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.

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

10. “Drought-induced reduction in global
terrestrial net primary production from 2000
through 2009.”
Zhao & Running, 2010.

11. 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?

12. 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.

13. Methods (Cont)
MODIS GPP/NPP (MOD17) Algorithm
Zhao & Running, 2010

14. 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

15. Results (cont)
Regional negative annual
NPP anomalies were due to
severe large-scale drought
events.
Zhao & Running, 2010

16. 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.

17. 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

18. 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).

19. “A remotely sensed global terrestrial
drought severity index.”
Mu et al, 2013

20. 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).

21. 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
•

22. Methods (cont)
Mu et al, 2013

23. 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

24. 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

25. 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

26. Drought stress and carbon uptake in an
Amazon forest measured with spaceborn
imaging spectroscopy
Asner, G.P., D. Nepstad, G. Cardinot, D. Ray,
2004

27. 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

28. Asner et al., 2004
● Methods
•
•
•
•
•
Throughfall exclusion
Time Domain Reflectometry
LAI-2000
Differential GPS & IKONOS
EO-1 Hyperion Imaging
Spectroscopy

29. Asner et al., 2004
● Methods

30. Asner et al., 2004
● Results

31. 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.

32. 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

33. 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.

34. Huang et al., 2010
● Methods

35. 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|]

36. 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)

37. 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.