Moisture dynamics of moss-dominated surface fuel in relation to the

structure of Picea abies and Pinus sylvestris stands

Heidi Tanskanen a,*, Anders Granstrom b, Ari Venalainen c, Pasi Puttonen d

a Department of Forest Ecology, PO Box 27, FI-00014, University of Helsinki, Finlandb Department of Forest Vegetation Ecology, Swedish University of Agricultural Sciences, 901-83 Umea, Sweden

c Finnish Meteorological Institute, PO Box 503, FI-00101 Helsinki, Finlandd Finnish Forest Research Institute, General Services, Helsinki Unit, Unioninkatu 40 A, 00170 Helsinki, Finland

Received 4 May 2005; received in revised form 17 December 2005; accepted 30 January 2006

Abstract

We examined moss-dominated surface fuel moisture in a southern boreal fire environment by carrying out destructive moisture sampling on 47

days in Pinus sylvestris L. stands aged 0, 15, 30, 45, and in Picea abies (L.) Karst stands aged 0, 40, and 60 years since regeneration cutting. The

moss layer was found to be consistently moister under dense canopy cover, formed by either of the studied tree species, than in the respective open

stands. In the most exposed stand type (clear-felled Pinus), the frequency distribution of daily surface fuel moisture was strongly skewed towards

the low end, with 37% of the observations falling below 50% MC. For 45-year-old Pinus, the corresponding figure was only 7%. Moisture

difference between open and closed stands increased during the latter half of fire season, probably due to increased canopy-interception of radiation

with lowering sun-angle. Average surface fuel moisture correlated with effective leaf area index and canopy openness with r ranging from�0.68 to

�0.84 ( p < 0.001). There was on average a two-fold difference in drying efficiency between the closed and cut-over stands. The Finnish fire risk

index (a weather-based fuel moisture index that uses the data input of precipitation, solar radiation, temperature, relative humidity, and wind)

predicted the average daily moisture content of surface fuels in most stand types with reasonable accuracy (R2: 0.56–0.98). Surface fuels reached a

postulated ignition threshold moisture content of 30% and below in Pinus clear-cuts at an FFI value of 3.6, and in closed Pinus sylvestris stands at

FFI 5.6, but never went below 40% in the closed P. abies stands. The results show that stand structure has a remarkable impact on surface fuel

moisture regimes. Integrating spatially explicit stand data such as LAI with weather-derived fire risk indices would greatly improve moisture

content predictions to the benefit of various fire management operations.

# 2006 Elsevier B.V. All rights reserved.

Keywords: Forest fires; Boreal forest; Surface fuel moisture; Moss; Stand structure; Picea abies; Pinus sylvestris

www.elsevier.com/locate/foreco

Forest Ecology and Management 226 (2006) 189–198

1. Introduction

Surface fuel moisture plays a critical part in the ignition,

spread, and fuel consumption properties of forest fires (Byram,

1959; Rothermel, 1972; Nelson, 2001). In dead fuels, moisture

dynamics are considered to depend mostly on relative humidity,

air temperature, wind, solar radiation, and precipitation (Van

Wagner, 1969, 1979; Nelson, 2001) and these variables have

been incorporated into most systems that predict fuel moisture

(e.g. Van Wagner, 1987; Stocks et al., 1989; Venalainen and

* Corresponding author.

E-mail addresses: heidi.tanskanen@helsinki.fi (H. Tanskanen),

anders.granstrom@svek.slu.se (A. Granstrom), ari.venalainen@fmi.fi

(A. Venalainen), pasi.puttonen@metla.fi (P. Puttonen).

0378-1127/$ – see front matter # 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.foreco.2006.01.048

Heikinheimo, 2003). Being able to provide real-time and

predictive information about fuel moisture conditions with the

minimal input of labor and time the weather-based fuel

moisture indices have largely replaced field fuel moisture

sampling in fire operations (Van Wagner, 1987; Stocks et al.,

1989). Fuel moisture estimates derived solely from general

weather can, however, be very different from the reality due to

additional variation caused by factors such as boundary layer

weather conditions (Fosberg et al., 1970; Oke, 1987; Kunkel,

2001) and the composition of fuel material (Nelson, 2001). The

lack of tools for making reliable fuel moisture estimates is

especially evident in the boreal European coniferous forest fire

environment.

In mesic boreal forests dominated by conifers Picea abies L.

