conditions. - H.T. Gisborne, 1936 in The Principles of Measuring Forest Fire Danger Forest fire managers work to protect people, property and forest areas from wildfire while constantly considering constantly changing environmental, social, and economic needs (Merrill and Alexander 1987). In addressing these needs, fire management has matured into a marriage of scientific principles and the art of individual experience and intuition. A wide array of tools is available to aid the forest management decision-making process, ranging from simple rules of thumb and basic knowledge of weather conditions to complex modular systems created to address fire management across an entire nation. Fire danger rating systems are one of the most important tools available to forest managers in Canada, as these systems have become the primary means by which the science of fire behaviour and fire occurrence prediction is fused with operational experience. Fire danger rating is defined as the process of systematically evaluating and integrating individual and combined effect of factors influencing fire potential (Taylor and Alexander 2006). Danger rating systems allow suppression and preparedness resources to be allocated in a way that minimizes economic loss and adverse environmental impacts by providing a means by which managers can properly assess hazard in their area. The first systems in Canada were largely developed to aid managers in making cost effective decisions for large and spatially variable areas. Fire danger rating systems have both tactical and strategic uses in wildland fire management. The following list of fire danger rating system applications is not all inclusive but demonstrates the importance and versatility of such a system: prevention planning (communication and education with public and industrial sectors), preparedness planning (pre-suppression readiness

19 2 and deployment of suppression resources), detection planning (aircraft routing and scheduling of lookout staff), initial attack dispatch (ranking importance of certain targets), suppression planning on active fires (short term fire behaviour predictions), firefighter safety (evaluating dangerous fire behaviour potential), escaped fire situation analysis, prescribed burn planning and execution, fuel management planning and fire behaviour training (Taylor and Alexander 2006). Implementation of such a system across all of Canada poses challenges. Developers had to represent variation in fire environments county-wide, which ranged from arid grasslands in the prairies to western coastal rainforests. The land management objectives associated with each different district were obviously important. One of the biggest hurdles for the federal government was that it only played a small role at the operational level which meant it had to be sensitive to local concerns and cooperate with provincial governments. Through the 1930s and into the 1960s, many regions had developed their own local danger rating systems. Eventually, a modular nation-wide system was proposed by the Canadian Forest Service (Muraro 1968) and data collection from experimental fires began for each of the major Canadian fuel types. The system was intended to be easily applied in field operations by multiple users, so required inputs like weather observations remained simple to collect. The Canadian Forest Fire Danger Rating System (CFFDRS) of today is continually evolving to account for changes in management priorities, improvements in technologies, and advancements in fire science. Outputs from the CFFDRS are based on idealized conditions and provide reasonable estimations for most situations, but really are approximations at best. Such systems are sometimes applied beyond their field of usefulness and outputs can be realistically adjusted through experiential judgment only to a limited degree (Taylor and Alexander 2006). The models and their underlying assumptions may not be fully understood by the operational staff that use them for daily fire management decision-making. Frequently there are complaints that the models do not represent local fuel type, fire climate or moisture conditions. Consider for example the Canadian Forest Fire Weather Index System, a component of the CFFDRS that uses common weather inputs to create numerical ratings of fire danger related to wind conditions and the moisture content of surface fuels (Van Wagner 1987). Predictions from

20 3 the three moisture codes and three fire behaviour based on a closed canopy jack pine stand along flat terrain. The model does not explicitly address the impact of slope or aspect on moisture content dynamics, causing fire managers in regions with complex terrain features to experience discrepancies between predicted and actual local fuel moisture. Topography influences moisture content primarily through solar radiation and its impact on instand weather and surface fuel moisture transport. Although simple physical relationships can be used to estimate temperature and relative humidity changes related to elevation, it is difficult to accurately quantify the length of exposure and amount of solar radiation received at the forest floor and how it may change ambient temperature and relative humidity conditions. Slope angle and orientation coupled with time of year and latitude create a complicated mosaic of solar insolation conditions, making it difficult to predict the resulting moisture content of the fine forest fuels on the surface. In the northern hemisphere, the strongest difference in solar radiation is observed between north and south-facing slopes. This study explores the relationship between fine fuel moisture content and solar radiation through a field experiment conducted in the eastern slopes of the Rocky Mountains of Canada. A destructive sampling program was conducted over two fire seasons to collect data on litter and moss moisture content through various drying and wetting periods along a valley with north and south-facing slopes. In-stand weather stations were installed to describe solar radiation and micro-climatic conditions in relation to slope percent and orientation. The objectives of the study were to: - identify and quantify differences in key meteorological drivers of moisture dynamics (solar radiation, temperature, relative humidity, precipitation and wind) along slopes in a north and south-aligned valley - identify and quantify differences in moisture content of fine fuels on the surface of the forest floor along slopes in a north and south-aligned valley - assess the ability of current predictive models within the FWI System to accurately represent fine fuel moisture conditions on slopes of different aspects

21 4 The following chapter (Chapter 2) first outlines the current fire danger rating system in Canada and discusses the Fine Fuel Moisture Code, a component of the Fire Weather Index System that is used to estimate moisture content of the litter layer. The influence of each of the weather inputs into this model are discussed both in terms of their impact on the code value, as well as how they change with mountain climatology. The physical transmission of solar radiation is outlined and discussed from the landscape perspective down to fuel surface level. Chapter 3 discusses the specifics of the methodology that was used to quantify differences in weather and moisture dynamics across the mountain landscape. The moisture content of the forest floor is inherently driven by changes in local weather conditions. By installing a series of in-stand weather stations in similar stands of trees on opposing north and south-facing slopes, the variability in temperature, relative humidity, wind, rain and solar radiation was quantified for two summer fire seasons in the eastern slopes of the Rocky Mountains of Alberta. Litter and moss moisture samples were collected throughout the valley in relation to the weather station locations during two field seasons, with samples first collected individually in 2008 and then simultaneously in In Chapter 4, the quantifiable differences in weather and moisture dynamics between north and south-facing slopes are presented in detail. Diurnal trends and significant differences in the mean values of temperature, relative humidity, solar radiation and wind are compared between sites. Variability surrounding the collection of destructive moisture samples is discussed, and the mean difference in moisture content between the sites reported for both the 2008 and 2009 data. In Chapter 5, each weather element s impact on moisture content is conceptualized using existing theories on fuel moisture transport. By examining the role weather variables play in theoretical physical concepts such as equilibrium moisture content and fuel temperature, the observed differences in moisture content between the north and south-facing slopes are put into context.

22 5 The final chapter (Chapter 6) is a summary of the results and observations and a discussion of their potential for applications in forest fire management. Future research directions are presented to serve as a catalyst to continued studies in the realm of fuel moisture dynamics and solar radiation.

23 6 Chapter 2 Background information 2.1 Forest Fire danger rating in Canada The first experimentation directed at understanding fire danger in Canada was initiated by J.G. Wright in 1925 with an experiment to address the influence of weather parameters on fuel moisture and resulting fire behaviour (Stocks et al. 1989b). Wright, his colleague H.W. Beall and their successors developed different systems over several decades continually improving their applicability to fuel conditions across the country (Van Wagner 1987). The system evolved over time, keeping true to its simple weather data collection and index calculation. The danger rating values were based on mostly field experimentation analyzed by empirical mathematics rather than physical theory (Van Wagner 1987). The currently used system was issued in 1970 by the Canadian Forest Service, with subsequent editions and supplements released in 1976, 1978 and 1984 (Van Wagner 1987) Components of the CFFDRS The current version of the CFFDRS is made up of four main components: the Fire Occurrence Prediction System, the Accessory Fuel Moisture System, the Fire Behaviour Prediction System and the Fire Weather Index System (Figure 2.1). The Fire Occurrence Prediction (FOP) System uses detailed models to predict flaming and smouldering ignition potential. The Accessory Fuel Moisture System relates moisture content to the environment and includes the results of research on sheltered duff moisture calculations (Wotton et al. 2005), ground-truthing drought codes (Lawson and Dalrymple 1996) and diurnal adjustments of the fine fuel moisture code based on varying meteorological parameters (Van Wagner 1977, Beck and Trevitt 1989, Lawson et al. 1996). Both the FOP System and the Accessory Fuel Moisture System are currently being developed but have not been formally documented and implemented on a national level.

24 7 Figure 2.1 Structure of the Canadian Forest Fire Danger Rating System (adapted from Taylor and Alexander 2006) The Fire Behaviour Prediction (FBP) System is a set of models that provides quantitative outputs such as forward rate of spread (meters/minute), head fire intensity (kw/meter), crown fraction burned (%) and can predict fire type (ground, intermittent crowning and continuous crowning) (Fire Danger Group 1992). Finally, the Fire Weather Index (FWI) System creates relative values representing the wetness of forest floor fuels. The indexes rise with increasing dryness, so higher values correspond to higher fire intensities (Van Wagner 1987). The models within the FWI System are based on commonly collected meteorological inputs. The advantage of such a system over simply using local weather forecasts to predict fire hazard is that the FWI System provides estimates the fuel moisture content of the litter layer of the forest floor, which, along with wind speed, are the primary factors influencing the predicted rate of spread of a fire (Wotton and Beverly 2007). The FWI System consists of three moisture codes (the Fine Fuel Moisture Code or FFMC, the Duff Moisture Code or DMC, and the Drought Code or DC) and three fire behaviour indexes (the Initial Spread Index or ISI, the Build-Up Index or BUI, and the Fire Weather Index or FWI).

25 8 Each index provides a relative indicator of potential fire hazard which can be useful when making fire management decisions (Table 2.1). For example, the BUI is a good representation of the potential for hold-over fires and difficulties in mop-up procedures. Table 2.1 Descriptions of indexes of the Fire Weather Index System and their contributions to fire management and fire behaviour predictions (adapted from Stocks et al. 1989a) Index Description Fire Management Implication FFMC Numerical rating of the moisture Indicates the relative ease of ignition DMC DC ISI BUI FWI content of litter and other fine fuels Numerical rating of the average moisture content of loosely compacted organic layers of moderate depth Numerical rating of the moisture content of deep, compact organic layers Numerical rating of the expected rate of forward fire spread Numerical rating combining DMC and DC Numerical rating combining ISI and BUI and flammability of fine fuels Gives and indication of fuel consumption in moderate duff layers and medium-sized woody material Useful indicator of seasonal drought effects on forest fuels and the amount of smouldering in deep duff layers and large logs Combines the effects of wind and FFMC on rate of spread without the influence of variable quantities of fuel Represents the total amount of fuel available for combustion Gives a numerical rating of fire potential and fire intensity Role of fine fuel moisture The moisture content of the fine dead forest fuels is represented by the Fine Fuel Moisture Code (FFMC) in CFFDRS. The code was developed from moisture content data collected in jack pine stands near Chalk River, Ontario (Van Wagner 1987). The data showed that current moisture content was not only related to current weather conditions, but also dependent on the previous day s moisture content; drying was not an instantaneous process as previously assumed. The FFMC estimates current moisture content based on adjusting yesterday s code value by today s weather conditions with a rate of change dependent on moisture transport mechanisms within the fuel and its equilibrium (Van Wagner 1987). Fine fuel moisture content is one of the primary factors influencing wildland fire behaviour (Barrows 1951, Rothermel 1986). The fine fuels on the forest floor are reactive to small changes in location weather conditions. Moisture transport in the litter layer, because it is composed of

26 9 fine small diameter material, is rapid with the shortest timelag of all forest fuels in the FWI System (roughly two-thirds of a day). The moisture content of the fine fuels also influences the ease at which an ignition source will spread into a sustaining surface fire. Within the CFFDRS, the FFMC is combined with the effective wind speed to create the Initial Spread Index (ISI) which is a relative indicator of the expected rate of forward fire spread. The FFMC is also an input in the primary equations for calculating forest floor fuel consumption for the spruce-lichen woodland (C-1) and Ponderosa pine/ Douglas fir (C-7) fuel types in the FBP System (Fire Danger Group 1992). Combined with wind in the ISI, the FFMC is also used as an input into rate of spread calculations for all of the natural conifer fuel types (C-1, C-2, C-3, C-4, C-5, C-6, and C-7) Assumptions in the FFMC Two significant general assumptions are made within the Fine Fuel Moisture Code (FFMC): 1) the index represents conditions at the peak burning period of the day (does not account for temporal variation due to diurnal wetting, drying and wind speed); and 2) the observations made at a particular fire weather station, and the fuel moistures calculated from these observations, are representative of the area being assessed (Taylor and Alexander 2006). The second of these assumptions is particularly pertinent to the FFMC, as it is reactive to small and sudden changes in weather parameters varying throughout the day and influenced by topography and fuel type. Both the equilibrium moisture content and the rate of drying (or wetting) within the FFMC are dependent on atmospheric conditions such as air temperature and relative humidity. The FFMC as calculated by the FWI System is representative of the fuel moisture content during peak fire hours (approximately 1600 hrs LST) in a standard fuel type on level terrain, based solely on consecutive observations of fire weather components measured at noon local standard time. Although these standard conditions are useful for easy hazard calculations and comparisons across a large region, they do not necessarily represent local fuel moisture conditions which can lead to complications in management operations. Topography is one of the three edges of the fire triangle and it affects fire behaviour both directly

27 10 through mechanical processes and indirectly through its influence on local fuel moisture conditions. Topography can directly influence local moisture conditions by altering meso- and micro-scale weather conditions. Changes in elevation will modify temperatures, relative humidities, rainfalls and local winds. Aspect also plays an important role by affecting the amounts of incoming solar radiation reaching forest fuels which can shape densities and types of vegetation found on the slope. Inversion conditions, thermal belts, up or down-slope wind conditions and rainfall reaching the forest floor will also be altered across the landscape. The indirect effect of topography through differences in solar radiation on moisture content is not represented in the standard FFMC. If a discrepancy exists between the predicted and actual moisture contents of fine fuels found on slopes in mountainous areas, there is potential for errors in prescribed burn planning activities, fire behaviour predictions and all other uses of the CFFDRS. Recent research has worked to create conversion factors that can be applied to these FWI System moisture codes that allow them to more accurately estimate moisture contents in various fuel types and at different times of the day (Van Wagner 1977, Beck and Trevitt 1989, Lawson et al. 1996, Wotton et al. 2005); however, little research has been done to estimate the influence of topographical features such as elevation, percent slope and aspect on local fuel moisture contents. 2.2 Moisture content of fine forest fuels Small diameter dead forest fuels such as pine needle litter and feathermoss are the most sensitive to changes in local weather conditions. When local air temperature and vapor pressure change rapidly, fine fuels also respond quickly. The FFMC issued at regular reporting time (1200 LST) represents peak burning hours (1500 to 1700 LST) but may actually vary greatly over time as weather conditions constantly change. Models have been developed to predict hourly FFMC based either on a diurnal moisture trend or on hourly weather observations (Van Wagner 1972, 1977, Beck and Trevitt 1989, Lawson et al. 1996) but none of the approaches are widely used for fire operations. The moisture present in a fuel particle determines the amount of energy required to raise that

28 11 piece of fuel to temperatures were ignition occurs readily. Small changes in the moisture content of fine fuels have great impacts on the likelihood of a source of ignition sustaining open flame (Beverly and Wotton 2007). As the fuel moisture content decreases (FFMC increases) fire managers can expect a greater probability that an ignition source will result in sustained flaming. The moisture content of the fine fuels also dictates the forward rate of fire spread through its influence on the Initial Spread Index (ISI) component of the Fire Behaviour Prediction (FBP) System (Fire Danger Group 1992). The ISI indicates the basic relative rate at which a fire will spread when the upper fuel layer is dry. The ISI increases positively with increases in the FFMC. The FFMC through its influence on ISI is used within the Fire Behaviour Prediction (FBP) System to estimate rates of spread, head fire intensity and fire type for the conifer forest types Modeling moisture in dead forest fuels The physics of the exchange of water vapor between the fuel and the atmosphere have been studied and reviewed thoroughly by researchers using theoretical, laboratory and field techniques (Simard 1968, Viney 1991). The moisture content of fine fuels in the forest reacts as described by the physics of diffusion in solids (Nelson 1969). Actual fuel moisture dynamics do not adhere exactly to the theoretical relationships, so specific adsorption and desorption curves and physical characteristic dependent relationships have been developed for various litter types (Nelson 1969, Blackmarr 1971, Fosberg 1975, Anderson et al. 1978, Anderson 1990a,b). The moisture content of the dead fine fuels is dependent on the structure and composition of the fine fuel, the surrounding environmental conditions and the moisture content of the underlying organic material and soil. Most of the woody material and vegetation found on the forest floor consists of cellulose, hemicellulose, lignin and extractives (some of which may be volatile) (Nelson 2001). The relative amount of each of these materials varies by species and the higher the concentration of cellulose materials, the more hygroscopic the fuel becomes (Blackmarr 1971).

