A Landscape-Scale Optimisation Model to Break the Hazardous Fuel Continuum While Maintaining Habitat Quality
dc.contributor.author | León Caballero, Javier | |
dc.contributor.author | Reijnders, Victor M. J. J. | |
dc.contributor.author | Hearne, John W. | |
dc.contributor.author | Ozlen, Melih | |
dc.contributor.author | Reinke, Karin J. | |
dc.date.accessioned | 2023-06-17T12:33:23Z | |
dc.date.available | 2023-06-17T12:33:23Z | |
dc.date.issued | 2019 | |
dc.description | This is a post-peer-review, pre-copyedit version of an article published in Environmental Modeling & Assesment. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10666-018-9642-2 | |
dc.description.abstract | Wildfires have demonstrated their destructive powers in several parts of the world in recent years. In an effort to mitigate the hazard of large catastrophic wildfires, a common practice is to reduce fuel loads in the landscape. This can be achieved through prescribed burning or mechanically. Prioritising areas to treat is a challenge for landscape managers. To help deal with this problem, we present a spatially explicit, multiperiod mixed integer programming model. The model is solved to yield a plan to generate a dynamic landscape mosaic that optimally fragments the hazardous fuel continuum while meeting ecosystem considerations. We demonstrate that such a multiperiod plan for fuel management is superior to a myopic strategy. We also show that a range of habitat quality values can be achieved without compromising the optimal fuel reduction objective. This suggests that fuel management plans should also strive to optimise habitat quality. We illustrate how our model can be used to achieve this even in the special case where a faunal species requires mature habitat that is also hazardous from a wildfire perspective. The challenging computational effort required to solve the model can be overcome with either a rolling horizon approach or lexicographically. Typical Australian heathland landscapes are used to illustrate the model but the approach can be implemented to prioritise treatments in any fire-prone landscape where preserving habitat connectivity is a critical constraint. | |
dc.description.faculty | Fac. de Ciencias Matemáticas | |
dc.description.refereed | TRUE | |
dc.description.sponsorship | Unión Europea. Horizonte 2020 | |
dc.description.sponsorship | Ministerio de Ciencia e Innovación | |
dc.description.sponsorship | Comunidad de Madrid | |
dc.description.sponsorship | Universidad Complutense de Madrid | |
dc.description.status | pub | |
dc.eprint.id | https://eprints.ucm.es/id/eprint/64261 | |
dc.identifier.doi | 10.1007/s10666-018-9642-2 | |
dc.identifier.issn | 1420-2026 | |
dc.identifier.officialurl | https://doi.org/10.1007/s10666-018-9642-2 | |
dc.identifier.relatedurl | https://link.springer.com/article/10.1007%2Fs10666-018-9642-2 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14352/12482 | |
dc.issue.number | 4 | |
dc.journal.title | Environmental Modeling & Assessment | |
dc.language.iso | eng | |
dc.page.final | 379 | |
dc.page.initial | 369 | |
dc.publisher | Springer | |
dc.relation.projectID | GEO-SAFE (691161) | |
dc.relation.projectID | MTM2015-65803-R | |
dc.relation.projectID | CASI-CAM-CM (S2013/ICE-2845) | |
dc.relation.projectID | CT27/16-CT28/16 | |
dc.rights | Atribución-NoComercial 3.0 España | |
dc.rights.accessRights | open access | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc/3.0/es/ | |
dc.subject.cdu | 519.8 | |
dc.subject.cdu | 614.841.3 | |
dc.subject.cdu | 504.4 | |
dc.subject.keyword | Wildfires | |
dc.subject.keyword | Spatial optimisation | |
dc.subject.keyword | Fuel continuum | |
dc.subject.keyword | Habitat quality | |
dc.subject.keyword | Multiperiod landscape planning | |
dc.subject.keyword | Mixed integer programming | |
dc.subject.ucm | Investigación operativa (Matemáticas) | |
dc.subject.unesco | 1207 Investigación Operativa | |
dc.title | A Landscape-Scale Optimisation Model to Break the Hazardous Fuel Continuum While Maintaining Habitat Quality | |
dc.type | journal article | |
dc.volume.number | 24 | |
