Climate change effects on wildfire hazards in the wildland-urban-interface – Blue pine forests of Bhutan

Forest Ecology and Management 461 (2020) 117927

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Climate change effects on wildfire hazards in the wildland-urban-interface – Blue pine forests of Bhutan

T

Lena Vilà-Vilardella,b, William S. Keetonb,c, , Dominik Thomb,c, Choki Gyeltshend, Kaka Tsheringe, Georg Gratzera ⁎

a

University of Natural Resources and Life Sciences (BOKU), Institute of Forest Ecology, Peter Jordan Strasse 82, 1190 Vienna, Austria University of Vermont, Rubenstein School of Environment and Natural Resources, 308 Aiken Center, Burlington, VT 05405, USA c Gund Institute for Environment, University of Vermont, 210 Colchester Avenue, Burlington, VT 05405, USA d National Biodiversity Centre, Ministry of Agriculture and Forests, Serbithang, Thimphu 11001, Bhutan e Ugyen Wangchuck Institute for Conservation and Environmental Research (UWICER), Lamai Goempa, Bumthang, Bhutan b

ARTICLE INFO

ABSTRACT

Keywords: Forest fire Blue pine Pinus wallichiana Fire hazards Fire behavior Wildfire simulation FlamMap Rural livelihoods Climate change Adaptive management Bhutan Himalayas

Increased wildfire activity in the Himalayan Mountains due to climate change may place rural livelihoods at risk, yet potential climate change effects on forest fires in this region are poorly investigated. Here we use Bhutan’s blue pine (Pinus wallichiana) ecosystems to study the sensitivity of fire behavior to climatic changes. Wildland fires are one of the biggest threats to forest resources in Bhutan; blue pine ecosystems, in particular, are of high concern because of their importance for rural livelihoods and relatively high frequency of forest fires. Due to the geographical and socioeconomic characteristics of Bhutan, the region is highly sensitive to climate change. We investigated fire hazards in the wildland-urban-interface (WUI) of two valleys in Bhutan (Thimphu and Jakar), where human settlements and infrastructure are surrounded by blue pine forests. We applied FlamMap, a spatially-explicit wildfire simulation model, to simulate fire behavior under four climate scenarios. As indicators of fire behavior, we used flame length, rate of spread, crown fire activity, burn probability, and fire size. With the simulation outcomes we constructed a fire hazard map showing the hotspots of forest fire susceptibility. FlamMap predicts a two-fold increase in fire hazards in the WUI for both study areas owing to climatic changes. The capital city of Thimphu has, on average, greater fire hazards than Jakar, though fire hazards are spatially variable within both study areas. Our study contributes to the understanding of and ability to predict forest fire hazards in Himalayan blue pine ecosystems. The findings will help to more precisely allocate fire management resources in the WUI, plan suburban development to minimize fire risk to livelihoods, and adapt forest management in the face of climate change.

1. Introduction Wildfires play a formative role in forest ecosystem dynamics, shaping vegetation distribution, structure, and composition in many systems. Wildland fire activity around the globe is predicted to increase with climate change (Seidl et al., 2017; Flannigan et al., 2009). In the Himalayan Mountains, climate change is expected to trigger cascading effects that will likely affect regional and global processes, such as water availability, biodiversity, monsoon precipitation, carbon cycling, and local livelihoods (Xu et al., 2009). Mountainous regions are especially susceptible to climate change because of their steep environmental gradients related to complex topography, high microclimatic variability over short distances, and fragile ecosystems, which are

⁎

largely specialized and may be restricted to upwards shifts in elevation (Dimri et al., 2018; Tse-ring et al., 2010; Xu et al., 2009). Rural communities whose livelihoods and economies depend on mountainous ecosystem services will likely need to adapt to shifting species distributions and resource availabilities (Gratzer and Keeton, 2017). On-going climatic and socioeconomic changes in the Himalayas (Tiwari and Joshi, 2015; Xu et al., 2009) will affect fire regimes by modifying fuel composition, loading, and connectivity (Hessl, 2011). Out-migration to urban areas in the Himalayas (Tiwari and Joshi, 2015; The World Bank, 2014) is driving residential and commercial sprawl into neighboring forests, increasing the human pressure on those ecosystems. Intensified fire disturbances due to climate change (Seidl et al., 2017), along with increased forest pressure in the wildland-urban-

Corresponding author at: University of Vermont, Rubenstein School of Environment and Natural Resources, 308 Aiken Center, Burlington, VT 05405, USA. E-mail address: [email protected] (W.S. Keeton).

https://doi.org/10.1016/j.foreco.2020.117927 Received 17 September 2019; Received in revised form 20 January 2020; Accepted 21 January 2020 0378-1127/ © 2020 Elsevier B.V. All rights reserved.

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interface (WUI) due to suburban sprawl, will put people and infrastructure at greater risk (Keeton et al., 2007). It is thus important to improve predictions of potential climate change effects on forest fires in order to understand vulnerabilities in critically important natural resources and ecosystem services.

In Bhutan, blue pine forests are semi-mesic (dry) ecosystems that grow at mid elevations (2100–3200 m.a.s.l.), where annual precipitation ranges from 450 to 1500 mm (Tenzin, 2001) and represent 3.7% of the total forest cover (DoFPS, 2016a). The most common parasitic plants growing on blue pine are dwarf mistletoe (Arceuthobium minutissimum [Hook f.]) (western Bhutan) and leafy mistletoe (Taxillus kaempferi [D.C. Danser]) (western and central Bhutan) (Dorji et al., 2012; Tenzin, 2001). Blue pine trees affected by dwarf mistletoe exhibit abnormal growth, such as development of “witches’ brooms”, and are more susceptible to other pests and diseases resulting in tree mortality, thereby increasing fuel loading and the likelihood of more intense and severe fires (Alexander and Hawksworth, 1975). Effects of dwarf mistletoe on blue pine are more severe than those caused by leafy mistletoe (Dorji et al., 2012), thus contributing more to fuel loading. Virtually all blue pine forests adjacent to settlements are grazed (Gyeltshen, 2016). Livestock grazing may decrease fire frequency and intensity by reducing the density of grasses (Tenzin, 2001). However, Gyeltshen (2016) found a positive relation between grazing and fire hazards in Bhutan, since fires intentionally lit to stimulate grass production, a common management practice, may escape. According to Gyeltshen (2016), the main tree mortality predictors due to wildfire in blue pine forests are distance to nearest road, grazing intensity, and nearest land-use type (settlements and orchards). Few previous studies have explored fire regimes in Bhutan, and past fire records are inconsistent or largely anecdotal. From 1992 to 2014, an average of 59 fire incidences per year were recorded in Bhutan. Annual area burned was 8722 ha on average (DoFPS, 2014). Despite the importance of wildfires in the country, there has been little scientific documentation on this topic (Gyeltshen, 2016; Tshering, 2015; Dema, 2014; Tshering, 2006; Chhetri, 1994). Here we expand on that research, presenting a tool to spatially assess potential fire hazards in blue pine ecosystems. This will inform and improve fire management strategies aiding, for example, determination of areas where fuel treatments are most needed.

1.1. Importance of fire hazard prediction The WUI presents major challenges for both forest managers and urban planners. Predictive models providing spatial information on potential fire hazards would inform allocation of resources to fuel treatments for fire risk reduction, as well as effective planning of suburban development to minimize jeopardy to people and property (Chuvieco et al., 2010). Both are needed particularly in the face of increasing fire activity under climate change. Spatially-explicit models predicting fire hazards will be of high value to plan adaptation strategies to changing fire regimes. For this study we define wildfire hazards as the physical properties of fire characterized by the following fire behavior indicators: flame length, rate of fire spread, crown fire activity, burn probability, and fire size. Fire risk includes fire effects on biophysical and socioeconomic systems (Finney, 2005), but is not the focus of this study. Wildland fires are considered one of the biggest threats to forest resources in Bhutan (Chhetri, 1994). Bhutan is a small landlocked country of 38,394 square kilometers (NSB, 2013), mostly mountainous (elevations ranging from 100 m to more than 7000 m above sea level (m.a.s.l.)) and highly forested (total forest cover is 71% (DoFPS, 2016b). Bhutan acts as a net sink for greenhouse gases, with an average net carbon sequestration of 1.2 Tg yr−1 (Cervarich et al., 2016). Forest fires occur mainly during the dry winter months and are caused almost entirely by human related activities, such as agricultural debris burning, pasture management, and uncontrolled camp fires (Chhetri, 1994). Most of the Bhutanese population works on their own land and relatively few are salaried employees (23.9%). The forestry and agriculture sector contributes strongly to household economies, employing 62.2% of the active population (NSB, 2013). Webb and Dorji (2008) estimated that 79% of the population is dependent on forest resources for their livelihoods. In this context, forests are vitally important for rural development and rural livelihoods. Due to the social (e.g., low economic resilience) and geographical (e.g., steep terrain, shallow and erodible soils) characteristics of Bhutan, the region is particularly sensitive to global climate change (Hoy et al., 2016; Tse-ring et al., 2010; Xu et al., 2009). Bhutan’s economy is largely dependent on hydropower, tourism, forestry, and agriculture, all of them climate-sensitive sectors vulnerable to climate change impacts (MoAF, 2016).

