1 00:00:12,210 --> 00:00:15,570 - This next talk is from William Keeton 2 00:00:15,570 --> 00:00:17,490 with the Rubenstein School of Environment 3 00:00:17,490 --> 00:00:20,050 and Natural Resources at the University of Vermont 4 00:00:20,050 --> 00:00:25,050 in the Gunn Institute of Environment presenting, 5 00:00:25,260 --> 00:00:27,280 and now for something completely different, 6 00:00:27,280 --> 00:00:31,150 Climate Change Effects on Forest Fire Hazards 7 00:00:31,150 --> 00:00:34,043 in the Wildland-Urban-Interface of Bhutan. 8 00:00:35,770 --> 00:00:37,760 - And thanks for attending. 9 00:00:37,760 --> 00:00:39,350 The title of my talk this morning 10 00:00:39,350 --> 00:00:42,070 is Climate Change Effects on Forest Fire Hazards 11 00:00:42,070 --> 00:00:45,790 in the Wildland-Urban-Interface of Bhutan. 12 00:00:45,790 --> 00:00:47,050 And this is gonna be a little bit different 13 00:00:47,050 --> 00:00:49,410 from other talks I've given for this conference 14 00:00:49,410 --> 00:00:52,460 over the years, but I'm really excited to share with you 15 00:00:52,460 --> 00:00:55,100 this project that we've been working on 16 00:00:55,100 --> 00:00:56,170 for a number of years. 17 00:00:56,170 --> 00:00:59,490 It's been a collaboration between UVM 18 00:00:59,490 --> 00:01:03,030 and a team of scientists from Austria, Germany, 19 00:01:03,030 --> 00:01:05,083 Spain, and Bhutan. 20 00:01:06,150 --> 00:01:10,063 So I'm gonna take you to a very different place in a minute, 21 00:01:11,170 --> 00:01:13,990 but let's start off with just a little bit of context. 22 00:01:13,990 --> 00:01:16,530 I'm sure everyone is aware 23 00:01:16,530 --> 00:01:20,080 of the increasing forest fire problems, 24 00:01:20,080 --> 00:01:21,380 both in the United States 25 00:01:21,380 --> 00:01:23,550 and in many other parts of the world. 26 00:01:23,550 --> 00:01:25,050 And I'm sure you're aware 27 00:01:25,050 --> 00:01:29,520 of some of the particular concerns around risks 28 00:01:29,520 --> 00:01:32,870 that that poses for the willdland-urban-interface 29 00:01:32,870 --> 00:01:36,900 where risks to human lives, and property, and infrastructure 30 00:01:36,900 --> 00:01:37,780 are the greatest. 31 00:01:37,780 --> 00:01:40,550 And there are really no lack of examples 32 00:01:40,550 --> 00:01:45,220 of these types of disasters from recent years, 33 00:01:45,220 --> 00:01:50,040 you know, such as the Paradise Fire in Northern California 34 00:01:50,040 --> 00:01:51,513 just two years ago. 35 00:01:52,670 --> 00:01:55,710 Other parts of the world are beginning to experience 36 00:01:55,710 --> 00:01:57,540 similar phenomena. 37 00:01:57,540 --> 00:02:00,470 We've seen similar catastrophic fires 38 00:02:00,470 --> 00:02:03,870 in the wildland-urban-interface in Greece, in Spain, 39 00:02:03,870 --> 00:02:07,486 in Indonesia, and Australia, in many parts of the world. 40 00:02:07,486 --> 00:02:12,030 And one place that people don't typically think of as much 41 00:02:12,030 --> 00:02:15,040 as having these types of fire hazards 42 00:02:15,040 --> 00:02:16,900 is the Himalayan Mountains. 43 00:02:16,900 --> 00:02:18,950 And yet there are forest types there 44 00:02:18,950 --> 00:02:21,150 that are highly fire prone 45 00:02:21,150 --> 00:02:24,620 and increasingly pose these types of risks 46 00:02:24,620 --> 00:02:27,770 to urban areas and suburban areas 47 00:02:27,770 --> 00:02:29,570 like the capital city of Bhutan 48 00:02:29,570 --> 00:02:32,490 that you're seeing here in this picture called Thimphu. 