29 12 In dead fuels, the permeability of the fuel to water and the moisture diffusivity of the fuel affect the processes through which water is stored or transported (Nelson 2001). Water is stored mainly in the cellulose of plant material where it either condenses in voids or is bonded to crystalline regions of cellulose bonds in the cell wall (Nelson 2001). The point at which the cell walls are saturated with water and extra moisture begins to condense as a liquid is known as the fibre saturation point (m fsp ) (Nelson 2001). The value for m fsp generally used by fire practitioners is 30% moisture content. The main water transport method above m fsp is through capillaries in response to changes in surface tension forces. The permeability of the fuel structure to water is related to its internal structure and composition of pit membrane pores, fibre cavities, fibrils and tracheids. Below m fsp, moisture diffusivity governs the exchange of moisture in the fuel particle. Moisture diffusivity is the rate at which water diffuse across a moisture gradient from areas with high concentrations to areas with low concentrations. Moisture diffusivities are generally influenced by differences in mass density, particle size and shape, and extractive content (Nelson 2001); however, it is generally assumed that diffusion in foliage such as pine needles occurs in a similar way to wood structures (Van Wagner 1979, Nelson 1969). Weathering also affects moisture transport; for example, weathered pine needles that have lost their protective wax coating can gain or lose moisture about three to six times faster than unweathered particles with the wax intact (Van Wagner 1969). Fire hazard increases as fuels dry beyond the fibre saturation point (moisture content of 30% corresponds to a FFMC near 75). Each component of the forest floor reaches a steady level of moisture content after being exposed for an extended period of time to air with constant relative humidity and temperature (Blackmarr 1971, Fosberg 1975 Anderson et al. 1978, Anderson 1990b). This equilibrium moisture content (EMC) occurs when the vapor pressure in the atmosphere around a fuel particle equals the vapor pressure within the fuel particle (vapor pressure is a measure of a substance s ease of evaporation). The EMC differs again due to inherent differences in chemical and physical structures of each kind of fuel, but also differs according to whether the EMC was approached from a previously dry or wet condition (Blackmarr 1971, Nelson 2001). There are two different relationships for drying and wetting curves (adsorption and desorption isotherms)

30 13 for cellulose EMCs. As relative humidity increases, the EMC increases and the process is called adsorption. As relative humidity decreases, the EMC decreases and the process is called desorption. The curves have a very similar shape and converge under wet conditions (Van Wagner 1987). The EMC is of interest to fire managers because it defines the critical point that a fuel is drying towards. It is well studied because it is measurable in laboratory environmental chambers. Many studies have been done to evaluate the differences in EMC among different fuel types (Nelson 1969, Blackmarr 1971, Anderson et al. 1978, Anderson 1990b). These experiments form the base for the models developed to predict moisture contents and fire behaviour in different fuel types. All forest fuels dry or wet towards their equilibrium moisture content at different rates, depending on the physical structure (surface-area-to-volume ratio, packing ratio, bed depth, bulk density, etc.) of the fuel layer (Fosberg 1975). The moisture response time or time lag quantifies this rate by determining the time required for a fuel to achieve 63.2% (or 1-1/e) of the total change between its initial moisture content and its EMC. Fuels like grass, lichen and some mosses have the shortest time lags (less than two hours) while conifer needle time lags range from 2 to 14 hours (weathered) to 5 to 34 hours (recently cast) (Anderson 1990a). Dead fuels also receive moisture directly in the form of rain (absorption). Rainfall first fills intercellular voids and then follows gravitational flow through the fuel layer. The amount of rainfall that is absorbed depends on how much water was in the fuel structure prior to the rain event and on the structural details of the fuel layers beneath (Stocks 1970). Rainfall is the primary method by which fuels can absorb moisture beyond the fibre saturation point The Fine Fuel Moisture Code

31 14 Moisture models allow fire managers to track day to day changes in moisture content through easily communicated codes in the absence of rain. The moisture models within the FWI System (FFMC, DMC, DC) can all be simplified down to the general exponential model of moisture exchange for forest fuels (Equation 1). [1] Equation 1 is the basic exponential model for fuel moisture exchange where m t represents the average moisture content of needles or litter at time t, m e is the equilibrium moisture content (EMC), m o is the initial moisture content, t is time in minutes and τ is the time lag in minutes. The original FFMC tracks the moisture content of the fuels that support surface fire spread. The necessary weather inputs are wind, rain, temperature and relative humidity. The larger the value of the code, the lower the moisture content of the surface litter layer. The time lag for the FFMC is approximately two thirds of a day (for temperature of 21.1 C, relative humidity 45%, wind speed 12 km/h in July). to the FFMC represents the litter layer that has a dry weight of 0.25 kg/m 2 and a nominal layer depth of 1.2 cm. The FFMC predicts moisture content of the fine dead fuels at the peak of the burning period (1600 LST) in a closed canopy jack pine stand on level terrain. Canada s original moisture model for dead fine fuels was the Tracer Index, a model initially developed in eastern pine forests (Van Wagner 1987). Researchers at the Petawawa Research Forest correlated late afternoon litter fuel moisture with noon weather and yesterday s index value content (Wright 1937). Van Wagner eventually converted the original tabular Tracer Index into a set of moisture equations using the well-known exponential drying rate formula (Equation 1), his own model for EMC for pine needle litter and a basic model for rainfall absorption. His model estimated the moisture content of the litter layer and then converted it to a code value where increasing dryness corresponded to an increasing value of the code for psychological effect. Several conversions have existed through the years; the current version used in practice is the FF-scale which ranges from 0 to 101 where F is the output code and m is the moisture content in percent (Equation 2).

32 15 [2] The FFMC model contains explicit definitions for adsorption and desorption isotherms. In the drying phase, the rate of change in moisture content from day to day is described by the log drying rate (k) and expressed in units of log moisture content per day. There are two separate equations that assign log drying rates to differentiate between days where yesterday s moisture content is greater than the equilibrium moisture content obtained from drying from above (E d ) and days where yesterday s moisture content is below the equilibrium moisture content obtained from wetting from below (E w ). In the rainfall phase of the FFMC, the influence of incoming precipitation on the moisture in the layer is calculated first. Then, the drying based on noon temperature, relative humidity and rain readings is estimated assuming that the fuels dried for the entire length of the day. The system assumes that all the rain for the twenty-four hour period fell before the forest floor started drying that day ( the increase in moisture content due to rain is assumed to occur always before the day s drying begins ) (Van Wagner 1987). There is a rainfall threshold in the FFMC of 0.5 mm; rainfall less than this value is ignored because it is assumed to have been intercepted by overhead forest canopy Variations in the Fine Fuel Moisture Code Estimates of local fine fuel conditions are limited by the FFMC in that the standard code predicts the moisture content for one point of the day (the peak burning period). The FFMC as defined by the FWI System will not correctly estimate moisture content at other times of the day if weather inputs from a time other than noon are used; litter moisture is known however to vary throughout the day (Van Wagner 1977). To allow estimation of litter moisture at other times, models have been developed to explicitly estimate its diurnal variation. The diurnally adjusted FFMC (dffmc) model assumes a typical diurnal cycle of temperature and relative humidity that occurs in stable weather patterns (Van Wagner 1977). The diurnal variation in litter moisture was observed in a field study in lodgepole pine near Prince George,

33 16 British Columbia (Muraro 1969). Based on this data, Van Wagner (1972) developed a tabular form of diurnal adjustments for morning and afternoon use. Lawson et al. (1996) converted the tabular results Van Wagner produced into equations and extended the model through the overnight hours. The model is only applicable to central latitudes (48 N to 60 N) and requires the same inputs as the traditional FFMC with the exception of a morning relative humidity class for calculating FFMC between 0600 and 1159 hrs LST. The hourly FFMC (hffmc) differs from the dffmc in that it uses continuous hourly weather inputs (temperature, relative humidity, wind speed, precipitation) to calculate FFMC values (Van Wagner 1977). It has been adjusted through trial and error so that the hour-to-hour trends still follow the standard day-to-day log drying and wetting trends. Van Wagner (1977) validated the hffmc curve using fuel moisture samples collected at the Petawawa National Forest Institute in Ontario. There are key differences to be noted between the dffmc and the hffmc (Lawson and Armitage 2008). The dffmc should only be used in the absence of rain because it does not account for any rainfall events outside of the typical prediction period (noon to noon). The hffmc responds immediately to each rainfall amount and both the hffmc and dffmc curves intersect near noon the following day (Figure 2.2). In the reverse, the hffmc tends to underpredict overnight moisture content during extended periods without rain. The hffmc is also then tends to be lower than the dffmc during peak burning hours, meaning that the fire behaviour predicted by the hffmc is less extreme. Also, the dffmc was found to better represent the drying and wetting trends of feathermoss while the hffmc better represented the jack pine needles (Lawson and Armitage 2008). The hffmc model tends to be preferred among fire managers as it accounts for deviations in the daily weather pattern.

34 hffmc dffmc 80 FFMC value hffmc is lower than dffmc during peak fire hours dffmc catches up to hffmc with the noon rain reading Hour of Day (MDT) Figure 2.2 The difference between hourly FFMC and diurnal FFMC values from the valley bottom open station from August 16 to August 20, 2008 Adjustments to the standard FFMC have also been investigated to account for changes in stand forest type found across Canada (Wotton and Beverly 2007). The study compared moisture content data collected from several different stand types across Canada from 1939 to The authors found a significant influence of stand type on the moisture relationship between the FFMC and actual moisture content of the upper litter layer; however, during extended drying periods, the differences in fine fuel moisture between the different fuel types became smaller Fire weather inputs The accuracy of the standard FFMC depends on the quality of the weather information that is entered into the system. The four key elements: temperature, relative humidity, wind, and rainfall are generally collected daily at noon local standard time (LST) or 1300 local daylight time (LDT).

35 Temperature and relative humidity The FFMC calculation requires noon dry-bulb temperature measured in degrees Celsius (Lawson and Armitage 2008). A common misconception amongst fire managers is that although temperature is easy to measure on site, it does not directly control the susceptibility of fuels to fire damage. Its link to fire potential lies in its positive relationship with other fundamental weather parameters such as insolation and evaporation. The ability of an air mass to hold water is a non-linear function of air temperature. A rise in temperature increases the amount of moisture the air volume can hold, which is reflected by a decrease in the relative humidity and increase in the vapor pressure deficit (Environment Canada 1987). Based on the absorption and desorption isopleth curves of the equilibrium moisture content relationship, a dropping relative humidity value will result in a lower equilibrium moisture content value. An increase in vapor pressure value will increase evaporation from the surface of the fuel and thus the drying rate. Relative humidity is the amount of water in the air at a given temperature compared to the maximum amount of water the air can hold under similar conditions. Relative humidity varies directly with the moisture content of the air at constant temperature and inversely with the temperature of air at constant absolute humidity (Environment Canada 1987). By doing some simple calculations, the importance of temperature and relative humidity in the FFMC can be graphically presented (Figure 2.3). Figure 2.3(a) shows a plot of temperature in degrees Celsius versus the log drying rate with simplifications of constant relative humidity and wind components. As temperature increases, the log drying rate of the fuel increases. By affecting the equilibrium moisture content of the fuels, air temperature affects the energy required to ignite the fuel. Figure 2.3(b) shows the relationship between relative humidity and the log drying rate. There is a much larger increase in drying rate with a drop in relative humidity, illustrating that although temperature is commonly referred to as the main component driving susceptibility of fuels to ignition, relative humidity clearly has a larger influence.

36 a) 0.8 k (log drying rate in log moisture content per day) Temperature (degrees Celsius) 2.5 b) k (log drying rate in log moisture content per day) Relative humidity (%) Figure 2.3 (a) Simplified graph showing temperature versus log drying rate with constant relative humidity and wind within the FFMC. (b) Simplified graph showing relative humidity plotted against log drying rate, keeping temperature and wind inputs constant within the FFMC Wind speed For the calculation of FFMC, wind is collected 10 m above the ground in a forest clearing (Lawson and Armitage 2008). Wind accelerates the drying process by creating turbulence in the air directly surrounding the fuel. Wind will carry away the thin skin of air on the surface of a fuel before it can become saturated with moisture as the fuel dries. A relatively weak effect of wind speed on the drying rate is evident in the FFMC model (Figure 2.4).

37 20 k (log drying rate in log moisture content per day) Wind speed (km/h) Figure 2.4 Diagram showing the log drying rate (log moisture content per day) plotted against wind speed with constant temperature and humidity values within the FFMC Sometimes the cooling effect of the wind can outweigh its drying effects. Cold air masses moving near the ground will bring moist air closer to the fuel surface. Even if the cool air mass has a relative humidity less than that of the fuel particle, the cool air circulation can slow the evaporation process. This effect will also be relatively weak compared to effects of other meteorological parameters. The greatest effect of wind on fuel drying is indirect. Wind patterns move parcels of air with varied temperatures and relative humidities over fuel particles. The effect of the humidity change is much greater than the effect of the wind speed on its own. In areas with significant topographical variation, wind patterns can also control the location and amount of precipitation received locally. Orographic effects can produce thundershowers as moist air masses are uplifted over physical barriers.

38 21 Although wind has a minimal effect on the drying rate of forest fuels, fire managers should be familiar with localized wind patterns. Tactical planning that involves night or early morning firefighting needs to address the possibility of lifting inversions and the onset of warm upslope winds. Atypical fire behaviour can usually be attributed to changes in topography or their influence on the active or passive wind phenomena. This discussion aims to focus only on the influences of these parameters on the fuel moisture content, but large scale effects of topographical wind effects on fire behaviour are serious and plentiful Precipitation Rain for the FFMC is summed over the 24 hour period beginning at just after 1200 hrs LST the previous day and ending at 1200 hrs LST on the present day. Precipitation is measured in the open, but net rainfall for the FFMC is reduced by 0.5 mm to account for canopy interception. Rainfall is the only way that fuel moisture content can be raised above the fibre saturation point (close to 30% moisture content which corresponds to an FFMC of about 75). In the FFMC, needle litter can reach maximum moisture contents of 250%. Variation in rainfall creates difficulty in interpreting FFMC across a landscape. At the largest scale, storm fronts will release moisture in unpredictable patterns across a fire management zone. At the stand level, differing amounts of rainfall infiltrate the canopy; of specific note, the fuels directly surrounding the bole of a tree can be significantly drier than those beneath more open canopy (Wotton et al. 2005)

39 Fine fuel moisture dynamics and topography For ease of use, models embedded within the FWI System are designed to predict conditions for level terrain. Topography adds multiple complications to estimating fuel moisture content. The angle and aspect of individual slopes can create complex interactions between soil type, vegetation pattern, drainage patterns, species diversity and other biological processes. These impacts are strongly tied to changes in mountain weather conditions and especially to the amount of incident solar radiation at the site. Changes to input weather components will directly change the way the FFMC predicts fine fuel moisture content. Systems have been developed to both interpolate weather parameters like relative humidity, temperature and wind patterns as well as to predict FWI System codes and indices across mountainous terrain. Solar radiation is not included in the model underlying the FFMC, but its impact on moisture transport has been investigated by past researchers Fire weather Standard fire weather recording stations are commonly installed in valley bottom locations or at fire lookout towers on mountain peaks. Data collected at a station is expected to represent the weather in an area with about a 40 kilometer radius (Lawson and Armitage 2008). The variations in weather conditions from valley bottom to mountain top are great in scale. By using readings from each of these stations managers may misinterpret local fuel moisture conditions, especially in areas with complicated terrain features. There have been numerous studies conducted to investigate changes in weather patterns related to slope, aspect and elevation (Sharples 2009). Others have attempted to use these physical relationships to adjust the fire weather inputs into the FWI System in order to better extrapolate the estimated moisture conditions across the landscape. Local topography affects temperature and relative humidity. Altitude is the main factor controlling variation in ambient temperature in mountainous terrain. The average temperature decrease with height is described by the environmental lapse rate which approximates 6 C per kilometer in the free atmosphere (Barry 1992). The dry adiabatic lapse rate is the rate at which

40 23 an unsaturated hypothetical air parcel cools when it is displaced upward (Barry 1992). As the parcel of air is lifted as air masses push over topographical features, its pressure decreases and its temperature falls due to the expansion. The dry adiabatic lapse rate (DALR) is represented by a cooling of 9.8 C per kilometer rise. As the air parcel is lifted, it will continue to cool at the DALR as long as it remains unsaturated. As it ascends, the parcel will eventually cool to its saturation temperature and the relative humidity will reach one hundred percent. At this point, condensation occurs and as this latent heat of condensation is absorbed by the air the rate of adiabatic cooling decreases. This is called the wet (or saturated) adiabatic lapse rate and it approximates a decrease in temperature of 5 C per kilometer rise (Barry 1992). So as temperature decreases with increasing altitude, relative humidities generally increase with increasing elevation. There are events where this process becomes inverted and temperature actually increases with height. Nocturnal inversions occur when cold air lakes form in valley bottoms as air sinks along the contours of the landscape. A weak inversion layer forms above the cool air mass which is warmer than the air below. This creates a thermal belt or zone, usually at the steepest part of the slope, where the warm air has been pushed up by the cold air causing temperature to increase with altitude (Yoshino 1984). Areas with varied terrain also tend to experience greater diurnal temperature and atmospheric moisture variations. The greatest differences are observed between valley bottoms and are smaller along slopes and at higher elevations (Environment Canada 1987). Valley bottoms are colder and moister during the night than upper slopes, and tend to be warmer and drier during the day. The upper slope is warmest at night with temperature inversion conditions; its daytime temperatures depend on its height, and its relative humidity is about average (Geiger 1965). Valley bottom air will experience a steep rise in temperature during peak burning hours but that rate of temperature increase with time does not exceed a certain value matched on the upper slope since the strengthening upslope wind will likely force a balance (Geiger 1965). The slope will have a much smaller diurnal variation in temperature and relative humidity, and it will be driest at night and wettest during the warm part of the day. Typically in the middle latitudes, the

41 24 temperature difference is approximately 1.5 C at 850 mb, 1.0 C at 700 mb and 0.7 C at 500 mb (Barry 1992). From his weather station data, Hayes (1941) delineated three altitudinal zones where predicted fire behaviour differed along the up and downslope transects. He identified a thermal belt zone characterized by the highest temperature at night and highest mean daily temperature compared to the other zones. The low zone corresponded to the valley bottom with highest humidities and greatest range in daily temperature fluctuations. The high zone was generally cooler and wetter than the thermal belt with small daily fluctuations. Few attempts have been made to measure transects of air temperature following slopes. Initial studies attempted hand measurements at different locations along a mountain trail, but analysis of the data was difficult due to differences in time between slopes. Wagner (1930) provided measurements of a mountain transect by using an aspirated thermometer to collect temperatures from a cable car. His morning measurements only show a 4 C change over the 1700 m interval, but in the afternoon the temperatures on the steep south-facing slopes were much higher. Modern technology has allowed more detailed collection of weather observations across steep terrain. The development of portable weather stations and complicated networking systems will continue to expand data collected from mountainous terrain. Other technologies such as aerial surveys with radiation thermometers have provided detailed evidence of the radiative patterns over steep terrain features, although the data is expensive to acquire and difficult to interpret (Fujita et al. 1968). A surge of the use of satellite imagery to describe weather patterns has also emerged in the last fifty years. For example, Bourgeau-Chavez et al. (2007) use satellite imaging radar (ERS-1 and ERS-2) to provide point source weather and moisture data to improve the accuracy of fire weather indexes in boreal Alaska. Images from the Moderate Resolution Imaging Spectroradiometer (MODIS) have also been used to measure large-scale global weather patterns, including changes in the radiation budget, cloud cover and processes in the lower atmosphere, although the fineness of the resolution may not be fine enough to evaluate individual mountain climatologies.