1.3. Study objectives Our goal was to investigate the effect of climate change on wildfire hazards in Bhutan’s blue pine forests (Plate 1). The specific objectives were to (a) identify the effect of climatic changes on wildfire hazards in the WUI, and (b) characterize and map wildfire hazards in two study areas. We hypothesized that (a) warmer, drier conditions associated with climatic changes are positively correlated with increased wildfire hazards in blue pine ecosystems, and (b) wildfire hazards are spatially variable and thus indicative of differing vulnerabilities.

1.2. Fire ecology of blue pine ecosystems

2. Materials and methods

A dominant forest type within important portions of Bhutan’s WUI, such as around the capital city of Thimphu, is blue pine (Pinus wallichiana [A.B. Jackson]). Blue pine is a fire prone species native to the Himalayas; younger trees are sensitive to wildfire-caused mortality due to their thin bark and high flammability (Tenzin, 2001). The species’ natural distribution includes mountainous regions of Afghanistan, Bhutan, China, India, Myanmar, Nepal, and Pakistan. Blue pine is an early successional species, typically growing adjacent to human settlements, mostly in abandoned farmland. Since blue pine forests are often intermingled or adjacent to settlements, they face high anthropogenic pressure, including demand for forest ecosystem services, such as timber, firewood, and forage for livestock, and they face high chances of escaped ignitions (Dukpa et al., 2018; Gyeltshen, 2016; Tenzin, 2001; Govil, 1999). Blue pine is highly valued economically in Bhutan, and the non-timber products (NTFP) from its forests are important for rural livelihoods (CF members, 2018; Phuntsho et al., 2011; Tenzin, 2001).

2.1. Study area This study focuses on two valleys of Bhutan (Thimphu, at 2334 m.a.s.l., and Jakar, at 2587 m.a.s.l), which have the largest human population densities within their respective administrative districts (Fig. 1). The forests surrounding settlements and infrastructure are dominated by blue pine. The area analyzed comprises the WUI and covers 7723 ha in Thimphu and 4075 ha in Jakar. Forest fires in Thimphu are more frequent than in Jakar (DoFPS, 2014; NLC, 2014), yet in both regions there is high utilization of forest resources. Climate in both valleys is characterized by warm and wet summers, and cold and dry winters. The southwest monsoon occurs from June to September, providing 73% and 64% of the total annual precipitation for Thimphu and Jakar, respectively. Mean annual precipitation is 607 mm in Thimphu and 752 mm in Jakar (HydroMet, 2017). The primary fire season in Bhutan stretches from November to April, and the peak fire month is February (Chhetri, 1994). 2

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Plate 1. A) Mature blue pine forest in the Thimphu Valley of Bhutan; B) the Wildland-Urban-Interface within a matrix of young blue pine stands on the outskirts of Thimphu, Bhutan’s capital city; C) the famous Paro Taktsang (“Tiger’s Nest”) buddhist temple surrouded by fire-prone blue pine, an example of cultural resources at risk; and D) fire effects above Thimphu and within a young blue pine stand regenerated naturally on abandoned agricultural land. Photo credits: W.S. Keeton.

2.2. Data collection and processing

within variable radius plots, using a prism of 2.3 metric basal area factor (BAF). For each tree we measured DBH, total tree height and height to crown base, crown class, decay stage (Bartels et al., 1985), and mistletoe presence. Downed woody debris was inventoried using the line intersect method (Van Wagner, 1968). Fine woody debris (FWD) (branches and twigs between 1 cm and 10 cm diameter) was inventoried along a nested 12.5 m line transect and required measurements of diameter at intercept and lean angle. Coarse woody debris (CWD) (logs greater than 10 cm diameter at intercept and longer than

We sampled vegetation in randomly distributed forest inventory plots, ensuring good representation of landscape heterogeneity. We inventoried a total of 58 plots in Thimphu and 44 plots in Jakar (Fig. 2). In each plot we collected data on site conditions, live and standing dead trees, and downed coarse and fine woody debris. Site information included canopy, shrub, and herbaceous cover. We measured live and standing dead trees greater than 5 cm diameter at breast height (DBH)

Fig. 1. Map of Bhutan’s political districts. Shown in blue is the distribution of blue pine forests; orange indicates forested landscapes assessed within the two study areas. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 3

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Fig. 2. Plot distribution in Thimphu (left) and Jakar (right). Orange lines delineate the borders of each study area. In each panel there is a shaded gray circle, representing an example of the kriging extent around each plot (1 km radius). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Table 1 Equations used to calculate input parameters for the wildfire simulation model. BA = Basal area (m2), TH = Tree height (m), FF = Form factor, SG = Specific gravity, BEF = Biomass expansion factor, RF = Reduction factor, L = Transect length (m), d = Diameter (cm), c = Slope correction factor, CCF = Carbon conversion factor, k = Constant for unit conversion, a = Nonhorizontal lean angle correction factor, fw = Foliar weight (kg), TEF = Tree expansion factor, CL = Crown length (m), cbh = Crown base height (m), HCover = Herbaceous cover (%), ShCover = Shrub cover (%), LD = Litter depth (cm), UH = Understory height (cm), h = Mean understory height (cm), C = Understory cover (%). Parameter

Equation

Reference

Volume (m ha ) Stem biomass (kg ha−1)

V = BA*TH*FF StB = V*SG*1000

Aboveground biomass (kg ha−1) Standing dead wood biomass (kg ha−1) Aboveground carbon (kg m−3) CWD volume (m3 ha−1) CWD biomass (kg ha−1) CWD carbon (kg ha−1) FWD volume (m3 ha−1) FWD biomass (kg ha−1) Canopy fuel load (kg m−2) Canopy bulk density (kg m−3) Canopy base height (m) Herbaceous biomass (Mg ha−1) Shrub biomass (Mg ha−1) Fuelbed depth (cm) Understory height (cm)

AGB = StB*BEF SDW.B = StB*RF

FF – Tenzin et al. (2016) SG – Neumann et al. (2016), Wani et al. (2014), Sheikh et al. (2011), Zhang (1995), Lawton (1984), Jain and Seth (1979) BEF – Teobaldelli et al. (2009) RF – Harmon et al. (2011)

AGC = AGB*0.5 CWD.V = (pi2/8l)*∑d2*c CWD.B = CWD.V*SG*1000*RF CWD.C = AGB*CCF FWD.V= (kac/L)*∑d2 FWD.B = FWD.V*SG*1000 CFL = ((∑(fwi*TEFi))/10,000)*RF CBD = CFL/CL CBH = ∑(cbhi*TEFi)/∑TEFi HB = HCover*2.1262/100 ShB = ShCover*0.8416/100 FD = LD + UH UH = h-h*((100 − C)/100)

Ford and Keeton (2017) Van Wagner (1968); c – Brown (1974) SG – Jain and Seth (1979); RF – Harmon et al. (2011) CCF – Harmon et al. (2008) Woodall and Williams (2005) SG – Jain and Seth (1979) Cruz et al. (2003); RF – Reinhardt et al. (2006) Cruz et al. (2003) Cruz et al. (2003) Muukkonen et al. (2006) Muukkonen et al. (2006) Fernandes et al. (2006) Fernandes et al. (2006)

3

−1

1 m) was sampled along a 25 m transect and required measurements of diameter at intercept and decay class (Bartels et al. (1985). We calculated forest structure and canopy fuel characteristics following the equations presented in Table 1. To scale up fuel data from plot to landscape, as required for the wildfire simulation, we applied ordinary

kriging using ArcGIS to canopy cover, stand height, canopy base height (CBH), and canopy bulk density (CBD). Kriging was limited to a 1 km buffer distance from the sample plots to improve prediction accuracy at the landscape scale. It resulted in 30 m resolution raster layers at landscape level. 4

