Atlas Station General Information
Each station in the Drought Risk Atlas is assigned to a homogeneous region or cluster with stations that have similar precipitation climatologies.
The station selected is highlighted with its homogeneous cluster below, and all the other recording stations available in the atlas are noted as well.
All of the stations in the cluster are also listed to give the user an idea of other stations they can consider when studying the climatology of the region.
In some instances, although the closest station may be part of another homogeneous cluster, it may provide valuable information.
Use one of the options below to view more information about this station. These options are also available in the toolbar at the top of the box to the left.
(Click to select)
L-Moment Ratios for Station and Region
Quantiles for Station and Region
Each station was clustered using summer precipitation values into homogenous regions and tested for homogeneity for each season. Some stations were discordant in the clusters for certain seasons, but not in others. These results are:
Clusters passing and failing the H(1) Homogeneity tests are noted.
Stations which were discordant are identified by COOP identification numbers. For some seasons, no stations may have been found to be discordant.
Precipitation and Temperature Data
The precipitation climatology will allow you to investigate historical precipitation using a weekly, monthly or annual time step.
Historical precipitation information by itself will not tell you if a drought is expected,
but it will show the wet and dry periods and how long they typically last for your location of interest.
Note: these precipitation totals may include days with no data values and these temperature averages may include days with no data values. Please see the tabular datasets for more information.
In this section, users can compare indices to see how they were indicating drought at various times.
Note: the drought period ends when the index returns to zero.
The deciles method is a fairly straightforward index that involves comparing precipitation values with a historical period of record.
It was developed in 1967 and has been the primary drought index in Australia. Deciles are calculated by taking a period of precipitation data,
ranking the values from smallest to largest, and dividing the record into ten parts. A value that falls within the lowest 10% of the record is said to be in the first decile and so on.
For the purposes of determining when a region is in drought, the first decile is typically used as the breaking point.
Any values that fall within the first decile are considered to be in drought. Like the SPI, deciles can be calculated using time-steps.
This is done by determining the length of the time-step and summing the precipitation values over the entire time-step.
A given value for one time-step is then compared to the historical record of values for that same time-step.
The deciles method has a number of advantages, with the most obvious being its simplicity.
It is much easier to calculate than other commonly used indices such as the Standardized Precipitation Index (SPI) and the Palmer Drought Severity Index (PDSI).
It is also more easily understood than most indices. However, the deciles method does have a few drawbacks that users must be aware of.
To be most effective, this method requires a fairly long and accurate record of precipitation data.
Another issue is the difficulty of using the deciles method in areas that may frequently receive no rainfall.
If a number of the values within the record are zero, then it is difficult to determine where the decile cut-off points are for the lower deciles.
The deciles method also assumes that all precipitation is rainfall, making it difficult to use this method in places that rely more on snowpack.
Standardized Precipitation Index (SPI)
The Standardized Precipitation Index (SPI) is an index based on the probability of precipitation for any time scale.
The underlying assumption is that a deficit of precipitation has different impacts on groundwater, reservoir storage, soil moisture, snowpack, and streamflow.
The SPI was designed to quantify the precipitation deficit for multiple time scales.
These time scales reflect the impact of drought on the availability of the different water resources.
Soil moisture conditions respond to precipitation anomalies on a relatively short scale.
Groundwater, streamflow, and reservoir storage reflect the longer-term precipitation anomalies.
The SPI calculation for any location is based on the long-term precipitation record for a desired period.
This long-term record is fitted to a probability distribution, which is then transformed into a normal distribution so that the mean SPI for the location and desired period is zero (Edwards and McKee, 1997).
Positive SPI values indicate greater than median precipitation, and negative values indicate less than median precipitation. Because the SPI is normalized,
wetter and drier climates can be represented in the same way, and wet periods can also be monitored using the SPI.
A drought event occurs any time the SPI is continuously negative and reaches an intensity of -1.0 or less.
The event ends when the SPI becomes positive. Each drought event, therefore, has a duration defined by its beginning and end, and an intensity for each month that the event continues.
The positive sum of the SPI for all the months within a drought event can be termed the drought's "magnitude".
Note: Only the shortest duration timestep that is selected will be displayed below.
U.S. Drought Monitor
The United States Drought Monitor (USDM) map is a composite index that has been released on a weekly basis since 1999.
No single definition of drought works for all circumstances, so people rely on drought indices to detect and measure droughts.
