Global Climate Change: Drought Assessment + Impacts
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Global Climate Change: Drought Assessment + Impacts

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This presentation outlined the purposes, methods, data analyses, results and conclusions of four selected articles in remotely sensed regional and global drought assessments and impacts for global ...

This presentation outlined the purposes, methods, data analyses, results and conclusions of four selected articles in remotely sensed regional and global drought assessments and impacts for global environmental change. This presentation was developed and presented by Richard Maclean, doctoral student in Geography at Clark University and Jenkins Macedo, Master of Science candidate in Envrionmental Science and Policy at Clark University.

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Global Climate Change: Drought Assessment + Impacts Presentation Transcript

  • 1. Drought Assessment + Impacts Jenkins Macedo & Richard Maclean GEOG 292/392 Remote Sensing of Global Environmental Change November 11, 2013
  • 2. Drought “A drought is a period of unusually persistent dry weather that persists long enough to cause serious problems such as crop damage and/or water supply shortages. The severity of the drought depends upon the degree of moisture deficiency, the duration, and the size and location of the affected area.” - NOAA Aral Sea, Kazakhstan Source: Wikimedia Commons
  • 3. Effects of Drought Environmental Impacts decreases NPP impacts global food security Triggers ecosystems disturbances/biodiversity Weakens terrestrial carbon sinks Alters the climate system Social Impacts Increase anxiety associated with economic losses Public health concerns with low water supply Public safety issues from forest fires Reduced income Forced displacements Economic Impacts Decreases farmers’ net potential production/income High cost of irrigation Hydroelectric power generation will be impacted Food and water prices will increase Sequence of drought occurrence and impacts for commonly accepted drought types. All droughts originate from a deficiency of precipitation or meteorological drought but other types of drought and impacts cascade from this deficiency. (Source: National Drought Mitigation Center, University of Nebraska-Lincoln, U.S.A.)
  • 4. Measures of Drought Drought Impact Reporter Vegetation Drought Response Index
  • 5. Remote Sensing of Drought • VDRI: o • AVHRR NDVI for Percent Average Seasonal Greenness (PASG) and Start of Season Anomaly (SOSA) ESI: o • • GOES-East and West for AET vs PET estimate from the Two-Source Energy Balance algorithm o MODIS for albedo and LAI NDVI Greenness: o AVHRR NDVI GRACE
  • 6. Drought & Climate Change NASA | Projected U.S. Temperature Changes by 2100 - YouTube
  • 7. Drought & Climate Change NASA | Projected U.S. Precipitation Changes by 2100 - YouTube
  • 8. Remote Sensing of Drought ● EO-1 Hyperion • • • • • Launched Nov. 2000 Extended Nov. 2001 Goal of developing and validating instruments to compliment the LDCM stalks Landsat 7, runs from Terra Advanced Land Imager (ALI), Hyperion & Linear Etalon Imaging Spectrometer Array (LEISA) Atmospheric Corrector (LAC) source: NASA.gov
  • 9. “Drought-induced reduction in global terrestrial net primary production from 2000 through 2009.” Zhao & Running, 2010.
  • 10. Purpose to test the hypothesis whether warming climate of the past decade continued to increase Net Primary Production (NPP), or if different climate constraints were more important?
  • 11. Methods Remotely sensed data (MODIS Gross Primary Production) ❏ calculated global 1-km MODIS NPP from 2000 to 2009. ❏ used collection 5 (C5) 8-day composite 1-km FPAR and LAI from MODIS sensor. ❏ collected 4 (C4) MODIS 1-km land cover (MOD12Q1). ❏ collected 5 (C5) MODIS CMG 0.5 degree 8-day snow cover (MOD10C2). ❏ collected 5 (C5) MODIS 16-day 1-km NDVI/EVI (MOD12A2). MODIS GPP/NPP (MOD17) Algorithm ❏ calculated daily gross primary production (GPP) ❏ calculated annual growth respiration (Rg ) as a function of LAI Meteorological data ❏ National Center for Environmental Prediction (NCEP) ❏ Palmer Drought Severity Index (PDSI) 0.5 degree resolutions was used. ❏ combined water stress information from precipitation and evaporation were used.
  • 12. Methods (Cont) MODIS GPP/NPP (MOD17) Algorithm Zhao & Running, 2010
  • 13. Results ❏ Slight decrease for the past decade in global NPP (-0.55 Pg C). ❏ negative correlation of interannual variations in atmospheric CO2 growth rates (CC R= -0.89, p<0.0006). ❏ which suggests that global terrestrial NPP drives interannual CO2 growth rate. Zhao & Running, 2010
  • 14. Results (cont) Regional negative annual NPP anomalies were due to severe large-scale drought events. Zhao & Running, 2010
  • 15. Results (cont) ❏ Tropical NPP explain 93% (p<0.0001) of variations in global NPP. Zhao & Running, 2010 ❏ of which tropical NPP contributes 61% (0.0080) of the global NPP variations.
  • 16. Results (cont) ❏ Unlike rainforests growth in Islands of SEA, Africa and Amazon rainforests growth are constrained by limited water availability. Only Africa has an increasing trend NPP of 0.189 Pg C/decade due to decreased water vapor deficit. Zhao & Running, 2010
