Similar in methodology to VegDRI, QuickDRI is a hybrid drought index that incorporates multiple sources of input data informative of drought or rapidly drying conditions. The QuickDRI methodology includes many steps shown in Figure 1.
Steps A, B, and C form the model and data development phase. Steps D, E, and F comprise the weekly operational map production phase. Step A demonstrates the many input satellite, climate, and biophysical data layers used in QuickDRI. Step B outlines the extraction and assembly of a large historical database of all input data designed for QuickDRI model training. In Step C, the training data are input into a regression tree analysis to derive multiple models through time during overlapping 8-week periods. Each model can then be used to estimate drought conditions for a certain time of year based on the condition of the dependent variable, the Standardized Precipitation and Evaporation Index (SPEI). In Step D, all input geospatial data layers are assembled. Then in Step E, the operational QuickDRI system software is implemented where the QuickDRI model is applied to the geospatial input data. And finally, in Step F, the QuickDRI map is output in various image and graphic formats and distributed through project websites and map viewers.
Step A
Step A shows the many input satellite, modeled, climate, and biophysical data layers used in QuickDRI. Satellite data related to vegetation condition include the eMODIS Standardized Vegetation Index (SVI) and the Start of Season Anomaly (SOSA). Climate variables include the SPEI and the Standardized Precipitation Index (SPI). The Evaporative Stress Index (ESI) 1-month anomaly provides a relative estimate of water use via evapotranspiration at the land surface. A Soil Moisture Anomaly (SMA), representing the top 1-meter for a 1-month period, is simulated from the Variable Infiltration Capacity (VIC) land surface model (LSM). Four biophysical data layers provide the environmental and geographic setting for the climate and satellite vegetation conditions and are used as categorical data in the QuickDRI model. They include land use/land cover type, irrigated agricultural land, soil available water capacity, and elevation.
Two indicators are calculated from time-series eMODIS NDVI: the SVI and the SOSA. The SVI is an indicator of the probability of a certain weekly NDVI based on its history. It was selected for QuickDRI because the calculation standardizes NDVI deviations from a historical normal, allowing SVI anomalies to be directly comparable both spatially and temporally. The training SVI data were initially calculated based on historical Terra NDVI, but as we switch to Collection 6 MODIS, it will be based on Aqua NDVI from 2017 forward.
The SOSA is a measure of the temporal difference in the start of the growing season (SOS) for a given year compared to the historical average SOS for a location. SOS can be defined as the time when vegetation initiates growth (that is, photosynthetic activity) after winter as observed from satellite observations. A negative SOSA indicates that the SOS for a specific year is earlier than the average date and a positive SOSA appears when green-up occurs later than normal. The SOSA is used to quantify the variability in timing of vegetation phenology that may occur from year-to-year.
The ESI consists of standardized anomalies (z-scores) representing the actual-to-potential ET ratio (fPET), as determined using the Atmosphere-Land Exchange Inverse (ALEXI) thermal-infrared remote sensing surface energy balance algorithm. Time-differential thermal information provided by geostationary satellites (GOES) is used to reduce model sensitivity to errors in absolute surface temperature retrievals. The ESI represents an independent drought tool that focuses on water use rather than water supply (and requires no precipitation data) and has been shown to be well correlated with standard drought indicators. The TIR band incorporated in ALEXI is sensitive to canopy temperature increases associated with early vegetation stress, which is sensitive to a rapidly changing dryness signature.
Soil moisture data from a LSM is incorporated into QuickDRI. The VIC Monthly Anomaly (VMA) is the monthly anomaly of soil moisture in the top 1-meter in the North American Land Data Assimilation System Phase 2 (NLDAS-2) VIC LSM. NLDAS-2 is a collaboration project between NOAA and NASA. NASA provides a customized VIC monthly anomaly product specifically for QuickDRI.
Two climate variables are used in QuickDRI to indicate dry or wet conditions in the meteorological sense: the SPEI and the SPI. A 1-month SPEI is the dependent variable in the QuickDRI models. SPEI characterizes dryness with a metric describing the difference between precipitation and potential evapotranspiration, computed using both precipitation and temperature data. The SPEI can be calculated at multiple time steps. The 1-month SPEI is used in QuickDRI models to reflect the very recent climatic conditions that influence short-term vegetation health. The modeled QuickDRI data output is scaled on a continuous dryness-wetness scale using the SPEI scaling and represents the relative landscape dryness compared to longer-term historical conditions.
The SPI was designed to quantify precipitation departures from normal over multiple time intervals (for example, 1- to 12-month periods). This method is based on fitting a long-term precipitation record at a given location over a specified interval to a probability distribution, which is then transformed into a gamma distribution such that the mean SPI value for that location and time period is zero. SPI values are positive if the precipitation over a specific time period is higher than the historical average precipitation over that same period and negative if precipitation is less than the historical mean.