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The Forest Resource Management Division, part of the Department of Forests and Park Services (DoFPS) of Bhutan, provided a digital elevation model (DEM) at 30-meter resolution, and a land use land cover thematic map from 2016. Climate data from 1996 to 2017 was provided by the National Center for Hydrology and Meteorology of Bhutan; it included daily measures of temperature, precipitation, relative humidity (RH), wind speed, wind direction, and cloud cover. We used measurements of Simtokha weather station in Thimphu (27° 26′ 29.31″ N latitude, 89° 39′ 45.98″ E longitude, 2310 m.a.s.l.) and Chamkhar weather station in Jakar (27° 32′ 43.43″ N latitude, 90° 45′ 13.41″ E longitude, 2470 m.a.s.l.). A forest fire records database from 1992 to 2014 was provided by the Forest Protection and Enforcement Division of the DoFPS. Those records were not consistent and were only used to calculate an average fire size for each valley. 2.3. Simulation modelling and climate scenarios We employed FlamMap 5.0 to simulate wildfire behavior in the kriged landscapes (Finney et al., 2016). FlamMap is a spatially-explicit wildfire simulation model that calculates potential fire behavior under given environmental conditions. The model predicts the effect of spatial fuel arrangement and topography on fire behavior (Finney, 2006). As fire behavior indicators we used flame length, rate of fire spread, crown fire activity, burn probability, and fire size (Scott et al., 2013). Flame length, rate of fire spread, and crown fire activity are calculated independently for each 30 m × 30 m pixel of the landscape (Finney, 2006). Burn probability and fire size are computed using the minimum travel time fire growth algorithm (MTT) developed by Finney (2002). MTT calculates fire growth after simulating a certain number of random ignitions (Finney, 2002), which should ensure that most of the pixels of the landscape burn at least once (Ager et al., 2010). This condition was met when simulating 2000 ignitions in Thimphu and 1000 in Jakar. Running FlamMap simulations in our study required the following data: (i) weather data (temperature, RH, precipitation, wind direction, wind speed, and cloud cover), (ii) topography data (elevation, slope, and aspect), and (iii) fuels data (canopy fuels, including canopy cover, stand height, CBH, and CBD; fuel moisture content; and a fire behavior fuel model [see below]). Climate parameters were calculated as the average values in February, the peak fire month, over the years 1996–2017. A longer time span was not possible due to lack of regional climate data (Hoy et al., 2016). We used the WindNinja extension in FlamMap to account for wind direction variation at finer scale as influenced by topography. To assess wildfire sensitivity to climatic changes, we constructed four climate scenarios. These represented climate change projections of higher temperatures and weaker monsoons (Choudhary and Dimri, 2018; Dimri et al., 2018), by increasing temperature and decreasing RH. We constructed scenario A using the baseline climate from 1996 to 2017. Extreme temperature and RH were derived from the 97th percentile (Fig. 3), following the same approach as previous studies using FlamMap (Alcasena et al., 2018; Salis et al., 2013; Ager et al., 2012; Ager et al., 2010). Temperature increased by 6–7 °C in scenarios B and D relative to the baseline temperature of scenarios A and C, reflecting the warming trend predicted under the RCP8.5 scenario for the end of the century in the Himalayas (Dimri et al., 2018).

Fig. 3. Climate scenarios modeled in FlamMap. Shown are minimum and maximum values of temperature (T) and relative humidity (RH) used for scenario A: baseline scenario; scenario B: increase in temperature scenario; scenario C: decrease in RH scenario; scenario D: increase in temperature and decrease in RH scenario.

the wind reduction factor under the forest canopy (Finney, 1998). CBH is used to define the threshold for transition to crown fire. CBD determines the threshold needed to achieve an active crown fire (Finney, 1998). Fuel moisture content (FMC) influences rate of combustion of forest fuels, affecting fire intensity and spread (Simard, 1968). FlamMap requires information on initial FMC of dead and live fuels as well as foliar moisture content. Dead fuels moisture content varies on a daily basis reflecting the immediate weather. In FlamMap, initial fuel moisture of dead fuels is locally modified by a conditioning period that accounts for weather, topography, and shading (Finney et al., 2016). We computed moisture content of dead fuels following Cohen and Deeming (1985). Live fuels (herbaceous and woody fuels) moisture changes are more complex, since moisture content not only depends on direct weather changes but also on the balance between moisture loss by transpiration and replacement from stem water or soil moisture uptake (Agee, 1993). During the dry season, herbaceous fuels have a moisture content lower than 50%, and woody fuels between 50 and 80% (Burgan, 1979). Foliar moisture content is used along with canopy base height to calculate the threshold for transition to crown fire. For the baseline scenario (A) we assumed 100% foliar moisture content, compared to 90% for scenarios B and C, and for the most extreme scenario (D) we used a value of 80%, following the approach in Scott and Reinhardt (2001). A fire behavior fuel model (FBFM) consists of a set of fuelbed inputs which provide a description of the surface fuel structure (Finney, 1998). To create a FBFM for Thimphu and Jakar, we parameterized an existing FBFM (TU5) developed by Scott and Burgan (2005) using data we derived in the field. The main fire carrier of TU5 is forest litter in combination with herbaceous and shrubs fuels, the fuelbed is characterized by a high load of conifer litter with shrub or small tree understory (Scott and Burgan, 2005). A FBFM requires the following data: fuel loading (dead and live fuels), type of live herbaceous fuel load, surface-area-tovolume (SAV) ratio (1 h time-lag class dead fuels and live fuels), fuelbed depth, moisture of extinction, and heat content. Fuel loading and fuelbed depth were derived from our field data for Thimphu and Jakar

2.4. Model inputs and parameters FlamMap requires a topographic raster. The model uses elevation to adjust temperature and relative humidity at the adiabatic lapse rate from the reference elevation of the weather station to any point on the landscape, slope to calculate fire spread, and aspect along with slope to determine the angle of incident solar radiation (Finney, 1998). Canopy fuels are spatial inputs in FlamMap. Canopy cover is used to calculate the shading of surface fuels, which in turn influences fuel moisture content. Along with stand height, these variables are used to compute 5

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(Table 1). Type of herbaceous fuel load was set as “dynamic”, implying that herbaceous fuels move from live to dead depending on their moisture content (Scott and Burgan, 2005). We validated our fuel model by comparing flame length output to scorch height of past fires in blue pine forests of Thimphu. Our model underestimated flame length by 0.1 m. Average scorch height in blue pine forests burned between 2010 and 2015 was 4.5 m (Gyeltshen, 2016). To validate the outcomes of FlamMap we compared fire size to area burned obtained from fire records in Thimphu and Jakar. Average fire size over the years 1992 to 2014 was 39 ha in Thimphu and 78 ha in Jakar (DoFPS, 2014). FlamMap overestimated fire size by 42 ha in Thimphu and by 4 ha in Jakar.

35% steepness, respectively). In Thimphu blue pine represents 88.2% of the total basal area; associated tree species mostly occupied the sub-canopy (Quercus griffithii [Hk.], Populus ciliata [Wall. ex Royle], Quercus semecarpifolia [Sm.], Rhododendron arboreum [Sm.], and Picea spinulosa [Griff.]). Forest cover in Jakar is strongly dominated by blue pine (99.9% of total basal area). Shrubs and herbs covered almost half of the sub-canopy layer in both study areas, providing enough fuel for a surface fire to spread. Litter accumulated in less quantity in Thimphu compared to Jakar. In Thimphu, fire evidence (assessed through presence of burned stumps or trees) was almost four times higher than in Jakar. Blue pine forests showed high grazing pressure in both study regions (Table 2). Total volume and biomass of live and standing dead trees was lower in Thimphu than in Jakar, and thus, also carbon stored. For downed woody debris, CWD did not differ significantly between study areas, whereas FWD accumulation was less than the half in Thimphu compared to Jakar. FWD greatly contributes to the spread of a surface fire. Tree canopy layer was narrower and started closer to the ground in Thimphu, average crown length was 7.83 m in Thimphu and 10.36 m in Jakar. Canopy fuel load was lower in Thimphu compared to Jakar, CBD did not show any significant difference between the two study areas (Table 3). The landscape derived from kriging simplified the variability in canopy fuel structure measured at the plot scale. Root-mean-square errors (RMSE) of the resulting landscapes in Thimphu and Jakar were 19.8% and 13.71% for canopy cover, 6.73 m and 5.64 m for stand height, 3.74 m and 2.59 m for CBH, and 0.15 kg m−3 and 0.18 kg m−3 for CBD, respectively. Predicted values were within the borders of the interquartile range of measured values, therefore we concluded that they represented the variability of the landscape well.

2.5. Fire hazard index and map We created a fire hazard index in order to identify areas with higher fire hazards by combining the outputs of the FlamMap simulation (flame length, rate of spread, crown fire activity, and burn probability) into a common hazard scale. For each indicator, results of both study areas were first standardized (so variables of different magnitude can be compared), then log-transformed (to mitigate the effect of outliers and convert to normal distribution), and finally, re-scaled to a 0–1 range. We considered all variables to have the same weight in defining the wildfire hazard, therefore they were summed in each raster cell, and the resulting 0–4 range was converted to a categorical scale: low, moderate, high, and extreme. To test for significant differences in fire behavior between baseline and extreme weather conditions, paired Wilcoxon signed-rank tests with an alpha error of 0.05 were applied (Thom et al., 2017) using the R language and environment for statistical computing (R Core Team, 2017).

3.2. Climatic changes increase fire hazards

3. Results

Under extreme weather conditions, fire hazards in both study areas were predicted to experience a two-fold increase (Table 5). Thimphu showed a higher potential for more intense and severe wildfires under all climate scenarios (Table 4, Fig. 4, Fig. 5). This held true for all but one of the indicators (burn probability). Under all scenarios the majority of both study areas was categorized as having moderate fire hazard (Fig. 6, Table 5). The proportion of areas with low fire hazard did not change significantly when simulating extreme weather conditions in both landscapes. However, many areas classified as moderate under scenario A, shifted to category high when simulating extreme weather conditions. A larger portion of Thimphu compared to Jakar was categorized under high fire hazard for all climate scenarios. Extreme fire hazard areas also increased under extreme weather, particularly in Jakar, yet remaining always a small proportion of the landscape (Table 5, Fig. 6). We found a high spatial variability of fire hazards within each study area. Overall, we found highest fire hazards in the northern and southern areas in Thimphu, and in the southern regions in Jakar (Fig. 6). Low fire hazard was slightly positively correlated with stand height and CBH, while high fire hazard was negatively correlated with these variables. Canopy cover and CBD were not strongly correlated with any fire hazard category (Fig. 7). We applied paired Wilcoxon signed-rank tests to FlamMap outputs to assess the effects of increased temperature, decreased RH, or the combination of both on wildfire activity. We found highly significant differences between climate scenarios across all fire behavior indicators modeled (P < 0.001). No significant difference was found for burn probability and fire size under scenario B both in Thimphu (P = 0.53 and P = 0.69, respectively) and in Jakar (P = 0.68 and P = 0.46, respectively). In both study areas the greatest change in all modeled wildfire indicators was a consequence of either the combination of an increase in temperature and decrease in RH (scenario D) or a decrease in RH only (scenario C). On average, an increase in temperature (scenario B) did not cause any change compared to the baseline scenario.