49 00:02:32,490 --> 00:02:33,980 No, we see this phenomena 50 00:02:33,980 --> 00:02:36,080 in many of these Himalayan countries 51 00:02:36,080 --> 00:02:38,217 of out-migration from rural areas 52 00:02:38,217 --> 00:02:41,790 and in-migration into the the few urban areas 53 00:02:41,790 --> 00:02:43,730 that there are in a country like Bhutan 54 00:02:43,730 --> 00:02:46,830 and that leads to sprawl into the fire-prone vegetation 55 00:02:46,830 --> 00:02:49,890 just like we see in California and other parts of the west. 56 00:02:49,890 --> 00:02:54,120 So I'm gonna talk a little bit today about these hazards 57 00:02:54,120 --> 00:02:57,750 and models that we can use to try to predict 58 00:02:57,750 --> 00:03:01,350 where these fire hazards might be the greatest in the future 59 00:03:01,350 --> 00:03:02,780 with climate change. 60 00:03:02,780 --> 00:03:06,940 And even though this is an example from far, far away, 61 00:03:06,940 --> 00:03:09,180 I think there's relevance to our own region 62 00:03:09,180 --> 00:03:13,320 in terms of models, and methodologies, and other approaches 63 00:03:13,320 --> 00:03:15,820 that we might use here 64 00:03:15,820 --> 00:03:19,330 to think about fire risks in the future, 65 00:03:19,330 --> 00:03:23,090 especially in some of our more fire prone forest systems. 66 00:03:23,090 --> 00:03:25,440 So that's really the connection to New England. 67 00:03:26,960 --> 00:03:28,990 Okay, So I'll be reporting on this paper 68 00:03:28,990 --> 00:03:31,380 that was just published last spring. 69 00:03:31,380 --> 00:03:33,890 The lead author, Lena Vila-Vilardell 70 00:03:33,890 --> 00:03:35,230 was a Master's student with me. 71 00:03:35,230 --> 00:03:38,140 And I'd also like to thank specifically Choki Gyletshen, 72 00:03:38,140 --> 00:03:39,740 who was another Master's student with me 73 00:03:39,740 --> 00:03:42,933 who contributed to another component of this project. 74 00:03:43,930 --> 00:03:45,300 Okay, so this is where we're headed. 75 00:03:45,300 --> 00:03:47,000 We're headed to the kingdom of Bhutan, 76 00:03:47,000 --> 00:03:50,960 which is a mountain kingdom in the Himalayan Mountains 77 00:03:50,960 --> 00:03:53,270 just north of India, just south of China, 78 00:03:53,270 --> 00:03:57,020 a place that was closed off to the Western world 79 00:03:57,020 --> 00:04:00,600 until fairly recently, until the 1970s and 80s. 80 00:04:00,600 --> 00:04:02,470 There's now some degree of tourism there, 81 00:04:02,470 --> 00:04:04,453 but it's highly regulated. 82 00:04:05,510 --> 00:04:07,840 It's a constitutional monarchy, 83 00:04:07,840 --> 00:04:12,830 but the King still holds great sway over the society. 84 00:04:12,830 --> 00:04:14,270 It's a fascinating place to work. 85 00:04:14,270 --> 00:04:15,103 I thought I would just show you 86 00:04:15,103 --> 00:04:17,650 a couple of pictures quickly to get you in the mood. 87 00:04:17,650 --> 00:04:19,690 So these are the high Himalayas, 88 00:04:19,690 --> 00:04:24,690 the peaks there are no less tall than the more famous ones 89 00:04:24,800 --> 00:04:26,393 not too far away in Nepal. 90 00:04:28,640 --> 00:04:32,520 Fascinating culturally, it's a majority Buddhist country. 