This document summarizes a presentation on analyzing the relationship between environmental risk and surface energy budget over the Tibetan Plateau using remote sensing data. Validation showed the remote sensing data had acceptable accuracy. Analysis found variability in surface radiation metrics like downward shortwave flux, albedo, and upward longwave flux were linked to occurrences of natural disasters in the region, including droughts, floods, rainstorms and locust disasters. Trend analysis found summer dimming of shortwave radiation, surface brightening through increased albedo, and general atmospheric warming, indicating increased risk of events like hot waves, severe floods and rainstorms in recent decades. The study provides a way to incorporate satellite observations of surface energy into assessing

1. 4th International Disaster and Risk Conference IDRC Davos 2012
"Integrative Risk Management in a Changing World - Pathways to a Resilient Society"
26-30 August 2012
Davos, Switzerland
Relationship of the Environmental Risk and Surface
Energy Budget over the Tibetan Plateau
-A Remote Sensing Evidence Approach
Qinqing Shi1, Shunlin Liang1, Peijun Shi2,3,4
Presenter: Peijun Shi
1. Department of Geographical Sciences, University of Maryland, College Park, USA.
2. State Key Laboratory of Earth Surface Processes and Resource Ecology of Beijing Normal University, Beijing, China.
3. Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing, China.
4. Academy of Disaster Reduction and Emergency Management of Ministry of Civil Affairs and Ministry of Education, Beijing, China.

2. Outline
• Introduction
• Data Sets and Methodology
• Results
• Conclusions
Research Area of the Tibetan Plateau

3. Outline
• Introduction
• Data Sets and Methodology
• Results
• Conclusions

4. Introduction (1/2)
• Surface Radiation Budget (SRB)
– Downward shortwave flux (DSW)
– Upward shortwave flux (USW)
– Surface albedo (USW/DSW)
– Downward longwave flux (DLW)
– Upward longwave flux (ULW)
– Net radiation
• SRB of the Tibetan Plateau (TP)
– Provides evidence to the environmental risk of climatic disasters through the
spatial-temporal variation of atmosphere-surface radiation/energy interaction
– Indicates the variation of atmospheric condition and land cover change
– Indicates the impact and response of climate change over TP

5. Introduction (2/2)
• Five categories of environmental risks related to variation of SRB over
TP
1) Risk of snow-permafrost grassland ecosystem from variation through
retreating of glaciers and the variation of the snow cover
2) Risk of regional variation of precipitation through thermal forcing to the
Asian Summer monsoon
3) Risk of desertification from enhanced soil and permafrost degradation
4) Risk of regional agriculture from variation of hydrological cycle , temperature
and insect diseases
5) Risk of drought, heat waves with increased temperature with global warming
• Objectives
– To identify and analyze the environmental risk of climatic changes with
variability of surface energy budget over the Tibetan Plateau based on two-
decades observation from remote sensing product.

6. Outline
• Introduction
• Data Sets and Methodology
• Results
• Conclusions

7. Data Sets and Methodology (1/2)
• GEWEX SRB (July 1983-December 2007)
– Version 3.0 for SW and version 3.1 for LW
– Produced by NASA/GEWEX to support study of Earth radiation budget in
global/regional climate change
• Ground observations at 29 sites (1997-2007)
– During the temporal period where most available and reliable ground
measurement of surface radiation fluxes existed 29 observation sites from
five networks (AsiaFLUX, ChinaFLUX, CAMP-Tibet, CEOP-Himalayas, GAME-
Tibet)
• Historical records of natural disaster (1949-2010)
– Provided by Key Laboratory of Regional Geography Research, BNU, China
– Includes natural disaster records (disaster type, county names, begin dates,
end dates) in Qinghai, Xizang provinces in China.