42 25 Landscape level and localized wind patterns created by a broken landscape influence fuel flammability and eventual fire behaviour by controlling local weather parameters like distribution of rainfall events, movements of air masses and general circulation of local air. To simplify discussion, topography can have both passive and active influences on air flow. The influence on air flow is passive when relief features such as mountains, valleys and slopes modify the existing wind field. On average, wind speed increases with height at middle and high latitudes based on the characteristics of the global westerly wind belts (Barry 1992). Ridges and peaks experience high and extreme wind speeds due to the lack of speed reduction from friction as the air moves over smooth, rocky outcroppings associated with these features. The frictional effects work to slow the air mass, but a vertical compression of airflow causes acceleration. Studies by Schell (1936) demonstrated that, in the case of peaks and ridges, the compression effect almost always outweighs the frictional effect, leading to even stronger winds 50 m above the summit. This acceleration due to compression is caused by a pressure reduction as a result of the streamline curvature over the crestline which is termed the Bernoulli Effect (Barry 1992). The Bernoulli equations relate kinetic energy, pressure forces and potential energy to predict an increase of 4 to 5 meters per second in wind speed with a 1 mb drop in pressure (Barry 1992). Orientation and dimensional characteristics of the barrier such as height, length, width and spacing between ridges affects the pattern of air movement. An airstream will separate around an isolated mountain peak, but a range extending several hundred kilometers along may cause deflection, funneling or concentration of air masses. Gap winds can be especially turbulent, with wind speeds sometimes increased two to three-fold through the break in the mountain range (Barry 1992). An active topographic effect occurs when gradients of temperature and air pressure caused by differential heating create air currents. Localized mountain winds vary throughout the day, and are dependent not only on the shape of the land, but also the amount of incoming solar radiation received by the slope. These events can be categorized into three types: compensating winds, mountain and valley winds, and slope winds, with the strength and influence of the winds decreasing in that order (Barry 1992). Many of the differences in temperature and relative

43 26 humidity across slopes are moderated by up- and down-slope winds that form due to differential heating. In fact, although lower elevations tend to have drier air masses associated with them, as the season progresses the cumulative drying effect will eventually even out elevation and exposure differences, although not entirely (Environment Canada 1987). Another active wind effect caused by topography is the Chinook or foehn wind. This is a strong, gusty, dry down-slope wind caused by moist air moving over a terrain feature. The air will lose its moisture as it ascends and cools; as it reaches the lee side, the air subsides and warms by compression. This is of concern for fire managers as a combination of warm, windy conditions often with humidities less than 5 percent increases potential for ignition and for dangerous fire behaviour in flashy and fine fuels Interpolating fire danger across the landscape Various models have been developed in an attempt to quantify the differences between the fire danger predicted by a standard location and the hazard actually experienced at the fire location. In general, there are two ways to interpolate fire danger across the landscape: 1) use weather variables from a standard station, interpolate them across the landscape using known physical relationships, and then calculate site specific FWI System values; or 2) calculate FWI System values from the standard station and then interpolate them across the landscape (Flannigan 1998). The second option is used most commonly by fire danger agencies due to increased complexity of calculations of physical parameters as well as the inability to track random convective summer precipitation events (Flannigan and Wotton 1989) associated with the first approach. At the basic level, documents exist for fire practitioners that provide adjustments for differences in relative humidity and temperature due to elevation (Cramer 1961). At most they provide an informational background on weather patterns and what to expect when air masses move through complicated terrain. They are guidelines for ground crews but are not integrated at the higher organizational levels.

44 27 The San Antonio Mountain Experiment (SAMEX) collected hourly observations of temperature, relative humidity, wind speed and direction, and precipitation on a conically-shaped isolated mountain in New Mexico over a five year period (McCutchan et al. 1982). This study was unique in that it presented data for three separate aspects on homogeneous slopes for each cardinal direction. The data helped to clarify the effect of elevation and aspect on the meteorological variables, to compare local slope weather measurements to those in the surrounding free air, and to validate temperature, humidity and wind models in complex terrain (McCutchan et al. 1982, McCutchan 1983). The MTCLIM model is able to extrapolate meteorological variables from a point of measurement (a standard reporting station) to an area of interest in mountainous terrain (Hungerford 1989). The model creates outputs for incoming solar radiation, air temperature, relative humidity and precipitation based on inputs from basic weather station inputs. The accuracy of the extrapolation decreases as the distance between the base station and the area of interest increases because of changes to air masses, cloud cover and localized precipitation. These adjusted weather inputs can be used to calculate the FWI System indices then for any location in the mountains. Calculating the FWI System codes first and then interpolating them across the landscape eliminates one step in the computation of adjusted codes. Flannigan and Wotton (1989) evaluated multiple interpolation techniques commonly used in meteorological processes for use in interpolating FWI System values. Their work indicated that certain methods fit observed data well at both high and low fire danger values, and that there was potential for their implementation among fire agencies. However, the accuracy of all the interpolation techniques decreased due to the lack of knowledge in predicting convective summer rainfall. The spatial variability in rainfall across the landscape is great, and the understanding of the processes involved in minimal. Flannigan et al. (1998) attempted to remedy this issue by using precipitation estimates from radar. Using radar rainfall estimates slightly improved the interpolation of FWI System values, particularly for the FFMC where rainfall events need only to be greater than 0.5 mm to have an influence on the value.

45 Solar radiation Incoming energy from the sun directly influences the forest environment. Certain tree species grow more efficiently in sun rather than shade, understory species composition is influenced by the amount of sunlight reaching the forest floor, timing of snowmelt is affected by amounts and durations of energy from the sun, and so forth. It is logical then, to assume that the moisture content of the fine forest fuels beneath the canopy is affected by incoming solar radiation. Although never fully quantified, there appear to be both indirect and direct impacts of solar radiation on fuel moisture content. Indirectly, incoming solar radiation influences the ambient air temperature and relative humidity of air masses (Barry 1992). Differential heating from the sun also drives various wind patterns across the landscape (Barry 1992). The composition of the forest stand itself is driven by solar radiation, which in turn influences the type, amount and dryness of forest floor fuels. Directly, sunlight heats the surface of a fuel causing changes in local vapor pressure, diffusivity and rates of evaporation. These effects are amplified over complex topographical features. Aspect and slope angle affect the exposure and intensity of incoming solar radiation, so this in turn must amplify the affect on moisture content. The greatest difference in solar radiation driven differences in in-stand moisture content should then be expected on north and south-facing slopes in the northern hemisphere Influences at the landscape level The sun emits shortwave energy as ultra-violet light, visible light and near-infrared wavelengths. This energy is absorbed and reemitted by terrestrial elements. Emitting bodies including the forest canopy itself, clouds and haze are secondary sources of radiation. These sources emit longwave energy at a rate dependent on temperature.

46 29 Basic principles show that the amount of incoming solar radiation is affected by the rotation of the earth through cycles of night and day and variation in sun angle and altitude. The revolution of the earth about the sun and the tilt of its axis also affect solar radiation by determining day length, the amount of radiation received at the surface as based on latitude, the seasonal variation in incoming solar rays and the maximum altitude of the sun in the sky at noon (Garg and Datta 1993). Shortwave radiation is absorbed at the earth s surface during the day, while longwave radiation is continually emitted by the surface. Some longwave radiation is absorbed by water vapor in the atmosphere resulting in heating. Incoming solar radiation is diminished by reflection by clouds and other surfaces such as snow, by scattering by atmospheric gases, dust and pollutants, and through absorption by water vapor and carbon dioxide in the air (Environment Canada 1987). Clouds can also have the reverse effect whereby they absorb outgoing longwave radiation and then reradiate it back to the earth s surface, enhancing the heating effect. A surface that is exactly perpendicular to the incoming radiation receives the maximum effect of that radiation (Reifsnyder and Lull 1965). Any other surface not perpendicular to that beam will receive less energy per unit area as the radiation is spread out across a greater area. The effect of the slope depends on the sun s position in the sky and different slopes will be heated at different rates throughout the day as the sun follows its celestial path across the sky. In a similar way, a tree located on flat ground will cast a longer shadow than a similar tree located on a slope, meaning that there are periods during the day where more of the valley bottom forest floor is shaded than on a nearby slope. In mountain climates, solar radiation is seen to increase with altitude. In a study conducted by Steinhauser (1939), a rapid increase in solar radiation was detected up to 2000 m after which the rate broadly declined, based on the relative concentration of water vapor in the atmosphere. A similar study determined that the global solar radiation received by a slope at 3000 m is 32% greater than at 200 m in December, 25% greater in March and September, and 22% greater in June (Barry 1992). Sky radiation is much more intense on mountains owing to generally

47 30 shallower cloud layers overhead, and by the increased atmospheric transparency and lower temperatures at high elevations. The aspect angle determines a slope s exposure to sunlight and prevailing winds, which in turn modify precipitation, temperature, humidity and fuel regimes (Table 2.2) (Whiteman 2000). As the sun moves across the sky, its rays will become nearly perpendicular to different aspects and slopes at different times causing large variations in heating across a site. Table 2.2 General description of the vegetation and fuel characteristics of a slope dependent on aspect angle in the northern hemisphere (adapted from Whiteman 2000). South-facing - lowest density - lowest fuel moisture - lowest average relative humidity - highest average temperature - highest rate of spread - earlier curing of fuels - earlier snow melt West-facing - density transition - later heating - later cooling - generally the windward side of the mountain North-facing - highest density - highest fuel moisture - highest average relative humidity - lowest average temperature - lowest rate of spread - later curing of fuels - late snow melt East-facing - density transition - earlier heating - earlier cooling - generally the lee side of the mountain Influences at the stand level The forest canopy absorbs somewhere between 60 and 90% of the total incident solar energy received, dependent on its density and the structure of the foliage (Reifsnyder and Lull 1965). Forest canopy greatly reduces shortwave radiation (by 73 to 86%), but has very little effect on outgoing long-wave radiation. It reflects about 15 to 29% based on its albedo value (albedo refers to the reflected portion of a specified spectral band, where 0 represents a perfect reflector and 1 is perfect absorber or black body (Garg and Datta 1993)). The proportion of sunlight reaching the forest floor, or transmissivity of a forest canopy is dependent on density, vegetation type and other physiological characteristics of the shading

48 31 trees. By shading out up to 90% of the incoming shortwave radiation, the forest can reduce the monthly maximum air temperature in the summer by about 12 C below that out in the open (Reifsynder and Lull 1965). For coniferous stands the ratio between the amount of light in the open to the amount of light beneath the canopy has been found to vary between 0.5 to 6.7 (Reifsnyder and Lull 1965). Solar radiation and light intensity decrease from the forest canopy to the surface of the forest floor (Reifsnyder and Lull 1965). Sunflecks or small beams of sun shining through the canopy and onto the forest floor can actually contribute a substantial amount of heat energy to the forest fuels. One study showed that in their movement with cloud cover and day lapse, they warm the soil momentarily and produce variations in the temperature of the litter as much as 16 C in 20 minutes (Reifsnyder and Lull 1965) Influences at the fuel level Most of the solar energy absorbed by matter is converted to thermal energy which manifests itself in the temperature of the matter (Reifsnyder and Lull 1965). Emissivity and absorption are directly related through Kirchoff s Law which considers a body completely surrounded by an enclosure of uniform temperature with which the body is in equilibrium. If absorptivity is larger or smaller than emissivity, the body will experience a net gain or loss of heat. Solar radiation increases the temperature of the fuel surface, sometimes by as much as 5 ºC in ponderosa pine (Pinus ponderosa) litter (Anderson et al. 1978). Most of the incident heat from incoming solar radiation is absorbed by the air, but the albedo of the fuel allows its surface to absorb this heat more efficiently (Rothermel et al. 1986). The conductivity of the fuel surface also affects the change in surface temperature. Energy absorbed by a good conductor will spread as heat evenly throughout the material so its temperature rises uniformly. A poor conductor tends to concentrate heat near the surface causing uneven heating. These objects tend to become much hotter on the surface during the day but tend to cool quickly at night. Many organic substances are poor conductors including wood, leaf litter and dry organic fuels (Environment Canada 1987).

49 32 A higher surface temperature corresponds to an exponential increase in vapor pressure, which is essentially a measure of a substance s propensity to evaporate. Water particles at the fuel surface vaporize more easily, decreasing the equilibrium moisture content of the fuel (Blackmarr 1971). By decreasing the moisture content which a fuel will attain after exposure to constant environmental conditions, the time it takes to reach that equilibrium is also decreased. A shorter timelag means a fine fuel exposed to solar radiation will theoretically dry faster Impacts for fine fuel moisture content South facing slopes in the northern hemisphere receive the most solar radiation throughout the summer season. Incoming radiation may be the primary meteorological parameter affecting fuel flammability prior to ignition (Environment Canada 1987). Energy from the sun influences both the temperature of the fuel surface and in turn its moisture content. Longwave radiation is released from the fuels during cool, clear nights, sometimes cooling the fuel beyond the dew point causing surface condensation. Fuels that are not directly exposed to the open sky will not lose as much heat or gain as much fuel moisture content (Byram 1948). Historical research on the effects of solar radiation on fine fuel moisture content began in 1929 when Gast and Stickel designed a simple experiment showing moisture content differences ranging from 5 to 10 percent lower in duff exposed to full sun light versus duff shielded by bobbinet cloth. A study conducted near Spokane, Washington installed six weather stations in clearings on opposing north and south slopes and at three different elevations and collected daily values for fuel moisture content using duff hygrometers (instrument that uses wet and dry bulb temperatures to determine localized humidity) and moisture dowels (Hayes 1941). Results revealed significant differences in the diurnal drying and wetting trends observed on the opposing slopes. On the south-facing slope, the duff moisture followed a typical drying curve throughout the hourly cycle. The thermal belt zone was the driest, the valley bottom the wettest and the upper slopes mid-range. However, when the sun became high enough in the sky to begin the drying process, duff moisture became approximately the same at all altitudes by 1000 h and remained

50 33 the same throughout the day. On the north-facing slope, the thermal belt zone was drier in the morning than the valley bottom, but the high zone was even drier for much of the day. Minimum duff moisture was reached at 1400 h and the moisture content was practically the same at all elevations. Overall, duff moisture was lower on the south-facing slopes ranging from 1 to 2 percent more moist at the fully exposed valley bottom station, 2 to 4 percent more moist on north-facing slopes and 6 to 8% more moist at the in-stand valley bottom station. Duff had higher moisture contents on the north-facing side than the south-facing side at all hours and all elevations. The difference was the greatest at the lowest elevation (mean=4.8%) and smallest at the highest elevation (mean=1.8%). The smallest difference in duff moisture between aspects occurred between 1400 h and 1600 h and the greatest difference occurred between 0800 and On the south slopes, he also found that the minimum moisture content varied as little as ±1 percent day to day, although maximum air temperatures ranged from 21 C to 35 C and minimum relative humidity from 30 to 15 percent. Assuming that the level of incoming solar radiation was constant (cloud-free), he concluded that radiation was the primary controller of moisture content rather than temperature and humidity. Byram and Jemison (1943) investigated the quantitative relationship between solar radiation and hardwood leaf litter moisture content. First, they collected solar radiation data on north and south-facing slopes to show differences in the amount and timing of solar radiation received on the slopes. They found that in the summer, the southern exposure with 20 percent slope received the greatest amount of solar radiation because it was almost perpendicular to the sun s rays at noon. They also noted that at the research location in the southern Appalachians the 20 percent north-facing slope received almost as much solar radiation as the 20 percent south-facing slope during the summer. Following this, Byram and Jemison (1943) attempted to demonstrate the effect of differing levels of solar radiation on the moisture content of hardwood forest leaf litter. They created an artificial radiation source using twelve 150 watt bulbs and a reflective box equipped with electric fans for air circulation. By controlling the conditions within the box, they were able to separate the relationships between temperatures and humidities of the fuel bed and the independent variables of wind, air humidity and temperature and insolation. The researchers first addressed

51 34 the relationship between fuel temperature and ambient air temperature. Without the presence of sunlight, the fuel temperature on a north-facing slope shaded from the sun could be as much as 8 C below the air temperature. Fuel temperature increased with the presence of solar radiation, sometimes by up to 23 C (radiation intensity of 0.70 kw/m 2 ) By varying wind speeds within their chamber, Byram and Jemsion (1943) went on to discover that increases in fuel surface temperature due to solar radiation were negated as surface wind speed increased. It was hypothesized that turbulent mixing around the fuel particle tended to cool the air and return it to its original temperature. So, fuels in clear windy weather would have higher EMCs than fuels with clear calm weather, and this observed difference could be as great as 6 percent (differences were greatest when EMC was less than 15 percent). Interestingly, the reverse is true when solar radiation is absent; with no solar radiation present, the fuels exposed to windy conditions had faster drying rates than those in still conditions. A similar study by Van Wagner (1969) also observed higher surface temperatures when the fuel was exposed to full sunlight in still air. Using floodlamps and fans to simulate the combined effects of wind and solar heating, Van Wagner showed that, at a constant radiation of 1.0 kw/m 2 (full summer sunlight), temperature rise on the surface of the jack pine litter decreased as the wind speed increased. In his equations, which were derived from Byram and Jemison s (1943) study of hardwood litter, if solar radiation input was zero, then the surface temperature was equal to the air temperature, suggesting that wind has no effect on surface temperature in the absence of radiation. Gisborne s (1933) findings agreed with the above when he determined that roundwood placed on litter dried faster than wood elevated 25 centimeters above the litter surface. He determined that fuel particles exposed to both radiation and wind had higher equilibrium moisture contents than those exposed to radiation in calm conditions. He found differences in EMCs as great as six percent depending on the ambient air temperature, relative humidity and wind speed. In his continued research, Byram (1948) determined that the expected difference between moisture on north and south-facing slopes could be even greater if the effects of terrestrial