3.1. Fuel structure in Thimphu and Jakar Blue pine ecosystems in Thimphu are more susceptible to wildfires compared to Jakar. Vertical and horizontal forest structure and fuel profile differed considerably between study areas (Tables 2 and 3). In Thimphu, canopy cover is sparser than in Jakar, thus surface fuels receive more solar radiation and wind easily reaches surface fuels. The percentage of trees infected by mistletoe was almost three times higher in Thimphu than in Jakar, increasing the overall fuel load in the forest canopy. Mistletoe infection was restricted to one third of the crown in more than 60% of the infected trees in both study areas. Thimphu lies in a lower valley than Jakar (2334 m.a.s.l. and 2587 m.a.s.l., respectively), and its surrounding mountains are generally steeper (45% and Table 2 Site information. Shown are average values, standard deviations, p-values, and effect sizes (percent change from Thimphu to Jakar). Significance codes: *: p < 0.05, **: p < 0.01, ***: p < 0.001.

Elevation (m) Slope (%) Canopy cover (%) Mistletoe presence (% trees infected) Shrub cover (%) Herbaceous cover (%) Litter depth (cm) Fire evidence (% total plots) Grazing evidence (% total plots)

Thimphu

Jakar

p-value

Effect size (%)

2616 ± 140 45 ± 21 61 ± 20 29.7

2807 ± 132 35 ± 19 75 ± 14 12.4

< 0.001*** < 0.001***

23.0 −139.5

47 ± 25 39 ± 25 4.3 ± 3.2 20.7

47 ± 23 45 ± 25 6.4 ± 3.6 4.5

0.957 0.259 < 0.001*** 0.02*

0.0 15.4 48.8 −360.0

93.1

97.8

0.291

5.0

6

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Table 3 Forest structure and canopy fuel characteristics. Shown are average values, standard deviations, p-values, and effect sizes (percent change from Thimphu to Jakar). Significance codes: *: p < 0.05, **: p < 0.01, ***: p < 0.001.

Blue pine (% of total BA) Basal area (m2 ha−1) Density (trees ha−1) Volume (m3 ha−1) Aboveground biomass (Mg ha−1) Carbon storage (Mg ha−1) Biomass CWD (Mg ha−1) Biomass FWD (Mg ha−1) Stand height (m) Canopy fuel load (kg m−2) Canopy base height (m) Canopy bulk density (kg m−3)

Thimphu

Jakar

p-value

Effect size (%)

88.2 22.13 ± 10.3 1051.0 ± 1073.7 212.36 ± 152.1 135.74 ± 97.3 67.71 ± 48.5 7.21 ± 24.3 2.31 ± 2.9 12.6 ± 7.0 1.43 ± 1.1 4.77 ± 3.5 0.23 ± 0.2

99.9 28.02 ± 10.7 950.3 ± 978.0 308.96 ± 149.0 196.51 ± 94.7 98.05 ± 47.3 4.19 ± 8.3 4.81 ± 4.5 16.2 ± 5.8 2.15 ± 1.1 5.84 ± 2.5 0.27 ± 0.2

0.011* 0.906 < 0.001*** < 0.001*** < 0.001*** 0.743 < 0.001*** 0.001** < 0.001*** 0.009** 0.341

−13.3 26.6 −10.6 45.5 44.8 44.8 −72.1 108.2 28.6 50.3 22.4 17.4

The greatest changes in flame length and rate of fire spread with respect to scenario A were under scenario D both in Thimphu and Jakar (average increase of 2.50 m and 1.75 m in flame length, and 2.14 m min−1 and 0.96 m min−1 in rate of fire spread, respectively). The greatest increase in burn probability was predicted for both scenario C and scenario D in Thimphu and for scenario C in Jakar (average increase of 0.3% in both study areas). Regarding fire size, the greatest increase was under scenario D in Thimphu, and scenario C in Jakar (average increase of 27.68 ha and 10.05 ha, respectively). Total area experiencing a crown fire was doubled under scenario D compared to scenario A in both landscapes (increase of 82.9% in Thimphu and 122.6% in Jakar), yet Thimphu’s forests experienced more crown fires than Jakar under all scenarios (Fig. 4).

Table 5 Percent of area under each category of fire hazard by scenario and study area. Low

Moderate

High

Extreme

Thimphu

Scenario Scenario Scenario Scenario

A B C D

14.87 14.75 11.93 11.81

67.69 66.26 56.76 53.10

17.43 18.99 31.28 35.03

0.01 0.00 0.02 0.06

Jakar

Scenario Scenario Scenario Scenario

A B C D

13.85 14.08 12.96 12.85

76.45 73.20 70.49 64.82

9.69 12.68 16.44 22.20

0.01 0.04 0.11 0.13

4. Discussion With this study we show the potential for significantly increased wildfire activity in Himalayan blue pine ecosystems in the face of climate change. Fire hazards in general, and in particular under climate change, are poorly explored in the Himalayas (Bali et al., 2017; Kale et al., 2017; Matin et al., 2017; Kanga et al., 2014; Wang et al., 2007; Chandra, 2005; Jain et al., 1996). In Bhutan, a relatively small number of previous studies have investigated wildfires from different perspectives, including the identification of factors influencing blue pine mortality due to wildfire (Gyeltshen, 2016), the development of an expert-based forest fire prone areas map (Tshering, 2015), the design of a forest fire management strategy for Bhutan (Tshering, 2006), a longterm study on the effects of prescribed burning on NTFP production in chir pine (Pinus roxburghii [Sarg.]) forests (Darabant et al., 2016; Darabant et al., 2014), as well as the characterization of post-fire succession in blue pine forests (Dema, 2014). Our study adds novel insights to the Himalayan wildfire research by exploring the sensitivity of fire behavior to climatic changes in blue pine ecosystems of Bhutan. The findings will help others in predicting how extreme weather may affect fire behavior in similar systems elsewhere in the Himalayan Mountains. The results support our initial hypotheses and point out the high sensitivity of wildfire activity in blue pine ecosystems to extreme weather.

Fig. 4. Frequency distribution of crown fire activity outputs by scenario and study area.

Table 4 Average values and standard deviations for fire behavior indicators by scenario and study area.

Scenario A Scenario B Scenario C Scenario D

Thimphu Jakar Thimphu Jakar Thimphu Jakar Thimphu Jakar

Flame length (m)

Rate of spread (m min−1)

Burn probability

Fire size (ha)

4.44 3.05 4.70 3.35 6.36 4.06 6.94 4.80

8.03 ± 6.1 6.40 ± 4.6 8.00 ± 6.1 6.33 ± 4.6 9.86 ± 7.4 7.08 ± 5.3 10.17 ± 7.7 7.36 ± 5.7

0.010 0.019 0.010 0.019 0.013 0.022 0.013 0.021

81.04 ± 41.4 82.56 ± 40.4 80.50 ± 39.8 81.70 ± 40.5 105.54 ± 53.1 96.13 ± 46.1 106.01 ± 53.6 90.62 ± 46.9

± ± ± ± ± ± ± ±

5.0 4.2 5.1 4.6 6.2 5.4 6.3 6.1

7

± ± ± ± ± ± ± ±

0.006 0.012 0.006 0.012 0.008 0.012 0.008 0.012

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Fig. 5. Median and interquartile range of fire behavior indicators by scenario and study area.

