Mapping Grassland Wildfire Risk of the World Xin Cao, Yongchang Meng, and Jin Chen
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Background
Recent researches indicated an increasing frequency and intensity of grassland wildfire (Running 2006; Balshi et al. 2009), which arose the debate whether grassland wildfire can accelerate global warming (Randerson et al. 2006). Fluctuations of weather and fuel due to climate change will enhance the spatio-temporal uncertainty of grassland wildfire. Therefore, analyzing fire ignition probability, assessing fire propagation damage, and modeling grassland wildfire risk are of great importance with the climate change context. Existing methods for grassland wildfire risk assessment focus on fire danger monitoring and assessment of fire potential damage and can be classified as fire danger index methods (Gonzalez-Alonso et al. 1997; Burgan et al. 1998; Lopez et al. 2002; Peng et al. 2007), fire causing factors
(Jaiswal et al. 2002; Xu et al. 2005), fire spread model using historic fire database (Mbow et al. 2004; Carmel et al. 2009), and integrated wildfire risk assessment (Tong et al. 2009; Chuvieco et al. 2010). Various fire danger rating systems (FDRSs) have been developed based on the fuel-burning model and climate factors, such as fire behavior prediction and fuel modeling system (BEHAVE) (Burgan and Rothermel 1984), National Fire Danger Rating System (NFDRS) (Bradshaw et al. 1983), Canadian Forest Fire Danger Rating System (CFFDRS) (Canadian Forest Service 1992), Fire Area Simulator (FARSITE) (Finney 2004), etc. This study performs a quantitative assessment and mapping of grassland wildfire risk at the global scale by multivariate logistic regression based on the long time-series data acquired from MODIS products.
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Mapping Editors: Jing’ai Wang (Key Laboratory of Regional Geography, Beijing Normal University, Beijing 100875, China) and Fang Lian (School of Geography, Beijing Normal University, Beijing 100875, China). Language Editor: Kai Liu (Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China). X. Cao State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China Y. Meng Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs and Ministry of Education, Beijing Normal University, Beijing 100875, China
Method
Figure 1 shows the technical flowchart for mapping grassland wildfire risk of the world. The grassland wildfire risk is assessed with a 1 km × 1 km grid cell which contains the land cover types of grassland (Appendix III, Exposures data 3.19). In this study, three types of land cover were selected as grassland: woody savannas, savannas, and grasslands.
2.1
Intensity
A multivariate logistic regression model was used to predict grassland burning probability (Cao et al. 2013). Logistic regression is used in the condition of the dichotomous (i.e., binary) response variable. The specific form of the multivariate logistic regression model is as follows:
J. Chen (&) College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China e-mail:
[email protected]
P. Shi and R. Kasperson (eds.), World Atlas of Natural Disaster Risk, IHDP/Future Earth-Integrated Risk Governance Project Series, DOI 10.1007/978-3-662-45430-5_15 © Springer-Verlag Berlin Heidelberg and Beijing Normal University Press 2015
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Fig. 1 Technical flowchart for mapping grassland wildfire risk of the world
Assume response variable y has a binomial distribution (Eq. 1): ( 1 y¼ ð1Þ 0 where y = 1 indicates burned grasslands, y = 0 indicates randomly selected unburned areas. The logistic regression model is defined in Eq. (2): Py¼1 ¼
1 1þe
ðb0 þ
P
bi X i Þ
ð2Þ
where Py=1 represents the burning probability, β0 is the constant value of the logistic regression model, and βi is the coefficient for variable Xi. Xi takes into account the properties of fuels and topography. The following factors were selected or calculated from MODIS 1-km reflectance product (Appendix III, Environments data 2.14) and DEM data (Appendix III, Environments data 2.1) and then used as the explanatory variables for burning probability. It should be noted that the properties of fuels were represented by VIs (vegetation indices) calculated from MODIS data rather than the specific physical indicators of fuel properties. • Live fuel load: Normalized Difference Vegetation Index (NDVI) and Optimized Soil-Adjusted Vegetation Index (OSAVI) • Live fuel moisture content: Global Vegetation Moisture Index (GVMI) and Moisture Stress Index (MSI) • Dead fuel (coverage): Dead Fuel Index (DFI) • Topography: Digital Elevation Model (DEM), slope, and aspect
Based on the historical burned areas database acquired from MODIS (Appendix III, Hazards data 4.17), we built up the grassland burning probability model by using 2000– 2010 historical burned areas as the response variable, while the information of fuels in grassland together with topographic factors is taken as the explanatory variables.
2.2
Vulnerability
Considering the main potential loss of grassland fire is the stockbreeding industry, and the stock capacity is directly dependent on the biomass of grassland. Net primary product (NPP) was then used as a surrogate to represent the potential loss of grassland fire. The average NPP distribution was calculated based on the data from 2000 to 2010 (Appendix III, Exposures data 3.20).
