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Effects of weather, landscape structure, and management on fire spread: Comparison between WI hardwoods and NJ Pinelands, USA Zheng, D 1* ., J.J. LaCroix 1 , S. Ryu 1 , J. Chen 1 , J. Hom 2 , and K. Clark 2 1 University of Toledo, Toledo, OH 43606, USA.

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Effects of weather, landscape structure, and management on fire spread: Comparison between WI hardwoods and NJ Pinelands, USA

Zheng, D1*., J.J. LaCroix1, S. Ryu1, J. Chen1, J. Hom2, and K. Clark2

1 University of Toledo, Toledo, OH 43606, USA.

2 USDA Forest Service, Newtown Square, PA 19073, USA


  • OBJECTIVES fire spread: Comparison between WI hardwoods and NJ Pinelands, USA

  • Fire physics, single fire event, landscape persp. (single out one factor in a given test—see Meth.). 15-day hypothetical simulations.

  • How fire spread is affected by landscape structure?

  • Does harvest increase or decrease surface fire spread across the landscapes?

  • Is there a significant difference in harvesting methods on fire spread?

  • Does fire spread vary seasonally and to what degree?


  • METHODS & MATERIALS fire spread: Comparison between WI hardwoods and NJ Pinelands, USA

  • Study areas – WI & NJ landscapes differ in:

  • Vegetation – fuel types (4 in WI and 10 in NJ grouped from LULC);

  • Land-use history – structure;

  • Both have less contrast in topography, thus, minimize the effects of TOPO on fire spread

  • 2)Study design

  • Fire ignition locations and fuel type assignments (13 nationally established fuel types (Anderson 1982). 3 to 6 replicates for each fuel type depending on area proportion (total of 24 locations)—Fig. 1 & Table 1;

  • Management scenarios and simulations

  • *** Weather data Table 2;

  • *** road vs no_road;

  • *** keep weather constant while varying landscape (August, 2002);

  • *** MROS (183 m/hr in WI, 334/173 m/hr in NJ);

  • *** Summer vs. Spring -- seasonal;

  • *** 4% cutting (DISP. vs. CLUS.) -- management


Spatial distribution of 24 fire ignition points in relation to fuel types in the CNF (left) and NJP (right) USA. 5 = Brush < 0.8 m with scattered trees, 8 = Litter layer without under story, 10 = Litter layer with under story, and 11 = light logging/Swamps.

Eastern USA


3) Model linkage and applications to fuel types in the CNF (left) and NJP (right) USA. 5 = Brush < 0.8 m with scattered trees, 8 = Litter layer without under story, 10 = Litter layer with under story, and 11 = light logging/Swamps.


RESULTS to fuel types in the CNF (left) and NJP (right) USA. 5 = Brush < 0.8 m with scattered trees, 8 = Litter layer without under story, 10 = Litter layer with under story, and 11 = light logging/Swamps.

The mean burned area (MBA) of 24 fires in the CNF after a 15-day burning duration was 3,867 ha, while it was 8% larger in the NJP (4,177 ha) using the same weather inputs even though the fuel’s mean rate of spread (MROS, m/hr) in the NJP increase to 334 m/hr from 183 m/hr in the CNF, by 83%, indicating effects of LS structure.


Relative differences (VALUE to fuel types in the CNF (left) and NJP (right) USA. 5 = Brush < 0.8 m with scattered trees, 8 = Litter layer without under story, 10 = Litter layer with under story, and 11 = light logging/Swamps.NJP / VALUECNF) of the 4 LS indices in the CNF and NJP (CNF rescaled to 1). NP = #of patches, PD = patch density (No./100 ha), MPS = mean patch size (ha), and ED = edge density (m/ha). Higher values of NP, PD, and ED or lower values of MPS = more fragm.



Effects of harvesting and harvesting methods on MBA (relative change) in a) NJP; and b) CNF, compared to the MBA in spring and summer for the control landscapes. Numbers above the bars indicate relative changes in %. * significant (0.01).


CONCLUSIONS (relative change) in a) NJP; and b) CNF, compared to the MBA in spring and summer for the control landscapes. Numbers above the bars indicate relative changes in %. * significant (0.01).

*** Burned areas in the NJP > in CNF due to highly volatile fuels.

*** Fire spread was + with landscape fragmentation. The combination of fuel/structure determine fire areas/patterns if other factors are constants.

*** In CNF C & D (4%) could reduce BA up to 2.4% (ns). In the NJP (less fragm) increase BA especially under extreme weather conditions (*16% in SUM). Cutting methods on fire spread are more complicated and interacted with land-use history, fuel type composition, and weather. Thus, forest management planning should be flexible & aim to the characteristics of a given landscape to minimize fire spread. More studies are desired.

*** Cross-season variation >? within-season variation, depending on weather combination.

*** Road effects on fire are not simply related to density (density==0.63/0.33=191%, effect=0.685/0.275=249%). Furthermore,if human factors are considered (95% between 1985-95 in GLR were human caused), the effects could go opposite way. Need more comprehensive studies. Road effects can be enhanced when weather conditions are more favorable to fire spread.


Acknowledgements (relative change) in a) NJP; and b) CNF, compared to the MBA in spring and summer for the control landscapes. Numbers above the bars indicate relative changes in %. * significant (0.01).

The Joint Fire Science Project (JFSP) and the Northern Global Change Program (NGCP) primarily support this research.


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