8. Data Sets and Methodology (2/2)
• Validations of surface radiation budget
– Three statistical quantities: root mean square error (RMSE), Mean bias error
(MBE), Correlation of determination (R2)
• Characterizations of natural disaster
– Two statistics in county level: monthly occurrences, the dates of duration
• Relationship between surface radiation budget and natural disasters
– Linked to gridded GEWEX-SRB through interpolation from county level
– Divided TP with disaster occurrences to four types
• Zero (no disaster), low (20%), medium (60%), high (20%) risk
– Calculated the seasonal mean and standard deviation (STD) of DSW, albedo,
DLW, ULW from grids in four level risk areas from GEWEX-SRB
– Detected linear trend of DSW, albedo, DLW, ULW in 24 years

9. Outline
• Introduction
• Data Sets and Methodology
• Results
– Validation
– Relationship between Variability of Surface
Radiation Budget and Environmental Risk
– Trend analyses
• Conclusions

10. Validation
• The validation results
– RMSE, MBE and R2 for DSW, USW, DLW, ULW from GEWEX SRB 1° monthly
products proves an acceptable accuracy (±10W/m2) to explore relationship
between environmental risk and surface radiation budget over the Tibetan
Plateau.
Table 1: Validation result of RMSE, MBE, R2 of GEWEX SRB products
Validation DSW USW Albedo DLW ULW
RMSE 28.11 W/m2 13.39 W/m2 0.06 18.30 W/m2 20.37 W/m2
MBE -3.55 W/m2 -3.73 W/m2 -0.01 8.42 W/m2 9.11 W/m2
R2 0.66 0.17 0.10 0.91 0.80

11. Outline
• Introduction
• Data Sets and Methodology
• Results
– Validation
– Relationship between Variability of Surface
Radiation Budget and Environmental Risk
– Trend of Surface Radiation Budget and Implication
to Environmental Risk
• Conclusions

12. Relationship between Variability of Surface Radiation Budget
and Environmental Risk (1/4) : Droughts and DLW
320 12
30
300
2)
11
2)
280 25
10 Zero
260 20 Low
9
240 Low
15 Medium
D
a
8
e
t
220 Medium
10
200 7 High
High
5
W
M
m
6
D
180
W
L
n
m
a
e
D
L
T
(
/
S
o
(
f
/
160 5 0
J F MAM J J A S O N D J F MAM J J A S O N D J F MAM J J A S O N D
Fig. 1: Monthly variability of dates of droughts and mean, STD of DLW in four risk areas in TP
• Decrease of DLW in drought area is related to decrease of water vapor in
the atmosphere, while STD of DLW varied with drought occurrence and
duration
– DLW of medium drought areas is about 10W/m2 lower in summer and
winter.
– Monthly variation of STD for DLW increases in summer decreases in winter
and spring from low to medium drought risk areas
– Area with high drought risk has a higher mean DLW in spring and autumn
but STD is lower than that of medium risk area

13. Relationship between Variability of Surface Radiation Budget
and Environmental Risk (2/4) : Floods and Albedo
0.27 0.07 30
0.25 25
0.06
Zero
0.23 20 Low
0.05 Low
0.21 15 Medium
D
a
e
t
0.04 Medium
0.19 10
High
M
A
d
b
n
o
a
e
A
D
High
T
l
d
b
S
o
e
f
l
0.17 0.03 5
0.15 0.02 0
J F MAM J J A S O N D J F MAM J J A S O N D J F MAM J J A S O N D
Fig. 2: Monthly variability of dates of floods and mean, STD of albedo in four risk areas in TP
• Low flood risk area is related to the decrease of albedo, and the
seasonal contrast of STD between winter and summer albedo increases
from to low risk to medium risk
– Albedo is lower by 0.006 for area with a low flood risk when flood occurs
– High flood occurrences with peaks of albedo STD in spring and summer
– Compare to the low risk area, albedo of medium risk area is higher in
autumn and early winter, lower in spring, indicating the flood occurrence
and intensity also varies with the fluctuation of winter and spring snow
cover