Karst (Norway spruce) and Pinus sylvestris L. (Scots pine),

moss mixed with litter is a principal surface fuel material, and

H. Tanskanen et al. / Forest Ecology and Management 226 (2006) 189–198190

live vascular plants such as Vaccinium sp. dwarf shrubs

typically constitute only a minor part of total fuel quantity (Van

Wagner, 1983; Schimmel and Granstrom, 1997). Boreal forest

mosses have high surface-to-volume ratios of 115–135 cm�1

which make them very flammable (Brown, 1970; Norum, 1982;

Schimmel and Granstrom, 1997) and sensitive to changes in

atmospheric conditions (Fons, 1950). Due to lack of roots and

stomatal control mosses are highly dependent on incoming

precipitation in their moisture acquisition and prone to rapid

desiccation in the absence of rain (Proctor, 1981; Zotz et al.,

2000). In vascular plants, moisture content typically presents

slow seasonal changes as a result of phenological processes

(Nelson, 2001) and little short-term variation. Live moss

moisture dynamics thus resemble more those occurring in fine

dead forest fuels (Norum, 1982), the main difference being the

higher maximum moisture holding capacity, approximately

500–600% (dry weight basis) (Dilks and Proctor, 1979;

Granstrom and Schimmel, 1998), in comparison to 250% of

fine dead fuels like conifer needles and twigs (Simard, 1968).

The moisture regimes of the forest floor may vary

remarkably due to the influence of canopies (Chen et al.,

1999; Kunkel, 2001). A closed canopy, whether created by trees

or understory vegetation, reduces the amount of precipitation

reaching the forest floor (e.g. Lukkala, 1942; Oke, 1987). On

the other hand, the net radiation, considered the primary factor

sustaining drying, is below the canopy only a fraction of that

above the canopy (e.g. Baldocchi and Vogel, 1996). Addition-

ally, the canopy abates evaporation in the surface fuel layer by

reducing the ventilation of the forest floor by the wind (Raynor,

1971; Kunkel, 2001).

Little information exists on surface fuel moisture dynamics

in boreal European coniferous forests. This is a severe problem

both for fire control operations and prescribed burning which

today is becoming increasingly used as a forest management

tool to maintain biological diversity (Granstrom, 2001;

Kuuluvainen et al., 2002). The aim of this study is to map

and analyze surface fuel moisture dynamics in P. abies and

Pinus sylvestris stands at different stages of stand succession.

The observed moisture contents are linked to a fire-weather

Table 1

Structural characteristics of the study plots

Stand typea

(no. of plots)

Stand height

(m)/canopy depth (m)

DBH (cm) Stocking/opennessb

ha�1 %

Picea_0 (3) 0/0 – 0 74.4

Picea_40 (3) 15.6/9.6 15.9 1170 6.5

Picea_60 (2) 21.3/11.6 21.4 720 8.9

Pinus_0 (2) 0/0 – 0 60.2

Pinus_15 (3) 4.6/3.8 7.2 760 44.3

Pinus_30 (3) 12.2/5.1 12.4 2000 11.7

Pinus_45 (3) 18.1/5.7 18.4 850 14.1

a Picea indicates the dominance (>85% of the basal area) of Picea abies, Pinus the

passed since the last regeneration cutting.b The percentage of open sky as seen from beneath a forest canopy, calculated uc The cover percentages of total field layer vegetation (the dominant species indi

Vaccinium myrtillus L., (3) Vaccinium vitis-idaea L.), mosses and litter on the ford The depth and the dry matter fuel loads of the litter and moss layer, measured

index called the Finnish Fire Risk Index (hereafter, referred to

as FFI) which is a system calculating fuel moisture in boreal fire

environment as a function of precipitation, solar radiation,

temperature, relative humidity, and wind (Heikinheimo et al.,

1998; Venalainen and Heikinheimo, 2003) without the effect of

canopy cover on drying or wetting processes. The findings will

serve prescribed burning activities and wildfire risk assess-

ments, by providing more accurate fuel moisture predictions as

modified by stand structure.

2. Methods

2.1. Study site

The field data was collected in the southern boreal

coniferous forest zone (Ahti et al., 1968) in southern Finland

in the districts of Lammi and Padasjoki (618120N, 258070E;

140–180 m above sea level) within a total area of

30 km � 30 km. In the study region, Pinus sylvestris dominates

on 57% and P. abies on 32% of the forest land area (Finnish

Forest Research Institute, 2000). Average annual precipitation

is 631, 250 mm of which falls as snow. The mean annual

temperature is 3.9 8C and snow cover is normally present from

early December to mid-April (Drebs et al., 2002). The length of

the growing season is approximately 168 days (Drebs et al.,

2002). In July, the mean temperature is 16.2 8C and average

monthly rainfall 81 mm.

In April and May 2001, 24 experimental plots (40 m �40 m) were established in Pinus sylvestris stands of 0, 15, 30,

and 45 years of age after regeneration cutting and in P. abies

stands of 0, 40, and 60 years of age (Table 1). Site-species

composition was chosen to be consistent with the traditional

Finnish site type classifications: Pinus sylvestris on xeric to

sub-xeric, low nutrient soils and P. abies on mesic, medium

fertile soils (Cajander, 1926). Stand age classes were chosen to

present the widest available variation in canopy cover and stand

structure (Fig. 1). Paludified depressions, ditches, and slopes

(inclination > 5%) were excluded on the map before random

plot placement.

