| hasData_Center_Short_Name |
|
| hasDataset_Online_Resource |
|
| hasDataset_Title |
- Standardized Precipitation Index (SPI) for Global Land Surface (1949-2012)
|
| hasEntry_ID |
|
| hasReference |
-
O’Loughlin, J., A. Linke, F. Witmer, A. Laing, A. Gettelmann, J.
Dudhia, 2012: Climate variability and conflict risk in East Africa.
Proc. Natl. Acad. Sci., 109(45), 18344-9 (DOI:
10.1073/pnas.1205130109), URL:
http://www.pnas.org/content/early/2012/10/17/1205130109.
McKee, T. B., N. J. Doesken, and J. Kleist, 1993: Drought monitoring
with multiple time scales. Proceedings of the Proc. Ninth Conference
on Applied Climatology, Amer. Meteor. Soc., Dallas, TX, 233-236.
McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship
of drought frequency and duration to time scales. Proceedings of the
Proc. Eighth Conference on Applied Climatology, Amer. Meteor. Soc.,
Anaheim, CA, 179-186.
|
| hasSummary |
-
This dataset includes the Standardized Precipitation Index (SPI) at
three-, six-, and 12-month scales for global land surfaces. It was
produced for a study to determine the relationship between climate
variability and armed conflict in Sub-Saharan Africa (O'Loughlin et
al. 2012). The precipitation data (1949-2012) are resampled from the
original University of East Anglia Climate Research Unit (CRU)
global time series, TS3.21, monthly 0.5 degree by 0.5 degree grids
to the study unit of analysis, 1.0 degree by 1.0 degree grids,
thereby facilitating regression with environmental and socioeconomic
variables.
The Standardized Precipitation Index (SPI) is commonly used to
monitor drought and anomalous wet periods. It was formulated by Tom
McKee, Nolan Doesken, and John Kleist of the Colorado Climate
Center, Colorado State University (McKee et al. 1993). The SPI at a
given location is based only on the long-term precipitation record
for a desired period. The long-term precipitation time series is
fitted to a gamma probability distribution, which is then
transformed into a normal distribution so that the mean SPI is zero.
Theoretically, the SPI is the number of standard deviations by which
the observed value would lie above or below the long-term mean, for
a normally distributed random variable. Thus, the index can be used
to compare precipitation across a region with different climates.
The SPI can be calculated for multiple time scales, which allows
assessment of impacts on different water resources. For example,
soil moisture responds to precipitation departures on a short time
scale, while stream flow responds to anomalies on a longer time
scale. Precipitation amounts that indicate wet conditions at one
time scale could indicate dry conditions at another time scale.
|
| GCMD Sciencekeywords describing the dataset. Click on Keyword to find similar datasets | |
