PowerPoint Presentation
Quick Drought Response Index (QuickDRI) A Composite Index for
Monitoring Rapid-Onset Agricultural DroughtTsegaye Tadesse1,
A/ProfessorClimatologist/Remote Sensing Expert, UNL/NDMCBrian
Wardlow2, Mark Svoboda1, Jesslyn Brown3, Martha Anderson4, Chris
Hain5, Matt Rodell6, and David Mocko6
1National Drought Mitigation Center (NDMC), University of
Nebraska-Lincoln2Center for Advanced Land Management Information
Technologies (CALMIT), University of Nebraska-Lincoln3USGS Center
for Earth Resources Observation Science (EROS)4USDA Agricultural
Research Service (ARS)5Earth System Science Interdisciplinary
Center, University of Maryland6NASA Goddard Space Flight Center
(GSFC)
2015 U.S. Drought Monitor Forum Reno, NevadaApril 14-16,
2015
Quick Drought Response Index (QuickDRI)QuickDRI is a hybrid
drought index that monitors rapid, short-term changes in
agricultural drought conditions through the integration of: -
satellite-based observations of vegetation conditions-
evapotranspiration (ET) estimates from satellite- root-zone soil
moisture (satellite-estimated or modeled) - climate-based drought
index data - biophysical characteristics of the environment.
Goal: Use recently available remote sensing products that are
shorter-term indicators of drought-related environmental conditions
to develop complimentary, operational tool over the CONUS to VegDRI
that characterizes shorter-term, rapid-onset agricultural drought
conditions on the order of weeks to a month.
Vegetation Drought Response Index (VegDRI)VegDRI is a composite
drought index that integrates: - satellite-based observations of
vegetation conditions - climate-based drought index data -
biophysical characteristics of the environmentto produce 1-km
spatial resolution maps that depict drought-related vegetation
stress.
Timeline/Milestones:2002: USGS-funded proof-of-concept work2005:
Development of operational VegDRI for CONUS (USDA RMA and
NASA)2009: Completion of operational VegDRI tool and products
3
VegDRI ResultsUsed by the operational USDM map development
processTechnology transfer to other regions of the
world.CanadaCentral Europe (Czech Republic prototype)Greater Horn
of Africa/EthiopiaIndiaMexicoChinaSouth KoreaVegDRI has proven to
be a useful indicator of seasonal agricultural drought conditions,
but has limited ability to characterize rapid, short-term changes
in conditions:inherent lag of NDVI response to drought stress
longer time interval of climate data inputs
Canada-US VegDRIJuly 30, 2002
Czech Republic VegDRI PrototypeJune 22, 2014
Ethiopia/ Greater Horn of AfricaVegOut PrototypeAugust, 2011
Emerging Satellite-based Drought Monitoring ToolsOver the past
10+ years, there has been a rapid development of remote
sensing-based drought monitoring tools characterizing different
parts of the hydrologic cycle that influence drought
conditions.
ExamplesEvaporative Stress Index (ESI)GRACE Terrestrial Water
Storage (TWS) anomaliesNLDAS soil misture anomalies
QuickDRI MethodologyIndicators responding on shorter time scales
(1 to 2 months)
QuickDRI models developed from analysis of 12-year time-series
data of dynamic variables over CONUS for period of 2000 to
2012.Biophysical variables static over the entire history. QuickDRI
Training Database Development
Locations of the 2,427 weather stations used to extract the
training data for the QuickDRI models.Vegetation IndexETSoil
MoisturePrecip.Biophysical
Example of QuickDRI Model from CubistExample of QuickDRI models
output from regression tree analysis (Cubist) of historical input
variables.Dependent variableSPEI (1 month)
Independent variables:SPI (1 month)ESI anomaly (4 week)SVIVIC
soil moistureStart of Season Anomaly (SOSA)
Static independent variables:LULCIrrigation statussoil
AWCelevationecoregion
June 26, 2012
June 25, 2012USDMVegDRIFigure 1. Comparison of USDM and VegDRI
vs. QuickDRI (Model R_N) for end of June 2012.
