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An Architecture for Adaptive Robust Modelling of Wildfire Behaviour under Deep Uncertainty

  • Daniele de Rigo
  • Dario Rodriguez-Aseretto
  • Claudio Bosco
  • Margherita Di Leo
  • Jesús San-Miguel-Ayanz
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 413)

Abstract

Wildfires in Europe – especially in the Mediterranean region – are one of the major treats at landscape scale. While their immediate impact ranges from endangering human life to the destruction of economic assets, other damages exceed the spatio-temporal scale of a fire event. Wildfires involving forest resources are associated with intense carbon emissions and alteration of surrounding ecosystems. The induced land cover degradation has also a potential role in exacerbating soil erosion and shallow landslides. A component of the complexity in assessing fire impacts resides in the difference between uncontrolled wildfires and those for which a control strategy is applied. Robust modelling of wildfire behaviour requires dynamic simulations under an array of multiple fuel models, meteorological disturbances and control strategies for mitigating fire damages. Uncertainty is associated to meteorological forecast and fuel model estimation. Software uncertainty also derives from the data-transformation models needed for predicting the wildfire behaviour and its consequences. The complex and dynamic interactions of these factors define a context of deep uncertainty. Here an architecture for adaptive and robust modelling of wildfire behaviour is proposed, following the semantic array programming paradigm. The mathematical conceptualisation focuses on the dynamic exploitation of updated meteorological information and the design flexibility in adapting to the heterogeneous European conditions. Also, the modelling architecture proposes a multi-criteria approach for assessing the potential impact with qualitative rapid assessment methods and more accurate a-posteriori assessment.

Keywords

Wildfire Behaviour Deep Uncertainty Integrated Natural Resources Modelling and Management Semantic Array Programming 

References

  1. 1.
    Bengtsson, J., Nilsson, S.G., Franc, A., Menozzi, P.: Biodiversity, disturbances, ecosystem function and management of European forests. For. Ecol. Manage. 132(1), 39–50 (2000)CrossRefGoogle Scholar
  2. 2.
    Turner, M.G.: Landscape ecology: What is the state of the science? Annu. Rev. Ecol. Evol. Syst. 36(1), 319–344 (2005)CrossRefGoogle Scholar
  3. 3.
    Turner, M.G.: Disturbance and landscape dynamics in a changing world. Ecology 91(10), 2833–2849 (2010)CrossRefGoogle Scholar
  4. 4.
    Seidl, R., Fernandes, P.M., Fonseca, T.F., Gillet, F., Jönsson, A.M., et al.: Modelling natural disturbances in forest ecosystems: a review. Ecol. Model. 222(4), 903–924 (2011)CrossRefGoogle Scholar
  5. 5.
    de Rigo, D.: Behind the horizon of reproducible integrated environmental modelling at European scale: ethics and practice of scientific knowledge freedom. F1000 Research (submitted 2013)Google Scholar
  6. 6.
    San-Miguel-Ayanz, J., Schulte, E., Schmuck, G., Camia, A., Strobl, P., et al.: Comprehensive Monitoring of Wildfires in Europe: The European Forest Fire Information System (EFFIS). In: Tiefenbacher, J. (ed.) Approaches to Managing Disaster - Assessing Hazards, Emergencies and Disaster Impacts, ch. 5. InTech (2012)Google Scholar
  7. 7.
    Díaz-Delgado, R., Lloret, F., Pons, X., Terradas, J.: Satellite evidence of decreasing resilience in Mediterranean plant communities after recurrent wildfires. Ecology 83(8), 2293–2303 (2002)Google Scholar
  8. 8.
    Pausas, J.G., Llovet, J., Rodrigo, A., Vallejo, R.: Are wildfires a disaster in the Mediterranean basin? – a review. Int. J. Wildland Fire 17(6), 713+ (2008)Google Scholar
  9. 9.
    Rodriguez-Aseretto, D., de Rigo, D., Di Leo, M., Cortés, A., San-Miguel-Ayanz, J.: A data-driven model for large wildfire behaviour prediction in Europe. Procedia Computer Science 18, 1861–1870 (2013)CrossRefGoogle Scholar
  10. 10.
    Barnaba, F., Angelini, F., Curci, G., Gobbi, G.P.: An important fingerprint of wildfires on the European aerosol load. Atmos. Chem. Phys. 11(20), 10487–10501 (2011)CrossRefGoogle Scholar
  11. 11.