2.3
Risk
Under the framework of disaster risk assessment, the grassland fire risk model is constructed in Eq. (3): R¼HV E
ð3Þ
where R is the risk of grassland fire; H is the grassland fire, i.e., the probability of fire ignition; V is the vulnerability, i.e., the probability of fire propagation; E is the exposure, i.e., the potential loss or NPP. In this model, both fire ignition and propagation information are considered, the probability of
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Fig. 2 Expected annual grassland NPP loss risk of wildfire of the world. 1 (0, 10 %] Brazil, United States, Australia, Russia, Kazakhstan, Mozambique, Madagascar, China, Tanzania, Canada, Angola, South Africa, Venezuela, Argentina, Nigeria, Sudan, Colombia. 2 (10, 35 %] Mexico, Zimbabwe, Zambia, Democratic Republic of the Congo, Botswana, Mongolia, Bolivia, Kenya, India, Namibia, The Republic of Côte d’Ivoire, Central African Republic, Turkey, Burma, Paraguay, Ethiopia, Uruguay, Ghana, Spain, Thailand, Congo, Indonesia, Chad, Somalia, Mali, Burkina Faso, Vietnam, Cameroon, Guinea, Portugal, France, Ecuador, Benin, Malawi, Chile, Italy, Cambodia, Peru, Senegal, New Zealand, Nicaragua, Niger, Togo, Laos. 3 (35, 65 %] Honduras, Uganda, Gabon, Guyana, Romania, Iran, Germany, Kyrgyzstan, Morocco, Japan, Papua New Guinea, Belarus, United Kingdom, Greece, Georgia, Swaziland, Ukraine, Croatia, Guatemala, Sweden, Cuba, Mauritania, Norway, Bosnia and Herzegovina, Dominican
Republic, Finland, Sierra Leone, Nepal, Serbia, Afghanistan, Uzbekistan, Poland, Azerbaijan, Philippines, Bangladesh, South Korea, Turkmenistan, Sri Lanka, Tajikistan, Iceland, Guinea-Bissau, El Salvador, Panama, Czech Republic, North Korea, Costa Rica, Timor-Leste, Bulgaria, Armenia, Haiti, Ireland, Algeria. 4 (65, 90 %] Latvia, Austria, Slovakia, Malaysia, Hungary, Lesotho, Burundi, Lithuania, Switzerland, Tunisia, Slovenia, Rwanda, Gambia, Bahamas, Bhutan, Albania, Pakistan, Estonia, Macedonia, Belize, Montenegro, Eritrea, Iraq, Suriname, Cyprus, Denmark, Trinidad and Tobago, Liberia, Jamaica, Fiji, the Netherlands, Belgium, Mauritius, Israel, Syria, Egypt, Lebanon, Oman, Cape Verde, Libya, Moldova, Yemen, Comoros, Luxembourg. 5 (90, 100 %] Palestine, Barbados, Equatorial Guinea, Saudi Arabia, Gaza Strip, Antigua and Barbuda, Jordan, San Marino, Singapore, Andorra, Baker Island, Saint Lucia, Liechtenstein, Djibouti, United Arab Emirates, Solomon Islands, Western Sahara, Kuwait
grassland burning is therefore taken as a combination of the probability of ignition and propagation.
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Results
Based on the grassland fire risk assessment model, we firstly calculated the grassland burning probability at 8-day scale and then calculated the annually averaged grassland burning probability. The yearly grassland fire ‘risk’ was then modeled by the product of yearly grassland burning probability and NPP. The final global grassland burning probability map and risk map were obtained by averaging the above results during 2000–2010.
3.1
Hazard Intensity
The intensity of grassland fire was represented by the grassland burning probability. A higher probability of grassland burning means the higher intensity of hazard. Grasslands with high burning probabilities concentrate in the central part of Asia, western Europe, western Africa, northern Oceania, central part of North America and eastern part of South America. The grassland in Kazakhstan, western Russia, eastern Mongolia, Ukraine, Somalia, Kenya, Madagascar, northwestern Australia, northern United States, southern Canada, and eastern Brazil is prone to be affected by grassland fire.
The NPP Loss Risk
The risk of grassland fire is represented by the product of the grassland burning probability and NPP. The higher average potential loss of NPP means the higher risk of grassland fire. It can be observed that the high-grassland-fire-risk regions are concentrated in the central part of Asia, western Europe, southwestern Africa, northern Oceania, central part of North America, and northeastern South America, including Kazakhstan, western Russia, eastern Mongolia, Ukraine, Somalia, Kenya, Mozambique, Tanzania, Madagascar, northwestern Australia, central part of United States, southern Canada, Columbia, Venezuela, and eastern Brazil. By zonal statistics of the expected risk result, the expected annual grassland NPP loss risk of wildfire of the world at national level is derived and ranked (Fig. 2). The top 1 % countries with highest expected annual grassland NPP loss risk of wildfire are Brazil and United States, and the top 10 % countries are Brazil, United States, Australia, Russia, Kazakhstan, Mozambique, Madagascar, China, Tanzania, Canada, Angola, South Africa, Venezuela, Argentina, Nigeria, Sudan and Colombia.
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