Field layerc

(%)

Mossc

(%)

Litterc

(%)

Fueld depth

(cm)

Fueld load

(kg m�2)

37.6 (1) 54 44.4 1.9 1.01

10.5 (2) 97 18.2 4.1 1.37

28.7 (2) 84 20.5 4.5 1.18

18.4 (3) 76 31.8 1.9 1.03

19.2 (3) 80 25.4 – 0.96

9.7 (3) 98 15.2 4.5 0.98

14.3 (3) 93 12.8 4.0 1.09

dominance of Pinus sylvestris. The numbers 0–60 in the titles refer to the years

sing fisheye photos.

cated with the number in parentheses: (1) Deschampsia flexuosa (L.) Trin., (2)

est floor.

and calculated based on the moisture samples of summer 2001.

H. Tanskanen et al. / Forest Ecology and Management 226 (2006) 189–198 191

Fig. 1. Examples of canopy cover in the experimental stands, presented as the Gap Light Analyzer working images of hemispherical photos. (A) A total clear-cut of

Picea (Picea_0), (B) a clear-cut of Pinus with a few mature seed trees left on the site (Pinus_0), (C) a thinned, 18-m-tall Pinus stand (Pinus_45) and (D) a dense 16-m-

tall Picea stand (Picea_40). Original photos: Ilkka Taponen, Finnish Forest Research Institute.

In each plot, diameter at breast height (DBH) was recorded

with a caliper on every tree having DBH of 5.0 cm or greater. The

tree height and the height of the lower canopy limit were defined

from every 5th tree in a diameter class using a hypsometer

(Forestor Vertex, Forestor Instrument AB, Sweden). Hemi-

spherical photographs were taken of the canopy in five

systematically placed locations. The coverage (%) of surface

vegetation (Pakarinen, 1984) and litter/cutting residual were

inventoried in each plot on six to nine systematically placed

quadrates of 0.5 m � 0.5 m. The depth of the litter–moss layer

(cm) (Table 1) down to the fermentation boundary layer was

measured in August 2001 for all stand types except Pinus_15.

Average DBH (cm) and stand dominant height (m) were

calculated for seven stand types defined by dominant tree

species and stand age (Table 1). Canopy depth (m) was defined

as the average difference between the tree height and the height

of the crown base. Estimates of site canopy openness (%) and

effective leaf area index were produced using fisheye

photographs and Gap Light Analyzer (GLA) imaging software

Version 2.0 (Frazer et al., 1999). Cover percentages of moss,

litter, and the field vegetation, as an average of all observed field

Table 2

The pair-wise t-test comparisons of surface fuel moisture contents between differe

Compared stands Mean S.D. S.E. 9

o

L

Picea_60 � Picea_40 �6.879 60.618 8.660

Picea_60 � Picea_0 98.200 87.315 12.474

Picea_60 � Pinus_45 8.733 73.332 10.371

Picea_60 � Pinus_30 �34.069 71.398 10.200

Picea_60 � Pinus_0 102.506 88.754 12.811

Picea_40 � Picea_0 104.224 84.703 12.226

Picea_40 � Pinus_45 18.285 54.417 7.774

Picea_40 � Pinus_30 �27.190 64.500 9.214

Picea_40 � Pinus_0 111.499 80.271 11.586

Picea_0 � Pinus_45 �89.112 92.041 13.149 �Picea_0 � Pinus_30 �131.823 107.646 15.537 �Picea_0 � Pinus_0 4.616 77.221 11.264

Pinus_45 � Pinus_30 �45.335 60.881 8.136

Pinus_45 � Pinus_0 92.522 60.983 8.223

Pinus_30 � Pinus_0 135.163 82.219 10.987

Picea indicates the dominance (>85% of the basal area) of Picea abies, Pinus the d

passed since the last regeneration cutting. Mean = mean difference of fuel moistur* Significant difference.

layer species pooled, were calculated for each stand type

(Table 1).