4.1. Forest structure effects on fire behavior

the tree canopy. When the vertical structure of the forest is more complex (e.g., multi-layered), fires are more likely to climb ladder fuels and become crown fires (Alexander, 1988). The transition from surface to crown fire depends on canopy base height, foliar moisture content, and fire intensity (Alexander, 1988). Once the crown fire has started, its rate and extent of spread will be strongly influenced by canopy bulk density (Alexander, 1988). Canopy cover and stand height indirectly affect transition from surface to crown fire, since they influence wind speed under the canopy and moisture content of dead fuels (Reinhardt et al., 2006). Dense canopy cover provides the understory fuels with shading (Finney, 1998), thus their moisture content is less likely to drop to the ignition point (Agee, 1993). Our data suggest a substantial window or gap between the shrub and canopy layers, making it more difficult for fires to jump from ground vegetation to the canopy. For example, the average height of the shrub layer in Bhutan's blue pine ecosystems is 1.72 m (Gyeltshen,

Blue pine forests in Thimphu are predicted to experience higher fire hazards under all climate scenarios compared to Jakar based on our results (Table 5). We also found a high spatial variability of fire hazards, and by inference vulnerability to climate change, within each study area. Because FlamMap considers the interaction among topography, forest structure, and weather to simulate wildfire behavior, it is not possible to isolate completely the effects that variations in vegetation structure have on potential fire behavior. Stand height and CBH appeared to influence the overall fire hazard in both study areas (Fig. 7). However, forest structure differences between study areas were apparent in the results. Forest vertical and horizontal continuity (i.e., spatial continuity of fuels) greatly affects wildfire behavior (Agee, 1993). Vertical continuity is represented by ladder fuels, which influence the transition from surface to crown fire by carrying the fire into 8

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Thimphu

A

B

A

B

C

D

Jakar

Low

C Moderate

High

D Extreme

Fig. 6. Fire hazards predicted in Thimphu and Jakar based on modeling in FlamMap. From left to right: scenario A (baseline scenario), scenario B (increase in temperature scenario), scenario C (decrease in RH scenario), and scenario D (most extreme weather scenario). In black, areas of low fire hazard; in green, areas of moderate fire hazard; in orange, areas of high fire hazard; in red, areas of extreme fire hazard. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

2016), whereas CBH is on average 4.77 m in Thimphu and 5.84 m in Jakar (Table 3) Lower CBH in Thimphu can be partly attributed to admixed, sub-canopy tree species. Most of the simulated wildfires in FlamMap behaved as surface fires in both study areas, yet Thimphu experienced higher frequency of crown fires than Jakar (Fig. 4). This was consistent with the findings of Gyeltshen (2016), who analyzed scorch height in blue pine ecosystems and concluded that most past fires were low intensity surface fires. Crown fire probability can also be examined by comparing flame length to canopy base height. On average, flames in Thimphu reached the height of the canopy base under scenarios B (increase in temperature), C (decrease in RH), and D (increase in temperature and decrease in RH), while they did not in Jakar (Table 4). Wind speed and slope steepness also influence the transition from surface to crown fire (Alexander, 1988), yet they are not included in the calculation of crown fire in FlamMap. On the one hand, steep slopes facilitate the preheating of fuels and fire can spread faster or climb more readily to the tree canopy. On the other hand, when canopy cover is sparse more wind and solar radiation penetrate through the canopy, so that fuels are drier and easier to ignite. These steep, windy conditions increase fire intensity, rate of fire spread, and probability of crown fire. In Thimphu, the forest canopy is less dense, and the terrain is steeper (Table 2). Therefore, we likely underestimated crown fire activity in Thimphu more strongly than in Jakar. Mistletoe infection tends to increase fire behavior potential. Heavily infected stands accumulate greater amounts of dead fuels. Moreover, infected branches have slower decay rates, which adds fuel loading to the canopy (Alexander and Hawksworth, 1975). The percentage of infected

trees in Thimphu is almost three times higher than in Jakar (Table 2). Mistletoe infection was not included in FlamMap; thus, again, crown fire potential was likely underestimated more strongly in Thimphu than in Jakar. For fire ignition, the surface-area-to-volume ratio of dead fuels is a particularly important factor (Agee, 1993). Smaller particles, represented in this study as FWD, ignite easier. FWD volume in Jakar is more than twice as in Thimphu (Table 3). Under sunny and dry conditions, chances of fire ignition are higher in Jakar (Table 4, Fig. 5). However, in Bhutan almost all forest fires are human-caused (Chhetri, 1994), and ignition probability depends highly on distance to roads and type of nearest land use (Gyeltshen, 2016). In fact, even though Jakar has higher FWD accumulation, fires ignite more frequently in Thimphu (DoFPS, 2014). Wildfire hazard is higher in Thimphu than in Jakar (Table 5). This also might be explained by differences in weather conditions (Bessie and Johnson, 1995). Jakar lies within a higher elevation valley compared to Thimphu and has lower temperatures and higher RH, generally true also under extreme weather conditions. Therefore, fuels in Jakar have higher moisture content and are likely to burn less easily in comparison. 4.2. Sensitivity of fire behavior to climatic changes Climate change is expected to affect wildland fires in several ways, including alterations in fire frequency, intensity, duration, timing, and interactions with other disturbances (Seidl et al., 2017). An increase in 9

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changes due to extreme fire weather were more pronounced than in Jakar. Moreover, RH had a stronger effect on potential fire behavior than temperature, and the combination of both induced the highest change. Results of this study suggest that blue pine forests are more prone to wildfires of greater severity in years of deficit monsoon precipitation. However, an increase in temperature or a decrease in RH cannot be directly related to greater fire disturbances, as fires are a product of the interaction between weather, fuels, topography, and ignitions (Flannigan et al., 2000). Higher temperatures may lengthen the growing season (increasing fuel volume and its continuity) and increase evapotranspiration (higher accumulation of drier fuels) (Hessl, 2011; Flannigan et al., 2009). Long-term changes in fuel structure related to increased temperature were not included in our wildfire simulation. Thus, future studies should incorporate dynamic landscape simulations that model both long-term stand dynamics and fire behavior. Changes in fire regime and length of fire season may threaten postfire regeneration and change landscape species composition (Flannigan et al., 2000). For example, fire size and intensity may affect regeneration dynamics, as they influence distance from burn area cores to seed sources depending on survivorship patterns (Flannigan et al., 2000). Blue pine does not have serotinous cones, rather its seeds are dispersed by animals (zoochory) and can often reach far within disturbed areas (Darabant et al., 2012). Forest fires in blue pine ecosystems tend to be of low severity and size (DoFPS, 2014). However, given extreme fire weather conditions, fire could intensify (Table 4), not only threatening people and infrastructure from the WUI but also post-fire regeneration.

Fig. 7. Principal component analysis (PCA) of forest structure variables at the plot level by fire risk category (SH: stand height; CBH: canopy base height; CC: canopy cover; CBD: canopy bulk density). Plots (n = 76) were only included in the ordination if they fell into the same risk category under all scenarios. The first two axis explained 81% of the variation.

4.3. Social dimension of forest fires in Bhutan

the frequency and intensity of fires in Himalayan forests will threaten fragile mountain ecosystems, for instance by reducing carbon storage and soil stability, and limit access to forest resources by rural people. Our findings suggest an increase in fire hazards within the WUI if extreme weather conditions increase in frequency and intensity in the future. In fact, fire dynamics in the Himalayan region are already changing with altered monsoon rainfall patterns. In the last decades, monsoon precipitation has decreased (Sano et al., 2012; Bhutiyani et al., 2010) through aerosol forcing and altered albedos caused by land use changes (Krishnan et al., 2016). Long-term spatiotemporal variability of summer monsoons over Asia is unclear (Cook et al., 2010). Analyses of past monsoon precipitation patterns indicate that there is a high inter-annual and regional variability of precipitation amount (Varikoden et al., 2018; Cook et al., 2010; Shrestha et al., 2000). However, anthropogenic climate change has increased both variability in monsoon dynamics (Sharmila et al., 2015) and the likelihood of monsoon failures (Hijioka et al., 2014; Schewe and Levermann, 2012). After low monsoon precipitation years, forest landscapes tend to be drier, which likely results in increased wildfire activity (MoAF, 2016). Increased frequency of weaker monsoons due to climate change (Hijioka et al., 2014) would threaten not only Bhutanese forests but also water resources and local livelihoods. Global climate phenomena, such as El Niño-Southern Oscillation (ENSO), impact monsoon dynamics, including precipitation quantity and timing. Previous studies have reported a link between ENSO events and both weaker monsoons (Cook et al., 2010; Shrestha et al., 2000; Thompson et al., 2000) and increased wildfire activity in the Himalayas (Tenzin et al., 2018; Kale et al., 2017). Consistent with previous research, our results predict an increase in wildfire hazards with more frequent weakened monsoons due to climate change. By comparing each extreme climate scenario with the baseline scenario, it was possible to distinguish the effect that an increase in temperature and/or a decrease in RH had on fire behavior. In both study areas, maximum change in fire behavior was predicted under scenario D (increase in temperature and decrease in RH), followed by scenario C (decrease in RH) (Fig. 5). In Thimphu, potential fire behavior

Blue pine forests experience high human pressure because of their location, increasing adjacency to settlements, and the social characteristics of the rural population, whose subsistence livelihoods are highly dependent on forest resources. Wildfire hazards in the WUI in Bhutan are growing due to forest redevelopment on abandoned rural lands and the related increase in fuel continuity across the landscape. Simultaneously, sprawl of cities and infrastructure into forested areas increases pressure on those systems and raises the likelihood of escaped ignitions. (Dukpa et al., 2018; Gyeltshen, 2016; The World Bank, 2014; Tenzin, 2001). Human influence in shaping blue pine ecosystems was not considered in our simulation. Models including a human component would increase the accuracy of fire hazard predictions. Fire management is important to reduce fire hazards in the WUI (Keeton et al., 2007). In Bhutan prescribed burning was legalized in 2012, yet its effects have not yet been studied (Darabant et al., 2014). Thinning-from-below-the-canopy and other “fuel treatments” can reduce fire hazards by removing fuel ladders and improving stand physiological vigor. Rai et al. (2010) and Dukpa et al. (2018) found that moderate intensity thinning (25% of volume removal) in blue pine stands had the most positive effects on individual tree growth. Moderate thinning would, thus, reduce physiological stress, for instance to drought. Darabant et al. (2012) suggested combining thinning operations with fire break creation for fire prevention in the Himalayas. Understanding of monsoon dynamics has rapidly increased in recent years (Wang et al., 2019; Chen et al., 2015). This will facilitate assessments of altered monsoon precipitation patterns on fire regimes and may allow forecasting of years with elevated fire risks. Combining this knowledge with the outcomes of this study, such as the spatial distribution of wildfire hazards and vulnerabilities (Fig. 6), fire managers will be able to optimize fuel treatment allocation for fire prevention and plan fire management strategies accordingly. The combination of our fire hazard map with spatial data on settlements, orchards, and roads, would be a powerful tool for predicting future fire ignition probability. The fire hazard map might also be used by urban planners to adapt further urban/sub-urban expansion of houses and infrastructure into forested land, for example by avoiding or curtailing development 10

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within areas having the greatest probabilities and vulnerabilities to high intensity fires. Other mitigation strategies, such as fire-resistant building design and landscaping, might also be encouraged in high vulnerability areas. The methodology applied in this study could be employed elsewhere in the Himalayan region for similar purposes.

