Principles For Verification Of Mathematical Fire Models

Use of Fire Models in Post Fire Investigations—A Case Study

Journal of Applied Fire Science ◽

10.2190/af.22.3.b ◽

2012 ◽

Vol 22 (3) ◽

pp. 259-277

Author(s):

Rajiv Kumar ◽

R. K. Sharma ◽

P. K. Yadav ◽

A. K. Gupta

Keyword(s):

Fire Models

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Response of simulated burned area to historical changes in environmental and anthropogenic factors: a comparison of seven fire models

Biogeosciences ◽

10.5194/bg-16-3883-2019 ◽

2019 ◽

Vol 16 (19) ◽

pp. 3883-3910 ◽

Cited By ~ 5

Author(s):

Lina Teckentrup ◽

Sandy P. Harrison ◽

Stijn Hantson ◽

Angelika Heil ◽

Joe R. Melton ◽

...

Keyword(s):

Land Use ◽

Co2 Concentration ◽

Fire Regimes ◽

Atmospheric Co2 ◽

Anthropogenic Factors ◽

Burned Area ◽

Earth System ◽

Atmospheric Co2 Concentration ◽

Fire Models ◽

The Impact

Abstract. Understanding how fire regimes change over time is of major importance for understanding their future impact on the Earth system, including society. Large differences in simulated burned area between fire models show that there is substantial uncertainty associated with modelling global change impacts on fire regimes. We draw here on sensitivity simulations made by seven global dynamic vegetation models participating in the Fire Model Intercomparison Project (FireMIP) to understand how differences in models translate into differences in fire regime projections. The sensitivity experiments isolate the impact of the individual drivers on simulated burned area, which are prescribed in the simulations. Specifically these drivers are atmospheric CO2 concentration, population density, land-use change, lightning and climate. The seven models capture spatial patterns in burned area. However, they show considerable differences in the burned area trends since 1921. We analyse the trajectories of differences between the sensitivity and reference simulation to improve our understanding of what drives the global trends in burned area. Where it is possible, we link the inter-model differences to model assumptions. Overall, these analyses reveal that the largest uncertainties in simulating global historical burned area are related to the representation of anthropogenic ignitions and suppression and effects of land use on vegetation and fire. In line with previous studies this highlights the need to improve our understanding and model representation of the relationship between human activities and fire to improve our abilities to model fire within Earth system model applications. Only two models show a strong response to atmospheric CO2 concentration. The effects of changes in atmospheric CO2 concentration on fire are complex and quantitative information of how fuel loads and how flammability changes due to this factor is missing. The response to lightning on global scale is low. The response of burned area to climate is spatially heterogeneous and has a strong inter-annual variation. Climate is therefore likely more important than the other factors for short-term variations and extremes in burned area. This study provides a basis to understand the uncertainties in global fire modelling. Both improvements in process understanding and observational constraints reduce uncertainties in modelling burned area trends.

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Event-Driven Simulations of Nonlinear Integrate-and-Fire Neurons

Neural Computation ◽

10.1162/neco.2007.19.12.3226 ◽

2007 ◽

Vol 19 (12) ◽

pp. 3226-3238 ◽

Cited By ~ 18

Author(s):

Arnaud Tonnelier ◽

Hana Belmabrouk ◽

Dominique Martinez

Keyword(s):

Neural Networks ◽

Spiking Neural Networks ◽

Synaptic Currents ◽

Fire Model ◽

Integrate And Fire ◽

Fire Models ◽

Event Driven

Event-driven strategies have been used to simulate spiking neural networks exactly. Previous work is limited to linear integrate-and-fire neurons. In this note, we extend event-driven schemes to a class of nonlinear integrate-and-fire models. Results are presented for the quadratic integrate-and-fire model with instantaneous or exponential synaptic currents. Extensions to conductance-based currents and exponential integrate-and-fire neurons are discussed.

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Identifying required model structures to predict global fire activity from satellite and climate data

10.5194/gmd-2016-301 ◽

2016 ◽

Cited By ~ 3

Author(s):

Matthias Forkel ◽

Wouter Dorigo ◽

Gitta Lasslop ◽

Irene Teubner ◽

Emilio Chuvieco ◽

...

