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Simulating fire regimes in human-dominated ecosystems: Iberian Peninsula case study
Abstract
A new fire model is proposed which estimates areas burnt on a macro‐scale (10–100 km). It consists of three parts: evaluation of fire danger due to climatic conditions, estimation of the number of fires and the extent of the area burnt. The model can operate on three time steps, daily, monthly and yearly, and interacts with a Dynamic Global Vegetation Model (DGVM), thereby providing an important forcing for natural competition. Fire danger is related to number of dry days and amplitude of daily temperature during these days. The number of fires during fire days varies with human population density. Areas burnt are calculated based on average wind speed, available fuel and fire duration. The model has been incorporated into the Lund‐Potsdam‐Jena Dynamic Global Vegetation Model (LPJ‐DGVM) and has been tested for peninsular Spain. LPJ‐DGVM was modified to allow bi‐directional feedback between fire disturbance and vegetation dynamics. The number of fires and areas burnt were simulated for the period 1974–94 and compared against observations. The model produced realistic results, which are well correlated, both spatially and temporally, with the fire statistics. Therefore, a relatively simple mechanistic fire model can be used to reproduce fire regime patterns in human‐ dominated ecosystems over a large region and a long time period.
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“Although there is a general relationship between human population density and fire activity in some regions, these variables are not necessarily linearly related, being strongly influenced by the environmental setting (Venevsky et al., 2002; Syphard et al., 2007; Li et al., 2009).”
From The human dimension of fire regimes on Earth (2011) · cites this paper as Venevsky et al., 2002
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Cites
A mathematical model for predicting fire spread in wildland fuels
Rothermel, R. C. (1972) · doi:10.2737/int-rp-115 · Cited by 2,247
Flagged first, then by citations
Research works printed but not linked: 43
- Albini FA, 1976, Estimating Wildfire Behaviour and Effects.
- 10.1029/1999GB900042 · DOI
- 10.1007/978-1-4613-8395-6_7 · DOI
- Davis KP, 1959, Forest Fires: Control and Use
- Deeming JE, 1974, The National Fire‐Danger Rating System, 84.
- Demidov PG, 1975, Gorenie I Svoistva Goriuchich Materialov
- Finney MA, 1994, 12th Conference on Fire and Forest Meteorology, 138
- Gardner RH, 1999, Spatial Modelling of Forest Landscapes: Approaches and Applications, 163
- Goldammer JG, 1990, Fire in Ecosystem Dynamics: Mediterranean and Northern Perspectives
- 10.2307/2390165 · DOI
- Hulme M, 1995, A Historical Monthly Precipitation Dataset for Global Land Areas from 1990 to 1997, Gridded at 3.75 X 25 Resolution Climate Research Unit
- IPCC, 1995, Climate Change 1995–the Science of Climate Change
- 10.1016/S0065-2504(08)60216-0 · DOI · Paper page
- 10.1175/1520-0442(1994)007<1794:HSATVA>2.0.CO;2 · DOI
- Keane RE, 1998, A Comparison of Coarse Scale Fire Effects Simulation Strategies, Northwest Science, 72, 79
- Keane RE, 1996, Simulating Coarse‐Scale Vegetation Dynamics Using the Columbia River Basin Succession Model‐CRBSUM, 340
- Keane RE, 1996, USDA, 484
- 10.1007/978-94-015-8737-2_8 · DOI
- Lenihan JM, 1998, Simulating Broad‐Scale Fire Severity in a Dynamic Global Vegetation Model, Northwest Science, 72, 91
- Levine JS, 1996, Biomass Burning and Global Change
- 10.1071/WF9960165 · DOI
- Melekhov IS, 1978, Lesnaia Pirologia
- Merida J‐C, 1999, Àrea de Defensa Contra Incendios Forestales
- Moreno JM, 1998, Large Forest Fires, 159
- 10.1890/1051-0761(1997)007[1252:AEDEFI]2.0.CO;2 · DOI
- Ndambiri JK, 2001, Fire situation in Kenya, International Forest Fire News, 25, 12
- 10.1007/978-3-642-60164-4_2 · DOI
- 10.1007/978-94-011-2866-7_33 · DOI
- 10.1139/x87-081 · DOI
- Sitch S, 2002, Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ Dynamic Global Vegetation Model, Global Change Biology
- Stocks BJ, 1991, Global Biomass Burning: Atmospheric, Climatic, and Biospheric Implications, 197, 10.7551/mitpress/3286.003.0030 · DOI
- 10.1007/978-94-015-8737-2_10 · DOI
- Telitsyn HP, 1996, Fire in Ecosystems of Boreal Eurasia, 139
- Telitsyn, 1988, Lesnye Pozhary, Ich Preduprezhdenie I Bor'ba S Nimi V Khabarovskom Krae
- 10.1046/j.1466-822X.2001.00175.x · DOI · Paper page
- Valendik EN, 1978, Krupnye Lesnye Pozhary
- 10.5558/tfc45103-2 · DOI
- Van Wagner CE, 1987, Development and Structure of the Canadian Forest Fire Weather Index System, 35
- 10.1071/WF9980103 · DOI
- Vélez R, 1997, Forest Fire Risk and Management, 15
- Whelan RJ, 1995, The Ecology of Fire
- Wong D, 1996, Practical Handbook of Spatial Statistics, 83
- Zobler L, 1986, A World Soil File for Global Climate Modelling
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Venevsky, S., Page, Y. L., Pereira, J. M. C., & Wu, C. (2019) · Geoscientific model development · doi:10.5194/gmd-12-89-2019 · Cited by 34
“Fire weather danger index FDI(d) , measured from 0 (“no fire danger”), to 1 (“extreme fire danger”), is estimated in SEVER-FIRE based on the Reg-FIRM fire danger index (Venevsky et al., 2002 (Thonicke et al., 2001).”
