The Integrated Water Portal includes some legacy products from the original Integrated Water Portal hosted by North Carolina State University, but with an ever-improving and expanding product suite. The key input data for the products is gridded PRISM precipitation data, downloaded from https://prism.oregonstate.edu/. The percent of normal and fraction of normal are relative to the gridded Oregon State University PRISM normals for 1991-2020. The SPI and SPI Blend maps are currently still using NCEP gridded Stage IV Daily Accumulations, downloaded from https://water.noaa.gov in NetCDF format (see https://water.noaa.gov/about/precipitation-data-access). PRISM datasets are not available until the following day, therefore the current day's maps supplement the PRISM data with one day of the NCEP Stage IV data for the current day.

The SPI calculations are performed using historical accumulated precipitation probability distributions using software originally developed at NCSU (Cumbie-Ward and Boyles 2016) that implemented a technique developed by McRoberts and Nielsen-Gammon (2012). Briefly, data from COOP stations are grouped into quasi-homogeneous regions and the data pooled to calculate the moments of a Pearson type-III distribution for different durations and times of year. The higher-order moments are normalized by the location parameter and interpolated to the Stage IV grid, where they are multiplied by the OSU PRISM normals to recover distributions consistent with the spatial variations of precipitation represented by the PRISM normals. The cumulative probability of the actual precipitation for a given grid point, ending time, and duration is mapped using the Pearson type-III distribution onto a standard normal distribution, and the corresponding normal Z-score is the SPI value.

The SPI Blends use the concept of weighting kernels to cause recent precipitation to have a greater influence on drought index values than precipitation in the more distant past (Beguería et al. 2014). This minimizes the problem often encountered with the SPI in which the index value changes suddenly because precipitation that occurred near the beginning of the measurement interval is no longer included in the accumulated precipitation calculation. The specific weight used here is a partial ramp weight, whereby for a nominal duration of n days or n months, precipitation during the first n/2 is given a weight of 1, and the weight for precipitation between n/2 and 3n/2 decreases linearly from 1 to 0. Note that an alternate interpretation of this weighting procedure is to average together all the accumulated precipitation values between 0-n/2 and 0-3n/2, effectively blending the SPI indices for that range of durations. The Pearson type-III calculations and other steps proceed as with the SPI methods described above.

The color tables for low values of the SPI and SPI blends are designed to coincide with the US Drought Monitor percentile values for D0 through D4, except with higher numerical precision than is listed on the USDM web site. High values of SPI and SPI blends are color-coded analogously to represent unusually wet conditions.

Further Descriptions of Indices and Datasets:
Drought indices represent one simple way of assessing drought severity. The SPI is perhaps the most popular meteorological drought index. Interpreting agricultural or hydrologic drought impacts from a meteorological drought index requires intimate knowledge of a given location’s hydrological and agricultural sensitivities. To aid in this assessment process, the Southern Regional Climate Center has improved and expanded its Integrated Water Portal and Climate Data Portal offerings, including both parametric and non-parametric SPI maps, SPI values based on effective precipitation and runoff precipitation, and drought fingerprint plots.

SPI Effective:
SPI Effective is a modified version of the Standardized Precipitation index that focuses on “effective precipitation”: the portion of rainfall that is readily available for use by crops. This is done by using the curve number method, which estimates how much rain soaks into the soil versus how much is lost to runoff. This helps distinguish between high-intensity storms that may produce runoff & little soil moisture compared to smaller, more frequent rains that replenish soil moisture. SPI Effective is specifically designed for diagnosing potential agricultural drought impacts. The current version is a simplified one that assumes a single common soil type everywhere, but future versions will be more tailored to local conditions.

SPI Runoff:
SPI Runoff is a modified version of the Standardized Precipitation Index that focuses on “runoff precipitation”. This is the portion of rainfall that flows across surfaces into streams, rivers, and reservoirs. Utilizing the curve number method, SPI Runoff estimates how much rainfall becomes runoff instead of soaking into the soil. This tool helps distinguish between rainfall patterns that generate meaningful water supply versus those that do not, making it useful for diagnosing potential hydrologic drought impacts. Future improvements in SPI Effective will also improve SPI Runoff.

Nonparametric SPI (SPINP):
The Non-Parametric SPI (SPINP) is an alternative form of the Standardized Precipitation Index that relies on historical precipitation percentages and calculates drought intensity. It directly compares current precipitation directly to past observed values that determine how rare or extreme conditions are. SPINP is useful for representing drought rarity relative to the actual historical record, allowing the highlighting of situations where precipitation falls below historically recorded low values. It may differ from SPI especially in areas where past extreme droughts haven't followed a typical statistical distribution, as the standard SPI assumes a typical statistical distribution. Differences between SPINP and SPI will tend to be largest for the most extreme droughts: SPI estimates their severity using the statistics of less-extreme droughts, while SPINP scales severity relative those few very extreme droughts in the historical record that have actually occurred.

PRISM Data:
PRISM is a high-resolution precipitation dataset used to provide detailed and geographically accurate rainfall information. By blending weather station data with the knowledge of terrain, climate patterns, and radar signals to estimate precipitation across various land types. PRISM allows drought tools such as SPI products to reflect local variations in climate more accurately than raw station data alone. PRISM serves as the primary source of precipitation normals and gridded data used across the Integrated Water Portal. Legacy IWP products rely upon Stage IV or AHPS, a gauge-based radar product available in near-real time. Experience has shown that PRISM is better for the western United States because it relies more heavily on gauge data and does a good job with topographically driven precipitation variations. AHPS rainfall data is still used to fill gaps in PRISM data, usually the most recent day when PRISM grids have not yet been produced.

For technical questions, please contact the Southern Regional Climate Center Director, John Nielsen-Gammon, at n-g@tamu.edu

Beguería, S., S. M. Vicente-Serrano, F. Reig, and B. Latorre, 2014: Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol., 34, 3001-3023, https://doi.org/10.1002/joc.3887.

Cumbie-Ward, R. V., and R. P. Boyles, 2016: Evaluation of a high-resolution SPI for monitoring local drought severity. J. Appl. Meteor. Climatol., 55, 2247-2262, https://doi.org/10.1175/JAMC-D-16-0106.1

McRoberts, D. B., and J. W. Nielsen-Gammon, 2012: The use of a high-resolution standardized precipitation index for drought monitoring and assessment. J. Appl. Meteor. Climatol., 51, 68-83, https://doi.org/10.1175/JAMC-D-10-05015.1.