52 35 radiation were considered (terrestrial radiation is the heat lost from the earth and lost to the atmosphere). He compared surface temperature of litter on north and south-facing slopes to ambient air temperature and found that the curves tracked each other reasonably well until the daylight hours. When illuminated directly by sunlight, the litter temperature rose rapidly and remained higher than the air temperature until the sun was low enough for outgoing terrestrial radiation to balance the incoming solar radiation. The cooling of fuels by terrestrial radiation is important for predicting their overnight fuel moisture recovery. Byram (1948) conducted another experiment with a radiator apparatus designed to magnify the effects of radiation on objects exposed to clear sky. Fuels exposed to clear skies lose radiation at a greater rate, sometimes to a point where their surface temperature drops below the dew point. In this case, liquid water condenses on the fuel as dew and their resulting moisture content is determined by their ability to absorb the water. Fuels beneath the forest canopy did not experience the same level of moisture recovery overnight. Records from the northern Rocky Mountains from 1931 to 1945 showed that although the greatest number of fires occurred at the top of a slope (linked to a greater amount of lightning strikes on ridges and peaks), the greatest number of large fires actually originated at the base of a slope (Barrows 1951). The author suggested that fires originating on lower slopes may burn more vigorously in the day when the temperatures are higher and the relative humidities are lower, and ease off in the evening when temperatures drop and moist air moves in along the slope. However, in higher elevations where nocturnal inversions are common, fires often burn actively through the night in thermal belts where temperatures remain warmer and relative humidities remain lower. Another study conducted near Kamloops, British Columbia installed moisture indicator sticks and weather stations along five south-facing slopes as well as in the valley bottom in order to address the influence of degree of slope on moisture content (Muraro 1964). He found that temperatures generally decrease as the steepness of the slope increases (1 C between 38 to 62% slope and 2 C between 0 and 62% slope). Also, the temperature at noon increased as the degree of slope increased up to 51 percent. Although temperature gradients were present, he was unable to identify a relationship between the fuel moisture content and the steepness of the slope (the

53 36 differences were small and could not be ruled out as error in the recording process). He did note that the soil moisture content produced an inverse relationship with the degree of slope on clear days. Furman (1978) used cluster analysis on Hayes (1941) data to predict the equilibrium moisture content (EMC) along a mountain ridge. He found that the valley bottom and the lower site on the north-facing slope had the lowest midday fine fuel moisture. In the evening though, sinking cool air drops the temperatures and raises relative humidities increasing the moisture content. Along the mountain top and upper south-facing slope, the EMC decreased faster midday, meaning the moisture in the fuels does not recover at night as much as other locations on the slope. So, fire danger is greatest in the valley bottom and lower slopes midday and at night the ignition hazard remains fairly high at high elevations. A field version of Byram and Jemison s (1943) and Van Wagner s (1969) initial laboratory studies on solar radiation, Countryman (1977) found a difference of ± 2 percent between the mean moisture content of ponderosa pine needle litter samples measured in the shade and those measured in the direct sun (samples in the sun being drier than those in the shade). Although this may appear to be a low number, a change as small as 0.5 percent is considered significant when considering the ignition potential of a forest fuel, suggesting that this sun/shade difference should not be ignored. In his study Countryman proved that litter exposed to sunlight even for a short period of time had significantly lower fuel moisture content. But the overall determination of the current fuel moisture appeared to be more dependent on the history of incoming solar radiation at the site than the existing exposure conditions. Models have emerged to predict fuel moisture content in relation to incoming solar radiation based on the above relationships. Of the available models, the common thread is the use of an adjustment to the equilibrium moisture content related to the increase in fuel surface temperature brought on by exposure to direct sunlight. The American BEHAVE Fire Prediction System (Rothermel et al. 1986) uses the fuel temperature relationships developed by Byram and Jemison (1943) to predict the moisture content of 10-hour fuels. The Canadian model for fine fuel moisture content is based on empirical data and does not include the influence of solar radiation.

54 37 Very little field data has been collected to investigate the actual impact of solar radiation on fuels in the mountains Impacts for forest and fire management As discussed above, historical research indicates potential for a change of 1 to 2 percent moisture content between north and south-facing slopes or at sites with longer exposure to direct sunlight. This value may seem inconsequential to everyday fire and private forestry operations, but when considered in detail, it can have serious impacts on prescribed burn planning, fire operations decision-making, community forest protection and private forestry sectors, especially during periods of high fire hazard Prescribed fire planning and operational decision-making The FFMC relationship is not linear; the rate at which a fuel dries is dependent on the moisture content it begins drying from. Fuels with higher moisture contents (above fibre saturation point of 30%) will lose more moisture in a given drying period than those that are closer to their equilibrium moisture content (Blackmarr 1971). When moisture contents are at the wet end of the moisture scale (>30%), a change of 1 or 2 % moisture content will have a minimal effect on fire behaviour. Conversely, during long periods of hot and dry weather, a change of 1 or 2 % moisture content could mean the difference between a surface fire and a crown fire. For example, a change in moisture content of 2% will change the ISI by less than a point when the FFMC is between 80 and 85, but when the FFMC approaches 90, the ISI begins to change by 3 or 4 points (Figure 2.5).

55 Standard Gain 2 % MC Lose 2% MC ISI FFMC Figure 2.5 The difference in ISI using the standard FFMC relationship versus the FFMC adjusted by ±2% moisture content (using constant wind speed of 10 km/h in C-3 fuel type) Differences in ISI lead to changes in the equilibrium rate of spread, the fire intensity class and the fire type predicted by the FBP System (Table 2.3). A difference of 2% in actual moisture content at an FFMC of 94 could mean the difference between predicting an intermittent crown fire, a surface fire, or a crown fire in a C-3 fuel type.

56 39 Table 2.3 Difference in fire behaviour predictions in a C-3 fuel type based on changes (±2%) in the moisture content of the fine fuels (constant wind speed of 10 km/h) Starting FFMC ISI Equilibrium Intensity Rate of Spread Class* (m/min) Fire Type 85 Standard Surface Plus 2% Surface Lose 2% Surface 90 Standard Surface Plus 2% Surface Lose 2% Surface 92 Standard Surface Plus 2% Surface Lose 2% Intermittent crown 94 Standard Intermittent crown Plus 2% Surface Lose 2% Crown 95 Standard Intermittent crown Plus 2% Intermittent crown Lose 2% Crown *Intensity classes: 1: <10 kw/m, 2: kw/m, 3: kw/m, 4: kw/m, 5: kw/m, 6: > kw/h Operationally, there are situations where a variation of 1 or 2 percent moisture content or FFMC points could mean the difference between a successful prescribed fire and an escaped wildfire. When making a prescription for a planned fire, managers create a very detailed and often narrow window for burn conditions. There are upper limits on indices like the FFMC, and exceeding the prescription by even one point means proceeding beyond prescription and beyond the manager s authorization. Only the Incident Commander can make the decision to proceed at time of ignition and take responsibility for the consequent fire behaviour (Finn 2009). Understanding the difference in drying patterns between north and south-facing slopes may aid the Incident Commander in his/her decision-making, especially when prescriptions are likely to be built on worst-case scenario or predicated on the conditions on the hotter south and west slopes. On a larger scale, the major factors influencing fire occurrence are ease of ignition (Tanskanen et al. 2005) and the potential number of sources of ignition (Cunningham and Martell 1973). An estimation of the expected number of fires can be calculated using daily records of the observed FFMC and the corresponding number of fires that occurred historically along with a Poisson distribution regression technique (Martell 2001, Wotton 2009b). The FFMC values used for this

57 40 procedure could be adjusted to suit local topographic needs in order to give a more accurate representation of the expected number of fires. This would align with evidence of increased fire frequency on south over north-facing slopes (Barrows 1951, Heyerdahl et al. 2007). An improved ability to predict local fire occurrence is useful in allocating suppression resources, in deploying initial attack equipment and crews, and in scheduling aerial fire detection routes in ways that minimize cost plus loss figures (Martell 2001) Community protection and fuel treatment Preventative fuel treatments involving thinning of stands or underburning are becoming more common as managers attempt to deal with spreading populations and increasing forest fuel loads and increased risk of crown fire events (Canadian Council of Forest Ministers 2005, USDA 2006). The goals of such treatments are often to reduce surface fuels, increase height to live crown, decrease crown density and retain large trees (Agee and Skinner 2005). In doing so, the understory is often removed causing in-stand wind speed and exposure to solar radiation to increase and the resulting moisture content to drop by a few percentages. As these treatments are often located around communities, the increase in probability of ignition related to the drop in moisture content has the potential outweigh the intended decrease in crown fire potential. These conditions are exacerbated further by lack of maintenance and improper reduction of fine forest fuels post-harvest (Agee and Skinner 2005, Schroeder et al. 2006, Valliant 2009). In order to make decisions about location and size of treated stands, managers often consider the resulting impact on modeled fire type, fireline intensity, rate of spread and probability of ignition (Valliant et al. 2009). If one could predict the change in FFMC related to decreasing stand canopy closure, this would translate into calculations of ISI, ROS and intensity class, making them more representative of the thinned stand conditions. In terms of probability of ignition, if surface fuel loading increases after treatment, so does the number of points of contact for a potential source of ignition. Effectively, there are more fuels available to burn and they are exposed to more extreme fire weather conditions (in terms of wind speed, temperature, relative humidity and solar radiation).

58 Private forestry sector Private logging and silviculture operations may also benefit from improved knowledge of stand moisture dynamics, especially in terms of its influence of forest floor ignitability. For example, the province of British Columbia is divided into Danger Index regions determined from analysis of historical fire weather and occurrence data (Turner 1973, British Columbia Forest Service 1975). Within each region, Danger Classes are defined by ranges of FWI and BUI and linked to guidelines for resource levels, patrols, and alert status and to regulations for private forestry activities (Government of British Columbia 2009). The system is a tool to aid decision-making and requires interpretation at the local level, meaning that it is possible to have a high danger rating based on yesterday s weather when it is presently raining. The FWI System codes are usually calculated based on one noon (LST) observation per day and represent a large administrative area (Beck 1986). Actual hazard across the different zones will vary greatly, particularly in areas with complicated topography. The blanket index for the zone will likely misrepresent the conditions in individual harvesting blocks, leading to days where high hazards are predicted but local conditions are benign. By increasing understanding of fire risk, the value of information provided by the fire prediction system to forest landowner is improved (Amacher et al. 2005). Consider the following theoretical decision-making problem for a harvesting company with forest management unit where it harvests from predominantly north-facing slopes in lodgepole pine stands. There is a standard open fire weather station located within 40 km of the block in the valley bottom which is used to produce daily fire danger rating indices for the entire valley. The fire danger rating indices are used both for pre-preparedness planning and the deployment of fire suppression resources as well as determining when to cease harvesting activities because the risk of fuel ignition from a spark becomes too great. Imagine that one of the forest managers has studied fire danger literature and believes that the fire danger rating indices observed from the fire weather station located in the valley bottom are not representative of the fuel moisture conditions on the north-facing slopes. He believes that the fire danger rating system is causing a Type I error, where the system sounds an alarm when no serious potential for fire exists (Taylor and Alexander 2006).

59 42 He feels that the probability of ignition for his site is lower than what is estimated in the valley bottom based on weather conditions collected at a valley bottom weather station, which is a Type I system error defined by a false alarm from the system when no serious potential for fire exists (Taylor and Alexander 2006). He also thinks that the forest closure rules that are based on the indices need to be modified for use on the north-facing slopes. Suppose that the probability of harvesting activities igniting a fire on the north-facing slope is based on historical data and the average number of fires per day is a function of the FFMC observed at the valley bottom fire weather station (Table 2.4). Table 2.4 Hypothetical number of fires per day related to FFMC value from the valley bottom weather station Expected FFMC from valley bottom number of fires per day (λ) Suppose the company incurs a loss of $PL/day for each day that it must cease harvesting due to fire hazard. Suppose, also that if an industrial fire starts in the company s holding, the expected loss is $FL per fire. During an average summer, the FFMC from the valley bottom has a probability distribution FFMCD(i) where FFMCD(i) is the fraction of days during a fire season that the FFMC at the valley bottom station is equal to i. On a day where the FFMC is FFMC Today the expected cost if the company must cease work is equal to $PL. If the company decides to continue to work, the expected cost is $FL/fire multiplied by the λ for the value of FFMC Today. To calculate the optimal solution for when the company should work and when it should cease activities, first a threshold value for FFMC should be chosen. This threshold represents the FFMC above which the company should not operate, and for this example choose a FFMC of 90. Generate multiple days of FFMC values

60 43 (for example 150 days) based on the FFMCD distribution above, assuming that the days are independent (although they are not). If the FFMC Today is greater than or equal to the threshold, then the incurred cost is %PL. If the FFMC Today is less than the threshold, then generate the number of fires using λ according to a Poisson distribution and each fire will cost the company $FL. Sum up the total costs for the fire season. Repeat this for as many fire seasons (say 1000) and compute the expected cost as a function of the FFMC threshold value and the FFMCD. This threshold can then be made larger or smaller until the value that will minimize the average harvesting penalty cost plus loss per season is identified. The company forester will complain that on a day when the FFMC Today from the valley bottom is 90, that the conditions are milder on his site. According to the results from this study, say the FFMC at his slope is actually 88. The initial problem is that the fire occurrence model for λ on the north-facing slopes is based on FFMC from the valley bottom. If a new occurrence model is developed by sampling assuming that the FFMC on the upper slope is always two points less than that in the valley bottom, and the optimal solution determined as above, the threshold value for the north-facing slope will end up being exactly two points below the optimal threshold for the valley bottom FFMC. Perhaps it is easier for the company and the government fire agency to agree to a simpler rule. For example, the government may request that the company must cease all activities when the moisture content is less than 10% (close to an FFMC of 90). This means that the company can continue operations in the valley bottom when the FFMC (valley bottom weather station) is less than 90, but it can continue operations on the north-facing slope until the FFMC is 92 with low likelihood of incurring addition fire costs. To further this example, the value of an improvement to the FFMC to better suit the local slope conditions can be calculated. Choose an FFMC threshold value and then calculate the expected cost for multiple fire seasons. Then, adjust the FFMC threshold value to better represent the north-facing slopes and recalculate the expected cost for the fire season. The difference between

61 44 the two costs is the representative dollar value increase for optimizing the solution with more accurate local fire hazard. There is a caveat to the above theoretical model. If the difference of 1 or 2 % moisture content occurs when forest fuels are dry (<30%), in most cases the forester would not be allowed to operate in the higher FFMC values regardless of an adjustment to the FFMC code values.

62 45 3 Chapter 3 Methodology The sample plan and protocol for this study were drawn from multiple resources in order to meet specific project objectives. Further technical details concerning site description, weather station installation and destructive sampling are outlined in Appendix A. 3.1 Study location Site selection After assessing potential sites throughout the Rocky Mountains, Nordegg, Alberta was chosen as the best location for the field study. The following factors were used to assess site suitability: - Distance from a research base (location with sample storage, drying oven, scale, etc.) - Distance between north and south-facing slopes (width of the valley) o Time between sampling must be less than 1.5 hours - Similarity of steepness of slopes - Homogeneity of individual slope along up/down-slope transect - Similarity of elevation of both slopes - Proximity to a nearby standard (open) fire weather station - Similarity of stand composition and density on both the north and south-facing slopes o Mature jack or lodgepole pine with 1500 stems per hectare (C-3 fuel type) was preferred (this stand type was used to create the original FFMC in the FWI System) - Accessibility for daily destructive sampling and weather station maintenance - Vicinity to participating partners and data collectors The town of Nordegg is situated at the eastern reach of a 100 kilometer long east-west aligned valley. The north and south-facing slopes are oriented nearly exactly to the poles, with similar stand types and slope steepness at equal elevations. Sites were easily accessible by foot, and the

63 46 time to travel by vehicle between the north and south-facing slopes was less than 15 minutes. Alberta Sustainable Resource Development (ASRD) provided space for drying ovens and other equipment at the Nordegg Ranger Station in the valley bottom, as well as researcher accommodations at a nearby active fire base. Nordegg is located in a montane ecoregion on the most eastern edge of the Rocky Mountains (N , W ). The research area was located in stands of trees predominantly dominated by lodgepole pine (Pinus contorta) with a white spruce (Picea glauca) understory. The forest floor consisted of a continuous layer of feathermoss with light understory herbaceous species such as kinnikinnik (Arctostaphylos uva-ursi), northern bedstraw (Galium boreale), bunchberry (Cornus canadensis) and buffaloberry (Shepherdia canadensis). Underlying the north-facing site was a non-calcareous, well-drained Eutric Brunisol soil type. On the southfacing side, the soil-type was a strongly calcareous, moderately drained Brunisolic Gray Luvisol Stand characterization methods Five stands of trees within the valley were selected as study sites and identified as: north-facing high, north-facing low, valley bottom, south-facing high and south-facing low. In the second field season, an additional site was added in the valley bottom in a thinned stand and was referred to as the FireSmart stand. An in-stand weather station was installed and three separate destructive fuel moisture sampling locations delineated at each site. A fixed radius plot was used to determine stand description data such as tree age, tree height, crown base height, species composition and diameter at breast height (Appendix A). From this data stand density and basal area were calculated for each site Forest floor characterization methods A general forest floor survey was conducted at each destructive sampling location and percent coverage of litter, moss and plant species recorded. Three fuel loading samples were collected at each sampling location using a 900 cm 2 grid. Each fuel loading replicate was broken down into

64 47 independent loading layers: surface needles (non-rotten needles at least two thirds above the forest floor), deep needles (blackened and rotten needles buried in forest floor), woody debris (twigs, cones, bark above and below the surface), moss tips (top 2 cm of moss), moss (green and brown moss minus the tips), and duff (decaying dark material below the moss and above the mineral soil) (Figure 3.1). Averages amongst the three replicates for each substrate layer were used to determine a value for total fuel loading at each site. Moss and duff depths were also recorded for each location and averaged for the site. Figure 3.1 Sampling protocol for forest floor characterization 3.2 Weather station installation At each of the 5 standard sites and thinned site (in 2009), an in-stand weather station was installed to collect various meteorological elements. An open station designed to meet current operational fire weather specifications was deployed in a forest opening in the valley bottom (Figure 3.2).

65 48 Figure 3.2 Installation of weather stations at the standard sites (FireSmart stand not shown) In-stand weather station specifications Each in-stand weather station consisted of Campbell Scientific components: one multi-channel datalogger (CR ), two solar radiometers (LI200X-20 LI-COR silicon pyranometer), two electronic fuel moisture sensors (CS505-L and L10824), one set of manually weighed 10 hour moisture dowels, one shielded temperature and relative humidity sensor (HC-S3 with X), three tipping bucket rain gauges (TE525M with 0.1mm tip) and five fencepost rain gauges (Figure 3.3). In 2009, an anemometer (Windsonic sensor) was installed 1.5 m above the ground to represent in-stand wind conditions. 1 Product numbers presented in brackets are from Campbell Scientific Canada catalogue.