References Agee, J.K., 1993. Fire ecology of Pacific Northwest Forests. Island Press. Ager, A.A., Vaillant, N.M., Finney, M.A., 2010. A comparison of landscape fuel treatment strategies to mitigate wildland fire risk in the urban interface and preserve old forest structure. For. Ecol. Manage. 259, 1556–1570. https://doi.org/10.1016/j.foreco. 2010.01.032. Ager, A.A., Vaillant, N.M., Finney, M.A., Preisler, H.K., 2012. Analyzing wildfire exposure and source–sink relationships on a fire prone forest landscape. For. Ecol. Manage. 267, 271–283. https://doi.org/10.1016/j.foreco.2011.11.021. Alcasena, F.J., Ager, A.A., Salis, M., Day, M.A., Vega-Garcia, C., 2018. Wildfire spread, hazard and exposure metric raster grids for central Catalonia. Data Brief 17, 1–5. https://doi.org/10.1016/j.dib.2017.12.069. Alexander, M. E., 1988. Help With Making Crown Fire Hazard Assessments. General technical report-US Department of Agriculture, Forest Service, Intermountain Research Station (USA). Alexander, M.E., Hawksworth, F.G., 1975. Wildland Fires and Dwarf Mistletoes: A Literature Review of Ecology and Prescribed Burning. Gen. Tech. Rep. RM-GTR-14. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station. 12 p., 14. Bali, K., Mishra, A.K., Singh, S., 2017. Impact of anomalous forest fire on aerosol radiative forcing and snow cover over Himalayan region. Atmospheric Environ. 150, 264–275. https://doi.org/10.1016/j.atmosenv.2016.11.061. Bartels, R., Dell, J.D., Knight, R.L., Schaefer, G., 1985. Dead and down woody material. Management of wildlife and fish habitats in forests of western Oregon and Washington. Part, 1, pp. 171–186. Bessie, W.C., Johnson, E.A., 1995. Relative importance of fuels and weather on fire behavior in subalpine forests. Ecology 76, 747–762. https://doi.org/10.2307/1939341. Bhutiyani, M.R., Kale, V.S., Pawar, N., 2010. Climate change and the precipitation variations in the northwestern Himalaya: 1866–2006. Int. J. Climatol. 30, 535–548. https://doi.org/10.1002/joc.1920. Brown, J.K., 1974. Handbook for inventorying downed woody material. Gen. Tech. Rep. INT-16. Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 24 p., 16. Burgan, R.E., 1979. Estimating live fuel moisture for the 1978 National Fire Danger Rating System. Res. Pap. INT 226. Ogden, UT: Intermountain Forest and Range Experiment Station, Forest Service, U.S. Department of Agriculture; 1979. 17 p. https://doi.org/10.5962/bhl.title.68713. Cervarich, M., Shu, S., Jain, A.K., Arneth, A., Canadell, J., Friedlingstein, P., Houghton, R.A., Kato, E., Koven, C., Patra, P., 2016. The terrestrial carbon budget of South and Southeast Asia. Environ. Res. Lett. 11, 105006. https://doi.org/10.1088/1748-9326/ 11/10/105006. CF members, 2018. Community Forest members' interviews. Thimphu, Bumthang. Chandra, S., 2005. Application of Remote Sensing and GIS Technology in Forest Fire Risk Modeling and Management of Forest Fires: A Case Study in the Garhwal Himalayan Region, Geo-information for Disaster Management. Springer, pp. 1239–1254 http:// dx.doi.org/10.1007/3-540-27468-5_86. Chen, F., Xu, Q., Chen, J., Birks, H.J.B., Liu, J., Zhang, S., Jin, L., An, C., Telford, R.J., Cao, X., 2015. East Asian summer monsoon precipitation variability since the last deglaciation. Sci. Rep. 5, 11186. https://doi.org/10.1038/srep11186. Chhetri, D., 1994. Seasonality of forest fires in Bhutan. Int. Forest Fire News 10, 5–9. Choudhary, A., Dimri, A., 2018. Assessment of CORDEX-South Asia experiments for monsoonal precipitation over Himalayan region for future climate. Clim. Dyn. 50, 3009–3030. https://doi.org/10.1007/s00382-017-3789-4. Chuvieco, E., Aguado, I., Yebra, M., Nieto, H., Salas, J., Martín, M.P., Vilar, L., Martínez, J., Martín, S., Ibarra, P., 2010. Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecol. Model. 221, 46–58. https://doi.org/10.1016/j.ecolmodel.2008.11.017. Cohen, J.D. Deeming, J.E., 1985. The national fire-danger rating system: basic equations. Gen. Tech. Rep. PSW-82. Berkeley, CA: Pacific Southwest Forest and Range Experiment Station, Forest Service, US Department of Agriculture; 16 p, 82. Cook, E.R., Anchukaitis, K.J., Buckley, B.M., D’Arrigo, R.D., Jacoby, G.C., Wright, W.E., 2010. Asian monsoon failure and megadrought during the last millennium. Science 328, 486–489. https://doi.org/10.1126/science.1185188. Cruz, M.G., Alexander, M.E., Wakimoto, R.H., 2003. Assessing canopy fuel stratum characteristics in crown fire prone fuel types of western North America. Int. J. Wildland Fire 12, 39–50. https://doi.org/10.1071/WF02024. Darabant, A., Dorji, B.T., Rai, P.B., Kleine, M., Dorji Gratzer, G., 2014. Legalizing prescribed burning in Bhutan: A successful example of an evidence-based policy decision. In: Miner, C. L., Sands, Y. Pierre, H. (Eds.) Comunicating Forest Science. IUFRO Internation Union of Forest Research Organizations. Vienna, Austria. Darabant, A., Rai, P.B., Eckmüllner, O., Gratzer, G., Gyeltshen, D., 2012. Guidelines for Nation-Wide Thinning of Blue Pine. Department of Forests and Park Services, Ministry of Agriculture, Thimphu, Bhutan. Darabant, A., Staudhammer, C.L., Rai, P.B., Gratzer, G., 2016. Burning for enhanced nontimber forest product yield may jeopardize the resource base through interactive effects. J. Appl. Ecol. 53, 1613–1622. https://doi.org/10.1111/1365-2664.12746. Dema, N., 2014. Succession After Forest Fire in the Blue Pine Forest. Master thesis. Noida International University, India. Dimri, A., Kumar, D., Choudhary, A., Maharana, P., 2018. Future changes over the Himalayas: maximum and minimum temperature. Glob. Planet. Change 162, 212–234. https://doi.org/10.1016/j.gloplacha.2018.01.015. DoFPS, 2014. Fire records (1992–2014). Forest Protection and Enforcement Division, Department of Forests and Park Services, Ministry of Agriculture and Forests, Royal Government of Bhutan.

5. Conclusion This study is among the first to characterize the spatial sensitivity of wildfire behavior to climate change in Himalayan blue pine ecosystems. Climatic changes are likely to profoundly alter natural disturbances regimes (Seidl et al., 2017). These alterations will likewise play a key role in determining trajectories of future ecosystem change and adaptation. In regions where climate change is expected to have large impacts, such as mountainous regions (Gratzer and Keeton, 2017), it is of particular importance to anticipate altered disturbance behavior so that impacts on rural, natural-resource dependent livelihoods can be minimized. In this context, and at a regional scale, our study contributes to improving knowledge of how climate change may affect wildland fire in blue pine ecosystems in the Himalayas. Thus, fire prevention strategies can be accurately allocated to reduce negative impacts of exacerbated fire risks of rural livelihoods. Findings of this study indicate a likely doubling of fire hazards in Bhutan blue pine ecosystems under extreme weather conditions. The fire hazard map can be used both by fire managers to more efficiently allocate fuel treatment resources for fire risk reduction, and by urban planners to shift suburban development to areas where fire hazards are low. Furthermore, results of this study may be used as a scientific basis for both policy-making and forest and fire management in Bhutan in the face of climate change. Author contributions LVV lead authored the paper for her M.S. thesis. WSK supervised the project, with assistance from GG and DT. WSK and LVV designed the study. LVV conducted the literature review, performed the analysis, and wrote the first draft of the manuscript. LVV and WSK collaborated on subsequent drafts. DT provided statistical and analytical support. CG and KS assisted with data collection and logistical support in the field. All authors contributed to, edited, and revised the manuscript. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This research was supported by the project “Climate Change Adaptation Potentials of Forest in Bhutan” (BC CAP II) funded by the Austrian Federal Ministry of Agriculture, Forestry, Environment and Water Management (G. Gratzer, P.I.), and by grants from the Austrian Marshall Plan Foundation and the USDA McIntire-Stennis Forest Research Program (W. Keeton, P.I.). The authors wish to thank the field crew and staff members of the Ugyen Wangchuck Institute for Conservation and Environmental Research (UWICER, Department of Forest and Park Services, Ministry of Agriculture, Bhutan) for outstanding support during the field work portion of this study. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foreco.2020.117927. 11

