Pyrogeography of savanna fire intensity and severity
•Download as PPTX, PDF•
1 like•492 views
Fire intensity is considered an important component of our understanding of savannas. Fire intensity can be used to predict probability of top kill of woody stems, and thus can deterministically link fire to demographic bottlenecks observed in savanna woody species. However, our understanding of the spatial processes of fire intensity is limited: What tree neighborhood effects on fire intensity exist? How does intensity vary across woody cover gradients? Is fire intensity really the best metric for understanding impact of fire on savanna plant communities? My research hopes to expand on these topics while suggesting future directions for research and applications in fire management.
Recommended
Recommended
More Related Content
Similar to Pyrogeography of savanna fire intensity and severity
Similar to Pyrogeography of savanna fire intensity and severity (20)
Recently uploaded
Recently uploaded (20)
Pyrogeography of savanna fire intensity and severity
- 1. What are we measuring and why? Daniel Godwin Kruger National Park, 2014
- 3. fuel + oxygen + heat -> water + hydrogen + carbon dioxide + carbon monoxide + heat
- 4. For a given interaction of fuel + oxygen + heat… How much energy is released?
- 5. Intensity explains p(topkill) Demographic bottlenecks Structurally defines savannas and allows for coexistence of trees, grass Although not the only factor… Fire limits tree establishment
- 6. If fire is a strong factor in limiting tree establishment in savannas… In areas of high tree cover, you could expect either infrequent or low intensity fires In areas of low tree cover, you could expect either frequent or high intensity fires.
- 7. I = Hwr I – Fireline Intensity (kW m-1) H – Heat content (kJ g-1) w – Mass of combusted fuel (g) r – Rate of Spread (m s-1)
- 10. Why do we see increasing woody cover at the same time as increasing p(top kill)? Confounding effects of granitic vs. basaltic? Fire doesn’t limit as much as we think Fire intensity as we measure it doesn’t capture the effects on woody cover that we think it should
- 11. Severity is the ecological impact of a fire Plants Animals Soil Intensity is energy released Intensity != Severity… …but no standard metric for severity in savannas
- 12. Remote Sensing using MODIS Fire Product Modeled fireline intensity Analysis of fire scar reports from KNP Local-scale intensity variation
- 14. Ten years of data (1 Jan 2004 – 1 Jan 2014 Subset to only include fires of > 90% accuracy
- 17. No clear relationship with MAP No clear relationship with woody cover FRP bad metric for savanna fires?
- 19. Mean Low RH Wind Speed Range MAP MFRI Fuel Load: f(Time Since Fire, Rainfall) Fire Intensity: f(Fuel Load, RH, Fuel Moisture, Wind Speed) Fuel Moisture Range Probability of Top Kill f(Intensity, Height)
- 24. Intuitive: hotter fires near PRET Clear relationship with MAP…but that’s expected given the model. No clear relationship with woody cover, though differences in intensity between geologic material.
- 26. Date Location Size Impact on Woody Cover Impact on Herbaceous Material
- 27. Impact on Woody Cover Very severe – None Impact on Herbaceous Material Very patchy – Clean
- 32. Most of this is intuitive To do: Link to previous summer’s rainfall Use as training samples for remotely sensed data
- 34. How “spatial” is fire intensity? Fire intensity has deterministic drivers (fuel moisture, available oxygen, fuel amount) Do these vary enough around trees to drive potential effects on p(top kill)?
- 35. Ground level 5 cm 3 cm 30 cm Sand iButton
- 38. Classic point clouds. Similar to earlier studies (Moustakas et al. 2013), at wetter end, grass biomass is decoupled from canopy coverage
- 39. Variation in fuel moisture content ~ Canopy Coverage. Better assessment of intensity from temperature curves Canopy opening patch size effect on fire intensity, controlling for RH%
- 41. When measured or modeled, intensity doesn’t appear to predict woody cover. …so does it matter as much as we think? Maybe we need to shift away from intensity as a metric.. And what, exactly, does fire severity look like in savannas?
- 44. LAL (lighting activity level) Dispersion (atmospheric stability) Mixing Height (“ceiling”) Haines Index (atmospheric driven large fire growth)
- 45. ERC is the potential energy released from a headfire Calculated based on dynamic fuel models Fuel moisture calculated based on magnitude/intensity of rain, RH%, live and dead fuel moisture Extensive monitoring of live and dead fuel moisture
- 46. Oak savanna research: 10 hr live fuel moisture content key to top kill Haines Index: Likelihood of “firestorm”
Editor's Notes
- So Byram’s Fireline Intensity assumes a trade off between amount combusted and rate of spread. Each of these lines is a fire of equal intensity. We thus assume that low rate of spread will consume more material, and vice versa. Thus if we plug this into our
- So I modeled some broad scale relationships. A) here is average woody cover % by MAP from the Bucini data set. B. Is fuel load by MAP, as described by Navashni in 2006. C. Is the relationship between fireline intensity and fuel load. Linking B and C, we get D. E, then, takes the Higgins et al.’s relationship between p(topkill) and links it to D.
- Product of MODIS 4 and 11 micrometer radiances Detects thermal anomalies and calculates megawatts / km^2
- MFRI came from Izak’s work. MAP is from BioClim. Fuel Moisture and wind speed values are from published values from Winston Trollope. Mean Low RH is from a gridded South African ag dataset. Fuel load function comes from Govender et al. 2006, as does fire intensity. P(top kill) is Higgins most recent one that used a likelihood estimation framework. South African Atlas of Climatology and Agrohydrology
- Lots of noise, no clear signal. Not really seeing otter fires in high woody cover or low woody cover areas.
- A. Is high, B is avg
- N=75
- For a system for measuring maxima ~ biomasss…sort of works. Bit of a point cloud still. Need to account for RH in this. May integrate under the curve for each of these instead of max temp.
- Trend is non-significant and is weighted by outliers. This doesn’t include this year’s data, which amounts to ~ 300.
- RAWS are scattered throughout the country – over 2200 of them