66 49 Fence post rain gauge Rain gauge RH and Temp Solar Radiometer Solar Radiometer CR1000 Rain gauge Rain gauge Electronic moisture stick Manual moisture stick Figure 3.3 Configuration of in-stand Campbell Scientific weather station (not showing wind sensor) Multiple rain gauges and solar radiation sensors were included to attempt to capture variability in rain and sunlight conditions under the canopy. Tipping bucket rain gauges and solar radiometers were rotated bi-weekly around the central installation to avoid bias in particular canopy conditions. Manual rain gauges were emptied after every rainfall event or noted as composite samples if the precipitation occurred overnight or in multiple storm events Open fire weather station specifications A weather station was installed in an open area of the Nordegg Ranger Station and conformed to standard fire weather data collection specifications (Lawson and Armitage 2008). A portable communications tower was used to mount a 10 m high R.M.Young wind monitor (05103), and a datalogger recording temperature, relative humidity, solar radiation, electronic moisture content and rainfall was installed nearby.

67 Sampling intervals for all weather stations Each weather station recorded observations at 15 minute intervals. Electronic tipping bucket rain gauges summed the amount measured during the 15 minute period. Wind and electronic moisture content values were averaged over the interval, but the value at the 15 minute mark was recorded for temperature and relative humidity. Dataloggers stored solar radiation information as both the average over 15 minutes as well as the measured value at the 15 minute mark Statistical analysis of weather data Before any paired comparisons of micrometeorological conditions between sites were made, the accuracy of all the temperature and relative humidity sensors was tested by running them for 24 hours under the same environmental conditions and comparing the resulting values. Once it had been established that the sensors were collecting similar values for temperature and relative humidity, the data were plotted in time series graphs to identify diurnal trends related to site elevation and aspect. The data from the sites were then paired by hour and stratified into time intervals (morning: 0800 to 1200 hrs, peak: 1300 to 1700 hrs, evening: 1800 to 2200 hrs, overnight: 2300 to 0700 hrs) and compared using a Student s t-test to determine the mean difference in temperature and relative humidity between sites. Because the sensors that recorded solar radiation data at the site were installed and leveled to the horizontal plane, the data needed to be corrected to represent conditions on different slopes and aspects. To do this, a basic solar radiation model was constructed using data from existing sources (Sellers 1965, Allen et al. 2006) and is described in Appendix B. From the model, a conversion was calculated for each sloped site and the observed values adjusted accordingly. Data from a clear day were plotted to show diurnal variation in solar radiation between sites. The difference in solar radiation in kw/m 2 between sites was determined using paired Student s t-tests. The ratio of incident solar radiation reaching the forest floor, or transmittance, was calculated for each site using Equation 3, where I is the solar radiation received in-stand and I o is

68 51 the solar radiation in the open (measured at every 15 minute interval). T= I / I o [3] Significant differences in transmittance between sites were also determined using a paired Student s t-test. The Leaf Area Index (LAI), or the fraction of photosynthetically active radiation absorbed by forest canopies, was not physically collected during the field season due to a sensor malfunction so it was estimated using mean transmittance values for each site and the Beer-Lambert Law in the following equation (Bréda 2003): I= I o e -k* LAI [4] where I is the solar radiation received beneath the canopy, I o is the solar radiation received in the open, and k is the extinction coefficient (assumed to be 0.52 for conifers following Bréda 2003). The ratio between the wind speed collected in-stand and that collected at 10 m height in the open was calculated for each site for each time period of the day (morning, peak, evening, overnight). The observed wind speed was paired by hour and the differences between sites determined using a Student s test for each of the designated time periods. The difference in in-stand to open wind ratio was also quantified in the same way. Individual variation between the electronic moisture sensors was addressed by comparing the measurements between sensors installed at the same site, and the data were plotted in time series across multiple days to illustrate diurnal drying and wetting trends in the data. Values from the destructive litter sampling were plotted against the sensor estimated diurnal curves to assess the ability of the sensor to predict moisture content at the sites. Values from the sensor were then paired by sample time with the observed value and the difference between them determined using a Student s t-test.

69 Fine fuel moisture content destructive sampling Data collection for this study occurred over two separate field seasons. The physical sample collection remained similar, but the method and timing of sampling events differed between seasons. In 2008, data were collected from early June to mid-september, covering seasonal variation in weather and fuel moisture conditions. The summer of 2008 lacked a substantial drying trend, so an additional campaign-style field study was conducted from mid-july to mid- August During each sampling event, two forest floor substrates were collected: fallen needle litter on the moss surface and the top two centimeters of the feathermoss layer. A more detailed account of destructive moisture content sampling field procedures is outlined in Appendix A The 2008 field season In 2008, sites were visited each rain-free day throughout the length of the fire season, beginning in early spring and continuing though late summer. The fire weather conditions in Nordegg were uncharacteristically cool and rainy for most of the season, with the longest period without rain lasting only three days. A single individual collected samples from each site while covering the length of the valley as the day progressed. For example, on a typical day sample collection would being at 0900 hrs (MDT) at the north-facing high site, progress from the high site to the low site and into the valley bottom by 1300 hrs and commence sampling at the south-facing high site by 1800 hrs. The sites were visited as randomly as possible, but usually the high and low sites on a particular slope were visited one after another. At each site, samples were collected at two of three designated sampling locations (Figure 3.4). During each sampling event at each location, three tins of needle litter and two tins of moss-tips were collected, making a total of six litter and four moss tins from each location.

70 The 2009 field season During a drying trend in 2009, a campaign-style sample plan was executed to capture moisture data during higher fire hazard. Ten days into a drying trend in mid-july, up to five people participated in sampling events, collecting simultaneous litter and moss-tip samples at all sites. The sampling day was divided into two hour target intervals starting at 0830, then through 1000, 1200, 1400 and ending at 1600 hrs. Three people was the minimum number required to sample all six sites, with one individual collecting data from both the upper and lower site at the same aspect within the two-hour interval. After preliminary analysis into intra-site variation it was determined that no significant difference in moisture content existed between the three designated sampling locations at each site. Subsequently, a single sample location was visited during each sample event; during each event four litter and four moss-tip samples were collected Sample methods Each sampling location was marked with flagging tape in order to prevent trampling and compaction of the forest floor. During sample collection, additional flagging and pins were used to identify a cross-slope transect from which composite samples were collected along its length (Figure 3.4). Samples were not collected from strongly sheltered areas (close to boles of trees, under leaning trees or areas of atypical vegetation density), from uncharacteristically open areas, from depressions in the forest floor that were clearly wetter, from areas obviously compressed from walking or kneeling or from areas where the litter has become unnaturally compacted (animal or hiking trails).

71 54 Figure 3.4 Schematic of in-stand weather station set-up and destructive sampling routine Down-time between site visits was minimized in order to keep sample collection time less than 1.5 hours. Sky condition and time of sampling were noted at every sampling event. The sky condition as defined by general cloud type as well as in tenths of the celestial concave occupied by clouds. Dead needle litter was collected from the surface of the feathermoss and placed in an air-tight metal container until it was two-thirds full. Needles were not collected if they showed signs of decay or were buried more than a third of their length in the moss. Needles were also not collected if they were elevated above the forest floor. Organic material such as bits of moss, green needles, bark, small twigs or herbaceous vegetation were removed from the sample before sealing the tin. Before collecting moss-tips, the depth of the feathermoss layer was measured. Following that, scissors were used to clip the top two centimeters from the surface of the moss and placed in a tin. Again, care was taken to exclude unwanted materials such as pine cones, twigs, etc.

72 55 Once the tins were full of appropriate materials, they were sealed with painter s tape and labeled with the sample site, location and time. They were returned to the lab for drying as soon as possible to avoid condensation in tins and formation of mould. The tape was removed and each tin was weighed wet. After 24 hours in a 95 to 105 C oven, the samples were removed and weighed immediately dry. After discarding the sample and recording a tare value, the gravimetric moisture content was calculated using: moisture content (%) = ( wet dry ) / ( dry tare )*100 [5] Statistical analysis of moisture data Initially, the distribution and descriptive statistics for the entire moisture dataset from both seasons was explored. The daily FFMC was evaluated for accuracy against the destructive moisture content values and intra-site variation discussed. Because they were collected using different approaches, the analysis of inter-site variations in destructive moisture content was different for each year Moisture data from 2008 The moisture content of fine fuels varies diurnally; early in the morning, the fuels have higher moisture contents, resulting from exposure to cool temperatures and high humidities during the overnight period (Stocks 1970, Van Wagner 1977). Throughout the day, the fuel dries towards its equilibrium moisture content, eventually reaching its lowest moisture value between 1400 and 1700 hrs. Because the fine fuel moisture content varies greatly with time, the samples collected in 2008 could not be used for paired site to site comparisons. Traveling between sites meant that there were often periods of two hours or more between sampling, so weather conditions at one site could have been much different at another site two hours later. To overcome this, hourlycalculated (Van Wagner 1977) and diurnally-adjusted (Lawson et al. 1996) models of the FFMC were used to make inter-site comparisons.

73 56 First, a moisture content from the hourly-calculated FFMC was associated with an observed moisture content value. The hourly-calculated FFMC and diurnally adjusted FFMC values were calculated from weather data collected at the nearby open station. An hourly value from each model was paired with the hour that the destructive sample was collected and a Student s t-test used to compare the mean difference. Ideally, the samples collected in the valley bottom would have very similar moisture contents to that predicted by the FFMC. At the south-facing sites, the difference between the predicted and actual moisture content is hypothesized to be positive, suggesting that the FFMC overpredicts moisture content. At the north-facing site, the observed moisture content is hypothesized to be higher than the moisture content predicted by the FFMC at by the valley bottom. To reduce the effect of absolute errors in the hourly and diurnally adjusted models, the general drying curve from each was assumed to be similar amongst sites based on observed temperature and relative humidity trends and used to adjust all observations to a common time (Figure 3.5). By assuming that the fuels dry towards equilibrium in a similar pattern but differ by slight changes in weather conditions, estimations of moisture content can be made by moving along the moisture curve. Figure 3.5 Visual representation of estimating moisture content along a diurnal drying curve

74 57 For example, if a destructive moisture content sample was collected at a site at 1100 hrs the hourly-calculated curve can be used to estimate the value of moisture content at that site for 1600 hrs using the following relationship where is the difference in modeled moisture content (by hourly-calculated or diurnally-adjusted FFMC) between time t and time p, t is the time at destructive sampling and p is the time that the moisture content is predicted for: Actual mc p = Actual mc t [6] Using the destructive sample as a reference point, the moisture content was predicted for a similar time for all sites and inter-site comparisons were made using a Student s paired t-test Moisture data from 2009 For the 2009 data, daily trends in diurnal drying and wetting were initially examined using time series plots. Following that, the simultaneous nature of the moisture sampling during the 2009 field campaign allowed for direct comparisons in moisture content between sites using paired Student s t-tests. The rate of drying between sites was also investigated. First, a basic drying rate in terms of moisture percentage change per hour was calculated using Equation 7 where MC is the moisture content and t 1 and t 2 are two consecutive samples. [7] The second method of determining drying rate is dependent on the equilibrium moisture content (EMC). The EMC is calculated using two different equations within the FFMC model (Van Wagner 1987). Under constant temperature and relative humidity, two parallel isotherms demonstrate a condition of hysteresis, where the moisture content reached by wetting from below is one or two moisture percentage points lower than that reached by drying from above. During a period without rain, the following equation was used to calculate EMC obtained by drying from above using in-stand temperature in degrees Celsius (T) and relative humidity in as a percentage (H) (Equation 8).

75 58 [8] The EMC values were paired by time of day and Student s t-tests used to test for significant differences between means of different sites. The drying rate was then calculated using the following relationship (Equation 9). This equation determines a dimensionless drying rate, where the value represents the fraction of total evaporable moisture remaining in the fuel after drying (or wetting) has occurred where MC is the observed moisture content, EMC is the equilibrium moisture content and t 1 and t 2 are successive times. These rates were compared between sites using Student s paired t-tests. [9]

76 59 4 Chapter 4 Results and Preliminary Discussion Prior to making statistical comparisons, the variability, distribution and trends in the weather and moisture data were examined using basic charts and tables. Information from the preliminary results led to further results and discussion in subsequent chapters. 4.1 Site description Stand characterization The sites were similar in stand density, with the north-facing sites being slightly less dense (1265 and 1369 stems/hectare) than the south-facing sites (1424 and 1432 stems/hectare) (Table 4.1). The FireSmart stand was approximately 25% less dense than the other treated stands, but the number may not be accurate as the tally survey was conducted after a storm blew most of the trees down. The average pine age was lower on the south-facing site (80 years) and higher on the north-facing side (121 years). All the sites had at least 50% pine in the overstorey, with mature white spruce as the remaining component of the canopy. The spruce trees were slightly younger on the south-facing side (75 years) than the north-facing side (80 years). Basal area was determined for the entire stand (pine and spruce together), and ranged from 36.4 m 2 /ha in the FireSmart stand to m 2 /ha in the valley bottom. Tree height was similar for all sites, with an average of 20 m (standard deviation 3.6 m). The mean distance from the crown to the ground was also similar for all sites with a value of 10 m (2.6 m standard deviation).

77 60 Table 4.1 Stand and forest floor characteristics for each site (standard deviation is reported in italics in brackets) Lodgepole Slope Elevation Density Stand Basal Site Pine (%) (m) (stems/ha) Area (m 2 /ha) (%) Mean Pine DBH (cm) North-facing high (4.7) North-facing low (2.9) South-facing high (2.8) South-facing low (2.3) Valley Bottom (1.5) FireSmart unknown (2.3) Forest floor characterization The highest fuel loading value for surface needles was measured at the north-facing high site (0.06 kg/m 2 ) (Table 4.2). Moss fuel loading ranged from 0.43 kg/m 2 at the FireSmart site to 0.73 kg/m 2 at the south-facing low site. Moss tips were not collected at the FireSmart stand, as the mean depth of moss rarely exceeded 2 centimeters. Duff loading was largest at the north-facing low site (9.08 kg/m 2 ) and smallest at the FireSmart site (3.12 kg/m 2 ). The site with the highest total fuel loading (the sum of the loading values for surface needles, deep needles, moss tips, moss and duff) was the north-facing low site (9.76 kg/m 2 ). The lowest fuel loading value was observed at FireSmart stand (3.53 kg/m 2 ). The nominal fuel loading value for the FFMC is 0.25 kg/m 2 (Van Wagner 1987). Surface needle and moss tip fuel loading values were summed from each site to obtain the FFMC loading. FFMC loading values were generally lower than the nominal value, except for loading in the FireSmart stand (0.42 kg/m 2 ).

78 61 Table 4.2 Mean forest floor fuel loading values for each site (standard deviation is reported in brackets in italic font) Site Surface Needles (kg/m 2 ) Deep Needles (kg/m 2 ) Woody Debris (kg/m 2 ) Moss Tips (kg/m 2 ) Moss (kg/m 2 ) Duff (kg/m 2 ) North-facing high 0.06 (0.048) 0.06 (0.025) 0.23 (0.173) 0.15 (0.021) 0.60 (0.026) 3.99 (1.60) North-facing low (0.015) (0.030) (0.407) (0.003) (0.033) (1.52) South-facing high (0.007) (0.023) (0.056) (0.046) (0.17) (5.61) South-facing low (0.006) (0.053) (0.22) (0.021) (0.12) (2.46) Valley bottom (0.006) (0.017) (0.027) (0.016) (0.113) (0.26) FireSmart (0.003) (0.11) (0.094) (1.03) * Total loading is the sum of the loading values for surface needles, deep needles, moss tips, moss and duff (woody debris is not included in forest floor loading) The loading of the fine fuels (FFMC loading) is the sum of the loading values for surface needles and moss tips Total Site Loading (kg/m 2 ) * 4.66 (1.85) 9.76 (1.53) 8.56 (5.59) 4.77 (2.40) 6.60 (0.14) 3.53 (0.94) FFMC Loading (kg/m 2 ) 0.21 (0.06) 0.14 (0.01) 0.20 (0.05) 0.11 (0.03) 0.11 (0.02) 0.42 (0.10) Moss and duff depth data were also collected. Depths of the moss and duff layers were fairly consistent through all the sites (Table 4.3). The FireSmart stand had shallower measurements, likely due to disruption of the forest floor during mechanical thinning. Table 4.3 Total and green moss mean depth for each site (standard deviation is reported in brackets in italic font) Site Total depth (cm) Green depth (cm) Mean Duff Depth (cm) North-facing high 10.3 (3.6) 3.6 (0.9) 6.0 (2.0) North-facing low 7.6 (2.1) 3.2 (0.8) 8.0 (1.7) South-facing high 8.1 (2.3) 3.6 (0.8) 6.0 (3.0) South-facing low 9.1 (2.8) 3.8 (1.0) 6.0 (1.4) Valley bottom 8.3 (3.4) 3.5 (1.0) 5.0 (1.5) FireSmart 3.5 (1.2) 2.1 (0.5) 2.0 (1.0)

79 In-stand micrometeorology Sensor accuracy Prior to installing the in-stand weather stations for the 2009 field season, the temperature and relative humidity sensors were tested for consistency between units. They were arranged together in an office and allowed to run in tandem for nineteen hours (Figure 4.1). Temperature (degrees celsius) or Relative humidity (%) NFHigh Temp NFLow Temp SFHigh Temp SFLow Temp VBin Temp NFHigh RH NFLow RH SFHigh RH SFLow RH Vbin RH Hour of the day (MST) Figure 4.1 Temperature and relative humidity sensor time series under similar environmental conditions. The FireSmart and valley bottom open sensors were also tested separately and displayed similar results. All the temperatures and relative humidities remained within one half of a degree Celsius and one percent relative humidity (Table 4.4). Although the sensors were placed on the same table, the differences between the sensors changed over time, suggesting that there may have been slight variations in the conditions each sensor was exposed to, rather than biases between the sensors themselves. These differences were deemed small enough to not affect the outcome of further comparisons between the in-stand weather station installations.