  • 17. Conclusion ❏ Warming climate increased NPP over high latitudes and high elevations, which constitute about 16% of global total NPP and 24% of vegetated land. ❏ There is a negative correlation of NPP with temperature in area between low latitude and altitude, which is due to warming from water stress and autotrophic respiration in the SH (r = 0.94, p< 0.0001). ❏ Globally, NPP is negatively correlated with air temperature over vegetated land (r = -64, p< 0.05).
  • 18. “A remotely sensed global terrestrial drought severity index.” Mu et al, 2013
  • 19. Purpose to use previous model of MODIS ET developed by Mu et al., 2007, 2009, 2011b to estimate ET and PET using MODIS data to calculate remotely sensed global drought severity index (DSI).
  • 20. Methods MOD16 ET & PET primary inputs to calculate global DSI. o terrestrial ecosystems at continuous 8-day, monthly, and annual steps at 1-km spatial resolution Daily Meteorological Reanalysis Data ○ 8-day remotely sensed vegetation dynamics were used as inputs to MODIS16 ET/PET o Penman-Monteith equation (P-M) to calculate global remotely sensed ET and integrates both P-M and Priestley-Taylor methods to estimate PET. o ET algorithm parameters include surface energy partitioning, environmental constraints on ET, wet and moist soil surfaces, and transpiration from canopy stomata. o used the atmospheric relative humidity to quantify proportion of wet soil and wet canopy features Ancillary Data > MODIS17 provides estimates of GPP and NPP •
  • 21. Methods (cont) Mu et al, 2013
  • 22. Results ❏ High frequency and intensity of drought impacts were detected in parts of Asia captured by the annual DSI data (2000-11) impacting 6.7 million hectares of rice production fields. ❏ Sharp decline in the water table was detected as a result of consecutive drought in South Asia and parts of Pakistan and Northwestern India (2000-03). ❏ Dought in Thailand in 2004 affected 2 million hectares of cropped areas and over 8 million people. ❏ The 2004 drought in SEA accounted for crop failures on millions of hectares, cost millions of dollars, and water shortages for irrigation and drinking. ❏ The data also indicates severe drought in Australia that are documented between (2000-11). Mu et al, 2013
  • 23. Results (cont) Results from the annual DSI (Figure 1) were evaluated with the global growing-season datasets PDSI (Figure 2): ❏ 0.43 area-weighted average coefficient correlation between 0.5 degree annual DSI and PDSI. ❏ DSI and PDSI correlations are the highest in areas where weather stations are dense (e.g. Southern US and portions of Eurasia). ❏ Correlation is Western Europe (0.41) and Western Russia (0.60). Mu et al, 2013
  • 24. Conclusion ❏ DSI seems to have nicely captured droughts that were documented over the last decade (2000-11). ❏ The last decade was the warmest in the twentyfirst century. ❏ Under warming drought persistency may increase ❏ The resulting correlation in Figure 6 is significant for each year in each experiment (35,594 0.5 degree). Mu et al, 2013
  • 25. Drought stress and carbon uptake in an Amazon forest measured with spaceborn imaging spectroscopy Asner, G.P., D. Nepstad, G. Cardinot, D. Ray, 2004
  • 26. Asner et al., 2004 ● Goals • • • source: NASA & Wikimedia Commons “Observe the location extent and severity of drought stress [in tropical forests]” Use imaging spectroscopy to overcome problems with field sampling and NVDI saturation. Relate drought stress to NPP
  • 27. Asner et al., 2004 ● Methods • • • • • Throughfall exclusion Time Domain Reflectometry LAI-2000 Differential GPS & IKONOS EO-1 Hyperion Imaging Spectroscopy
  • 28. Asner et al., 2004 ● Methods
  • 29. Asner et al., 2004 ● Results
  • 30. Asner et al., 2004 ● Results • • • SWAM performed better than NDVI or the simple ratio (SR). PRI and ARI demonstrated opposite patterns between the two imaging dates. Scenario 4 (fAPAR estimated from SWAM and ε estimated from PRI) demonstrated the greatest change between July & December.
  • 31. Regional aboveground live carbon losses due to drought-induced tree dieback in piñon-juniper ecosystems Huang, C., G.P. Asner, N.N. Barger, J.C. Neff, M.L. Floyd, 2010
  • 32. Huang et al., 2010 ● Goals • • Monitor landscape level changes in C storage associated with large scale mortality events. Quantify the change in piñon-juniper aboveground biomass (AGB) with remote sensing techniques.
  • 33. Huang et al., 2010 ● Methods
  • 34. Huang et al., 2010 ● Methods • • • • 12, 30 x 45 m plots: all live/dead RCD measured 2005 6 Landsat TM and ETM+ images; 1 image per year 2000-2005 PV determined through CLAS ΔPVmax = MAX[|PV00-PV01-05|]
  • 35. Huang et al., 2010 ● Results • • • Significant correlation b/w dead AGB and ΔPVmax Magnitude of AGB loss per plot tended to be related tree size class Die back C loss was 39 times wildfire and treatment C loss (4.6 Tg - 0.12 Tg C respectively)
  • 36. Bibliography Asner, G.P., Nepstad, D., Cardinot, G., and Ray, D. (2004). Drought Stress and Carbon Uptake in an Amazon Forest Measured with Spaceborne Imaging Spectroscopy. PNAS, Vol. 101, No. 16, pg. 6039-6044. Huang, C., Anser, G.P., Barger, N.N., Neff, J.C., and Floyd, M.L. (2010). Regional Aboveground Live Carbon Losses due to Drought-Induced Tree Dieback in Pinon-Juniper Ecosystems. Remote Sensing of Environment, Vol. 114, pg. 1471-1479. Mu, Q., Zhao, M., Kimball, J.S., McDowell, N.G., and Running, S.W. (2013). A Remotely Sensed Global Terrestrial Drought Severity Index. American Meteorological Society, pg. 83-98. Zhao, M. & Running, S.W. (2010). Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 through 2009. Science, Vol. 329, pg. 940-943.