14. Relationship between Variability of Surface Radiation Budget
and Environmental Risk (3/4) : Rainstorms and DSW
280 20 30
2)
2)
260 25
18
240 Zero
220 16 20 Low
Low
200 14 15 Medium
D
a
e
t
180 Medium
12 10
160 High
High
5
W
M
m
10
D
W
S
n
a
140
e
m
(
D
/
T
S
o
(
f
/
120 8 0
J F MAM J J A S O N D J F MAM J J A S O N D J F MAM J J A S O N D
Fig. 3: Monthly variability of dates of rainstorms and mean, STD of DSW in four risk areas in TP
• Mean and STD of DSW is different among area with low, medium and
high rainstorm frequencies, which is caused by the dimming effects of
cloud cover to DSW when rainstorm happens
– DSW decreases in summer with more clouds due to rainstorms.
– In June and July, the increase of DSW STD in low rainstorm area is related to
the variability of cloud
– Mean, STD of DSW decreases in June, July from area with rainstorm
occurrences from zero to medium, and from medium to high

15. Relationship between Variability of Surface Radiation Budget
and Environmental Risk (4/4) : Locust Disasters and ULW
400 16 30
2)
2)
380 14 25
360 12 Zero
340 20 Low
10 Low
320 15 Medium
D
a
e
t
300 8 Medium
10 High
280 6 High
5
W
M
m
U
W
L
4
n
260 m
a
e
U
D
L
T
(
/
S
o
(
f
/
240 2 0
J F MAM J J A S O N D J F MAM J J A S O N D J F MAM J J A S O N D
Fig. 3: Monthly variability of dates of locust disasters and mean, STD of ULW in four risk areas in
TP
• The locust disaster records as the indirect implication for hot waves is
compared with ULW
– The locust disaster in July happens in low risk area where ULW is the higher
than zero risk area by 26 W/m2
– ULW of medium locust disaster risk area has the highest STD in most
months.
– The high locust disaster happens in area with higher mean, but lower STD of
ULW in winter and spring, which is relates to higher risk of severe locust
disaster if the winter and spring is warmer.

16. Outline
• Introduction
• Data Sets and Methodology
• Results
– Validation
– Relationship between Variability of Surface
Radiation Budget and Environmental Risk
– Trend of Surface Radiation Budget and Implication
to Environmental Risk
• Conclusions

17. Trend of mean, STD of DSW, albedo, DLW, ULW averaged over
TP areas with Disaster Occurrences from 1984 to 2007
• Summer dimming , surface brightening and warming in all seasons
– In summer, DSW decreases by -0.770 W/m2 per year and DLW increases by
0.238 W/m2 per year, increasing risk of rainstorms with more cloud cover and
water vapor
– The increase of trend of albedo about 0.002 in four seasons is related to the
increase risk of severe flood frequently
– The increase trend of ULW in four seasons corresponding to surface warming
creates threads for hot waves, increasing risk of severe locust disasters
Table 2: Trend of mean, STD of DSW, albedo, DLW, ULW averaged over TP with Disaster
Occurrences
Season Spring Summer Autumn Winter
Mean STD Mean STD Mean STD Mean STD
Slope
DSW 0.163 -0.465** -0.779*** -0.328** -0.040 0.131 0.099 -0.040
Albedo 0.002*** 0.0003 0.002*** 0.000 0.002*** 0.000 0.002*** 0.000
DLW -0.021 0.137* 0.238*** -0.014 -0.008 0.018 -0.125 0.095
Note- * p<0.1, ** p<0.05, *** p<0.01
ULW 0.399** 0.101 0.277** -0.010 0.593*** 0.096 0.512*** -0.099

18. Outline
• Introduction
• Data Sets and Methodology
• Results
• Conclusions

19. Conclusions
• This study applies remote sensing retrieval of surface radiation budget
from GEWEX SRB to access the environmental risk of climatic disasters
over the Tibetan Plateau
– GEWEX SRB has been validated with accuracy (±10W/m2) for climatic research
– The variability of seasonal cycle of mean and standard deviation for DSW,
albedo, DLW, ULW is linked to climatic disasters: rainstorms, floods,
droughts, and locust disasters respectively
• The solar dimming trend of DSW and the atmospheric warming trend of
DLW in summer, the increasing albedo and surface warming of ULW
together indicate increase environmental risk of hot waves, locust
disasters, severe flood, and summer rainstorms, in recent decades
– Provides an alternative way to incorporating surface radiation budget from
remote sensing observation into risk assessment, governance, and
projection for climatic disasters in the Tibetan Plateau

20. Thanks