91 00:04:32,520 --> 00:04:34,480 There's a Hindu minority, 92 00:04:34,480 --> 00:04:38,190 even a Baptist and some Christian minority groups, 93 00:04:38,190 --> 00:04:42,203 Animist religions, a fascinating place culturally. 94 00:04:43,910 --> 00:04:46,620 The national sport is archery, 95 00:04:46,620 --> 00:04:50,493 and these folks are just tremendous archers. 96 00:04:52,050 --> 00:04:55,620 So again, back to the point about most people 97 00:04:55,620 --> 00:04:59,540 not thinking of Himalayan forests as having fire. 98 00:04:59,540 --> 00:05:00,850 Well, that's because, of course, 99 00:05:00,850 --> 00:05:03,130 these are high elevation mountains, 100 00:05:03,130 --> 00:05:04,970 so highly mountainous terrain, 101 00:05:04,970 --> 00:05:07,410 and the higher elevation forest there 102 00:05:07,410 --> 00:05:11,670 are wet tempered forests, or boreal forests 103 00:05:11,670 --> 00:05:13,450 that don't have much fire, 104 00:05:13,450 --> 00:05:17,360 but there's a thin band of forest and mid elevations 105 00:05:17,360 --> 00:05:21,220 that tends to surround settlements in urban areas. 106 00:05:21,220 --> 00:05:23,730 It's dominated by this species that you're seeing here, 107 00:05:23,730 --> 00:05:28,300 blue pine or Pinus wallichiana, that is highly fire-prone. 108 00:05:28,300 --> 00:05:32,410 It's much like Ponderosa pine, a close relative out west. 109 00:05:32,410 --> 00:05:34,890 And because we're at mid elevations, you know, 110 00:05:34,890 --> 00:05:36,710 just up the mountain slopes, 111 00:05:36,710 --> 00:05:38,800 this is where you find a lot of the most important 112 00:05:38,800 --> 00:05:40,330 cultural sites in Butan, you know, 113 00:05:40,330 --> 00:05:44,300 Buddhist temples, monasteries, that sort of thing 114 00:05:44,300 --> 00:05:47,730 that are surrounded by these blue pine forest 115 00:05:47,730 --> 00:05:50,253 that are highly combustible. 116 00:05:51,190 --> 00:05:54,210 Okay, so here's a map of vegetation in Bhutan, and you know, 117 00:05:54,210 --> 00:05:56,930 you're seeing everything from tropical to boreal, 118 00:05:56,930 --> 00:06:01,630 but the purple is this margin or this band 119 00:06:01,630 --> 00:06:06,270 of blue pine forest at mid elevations that surrounds, again, 120 00:06:06,270 --> 00:06:10,900 some of the most important urban areas in the country. 121 00:06:10,900 --> 00:06:14,450 It's an early successional species, it's shade intolerant, 122 00:06:14,450 --> 00:06:17,150 but there's a lot of young blue pine now 123 00:06:17,150 --> 00:06:20,880 that has regenerated on abandoned agricultural lands. 124 00:06:20,880 --> 00:06:23,330 And one of our hypotheses in this project, 125 00:06:23,330 --> 00:06:24,220 the larger project, 126 00:06:24,220 --> 00:06:28,390 was that seems to be particularly susceptible to fire, 127 00:06:28,390 --> 00:06:30,610 more so perhaps than some of the older, 128 00:06:30,610 --> 00:06:33,000 more fire-resistant forest structures. 129 00:06:33,000 --> 00:06:34,220 The other really important thing to know 130 00:06:34,220 --> 00:06:36,230 about this case study is that the climate there 131 00:06:36,230 --> 00:06:37,580 is strongly influenced 132 00:06:37,580 --> 00:06:40,390 by the monsoon precipitation dynamics. 133 00:06:40,390 --> 00:06:43,750 The monsoon season is roughly June to September. 134 00:06:43,750 --> 00:06:47,370 More than 70% of the precipitation falls 135 00:06:47,370 --> 00:06:49,670 during the monsoon season. 