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

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
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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

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Outline
1. Research Advance of SDS
2. Data and Methods
3. Results and Analysis

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1. Research Advance Of SDS

Korla City
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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.

Engelstaedter, et al.，2003
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• 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 .

• 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.
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2. Data and Methods

2. Data and Methods
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Previous H
Flow chart

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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

R  EHV
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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).

max 
x
max -min
x
I

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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.

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2.2. Methods
(2) Data Processing
 Hazard-formative environment
Fig. 1 Normalized aridity map of global areas prone to sand and dust storms

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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).

10 5 10 vis PM V    
8 1.5977
1.5977 2 1
4 p vis E V v    
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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.

x -min
max -min
x
I

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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.

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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

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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.

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2.2. Methods
(2) Data Processing
 Exposure
Fig. 3 Normalized map of global population distribution in 2010

3. Results and Analysis
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——Risk R  EHV
——global RPOP-SDS was divided into five grades, namely, extremely high,
high, moderate, low, and extremely low.

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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)

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• 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

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• 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

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To the end!
Thank you for your attention！

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

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