dcterms.references | 1. Agee, J.K., & Skinner, C.N. (2005). Basic principles of forest fuel reduction treatments. Forest Ecology And Management, 211(1), 83–96. 2. Ager, A.A., Vaillant, N.M., McMahan, A. (2013). Restoration of fire in managed forests: a model to prioritize landscapes and analyze tradeoffs. Ecosphere, 4(2), 1–19. 3. Alcasena, F.J., Ager, A.A., Salis, M., Day, M.A., Vega-García, C. (2018). Optimizing prescribed fire allocation for managing fire risk in central catalonia. Science of the Total Environment, 621, 872–885. 4. Bezanson, J., Edelman, A., Karpinski, S., Shah, V.B. (2017). Julia: a fresh approach to numerical computing. SIAM Review, 59(1), 65–98. 5. Boer, M.M., Sadler, R.J., Wittkuhn, R.S., McCaw, L., Grierson, P.F. (2009). Long-term impacts of prescribed burning on regional extent and incidence of wildfires-evidence from 50 years of active fire management in SW Australian forests. Forest Ecology and Management, 259(1), 132–142. 6. Brown, S., Clarke, M., Clarke, R. (2009). Fire is a key element in the landscape-scale habitat requirements and global population status of a threatened bird: the Mallee Emu-wren (Stipiturus Mallee). Biological Conservation, 142(2), 432–445. 7. Burrows, N. (2008). Linking fire ecology and fire management in south-west Australian forest landscapes. Forest Ecology and Management, 255(7), 2394–2406. 8. Carey, H., & Schumann, M. (2003). Modifying wildfire behavior – the effectiveness of fuel treatments, the status of our knowledge. National Community Forestry Center, Southwest Region Working Paper 2. Available at: https://www.energyjustice.net/files/biomass/library/Carey-Schumann.pdf [Verified 2018]. 9. Cheal, D. (2010). Growth stages and tolerable fire intervals for Victoria’s native vegetation data sets. In Fire and adaptive management report No. 84. East Melbourne, Victoria, Australia: Department of Sustainability and Environment. 10. Chung, W. (2015). Optimizing fuel treatments to reduce wildland fire risk. Current Forestry Reports, 1(1), 44–51. 11. Stefano, J.D., McCarthy, M.A., York, A., Duff, T.J., Slingo, J., Christie, F. (2013). Defining vegetation age class distributions for multispecies conservation in fire-prone landscapes. Biological Conservation, 166, 111–117. 12. Dunning, I., Huchette, J., Lubin, M. (2017). Jump: a modeling language for mathematical optimization. SIAM Review, 59(2), 295–320. 13. Fernandes, P.M., & Botelho, H.S. (2003). A review of prescribed burning effectiveness in fire hazard reduction. International Journal of Wildland Fire, 12(2), 117–128. 14. Finney, M.A. (2001). Design of regular landscape fuel treatment patterns for modifying fire growth and behavior. Forest Science, 47(2), 219–228. 15. Finney, M.A. (2008). A computational method for optimising fuel treatment locations. International Journal of Wildland Fire, 16(6), 702–711. 16. Finney, M.A., Seli, R.C., McHugh, C.W., Ager, A.A., Bahro, B., Agee, J.K. (2008). Simulation of long-term landscape-level fuel treatment effects on large wildfires. International Journal of Wildland Fire, 16(6), 712–727. 17. USGAO. (2003). Wildland fire management: Additional actions required to better identify and prioritize lands needing fuels reduction, GAO-03-805. Washington, DC.: USGAO. 18. (2018). LLC Gurobi Optimization. Gurobi optimizer reference manual, p 786. 19. Hof, J., & Omi, P. (2003). Scheduling removals for fuels management, USDA Forest Service Proceedings RMRS-p-29, pp. 367–378. Available at : http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.588.7432&rep=rep1&type=pdf [Verified 2018]. 20. Keith, D.A., McCaw, W.L., Whelan, R.J. (2002). Fire regimes in Australian heathlands and their effects on plants and animals. Flammable Australia: the fire regimes and biodiversity of a continent. In Bradstock, R.A., Williams, J.E., Gill, M.A. (Eds.) Flammable Australia: the fire regimes and biodiversity of a continent, 463p. Cambridge University Press (pp. 199–237). 21. Kim, Y.-H., Bettinger, P., Finney, M.A. (2009). Spatial optimization of the pattern of fuel management activities and subsequent effects on simulated wildfires. European Journal of Operational Research, 197(1), 253–265. 22. King, K.J., Bradstock, R.A., Cary, G.J., Chapman, J., MarsdenSmedley, J.B. (2008). The relative importance of fine-scale fuel mosaics on reducing fire risk in South-West Tasmania, Australia. International Journal of Wildland Fire, 17(3), 421–430. 23. King, K.J., Cary, G.J., Bradstock, R.A., Chapman, J., Pyrke, A., Marsden-Smedley, J.B. (2006). Simulation of prescribed burning strategies in south-west Tasmania, Australia: effects on unplanned fires, fire regimes, and ecological management values. International Journal of Wildland Fire, 15(4), 527–540. 