2.2. Moisture sampling

Surface fuel moisture sampling was carried out during the

period of 5 June–29 August 2001 between 11:00 and 15:00 h

(solar time) in seven types of stands with two to five replicates

(Table 2) weekends and rainy afternoons excluded. There were

47 sampling days for all stand types except Pinus_15 which was

excluded from the scheme for the last month. To extract

moisture content information, samples 15 cm � 15 cm in

surface area, consisting of litter, green and brown parts of

the growing moss down to the more compact humus layer, were

cut in randomly chosen position on each plot, and sealed in

plastic bags. Field moisture content (hereafter referred to as

MC) was calculated on dry-weight basis based on differences

between the fresh and dry (after 24 h at 105 8C) weights of

entire sample contents. The litter–moss fuel loads (kg m�2)

were derived relating average stand-specific dry-matter values

to the sample size (Table 1).

nt stand types

5% Confidence interval

f the difference

t d.f. Significance

(two-tailed)

ower Upper

�24.291 10.533 �0.794 48 0.431

73.120 123.280 7.873 48 0.000*

�12.107 29.574 0.842 49 0.404

�54.577 �13.562 �3.340 48 0.002*

76.734 128.277 8.002 47 0.000*

79.628 128.819 8.525 47 0.000*

2.654 33.915 2.352 48 0.023*

�45.717 �8.664 �2.951 48 0.005*

88.191 134.808 9.624 47 0.000*

115.550 �62.675 �6.777 48 0.000*

163.080 �100.566 �8.484 47 0.000*

�18.057 27.289 0.410 46 0.684

�61.639 �29.031 �5.572 55 0.000*

76.036 109.008 11.252 54 0.000*

113.145 157.181 12.302 55 0.000*

ominance of Pinus sylvestris. The numbers 0–60 in the titles refer to the years

e (MC%), S.D. = standard deviation, S.E. = standard error.

H. Tanskanen et al. / Forest Ecology and Management 226 (2006) 189–198192

2.3. Weather data and the Finnish fire risk index

The weather data were acquired from the meteorological

station network of the Finnish Meteorological Institute (http://

www.fmi.fi/weather/stations.html, Venalainen and Heikin-

heimo, 2002). The network uses automatic stations of type

MILOS 500 equipped with standard instruments used by

national meteorological services (manufactured by Vaisala,

Vantaa, Finland). Precipitation measurements are sent to

climatological database daily by about 200 stations and

3-hourly precipitation values are calculated from the daily

values with the help of synoptic weather observations made at

the nearest available station. Air temperature, air humidity and

wind speed observations are available from about 160 stations.

Direct solar radiation measurements are being received from

about 20 stations and indirect radiation estimates from about

40 stations making cloud observations (Venalainen and

Heikinheimo, 2002). The distance between the nearest weather

station and our study sites was 5–20 km, the three next closest

weather stations being located within 40–60 km radius from

the study area.

Weather measurements were interpolated to our study area

from the total station network using the spatial statistics method

of kriging (Ripley, 1981) with a program especially designed

for climatological applications in forestry (Henttonen, 1991).

The method has been previously used in many studies and

proved to provide reasonably good local estimates of most

weather variables. In a sensitivity test study, where climato-

logical parameters sampled by the network stations were

interpolated onto a 1 km � 1 km grid in northern Finland where

the between-station distances are higher, the error in the case of

average summer daily mean temperature varied between �0.6

and +0.6 8C with an RMS error of 0.3 8C (Vajda and

Venalainen, 2003). The interpolation of precipitation, however,

has been found to be more problematic due to the nature of local

rain showers that can cause very high and abruptly changing

spatial variation. The calculation has been observed to create

systematic error in the interpolated values because the method

cuts off the peak values when the measured precipitation values

are above 10 mm/day (Venalainen et al., 2005). To improve the

accuracy of precipitation observations, eight rain collectors

were located within the 30 km � 30 km study area in the

proximity of the study plots and monitored daily at noon to

control the spatial variation of precipitation and eliminate the

disturbance of the local rain showers in the data series. If large

differences in timing or amount occurred between locally

recorded and interpolated precipitation, the respective moisture

observations were excluded from the fire risk index-analysis.

Local precipitation values were used in stand-specific fuel

wetting-analysis.

Fire weather conditions were evaluated using the Finnish

fire risk index (FFI), a system designed to estimate the

volumetric moisture content of organic layer (a 6-cm-deep

fuel layer consisting of litter, moss, and humus) in clear-cut

areas as a function of precipitation and evaporation

(Heikinheimo et al., 1998; Venalainen and Heikinheimo,

2003). The FFI uses the input of air temperature, relative

humidity, 10-m wind, and surface net radiation for fuel drying

and precipitation for fuel wetting. Fuel drying is modeled as a

function of potential evaporation, calculated using Penman–

Monteith equation for evapotranspiration (Monteith, 1981)

with the surface resistance parameter set to zero. The FFI is

scaled to range 1.0–6.0, 1.0 indicating the lowest possible fire

risk (usually occurs after snowmelt or heavy long-term rain)

and 6.0 conditions of the highest fire risk that occur only rarely,

after a prolonged drought. FFI is calculated during fire season

every 3 h; in this study, we used the FFI values for noon.

