Palmer Drought Severity Index (PDSI)

One of the first and most highly used drought indices is the Palmer drought severity index (PDSI; Palmer 1965), which is based on a simplified soil water balance and is a measure of the departure of average soil moisture conditions. Instead of the typical parameterization of PDSI using Thornthwaite temperature only based potential ET, we utilize more physically based Penman-Monteith reference ET, which is a function of solar radiation, temperature, humidity and windspeed. A PDSI value between -.5 and 0.5 represents near normal soil moisture conditions, with positive/negative values representing wet/dry conditions. The magnitude of PDSI gives an indication as to the severity of the departure from normal conditions. PDSI> 4 represents very wet conditions, while PDSI<-4 represents an extreme drought.

Palmer, W. C., 1965, Meteorological drought. U.S. Department of Commerce Weather Bureau Research Paper 45, 58 pp.

Reference Evapotranspiration (ETo)

Reference evapotranspiration represents ET from a well-watered idealized reference surface and is a function of solar radiation, air temperature, humidity, and windspeed. Reference ET is often considered an upper limit on actual ET. Actual ET is usually estimated by scaling reference ET downward based on estimates of the fraction of reference ET (EToF) based on remotely sensed or simulated soil and vegetation moisture conditions, and vegetation type and phenology. ETo estimates in CLIM Engine are derived from the Penman-Monteith model (ASCE-EWRI, 2005; Allen et al., 1998) under ambient meteorological and radiative conditions derived from meteorological reanalyses, gridMET (Abatzoglou, 2013). ETo assumes a reference surface of short grass (0.12 m high), while ETr assumes a reference surface of tall grass (or alfalfa).

Abatzoglou, J. T., 2013, Development of gridded surface meteorological data for ecological applications and modeling. Int. J. Climatol., 33, 121–131

Allen, R.G., L.S. Pereira, D. Raes, and M. Smith, 1998. Crop Evapotranspiration: Guidelines for Computing Crop Requirements. Irrigation and Drainage Paper No. 56, United Nations Food and Agricultural Organization (FAO), Rome, Italy.

ASCE-EWRI, 2005. The ASCE Standardized Reference Evapotranspiration Equation. ASCE-EWRI Standardization of Reference Evapotranspiration Task Committee Report. American Society of Civil Engineers, Reston, Virginia.

Evaporative Demand Drought Index (EDDI)

Standardization of ETo similar to SPI has shown to be useful for drought monitoring and analysis of atmospheric land surface coupling and feedbacks. One example is the Evaporative Demand Drought Index (EDDI; Hobbins et al., 2016; McEvoy et al., 2016), which is showing promise as a leading indicator of agricultural drought at time-frames pertaining to both flash (i.e., fast-developing) and extended droughts. For time periods of interest, if ETo is higher than normal it is usually indicates dry and hot conditions, whereas lower than normal ETo usually indicates moist and cool conditions. ETo responds positively to both flash droughts and sustained droughts. ETo rises in response to drought via the complementary relationship, where drought typically increases air temperature and lowers humidity levels due to the lack of precipitation and subsequent lack of actual ET. ET based drought metrics complement other in drought metrics.

Climate Engine computes EDDI (and SPEI) using a non-parametric standardized probability based method. Plotting positions are used to obtain probabilities and then converted to EDDI and SPEI values using an inverse-normal distribution.

Hobbins, M., A. Wood, D.J. McEvoy, J. Huntington, and C. Morton, James Verdin, Martha Anderson, and Christopher Hain, 2016: The Evaporative Demand Drought Index: Part I – Linking Drought Evolution to Variations in Evaporative Demand. Journal of Hydrometeorology. 17, 1745-1761, doi: 10.1175/JHM-D-15-0121.1

McEvoy, D.J., J.L. Huntington, M. Hobbins, A. Wood, and C. Morton, James Verdin, Martha Anderson, and Christopher Hain, 2016: The Evaporative Demand Drought Index: Part II – CONUS-wide Assessment Against Common Drought Indicators. Journal of Hydrometeorology. 17, 1763-1779, doi: 10.1175/JHM-D-15-0122.1.