    Saarikoski, S., Hillamo, R.: Wildfires as a source of aerosol particles transported to the northern european regions. In: The Handbook of Environmental Chemistry, pp. 1–21. Springer, Heidelberg (2012)Google Scholar
  12. 12.
    de Rigo, D.: Software Uncertainty in Integrated Environmental Modelling: the role of Semantics and Open Science. Geophys. Res. Abstr. 15, 13292+ (2013)Google Scholar
  13. 13.
    de Rigo, D.: Semantic Array Programming for Environmental Modelling: Application of the Mastrave Library. In: Int. Congress on Environmental Modelling and Software. Managing Resources of a Limited Plant, Pathways and Visions under Uncertainty, Sixth Biennial Meeting, pp. 1167–1176 (2012)Google Scholar
  14. 14.
    de Rigo, D.: Semantic array programming with Mastrave - introduction to semantic computational modelling (2012)Google Scholar
  15. 15.
    de Rigo, D., Corti, P., Caudullo, G., McInerney, D., Di Leo, M., San Miguel-Ayanz, J.: Toward Open Science at the European Scale: Geospatial Semantic Array Programming for Integrated Environmental Modelling. Geophys. Res. Abstr. 15, 13245+ (2013)Google Scholar
  16. 16.
    de Rigo, D., Guariso, G.: Rewarding Open Science: A Collaborative Review System for Semantically-Enhanced Free Software and Environmental Data Modelling (in prep., 2013)Google Scholar
  17. 17.
    Bosco, C., de Rigo, D., Dijkstra, T., Sander, G., Wasowski, J.: Multi-Scale Robust Modelling of Landslide Susceptibility: Regional Rapid Assessment and Catchment Robust Fuzzy Ensemble. In: Hřebíček, J., Schimak, G., Kubásek, M., Rizzoli, A. (eds.) ISESS 2013. IFIP AICT, vol. 413, pp. 321–335. Springer, Heidelberg (2013)Google Scholar
  18. 18.
    Bosco, C., de Rigo, D., Dewitte, O., Poesen, J., Panagos, P.: Modelling Soil Erosion at European Scale. Towards Harmonization and Reproducibility (in prep.)Google Scholar
  19. 19.
    de Rigo, D., Bosco, C.: Architecture of a pan-european framework for integrated soil water erosion assessment. In: Hřebíček, J., Schimak, G., Denzer, R. (eds.) ISESS 2011. IFIP AICT, vol. 359, pp. 310–318. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  20. 20.
    de Rigo, D., Barredo, J.I., Busetto, L., Caudullo, G., San-Miguel-Ayanz, J.: Continental-Scale Living Forest Biomass and Carbon Stock: a Robust Fuzzy Ensemble of IPCC Tier 1 Maps for Europe. In: Hřebíček, J., Schimak, G., Kubásek, M., Rizzoli, A. (eds.) ISESS 2013. IFIP AICT, vol. 413, pp. 271–284. Springer, Heidelberg (2013)Google Scholar
  21. 21.
    Barredo, J.I., San-Miguel-Ayanz, J., Caudullo, G., Busetto, L.: A European map of living forest biomass and carbon stock. In: Reference Report by the Joint Research Centre of the European Commission. EUR – Scientific and Technical Research, vol. 25730 (2012)Google Scholar
  22. 22.
    Darema, F.: Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 662–669. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  23. 23.
    Denham, M., Cortés, A., Margalef, T., Luque, E.: Applying a Dynamic Data Driven Genetic Algorithm to Improve Forest Fire Spread Prediction. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008, Part III. LNCS, vol. 5103, pp. 36–45. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  24. 24.
    Castelletti, A., de Rigo, D., Tepsich, L., Soncini-Sessa, R., Weber, E.: On-Line design of water reservoir policies based on inflow prediction. IFAC-PapersOnLine 17, 14540–14545 (2008)Google Scholar
  25. 25.
    Di Leo, M., de Rigo, D., Rodriguez-Aseretto, D., Bosco, C., Petroliagkis, T., Camia, A., San-Miguel-Ayanz, J.: Dynamic Data Driven Ensemble for Wildfire Behaviour Assessment: A Case Study. In: Hřebíček, J., Schimak, G., Kubásek, M., Rizzoli, A. (eds.) ISESS 2013. IFIP AICT, vol. 413, pp. 11–22. Springer, Heidelberg (2013)Google Scholar
  26. 26.
    Stallman, R.M.: Viewpoint: Why “open source” misses the point of free software. Commun. ACM 52(6), 31–33 (2009)CrossRefGoogle Scholar
  27. 27.
    Rodriguez Aseretto, D., Di Leo, M., de Rigo, D., Corti, P., McInerney, D., et al.: Free and Open Source Software underpinning the European Forest Data Centre. Geophys. Res. Abstr. 15, 12101+ (2013)Google Scholar
  28. 28.