Because the FFI evaluates moisture content in a fairly thick

surface fuel layer it is more likely a tool for estimating

expected ignition potential and fuel consumption than fire

spread (Stocks et al., 1989). Due to the large potential

absorbance volume of the deep fuel layer feature, the FFI tends

to overestimate fire risk following shallow precipitation events

(Tanskanen et al., unpublished data).

2.4. Analysis

The range and frequency distribution of MC during the

study season were calculated for each stand type. The MC

status differences were analyzed using paired sample t-tests of

the SPSS program, version 10 (SPSS Inc., Chicago, IL). In the

analysis, averaged moisture observations of each stand type

were paired with average moisture observed on respective

dates in every other stand type resulting in 15 pair-wise

comparisons (Pinus_15 was excluded from the comparisons

because this stand type had less than 40 sampling days which

could have weakened the quality of the analysis). The

assumptions of paired t-test were reasonably well met due

to the large sample size (Moore and McCabe, 1989) which

ranged 46–56 in the different strings. Due to the nature of the

observed phenomenon all the data series contained simulta-

neous temporal autocorrelation (stands were drying and

wetting simultaneously). Significant autocorrelation has

potential to seriously impair the results of t-test analysis by

overestimating the difference between the variables. The

impact of the autocorrelation on our results was examined by

comparing each paired t-test result to the time series graph of

the respective pair of stands (e.g. Fig. 2). The results of the

statistical analysis appeared to be in good concordance with the

original data and correctly indicating large, small, or non-

existent difference. Therefore, no corrections were applied to

standard paired samples t-test of SPSS 10. Correlation analysis

was used to search for dependency between the average plot-

wise MC (as sub-sets of June, July, August, and total season)

and corresponding stand characteristics: canopy openness, leaf

area index, and canopy depth.

Based on precipitation records, 15 drying and 28 wetting

sequences (24-h periods containing observations of two

subsequent days) were extracted from the total dataset. The

24-h change in MC status was calculated for all stand types by

subtracting the observed MC value of an afternoon (n) from the

MC recorded on the previous afternoon (n � 1). Changes were

analyzed using both MC % values as well as MC as mm of

water, derived using the information on the surface area and the

H. Tanskanen et al. / Forest Ecology and Management 226 (2006) 189–198 193

Fig. 2. Precipitation and moisture content of surface fuels (moss and litter) in

Pinus_45 and Pinus_0 stands (above panel), interpolated noon values of solar

radiation (W/m2) and relative humidity (%) (mid-panel), and precipitation, FFI,

and surface fuel moisture content in Picea_60 and Picea_0 stands (bottom

panel) from June through August 2001.

average dry matter content of the samples. The change in water

storage during dry weather was compared with the correspond-

ing values of potential evaporation, FFI, and the initial moisture

content on day (n � 1). The change in storage during wet

weather conditions was analyzed in relation to the amount of

precipitation recorded during the past 24 h and the initial

moisture content on day (n � 1). Both drying and wetting of

fuels were studied as a manifestation of respective canopy

effect, because there is no accurate method to define neither the

amount of radiation or precipitation intercepted in the different

canopies and we were unable to arrange representative

measurements of below-canopy weather for this amount of

experimental stands.

Regression models of the negative exponential type were

developed between the daily noon FFI values and the

able 3

he relationshipa between the FFI and surface fuel moisture content in the studied stand types

a (S.E.) b (S.E.) R2 MCFFI4 MCFFI5 MCFFI6 FFIMC30

icea_0 156.1 (37.6) �0.218 (0.09) 0.10 88.1b – – –

icea_4060 467.4 (44.3) �0.378 (0.04) 0.69 103.0 70.6 48.4 –c

inus_0 296.2 (56.6) �0.622 (0.08) 0.56 24.6 13.2 7.1 3.7

inus_15 392.1 (101.8) �0.622 (0.10) 0.56 32.6 17.5 9.4 4.1

inus_30 664.6 (63.7) �0.515 (0.04) 0.77 84.7 50.6 30.2 6.0

inus_45 556.0 (47.8) �0.524 (0.03) 0.81 68.4 40.5 24.0 5.6

a The columns show regression parameters (a and b, Eq. (1)), coefficient of the determination (R2), estimates of moisture content at FFI values 4–6, and the

stimated minimum FFI value for reaching MC of 30%.b Calculated as the average of the observed MC at index values 3.5–4.5.c Value out of index range.

T

T

P

P

P

P

P

P

e

corresponding stand type-specific afternoon MC values

(MCstand) as

MCstand ¼ ai expðbiFFIÞ; (1)

where ai and bi are stand type-specific parameters (ai > the

observed maximum MC in stand type i, bi < 0) estimated using

nonlinear regression analysis tool (SPSS, version 10, SPSS Inc.,

Chicago, IL). Using the achieved regression parameters, ai and

bi, the status of MC at FFI values 4.0, 5.0, and 6.0 was

calculated for each stand type (Table 3). By solving Eq. (1)

for the FFI as follows:

FFIMC30 ¼ lnMC30%=ai

bi; (2)

the minimum FFI value (FFIMC30) at which moisture content in

different stands would reach 30% (considered here as the

maximum moisture at which a fire is able to ignite and spread

in a moss layer (Tanskanen et al., 2005)) was predicted.

