A joint NASA-supported project team involving the National Drought Mitigation Center (NDMC) and Center for Advanced Land Management Information Technologies (CALMIT) at the University of Nebraska-Lincoln, U.S. Geological Survey Earth Resources Observation and Science (USGS EROS) Center, U.S. Department of Agriculture (USDA) Agricultural Research Service (USDA ARS), and NASA Goddard Space Flight Center (GSFC) has developed a drought monitoring tool called the Quick Drought Response Index (QuickDRI), which is tailored to detect short-term changes and rapid intensification of drought conditions through the integration of satellite, climate, and biophysical information.
QuickDRI is a geospatial tool that characterizes the intensification of short-term drought condition patterns on a weekly basis across the continental United States (CONUS) at a 1-km gridded spatial resolution. The primary goal of QuickDRI is to serve as an "alarm" indicator of rapidly emerging events such as "flash drought" that manifest rapidly on the order of a few days to weeks, are often difficult to detect using traditional drought indicators, and can having devastating negative impacts on agriculture and natural resources.
QuickDRI information is designed to improve the spatial detail and sensitivity of the U.S. Drought Monitor, which is used as a trigger by several federal and state drought assistance programs (e.g., USDA Range and Forage Program), as well as the drought monitoring and planning activities of the National Integrated Drought Information System (NIDIS) regional drought early warning systems, state drought task forces, and other agricultural and natural resource drought management and response activities.
What does QuickDRI show?
QuickDRI represents a drought "alarm" indicator of emerging or rapidly changing drought conditions that can support drought severity assessment in combination with traditional, longer-term and/or application-specific drought indicators.
What drought categories correspond with the legend?
QuickDRI only presents the relative intensity of conditions on a gradient rather than individual drought categories. The QuickDRI gradient spans a range from stress intensification to normal to improvement. On one end, intensification of drought conditions may indicate where drought stress may be emerging or rapidly changing. The other end of the gradient shows where conditions are improving or becoming less severe or drought is not a problem. At the center of the range stable conditions are occurring, where there is no marked drought intensification or improvement, which would be expected during time periods when near-normal precipitation is received and conditions are not excessively dry or wet.
What time period does QuickDRI consider when determining "normal" conditions?
The historical period of record for QuickDRI model development is 2000-2012.
How is QuickDRI different from VegDRI?
QuickDRI looks at vegetative drought indicators over a short (1-month) timeframe to show the degree of environmental stress, whereas VegDRI looks at vegetation condition over a longer (seasonal 6- to 9-month) period.
In addition, some of the underlying datasets that go into the QuickDRI and VegDRI models are different. QuickDRI ingests input data that characterize general vegetation conditions (MODIS-based Standardized Vegetation Index), shorter-term hydrologic information (evapotranspiration: 1-month Evaporative Stress Index anomaly and soil moisture: VIC monthly anomaly), and short-term climate information (1-month Standardized Precipitation Index). VegDRI ingests input data representing longer-term climate (36-week Standardized Precipitation Index) and vegetation condition (AVHRR-based Percent Average Seasonal Greenness) information.
And the time period used to develop the models is different. QuickDRI is trained based on data from 2000-2012, VegDRI is trained based on data from 1989-2008.
What datasets went into developing the QuickDRI model?
The QuickDRI model uses 2 vegetation, 2 hydrologic (evapotranspiration and soil moisture), 1 climate, and 4 static biophysical variables to estimate drought intensification or improvement conditions scaled using the Standardized Precipitation Evaporation Index (SPEI) dryness intensity characterization scheme based on weekly models developed using a regression tree analysis method applied to the historical data record of all input variables.
Specifically, the following 9 input variables are used:
Vegetation Condition Variables
The MODIS-based Standardized Vegetation Index (SVI) and Start of Season Anomaly (SOSA), produced by USGS EROS.
The 4-week Evaporative Stress Index Anomaly (ESI), produced by USDA ARS.
The VIC Monthly Anomaly (VMA), which is the monthly anomaly of soil moisture in the top 1-meter in the North American Land Data Assimilation System Phase 2 (NLDAS-2) Variable Infiltration Capacity
(VIC) land surface model (LSM). NLDAS is a collaboration project between NOAA/NCEP and NASA/GSFC. NLDAS Phase 2 runs operationally at NOAA/NCEP, and NASA/GSFC provides a customized VIC monthly anomaly product specifically for QuickDRI.
The 1-month Standardized Precipitation Index (SPI), produced with cooperation of the High Plains Regional Climate Center (HPRCC).
Root-zone Available Water Storage (AWS or AWC) (gSSURGO 2016 produced by USDA NRCS)
Digital Elevation Model (GMTED2010 mn75 produced by USGS EROS)
MODIS-based Irrigated Agriculture Dataset (MIrAD-US 2007 produced by the USGS EROS)
National Land Cover Database (NLCD 2006 produced by USGS EROS)
How do I interpret the QuickDRI map?
We recommend QuickDRI information to be interpreted within the context of the current drought conditions because QuickDRI is designed to be sensitive to short-term changes in the current conditions that highlight areas of possible intensification or improvement within the last few weeks to a month.For example, a non-drought location may show an intense QuickDRI signal that may indicate the emergence of drought for that area. Alternatively, a location under prolonged extreme drought with very little change in dryness conditions may appear "normal" or having no intensification in the QuickDRI, since those conditions are fairly stable.
What does "NoData" mean?
"NoData" in the map means that one or more of the input layers has no data for that week at a pixel location, so a QuickDRI value cannot be calculated. Typically the layers that might show NoData are the satellite and modeled variables such as ESI and VMA. A primary example of why an input such as ESI may have areas of no data across the continental United States is because of persistent cloud cover that prevent land surface temperatures (a primary input into the ESI calculation) from being observed from satellite and the corresponding ESI to be calculated. Typically, only a very small proportion of the continental United States will contain no data for a given week.
What does "Out Of Season" mean?
When the QuickDRI map shows "Out of Season" it means that vegetation in those areas was dormant that week for a 20-year historical record of satellite NDVI data (AVHRR 1989 through 2009) was used to identify "out of season" areas. The "Out of Season" class is only applied to the QuickDRI maps during the general non-growing season for most of the continental United States, which has been defined as the winter period spanning from early December through the end of February. An out of season mask is implemented for time periods and areas where the QuickDRI may not be meaningful because of dormant vegetation, snow cover and frozen soils, which could lead to a false signal that is not associated with a change in drought condition.
How does the QuickDRI map fit into the suite of existing drought monitoring tools by a USDM author or other drought decision makers?
In areas showing an intense signal, this may indicate that drought should be introduced in that location or the severity category changed. In other words, a non-drought location may have an intense QuickDRI signal prompting the USDM author to consider introducing drought for that area. On the flip side, a location under prolonged extreme drought with very little change in dryness conditions may show no intensification in the QuickDRI since those conditions are fairly stable.