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| Titel |
Towards predictive data-driven simulations of wildfire spread – Part II: Ensemble Kalman Filter for the state estimation of a front-tracking simulator of wildfire spread |
| VerfasserIn |
M. C. Rochoux, C. Emery, S. Ricci, B. Cuenot, A. Trouvé |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Sciences ; 15, no. 8 ; Nr. 15, no. 8 (2015-08-04), S.1721-1739 |
| Datensatznummer |
250119631
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| Publikation (Nr.) |
 copernicus.org/nhess-15-1721-2015.pdf |
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| Zusammenfassung |
This paper is the second part in a series of two articles, which aims at
presenting a data-driven modeling strategy for forecasting wildfire spread
scenarios based on the assimilation of the observed fire front location and
on the sequential correction of model parameters or model state. This model
relies on an estimation of the local rate of fire spread (ROS) as a function
of environmental conditions based on Rothermel's semi-empirical formulation,
in order to propagate the fire front with an Eulerian front-tracking
simulator. In Part I, a data assimilation (DA) system based on an ensemble
Kalman filter (EnKF) was implemented to provide a spatially uniform
correction of biomass fuel and wind parameters and thereby, produce an
improved forecast of the wildfire behavior (addressing uncertainties in the
input parameters of the ROS model only). In Part II, the objective of the
EnKF algorithm is to sequentially update the two-dimensional coordinates of
the markers along the discretized fire front, in order to provide a
spatially distributed correction of the fire front location and thereby, a
more reliable initial condition for further model time-integration
(addressing all sources of uncertainties in the ROS model). The resulting
prototype data-driven wildfire spread simulator is first evaluated in a
series of verification tests using synthetically generated observations;
tests include representative cases with spatially varying biomass properties
and temporally varying wind conditions. In order to properly account for
uncertainties during the EnKF update step and to accurately represent error
correlations along the fireline, it is shown that members of the EnKF
ensemble must be generated through variations in estimates of the fire's
initial location as well as through variations in the parameters of the ROS
model.
The performance of the prototype simulator based on state estimation (SE) or
parameter estimation (PE) is then evaluated by comparison with data taken
from a reduced-scale controlled grassland fire experiment. Results indicate
that data-driven simulations are capable of correcting inaccurate predictions
of the fire front location and of subsequently providing an optimized
forecast of the wildfire behavior at future lead times. The complementary
benefits of both PE and SE approaches, in terms of analysis and forecast
performance, are also emphasized. In particular, it is found that the size of
the assimilation window must be specified adequately with the persistence of
the model initial condition and/or with the temporal and spatial variability
of the environmental conditions in order to track sudden changes in wildfire
behavior. The present prototype data-driven forecast system is still at an
early stage of development. In this regard, this preliminary investigation
provides valuable information on how to combine observations with a fire
spread model in an efficient way, as well as guidelines to design the future
system evolution in order to meet the operational requirements of wildfire
spread monitoring. |
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