The results principally are being presented in two layers.

Quantitative canopy variables are being used to explain

moisture variation in forest landscape. Alongside, we present

a category analysis of surface fuel moisture dynamics in

common stand types, as classified according to dominant tree

species and developmental stage, to facilitate the interpretation

and implementation of the observations in field operations.

3. Results

3.1. MC and stand structure

During the experimental season, the 3-month average noon

air temperature was 19.7 8C, July being the warmest month

(22.1 8C). There were on average 14–16 rainy days per month

(Fig. 2), the accumulated monthly precipitation ranging from

32 mm (August) to 116 mm (June). The two longest

precipitation-free periods lasted for 13 and 8 days. The range

of FFI during the test season varied from 1.0 to 5.1. Surface fuel

moisture content during the whole study season varied between

10% and 490% (Fig. 2). The gain periods of fuel moisture were

fairly accurately synchronized with the occurrence of

precipitation. The magnitude of moisture content increase,

H. Tanskanen et al. / Forest Ecology and Management 226 (2006) 189–198194

however, seemed to be somewhat indifferent to amount of

precipitation. The longest dry period at the beginning of July

coincides with the prolonged high level of FFI values and solar

radiation (Fig. 2). The fluctuation of FFI presented in large part

a mirror trend of the direction of fuel moisture content changes

(Fig. 2). The connection between the magnitude of FFI changes

and the amount of precipitation remained difficult to evaluate

because precipitation events were somewhat clustered affecting

other observations (Fig. 2).

The frequency distribution of daily MC varied substantially

between stand types (Fig. 3). In open or semi-open stands, the

distributions were markedly skewed towards low MC values,

whereas under canopy cover, moisture regime presented a

wider range of variation and the observations of moisture of

below 50% were rare (Fig. 3). In Pinus_0 stands, the driest fuel

environment in this survey, over 35% of the days ranked within

the MC range of 0–50% in which Picea_60 had no observations

Fig. 3. Frequency distribution histograms with fitted normal curves of the

observed daily MC in surface fuels of four stand types having an open (Pinus_0

and Picea_0) or closed (Pinus_45, Picea_60) canopy.

(Fig. 3). The standardized difference of moisture contents

between closed and open stand types expressed as t-values

ranged between 7.9 and 12.3, with p < 0.001 (Table 2).

Pinus_30 appeared to have moister surface fuel layer than

Picea_40 (t: �2.0, p < 0.05), and Picea_60 (t: �3.3, p < 0.05)

(Table 2). Pinus_45 stands were significantly drier than

Pinus_30 (t: �3.1, p < 0.005) but similar stand density change

did not lead to significant surface fuel moisture differences

between Picea_60 and Picea_40 stands (Table 2).

Moisture differences between open and closed sites

increased as the season progressed (Fig. 4). During mid-

summer (summer solstice � 3 weeks), the average MCopen/

MCclosed ratio ranged between 0.54 and 0.66, whereas during

late season (4–9 weeks after the solstice) the ratio was 0.42–

0.44 (Fig. 4) indicating that open sites became relatively drier.

Canopy openness and effective leaf area index were

structural parameters that best correlated with the variation

of observed moisture contents. Observed surface fuel MC

decreased with the increase in canopy openness, the correlation

coefficient r being �0.792 for the plot-wise summer averages

with p < 0.01. In similar analysis for the effective leaf area

index, MC increased with the increasing leaf area (Fig. 5) and

correlation coefficient r was 0.780 with p < 0.01. Crown depth

also correlated (r: 0.514–0.714) with the same moisture sub-

series with a slightly weaker significance, 0.01 < p < 0.05.

3.2. Change of MC, stand structure, and weather

For the 15 24-h periods without precipitation, water loss was

related both to initial moisture content and to stand type

(Fig. 6). The data indicate a linear decrease in absolute water

loss with decreasing initial moisture content, although variation

was large (Fig. 6). In open stands, the change in MC from high

initial level was without exception negative; at low initial

levels, MC remained stable or even increased in the absence of

rain (Fig. 6). Under a closed canopy, the drying process

stagnated at relatively high initial MC levels (e.g. Pinus_30 in

Fig. 6). Drying was most efficient in the open stand types

(Pinus_0 and Pinus_15, Fig. 6; Picea_0, not shown), where

on average 32% of the initial moisture storage evaporated daily.