BackgroundMethodInfluentialSelf-citationAir quality impacts of European wildfire emissions in a changing climate
Knorr, W., Dentener, F. J., Hantson, S., Jiang, L., Klimont, Z., & Arneth, A. (2016) · Atmospheric chemistry and physics · doi:10.5194/acp-16-5685-2016 · Cited by 47
“In fact, most of the existing global fire models to-date that are able to predict burned area use the approach by Venevsky et al. (2002), where burned area is considered at the end of a chain of predictions that starts from the number of ignitions.”
European vulnerability to global change: a spatially explicit and quantitative assessment
Metzger, M. (2005) · doi:10.18174/121725 · Cited by 25
“…& Jenkinson, 1996; Coleman et al., 1997) Forestry GOTILWA+ (Sabaté et al., 2002) Carbon storage LPJ (biogeochemistry) (Sitch et al., 2003; , fire dynamics: Venevsky et al., 2002) Biodiversity and nature conservation statistical niche modelling (Araújo et al., 2002; Thuiller, 2003) Chapter 2 33”
Geospatial Analysis Application to Forecast Wildfire Occurrences in South Carolina
Feltman, J. A., Straka, T., Post, C., & Sperry, S. (2012) · Forests · doi:10.3390/f3020265 · Cited by 24
Crévoisier, C., Shevliakova, E., Gloor, M., Wirth, C., & Pacala, S. (2007) · doi:10.1029/2006jd008372 · Cited by 24
A statistical model linking Siberian forest fire scars with early summer rainfall anomalies
Jupp, T., Taylor, C., Balzter, H., & George, C. (2006) · doi:10.1029/2006gl026679 · Cited by 24
Valuing Weather Observation Systems For Forest Fire Management
Khabarov, N., Moltchanova, E., & Obersteiner, M. (2008) · IEEE Systems Journal · doi:10.1109/jsyst.2008.925979 · Cited by 23
“The average number of ignitions during a day according to [10] is expressed in the form”
BackgroundZou, Y., Wang, Y., Ke, Z., Tian, H., Yang, J., & Liu, Y. (2019) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2018ms001368 · Cited by 47
“The natural fire ignition is a function of the cloud‐to‐ground fraction of lightning flashes Il (flash per square kilometer per time step) and latitude λ (°) (Li et al., 2012; Prentice & Mackerras, 1977), In ¼ 15:16þ 2:16 cos 3λð Þ Il; (2) and the anthropogenic fire ignition is a function of population density Dp (per…”
BackgroundModeling Burned Areas in Indonesia: The FLAM Approach
Krasovskii, A. A., Khabarov, N., Pirker, J., Kraxner, F., Yowargana, P., Schepaschenko, D. G., & Obersteiner, M. H. (2018) · Forests · doi:10.3390/f9070437 · Cited by 24
Is Landscape a Driver of Short-term Wildfire Recurrence?
