Drought Assessment +

Impacts

Jenkins Macedo & Richard Maclean

GEOG 292/392 Remote Sensing of Global Environmental Change

November 11, 2013

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

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.)

Measures of Drought

Drought Impact

Reporter

Vegetation Drought

Response Index

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

• Many more!

Drought & Climate Change

NASA | Projected U.S. Temperature Changes by 2100 - YouTube

Drought & Climate

Change

NASA | Projected U.S. Precipitation Changes by 2100 - YouTube

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

“Drought-induced reduction in global

terrestrial net primary production from 2000

through 2009.”

Zhao & Running, 2010.

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?

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.

Methods (Cont) MODIS GPP/NPP (MOD17) Algorithm

Zhao & Running, 2010

Results

Zhao & Running, 2010

❏ 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.

Results (cont)

Regional negative annual

NPP anomalies were due to

severe large-scale drought

events.

Zhao & Running, 2010

Results (cont)

Zhao & Running, 2010

❏ Tropical NPP explain 93% (p<0.0001) of variations in

global NPP.

❏ of which tropical NPP contributes 61% (0.0080) of the

global NPP variations.

Results (cont)

Zhao & Running, 2010

❏ 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.

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).

“A remotely sensed global terrestrial

drought severity index.”

Mu et al, 2013

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).

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

Methods (cont)

Mu et al, 2013

Results

Mu et al, 2013

❏ 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).

Results (cont)

Mu et al, 2013

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).

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 twenty-

first 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

Drought stress and carbon uptake in an

Amazon forest measured with spaceborn

imaging spectroscopy

Asner, G.P., D. Nepstad, G. Cardinot, D. Ray,

2004

Asner et al., 2004

● Goals

• “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

source: NASA & Wikimedia Commons

Asner et al., 2004

● Methods

• Throughfall exclusion

• Time Domain Reflectometry

• LAI-2000

• Differential GPS & IKONOS

• EO-1 Hyperion Imaging

Spectroscopy

Asner et al., 2004

● Methods

Asner et al., 2004

● Results

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.

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

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.

Huang et al., 2010

● Methods

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|]

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)

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.