Fig. 4. Moisture content gradient between closed and open stands of Pinus

sylvestris during mid-summer (from the beginning of June to mid-July) and late

summer (from mid-July to the end of August). Diagonal line marks the 1:1 ratio.

H. Tanskanen et al. / Forest Ecology and Management 226 (2006) 189–198 195

Fig. 6. Absolute change in MC of surface fuels in relation to initial MC within

24-h without precipitation for different Pinus canopy conditions: LAI1 = open

(Pinus_0 pooled with Pinus_15), LAI2 = sparse mature (Pinus_45), and

LAI3 = dense semi-mature (Pinus_30). Linear correlations indicated.

Fig. 5. Average moisture content (�2S.E.) of surface fuels during the observa-

tion period in relation to leaf area index. Estimated linear regression and

coefficient of determination indicated.

In closed stands (Picea_60, Picea_40, Pinus_45, and Pinus_30),

average 24-h moisture loss was 16–23% of the initial storage.

Surface fuel drying efficiency (lost moisture vs. initial storage)

was approximately twofold in the open Pinus stands (Pinus_0,

Pinus_15) in comparison with closed stands (Pinus_30,

Picea_4060). Average absolute 24-h evaporation rates ranged

from 0.43 mm (in the open stands) to 0.68 mm (in closed Picea

stands). The estimates of potential evapotranspiration (Pen-

man–Monteith with surface resistance set to zero) did not

produce significant correlation with observed drying rates in

any stand type or initial moisture range (Fig. 7a).

Fig. 7. (A) Change in fuel moisture storage in relation to the estimated potential eva

initial moisture storage, the base of the columns = the new moisture storage after rain

lost. (B) Change in fuel moisture storage as a result of rain within the last 24 h. The ini

if not otherwise indicated with arrowheads (=instead of gaining, some moisture ha

In the wetting dataset, the effect of the initial MC was

important for the net effect of precipitation on fuel moisture:

samples with a high initial MC remained indifferent or

decreased in moisture despite a heavy rainfall (Fig. 7b). The

maximum moisture storage appeared to be around 6–7 mm

(Fig. 7b).

3.3. Modeling of MC status with fire index

The exponential regressions between the daily FFI and

stand-specific moisture contents presented high coefficients of

potranspiration (Penman–Monteith) (mm). The upper end of each column = the

less 24-h sequences. The length of each column shows the amount of moisture

tial storage at the base and the new storage (mm) at the upper end of the columns

s been lost). Data in both panels from Pinus_0 and Pinus_15 stands.

,

H. Tanskanen et al. / Forest Ecology and Management 226 (2006) 189–198196

Fig. 8. Daily observations of surface fuel MC in relation to index value for the FFI system. Data from three different stand types (Pinus_0, Pinus_45, and pooled

Picea_40 and Picea_60). Curves show fitted exponential regressions (Eq. (1)). The dash lines indicate MC = 30%, a likely limit for fire propagation in these fuels

(Tanskanen et al., 2005).

determination, with the exception of MCPicea_0 (Table 3). The

best fits were produced between the FFI and the MC of closed

stands: MCPinus_45, MCPinus_30, and MCPicea_4060 (Table 3,

Fig. 8). In the models, Pinus_0 and Pinus_15 stands reached

MC levels below 30% at an FFI-value as low as 3.5. The fast

drying of surface fuels in open sites during the first days after

rain causes the model to underestimate the maximum MC of

those stand types (Fig. 8). Pinus_30 and Pinus_45 moisture

prediction curves reached 30% MC level within FFI range 5–6,

but Picea_4060 MC seemed unable to go below 40% within the

functional FFI range (Table 3).

4. Discussion

4.1. Effect of stand structure on fuel moisture

The moisture regime of the surface fuels differed

substantially between stand types and much of that variation

could be tied to the canopy variables. Dried-out fuel conditions

(MC < 30%) occurred frequently during the study season in

stands with an open or semi-open canopy whereas stands with

the most extensive canopy cover never reached a MC of below

50%. In addition or as an alternative to tree canopy (Oke, 1987)

herbs and dwarf-shrubs should have a similar effect on forest

floor moisture relations. The observed difference in MC

between the open Picea_0 and Pinus_0 stands is probably due

to the more extensive field-layer cover (38%) of grass and tall

herbs (Deschampsia flexuosa and Epilobium angustifolium L.)

present on Picea_0 sites (Table 1). The moisture content

gradient between open and closed sites became more

pronounced during the late summer. In northern latitudes,

the effect of seasonal change in sun position is assumed to be

strong enough to cause this phenomenon. Besides enhanced

atmospheric absorbance, the decreasing solar altitude angle

causes the sun-rays to travel a longer distance within canopy

layer, having more occasions of absorbance, reflectance, and

scattering which reduces the amount of radiant energy to reach

the surface fuel layer (Byram and Jemison, 1943; Bonan, 2002).