Keyword(s):

Satellite Observations ◽

Burned Area ◽

Human Society ◽

Fire Emissions ◽

Fire Models ◽

Fire Activity ◽

Process Oriented ◽

Density And Biomass ◽

Global Vegetation ◽

Model Structures

Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. In particular, extreme fire conditions can cause devastating impacts on ecosystems and human society and dominate the year-to-year variability in global fire emissions. However, the climatic, environmental and socioeconomic factors that control fire activity in vegetation are only poorly understood and consequently it is unclear which components, structures, and complexities are required in global vegetation/fire models to accurately predict fire activity at a global scale. Here we introduce the SOFIA (Satellite Observations for FIre Activity) modelling approach, which integrates several satellite and climate datasets and different empirical model structures to systematically identify required structural components in global vegetation/fire models to predict burned area. Models result in the highest performance in predicting the spatial patterns and temporal variability of burned area if they account for a direct suppression of fire activity at wet conditions and if they include a land cover-dependent suppression or allowance of fire activity by vegetation density and biomass. The use of new vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. The SOFIA approach implements and confirms conceptual models where fire activity follows a biomass gradient and is modulated by moisture conditions. The use of datasets on population density or socioeconomic development do not improve model performances, which indicates that the complex interactions of human fire usage and management cannot be realistically represented by such datasets. However, the best SOFIA models outperform a highly flexible machine learning approach and the state-of-the art global process-oriented vegetation/fire model JSBACH-SPITFIRE. Our results suggest using multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with model-data integration approaches to guide the future development of global process-oriented vegetation/fire models and to better understand the interactions between fire and hydrological, ecological, and atmospheric Earth system components.

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First passage time densities in resonate-and-fire models

Physical Review E ◽

10.1103/physreve.73.031108 ◽

2006 ◽

Vol 73 (3) ◽

Cited By ~ 39

Author(s):

T. Verechtchaguina ◽

I. M. Sokolov ◽

L. Schimansky-Geier

Keyword(s):

First Passage Time ◽

Passage Time ◽

First Passage ◽

Fire Models

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Generalized Integrate-and-Fire Models of Neuronal Activity Approximate Spike Trains of a Detailed Model to a High Degree of Accuracy

Journal of Neurophysiology ◽

10.1152/jn.00190.2004 ◽

2004 ◽

Vol 92 (2) ◽

pp. 959-976 ◽

Cited By ~ 158

Author(s):

Renaud Jolivet ◽

Timothy J. Lewis ◽

Wulfram Gerstner

Keyword(s):

Cortical Neurons ◽

Direct Reduction ◽

Spike Trains ◽

Model Parameters ◽

Detailed Model ◽

Fire Model ◽

Integrate And Fire ◽

Fire Models ◽

Wide Range ◽

Simple Models

We demonstrate that single-variable integrate-and-fire models can quantitatively capture the dynamics of a physiologically detailed model for fast-spiking cortical neurons. Through a systematic set of approximations, we reduce the conductance-based model to 2 variants of integrate-and-fire models. In the first variant (nonlinear integrate-and-fire model), parameters depend on the instantaneous membrane potential, whereas in the second variant, they depend on the time elapsed since the last spike [Spike Response Model (SRM)]. The direct reduction links features of the simple models to biophysical features of the full conductance-based model. To quantitatively test the predictive power of the SRM and of the nonlinear integrate-and-fire model, we compare spike trains in the simple models to those in the full conductance-based model when the models are subjected to identical randomly fluctuating input. For random current input, the simple models reproduce 70–80 percent of the spikes in the full model (with temporal precision of ±2 ms) over a wide range of firing frequencies. For random conductance injection, up to 73 percent of spikes are coincident. We also present a technique for numerically optimizing parameters in the SRM and the nonlinear integrate-and-fire model based on spike trains in the full conductance-based model. This technique can be used to tune simple models to reproduce spike trains of real neurons.

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