Windthrow Hazard Mapping using GIS, Canadian Forest Products ...

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Table 8. Effect of increasing severity thresholds for classifying a segment as‘damaged’ on variable coefficients. Dataset ‘E’.Without time since logging (timelog) included:Parameter WTT3010 WTT5050 WTT9050n 21457 21457 21457Damaged segments 3157 723 574% Damaged 14.7 3.4 2.8Intercept -7.5842 -6.3140 -6.8061slp100 -0.0340 -0.0409 -0.0421cbrgp 0.00697 0.00738 0.00652height 0.0507 0.0274 0.0245mwspeed 0.1824 -0.1733 -0.2213strmaxs 0.1918 0.1007 0.1331score -0.00012 -0.00013 -0.00014direx 0.3249 0.4214 0.4807c-value 0.70 0.70 0.71With time since logging (timelog) included:Parameter WTT3010 WTT5050 WTT9050n 16618 16618 16618Damaged segments 2927 654 512% Damaged 17.6 3.9 3.1Intercept -6.9186 -6.3134 -7.5138slp100 -0.0264 -0.0350 -0.0350cbrgp 0.00778 0.00809 0.00714height 0.0494 0.0223 0.0183mwspeed 0.0944 0 0strmaxs 0.1374 0 0.0714score -0.00008 -0.00010 -0.00009direx 0.3350 0.4522 0.5106timelog 0.0462 0.0754 0.0742H-L GF 16.92 21.3 20.7c-value 0.69 0.71 0.71DiscussionKey findingsThe proportion of segments damaged (lowest severity threshold model) increased withincreasing mean annual windspeed, increasing storm wind speed, increasing standbasal area, increasing stand height, decreasing slope, and increasing number ofdirections of boundary exposure. Damage also increased up to 7 years followinglogging and then levelled.The species results were generally consistent with those for other locations.Deciduous, redcedar and subalpine fir leading stands were damaged less than otherstands of other species. The site index variable was missing for most polygons, so it16
was not possible to evaluate this effect directly. In other studies, vulnerability increaseswith site quality. The relatively high vulnerability of Douglas-fir leading stands wasunexpected, but this may reflect the quality of these sites. There was no clearassociation of damage with soil drainage, however damage as higher on sands andgravels than on finer textured substrates. The increase in damage with stand height isexpected and very consistent with other studies, as is the increase with basal area.Wind exposure variables are typically more important than stand or soil variables inpredicting risk.The greater vulnerability of south facing boundary segments isconsistent with BC Hydro’s wind resource data for the area, which shows a southerlycomponent to mean winds. This directionality is also consistent with results for otherBC studies. The dominant wind direction during the strong wind event was from thesoutheast. The number of directions to which a segment is exposed to windscontributed to all models. The negative coefficient for opening size (fetch), likelyreflects the fact that most openings are much larger than the 5 tree lengths at whichshelter from upwind stands takes effect. It is likely that the boundaries of the largestopenings follow natural forest type changes such as wetlands where the edges arenaturally windfirm.This study provided a unique opportunity to evaluate correlations between differentmeasures of wind regime, including topographic exposure (TOPEX), elevation, BCHydro numerically modelled mean wind and numerically modelled extreme winds. Asexpected elevation and mean wind speed were strongly correlated, however, elevationwas weakly inversely correlated with extreme winds. Topex was weakely positivelycorrelated with mean wind speed, but uncorrelated with extreme winds. The lack ofcorrelation between these variables indicates the difficulty in characterizing windregime. Topex is a key variable in our models in other BC locations, however it wasnot significant in any model for TFL 30, even when other topographic and windmeasures were excluded. This may reflect the relatively low complexity of topographyin TFL 30 and that the dominant terrain effect is the funnelling southeast winds.Fortunately, the extreme wind variable was available. Including mean wind improvedthe fit for the overall data set, but the fact that this variable took a negative coefficientfor high severity threshold models indicates the underlying complexity of standstability-wind regime interactions.The dependent variables used for map production were WTT3010 (low severitythreshold) and WTT9050 (high severity threshold). These variables capture bothsegment area damaged and canopy loss. The maps resulting from these models wereexamined. The pattern of higher edge windthrow probability areas produced with thismodel was logical in relation to the landscape features such as elevation, windinessand stand condition. Including time since logging did not change the overall pattern ofrisk, but provided a greater range of risk results.17
Page 3 and 4: models have been successfully devel
Page 5 and 6: angle between peak wind direction a
Page 7 and 8: Determination of independent variab
Page 9 and 10: anking of risk groups. For practica
Page 11 and 12: Contingency tablesTrends in the pro
Page 13 and 14: k)l)0.2060001612000Proportion0.150.
Page 15: Observed0.450.40.350.30.250.20.150.
Page 19 and 20: ReferencesAnderson, C. J., Coutts,
Page 21 and 22: Appendix 1. Procedures used to buil
Page 23 and 24: Table A1-3. Map formulas used to ca
Page 25: *a pdf version of this map is attac

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