The biophysical data layers help to differentiate drought stress by giving information on environmental and geographic context. The land use/land cover (LULC) type represents broad categories of vegetation with various seasonal cycles and climate-vegetation responses. An irrigated agriculture variable differentiates irrigated lands, which are less susceptible to drought stress because of targeted water applications, from rain-fed agriculture. The root-zone available water capacity (AWC) variable represents the potential of the soil to hold moisture that is available to plants, which in turn influences the sensitivity of vegetation to drought stress. Elevation data help to distinguish regions where typical seasonal development of vegetation may differ.
Step B
In Step B, all model input data were extracted and assembled in a training database for use in training QuickDRI models. Historical, point-based SPEI and SPI data records at over 2,000 weather stations first populated the QuickDRI training database. Training data were also extracted from the satellite and biophysical datasets (that are in the form of image grids) at the location of each weather station. A 3-by-3 pixel window centered on each station location was used to calculate the average value across all pixels in the window for continuous variables (for example, SVI or ESI) and the majority value for categorical variables (for example, LULC). Data values associated with both urban and water locations were excluded. The biophysical variable values calculated for each station were held static across the historical period. Historical records of all stations in the database were then temporally subset into 52 weekly periods across the calendar year to train a series of separate, weekly QuickDRI models.
Step C
Step C encompasses QuickDRI model development. For each period, a commercial classification and regression tree (CART) algorithm called Cubist was used to analyze the historical data in the training database and generate a rule-based, piecewise linear regression QuickDRI model. Each model incorporates historical data for the ‘dynamic’ climate and satellite-based variables while holding the biophysical variables constant over a 4-week window that includes the current period and extends to the 3 prior weeks. As discussed earlier, the SPEI serves as the dependent variable in these empirical-based models, providing a gradient of dryness to wetness at the land surface based on the analysis of the other biophysical, climate, and satellite variables. Fifty-two, period-specific QuickDRI models were developed through the year.
Each QuickDRI regression tree model consists of an unordered set of rules with each rule having the syntax ‘if x conditions are met then use the associated linear regression model’ to calculate a QuickDRI value at each pixel. The following is an example of one of many rules in a single model:
Rule1:
if: LandCover in {Grassland, Pasture/Hay, Row Crops}
Percent irrigated agriculture ≤ 0.20
1-month SPI ≤ -1.52
AWC ≤ 4.5
SPI ≤ -0.96
SVI ≤ 57
then: QuickDRI = -3.5 + 0.6 SVI + 1.48 SPI – 0.14 AWC
Step D
In Step D, the gridded input data layers listed in Step A are assembled together for operational map production. Step D also consists of near real time processing of the dynamic satellite and climate data input grids. The satellite source (MODIS) data are processed through a system called eMODIS (expedited MODIS) that provides imagery less than 24 hours later than the last observation in a weekly composite. Additional processing steps such as temporal smoothing and mathematical calculations are necessary to create the SVI and SOSA variables for input into the step E. The gridded climate data (SPI) are calculated from daily precipitation observations acquired from the Applied Climate Information System (ACIS; https://www.rcc-acis.org)) and then an interpolated continuous grid is estimated using a method called inverse weighted difference. The VMA data are acquired from NASA and the ESI data are acquired from NASA SPoRT. Each data layer is reformatted and reprojected to snap to a common spatial domain and grid cell size (1000 m).
Step E
In Step E, software code called MapCubist is used to apply the model rules for each specific period to data in a geospatial format. This is the main step for creating QuickDRI map output. In this step, model rules that were developed in Step C are applied to each grid cell in the QuickDRI spatial domain. In other words, if the data associated with a grid cell meet the threshold criteria for the continuous variables (e.g., SVI, SPI, ESI) and are represented by one of three land cover types (Grassland, Pasture/Hay, Row Crops), then the linear regression equation is used to calculate a QuickDRI value. Each independent input variable is incorporated into a subset of the multiple rules and regression equations that are collectively utilized to calculate the final QuickDRI value for each grid cell.
Step F
Finally, Step F consists of distributing data thttps://vegdri.cr.usgs.gov/viewer/https://vegdri.cr.usgs.gov/viewer/o collaborators and other users, generating a period-specific map pdf, and loading the data into a database for viewing in project map viewers. The USGS viewer (https://vegdri.cr.usgs.gov/viewer/) offers interactive viewing and web services. Operational QuickDRI data is provided from Earth Explorer. Static national, regional, and state maps and time series anima
Similar in methodology to VegDRI, QuickDRI is a hybrid drought index that incorporates multiple sources of input data informative of drought or rapidly drying conditions. The QuickDRI methodology includes many steps shown in Figure 1.
Steps A, B, and C form the model and data development phase. Steps D, E, and F comprise the weekly operational map production phase. Step A demonstrates the many input satellite, climate, and biophysical data layers used in QuickDRI. Step B outlines the extraction and assembly of a large historical database of all input data designed for QuickDRI model training. In Step C, the training data are input into a regression tree analysis to derive multiple models through time during overlapping 8-week periods. Each model can then be used to estimate drought conditions for a certain time of year based on the condition of the dependent variable, the Standardized Precipitation and Evaporation Index (SPEI). In Step D, all input geospatial data layers are assembled. Then in Step E, the operational QuickDRI system software is implemented where the QuickDRI model is applied to the geospatial input data. And finally, in Step F, the QuickDRI map is output in various image and graphic formats and distributed through project websites and map viewers.
Step A
Step A shows the many input satellite, modeled, climate, and biophysical data layers used in QuickDRI. Satellite data related to vegetation condition include the eMODIS Standardized Vegetation Index (SVI) and the Start of Season Anomaly (SOSA). Climate variables include the SPEI and the Standardized Precipitation Index (SPI). The Evaporative Stress Index (ESI) 1-month anomaly provides a relative estimate of water use via evapotranspiration at the land surface. A Soil Moisture Anomaly (SMA), representing the top 1-meter for a 1-month period, is simulated from the Variable Infiltration Capacity (VIC) land surface model (LSM). Four biophysical data layers provide the environmental and geographic setting for the climate and satellite vegetation conditions and are used as categorical data in the QuickDRI model. They include land use/land cover type, irrigated agricultural land, soil available water capacity, and elevation.
Two indicators are calculated from time-series eMODIS NDVI: the SVI and the SOSA. The SVI is an indicator of the probability of a certain weekly NDVI based on its history. It was selected for QuickDRI because the calculation standardizes NDVI deviations from a historical normal, allowing SVI anomalies to be directly comparable both spatially and temporally. The training SVI data were initially calculated based on historical Terra NDVI, but as we switch to Collection 6 MODIS, it will be based on Aqua NDVI from 2017 forward.
The SOSA is a measure of the temporal difference in the start of the growing season (SOS) for a given year compared to the historical average SOS for a location. SOS can be defined as the time when vegetation initiates growth (that is, photosynthetic activity) after winter as observed from satellite observations. A negative SOSA indicates that the SOS for a specific year is earlier than the average date and a positive SOSA appears when green-up occurs later than normal. The SOSA is used to quantify the variability in timing of vegetation phenology that may occur from year-to-year.
The ESI consists of standardized anomalies (z-scores) representing the actual-to-potential ET ratio (fPET), as determined using the Atmosphere-Land Exchange Inverse (ALEXI) thermal-infrared remote sensing surface energy balance algorithm. Time-differential thermal information provided by geostationary satellites (GOES) is used to reduce model sensitivity to errors in absolute surface temperature retrievals. The ESI represents an independent drought tool that focuses on water use rather than water supply (and requires no precipitation data) and has been shown to be well correlated with standard drought indicators. The TIR band incorporated in ALEXI is sensitive to canopy temperature increases associated with early vegetation stress, which is sensitive to a rapidly changing dryness signature.
Soil moisture data from a LSM is incorporated into QuickDRI. The VIC Monthly Anomaly (VMA) is the monthly anomaly of soil moisture in the top 1-meter in the North American Land Data Assimilation System Phase 2 (NLDAS-2) VIC LSM. NLDAS-2 is a collaboration project between NOAA and NASA. NASA provides a customized VIC monthly anomaly product specifically for QuickDRI.
Two climate variables are used in QuickDRI to indicate dry or wet conditions in the meteorological sense: the SPEI and the SPI. A 1-month SPEI is the dependent variable in the QuickDRI models. SPEI characterizes dryness with a metric describing the difference between precipitation and potential evapotranspiration, computed using both precipitation and temperature data. The SPEI can be calculated at multiple time steps. The 1-month SPEI is used in QuickDRI models to reflect the very recent climatic conditions that influence short-term vegetation health. The modeled QuickDRI data output is scaled on a continuous dryness-wetness scale using the SPEI scaling and represents the relative landscape dryness compared to longer-term historical conditions.
The SPI was designed to quantify precipitation departures from normal over multiple time intervals (for example, 1- to 12-month periods). This method is based on fitting a long-term precipitation record at a given location over a specified interval to a probability distribution, which is then transformed into a gamma distribution such that the mean SPI value for that location and time period is zero. SPI values are positive if the precipitation over a specific time period is higher than the historical average precipitation over that same period and negative if precipitation is less than the historical mean.
The biophysical data layers help to differentiate drought stress by giving information on environmental and geographic context. The land use/land cover (LULC) type represents broad categories of vegetation with various seasonal cycles and climate-vegetation responses. An irrigated agriculture variable differentiates irrigated lands, which are less susceptible to drought stress because of targeted water applications, from rain-fed agriculture. The root-zone available water capacity (AWC) variable represents the potential of the soil to hold moisture that is available to plants, which in turn influences the sensitivity of vegetation to drought stress. Elevation data help to distinguish regions where typical seasonal development of vegetation may differ.
Step B
In Step B, all model input data were extracted and assembled in a training database for use in training QuickDRI models. Historical, point-based SPEI and SPI data records at over 2,000 weather stations first populated the QuickDRI training database. Training data were also extracted from the satellite and biophysical datasets (that are in the form of image grids) at the location of each weather station. A 3-by-3 pixel window centered on each station location was used to calculate the average value across all pixels in the window for continuous variables (for example, SVI or ESI) and the majority value for categorical variables (for example, LULC). Data values associated with both urban and water locations were excluded. The biophysical variable values calculated for each station were held static across the historical period. Historical records of all stations in the database were then temporally subset into 52 weekly periods across the calendar year to train a series of separate, weekly QuickDRI models.
Step C
Step C encompasses QuickDRI model development. For each period, a commercial classification and regression tree (CART) algorithm called Cubist was used to analyze the historical data in the training database and generate a rule-based, piecewise linear regression QuickDRI model. Each model incorporates historical data for the ‘dynamic’ climate and satellite-based variables while holding the biophysical variables constant over a 4-week window that includes the current period and extends to the 3 prior weeks. As discussed earlier, the SPEI serves as the dependent variable in these empirical-based models, providing a gradient of dryness to wetness at the land surface based on the analysis of the other biophysical, climate, and satellite variables. Fifty-two, period-specific QuickDRI models were developed through the year.
Each QuickDRI regression tree model consists of an unordered set of rules with each rule having the syntax ‘if x conditions are met then use the associated linear regression model’ to calculate a QuickDRI value at each pixel. The following is an example of one of many rules in a single model:
Rule1:
if: LandCover in {Grassland, Pasture/Hay, Row Crops}
Percent irrigated agriculture ≤ 0.20
1-month SPI ≤ -1.52
AWC ≤ 4.5
SPI ≤ -0.96
SVI ≤ 57
then: QuickDRI = -3.5 + 0.6 SVI + 1.48 SPI – 0.14 AWC
Step D
In Step D, the gridded input data layers listed in Step A are assembled together for operational map production. Step D also consists of near real time processing of the dynamic satellite and climate data input grids. The satellite source (MODIS) data are processed through a system called eMODIS (expedited MODIS) that provides imagery less than 24 hours later than the last observation in a weekly composite. Additional processing steps such as temporal smoothing and mathematical calculations are necessary to create the SVI and SOSA variables for input into the step E. The gridded climate data (SPI) are calculated from daily precipitation observations acquired from the Applied Climate Information System (ACIS; https://www.rcc-acis.org)) and then an interpolated continuous grid is estimated using a method called inverse weighted difference. The VMA data are acquired from NASA and the ESI data are acquired from NASA SPoRT. Each data layer is reformatted and reprojected to snap to a common spatial domain and grid cell size (1000 m).
Step E
In Step E, software code called MapCubist is used to apply the model rules for each specific period to data in a geospatial format. This is the main step for creating QuickDRI map output. In this step, model rules that were developed in Step C are applied to each grid cell in the QuickDRI spatial domain. In other words, if the data associated with a grid cell meet the threshold criteria for the continuous variables (e.g., SVI, SPI, ESI) and are represented by one of three land cover types (Grassland, Pasture/Hay, Row Crops), then the linear regression equation is used to calculate a QuickDRI value. Each independent input variable is incorporated into a subset of the multiple rules and regression equations that are collectively utilized to calculate the final QuickDRI value for each grid cell.
Step F
Finally, Step F consists of distributing data thttps://vegdri.cr.usgs.gov/viewer/https://vegdri.cr.usgs.gov/viewer/o collaborators and other users, generating a period-specific map pdf, and loading the data into a database for viewing in project map viewers. The USGS viewer (https://vegdri.cr.usgs.gov/viewer/) offers interactive viewing and web services. Operational QuickDRI data is provided from Earth Explorer. Static national, regional, and state maps and time series anima