IDRC global risk of population by SDS-Yang Hui-Min_template2

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5th International Disaster and Risk Conference IDRC 2014 Integrative Risk Management - The role of science, technology & practice 24-28 August 2014 in Davos, Switzerland

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  • 方法与模型 method and model H for short (缩写为H) definition Because of difference in unit of these three parts.so normaliztion is needed.
    技术路线图 technology roadmap  global aridity index(AI)
    Years encounter 年遇型 No vulnerability curve→relative risk level
  • Where Ix is dimensionless normalized data normalized by original data, x is original data, min is minimum of original data, and max is maximum of original data.
  • Tunisia syria
  • IDRC global risk of population by SDS-Yang Hui-Min_template2

    1. 1. Assessment of global affected demographic risk 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org by sand-dust storm and its spatial pattern Huimin Yang, Lianyou Liu, Xingming Zhang, Jing’aWang, Peijun Shi State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Geography, Beijing Normal University, China
    2. 2. 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org Outline 1. Research Advance of SDS 2. Data and Methods 3. Results and Analysis
    3. 3. 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 1. Research Advance Of SDS
    4. 4. Korla City 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 1. Research advance of SDS • Sand and dust storm(SDS)is a type of disastrous weather which occurs chiefly in arid and semi-arid desertified regions. • SDS is a weather phenomenon of wind-borne sand and dust particles blown by strong winds on the ground and makes air particularly turbid, with horizontal visibility less than 1 km. • Adverse effects:such as reducing land productivity, affecting transport facilities and power lines, polluting the air, threating human health, affecting global climate, etc.
    5. 5. 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland Engelstaedter, et al.,2003 www.grforum.org 1. Research advance of SDS • Global SDS-prone areas are located in North Africa, Middle East, central Asia, north America, south America, Australia and other places. • A lot of studies have been conducted on the temporal and spatial pattern of SDS from regional perspective: such as the spatial-temporal distribution of SDS in central Asia, Turkmenistan, China, etc .
    6. 6. 1. Research advance of SDS • At present, a lot of studies have been conducted on the spatial-temporal distribution, causes, source regions, and disaster theories of SDS with fruitful results. SDS disaster risk assessment is important for SDS disaster reduction, especially from regional perspective to a global scale. • In this study, the global affected demographic risk by SDS(RPOP-SDS ) was evaluated and its spatial pattern was analysed in the aspects of hazard, hazard-formative environment and hazard-affected body (exposure) , With SDS kinetic energy as the key indicator of hazard, this study is intended to provide a new solution to assessing SDS risk. 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org
    7. 7. 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 2. Data and Methods
    8. 8. 5th International Disaster and Risk Conference IDRC 2014 2. Data and Methods ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org Previous H Flow chart
    9. 9. 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 2. Data and Methods Data Source Remarks Global surface China Meterological Administration synoptic timing data (CMA),http://cdc.cma. gov.cn/home.do set January 1, 1982--2011, 65 indices, 9728stations。 Population Oak Ridge National Laboratory (ORNL) ,http://www.ornl.gov/gist 2010,resolution of 1km*1km,raster Global Aridity Index Climate Database Consultative Group for International Agriculture Research(CGIAR), http://www.csi.cgiar.org resolution of 30 arc-second, raster Circum-Arctic Map of Permafrost National Snow and Ice Data Center, ftp://sidads.colorado.edu /pub/DATASETS/fgdc/ggd318_map_cir cumarctic/ vector data(*.shp) 2.1 Data sources
    10. 10. R  EHV 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 2.2. Methods (1) Three-dimensional evaluating model of risk Disaster is the comprehensive product of hazard-formative environment (E), hazards (H) and hazard-affected body (exposure). disaster risk(R) is the potential loss induced by natural hazards. hazard-formative environment could reduce or magnify the effect of hazards on exposures. (1) Where R is risk, E is stability of hazard-formative environment, H is danger of hazards; V is vulnerability of exposure. This three- dimensional model is a modification of previous two- dimensional models。 The RPOP-SDS was determined by product of normalized kinetic energy, population density and aridity index by grid, country unit and comparable area unit, respectively (Fig. 4, Fig. 5 and Fig. 6).
