English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

Forkel, M., Dorigo, W., Lasslop, G., Teubner, I., Chuvieco, E., Thonicke, K. (2017): A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1). - Geoscientific Model Development, 10, 12, 4443-4476.
https://doi.org/10.5194/gmd-10-4443-2017

Item is

Files

show Files
hide Files
:
7847oa.pdf (Publisher version), 11MB
Name:
7847oa.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show

Creators

show
hide
 Creators:
Forkel, M.1, Author
Dorigo, W.1, Author
Lasslop, G.1, Author
Teubner, I.1, Author
Chuvieco, E.1, Author
Thonicke, Kirsten2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

Content

show
hide
Free keywords: -
 Abstract: Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of 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. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model–data integration approaches can guide the future development of global process-oriented vegetation-fire models.

Details

show
hide
Language(s):
 Dates: 2017
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.5194/gmd-10-4443-2017
PIKDOMAIN: Earth System Analysis - Research Domain I
eDoc: 7847
Working Group: Ecosystems in Transition
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Geoscientific Model Development
Source Genre: Journal, SCI, Scopus, p3, oa
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 10 (12) Sequence Number: - Start / End Page: 4443 - 4476 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals185