How to cite:
Abatzoglou, J.T., D.J. McEvoy, and K.T. Redmond, in press, The West Wide Drought Tracker: Drought Monitoring
at Fine Spatial Scales, Bulletin of the American Meteorological Society


What is WestWide Drought Tracker?

The western United States consists of complex terrain where local precipitation and temperature can vary dramatically across short distances, which in turn impact local drought conditions. The goal of WestWide Drought Tracker (WWDT) is to provide easy access to fine-scale drought monitoring and climate products that can be utilized by a variety of users.  The climate data sets, drought indices, and maps that are found on WWDT use monthly data which are updated with new values at the beginning of each month using data from the PRISM Climate Mapping Program.

What products are available on WWDT?

Data Sources:


The PRISM Climate Mapping Program (Oregon State University) is an ongoing effort to produce and disseminate the most detailed, highest-quality spatial climate datasets currently available (Daly et al., 1994). PRISM (Parameter-elevation Regressions on Independent Slopes Model) is an analytical tool that uses point data, a digital elevation model, and other spatial data sets to generate fine scale (4-km, 2.5 arc-minutes) grid-based estimates of monthly precipitation and temperature from 1895-present. PRISM uses point measurements of climate data and a digital elevation model of terrain and is constantly updated to map climate in the most difficult situations, including high mountains, rain shadows, temperature inversions, coastal regions, and associated complex meso-scale climate processes. We are currently using the AN81m and AN81d datasets available from

Drought Indices:

Palmer Drought Indices:

Palmer drought indices are based on a simplified water budget that considers water supply (precipitation), demand (evapotranspiration) and loss (runoff). Input data for these budget terms consist solely of monthly temperature and precipitation. Fixed soil characteristics are supplied independently by incorporating the available water holding capacity of the top 250 cm of the soil acquired from the State Soil Geographic Data Base (STATSGO). Snow and its effects are not represented.

PDSI (Palmer Drought Severity Index) uses temperature and precipitation data to determine the accumulated water excess or deficit (Palmer, 1965). Values are referenced to the local climate so that PDSI in different climates can be more readily compared. The value of PDSI for a particular month is obtained from a weight applied to the previous PDSI and an additional contribution from the present month's Z-index (below).

sc-PDSI (self-calibrated PDSI) is a locally calibrated version of the PDSI designed to make values of the PDSI more comparable across space (Wells et al., 2004). The sc-PDSI thus overcomes some of the limitations of the PDSI which were originally calibrated using a few locations in the Midwestern USA. The sc-PDSI ensures that values exceeding +4 occur 2 percent of the time, and likewise for values less than -4. Note that the mean value of sc-PDSI may not be 0 for a specific location and the sc-PDSI is not intended to be a normalized version of the PDSI.

Palmer Z-Index: The Palmer Z Index measures short-term drought on a monthly scale. The Z-value is also referenced to the specific location and the climate for that time of year.

Standardized Precipitation Index:

The SPI (McKee et al., 1993) utilizes only monthly precipitation in its calculation and was developed for two main reasons. First, water supply in the form of precipitation is usually the most dominant component of the soil's water budget, and strongly influences the Palmer calculations. Secondly, a given location can be simultaneously in short-term deficit, medium-term excess and long-term deficit. All forms of the SPI pertain to some explicit time scale, which is part of the name (e.g., 1-month SPI, 24-month SPI). The SPI is "standardized" so that all precipitation climate regimes are represented in common terms that can be compared with SPI from all other locations, based on the historical probability distribution. Monthly precipitation values are first added over the past N (N = 1, 2, 3...72) months. These are formed into a historical series, from which a histogram or empirical distribution is formed. This is then approximated by any of several possible theoretical distributions (the incomplete beta distribution is used here). Various points on the distribution (5th, 25th, 50th, 75th, 95th percentile, etc.) are then mapped onto a normal (bell-shaped curve) distribution. The SPI is then taken as the numerical value on a normal distribution of the departure from a long term average. SPI has no units, and values can be thought of as "like" standard deviations. Values exceeding +2 or -2 are seen only about 5 percent of the time.

Standardized Precipitation Evapotranspiration Index:

The SPEI utilizes monthly precipitation and average monthly temperature in its calculation and was developed to help overcome some limitations of the SPI (Vicente-Serrano et al., 2010).  With the temperature input potential evapotranspiration (PET) is calculated and a historical time series of the simple water balance (precipitation – PET) is used in place of the precipitation only time series used in the SPI. The SPEI and SPI are calculated in a similar manner and both indices have the advantage of being multi-scalar. The standardization procedure for SPEI follows the same steps as SPI, however, the developers of SPEI recommend using the three parameter log-logistic theoretical distribution to account for common negative values which are found in the time series (precipitation – PET). We use the entire period of record for calculating SPI and SPEI, rather than just 1981-2010.