136 00:06:49,670 --> 00:06:53,240 And there's some evidence now that with climate change, 137 00:06:53,240 --> 00:06:55,300 the monsoon dynamics are getting all messed up. 138 00:06:55,300 --> 00:06:56,920 Sometimes they fail completely, 139 00:06:56,920 --> 00:06:58,230 sometimes they're dampened, 140 00:06:58,230 --> 00:07:00,610 sometimes they arrive late. 141 00:07:00,610 --> 00:07:05,200 So much of the connection between climate change and fire 142 00:07:05,200 --> 00:07:07,950 is through this alteration of monsoon dynamics 143 00:07:07,950 --> 00:07:10,203 that we're seeing throughout the Himalayas. 144 00:07:11,380 --> 00:07:14,830 Here's an example of these younger forests 145 00:07:14,830 --> 00:07:17,940 regenerated on abandoned agricultural lands that I mentioned 146 00:07:17,940 --> 00:07:21,360 that seemed to be burning with ever greater frequency. 147 00:07:21,360 --> 00:07:25,740 And again, right down into these suburban and urban areas, 148 00:07:25,740 --> 00:07:30,563 this is the Royal Palace complex in Thimphu. 149 00:07:30,563 --> 00:07:32,760 And, you know, the fires coming right down 150 00:07:32,760 --> 00:07:34,363 to the outskirts of that. 151 00:07:35,750 --> 00:07:39,680 So that's just a little bit of a setup for our study. 152 00:07:39,680 --> 00:07:43,080 And we wanted to know whether climate change 153 00:07:43,080 --> 00:07:45,240 is likely to alter fire regimes. 154 00:07:45,240 --> 00:07:47,290 Does it pose particular threats 155 00:07:47,290 --> 00:07:49,550 to the wildland-urban-interface? 156 00:07:49,550 --> 00:07:52,110 And could we use models to predict and map 157 00:07:52,110 --> 00:07:55,720 where those threats would be greatest? 158 00:07:55,720 --> 00:07:59,900 So this is the workflow or a summary of the methodology. 159 00:07:59,900 --> 00:08:02,900 It began with a very intensive data collection in Bhutan 160 00:08:02,900 --> 00:08:05,030 over two field seasons. 161 00:08:05,030 --> 00:08:06,980 It was a lot of fun to be there and doing that, 162 00:08:06,980 --> 00:08:09,630 working with a couple of different crews. 163 00:08:09,630 --> 00:08:10,870 We collected data from something like, 164 00:08:10,870 --> 00:08:14,880 102 randomly distributed inventory plots 165 00:08:14,880 --> 00:08:16,210 in two different research areas 166 00:08:16,210 --> 00:08:17,790 that I'll mention in a minute. 167 00:08:17,790 --> 00:08:20,270 The data were fed into, well actually, 168 00:08:20,270 --> 00:08:21,830 there was a lot of processing of the data 169 00:08:21,830 --> 00:08:26,690 that had to be done first to parameterize and calibrate 170 00:08:26,690 --> 00:08:28,470 the fire behavior algorithms 171 00:08:28,470 --> 00:08:30,180 that we needed for this study. 172 00:08:30,180 --> 00:08:34,830 That all went to a wildfire behavior simulation model 173 00:08:34,830 --> 00:08:35,673 called FlamMap. 174 00:08:36,550 --> 00:08:40,040 And then finally, we calculated a fire hazard index 175 00:08:40,040 --> 00:08:42,382 based on the output of FlamMap. 176 00:08:42,382 --> 00:08:43,770 So I don't have a lot of time today, 177 00:08:43,770 --> 00:08:46,270 but I'll just walk you through this quickly. 178 00:08:46,270 --> 00:08:49,743 We used two major valley systems in Bhutan. 179 00:09:07,393 --> 00:09:10,230 The roads and infrastructure there to create 180 00:09:11,130 --> 00:09:14,500 what we considered to be the wildland-urban-interface, 181 00:09:14,500 --> 00:09:18,350 and then randomly distributed our inventory plots with that. 182 00:09:18,350 --> 00:09:19,710 And that's what you're seeing here 183 00:09:19,710 --> 00:09:22,653 in those pictures on the right. 