Agricultural Drought Indices - US Department of Agriculture

A drought indicator is simply any parameter (including an index) used to measure or describe
drought conditions. Indicators can include parameters such as precipitation, temperature, soil
moisture, streamflow, snowpack, and reservoir levels. A drought index takes it a step further and
attempts to numerically quantify the qualitative as a means of representing reality on the landscape.
It is a means of simplifying complex relationships in a way that can be (hopefully) easily
communicated.
This led to a period of a decade or so of crudely integrating multiple indices or indicators. But
much of this, before GIS and increased computing/modeling capacity, was still integrated manually
in a subjective nonsystematic way.
In the late 1990s, the internet and GIS really began to make their mark on how we monitor and
utilize drought early warning systems (DEWS) today. A resolution revolution began to emerge as
well. The original versions of the PDSI, CMI, and SPI were all calculated using preliminary
monthly data at the coarser climate division scale up to the late 1990s. After that (in the early to
mid 2000s) we began to get data weekly (and even daily), and GIS allowed us to interpolate from
stations and to extrapolate through gridded coverage at around 40 km 2 . Given the influence of
topography and a generally erratic distribution of precipitation, this seems to be a relatively
comfortable resolution to work with precipitation operationally, compared to a more uniform
parameter like temperature. An example of the daily updated and gridded SPI on a national scale
is seen below in Figure 2 and at a regional and county-level scale in Figure 3.
As the name would suggest, a composite index is “hybrid” in nature, as it combines many
parameters, indicators, and/or indices into a single product, or indicator. Decision makers prefer a
single map with a simple classification system. In order for tools and indices to be used by
decision and policy makers, it is important to understand and follow this simple premise. Another
advantage of a composite index is that users can extract and utilize/analyze all of the input
parameters individually as well.
Figure 2. Daily gridded (40 km resolution) SPI courtesy of the High Plains Regional Climate Center
and the National Drought Mitigation Center at the University of Nebraska-Lincoln.
(http://www.hprcc.unl.edu/maps/current/index.php?action=update_product&product=SPIData)
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