Normalized Difference Vegetation Index (NDVI)

The shortage of water available to vegetation in a drought limits the growth and productivity of vegetation. Chlorophyll, which is the pigment in plant leaves, strongly absorbs red light (from 0.6 to 0.7 µm) for photosynthesis. The cell structure of the leaves strongly reflects near-infrared light (from 0.7 to 1.1 µm). The magnitude of absorption and reflection of red and near-infrared light is strongly a function of leaf area and vegetation vigor. Satellite imagery has long been used to evaluate differences in plant reflectance and to determine their spatial distribution. A common satellite image index of vegetation vigor is the Normalized Difference Vegetation Index (NDVI) (Huete et al., 1985; Jackson and Huete, 1991), which ranges from -1 to 1, with ~ 0.5 to 1 representing high vegetation vigor. Effects of drought can be visualized through computing time series and spatial anomalies of NDVI.

Huete, A. R., Jackson, R. D., and Post, D. F. (1985), Spectral response of a plant canopy with different soil backgrounds, Remote Sens. Environ. 17:37-53.

Jackson, R. D., and Huete, A. R. (1991), Interpreting vegetation indices, J. Preventative Vet. Med. 11:185-200.

Enhanced Vegetation Index (EVI)

The Enhanced Vegetation Index is a common vegetation index that was developed to optimize the vegetation signal and improve the sensitivity to improve vegetation monitoring in through a de-coupling of the canopy background signal and atmosphere influences (Liu and Huete, 1995). Like NDVI drought can be visualized through computing time series and spatial anomalies of EVI.

Liu, H. Q., & Huete, A. (1995). A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33, 457−465

A. Huete, K. Didan, T. Miura, E. P. Rodriguez, X. Gao, L. G. Ferreira. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83(2002) 195-213.

Energy Release Component(ERC)

The Energy Release Component (ERC) is an index which is related to the potential heat released at the flaming front of a fire measured in units of available energy per square foot. This index can be converted to the available energy per unit area within the flaming front at the head of a fire (in units of BTU/sq. ft) by multiplying the index by a factor of 25. ERC is a commonly used fire danger index by fire management in the United States for tracking the fire season and serves as a guide for fire suppression and fuel treatment operations. The ERC is one of the outputs of the National Fire Danger Rating System (NFDRS, Bradshaw et al., 1983) that represents the cumulative drying effect of daily meteorology on both live fuel moistures and 100 and 1000-hour dead fuel moistures and is considered a build-up index as it’s values are carried over from day to day. As such, ERC generally tracks within season moisture specific to fuels that can potentially carry fire and thus represents concurrent moisture stress rather than longer-time drought stress like PDSI. ERC is most sensitive to variations in relative humidity and precipitation, but does not incorporate the influence of wind speed. We use a common fuel model (model G, or dense confer stand with heavy litter accumulation) in ERC calculations for consistency across space as well as its frequent use by regional fire management. ERC values are best viewed as either percentiles or anomalies from the historic value for individual locations as a value of ERC=60 can represents very different relative conditions from place to place.

Bradshaw, L.S., R.E. Burgan, J.D. Cohen, and J.E. Deeming. 1983. The 1978 National Fire Danger Rating System: Technical Documentation. USDA Forest Service; Intermountain Forest and Range Experiment Station, General Technical Report INT-169, Ogden, Utah. 44 pp.

Normalized Difference Water Index (NDWI)

The normalized difference water index can be utilized for evaluating vegetation liquid water contents or water inundated areas (Gao, 1996). NDWI is useful for evaluating reflectance from vegetation canopies that have similar scattering properties, but slightly different liquid water absorption due to canopy water content. As a result, NDWI is sensitive to changes in liquid water content of vegetation canopies and open water areas. The common range of NDWI for green vegetation is -0.1 to 0.4 with 0.4 indicating high vegetation water content.