    Richardson, L.A., Champ, P.A., Loomis, J.B.: The hidden cost of wildfires: Economic valuation of health effects of wildfire smoke exposure in southern California. J. For. Econ. 18(1), 14–35 (2012)Google Scholar
  29. 29.
    Herrando, S., Brotons, L., Llacuna, S.: Does fire increase the spatial heterogeneity of bird communities in Mediterranean landscapes? Ibis 145(2), 307–317 (2003)CrossRefGoogle Scholar
  30. 30.
    Torre, I., Díaz, M.: Small mammal abundance in Mediterranean post-fire habitats: a role for predators? Acta Oecologica 25(3), 137–142 (2004)CrossRefGoogle Scholar
  31. 31.
    Estreguil, C., Caudullo, G., de Rigo, D., Whitmore, C., San-Miguel-Ayanz, J.: Reporting on European forest fragmentation: Standardized indices and web map services. IEEE Earthzine 5(2), 384031+ (2012); 2nd quarter theme: Forest Resource Information Google Scholar
  32. 32.
    Estreguil, C., Caudullo, G., de Rigo, D., San-Miguel-Ayanz, J.: Forest landscape in Europe: pattern, fragmentation and connectivity. EUR – Scientific and Technical Research 25717(JRC 77295) (2013)Google Scholar
  33. 33.
    Moreira, F., Russo, D.: Modelling the impact of agricultural abandonment and wildfires on vertebrate diversity in Mediterranean Europe. Landsc. Ecol. 22(10), 1461–1476 (2007)CrossRefGoogle Scholar
  34. 34.
    Di Piazza, G.V., Di Stefano, C., Ferro, V.: Modelling the effects of a bushfire on erosion in a mediterranean basin. Hydrol. Sci. J. 52(6), 1253–1270 (2007)CrossRefGoogle Scholar
  35. 35.
    Moody, J.A., Martin, D.A., Haire, S.L., Kinner, D.A.: Linking runoff response to burn severity after a wildfire. Hydrol. Proces. 22(13), 2063–2074 (2008)CrossRefGoogle Scholar
  36. 36.
    Candela, A., Aronica, G., Santoro, M.: Effects of forest fires on flood frequency curves in a Mediterranean catchment. J. Hydrol. Sci. 50(2), 193–206 (2005)CrossRefGoogle Scholar
  37. 37.
    Rulli, M.C., Rosso, R.: Hydrologic response of upland catchments to wildfires. Adv. Water Resour. 30(10), 2072–2086 (2007)CrossRefGoogle Scholar
  38. 38.
    Ice, G.G., Neary, D.G., Adams, P.W.: Effects of wildfire on soils and watershed processes. J. Forestry 102(6), 16–20 (2004)Google Scholar
  39. 39.
    Shakesby, R., Doerr, S.: Wildfire as a hydrological and geomorphological agent. Earth Sci. Rev. 74(3-4), 269–307 (2006)CrossRefGoogle Scholar
  40. 40.
    Smith, H.G., Sheridan, G.J., Lane, P.N.J., Nyman, P., Haydon, S.: Wildfire effects on water quality in forest catchments: a review with implications for water supply. J. Hydrol. 396(1-2), 170–192 (2011)CrossRefGoogle Scholar
  41. 41.
    Smith, H.G., Blake, W.H., Owens, P.N.: Discriminating fine sediment sources and the application of sediment tracers in burned catchments: a review. Hydrol. Proces. 27(6), 943–958 (2013)CrossRefGoogle Scholar
  42. 42.
    Krawchuk, M.A., Moritz, M.A., Parisien, M.-A., Van Dorn, J., Hayhoe, K.: Global pyrogeography: the current and future distribution of wildfire. PLoS ONE 4(4), e5102+ (2009)Google Scholar
  43. 43.
    Pautasso, M., Dehnen-Schmutz, K., Holdenrieder, O., Pietravalle, S., Salama, N., et al.: Plant health and global change some implications for landscape management. Biol. Rev. 85(4), 729–755 (2010)Google Scholar
  44. 44.
    Nijhuis, M.: Forest fires: Burn out. Nature 489(7416), 352–354 (2012)CrossRefGoogle Scholar
  45. 45.
    Hou, Y., Burkhard, B., Müller, F.: Uncertainties in landscape analysis and ecosystem service assessment. J. Env. Manag. (2013)Google Scholar
  46. 46.