80 63 Table 4.4 Differences between temperature and relative humidity sensors from different sites operating under the same conditions. For these comparisons, the valley bottom sensor was chosen as the standard and subtracted from the site sensor (standard deviation is reported in brackets in italic font) Site Temperature Mean Difference ( C) Temperature Maximum Difference ( C) RH Mean Difference (%) RH Maximum Difference (%) North-facing high 0.18 (0.33) (0.41) North-facing low 0.11 (0.12) (0.18) South-facing high 0.24 (0.31) (0.42) South-facing low 0.16 (0.35) (0.52) Temperature and relative humidity Diurnal trends Distinct diurnal patterns were seen in temperature and relative humidity time series during a drying trend (Figure 4.2). Overnight, the differences between the sites were larger and mainly due to elevation. Cool moisture air settled as the evening progressed, leading to lower temperatures and higher relative humidities in the valley bottom and at the lower sites. The higher sites remained warmer and drier, which suggests that they may have been located in the thermal belt. Fire managers describe the thermal belt as a band of trees along a slope at the top of an inversion layer where it is drier and warmer and forest fires can remain active over night (Schroeder and Buck 1970). Stand types with less dense canopy cover such as the FireSmart and valley bottom sites also lose additional heat to the atmosphere overnight as terrestrial radiation (Byram 1948).

81 Temperature (`C) Elevation driven differences Aspect driven differences Valley bottom open Valley bottom in-stand South-facing low South-facing high North-facing low North-facing high FireSmart Hour of day (MDT) Relative humidity (%) Elevation driven differences Aspect driven differences Valley bottom open Valley bottom in-stand South-facing low South-facing high North-facing low North-facing high FireSmart Hour of day (MDT) Figure 4.2 Temperature and relative humidity time series during optimal drying conditions on July 25, 2009

82 65 During the peak burning period (1400 to 1700 hrs), the magnitude of the inter-site differences in temperature and relative humidity decreased and were influenced by aspect rather than elevation. The north-facing sites during this period had lower temperatures and higher relative humidities than the south-facing sites. The sites with less dense canopies like the FireSmart stand had the highest temperature and lowest humidities Inter-site differences In-stand values for temperature and relative humidity were compared to those collected by the standard open weather station (Table 4.5). During the peak conditions of the day, the temperature differences between sites ranged from 1.3 to 3.7 C. The relative humidity at the north-facing sites was higher than at the open station, while the relative humidity at the southfacing sites was lower. The standard deviation for these mean differences were sometimes quite high, likely owing to the variability in the weather data during the time period as well as limitations in sample size (morning, peak and evening n= 64, overnight n= 144). Table 4.5 Site temperature and relative humidity compared to that collected in the open at a standard reporting weather station during a drying trend between July 22 and July 25, 2009 (standard deviation is reported in brackets in italic font) Difference between valley bottom open and site (VBO- site) Morning (0800 to1200) Peak (1300 to 1700) Evening (1800 to 2200) Overnight (2300 to 0700) Site Temp RH Temp RH Temp RH Temp RH ( C) (%) ( C) (%) ( C) (%) ( C) (%) North-facing high 3.4 (2.8) -3.1 (10.2) 3.7 (1.0) -2.2 (3.4) 2.3 (1.4) 1.8 (5.8) 2.8 (2.4) 21.1 (9.4) North-facing low 3.0 (1.7) -3.3 (5.7) 3.3 (1.2) -1.4 (3.2) 2.5 (1.6) 0.7 (5.6) 1.5 (1.6) 13.6 (6.8) South-facing high 3.2 (2.3) -2.1 (8.0) 2.2 (0.8) 3.7 (4.2) 1.8 (1.4) 4.3 (5.6) 2.8 (2.0) 20.5 (7.4) South-facing low 3.5 (2.3) -5.9 (6.2) 1.9 (0.7) 3.7 (4.4) 2.1 (1.4) 3.1 (5.1) 1.1 (1.5) 15.0 (5.6) Valley bottom (1.3) FireSmart 2.3 (1.1) (7.4) -3.4 (4.2) (0.5) 1.3 (0.8) (3.3) 4.9 (5.2) (1.0) 1.8 (1.2) (4.8) 1.3 (4.7) (0.5) 0.4 (1.0) (2.7) 4.6 (3.5)

83 66 To examine slope and aspect driven differences between sites during a period of high fire danger, temperature and relative humidity data from the in-stand stations were compared using paired Student s t-tests (Table 4.6). Due to the observed differences in daytime and nighttime heating in the mountain valleys, daily observations were separated into four periods based on Mountain Standard Time (MST): morning (0800 to 1200 hrs), peak (1300 to 1700 hrs), evening (1800 to 200 hrs) and overnight (2300 to 0700 hrs). Table 4.6 Comparison between north and south-facing sites of hourly temperature and relative humidity data from July 22 to July 25, 2009 using Student s t-tests paired by hour and stratified by time of day SH - NH SL NL NH - NL SH - SL Temperature Mean Mean Mean Mean DF P-value P-value P-value ( C) Diff Diff Diff Diff P-value Morning * Peak <0.0001* 1.31 < * * * Evening * * * 0.33 <0.0001* Overnight * 1.29 <0.0001* 1.65 <0.0001* RH (%) Morning Peak <0.0001* <0.0001* * Evening * * * * Overnight <0.0001* <0.0001* * significant at the 0.05 level SH= south-facing high, NH= north-facing high, SL= south-facing low, SH= south-facing high During the morning period, there was little difference in temperature or relative humidity between the north and south-facing sites or between the higher and lower sites of the same aspect. By the peak burning hours of the day, where the effect of aspect on microclimate was most apparent, the south-facing sites were on average 1.3 and 1.5 C warmer than the northfacing sites; the largest difference occurred between the high sites. The accompanying significant decrease in relative humidity had mean differences of 5.1 % (low sites) and 5.9% (high sites). Further comparisons showed that during the peak, the low sites of the same aspect were significantly drier, though by a small amount (0.5 C and 1% relative humidity). The evening period showed a transition from aspect-driven to altitude-driven differences in temperature and relative humidity amongst sites. The difference in temperature and relative humidity between opposing aspects dropped to less than 0.5 C and 3% relative humidity while

84 67 the results for the differences between high and low sites reverse, with the low sites becoming significantly cooler and moister than the high sites. Overnight, there were very small (0.4 C) or no significant mean difference between the north and south-facing slopes. Driven by the difference in elevation, the high sites were warmer (1.3 and 1.7 C) and drier (5.5 and 7.5%) on average than the low site, evidence of the aforementioned diurnal trend. These conditions generally last overnight until about sunrise, when the difference between high and low sites begins to diminish and the south-facing sites become warmer and drier as the day progresses. For operational purposes, the FFMC is typically calculated from weather collected at open stations in the valley bottom. The FFMC as calculated then represents the in-stand conditions in the valley bottom. Paired Student s t-tests were used to examine whether or not there were differences between temperature and relative humidity at the sloped sites in relation to the valley bottom in-stand site (Table 4.7). Table 4.7 Comparison between sloped sites and valley bottom of hourly temperature and relative humidity data from July 22 to July 25, 2009 using Student s t-tests paired by hour and stratified by time of day NH - VBIN NL - VBIN SH - VBIN SL - VBIN Temperature Mean Mean Mean Mean DF P-value P-value P-value ( C) Diff Diff Diff Diff P-value Morning Peak <0.0001* * Evening * * * Overnight <0.0001* 2.04 <0.0001* 3.28 <0.0001* 1.63 <0.0001* RH (%) Morning * * * * Peak * * Evening <0.0001* <0.0001* <0.0001* <0.0001* Overnight <0.0001* <0.0001* <0.0001* <0.0001* VBIN= valley bottom in-stand site In the morning, temperature between the sloped and valley bottom site were not significantly different, but the valley bottom did have significantly higher relative humidities than the other sites. By the peak burning period of the day, the valley bottom in-stand site was significantly warmer than the north-facing side, but without a significant difference in relative humidity. On

85 68 the south-facing side, the temperature was not significantly different from the valley bottom and the relative humidity was significantly lower by a small amount. In the evening, most of the sloped sites became significantly warmer than the valley bottom site; the relative humidity remained statistically higher than all the sloped sites, with mean differences ranging from 6.5 to 10.2% relative humidity. Overnight, the temperatures in the valley bottom decreased significantly, with the high sites showing larger mean differences from the valley bottom (3.3 C) than the low sites (2.0 and 1.6 C). There was also a corresponding significant rise in relative humidity at the valley bottom, with the high sites differing from the valley bottom by a mean of 17.4 and 18.0 % and the low sites by 10.5% and 11.9%. Although the valley bottom in-stand site had a slightly lower density than the other sloped sites, the differences in temperature and relative humidity are of interest to any practitioner deploying portable weather station units for prescribed or wildfire events. Aspect does appear to influence ambient air temperature and relative humidity within each stand, with its greatest impact occurring during peak burning hours. Small differences in elevation can also influence temperature and relative humidity, especially during the overnight moisture recovery period. Comparisons were also made between the in-stand temperature and relative humidity of FireSmart stand and the south-facing low site (its closest neighbor geographically and less variable than the valley bottom in-stand site). In the morning, the FireSmart stand was significantly warmer (p<0.0001) but not drier in terms of measured relative humidity than the south-facing low site, with a mean temperature difference of 1.2 C. During peak burning conditions, FireSmart remained warmer (mean difference of 0.65 C, p<0.0001) but again did not show any significant difference in relative humidity. In the evening, no discernable significant difference in temperature was found between sites but the FireSmart stand had a significantly higher relative humidity by a mean of 1.8%. The overnight trend shows a significant drop in temperature and rise in relative humidity associated with the settling of cool, heavy air in the valley bottom. The FireSmart stand becomes on average 1.5 C cooler than the south-facing low site with a rise in relative humidity of 10.4 percent.

86 69 Although the FireSmart stand had 40% fewer trees than the other sites, there were little differences in temperature and relative humidity during the peak burning hours when compared with the south-facing low site. The overnight recovery period showed evidence of terrestrial radiation and cooling in the valley bottom Solar radiation at the forest floor Sensor adjustment The manufacturer s guidelines indicated that proper arrangement of solar radiation sensors required leveling them parallel to the horizontal plane, meaning that data collected did not necessarily represent conditions on the sloped surface. Lacking instructions for sloped installations, an adjustment was made to the observed values using a basic model created from existing literature (Sellers 1965, Allen et al. 2006, University of Oregon 2009). The model uses Julian date, hour of day, latitude, longitude and the angle of the receiving element relative to the horizontal to predict direct solar radiation received in the open on various slopes and aspects (detailed further in Appendix B). The model also was used to determine the theoretical ratio of solar radiation received in the open on the slope versus that on the horizontal using estimates for solar radiation predicted between June 1 and August 31 for the 2008 data and between July 22 and August 2 for the 2009 data (Table 4.8). During times of peak direct solar radiation (1100 to 1400 hrs), the steeper (60% slope) north-facing site received on average approximately 40% of that incident on the horizontal. The shallower north-facing slope (30%) received 70% of that at the horizontal. On the south-facing side, the steeper slope (45%) received 30% more than what was incident on the horizontal, while the shallow slope received about 20% more. This means that, on average, the south-facing slope high slope received close to 90% more solar radiation during the peak of the day than that received at the north-facing high slope. There was less of a difference between the shallower slopes, with south-facing low receiving on average 55% more solar radiation than the north-facing low site.

87 70 Table 4.8 Theoretical slope adjustment factor for sensors measuring solar radiation on slopes as determined by a basic model to predict open solar radiation. For the 2008 data, solar radiation was predicted for between June 1 and August 31 and for the 2009 data predicted between July 22 and August 2 (standard deviation is shown in bracket in italics) Mean ratio (slope/horizontal) Site North-facing high (0.17) (0.01) North-facing low (0.09) (0.02) South-facing high (0.32) (0.05) South-facing low (0.20) (0.01) The difference between the ratios of different years was related to the length of the sample season and the related change in solar angle. For each site, a ratio was calculated for each recording time and used to adjust the observed value for each 15 minute interval of recorded data. The adjusted solar radiation data were used for all further analyses Diurnal trends The wet 2008 field season had few completely cloud free days from which to study solar radiation amongst the sites. In 2009, several days of clear weather conditions were successfully recorded. For example, data from July 25 th showed a smooth curve from the valley bottom open station, which reached up to 0.85 kw/m 2 during the peak of the day (1200 hrs MST)(Figure 4.3). The FireSmart stand also showed a large increase in solar radiation close to 1300 hrs. The amount of solar radiation passing through the stand at to the solar radiometers at the forest floor is much lower than that measured in the open. At the sites, the solar radiation on a sunny day does not exceed 0.3 kw/m 2 except in the FireSmart stand.

88 71 Solar radiation (kw/m 2 ) FireSmart North-facing high North-facing low South-facing high South-facing low Valley bottom in-stand Valley bottom open Hour of day (MDT) Figure 4.3 Solar radiation received at all sites on July 25, 2009 The difference between solar radiation received at the south-facing and north-facing sites was tested using a paired Student s t-test. Observations of solar radiation greater than 0.8 kw/m 2 were compiled from both 2008 and 2009 to represent maximum solar radiation conditions received at the site. The analysis was stratified by time to determine peak differences between sites at certain hours of the day. For all hours between 1200 and 1500 hrs (MST) significant differences in solar radiation were found between the north and south-facing sites. The greatest difference was found at 1200 hrs where the south-facing high site received an average of 0.23 kw/m 2 (standard deviation = 0.07 kw/m 2 ) more solar radiation than the north-facing high site. The difference in solar radiation was smaller for the low sites, with the greatest mean difference occurring at 1500 hrs (0.14 kw/m 2, standard deviation = 0.06 kw/m 2 ) (Figure 4.4).

89 Solar radiation (kw/m 2 ) SH - NH SL - NL Hour of day (MDT) Figure 4.4 Mean difference in solar radiation by hour during peak solar radiation conditions (n=345) The magnitude of the difference in solar radiation between the FireSmart stand and the southfacing low site was comparable to the difference between the north and south-facing high slopes. The difference between FireSmart and south-facing low was significant between 1100 and 1600 hrs, with the greatest difference occurring at 1200 hrs (0.29 kw/m 2, standard deviation 0.09 kw/m 2 ) Transmittance In its basic form transmittance is measured as the fraction of radiant energy that passes through a substance (Reifsnyder and Lull 1965). In terms of a forest canopy, transmittance can be determined by calculating the ratio between the solar radiation received in-stand and that received in the open. Solar radiation data from both 2008 and 2009 were stratified by the amount of solar radiation received in the open in order to identify the sunniest days and used to

90 73 calculate transmittance (Table 4.9). Comparisons made on clear days are the most informative as they eliminate variation in reading due to random cloud cover. Table 4.9 Ratio between solar radiation received in-stand and in the open stratified by the amount of solar radiation received at the valley bottom open station Site Mean Ratio Mean Ratio N N Valley bottom open solar radiation > 0.5 kw/m 2 North-facing high (0.06) (0.04) 132 North-facing low (0.04) (0.04) South-facing high (0.12) (0.10) South-facing low (0.13) (0.10) Valley bottom in-stand (0.16) (0.13) FireSmart n/a (0.23) Valley bottom open solar radiation > 0.7 kw/m 2 North-facing high (0.03) (0.04) 79 North-facing low (0.03) (0.04) South-facing high (0.13) (0.09) South-facing low (0.13) (0.10) Valley bottom in-stand (0.19) (0.15) FireSmart n/a (0.23) Valley bottom open solar radiation > 0.8 kw/m 2 North-facing high (0.02) (0.03) 68 North-facing low (0.02) (0.04) South-facing high (0.12) (0.07) South-facing low (0.17) (0.11) Valley bottom in-stand (0.12) (0.16) FireSmart n/a (0.26) During the periods with the most incident solar radiation, the north-facing sites received between 3 and 5 % of the solar radiation received at the valley bottom open weather station, while the south-facing site received slightly more at 7 and 20%. The ratio for the north-facing high sites was consistently lower than the ratio for the south-facing sites, suggesting that transmittance is affected by aspect. The FireSmart stand had the greatest transmittance, with the forest floor receiving 30% of the solar radiation recorded in the open. Previous studies indicate that forest canopy can absorb between 60 and 90% of the total incident solar radiation received dependent on density and stand-type (Reifsnyder and Lull 1965), suggesting that transmittance varies between 10 and 40%. The research sites studied here seem to be on the low end of this range, even though the stocking densities are comparable to moderately stocked stands.

91 74 The difference in transmittance values between the two field seasons is due to in part to changes in solar angle related to time of year. The total amount of predicted incident solar radiation at each site decreased as the season progressed, with the smallest change occurring between June and July related to day length and the summer solstice (Table 4.10). The field season in 2008 began in June and spanned through to the end of August, so the average transmittance is determined by a wider range of values, with transmittance ratios in the early season being larger than those in the later season. The 2009 season began in mid July and ended early August when the sites received less of the direct solar radiation, and therefore had smaller transmittances. Table 4.10 Total incident solar radiation (mj) received at each site by month as determined by the basic solar radiation model (Appendix B) Total predicted incident solar radiation (mj) Site May June July August North-facing high North-facing low South-facing high South-facing low Valley bottom The difference in transmittance between north and south-facing slopes was evaluated using Student s t-tests paired by time during sunny days (Table 4.11). Significant differences in transmittance between the north and south-facing sites were found between 1200 and 1600 hrs MST. The greatest mean difference occurred at the high sites at noon where the forest floor at the south-facing aspect received on average 27% more of the incident solar radiation in the open than the north-facing site. The greatest difference between low sites occurred at 1500 hrs, with the south-facing side forest floor receiving on average 16% more of the incident solar radiation in the open than the north-facing side.