Barbati, A., Corona, P., D’amato, E., & Cartisano, R. (2013) · Landscape Research · doi:10.1080/01426397.2012.761681 · Cited by 27
“While there is extensive literature about the interplay between fire occurrence and human activities (e.g. Syphard et al., 2007; Venevsky et al., 2002) or climatological/meteorological conditions (e.g. Pausas, 2004; Pausas & Paula, 2012), only a few studies have focused on and tried to model…”
BackgroundImproving the LPJmL4-SPITFIRE vegetation-fire model for South America using satellite data
Drüke, M., Forkel, M., von Bloh, W., Sakschewski, B., Cardoso, M., Bustamante, M., Kurths, J., & Thonicke, K. (2019) · doi:10.5194/gmd-2019-92 · Cited by 21
“We aim to evaluate the performance of two alternative fire danger indices within SPITFIRE based on the already implemented Nesterov Index (Venevsky et al., 2002) and the newly implemented water vapor pressure deficit (VPD hereafter; Pechony and Shindell, 2009; Ray et al.”
MethodInfluentialSelf-citationGirod, C., Hurtt, G., Frolking, S., Aber, J., & King, A. (2007) · Earth Interactions · doi:10.1175/ei188.1 · Cited by 19
“Venevsky et al. ( Venevsky et al. 2002 ) included human ignitions, where the number of human ignitions was based on population density, lifestyle, and economic status.”
BackgroundEffect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence
Mukunga, T., Forkel, M., Forrest, M., Zotta, R.-M., Pande, N., Schlaffer, S., & Dorigo, W. (2023) · Fire · doi:10.3390/fire6050197 · Cited by 18
Integration of a Deep‐Learning‐Based Fire Model Into a Global Land Surface Model
Son, R., Stacke, T., Gayler, V., Nabel, J. E. M. S., Schnur, R., Alonso, L., Requena‐Mesa, C., Winkler, A. J., Hantson, S., Zaehle, S., et al. (2024) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2023ms003710 · Cited by 29
“Venevsky et al. (2002) added characteristics of fire spread by adopting the Rothermel's rate-of-spread (RoS) equations (Rothermel, 1972).”
MethodHuang, H.-L., Xue, Y.-K., Li, F., & Liu, Y. (2020) · Geoscientific Model Development · doi:10.5194/gmd-2020-122-supplement · Cited by 16
“35× 10−9 count per person s−1) and population density (Dp; person) (Venevsky et al. 2002):”
BackgroundInfluentialAn analytical review on the most widely used meteorological models in forest fire prediction
Hamadeh, N., Hilal, A., Daya, B., & Chauvet, P. (2015) · International Conference on Technological Advances in Electrical, Electronics and Computer Engineering · doi:10.1109/taeece.2015.7113633 · Cited by 16
Li, P., Irvine, B., Holden, J., & Mu, X.-M. (2017) · doi:10.1016/j.catena.2016.11.025 · Cited by 15
One extreme fire weather event determines the extent and frequency of wildland fires
Wang, X., Swystun, T., Oliver, J. A., & Flannigan, M. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/ac2f64 · Cited by 18
SPITFIRE-2 : an improved fire module for dynamic global vegetation models
Pfeiffer, M., & Kaplan, J. (2012) · doi:10.5194/gmdd-5-2347-2012 · Cited by 14
“These models include Glob-FIRM (Thonicke et al., 2001), Reg-FIRM (Venevsky et al., 2002), and CTEM-FIRE (Arora and Boer, 2005) and have been incorporated as modules into a number of DGVMs, including LPJ (Sitch et al., 20 2003), CTEM, CLM3 (Levis et al., 2004), ORCHIDEE (Krinner et al., 2005), SEIB- DGVM (Sato et al.,…”
BackgroundMethodLightning Forcing in Global Fire Models: The Importance of Temporal Resolution
Felsberg, A., Kloster, S., Wilkenskjeld, S., Krause, A., & Lasslop, G. (2017) · doi:10.1002/2017jg004080 · Cited by 12
“Current process-based fire models either use a globally constant lightning ignition rate (Hantson et al., 2016; Venevsky et al., 2002) or work with monthly lightning climatologies from observations (Hantson et al., 2016).”
BackgroundModelling and assessing fire regimes in mountain forests of Switzerland
Weibel, P. (2009) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-006088494 · Cited by 12
“The Nesterov Ignition Index is widely used in forest fire research (e.g. Viegas et al. 1999; Venevsky et al. 2002), but it did not perform well in our study region.”
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Multiscale assessment of North American terrestrial carbon balance
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“…(DGVMs) to explicitly describe the burned area, carbon emission, and fire disturbance on vegetation (Thonicke et al., 2010, 2001; Li et al., 2012; Arora and Boer, 2005; Pfeiffer et al., 2013; Rabin et al., 2018; Burton et al., 2019; Venevsky et al., 2002; Lasslop et al., 2014; Huang et al., 2020a).”