184 00:09:24,900 --> 00:09:26,330 A huge part of this work, 185 00:09:26,330 --> 00:09:29,100 and here's where I will try to make a connection 186 00:09:29,100 --> 00:09:31,460 to what we might do in Vermont. 187 00:09:31,460 --> 00:09:34,380 You know, if we were to use a model like FlamMap, 188 00:09:34,380 --> 00:09:38,010 or any models, 189 00:09:38,010 --> 00:09:41,660 a huge part of the work is just parameterizing 190 00:09:41,660 --> 00:09:43,280 all the equations and algorithms 191 00:09:43,280 --> 00:09:45,440 that have to go into a model like this. 192 00:09:45,440 --> 00:09:46,970 For a million different things, 193 00:09:46,970 --> 00:09:50,870 particularly fuel characteristic, you know, 194 00:09:50,870 --> 00:09:52,390 algorithms like you're seeing here, 195 00:09:52,390 --> 00:09:54,770 and this was a huge part of the work. 196 00:09:54,770 --> 00:09:56,990 And we were fortunate that we had 197 00:09:56,990 --> 00:09:59,340 very detailed inventory data that we could use 198 00:09:59,340 --> 00:10:00,200 for a lot of this, 199 00:10:00,200 --> 00:10:03,500 but in some cases we had to borrow coefficients 200 00:10:03,500 --> 00:10:06,130 from surrogate species and other things, 201 00:10:06,130 --> 00:10:07,160 but we felt like we ended up 202 00:10:07,160 --> 00:10:09,573 with a very robust set of equations. 203 00:10:11,850 --> 00:10:14,480 So from all of that, 204 00:10:14,480 --> 00:10:18,480 there are a number of kind of key parameters 205 00:10:18,480 --> 00:10:22,190 that go into the modeling work that you're seeing here. 206 00:10:22,190 --> 00:10:23,780 These fuel structure characteristics 207 00:10:23,780 --> 00:10:26,493 like stand height, canopy, bulk density, 208 00:10:28,460 --> 00:10:30,230 defined fuel, biomass, 209 00:10:30,230 --> 00:10:31,960 and other characteristics of the fuel, 210 00:10:31,960 --> 00:10:34,433 like foliar moisture content. 211 00:10:35,430 --> 00:10:37,620 And since we just had these inventory plots 212 00:10:37,620 --> 00:10:39,120 distributed across the valleys, 213 00:10:39,120 --> 00:10:41,030 like you've seen down here, 214 00:10:41,030 --> 00:10:45,270 we needed to interpolate a continuous coverage or layer 215 00:10:45,270 --> 00:10:47,220 for those characteristics. 216 00:10:47,220 --> 00:10:51,170 So we did that through kriging across the landscape, 217 00:10:51,170 --> 00:10:53,750 basically kind of interpolating 218 00:10:53,750 --> 00:10:58,750 a continuous spread of of data between our sample points. 219 00:10:59,280 --> 00:11:04,280 And there's a fairly robust cross validation routine 220 00:11:05,760 --> 00:11:06,920 that we needed to go through 221 00:11:06,920 --> 00:11:10,370 to substantiate that that kriging was accurate. 222 00:11:10,370 --> 00:11:12,800 And we felt like in the end, 223 00:11:12,800 --> 00:11:15,170 we had a very accurate layer 224 00:11:15,170 --> 00:11:18,903 representing fuel characteristics across those two valleys. 