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A review of recent advances in risk analysis for wildfire ma

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Berta Briñas García

on 18 October 2014

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Transcript of A review of recent advances in risk analysis for wildfire ma

A review of recent advances in risk analysis for wildfire management
Carol Miller & Alan A. Ager, 2013

Berta Briñas García, Tesfu Tesfazgi
Instituto Superior de Agronomia, Lisbon

Presentation of the paper:
Risk analysis
From the need to make
decisions concerning fire
What to analyse
of forest fires?
- Timing
- Location
- Potential effects
Last 10 years application of RA has increased.
Risk-based analytical tools evolution (Software; Systems integration; Data availability; GIS; Simulation techniques)
Wild range of fire & fuels management planning scales
Chance of loss, determined
from estimates of likelihood
and associated outcomes
Expectation of loss, including the assessment of:

Can be expressed in terms of:
- Ignition probability
- Burn probability
behaviour models (e.g.: NEXUS; BehavePlus, FOFEM...).
FlamMap: fire behaviour expected under presumed weather conditions for every pixel under a rasterised landscape.

higher resolution.
Uses spatial daily historical
climate to simulate daily fuel
moisture. Probabilities derived
from the temporal variability in
weather, not from likelihood of
ignition of fire ocurrences.
not necessarily negative. Mapping of positive and negative ecological fire effects by coupling loss-benefit functions for different ecological resources.

Integrating Likelihood and Hazard
Two approaches
Development of indices
Integral risk model (IRM)
Development of indices:

- Indices describe L, H and supporting information - Composite Risk score

Incorporate measures of rate of fire spead but do not use burn probability modeling

eg. DSS for assessing danger of severe fire and prioritising subwatersheds for fuel treatment (Hessburg et al. 2007)

Integral Risk Model (IRM)

- Dimensionless & Unitless
- Not expressed in probablistic terms
- Weight assigned to each indices

- Any geographic size but Static

Advances in modeling Fire risk
Temporal dynamics
Spatial Optimization
1 . Fire Threat Model (FTM) (Loboda and Csiszar 2007)

- FTM estimates fire risk from fire danger (likelihood and intensity) + values at risk ( Effects)

- Recovery potential and fire suppression capabilities

Recovery potential in FTM is estimated by severity, Resilience and time horizon

2. Intensity-weighted risk (Mercer et al. 2007)

Previous wildfires and Prescribed fires
Pulp wood volume, Housing density
3. Landscape Fire Simulation Models (LFSMs) (Scheller et al. 2007)

Simulate disturbance and succession :

Study the interplay between fuels managment and fires
Spatial optimization

Challenging due to spatiotemporal complexity

Simulation approaches to identify spatially efficient fuel treatment designs for disrupting landscape fire spread rates:

1. MTT algorithm to identify the
fastest fire travel routes
among nodes in a landscape (Finney 2007)

Then :
A heuristic approach to locate fuel treatments to efficiently disrupt these travel routes

Shortest path heuristic algorithm
to find harvest locations that would disrupt fire spread and protect timber volume (Palma et al. 2007)

3. Estimation of fire risk from uniform ignition probabilities, conditional probabilities of cell-to- cell fire spread, intensity and values at risk


locate treatments using a mixed integer programming model
(Wei et al. 2008)

4. Physical fire model with a stochastic spatial-dynamic optimization model (Konoshima et al. 2010)

Dynamic temporal optimisation model to determine the spatial configuration of forest management activities that would maximise NPVs and NFVs

Future directions for research

1. Integrate decision environments

- Management of fuels

- Management of ignition

Risk analysis needs to support both types of decisions and represent how actions taken in one decision environment transmit risk to the other

2. Address temporal dynamics

Changes in risk can extend through tme

3. Improve resource valuation

Risk analysis to evaluate strategies that increase the expected benefits of fire for some resource values while decreasing the expected losses to others

4. Build empirical knowledge

Information about longer-term fire effects is
particularly sparse

post-fire forest structure or habitat condition changes over several years or after repeated burning

5. Validation and communicate uncertainty

Verification tests and sensitivity analyses approaches
Wildfire risk concepts – common definition

IRM is robust framework that exemplifies and incorporates the more promising advances

Its ability to evaluate multiple resource values under variable weather conditions in a quantitative framework is powerful information for policy makers, budget planners and land managers

Existing fire risk tools are continually modified with new features, linked and hybridised with other tools and applied to new management problems
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