Climate Engine provides a couple different versions of NDWI, utilizing normalized differences of different bands, which can be more useful for different applications:

NDWI (NIR/SWIR1) = (NIR-SWIR1)/(NIR+SWIR1)

NDWI (GREEN/NIR) = (GREEN-NIR)/(GREEN+NIR)

NDWI (GREEN/SWIR1) = (GREEN-SWIR1)/(GREEN+SWIR1)

NDWI (GREEN/SWIR2) = (GREEN-SWIR2)/(GREEN+SWIR2)

NDWI (SWIR1/GREEN) = (SWIR1-GREEN)/(SWIR1+GREEN)

where NIR = near infra-red band, GREEN = green band, SWIR1 = 1.55 - 1.75 micrometer band, SWIR2 =2.08 - 2.35 micrometer band.

Gao, B.C., 1996, NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.

Normalized Difference Snow Index (NDSI)

Knowledge on the snow extent, depth, and water content is important for water resource management, planning, and forecasting. Monitoring of the snow extent using satellite imagery is useful for understanding snow depletion and recession rates, evaluating snow extent relative to long term average conditions, and is a useful drought metric. Snow cover area is often estimated using the Normalized Difference Snow Index (NDSI) (Crane and Anderson, 1984; Dozier, 1984). Snow is highly reflective in the visible part of the electromagnetic spectrum and highly absorptive in the near-infrared or short-wave infrared band of the spectrum. Reflectance of clouds is usually high in both the visible and infrared bands, allowing for separation of snow and clouds. NDSI usually ranges from -5 to 1, with ~0.5 to 1 representing snow cover. Time series and anomaly maps of NDSI clearly show changes in snow cover for a region. Positive anomalies indicate increased snow cover, whereas negative anomalies indicate decreased snow cover relative to average conditions.

Crane, R. G., and Anderson, M. R., 1984, Satellite discrimination of snow/cloud surfaces. International Journal of Remote Sensing, 5(1), 213 ­223.

Dozier, J., 1984, Spectgal signature of alpine snow cover from the Landsat Thematic Mapper. Remote Sensing of Environment, 28.9-22.

Short & Long Term Drought Blends

A blend of different drought indices for short and long term time scales can be useful to understand short and long term drought for a region. The experimental short and long term objective blends produced by the Climate Prediction Center (CPC) are an example of blends being produced as a weighting of percentiles for different drought metrics from past data where the weights are based on expert judgment from drought experts. The metrics that go into the experimental CPC blend are:

Palmer-Z Index (Z)

Palmer Drought Severity Index (PDSI)

Standardized Precipitation Index (SPI)( 30-day, 90-day, 180-day, 1-year, 2-year and 5-year)

Palmer Hydrological Drought Index (PDHI)

Soil Moisture from NOAH (SM-NOAH)

In Climate Engine, we are providing blends that are instead a weighting of the standardized indices coming from drought indices calculated from the gridMET data product (also in Climate Engine and based on 1981-2016). The weightings are the same as the experimental CPC blend with some differences:

the weightings for SM-NOAH (soil moisture from NOAH) and PHDI (Palmer Hydrological Drought Index) are added in with the weights for PDSI. Only PDSI is used in the blend construction to represent soil moisture.

in the construction, the Palmer drought indices (i.e Z and PDSI) will be divided by 2 to put them on roughly the same scale as the standardized indices

the colors and bins used to visualize the drought blends will be the US Drought monitor colors with non-linear standardized index bins.

The precise details of the construction of the blends is:

Short-term Blend= 0.2 *(PDSI/2) + 0.2 * SPI30d + 0.25 * SPI90d + 0.35 * (Z/2)
where

PDSI = Palmer Drought Severity Index

Z = Palmer's Z-Index

SPI30d = 30-day Standardized Precipitation Index (SPI)

SPI90d = 90-day Standardized Precipitation Index (SPI)

Long-term Blend= 0.35 *(PDSI/2) + 0.15 * SPI180d + 0.2 * SPI1y + 0.2 *SPI2y + 0.1 * SPI5y
where

PDSI = Palmer Drought Severity Index

SPI180d = 180-day Standardized Precipitation Index (SPI)

SPI1y = 1-year Standardized Precipitation Index (SPI)

SPI2y = 2-year Standardized Precipitation Index (SPI)

SPI5y = 5-year Standardized Precipitation Index (SPI)