225 00:11:20,890 --> 00:11:24,500 Okay, so then we're ready for the FlamMap simulations, 226 00:11:24,500 --> 00:11:27,680 and we're using FlamMap 227 00:11:27,680 --> 00:11:32,680 to predict a number of key indicators of fire behavior 228 00:11:32,980 --> 00:11:34,930 that are associated with risk. 229 00:11:34,930 --> 00:11:38,100 So everything from flame length vertically, 230 00:11:38,100 --> 00:11:42,263 the rate of fire spread, the amount of crown firing, 231 00:11:43,330 --> 00:11:48,230 burn probability on an individual pixel basis, 232 00:11:48,230 --> 00:11:52,470 this is all based on a raster digital elevation model 233 00:11:52,470 --> 00:11:53,303 in FlamMap, 234 00:11:54,780 --> 00:11:56,420 and many other assumptions 235 00:11:56,420 --> 00:11:58,060 that have to go into the modeling. 236 00:11:58,060 --> 00:12:01,870 I'm trying to make this quick in the interest of time. 237 00:12:01,870 --> 00:12:03,890 We ran those simulations 238 00:12:03,890 --> 00:12:07,570 under four different climate scenarios, 239 00:12:07,570 --> 00:12:10,530 beginning with a baseline scenario, that's A, 240 00:12:10,530 --> 00:12:13,000 and then using a factorial design 241 00:12:13,000 --> 00:12:15,600 to trade off predicted changes 242 00:12:15,600 --> 00:12:18,370 in temperature and relative humidity, 243 00:12:18,370 --> 00:12:19,940 two of the climate variabilities 244 00:12:19,940 --> 00:12:22,460 that most strongly influence fire behavior. 245 00:12:22,460 --> 00:12:25,470 And this was based on extreme values 246 00:12:25,470 --> 00:12:28,270 that we extracted from weather station data. 247 00:12:28,270 --> 00:12:30,900 We were fortunate to have that in these two valleys 248 00:12:30,900 --> 00:12:33,820 from 1996 to 2017. 249 00:12:33,820 --> 00:12:38,120 We used the 90th percentile of climate 250 00:12:38,120 --> 00:12:39,300 from the month of February, 251 00:12:39,300 --> 00:12:42,263 which is the peak fire season in Bhutan. 252 00:12:43,200 --> 00:12:46,120 And in the end, we ended up 253 00:12:46,120 --> 00:12:49,180 with temperature and relative humidity values 254 00:12:49,180 --> 00:12:52,170 that are very consistent with the IPCC scenario, 255 00:12:52,170 --> 00:12:55,820 the most likely IPCC scenario for climate change 256 00:12:55,820 --> 00:12:57,250 in the Himalayas. 257 00:12:57,250 --> 00:13:00,423 So that's what these four climate change scenarios are. 258 00:13:01,710 --> 00:13:03,070 Okay, so now we're ready to, 259 00:13:03,070 --> 00:13:04,985 I guess, run the model as you're seeing. 260 00:13:04,985 --> 00:13:09,985 FlamMap is designed based on the sort of basic idea 261 00:13:11,320 --> 00:13:12,470 of a fire triangle 262 00:13:12,470 --> 00:13:15,563 that you see in so many fire behavior models. 263 00:13:16,910 --> 00:13:20,390 So the end result of this research, 264 00:13:20,390 --> 00:13:22,630 where these layers that you're seeing here, 265 00:13:22,630 --> 00:13:25,530 maps representing fire hazard 266 00:13:25,530 --> 00:13:28,290 across these two study areas in Bhutan, 267 00:13:28,290 --> 00:13:29,370 and you're seeing this portrayed 268 00:13:29,370 --> 00:13:31,750 for those four different climate scenarios. 269 00:13:31,750 --> 00:13:35,160 And basically what we found is that with climate change, 270 00:13:35,160 --> 00:13:38,010 with changes in temperature and relative humidity, 271 00:13:38,010 --> 00:13:41,210 we're likely to see a doubling of forest fire hazards 272 00:13:41,210 --> 00:13:43,610 in blue pine forests in Bhutan. 273 00:13:43,610 --> 00:13:45,960 So a doubling of fire hazards. 274 00:13:45,960 --> 00:13:50,960 And that was particularly the case for elevated temperature. 275 00:13:51,190 --> 00:13:53,340 I'm sorry, elevated relative humidity, 276 00:13:53,340 --> 00:13:56,210 and the combination of elevated- 277 00:13:56,210 --> 00:14:00,730 I'm sorry, just got a little jumbled there when I lost you. 278 00:14:00,730 --> 00:14:02,490 It was particularly the case 279 00:14:02,490 --> 00:14:05,316 for a decrease in relative humidity 280 00:14:05,316 --> 00:14:09,630 or the combination of a reduction in relative humidity 281 00:14:09,630 --> 00:14:11,690 and an increase in temperature. 282 00:14:11,690 --> 00:14:13,540 So that's where we're likely to see 283 00:14:13,540 --> 00:14:15,880 the greatest effects on fire risks, 284 00:14:15,880 --> 00:14:17,950 again, through less moisture, 285 00:14:17,950 --> 00:14:21,890 particularly in fuels and elevated temperature. 286 00:14:21,890 --> 00:14:25,990 However, those effects of climate change 287 00:14:25,990 --> 00:14:28,140 are likely to be highly variable. 288 00:14:28,140 --> 00:14:29,230 What you're seeing here 289 00:14:29,230 --> 00:14:33,170 are the median and inner quartile ranges 290 00:14:33,170 --> 00:14:35,500 for the four different climate scenarios 291 00:14:35,500 --> 00:14:37,010 for these two different study areas. 292 00:14:37,010 --> 00:14:39,920 And what you're seeing is that there's a wide spread 293 00:14:39,920 --> 00:14:42,870 in these predicted values for fire behavior 294 00:14:42,870 --> 00:14:44,800 under these different climate scenarios. 295 00:14:44,800 --> 00:14:47,670 So there's tremendous spatial variability 296 00:14:47,670 --> 00:14:51,220 in terms of how climate change is gonna affect fire hazards. 297 00:14:51,220 --> 00:14:52,750 And that's really important 298 00:14:52,750 --> 00:14:55,990 if we start thinking about adaptation, 299 00:14:55,990 --> 00:15:00,990 because it allows us to zero in on the specific places 300 00:15:01,120 --> 00:15:03,700 on the landscape that are likely to have 301 00:15:03,700 --> 00:15:07,550 the greatest fire risks with climate change. 302 00:15:07,550 --> 00:15:09,510 Whereas other places, you know, 303 00:15:09,510 --> 00:15:12,070 where fuel profile is more adaptive, 304 00:15:12,070 --> 00:15:14,900 it's more fire resistant already, 305 00:15:14,900 --> 00:15:18,250 would be lower profile for things like fuels treatment 306 00:15:18,250 --> 00:15:22,300 and prescribed burning and other adaptive approaches. 307 00:15:22,300 --> 00:15:24,470 So the paper has a whole variety 308 00:15:24,470 --> 00:15:27,130 of adaptation recommendations 309 00:15:27,130 --> 00:15:30,660 that we presented to the ministries in Bhutan. 310 00:15:30,660 --> 00:15:35,230 And again, I think that there's a high degree of relevance 311 00:15:35,230 --> 00:15:37,350 in this general methodological approach 312 00:15:38,940 --> 00:15:41,750 to things that we might do here in New England. 313 00:15:41,750 --> 00:15:43,470 All right, thanks for your time. 314 00:15:43,470 --> 00:15:46,670 - The first question from Jenny Lauer. 315 00:15:46,670 --> 00:15:49,510 When you refer to middle range forested area, 316 00:15:49,510 --> 00:15:51,613 what elevations are you referring to? 317 00:15:52,810 --> 00:15:56,790 - Yeah, so this is about 1,000 to 3,000 meters. 318 00:15:56,790 --> 00:15:57,870 So that's, you know, 319 00:15:57,870 --> 00:16:02,790 3,500 feet to 9,000 feet, something like that. 320 00:16:02,790 --> 00:16:05,300 So I mean high elevation by our standards, 321 00:16:05,300 --> 00:16:07,500 but that's mid elevation for the Himalayas. 