    Spangenberg, J.H., Settele, J.: Precisely incorrect? Monetising the value of ecosystem services. Ecol. Complex. 7(3), 327–337 (2010)CrossRefGoogle Scholar
  47. 47.
    de Rigo, D.: Integrated Natural Resources Modelling and Management: minimal redefinition of a known challenge for environmental modelling. Excerpt from the Call for a Shared Research Agenda Toward Scientific Knowledge Freedom, Maieutike Research Initiative (2012)Google Scholar
  48. 48.
    Hollingsworth, A., Engelen, R., Textor, C., Benedetti, A., Boucher, O., et al.: Toward a monitoring and forecasting system for atmospheric composition. Bull. Amer. Meteor. Soc. 89(8), 1147–1164 (2008)CrossRefGoogle Scholar
  49. 49.
    Bouttier, F.: The Météo-France NWP system: description, recent changes and plans. CNRM (2010)Google Scholar
  50. 50.
    Perry, G.L.W.: Current approaches to modelling the spread of wildland fire: Areview. Prog. Phys. Geogr. 22(2), 222–245 (1998)MathSciNetGoogle Scholar
  51. 51.
    Andrews, P., Finney, M., Fischetti, M.: Predicting wildfires. Sci. American 297(2), 46–55 (2007)CrossRefGoogle Scholar
  52. 52.
    Countryman, C.M.: The concept of fire environment. Fire Manag. Today 64(1), 49–52 (2004)Google Scholar
  53. 53.
    Viegas, D.X.: Parametric study of an eruptive fire behaviour model. Int. J. Wildland Fire 15(2), 169+ (2006)Google Scholar
  54. 54.
    Rodriguez Aseretto, R., Cortés, A., Margalef, T., Luque, E.: An Adaptive System for Forest Fire Behavior Prediction. In: 11th IEEE Int. Conf. on Computational Science and Engineering, CSE 2008, pp. 275–282 (2008)Google Scholar
  55. 55.
    Bellman, R., Kalaba, R.: On adaptive control processes. IRE Transactions on Autom. Control 4(2), 1–9 (1959)CrossRefGoogle Scholar
  56. 56.
    Wang, F.-Y., Zhang, H., Liu, D.: Adaptive Dynamic Programming: An Introduction. IEEE Computational Intell. Mag. 4(2), 39–47 (2009)CrossRefGoogle Scholar
  57. 57.
    Lara, M., Doyen, L.: Sustainable Management of Natural Resources: Mathematical Models and Methods. Springer (2008)Google Scholar
  58. 58.
    Rani, D., Moreira, M.: Simulation-Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation. Water Resour. Manag. 24(6), 1107–1138 (2010)CrossRefGoogle Scholar
  59. 59.
    Ferreira, L., Constantino, M.F., Borges, J.G., Garcia-Gonzalo, J.: A Stochastic Dynamic Programming Approach to Optimize Short-Rotation Coppice Systems Management Scheduling: An Application to Eucalypt Plantations under Wildfire Risk in Portugal. Forest Sci. 58(4), 353–365 (2012)CrossRefGoogle Scholar
  60. 60.
    Minas, J.P., Hearne, J.W., Handmer, J.W.: A review of operations research methods applicable to wildfire management. Int. J. Wildland Fire 21(3), 189+ (2012)Google Scholar
  61. 61.
    Lee, J.M., Lee, J.H.: Approximate dynamic programming strategies and their applicability for process control: a review and future directions. Int. J. Control Autom. Syst. 2(3), 263–278 (2004)Google Scholar
  62. 62.
    de Rigo, D., Rizzoli, A.E., Soncini-Sessa, R., Weber, E., Zenesi, P.: Neuro-dynamic programming for the efficient management of reservoir networks. In: Proceedings of MODSIM 2001, Int. Congress on Modelling and Simulation Held in Canberra, Australia, vol. 4, pp. 1949–1954 (2001) ISBN: 0-867405252Google Scholar
  63. 63.
    de Rigo, D., Castelletti, A., Rizzoli, A.E., Soncini-Sessa, R., Weber, E.: A selective improvement technique for fastening neuro-dynamic programming in water resources network management. IFAC-PapersOnLine 16, 7–12 (2005)Google Scholar
  64. 64.
    de Rigo, D.: Applying semantic constraints to array programming: the module “check_is” of the Mastrave modelling library. Mastrave Project Technical Report (2011)Google Scholar
  65. 65.
    Bevins, C.D.: FireLib user manual and technical reference. Systems for Environmental Management (1996)Google Scholar
  66. 66.
    Rothermel, R.: How to predict the spread and intensity of forest and range fires. US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden (1983)Google Scholar
  67. 67.