Oberhagemann, L., Billing, M., von Bloh, W., Drüke, M., Forrest, M., Bowring, S., Hetzer, J., Batalla, J. R., & Thonicke, K. (2025) · Geoscientific model development · doi:10.5194/gmd-18-2021-2025 · Cited by 16
Self-citationFleskens, L., Kirkby, M., & Irvine, B. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2016.00031 · Cited by 9
“We have implemented a simplified fire model within PESERA, using simplified versions of algorithms developed and tested independently (Venevsky et al., 2002) for Portugal.”
MethodProjected changes in forest fire season, the number of fires, and burnt area in Fennoscandia by 2100
Kinnunen, O., Backman, L., Aalto, J., Aalto, T., & Markkanen, T. (2024) · Biogeosciences · doi:10.5194/bg-21-4739-2024 · Cited by 18
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Comment on “Carbon in Trees in Tasmanian State Forest”
Dean, C. (2011) · doi:10.1155/2011/212361 · Cited by 8
“decline, but it increases logarithmically with population [57].”
BackgroundVourlitis, G. L., Steinecke, D., Martinez, T., Konda, K., Rendon, R., Hall, V., Khor, S., & Sethuraman, A. (2021) · Applied Soil Ecology · doi:10.1016/j.apsoil.2021.104319 · Cited by 19
Integration of the Climate Impact Assessments with Future Projections
Goodess, C., Agnew, M., Giannakopoulos, C., Hemming, D., Salem, S. B., Bindi, M., Bradai, M., Congedi, L., Dibari, C., El-Askary, H., et al. (2013) · doi:10.1007/978-94-007-5769-1_5 · Cited by 8
Venevsky, S. (2006) · doi:10.1007/s11027-006-1024-4 · Cited by 8
Self-citationWildfire-smoke-precipitation interactions in Siberia: Insights from a regional model study.
Konovalov, I., Golovushkin, N. A., & Beekmann, M. (2024) · Science of the Total Environment · doi:10.1016/j.scitotenv.2024.175518 · Cited by 7
Yue, C., Ciais, P., Cadule, P., Thonicke, K., Archibald, S., Poulter, B., Hao, W.-M., Hantson, S., Mouillot, F., Friedlingstein, P., et al. (2014) · doi:10.5194/gmdd-7-2377-2014 · Cited by 7
Self-citationDynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
Shuman, J. K., Fisher, R. A., Koven, C. D., Knox, R., Kueppers, L. M., & Xu, C. (2024) · Geoscientific model development · doi:10.5194/gmd-17-4643-2024 · Cited by 10
“But in Venevsky et al. (2002), indeed daily temperature was used.”
BackgroundMethodInfluentialClimate-induced fire regimes in the Russian biodiversity hotspots
Wu, C., Wang, M., Lu, C., Venevsky, S., Сорокина, В., Kulygin, V. V., & Berdnikov, S. V. (2018) · Global Ecology and Conservation · doi:10.1016/j.gecco.2018.e00495 · Cited by 5
Self-citationMonmany, A. C., Gould, W., Andrade-Núñez, M., González, G., & Quinones, M. (2017) · InTech eBooks · doi:10.5772/67667 · Cited by 6
Hill, M. J., Roxburgh, S. H., Carter, J. O., & Barrett, D. J. (2005) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2005.06.008 · Cited by 4
“Fire danger is associated with dry weather, fire ignition with human influence; fire spread due to wind and fuel load; extinguishment of fire due to weather or suppression; and vegetation mortality due to fire ( Venevsky et al., 2002 ).”
BackgroundReviewing the "Hottest" Fire Indices WorldwidePreprint
Baijnath-Rodino, J., Foufoula-Georgiou, E., & Banerjee, T. (2020) · doi:10.1002/essoar.10503854.1 · Cited by 4
“A modified Nesterov Index (mNI) was developed by Venevsky et al. (2002).”