    11. 11. max  x max -min 5th International Disaster and Risk Conference IDRC 2014 x I  ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 2.2. Methods (2) Data Processing  Hazard-formative environment In this study, areas prone to desertification were taken as the main research areas. Aridity is considered as a key factor of the SDS hazard-formative environment because of its prominent influence on vegetation, soil property, the ultimate intensity and frequency of SDS. The aridity index data were normalized by formula (2): (2) The normalized aridity map of global areas prone to SDS is shown as Fig. 1.
    12. 12. 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 2.2. Methods (2) Data Processing  Hazard-formative environment Fig. 1 Normalized aridity map of global areas prone to sand and dust storms
    13. 13. 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 2.2. Methods (2) Data Processing  SDS hazard The SDS hazard is determined both by its magnitude and frequency. During an SDS event, SDS energy plays a decisive role in soil wind erosion, sand entrainment and dust emission, blown sand and dust transport, so it might be a more appropriate parameter for quantifying SDS. During typical SDS events, PM10 accounts for the majority of the particulate matter in atmosphere(Zhuang et al., 2001; Jayaratne et al., 2011).
    14. 14. 10 5 10 vis PM V     8 1.5977 1.5977 2 1 4 p vis E V v     5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 2.2. Methods (2) Data Processing SDS hazard In the centre of a sand desert, PM10 has negative power functions with visibility (Yang et al., 2006; Wang et al., 2008). (3) where Vvis is visibility, and PM10 is in μg·m-3. We combined formula (3) with the classical kinetic energy formula ,the kinetic energy per cubic meter of SDS (Ep) can be expressed by formula (4). (4) where v is the maximum wind velocity (m/s) at 10 m high.
    15. 15. x -min max -min 5th International Disaster and Risk Conference IDRC 2014 x I  ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 2.2. Methods (2) Data Processing  SDS hazard——SDS kinetic energy of 100-year return period  Information diffusion method was used in calculating SDS kinetic energy of different return periods, SDS kinetic energy of different return periods corresponded with a exceedance probability Pi, therefore making the exceedance probability Pi = 0.01 over 100- year return period. Then, SDS kinetic energy under this scenario was calculated. Finally kinetic energy was normalized with formula (5). (5)   Through the inverse distance weighted method, the normalized map of global SDS kinetic energy based on100-year return period is shown as Fig. 2.
    16. 16. 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 2.2. Methods (2) Data Processing  SDS hazard Fig. 2 normalized map of global sand and dust storm kinetic energy based on 100-year return period
    17. 17. 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 2.2. Methods (2) Data Processing  Exposure The global population distribution data is originally from the global population database(2010 ), with a resolution of 1 km × 1 km. For comparability, population density was normalized with formula (5), and the normalized map of global population distribution in 2010 is shown as Fig. 3.
    18. 18. 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 2.2. Methods (2) Data Processing  Exposure Fig. 3 Normalized map of global population distribution in 2010
    19. 19. 3. Results and Analysis 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org ——Risk R  EHV ——global RPOP-SDS was divided into five grades, namely, extremely high, high, moderate, low, and extremely low.
    20. 20. 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 3. Results and analysis • 3.1. Spatial pattern of RPOP-SDS by grid Fig. 4 Grade map of affected population risk by sand and dust storms (by grid)
    21. 21. 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 3. Results and analysis • 3.2. Spatial Pattern of RPOP-SDS by Country Unit Fig. 5 Grade map of affected population risk by sand and dust storms in different countries
    22. 22. 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org 3. Results and analysis • 3.3. Spatial pattern of RPOP-SDS by comparable area unit Fig. 6 Grade map of affected population risk by sand and dust storms in different regions
    23. 23. 5th International Disaster and Risk Conference IDRC 2014 ‘Integrative Risk Management - The role of science, technology & practice‘ • 24-28 August 2014 • Davos • Switzerland www.grforum.org To the end! Thank you for your attention!

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