322 00:16:07,500 --> 00:16:11,693 The peaks are 25, 26,000 feet. 323 00:16:13,210 --> 00:16:14,310 - Jay Shaffer asks, 324 00:16:14,310 --> 00:16:16,460 what's your favorite food to eat in Bhutan? 325 00:16:18,910 --> 00:16:22,050 - Well, I tell you, I have a somewhat mild palette. 326 00:16:22,050 --> 00:16:25,730 And so the really spicy chili peppers, 327 00:16:25,730 --> 00:16:29,500 they're green and red, which are in everything, 328 00:16:29,500 --> 00:16:31,550 were a little challenging for me at first, 329 00:16:31,550 --> 00:16:34,390 but I grew to like them a lot. 330 00:16:34,390 --> 00:16:36,460 And one of the fun things about doing field work there 331 00:16:36,460 --> 00:16:40,490 is that the field crews insist on having a hot meal 332 00:16:40,490 --> 00:16:42,080 every day at lunch, 333 00:16:42,080 --> 00:16:45,470 and they'll bring a hot meal in special containers 334 00:16:45,470 --> 00:16:46,570 that they'll share around. 335 00:16:46,570 --> 00:16:47,820 So that was a lot of fun. 336 00:16:49,850 --> 00:16:54,680 - Okay, Ellie Shaper, has there been any studies done 337 00:16:54,680 --> 00:16:56,500 or attempts to study the changes 338 00:16:56,500 --> 00:16:58,660 in cultural or human interactions 339 00:16:58,660 --> 00:17:02,930 due to the increase in wildfires or climate change effects? 340 00:17:02,930 --> 00:17:04,322 - Yeah, that's a great question. 341 00:17:04,322 --> 00:17:05,383 I really appreciate it. 342 00:17:05,383 --> 00:17:06,380 I meant to mention 343 00:17:06,380 --> 00:17:11,220 that like 99.9% of these fires are human caused. 344 00:17:11,220 --> 00:17:15,620 So they're mostly escaped agricultural fires. 345 00:17:15,620 --> 00:17:18,010 We think there might've been natural ignitions 346 00:17:18,010 --> 00:17:21,380 hundreds of years ago, but we have no evidence to go on. 347 00:17:21,380 --> 00:17:24,140 There are some fire scarred, you know, data sets 348 00:17:24,140 --> 00:17:25,530 that suggest that might be the case 349 00:17:25,530 --> 00:17:27,730 in some of the older blue pine forests. 350 00:17:27,730 --> 00:17:29,900 But in these younger forests, 351 00:17:29,900 --> 00:17:32,710 the vast majority of the fire is human caused. 352 00:17:32,710 --> 00:17:34,880 And so there's this really strong interaction 353 00:17:34,880 --> 00:17:38,860 between settlements pushing into this vegetation 354 00:17:38,860 --> 00:17:41,606 and increased frequency of escaped fires, 355 00:17:41,606 --> 00:17:45,330 particularly from orchards that are burned seasonally, 356 00:17:45,330 --> 00:17:48,150 and there's a lot of grazing in these forests, 357 00:17:48,150 --> 00:17:52,620 and they often will burn to produce forage for livestock 358 00:17:52,620 --> 00:17:54,570 and those fires will escape. 359 00:17:54,570 --> 00:17:56,650 So there's that side of the equation. 360 00:17:56,650 --> 00:17:59,820 And then in terms of cultural significance, 361 00:17:59,820 --> 00:18:01,500 this species, blue pine, 362 00:18:01,500 --> 00:18:04,060 is used for a million things over there. 363 00:18:04,060 --> 00:18:07,920 Everything from prayer flagpoles, 364 00:18:07,920 --> 00:18:11,250 to housing construction, and all sorts of things. 365 00:18:11,250 --> 00:18:13,850 So there's really an economic and a cultural impact 366 00:18:13,850 --> 00:18:14,903 of these fires.