92 75 Table 4.11 Difference in transmittance between north and south-facing sites on sunny days in 2008 and 2009 (valley bottom open solar radiation greater than 0.8 kw/m 2 ) Time Sites P-value Mean 95% Confidence Degrees of Difference Interval Freedom 11:00 NH SH * (-0.25, -0.02) 6 NL SL (-0.06, 0.05) 12:00 NH SH <0.0001* (-0.31, -0.24) 73 NL SL <0.0001* (-0.10, -0.06) 13:00 NH SH <0.0001* (-0.21, -0.16) 97 NL SL <0.0001* (-0.18, -0.13) 14:00 NH SH <0.0001* (-0.13, -0.11) 94 NL SL <0.0001* (-0.16, -0.13) 15:00 NH SH <0.0001* (-0.20, -0.13) 55 NL SL <0.0001* (-0.21, -0.12) 16:00 NH SH NL SL * < (-0.10, -0.03) (-0.09, -0.04) 14 NH= north-facing high, NL= north-facing low, SH= south-facing high, SL= south-facing low, FS=FireSmart, VBIN= valley bottom in-stand * significant at the 0.05 confidence level Although the south-facing sites regularly received significantly more solar radiation than the north-facing sites, the actual value of the difference in terms of that received in the open was quite small. The amount of solar radiation passing through the canopy to the forest floor was generally 10 to 20% of that received in the open (Table 4.9). For example, if 0.8 kw/m 2 was measured out in the open, the north-facing slope may receive 0.08 kw/m 2 at the forest floor while the south-facing slope receives 0.10 kw/m Leaf Area Index The Leaf Area Index (LAI) is an estimate of photosynthetically active radiation absorbed by plant canopies (Vose et al. 1994). The LAI can be estimate using direct methods with sensing equipment or indirect methods as estimations through physical relationships (Samson and Allen 1995). The LAI was to be measured using a TRAC sensor (Chen et al. 1997) at each of the sites but this could not be completed due to technical issues. Beer s Law (also know as Beer- Lambert) for the absorption of light can be used to describe how direct beam radiation behaves in a pine forest (Reifsnyder et al. 1971, Bréda 2003). The Beer-Lambert law expresses the attenuation of radiation through a homogenous medium and requires measurement of incident

93 76 and below-canopy radiation (Equation 4)(Strong 1952). By adjusting the equation to solve for LAI, the relationship becomes (Equation 10): [10] where I is the radiation transmitted below the canopy, I o is the incident radiation, and k is the extinction coefficient, representing functions of leaf angle distribution and leaf-azimuth angle. Based on previous research and field measurements, the value of 0.52 is said to be a reasonably robust value for k in coniferous stands (Bréda 2003). Using the unadjusted observed open and in-stand solar radiation for all days in the 2009 season where solar radiation in the open was greater than 0.8 kw/m 2, LAI was estimated for all sites using Equation 10 (Table 4.12). Table 4.12 Estimated LAI values for each site based on data from days with valley bottom open solar radiation > 0.8 kw/m 2 (cloud-free days) from 2009 (n=60) Site Mean LAI SD North-facing high North-facing low South-facing high South-facing low Valley bottom FireSmart The south and north-facing sites had estimated mean values of LAI that were similar, indicating similar canopy cover at all the sites. The valley bottom and FireSmart stand had slightly lower values, owing to lower densities and more open canopies. The south-facing sites had slightly lower values related to the angle of the incident solar radiation on the stand. The path length of the solar radiation passing through the canopy would decrease the more perpendicular to the slope the sun s rays became. Considering the latitude and longitude, the south-facing sites received more direct (perpendicular) sun rays throughout the day than do the north-facing sites. The absolute values of LAI for the sites were comparable to those recorded for forest types and densities observed in other studies (Table 4.13).

94 77 Table 4.13 Leaf Area Index (LAI) for a variety of Pinus species, environments and stand characteristics (adapted from Pearson et. al 1984 and Vose et al. 1994) Basal Age Density Geographic Species LAI area (yr) (stems/ha) (m 2 location /ha) P. contorta var latifolia Utah Utah Wyoming Wyoming P. strobus North Carolina Wisconsin P. elliottii Florida Florida P. ponderosa Montana P. sylvestris Sweden P. radiata Australia P. taeda North Carolina South Carolina Wind speed Diurnal trends Patterns in the wind throughout the day suggested a slight increase during the peak burning period of the day (Figure 4.5). The FireSmart stand with its decreased density appeared to have the highest wind speeds overall when compared to the other sites, especially during the driest part of the day.

95 78 Wind speed (km/h) Valley bottom open Valley bottom in-stand South-facing low South-facing high North-facing low North-facing high Firesmart Hour of day (MDT) Figure 4.5 Wind speed time series during optimal drying conditions on July 25, 2009 For each site, a ratio was calculated between the in-stand wind speed and the open 10 meter windspeed (Table 4.14). Values where 10 m open wind speed was less than 5 km/h were excluded to attempt to eliminate errors due to the lack of sensitivity of the 10 m wind sensor for low windspeeds. The largest ratio occurred at the north-facing high site overnight (0.62), but it had a large variability associated with it. The FireSmart stand had the highest ratio during all other time periods, with the most notable increase during the peak drying period. In general, the wind speed in-stand was between 30 and 40% of that recorded at 10 m in the open. Table 4.14 Ratios between in-stand and open wind speeds for all sites for 2009 field season Site Morning Peak Evening Overnight Mean (SD) Mean (SD) Mean (SD) Mean (SD) North-facing high 0.42 (0.2) 0.36 (0.1) 0.35 (0.2) 0.62 (0.3) North-facing low 0.40 (0.2) 0.31 (0.1) 0.34 (0.2) 0.59 (0.3) South-facing high 0.30 (0.1) 0.44 (0.1) 0.33 (0.1) 0.31 (0.1) South-facing low 0.30 (0.1) 0.36 (0.1) 0.28 (0.1) 0.31 (0.1) Valley bottom 0.32 (0.1) 0.36 (0.1) 0.28 (0.1) 0.29 (0.1) FireSmart 0.38 (0.2) 0.54 (0.1) 0.45 (0.2) 0.40 (0.2)

96 Inter-site differences Wind speeds were compared amongst the sites across different time periods (Table 4.15). Generally, the difference in wind speed was less than 1.0 km/h. During the peak burning conditions of the day, the FireSmart stand showed the greatest difference in wind speed (1.21 km/h), but there were no significant differences in wind speed between the north and southfacing sites. The greatest difference in wind speed between the north and south-facing sites occurred during the morning and overnight periods. Table 4.15 Wind speed at 1.5 m height stratified by time and compared between sites using paired Student s t-tests for the 2009 dataset (July 22 to August 3) Difference in wind speed (km/h) between sites NH - SH NL -SL NH - NL SH -SL FS- SL Mean Mean Mean Mean Mean P-value P-value P-value P-value Diff Diff Diff Diff Diff P-value Morning 0.78 <0.0001* 1.06 <0.0001* * 0.21 <0.0001* 0.54 <0.0001* Peak <0.0001* 0.51 <0.0001* 1.21 <0.0001* Evening 0.18 <0.0001* * 0.24 <0.0001* 0.21 <0.0001* 0.78 <0.0001* Overnight 1.07 <0.0001* 0.92 <0.0001* <0.0001* 0.44 <0.0001* The differences in the ratio between in-stand and open wind speed amongst all the sites were also compared using a paired Student s t-test (Table 4.16). During typical peak burning conditions, there was a significant difference in wind ratio between the north and south-facing slopes, but it was small (<10%). But, during this time period, the FireSmart stand received 18% more of the wind measured at the 10 m height than the south-facing low site. The greatest difference in wind ratio between the north and south-facing sites occurred during the overnight period. Table 4.16 Wind speed ratio between in-stand and 10 m open compared between sites using paired Student s t-tests Difference in wind speed ratio (in-stand wind speed/ open wind speed) between sites NH - SH NL -SL NH - NL SH -SL FS- SL Mean Mean Mean Mean Mean P-value P-value P-value P-value Diff Diff Diff Diff Diff P-value Morning * <0.0001* <0.0001* <0.0001* Peak <0.0001* <0.0001* <0.0001* <0.0001* <0.0001* Evening * <0.0001* <0.0001* Overnight <0.0001* <0.0001* <0.0001*

97 Electronic fuel moisture sensors Diurnal trends In 2008, a single electronic moisture sensor was installed at each site and was intended to be an analogue for litter moisture content. An additional sensor was installed at each site in 2009 due to anecdotal evidence of variation between individual sensors (Figure 4.6). Although they were placed directly beside one another (less than 0.5 m apart) it seems that one sensor may have been exposed to longer periods of direct sunlight than the other or perhaps one sensor unit was more sensitive to changes in temperature and relative humidity than the other. There were similar relationships amongst plots from the other sites Sensor 1 Sensor 2 Electronic moisture content % Hour of day (MDT) Figure 4.6 Time series from July 22 to July 25 showing electronic moisture content for two sensors installed at the south-facing low site The average between each pair of sensors was calculated and used to create a time series for each site during a period of dry weather (Figure 4.7). Absolute comparisons between sites using this analysis may not be reliable, due to the discrepancies previously discussed. The north-facing sites showed less diurnal variation that the south-facing and valley bottom sites, suggesting that

98 81 they were not exposed to direct sunlight as often. The FireSmart and south-facing high site were the driest during the peak burning period of the day. After the rain event on July 26, the FireSmart stand appeared to return to lower moisture contents more quickly than the other sites July 24, July 25, Moisture content % Valley bottom South-facing low South-facing high North-facing low North-facing high FireSmart Hour of day (MDT) July 26, 2009 Moisture content% Hour of day (MDT) Valley bottom South-facing low South-facing high North-facing low North-facing high FireSmart July 27, 2009 Moisture content % Hour of day (MDT) Valley bottom South-facing low South-facing high North-facing high North-facing low FireSmart Moisture content % Valley bottom South-facing low South-facing high North-facing low North-facing high FireSmart Hour of day (MDT) Figure 4.7 Average electronic moisture content from July 24 to July 26, On July 26, a rain event occurred with more precipitation occurring at the south-facing sites (7.3 mm) than at the north-facing sites (1.4 mm). The data for the north-facing low site on July 27, 2009 was accidentally truncated by a battery failure Sensor accuracy The observed litter moisture values for two sites were plotted against the moisture content values determined by the average of the two electronic moisture sensors to evaluate their ability to predict moisture content throughout the day (Figure 4.8). Data from drying conditions on July 25 were used from north and south-facing low sites, as the moisture content was much more variable in the valley bottom.

99 82 Moisture content% Destructive litter Sensor 1 Sensor 2 Average South-facing low July 24, Hour of day (MDT) 18 North-facing low July 24, 2009 Moisture content% Destructive litter Sensor 1 Sensor 2 Average South-facing low July 25, Hour of day (MDT) North-facing low July 25, Moisture content% Destructive litter Sensor 1 Sensor 2 Average Moisture content% Destructive litter Sensor 1 Sensor 2 Average Hour of day (MDT) Figure 4.8 Observed litter moisture content compared to moisture content estimated by an electronic sensor for two sites during drying conditions on July 24 and 25 th, The error bars represent plus or minus one standard deviation from the mean. Data from the south-facing site shows greater variation between individual sensors than the north-facing sites. This could be due to a greater incidence of perpendicular solar radiation directly on the sensor at solar noon (around 1346 MDT for this location). At the south-facing site, the rays of the sun aligned more perpendicular to the horizontal, allowing beams of light to shine directly through the standing trees (that grow perpendicular to the horizontal). At the north-facing site, the angle of the sun was lower and frequently shadowed by the trunks and canopies of the standing trees. The angle of the north-facing slope may have prevented the incidence of more direct solar radiation throughout the day Hour of day (MDT)

100 83 At the south-facing sites, the average of the two sensor values seems to track the diurnal trend of the observed values but is not all that accurate in predicting the absolute value. At the northfacing site, the sensor underpredicted moisture content throughout the day. The observed and estimated moisture contents from the sensors were paired by sample time and the mean difference between them compared using a Student s t-test (Table 4.17). When the entire dataset was used, the moisture sticks always underestimated the observed moisture content, except in the FireSmart stand. Table 4.17 Electronic moisture content compared to observed litter moisture content using paired Student s t-tests on all the data collected in the 2009 field season Electronic observed moisture content Site DF Mean 95% Confidence P-value Difference (%) Interval North-facing high <0.0001* (-15.0, -6.4) North-facing low <0.0001* (-15.9, -8.6) South-facing high <0.0001* (-25.4, -17.0) South-facing low <0.0001* (-15.9, -9.0) Valley bottom <0.0001* (-23.7, -13.6) FireSmart (-2.3, 2.0) When data from only the dry days are compared, the mean differences become smaller (Table 4.18). There were no significant differences in the means of the electronic and observed moisture content for the FireSmart or south-facing low site. The FireSmart stand had a very small sample size (n=3), so these results may not be truly representative. The mean differences that did exist been the sensor value and observed value were small in terms of moisture percentages and within the standard deviation associated with observed sampling. Table 4.18 Electronic moisture content compared to observed litter moisture content using paired Student s t-tests on data collected during the drying trend between July 22 and July 25, 2009 Electronic observed moisture content Site DF Mean 95% Confidence P-value Difference (%) Interval North-facing high * (-2.3, -0.6) North-facing low <0.0001* (-4.7, -3.4) South-facing high <0.0001* (-4.6, -3.3) South-facing low (-0.9, 0.7) Valley bottom <0.0001* (-5.9, -3.5) FireSmart (-1.6, 3.4)

101 84 The electronic moisture sensors were reasonably accurate in estimating the observed moisture content during the days with hot and dry weather, especially at the north-facing sites. Although the mean differences were similar to that determined for the south-facing sites, the variation between the sensor measurements was much smaller, meaning the mean difference between the predicted and observed value was weighted more by the variance in the observed value. 4.3 Destructive moisture content sampling Destructive sampling method variability The mean gravimetric moisture content was determined by averaging the values across the 4 to 8 tins collected from each site. An initial exploration of the variability of these sample means was carried out by plotting the mean moisture content against the estimated standard deviation from each sample (Figure 4.9). As the mean moisture content increased, the standard deviation also increased, suggesting larger variation amongst samples collected in the wet end of the scale Moss tip Litter 160 Standard deviation Mean moisture content % Figure 4.9 Standard deviation for observed moisture content of moss tip and needle litter samples collected in both 2008 and 2009

102 85 The majority of the samples were collected in the range of 16 to 30% moisture content, where the mean moisture content had an average standard deviation of 3 to 4% moisture content (Table 4.19). The lowest mean standard deviation is found for samples with moisture content less than 15%. Table 4.19 Mean standard deviation for moss and litter samples for ranges of observed mean moisture content for moisture data collected in both seasons Mean estimated standard deviation for ranges of observed mean moisture content < 15% 16-30% 31 50% 51 99% >100 % Mean SD N Mean SD N Mean SD N Mean SD N Mean SD N Litter Moss-tips The moss-tip samples tended to have larger standard deviations than the litter samples. Under dry conditions, moss and litter moisture contents can become quite similar, but after rainfall they can be quite different (Figure 4.10). After a rainfall, the moss layer becomes saturated, with 80 to 90% of the water held externally in leaf axils and capillary films on leaves and stems (Busby and Whitfield 1978). In this way, the moss can store much more liquid water than litter and moisture contents can easily rise to over 400%.

103 Litter Moss tips Moisture content % On July 26th from 0500 hrs to 0900 hrs the site received 5.7 mm of rain 0 7/25/09 12:00 AM 7/25/09 12:00 PM 7/26/09 12:00 AM 7/26/09 12:00 PM 7/27/09 12:00 AM Date and time (MDT) Figure 4.10 Litter and moss-tip moisture content data from the south-facing low site from July 25 to July 26 showing the difference after a rainfall event Because the moss-tip samples were more variable, litter sample values were used for most of the comparative analyses in this study. Moss data results are reported in a few cases during drying trends with low moisture content condition and thus decreased variance. The predominant moss type at the Nordegg site differs from the feathermoss typical in eastern Canadian forests where destructive sampling on which the FFMC was based on was carried out. The eastern Canadian species, Pleurozium schreberi was present at the site in smaller quantities than the dominant Hylocomium splendens. Qualitatively, the Pleurozium moss appeared to hold more water for a longer time, remaining greener during dry conditions while the Hylocomium turned brown. A visual observation of the two different species suggests differences in leaf arrangement and overall surface area, which may be conducive to differences in absorption rate (Figure 4.11)

104 87 Figure 4.11 Pleurozium schreberi (top left) and Hylocomium splendens (bottom left) feathermoss species found at research location. Density of the Hylocomium splendens at the north-facing high site (right). Although previous research indicates little difference in wetting and drying curves between common boreal forest mosses such as Pleurozium and Hylocomium (Busby and Whitfield 1978), a small set of exploratory samples were collected to further investigate (Table 4.20). No significant difference between mean moisture content was found. though little weight should be attributed to this finding due to the small sample size (n=4). However, the mean moisture content for the Hylocomium moss was in all samples greater than the mean moisture content for the Pleurozium moss samples. Table 4.20 Comparison of mean moisture content and standard deviation between Hylocomium splendens and Pleurozium schreberi samples collected under similar conditions (n=4) Collection Hylocomium splendens Pleurozium schreberi Date/ Time Mean SD Mean SD July 25, 16: % % July 27, 16: % % July 29, 18: % % July 30, 16: % % 31.82

105 Intra-site variability During the 2008 field season, multiple samples were collected at each site from two of three designated sampling locations (Figure 3.4). Typically, four litter samples were collected from each location. The means of these sub-samples were paired and a Student s t-test used to determine whether there were significant differences between the means observed at the different locations (Table 4.21). Table 4.21 Differences in litter moisture content between sampling locations at each site using paired Student s t-tests Site Location Mean Variance of N Comparison Difference Difference T-score P-value Valley Bottom 1 vs. 2 2 vs. 3 1 vs * * North-facing high North-facing low South-facing high South-facing low * Significant at the 0.05 level 1 vs. 2 2 vs. 3 1 vs. 3 1 vs. 2 2 vs. 3 1 vs. 3 1 vs. 2 2 vs. 3 1 vs. 3 1 vs. 2 2 vs. 3 1 vs Samples collected at Location 1 in the valley bottom had significantly higher moisture contents than those collected at the other locations. This location had increased vegetation cover and deeper hummocky areas when compared visually to the other locations. As there were no significant differences in moisture between the other locations, in 2009 samples were collected from a single area rather than from two of three. Samples were not collected from Location 1 in the valley bottom.