But no single index works under all circumstances, either. That's why we need the Drought Monitor, a synthesis of multiple indices and impacts that represents a consensus of federal and academic scientists.
The product has been refined over time as we find ways to make it better reflect the needs of decision-makers and others who use the information.
Palmer Drought Severity Index (PDSI)
The PDSI is a meteorological drought index, and it responds to weather conditions that have been abnormally dry or abnormally wet.
When conditions change from dry to normal or wet, for example, the drought measured by the PDSI ends without taking into account streamflow,
lake and reservoir levels, and other longer-term hydrologic impacts (Karl and Knight, 1985). PDSI calculations are based on precipitation and temperature data
as well as the local available water content (AWC) of the soil. From the inputs, all the basic terms of the water balance equation can be determined,
including evapotranspiration, soil recharge, runoff, and moisture loss from the surface layer. Human impacts on the water balance, such as irrigation, are not considered.
Complete descriptions of the equations can be found in the original study by Palmer (1965) and the more recent analysis by Alley (1984).
The Palmer Index varies roughly between -6.0 and +6.0. Palmer arbitrarily selected the classification scale of moisture conditions based on his original study areas in central Iowa and western Kansas (Palmer, 1965).
The Palmer Index is designed so that, ideally a -4.0 in South Carolina has the same meaning in terms of the moisture departure from a climatological normal as a -4.0 in Idaho (Alley, 1984).
Self-calibrated Palmer Drought Severity Index (SC-PDSI)
One of the inherent problems associated with the PDSI was that comparisons were being made using results from different regions,
especially those with very different climate regimes, and in many cases this was not appropriate.
The Self-Calibrated Palmer Drought Severity Index (scPDSI) is based upon the original PSDI work,
but takes all the constants and replaces them with values that are calibrated based upon the data for each individual location (Wells et al., 2004).
To make the process “self-calibrating”, several calculations from within the PDSI needed to be addressed, including the climatic characteristics and duration factors.
With the calculations for the scPDSI accounting for each individual location, the index becomes more reflective of what is happening at each site and allows for comparisons between regions to be more accurate.
With these new calculations, the data can be computed at different time steps (weekly, biweekly, and monthly), and the extreme events being calculated by the scPSDI are indeed rare because they are based on calculations at that location and are not a constant.
The positive consequences of the new calculations associated with the sc-PDSI provided the following results:
1) The range of the PDSI values is close to an expected range of -5.0 to 5.0, where values below -4 and above 4 represent extreme conditions. 2) The sensitivity of the index is based upon the local climate. 3) The sc-PDSI has different sensitivities to moist periods and dry periods.
Standardized Precipitation-Evapotranspiration Index (SPEI)
One of the more recently developed (in 2009) drought indices is the Standardized Precipitation Evapotranspiration Index (SPEI), which took the basic premise of the SPI and added a temperature component to capture a simplified water balance (Vicente-Serrano et al., 2010).
The SPEI, like the PDSI, uses a simple water balance calculation that is based on the Thornthwaite (1948) model for calculating potential evapotranspiration (PET) and a climatic water balance in which both wet and dry periods can be identified.
Several studies have shown that good estimates of PET can be obtained with various meteorological parameters, but for the purpose of a drought index these parameters are not needed because only a general estimation of the water balance is required.
This also keeps the calculations simple and usable given the additional data requirements needed for determining actual evapotranspiration values.
Like the SPI, the SPEI can be updated weekly using a moving window for each time step. The SPEI uses the difference between the basic calculations for PET and precipitation to determine a wet or dry period. Given the flexible nature of the SPEI, it has the capacity to be utilized in monitoring the various types of droughts because of the included water balance calculations.
As such, it has the potential to better track agricultural drought
Note: Only the shortest duration timestep that is selected will be displayed below.
Compare Drought Indices
A comparison of drought indices will give users a better understanding of which indicators are best suited for tracking drought in their area.
Choose the year of interest, the index of interest and the relevant timestep and then click this button to add the index to the comparison.
Select up to six datasets for comparison. To remove a dataset from the comparison, click the Remove Dataset button. To clear all datasets from the comparison, click the Clear All button. The datasets can be reordered at any time by dragging the rows.
All data for the comparisons is aggregated by week. Drought Monitor data represents the county-level data for the selected station.
Drought Index Frequencies
With this tool, it is possible to see how frequently a drought index hits a certain magnitude. These data also show how many times this value has occurred during the period of record for the currently selected station.
Choose the drought index, data aggregate and timestep and then click this button to view the frequency statistics.