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L. Vilà-Vilardell, et al. DoFPS, 2016a. Land use land cover thematic map. Forest Resource Management Division, Department of Forests and Park Services, Ministry of Agriculture and Forests, Royal Government of Bhutan. Dorji, S., Donaubauer, E., Wingfield, M.J., Kirisits, T., 2012. Himalayan dwarf mistletoe (Arceuthobium minutissimum) and the leafy mistletoe Taxillus kaempferi on blue pine (Pinus wallichiana) in Bhutan. J. Agric. Ext. Rural Dev. 4, 217–220. Dukpa, D., Cook, E.R., Krusic, P.J., Rai, P., Darabant, A., Tshering, U., 2018. Applied dendroecology informs the sustainable management of Blue Pine forests in Bhutan. Dendrochronologia 49, 89–93. https://doi.org/10.1016/j.dendro.2018.03.003. Fernandes, P., Luz, A., Loureiro, C., Ferreira-Godinho, P., Botelho, H.N., 2006. Fuel modelling and fire hazard assessment based on data from the Portuguese National Forest Inventory. For. Ecol. Manage. 234, S229. https://doi.org/10.1016/j.foreco. 2006.08.256. DoFPS, 2016b. National Forest Inventory Report: Stocktaking Nation’s Forest Resources. Thimphu: Forest Resources Management Division, Department of Forests and Park Services, Ministry of Agriculture and Forests, Royal Government of Bhutan. Finney, M.A., 1998. FARSITE, Fire Area Simulator–model development and evaluation, US Department of Agriculture, Forest Service, Rocky Mountain Research Station Ogden, UT. Finney, M.A., 2002. Fire growth using minimum travel time methods. Can. J. For. Res. 32, 1420–1424. https://doi.org/10.1139/x02-068. Finney, M.A., 2005. The challenge of quantitative risk analysis for wildland fire. For. Ecol. Manage. 211, 97–108. https://doi.org/10.1016/j.foreco.2005.02.010. Finney, M.A., 2006. An overview of FlamMap fire modeling capabilities. In: Andrews, Patricia L., Butler, Bret W., comps. 2006. Fuels Management-How to Measure Success: Conference Proceedings. 28-30 March 2006; Portland, OR. Proceedings RMRS-P-41. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station. pp. 213–220. Finney, M.A., Brittain, S., Seli, R.C., McHugh, C.W., Gangi, L., 2016. FlamMap 5 Version 5.0.3. Joint Fire Sciences Program, Rocky Mountain Research Station, US Bureau of Land Management. Flannigan, M.D., Krawchuk, M.A., de Groot, W.J., Wotton, B.M., Gowman, L.M., 2009. Implications of changing climate for global wildland fire. Int. J. Wildland Fire 18, 483–507. https://doi.org/10.1071/WF08187. Flannigan, M.D., Stocks, B.J., Wotton, B.M., 2000. Climate change and forest fires. Sci. Total Environ. 262, 221–229. https://doi.org/10.1016/S0048-9697(00)00524-6. Ford, S.E., Keeton, W.S., 2017. Enhanced carbon storage through management for oldgrowth characteristics in northern hardwood-conifer forests. Ecosphere 8. https:// doi.org/10.1002/ecs2.1721. Govil, K., 1999. Forest resources of Bhutan. Country report. Forest Resource Assessment Programme (FRA 2000). Rome: Food and Agriculture Organization of the United Nations (FAO). Gratzer, G., Keeton, W.S., 2017. Mountain forests and sustainable development: the potential for achieving the United Nations’ 2030 Agenda. Mt. Res. Dev. 37, 246–253. https://doi.org/10.1659/MRD-JOURNAL-D-17-00093.1. Gyeltshen, C., 2016. Fire Risks in Blue Pine Forests of Bhutan. Master thesis. University of Natural Resources and Life Sciences (Boku), Vienna, Austria. Harmon, M.E., Woodall, C.W., Fasth, B., Sexton, J., 2008. Woody detritus density and density reduction factors for tree species in the United States: a synthesis. USDA For. Serv. Gen. Tech. Rep. NRS-29. Harmon, M.E., Woodall, C.W., Fasth, B., Sexton, J., Yatkov, M., 2011. Differences Between Standing and Downed Dead Tree Wood Density Reduction Factors: A Comparison Across Decay Classes and Tree Species. Res. Pap. NRS-15. Newtown Square. US Department of Agriculture, Forest Service, Northern Research Station, PA 40 p., 15, 1-40. Hessl, A.E., 2011. Pathways for climate change effects on fire: models, data, and uncertainties. Prog. Phys. Geogr.: Earth Environ. 35, 393–407. https://doi.org/10. 1177/0309133311407654. Hijioka, Y., Lin, E., Pereira, J.J., Corlett, R.T., Cui, X., Insarov, G., Lasco, R., Lindgren, E., Surjan, A., 2014. Asia. In: Barros, V.R., Field, C.B., Dokken, D.J., Mastrandrea, M.D., Mach, K.J., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., Girma, B., Kissel, E.S., Levy, A.N., MacCracken, S., Mastrandrea, P.R., White, L.L. (Eds.) Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1327–1370. Hoy, A., Katel, O., Thapa, P., Dendup, N., Matschullat, J., 2016. Climatic changes and their impact on socio-economic sectors in the Bhutan Himalayas: an implementation strategy. Reg. Environ. Change 16, 1401–1415. https://doi.org/10.1007/s10113015-0868-0. HydroMet, 2017. Meteorological data (1996-2017). National Center for Hydrology and Meteorology, Royal Government of Bhutan. Jain, A., Ravan, S.A., Singh, R., Das, K., Roy, P., 1996. Forest fire risk modelling using remote sensing and geographic information system. Curr. Sci. 928–933. Jain, K., Seth, M., 1979. Intra-increment variation in specific gravity of wood in Blue pine. Wood Sci. Technol. 13, 239–248. https://doi.org/10.1007/BF00356967. Kale, M.P., Ramachandran, R.M., Pardeshi, S.N., Chavan, M., Joshi, P., Pai, D., Bhavani, P., Ashok, K., Roy, P., 2017. Are climate extremities changing forest fire regimes in India? an analysis using MODIS fire locations during 2003–2013 and gridded climate data of India meteorological department. Proc. Natl Acad. Sci. India Sect A: Phys. Sci. 87, 827–843. https://doi.org/10.1007/s40010-017-0452-8. Kanga, S., Sharma, L., Pandey, P.C., Nathawat, M.S., 2014. GIS Modelling approach for forest fire risk assessment and management. Int. J. Adv. Remote Sens. GIS Geogr. 2, 30–44. Keeton, W.S., Mote, P.W., Franklin, J.F., 2007. Chapter 13 Climate Variability, Climate Change, and Western Wildfire with Implications for the Urban–Wildland Interface.