QuickDRI Model Results for late-June 2012(Drought
Year)QuickDRI_June 30, 2012
vma_2012_w26sosa_2012svi_12wk26Figure 2. Comparing and
contrasting the input climate and satellite data with QuickDRI
(Model R_N) map at the end of June
2012.es08_2012w26spi1_2012_26
es04_2012w26
QuickDRI_June 30, 2012
awc_1kmdem_1kmus_econlcd06_1kmmirad07_1kmFigure 3. Contrasting
the input static biophysical parameters with QuickDRI (Model R_N)
map at the end of June 2012.
QuickDRI_June 30, 2012
July 29, 2012
July 23, 2012USDMVegDRIFigure 4. Comparison of USDM and VegDRI
vs. QuickDRI (Model R_N) for end of July 2012.
QuickDRI_July 30, 2012QuickDRI Model Results for late-July
2012(Drought Year)
vma_2012_w30sosa_2012svi_12wk30Figure 5. Comparing and
contrasting the input climate and satellite data with QuickDRI
(Model R_N) map at the end of July
2012.es04_2012w30es08_2012w30
spi1_2012_30
spi2_2012_30
QuickDRI_July 30, 2012
awc_1km
dem_1km
us_eco
nlcd06_1kmmirad07_1kmFigure 6. Contrasting the input static
biophysical parameters with QuickDRI (Model R_N) maps at the end of
July 2012.
QuickDRI_July 30, 2012
Aug. 28, 2012
Aug. 20, 2012USDMVegDRIFigure 7. Comparison of USDM and VegDRI
vs. QuickDRI (Model R_N) for end of August 2012.
QuickDRI Model Results for late-Aug. 2012(Drought
Year)QuickDRI_August 27, 2012
vma_2012_w34
sosa_2012svi_12wk34Figure 8. Comparing and contrasting the input
climate and satellite data with QuickDRI (Model N) map at the end
of August 2012.es08_2012w34spi1_2012_34spi2_2012_34
QuickDRI_Aug. 30, 2012es04_2012w3417
awc_1km
dem_1km
us_eco
nlcd06_1kmmirad07_1kmFigure 9. Contrasting the input static
biophysical parameters with QuickDRI (Model R_N)) map at the end of
August 2012.
QuickDRI_August 27, 2012
Example of Early-Onset Agricultural Drought Development Detected
by QuickDRI
Selection of Benchmarking Areas for QuickDRI Evaluation and
Validation
Benchmarking areas and time periods where earlier indicators of
drought would have been valuable to the USDM will be selected in
consultation with USDM authors and other drought experts.
Benchmark areas for the 2011 drought over U.S. Southern Great
PlainsSpatial Subset of QuickDRI inputs and results, VegDRI, and
USDM over Benchmark Area #4
Targeted Applications for QuickDRIUSDA Livestock Forage Disaster
Program (LFP)
The 2008 and 2014 Farm Bills mandated the USDA Farm Service
Agency (FSA) RFP use the USDM as the primary trigger to establish
county-level eligibility for financial assistance to cover
drought-related grazing losses. U.S. Drought Monitor (USDM)
Current state-of-the-art drought monitoring tool for the U.S.
that is used in federal and state programs and by the media to
communicate drought conditions.
Composite index that incorporates various types of data through
expert analysis by drought experts.
Challenge: Increasing demand to use the USDM for county- to
subcounty-level decisions requiring higher spatial resolution
information to characterize more spatially-detailed drought
patterns.
Current QuickDRI ActivitiesTesting a subset of QuickDRI models
for several flash drought events (e.g., 2011 and 2012), as well as
for other drought and non-drought periods.
Collecting drought-related information (e.g., drought impacts,
in situ observations, and expert insights) that characterizes the
development and impact of flash drought events for benchmark areas
to compare the QuickDRI model results.
Evaluating how QuickDRI results would improve the targeted
decision making processes (USDM and USDA Range Forage Program) for
retrospective flash drought events.
Engaging a network of key drought experts and decision makers to
evaluate QuickDRI results and define informational products and
other applications.
Developing operational QuickDRI processing system (at USGS EROS)
and data/product dissemination mechanisms.
Questions?