An additional factor for the seasonal trend is phenological

change in the canopy. Our measure of canopy variables was

taken at one time only but in conifers LAI is known to increase

well into the summer, as the current year shoots and needles

develop (Stenberg et al., 1994). The magnitude of LAI increase

from early summer to late summer, however, has been

estimated to be only 5–10%, being too small to be detected

by optical LAI/canopy openness measurement techniques

(Chen, 1996).

Though e.g. P. abies canopies intercept considerable

amounts (around 1 mm) of the precipitation (Lukkala, 1942)

and could prevent wetting of surface fuels, in this study the

principal effect of increasing canopy cover was diminished

drying. In open sites, drying was fast after a precipitation event

and continued to be so until it rained again or fuel moisture

declined below 50%. It is unknown what exactly made

desorption from mosses under dense canopies slow down or

nearly stagnate at moisture levels as high as 150–200%. The

results of this study support the idea that boreal mosses, despite

lacking effective internal water transport and cuticular

resistance to evaporation, can to some degree restrict desorption

through the overall structure of the moss mats (Dilks and

Proctor, 1979; Schofield, 1981; Proctor and Smith, 1995) and

sometimes stomata-resembling organs (Raven, 2003) espe-

cially under a sheltering canopy.

The absolute moisture loss rates observed in this study are

supported by other studies giving data on evaporation from the

boreal forest floor (e.g. Grelle et al., 1997; Heijmans et al.,

2004). The moisture loss values in open stands did not

correlate with potential evapotranspiration as approximated in

the drying function of the FFI-index with the Penman–

Monteith equation (Monteith, 1981) the surface resistance

parameter set to zero (Venalainen and Heikinheimo, 2003). A

reason for this may be the approximation ignoring the

diffusion resistance between the moss surface and the free

atmosphere (Proctor, 1981).

H. Tanskanen et al. / Forest Ecology and Management 226 (2006) 189–198 197

4.2. Modeling moisture development with fire weather

index

The surface fuel of the forest floor represents relatively small

water storage of around 4–6 mm, as shown from the maximum

observed MC (Fig. 6). Excessive precipitation simply sinks

deeper into the soil. The FFI index seems realistic in this respect

since in the simulations, it uses a maximal rain interception of

7 mm (Venalainen and Heikinheimo, 2003).

The results describe the general difficulty of modeling fuel

moisture conditions and fire risk on a landscape presenting wide

variety of canopy structures and fuel compositions. After only a

couple of days without rain the most exposed locations are dry

enough to burn (Fig. 8). As the dry spell continues and weather-

based fire risk estimates increase, fuel moisture under very

dense forest canopies may still remain too high to support any

fire spread (Fig. 8). Considering the simple structure of the FFI,

it provides remarkably consistent stand-specific relationships

with fuel moisture. The few deviating observations are likely

caused by the occurrence of slight rain events; since the FFI

models the moisture content of a 6-cm-thick fuel layer it reacts

only weakly to minor precipitation, which, however, are able to

raise the moisture content of mosses to a high level.

5. Conclusions

This study produced stand-specific thresholds of the FFI that

can have greatly improve the accuracy of fuel moisture

predictions for fire control efforts and prescribed burning. Our

results support the idea of using information on tree species and

stand age to improve the interpretation of fire-risk indices at a

landscape scale. It might be possible to merge satellite-derived

spatial information on forest composition with daily fire risk

indices, for field use in fire control operations or prescribed

burning. Attempts to produce fuel moisture and ignition potential

estimations based on satellite data have been made in other fire

environments (mainly for grasslands) with promising results

(Chuvieco et al., 2004). Development of such spatial tools for

closed boreal coniferous forests, however, requires some further

research, e.g. on more complex stand structures and the effects of

the seasonal variation of plant phenology on fuel moisture.

Acknowledgements

We thank Hanne Liukko, Pauli Pihlajamaki, Taneli Salonen,

Anni Uusi-Kuitti, and Timo Vesterinen for carrying out the fuel

moisture sampling, and the landowners (Finnish Forest

Research Institute, Hame Polytechnic, and UPM-Kymmene

Ltd.), the personnel of Vantaa Research Center of the Finnish

Forest Research Institute, project secretary Timo Heikkila

(Rescue Service, Ministry of the Interior, Finland), and

researcher Ilkka Vanha-Majamaa (Finnish Forest Research

Institute) for their contributions. This study has been financially

supported by the Finnish Fire Prevention Fund, the Finnish

Forest Research Institute, the Academy of Finland (FIRE-

project of the SUNARE Program), the Graduate School in

Forest Sciences, and a FORMAS-grant to Anders Granstrom.

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