24. Loehle, C. (2004). Applying landscape principles to fire hazard reduction. Forest Ecology and Management, 198(1), 261–267. 25. MacHunter, J., Menkhorst, P., Loyn, R.H. (2009). Towards a process for integrating vertebrate fauna into fire management planning. Arthur Rylah Institute for Environmental Research Department of Sustainability and Environment. 26. Minas, J.P., Hearne, J.W., Martell, D.L. (2014). A spatial optimisation model for multi-period landscape level fuel management to mitigate wildfire impacts. European Journal of Operational Research, 232(2), 412–422. 27. Nguyen, D.T. (2015). Develop a multistage stochastic program with recourse for scheduling prescribed burning based fuel treatments with consideration of future wildland fires and fire suppressions. Ph.D. thesis, Colorado State University. Libraries. 28. Oliveira, T.M., Barros, A.M.G., Ager, A.A., Fernandes, P.M. (2016). Assessing the effect of a fuel break network to reduce burnt area and wildfire risk transmission. International Journal of Wildland Fire, 25(6), 619–632. 29. Penman, T.D., Christie, F.J., Andersen, A.N., Bradstock, R.A., Cary, G.J., Henderson, M.K., Price, O., Tran, C., Wardle, G.M., Williams, R.J. (2011). Prescribed burning: how can it work to conserve the things we value? International Journal of Wildland Fire, 20(6), 721–733. 30. Rachmawati, R., Ozlen, M., Hearne, J., Reinke, K. (2018). Fuel treatment planning: Fragmenting high fuel load areas while maintaining availability and connectivity of faunal habitat. Applied Mathematical Modelling, 54, 298–310. 31. Rachmawati, R., Ozlen, M., Reinke, K.J., Hearne, J.W. (2015). A model for solving the prescribed burn planning problem. SpringerPlus, 4(1), 1–21. 32. Rachmawati, R., Ozlen, M., Reinke, K.J., Hearne, J.W. (2016). An optimisation approach for fuel treatment planning to break the connectivity of high-risk regions. Forest Ecology and Management, 368, 94–104. 33. Rayfield, B., Pelletier, D., Dumitru, M., Cardille, J.A., Gonzalez, A. (2015). Multipurpose habitat networks for short-range and long-range connectivity: a new method combining graph and circuit connectivity. Methods in Ecology and Evolution, 7, 222–231. 34. Rönnqvist, M., D’amours, S., Weintraub, A., Jofre, A., Gunn, E., Haight, R.G., Martell, D., Murray, A.T., Romero, C. (2015). Operations research challenges in forestry: 33 open problems. Annals of Operations Research, 232(1), 11–40. 35. Rytwinski, A., & Crowe, K.A. (2010). A simulation-optimization model for selecting the location of fuel-breaks to minimize expected losses from forest fires. Forest Ecology And Management, 260(1), 1–11. 36. Salazar, L.A., & González-Cabán, A. (1987). Spatial relationship of a wildfire, fuelbreaks, and recently burned areas. Western Journal of Applied Forestry, 2(2), 55–58. 37. Southwell, D.M., Lechner, A.M., Coates, T., Wintle, B.A. (2008). The sensitivity of population viability analysis to uncertainty about habitat requirements: implications for the management of the endangered southern brown bandicoot. Conservation Biology, 22(4), 1045–1054. 38. Strom, B.A., & Fulé, P.Z. (2007). Pre-wildfire fuel treatments affect long-term ponderosa pine forest dynamics. International Journal of Wildland Fire, 16(1), 128–138. 39. Thompson, M.P., Bowden, P., Brough, A., Scott, J.H., GilbertsonDay, J., Taylor, A., Anderson, J., Haas, J.R. (2016). Application of wildfire risk assessment results to wildfire response planning in the Southern Sierra Nevada, California, USA. Forests, 7, 64. 40. Venn, T.J., & Calkin, D.E. (2011). Accommodating non-market values in evaluation of wildfire management in the united states: challenges and opportunities. International Journal of Wildland Fire, 20(3), 327–339. 41. Wei, Y. (2012). Optimize landscape fuel treatment locations to create control opportunities for future fires. Canadian Journal of Forest Research, 42(6), 1002–1014. 42. Wei, Y., & Long, Y. (2014). Schedule fuel treatments to fragment high fire hazard fuel patches. Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS), 6(1), 1–10. 43. Wei, Y., Rideout, D., Kirsch, A. (2008). An optimization model for locating fuel treatments across a landscape to reduce expected fire losses. Canadian Journal of Forest Research, 38(4), 868–877. | |
dspace.entity.type | Publication | |
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