MethodIdentifying required model structures to predict global fire activityfrom satellite and climate data
Forkel, M., Dorigo, W., Lasslop, G., Teubner, I. E., Chuvieco, E., & Thonicke, K. (2016) · doi:10.5194/gmd-2016-301 · Cited by 4
“…is used as fire weather index in many fire modules of global vegetation models (Lasslop et al., 2014; Prentice et al., 2011; Thonicke et al., 2010; Venevsky et al., 2002; Yue et al., 2014), studies on forest fire 725 management rely more often on alternative fire weather indices such as from the…”
BackgroundSelf-citationReconstructing and analyzing the fire history in a dry continental valley (Valais) of the Swiss Alps
Zumbrunnen, T. (2010) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-006414388 · Cited by 2
Factors contributory to fire occurrences in two populated areas in the American southwest
Tong, S. T. Y., Wan, S., & Gao, Y.-H. (2021) · Management of environmental quality · doi:10.1108/meq-08-2021-0188 · Cited by 2
Drivers of recent forest cover change in southern South America are linked to climate and CO2
Ogunkoya, A., Kaplan, J., Whitlock, C., Nanavati, W. P., Roberts, D. W., & Poulter, B. (2021) · Landscape Ecology · doi:10.1007/s10980-021-01330-7 · Cited by 2
Wang, M., Venevsky, S., Wu, C., Berdnikov, S., Sorokina, V., & Kulygin, V. (2020) · Science of the Total Environment · doi:10.1016/j.scitotenv.2020.143492 · Cited by 2
Self-citationTechnical note: Low meteorological influence found in 2019 Amazonia fires
Kelley, D., Burton, C. A., Huntingford, C., Brown, M. A. J., Whitley, R., & Dong, N. (2020) · Biogeosciences · doi:10.5194/bg-2020-123 · Cited by 2
“Fire danger indices (FDIs), which can capture simultaneous drivers, are useful for calculating the level of risk of a fire spreading and becoming severe in a particular area (de Groot et al., 2015).”
InfluentialWildfire Hazard in Poland in a Warming Climate: Past and Future Impact of Extreme Weather
Pińskwar, I., Tyburski, Ł., Choryński, A., Dudzińska, A., & Graczyk, D. (2026) · International Journal of Climatology · doi:10.1002/joc.70439
“…etc., influence the potential fire danger, as reported in literature, which varies depending on the country or region (Keetch and Byram 1968; Zhdanko 1965; Nesterov 1949; Venevsky et al. 2002; Sharples et al. 2009; McArthur 1967; Fosberg 1978; Chandler et al. 1983; Zacharakis and Tsihrintzis 2023).”
A Raster-based forest fire regime model
Lu, A., & Zheng, Y.-T. (2011) · 2011 International Conference on Multimedia Technology · doi:10.1109/icmt.2011.6002718
“In this study, we follow the algorithm of fire occurrence in Reg-FIRM [12] and consider it as a function of the fire danger index, ignition sources (e.”
BackgroundMethodDynamic global vegetation models
Schulze, E., Beck, E., Buchmann, N., Clemens, S., Müller-Hohenstein, K., & Scherer-Lorenzen, M. (2019) · Springer eBooks · doi:10.1007/978-3-662-56233-8_22
INFERNO : a fire and emissions scheme for the Met 1 Office ’ s Unified Model 2
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Self-citationAssociation Between Fire Causative Agents Within Land Cover Types and Global Fire Occurrence
Pettinari, M. L., & Chuvieco, E. (2012) · Lecture notes in geoinformation and cartography · doi:10.1007/978-3-642-32714-8_18 · Cited by 1
Kobayashi, K., Jaafar, M. N. B., Ogata, S., & Tsukai, M. (2007) · doi:10.2208/jscejd.63.478
Metadata
- Authors
- Venevsky, S., Thonicke, K., Sitch, S., & Cramer, W.
- Affiliations
- Potsdam Institute for Climate Impact Research (DE)
- Corresponding author
- Venevsky, Sergey
- Venue
- Global Change Biology
- Publisher
- Wiley
- Published
- 2002-09-06
- Volume
- 8
- Issue
- 10
- Pages
- 984-998
- ISSN
- 1354-1013 (ISSN-L)
- 1365-2486
- OpenAlex
- W2138044865
- Cited by
- 190
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 16 · 2023: 6 · 2022: 11 · 2021: 17 · 2020: 6 · 2019: 3 · 2018: 6 · 2017: 8 · 2016: 8 · 2015: 8 · 2014: 7 · 2013: 11 · 2012: 11
- FWCI
- 2.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Global Change Biology (publishedVersion)
- HAL (submittedVersion)
- Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) (submittedVersion)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Fire effects on ecosystems
- Physical Sciences › Environmental Science › Global and Planetary Change › Plant Water Relations and Carbon Dynamics
- Life Sciences › Agricultural and Biological Sciences › Forestry › Forest Management and Policy
- Keywords
- fire regimes, fire modeling, fire danger, burned area estimation, human-dominated ecosystems, dynamic global vegetation models, fire-vegetation feedbacks, dry days, human population density, Iberian Peninsula, Peninsular Spain
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