In: Troy, A., Kennedy, R.G. (Eds.) Living on the Edge: Economic, Institutional and Management Perspectives on Wildfire Hazard in the Urban Interface (Advances in the Economics of Environmental Resources, Vol. 6). Emerald Group Publishing Limited, Bingley, UK, pp. 225–253. http://dx.doi.org/10.1016/S1569-3740(06)06013-5. Krishnan, R., Sabin, T., Vellore, R., Mujumdar, M., Sanjay, J., Goswami, B., Hourdin, F., Dufresne, J.-L., Terray, P., 2016. Deciphering the desiccation trend of the South Asian monsoon hydroclimate in a warming world. Clim. Dyn. 47, 1007–1027. https://doi. org/10.1007/s00382-015-2886-5. Lawton, R.O., 1984. Ecological constraints on wood density in a tropical montane rain forest. Am. J. Bot. 261–267. https://doi.org/10.1002/j.1537-2197.1984.tb12512.x. Matin, M.A., Chitale, V.S., Murthy, M.S., Uddin, K., Bajracharya, B., Pradhan, S., 2017. Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data. Int. J. Wildland Fire 26, 276–286. https://doi.org/10.1071/WF16056. MoAF, 2016. State of Climate Change Report for the RNR Sector. Ministry of Agriculture and Forests, Royal Government of Bhutan. Muukkonen, P., Mäkipää, R., Laiho, R., Minkkinen, K., Vasander, H., Finér, L., 2006. Relationship between biomass and percentage cover in understorey vegetation of boreal coniferous forests. Silva Fenn. 40, 231–245. https://doi.org/10.14214/sf.340. Neumann, M., Moreno, A., Mues, V., Härkönen, S., Mura, M., Bouriaud, O., Lang, M., Achten, W.M., Thivolle-Cazat, A., Bronisz, K., 2016. Comparison of carbon estimation methods for European forests. For. Ecol. Manage. 361, 397–420. https://doi.org/10. 1016/j.foreco.2015.11.016. NLC, 2014. Bhutan GeoSpatial Portal. Centre for GIS Coordination, National Land Commission, Royal Government of Bhutan. Available: http://geo.gov.bt/. NSB, 2013. Statistical Yearbook of Bhutan. National Statistics Bureau, Royal Government of Bhutan. Phuntsho, S., Schmidt, K., Kuyakanon, R., Temphel, K.J., 2011. Community Forestry in Bhutan: Putting people at the Heart of Poverty Reduction. Ugyen Wangchuck Institute for Conservation and Environment (UWICE), Jakar, Bhutan. R Core Team, 2017. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria https://www.R-project.org/. Rai, P.B., Darabant, A., Dukpa, D., Dorji, T., Eckmüllner, O., Sangay, G.G., Tenzin, K., Bürgi, A., Rai, T.B., Chhetri, Y.R., 2010. Effects of thinning intensities on productivity and stability of bluepine forests. J. Renew. Nat. Resour. Bhutan 6, 59–75. Reinhardt, E., Scott, J., Gray, K., Keane, R., 2006. Estimating canopy fuel characteristics in five conifer stands in the western United States using tree and stand measurements. Can. J. For. Res. 36, 2803–2814. https://doi.org/10.1139/x06-157. Salis, M., Ager, A.A., Arca, B., Finney, M.A., Bacciu, V., Duce, P., Spano, D., 2013. Assessing exposure of human and ecological values to wildfire in Sardinia. Italy. Int. J. Wildland Fire 22, 549–565. https://doi.org/10.1071/WF11060. Sano, M., Ramesh, R., Sheshshayee, M., Sukumar, R., 2012. Increasing aridity over the past 223 years in the Nepal Himalaya inferred from a tree-ring δ18O chronology. Holocene 22, 809–817. Schewe, J., Levermann, A., 2012. A statistically predictive model for future monsoon failure in India. Environ. Res. Lett. 7, 044023. https://doi.org/10.1088/1748-9326/ 7/4/044023. Scott, J.H., Burgan, R.E., 2005. Standard fire behavior fuel models: a comprehensive set for use with Rothermel's surface fire spread model. Gen. Tech. Rep. RMRS-GTR-153. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain ResearchStation. 72 p. Scott, J.H., Reinhardt, E.D., 2001. Assessing crown fire potential by linking models of surface and crown fire behavior. Res. Pap. RMRS-RP-29. Fort Collins, CO: U.S. Departmentof Agriculture, Forest Service, Rocky Mountain Research Station. 59 p. Scott, J.H., Thompson, M.P., Calkin, D.E., 2013. A wildfire risk assessment framework for land and resource management. Gen. Tech. Rep. RMRS-GTR-315.: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 83 p. Seidl, R., Thom, D., Kautz, M., Martin-Benito, D., Peltoniemi, M., Vacchiano, G., Wild, J., Ascoli, D., Petr, M., Honkaniemi, J., 2017. Forest disturbances under climate change. Nat. Clim. Change 7, 395–402. https://doi.org/10.1038/nclimate3303. Sharmila, S., Joseph, S., Sahai, A., Abhilash, S., Chattopadhyay, R., 2015. Future projection of Indian summer monsoon variability under climate change scenario: an assessment from CMIP5 climate models. Glob. Planet. Change 124, 62–78. https:// doi.org/10.1016/j.gloplacha.2014.11.004. Sheikh, M.A., Kumar, M., Bhat, J.A., 2011. Wood specific gravity of some tree species in the Garhwal Himalayas, India. For. Stud. China 13, 225. https://doi.org/10.1007/ s11632-011-0310-8. Shrestha, A.B., Wake, C.P., Dibb, J.E., Mayewski, P.A., 2000. Precipitation fluctuations in the Nepal Himalaya and its vicinity and relationship with some large scale climatological parameters. Int. J. Climatol. 20, 317–327. https://doi.org/10.1002/(SICI) 1097-0088(20000315)20:3%3C317::AID-JOC476%3E3.0.CO;2-G. Simard, A. J., 1968. Moisture Content of Forest Fuels – I A Review of the Basic Concepts. Information Report FF-X-14. Ottawa, ON, Canada: Forest Fire Research Institute, Department of Forestry and Rural Development. 47 p. Tenzin, J., Wangchuk, T., Hasenauer, H., 2016. Form factor functions for nine commercial tree species in Bhutan. For.: Int J. For. Res. 90, 359–366. https://doi.org/10.1093/ forestry/cpw044. Tenzin, K., 2001. The Management of Blue Pine (Pinus wallichiana) in Secondary Forest in Bhutan. Master thesis. The University of Edinburgh. Tenzin, K., Cook, E.R., Krusic, P.J., Dukpa, D., Gyeltshen, C., 2018. Forest fire history and its link to climate and ENSO in Bhutan Himalaya. Bhutan Ecological Society, Thimphu, Bhutan. Teobaldelli, M., Somogyi, Z., Migliavacca, M., Usoltsev, V.A., 2009. Generalized functions of biomass expansion factors for conifers and broadleaved by stand age, growing stock and site index. For. Ecol. Manage. 257, 1004–1013. https://doi.org/10.1016/j. foreco.2008.11.002.

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Forest Ecology and Management 461 (2020) 117927

L. Vilà-Vilardell, et al. The World Bank, 2014. Bhutan Development Update: Poverty Reduction and Economic Management, South Asia Region. The World Bank. Thom, D., Rammer, W., Dirnböck, T., Müller, J., Kobler, J., Katzensteiner, K., Helm, N., Seidl, R., 2017. The impacts of climate change and disturbance on spatio-temporal trajectories of biodiversity in a temperate forest landscape. J. Appl. Ecol. 54, 28–38. https://doi.org/10.1111/1365-2664.12644. Thompson, L.G., Yao, T., Mosley-Thompson, E., Davis, M., Henderson, K., Lin, P.-N., 2000. A high-resolution millennial record of the South Asian monsoon from Himalayan ice cores. Science 289, 1916–1919. https://doi.org/10.1126/science.289. 5486.1916. Tiwari, P.C., Joshi, B., 2015. Climate change and rural out-migration in Himalaya. Change Adapt. Socio-Ecol. Syst. 2. https://doi.org/10.1515/cass-2015-0002. Tse-ring, K., Sharma, E., Chettri, N. Shrestha, A.B., 2010. Climate Change Vulnerability of Mountain Ecosystems in the Eastern Himalayas. Climate Change Impact and Vulnerability in the Eastern Himalayas – Synthesis Report. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD). 101 p. Tshering, K., 2006. Development of an Effective Forest Fire Management Strategy for Bhutan. Master thesis. The University of Montana. Tshering, K., 2015. Spatial Modelling of Forest Fire Prone Areas in Bhutan using a combination of Expert Knowledge and Analytical Hierarchical Process in GIS. Bachelor thesis. Royal University of Bhutan. Van Wagner, C., 1968. The line intersect method in forest fuel sampling. For. Sci. 14, 20–26. Varikoden, H., Mujumdar, M., Revadekar, J., Sooraj, K., Ramarao, M., Sanjay, J., Krishnan, R., 2018. Assessment of regional downscaling simulations for long term

mean, excess and deficit Indian Summer Monsoons. Glob. Planet. Change 162, 28–38. https://doi.org/10.1016/j.gloplacha.2017.12.002. Wang, H., Xie, S.-P., Kosaka, Y., Liu, Q., Du, Y., 2019. Dynamics of asian summer monsoon response to anthropogenic aerosol forcing. J. Clim. 32, 843–858. https://doi. org/10.1175/JCLI-D-18-0386.1. Wang, M.Y., Shu, L.-F., Wand, J.-S., Tian, X.-R., Li, H., 2007. Forest fuel characteristics and the impacts of climate change on forest fires in southeast Tibet. Fire Saf Sci. 1. Wani, B.A., Bodha, R., Khan, A., 2014. Wood specific gravity variation among five important hardwood species of Kashmir Himalaya. Pak. J. Biol. Sci. 17, 395–401. https://doi.org/10.3923/pjbs.2014.395.401. Webb, E.L., Dorji, L., 2008. The evolution of forest-related institutions in Bhutan: adaptations of local people to the rising state. In: Webb, E.L., Shivakoti, G.P. (Eds.), Decentralization, Forests and Rural Communities: Policy Outcomes in South and Southeast Asia. Sage Press, India. Woodall, C., Williams, M., 2005. Sampling protocol, estimation, and analysis procedures for the down woody materials indicator of the FIA program. Gen. Tech. Rep. NC-256. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Research Station. 47 p. Xu, J., Grumbine, R.E., Shrestha, A., Eriksson, M., Yang, X., Wang, Y., Wilkes, A., 2009. The melting Himalayas: cascading effects of climate change on water, biodiversity, and livelihoods. Conserv. Biol. 23, 520–530. https://doi.org/10.1111/j.1523-1739. 2009.01237.x. Zhang, S., 1995. Effect of growth rate on wood specific gravity and selected mechanical properties in individual species from distinct wood categories. Wood Sci. Technol. 29, 451–465. https://doi.org/10.1007/BF00194204.

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