    Neteler, M., Bowman, M.H., Landa, M., Metz, M.: GRASS GIS: A multi-purpose open source GIS. Environmental Modelling & Software 31, 124–130 (2012)CrossRefGoogle Scholar
  68. 68.
    Xu, J., Lathrop, R.G.: GRASS GIS manual: r.spread. In: GRASS GIS 6.4.3svn Reference Manual (2006)Google Scholar
  69. 69.
    Xu, J.: GRASS GIS manual: r.ros. In: GRASS GIS 6.4.3svn Reference Manual (2009)Google Scholar
  70. 70.
    Andrews, P., LLoyd, P.: Fire modeling and information system technology. Int. J. Wildland Fire 10(4), 343–352 (2001)CrossRefGoogle Scholar
  71. 71.
    Certini, G.: Effects of fire on properties of forest soils: A review. Oecologia 143, 1–10 (2005)CrossRefGoogle Scholar
  72. 72.
    DeBano, L.F.: The role of fire and soil heating on water repellency in wildland environments: A review. J. Hydrol. 231-232, 195–206 (2000)CrossRefGoogle Scholar
  73. 73.
    Letey, J.: Causes and consequences of fire-induced soil water repellency. Hydrol. Processes 15, 2867–2875 (2001)CrossRefGoogle Scholar
  74. 74.
    Swanson, F.J.: Fire and Geomorphic Processes. In: Gen. Tech. Rep. USDA For. Serv. WO-26. Washington DC. pp. 401–420 (1981)Google Scholar
  75. 75.
    Pack, R.T., Tarboton, D.G., Goodwin, C.N.: The SINMAP Approach to Terrain Stability Mapping. In: 8th Congress of the International Association of Engineering Geology, Vancouver, British Columbia, Canada (1998)Google Scholar
  76. 76.
    Pack, R.T., Tarboton, D.G., Goodwin, C.N., Prasad, A.: SINMAP 2. A Stability Index Approach to Terrain Stability Hazard Mapping, technical description and users guide for version 2.0. Utah State University (2005)Google Scholar
  77. 77.
    Bosco, C., de Rigo, D., Dewitte, O., Montanarella, L.: Towards the reproducibility in soil erosion modeling: a new Pan-European soil erosion map. In: Wageningen conference on applied soil science: ’Soil Science in a Changing World, p. 209+ (2011)Google Scholar
  78. 78.
    Panagos, P., Jones, A., Bosco, C., Senthil Kumar, P.S.: European digital archive on soil maps (EuDASM): preserving important soil data for public free access. Int. J. of Digital Earth 4(5), 434–443 (2011)CrossRefGoogle Scholar
  79. 79.
    Myneni, R.B., Williams, D.L.: On the relationship between FAPAR and NDVI. Remote Sensing of Environment 49(3), 200–211 (1994)CrossRefGoogle Scholar
  80. 80.
    Kinnell, P.I.A.: Event soil loss, runoff and the Universal Soil Loss Equation family of models: A review. Journal of Hydrology 385, 384–397 (2010)CrossRefGoogle Scholar
  81. 81.
    Angima, S.D., Stott, D.E., O’Neill, M.K., Ong, C.K., Weesies, G.: Soil erosion prediction using RUSLE for central Kenyan highland conditions. Agriculture Ecosystems and Environment 97, 295–308 (2003)CrossRefGoogle Scholar
  82. 82.
    Bosco, C., Rusco, E., Montanarella, L., Panagos, P.: Soil erosion in the Alpine area: risk assessment and climate change. Studi Trent. Sci. Nat. 85, 117–123 (2009)Google Scholar
  83. 83.
    Cerdá, A., Doerr, S.H.: The effect of ash and needle cover on surface runoff and erosion in the immediate post-fire period. Catena 74, 256–263 (2008)CrossRefGoogle Scholar
  84. 84.
    Barbosa, P., Camia, A., Kucera, J., Libertà, G., Palumbo, I., San-Miguel-Ayanz, J., Schmuck, G.: Assessment of Forest Fire Impacts and Emissions in the European Union Based on the European Forest Fire Information System. Developments in Environmental Science 8, 197–208 (2008)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Daniele de Rigo
    • 1
    • 2
  • Dario Rodriguez-Aseretto
    • 1
  • Claudio Bosco
    • 3
  • Margherita Di Leo
    • 1
  • Jesús San-Miguel-Ayanz
    • 1
  1. 1.Joint Research Centre, Institute for Environment and SustainabilityEuropean CommissionIspraItaly
  2. 2.Dipartimento di Elettronica e InformazionePolitecnico di MilanoMilanoItaly
  3. 3.Department of Civil and Building EngineeringLoughborough UniversityLoughboroughUnited Kingdom

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