Modelling the Spatial Risk of Moorland Wildfire
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Modelling the spatial risk of Moorland wildfire Julia McMorrow and Sarah Lindley [email protected] [email protected] GeoInformatics Research Group Geography, School of Environment and Development, The University of Manchester, Manchester M13 9PL Final report, Moors for the Future small grant A79419_spg17. 31 Dec 2006 Modelling the Spatial risk of Moorland Wildfire McMorrow and Lindley 2006 _____________________________________________________________________________ Contents 1. Introduction............................................................................................................. 1 1.1 Wildfires ......................................................................................................... 1 1.2 Impacts of wildfires........................................................................................ 1 1.3 CCVE.............................................................................................................. 2 1.4 Climate change and wildfires.......................................................................... 2 1.5 Significance of PDNP fire risk studies ........................................................... 3 1.6 Aims of the study............................................................................................ 4 2. Overview of data and research methods ................................................................. 4 2.1 Approaches to fire risk modelling................................................................... 4 2.2 Fire distribution............................................................................................... 5 2.3 Building the model: MCE............................................................................... 6 2.4 Incorporating expert opinion........................................................................... 8 2.5 Defining the study area ................................................................................. 14 2.6 Fire database................................................................................................. 15 2.7 Habitat sensitivity analyses........................................................................... 16 2.8 Testing the model.......................................................................................... 18 2.9 Presenting the results .................................................................................... 19 2.9 Testing the model.......................................................................................... 20 3. Habitat factor........................................................................................................ 21 3.1 Comparison of empirical and stakeholder scores ......................................... 21 3.2 Effect of locational precision and class generalisation on habitat scores ..... 23 3.3 Habitat layers selected.................................................................................. 23 4. Human factors....................................................................................................... 30 4.1 Human factors selected................................................................................. 30 4.2 Settlements.................................................................................................... 31 4.3 Roads and vehicle tracks............................................................................... 33 4.4 Car parks....................................................................................................... 38 4.5 Footpaths....................................................................................................... 40 4.6 Other human factors...................................................................................... 52 5. Other physical factors ........................................................................................... 53 6. Results and discussion.......................................................................................... 53 6.1 Weighting results.......................................................................................... 53 6.1 Models tested................................................................................................ 55 6.2 Statistical test results..................................................................................... 57 6.2 Interpreting the final maps............................................................................ 59 6.5 Summary of recommendations ..................................................................... 64 Acknowledgements....................................................................................................... 67 References..................................................................................................................... 68 Appendix 1: Online survey ........................................................................................... 71 Appendix 2: Online survey results................................................................................ 75 Appendix 3: Attendance list for stakeholder workshop................................................ 77 Appendix 4: Summary of models ................................................................................. 78 ii Modelling the Spatial risk of Moorland Wildfire McMorrow and Lindley 2006 _____________________________________________________________________________ 1. Introduction 1.1 Wildfires Prescribed burning between 1st October and 15th April is a well-established moorland management tool (MFF, 2005). Wildfires are man-made accidental fires (such as those caused by cigarettes or litter acting as lenses) or natural fires due to lightning strikes. They also include fires started maliciously, and any managed burns which accidentally get out of control. Wildfires have been recorded in the Peak District National Park (PDNP) Rangers fire log since 1976. It is this database of 353 wildfires which forms the basis of the spatial fire risk assessment presented here. 1.2 Impacts of wildfires Five thousand acres of the Peak District were accidentally burnt in Spring 2003 alone. Such wildfires have direct and indirect environmental, social and economic costs. They present a threat to habitats, nesting birds and other fauna. The UK holds 75% of the world’s resource of open heather moorland. Two percent of this went up in flames in a two-week period in April 2003 (Baynes and Bostock, 2003), including a major fire on Bleaklow. Fire has been reported to increase water discoloration in peatland environments (Yallop, 2006) and wildfire adversely impacts on peatland carbon budget (Worall and Evans, 2006). Northern peatlands are the largest soil carbon store in the world, holding 30% of the world’s terrestrial carbon, of which the UK has 8%. Carbon stored in UK peatlands is equivalent to up to 3 years of national CO2 emissions (Worall and Evans, 2006). Burning releases carbon from vegetation and, if it back-burns into the peat substrate can permanently destroy this important carbon store. It has other, longer-term effects on the carbon budget; carbon-sequestering vegetation is lost and the exposed peat dries out more easily to release further carbon gradually by aerobic decomposition. Fire scars of exposed peat are persistent in the landscape (Mckay & Tallis, 1996; Anderson et al., 1997). Much of the two million pounds invested by MFF in moorland restoration has been spend on revegetating fire scars. Figure 1.1: Fighting peatland fire 1 Modelling the Spatial risk of Moorland Wildfire McMorrow and Lindley 2006 _____________________________________________________________________________ Other economic and social costs of wildfires include fire control and management (Figure 1.1) (Palutikof et al., 1997), loss of capital value and revenue from grouse moors and sheep grazing (Baynes and Bostock, 2003). Transport can also be disrupted due to smoke. Previous wildfires have necessitated the closure of the M62 and even Manchester airport (Trotter, 2003). 1.3 CCVE This report builds on spatial and temporal modelling carried out for the Climate Change and the Visitor Economy (CCVE) project. CCVE used spatial modelling to identify where risk of fire was highest in the Dark Peak part of the PDNP (northern part of the PDNP), based on past reported wildfires. Multi-criteria evaluation (MCE) was used to spatially model the risk of reported wildfires using the 28-year record of wildfires from the PDNP rangers’ fire log. Fire risk was investigated using habitat maps to represent vulnerability to ignition, and distance from access features as a proxy for the likelihood of anthropogenic ignition sources. The current report develops this spatial analysis further and for a wider area of the PDNP. CCVE also used temporal analysis to predict when wildfire risk was likely to be highest, based on preceding weather. The temporal model established a non-linear statistical relationship between past weather and wildfires. The model successfully predicted the most fire-prone months and days, especially April-May and July-August and spring bank holidays, reflecting the interplay between visitor numbers and the changing flammability of moorland vegetation (Fig 1.1). A typical British bank holiday is almost five times more perilous than seven days of dry weather (McMorrow et al., in review). 1.4 Climate change and wildfires Wildfire risk will increase under climate change scenarios (Conway. 1998). A gradual rise in mean temperature will have only a slight effect,