Document


Title

Causasilty among wildfire, ENSO, timber harvest, and urban sprawl: the vector autoregression approach
Document Type: Journal
Author(s): Jianbang Gan
Publication Year: 2006

Cataloging Information

Keyword(s):
ENSO - El Nino Southern Oscillation; Granger causality; impulse response function; timber harvest; urban sprawl; vector autoregression; wildfire activity; wildfire mitigation
Topic(s):
Record Maintained By:
FRAMES Staff; catalog@frames.gov
Record Last Modified: May 4, 2018
FRAMES Record Number: 55790

Description

This article demonstrates the applicability of vector autoregression (VAR) modeling in probing the causality relationships among wildfire, El Niño/Southern Oscillation (ENSO), timber harvest, and urban sprawl in the U.S. The VAR approach allows for the multi-directional, multi-faceted interactions among the variables concerned and enables us to portray the temporal impacts of ENSO, the volume of timber harvested, and urban sprawl on wildfire. The empirical analysis, though intended mainly for illustration, reveals that an individual factor may not affect wildfire activity (number of fires and area burned) when acting alone, but can significantly influence fire activity when coupled with other factors, and that wildfire activity has feedback effects on other variables. The impact of a change in ENSO, the volume of timber harvested, and urban population density on wildfire activity could last two decades with the most noticeable impact occurring in the initial 5-10 years. Though ENSO, timber harvest, and urban sprawl all Granger-cause wildfire activity, the impulse response functions show that wildfire activity is more responsive to urban population density than to the volume of timber harvested or ENSO. Thus, controlling urban sprawl represents another option for wildfire mitigation; and integrative wildfire management is essential.

Online Link(s):
Citation:
Gan, Jianbang. 2006. Causasilty among wildfire, ENSO, timber harvest, and urban sprawl: the vector autoregression approach. Ecological Modelling 191(2):304-314.