106 Inter-site variability After two consecutive summers collecting data, it became apparent that the valley bottom site conditions were anomalous in comparison to the other sites. Moisture content samples collected at this site tended to consistently have higher moisture contents than those collected at the same time in other sites. The valley bottom site had a lower overstorey density than the average for the other sites, which corresponds to a greater amount of solar radiation below the canopy; however shading of the forest floor by low-growing herbaceous species was also greater at the valley bottom site (Figure 4.12). Figure 4.12 Herbaceous vegetation cover at the valley bottom site was higher than that of the other sites Results from a general survey of herbaceous and shrub understory plant species indicated that the valley bottom had the greatest percent cover by plants (average of 60% coverage). The southfacing sites had greater percent cover (average of 30% coverage) than the north-facing sites (average of 20% coverage).

107 90 The standard deviation of the moisture content amongst the four sample replicates was higher for the valley bottom than for the other sites (Table 4.22). Any further statistical comparisons of differences between other sites and valley bottom were obscured by its high variability. Table 4.22 Standard deviations of the litter destructive moisture content samples from 2009 Site Maximum SD Minimum SD Mean SD Valley bottom North-facing high North-facing low South-facing high South-facing low FireSmart Accuracy of the FFMC The most commonly used form of the Fine Fuel Moisture Code (FFMC) is based on weather measurements collected at 1300 hrs (LDT) and is used to represent moisture conditions during the peak burning period of the day (1400 hrs to 1700 hrs) for fine fuels under a closed canopy C- 3 fuel type on level terrain. In order to investigate the accuracy in the FFMC s ability to predict moisture content, values of the daily, diurnal adjusted, and hourly FFMC were plotted against observed litter moisture content values. At the valley bottom site, the daily FFMC consistently underestimated the litter moisture content observed during peak burning hours (1400 to 1700 hrs) (Figure 4.13). As mentioned in earlier sections, the valley bottom site was considered anomalous, so the observed moisture content values from the south-facing low site were also plotted against the daily FFMC. The FFMC also underpredicted the moisture content at the south-facing low site.

108 Moisture Content (%) Valley bottom litter South-facing low litter Daily FFMC FFMC Figure 4.13 The predicted moisture content from the daily FFMC from July 15 to September 4, 2008 plotted against the afternoon (peak burning period) observed litter moisture content from the valley bottom and south-facing low sites. The daily FFMC was calculated from the open weather station in the valley bottom. The samples collected in 2008 had higher moisture contents, with the majority of the samples collected at or above fibre saturation point and few in the dry end of the moisture scale. The 2009 dataset had a wider spread of values, so the observed moisture content values from the valley bottom and south-facing low sites were compared to the daily FFMC to see if the relationship improved if there were more samples in the drier end of the scale (Figure 4.14). The daily FFMC still tended to underpredict the moisture content for both sites.

109 Valley bottom litter South-facing low litter Daily FFMC Moisture content % FFMC Figure 4.14 The predicted moisture content from the daily FFMC from July 23 to August 10, 2009 plotted against the afternoon (peak burning period) observed litter moisture content from the valley bottom and south-facing low sites. The daily FFMC was calculated from the open weather station in the valley bottom. The observed values are from 1600 MDT. There are calculation methodologies that use hourly weather observations or diurnal drying trends to estimate FFMC for a specific hour of the day (Van Wagner 1977, Lawson et al. 1996)(refer to section ). The hourly model (Van Wagner 1977) uses hourly weather data to estimate moisture content, while the diurnally adjusted model bases its estimates on a typical diurnal drying curve (Van Wagner 1972, 1979, Lawson et al. 1996). The hourly and diurnally adjusted FFMC model values were plotted against the observed litter moisture content from the south-facing low site (the valley bottom data was more variable) (Figure 4.15). The diurnally adjusted FFMC generally underpredicted the moisture content, while the hourly FFMC predicted the moisture content at the south-facing low site reasonably well in the dry end of the moisture scale (<20% moisture content), but underpredicted the moisture content overall. When the observed moisture content was less than 20%, the hourly model predicted the values reasonably well. These data points were collected during the drying trend between July 22 and July 25.

110 93 This could mean that the hourly model can predict moisture content reasonably well when conditions are dry and the observed sample variation was low South-facing low litter Diurnal FFMC Moisture content % Moisture content % South-facing low litter Hourly FFMC FFMC Figure 4.15 The diurnally adjusted FFMC (left) and hourly FFMC (right) calculated from hourly weather at the valley bottom open station compared to the observed litter moisture content from the south-facing low site for the 2009 dataset FFMC Moisture data from the 2008 field season Distribution of sample moisture content During the summer of 2008, moisture content samples were collected on 36 separate days between June 5 and August 30. In total, 1329 destructive samples were dried and weighed, of which 805 were litter and 524 were moss-tips. During this exceptionally wet summer, there were few samples collected with moisture contents below the fibre saturation point; of the 1329 samples, 200 had gravimetric moisture content less than 20% (Figure 4.16). The litter samples ranged from 10% to 99% moisture content, and the moss-tips ranged from 10% to 560% moisture content. Most of the analyses were conducted using the litter moisture data, as the variability of the moss data may not have been properly represented through the sample intensity of this study.

111 94 Figure 4.16 Histograms showing distribution of litter and moss-tip samples collected in Inter-site differences For the 2008 data, the lack of simultaneous observations meant that moisture content observations at each site could not be paired directly on any particular day. Comparisons could be made, however, between observed moisture content and at each site and the moisture content predicted by the hourly or diurnally adjusted FFMC models. The diurnal pattern in temperature and relative humidity observed amongst the sites indicated that it was reasonable to assume that the shape of the curve was similar for each site (Figure 4.2). Making the assumption that the diurnal curve in either of the hourly and diurnally adjusted FFMC models provided a reasonable approximation of the actual diurnal variation in moisture then it was also possible to estimate moisture content at 1700 hrs at each site based on the observations collected throughout the day. The observed moisture values for both litter and moss samples at each sample time were associated with the corresponding moisture values from hourly and diurnally adjusted FFMC models (Van Wagner 1977, Van Wagner 1987) to examine the accuracy of the model to predict fine fuel moisture content. If the model predicted well in the fuel type overall at the valley

112 95 bottom, the predicted value should not be significantly different from the observed value, while on the slopes the models may over or under-estimate the value. Previous comparisons indicated that the FFMC models tended to underestimate moisture content (Figure 4.15). The observed and predicted moisture content values were paired by hour and compared using a Student s t-test (Table 4.23). All of the comparisons showed a significant underestimation of moisture content by both FFMC models. The observed litter value in the valley bottom was on average 22 percent moisture content higher than the predicted value from the hourly model. The diurnal model also underestimated the valley bottom moisture content by a mean difference of 24 percent moisture content. The other sites had similar results, with the observed value having a significantly higher moisture content than that predicted by either model. Table 4.23 Mean difference between the observed moisture content and that predicted by either the hourly or diurnal FFMC model for the 2008 dataset Difference between observed and predicted moisture content Hourly FFMC Diurnally adjusted FFMC Site N Litter (%) Moss (%) Litter (%) Moss (%) Valley bottom South-facing low South-facing high North-facing low North-facing high Making the assumption that, while the absolute value by the hourly FFMC may be inaccurate, the diurnal trend predicted by the model was accurate, each daily litter moisture observation was adjusted to a common time. This difference was then added to the observed moisture content at a particular sample time for a particular site to get the estimated moisture content at 1700 hrs for that site (Equation 6). While this adjustment of the data relies on a very important (and yet untested) assumption, it was the only method that allowed direct comparisons of daily observations at each site using the 2008 data. These destructive samples adjusted to 1700 hrs were used in paired tests to examine inter-site differences (Table 4.24). First, the valley bottom in-stand site was compared to each of the other sloped sites. It was expected that the moisture content on the south-facing slopes would be lower

113 96 than that in the valley bottom and higher for the north-facing slopes due to the impact of solar radiation on ambient in-stand weather conditions (Reifsnyder and Lull 1965) and the fuel surface temperature (Van Wagner 1969, Byram and Jemison 1943). The valley bottom moisture content proved to be significantly greater than the moisture content at all the other sloped sites, except for the north-facing low site. Paired comparisons of the 1700 hrs moisture content estimates between the sites did not reveal any significant differences, except between the north-facing high and low sites. Table 4.24 Inter-site differences in observed moisture content adjusted to 1700 hrs MDT as determined by a paired Student s t-test Site Mean difference Max Min comparison (% moisture content) difference difference T score P value N VB - NH * 18 VB NL VB- SH * 19 VB SL * 28 SL NL SH NH SL SH NL - NH * 18 The observed difference between the sites was influenced by variation related to both the underlying assumptions in the FFMC models, as well as the process of collecting destructive samples. Each of the models has flaws in its assumptions; the hourly model tends to overpredict overnight moisture content during long periods without rain while the diurnal model does not account for any rainfall until noon the following day (Lawson and Armitage 2008). There was also large variation in observed moisture values as they were collected during unseasonably wet conditions with few drying days. Some of the standard deviations of the destructive samples ranged up to ±20 percent moisture content (Figure 4.9) because the sample replicate size was not large enough to properly address variability. Because historical research suggested that the difference in moisture content between and north and south-facing slopes was potentially small (2 to 5%) (Hayes 1941, Countryman 1977), any quantifiable signal of that size is likely to have been overshadowed by the cumulative variance described above.

114 Moisture data from the 2009 field season Distribution of sample moisture content In total, 998 litter and 988 moss-tip samples were collected on 15 different days in The litter moisture content ranged from 7% to 135%, and the moss-tips from 8% to 596%. Moisture data were collected simultaneously across the sites over a range of drying and wetting conditions (Figure 4.17). Sampling began on July 22 and had been preceded by 8 rain-free days of hot weather. Samples were collected for four days during hot, dry weather until a small rain event dropped 7.3 mm of rain on the south-facing sites and 1.4 mm at the north-facing sites. Reasonable drying conditions lasted from July 27 to 31 st, but multiple rain and wind storms delayed further sampling between August 1 and 9 th. Sampling recommenced following the poor weather, except in the FireSmart stand where a windburst or tornado event on August 3 blew down most of the trees. Litter moisture content at the sites began to recover from the rain events, but spot forecasts for marginal weather led to the decision to terminate sampling on August 13 th.

115 98 Moisture content % FireSmart N-facing high N-facing low S-facing high S-facing low Valley bottom Jul 25-Jul 29-Jul 2-Aug 6-Aug 10-Aug 14-Aug Date Figure 4.17 Observed moisture content for destructive litter samples collected during A period of rain extended from August 3 to August 9. Moss moisture data showed a similar pattern but with greater response to rainfall. The warm dry weather between July 22 and 25 th represents the best period of extended high fire danger captured in this study and thus has been examined in detail. Other conditions in 2009 provide further useful observations of low and moderate fire danger conditions. The diurnal variation in temperature and relative humidity remained relatively consistent and there was little cloud cover during the four day period between July 22 and July 25 th. This was the most informative dataset of diurnal fine fuel moisture drying trends, as it was not influenced by the presence of rainfall. The valley bottom site began with the highest moisture content in the morning (large overnight recovery due to settling of cool, moist air in the valley bottom) and generally remained the wettest at the end of the drying day (Figure 4.18). The slope (drying rate) of the south-facing site data appeared to be slightly steeper than the slope for the north-facing sites. During 2009, the FireSmart site, which was an addition to the main study, was sampled only at the end of the

116 99 regular sampling day. At the end of the drying day, the FireSmart site was at least 3 or 4 % moisture content lower than that recorded at all of the other sites. Moisture content % July 22, 2009 Firesmart North-facing high North-facing low South-facing high South-facing low Valley Moisture content % July 23, 2009 Firesmart North-facing high North-facing low South-facing high South-facing low Valley bottom Hour of day (MDT) Hour of day (MDT) Moisture content % July 24, 2009 Firesmart North-facing high North-facing low South-facing high South-facing low Valley bottom Moisture content % July 25, 2009 Firesmart North-facing high North-facing low South-facing high South-facing low Valley bottom Hour of day (MDT) Figure 4.18 Observed litter moisture for destructive litter samples collected during a drying trend from July 22 to July 25, Hour of day (MDT) Inter-site differences To eliminate some of the variability associated with the wet moisture samples collected in 2008 and the uncertainties in the underlying assumptions of the hourly and diurnally adjusted models, the 2009 destructive moisture data was collected simultaneously. Samples were paired directly by sample time and site to site comparisons could be made using Student s t-tests.

117 100 Historical research indicates that the time interval during which a particle will gain or lose moisture from a homogeneous state after a specific change in the environment for fine forest fuels like pine needle litter typically range between one to two hours, depending on the amount of weathering (Blackmarr 1971, Fosberg 1975, Anderson 1978). Samples that were collected within two hours of each other were then treated as temporally simultaneous samples and paired together for analysis with a Student s t-test (Table 4.25). Table 4.25 Comparison between moisture content of all 2009 samples using a Student s t-test paired by time and day collected Litter moisture content (%) Moss-tip moisture content (%) Site comparison Mean Mean DF P-value DF difference difference P-value NH - SH * NL - SL * NH - NL SH - SL * FS - SL <0.0001* SL - VBIN NL - VBIN <0.0001* * SH - VBIN NH - VBIN <0.0001* * Using the entire dataset collected in 2009, the results showed that contrary to expectations, the south-facing sites had significantly higher moisture contents than the north-facing sites. It is believed that this mean difference was largely driven by a rain event on July 26 where the southfacing sites received more precipitation than the north-facing sites. To determine if this was the case, a set of similar comparisons were made using just the observations from the drying period between July 22 and July 25 th. Again, the samples were paired by time and compared using Student s t-tests (Table 4.26). This analysis did not identify a significant difference in mean moisture content between the north and south-facing sites. The lack of significant difference in mean moisture content between the north and south-facing slopes during the drying period indicated that the first analysis (Table 4.24) was indeed influenced by the imbalance of rain received at the different sites.

118 101 Table 4.26 Student s paired t-test comparing samples collected at the same time on the same day from the drying trend between July 22 and July 25, 2009 Litter moisture content (%) Moss-tip moisture content (%) Site comparison DF Mean Mean P-value DF difference difference P-value NH - SH NL - SL NH - NL * SH - SL SL - VBIN * * NL - VBIN * SH - VBIN <0.0001* * NH - VBIN * During the drying trend, the moisture content ranged between 10 and 15% during the peak drying period of the day. When the fuel moisture content is below fibre saturation point (about 30 % moisture content), the increments in which the moisture content changes over time become much smaller, making any differences between the north and south-facing sites much more difficult to detect. At this drier end of the moisture scale, the litter layer may only change locally by 5% moisture content through the entire drying day, making any observable difference between the north and south-facing sites very small. The sample size was limited by the weather (n~20) and was not robust enough to be able to detect a significant difference in the range of a few percents moisture content Drying rate The rate at which fuels dried towards equilibrium was then examined for differences from site to site. Two methods were used to approximate drying rate from sample to sample throughout the day. The simplest method involves calculating the rate of moisture content change over time. It was determined by calculating the difference in moisture content over the 2 hour sampling period for each site (recall Equation 7 where t 1 and t 2 are consecutive sample times).

119 102 [7] When the entire 2009 dataset was used, a significant difference in drying rate could not be found between the north and south-facing sites. The variability in the dataset was large, stemming from variability in the sample mean, time between sampling, overhead canopy, etc. To decrease the variance in the destructive moisture content values, the drying rate was calculated again just for the drying trend from July 22 to July 25 th and the sites compared using paired Student s t- tests (Table 4.27). Table 4.27 Difference in percent moisture content change per hour between sites for the drying trend data (July 22 to July 25) determined by a Student s t-test paired by sample time Difference in drying rate (moisture %/ hour) Site comparison DF Mean Diff P-value 95% Confidence Interval NH SH * (-1.13, -0.33) NL SL (-1.22, 0.11) NH NL (-0.64, 0.44) SH SL (-0.63, 0.66) NH VBIN * (-1.88, -0.19) NL VBIN * (-1.76, -0.25) SH VBIN (-1.10, 0.52) SL VBIN (-0.95, 0.50) The south-facing high site dried significantly faster than the north-facing high site, with a mean difference of 0.7% per hour. The valley bottom dried at a faster rate than the north-facing sites, but there was no evidence that it differed from the south-facing sites. The actual difference in drying rate would be quite small, so perhaps this sampling routine was not rigorous enough to reveal the difference. The FireSmart stand could not be evaluated in this way, as there were not enough samples for comparison. The above method for calculating drying rate does not account for the moisture and environmental conditions that the fuels are drying from. The EMC is the estimated moisture content obtained by a fuel exposed to similar temperature and relative humidity for a prolonged time (Blackmarr 1971, Fosberg 1975, Anderson et al. 1978). Within the FFMC, the EMC is calculated using Equation 9 (Van Wagner 1987) during the drying phase.

120 103 [8] The value for EMC was determined using in-stand temperature (T) and relative humidity (H) data for each site. If time is considered constant (the change over 2 hours in this study design), then a drying ratio can be calculated by Equation 9. [9] This drying ratio compares the fraction of total evaporable moisture remaining at t 2 to the fraction the fuel began with at t 1, thus taking into consideration the moisture condition of the fuel prior to drying. This drying ratiowas calculated for each set of consecutive moisture samples at each site and compared amongst sites using a paired Student s t-test (Table 4.28). Because the EMC was determined using the equation for drying from above, only the drying trend data was used in the analysis. Table 4.28 Difference in the fraction of total evaporable moisture remaining in needle litter at time t during the drying trend determined by a Student s t-test paired by sample time Difference in drying ratio Site comparison DF Mean Diff P-value 95% Confidence Interval NH SH , 0.58 NL SL , 0.36 NH NL , 0.47 SH SL , 0.16 NH VBIN * 0.03, 0.70 NL VBIN * 0.02, 0.58 SH VBIN , 0.23 SL VBIN , 0.29 Evidence of a significant difference in drying ratios could not be found between the north and south-facing sites. But, the results showed that a significantly larger fraction of moisture remained in the litter on the north-facing site than in the valley bottom. On average, the northfacing side dried at a rate about 30% slower than that in the valley bottom. The same procedure was conducted for the moss data, but no significant difference in the fraction of moisture remaining between any sites could be found.