Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
4th International Disaster and Risk Conference IDRC Davos 2012"Integrative Risk Management in a Changing World - Pathways ...
Outline•   Introduction•   Data Sets and Methodology•   Results•   Conclusions                         Research Area of th...
Outline•   Introduction•   Data Sets and Methodology•   Results•   Conclusions
Introduction (1/2)•   Surface Radiation Budget (SRB)     –   Downward shortwave flux (DSW)     –   Upward shortwave flux (...
Introduction (2/2)•   Five categories of environmental risks related to variation of SRB over    TP     1) Risk of snow-pe...
Outline•   Introduction•   Data Sets and Methodology•   Results•   Conclusions
Data Sets and Methodology (1/2)•   GEWEX SRB (July 1983-December 2007)     – Version 3.0 for SW and version 3.1 for LW    ...
Data Sets and Methodology (2/2)•   Validations of surface radiation budget     – Three statistical quantities: root mean s...
Outline• Introduction• Data Sets and Methodology• Results  – Validation  – Relationship between Variability of Surface    ...
Validation•   The validation results     – RMSE, MBE and R2 for DSW, USW, DLW, ULW from GEWEX SRB 1° monthly       product...
Outline• Introduction• Data Sets and Methodology• Results  – Validation  – Relationship between Variability of Surface    ...
Relationship between Variability of Surface Radiation Budget                 and Environmental Risk (1/4) : Droughts and D...
Relationship between Variability of Surface Radiation Budget                 and Environmental Risk (2/4) : Floods and Alb...
Relationship between Variability of Surface Radiation Budget               and Environmental Risk (3/4) : Rainstorms and D...
Relationship between Variability of Surface Radiation Budget             and Environmental Risk (4/4) : Locust Disasters a...
Outline• Introduction• Data Sets and Methodology• Results  – Validation  – Relationship between Variability of Surface    ...
Trend of mean, STD of DSW, albedo, DLW, ULW averaged over        TP areas with Disaster Occurrences from 1984 to 2007•    ...
Outline•   Introduction•   Data Sets and Methodology•   Results•   Conclusions
Conclusions•   This study applies remote sensing retrieval of surface radiation budget    from GEWEX SRB to access the env...
Thanks
Upcoming SlideShare
Loading in …5
×

Relationship of the environmental risk and surface energy budget over the Tibetan Plateau - a remote sensing evidence approach

468 views

Published on

Qinqing SHI1, Shunlin LIANG1, Peijun SHI2,3,4

1Department of Geographical Sciences, University of Maryland, United States of America; 2State Key Laboratory of Earth Surface Processes and Resource Ecology of Beijing Normal University, China; 3Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, China; 4Academy of Disaster Reduction and Emergency Management of Ministry of Civil Affairs and Ministry of Education, Beijing Normal University, China

Published in: Education, Technology
  • Be the first to comment

  • Be the first to like this

Relationship of the environmental risk and surface energy budget over the Tibetan Plateau - a remote sensing evidence approach

  1. 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 2012Davos, 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. 2. Outline• Introduction• Data Sets and Methodology• Results• Conclusions Research Area of the Tibetan Plateau
  3. 3. Outline• Introduction• Data Sets and Methodology• Results• Conclusions
  4. 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. 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. 6. Outline• Introduction• Data Sets and Methodology• Results• Conclusions
  7. 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. 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. 9. Outline• Introduction• Data Sets and Methodology• Results – Validation – Relationship between Variability of Surface Radiation Budget and Environmental Risk – Trend analyses• Conclusions
  10. 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. 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. 12. Relationship between Variability of Surface Radiation Budget and Environmental Risk (1/4) : Droughts and DLW 320 12 30 300 2) 112) 280 25 10 Zero 260 20 Low 9 240 Low 15 Medium D a 8 e t 220 Medium 10 200 7 High High 5WMm 6D 180 WLn mae 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. 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 HighMAdbnoae A D High Tl 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. 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 5WMm 10D WSna 140e 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. 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 5WMmU WL 4n 260 mae 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. 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. 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 OccurrencesSeason Spring Summer Autumn Winter Mean STD Mean STD Mean STD Mean STDSlopeDSW 0.163 -0.465** -0.779*** -0.328** -0.040 0.131 0.099 -0.040Albedo 0.002*** 0.0003 0.002*** 0.000 0.002*** 0.000 0.002*** 0.000DLW -0.021 0.137* 0.238*** -0.014 -0.008 0.018 -0.125 0.095 Note- * p<0.1, ** p<0.05, *** p<0.01ULW 0.399** 0.101 0.277** -0.010 0.593*** 0.096 0.512*** -0.099
  18. 18. Outline• Introduction• Data Sets and Methodology• Results• Conclusions
  19. 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. 20. Thanks

×