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What is PNI (Percent of Normal Index)?

The PNI (Percent of Normal Index) was described by Willeke et al. (1994) as a percentage of normal precipitation. It can be calculated for different time scales (monthly, seasonally, and yearly). PNI (Percent of Normal Index) has been found to be rather effective for describing drought for a single region or/and for a single season (Hayes, 2006). PNI (Percent of Normal Index) is calculated as following: AgMerra Drought

The PNI (Percent of Normal Index) was developed for characterizing meteorological drought severity, but the PNI (Percent of Normal Index) procedure can also be applied to other variables such as streamflow for calculating streamflow drought severity. This index is simple, by definition, easy to calculate, and is easy understood by a general audience (Smakhtin and Hughes 2004). Main concept of this index was based on the ratio of the real rainfall to normal rainfall. Percent of Normal (PNI) index used by long-term average of rainfall for base values and then rainfall changes during monthly, seasonally and annual scales.

PNI (Percent of Normal Index) can be calculated for a variety of time scales, and usually these time scales range from a single month to a group of months representing a particular season to an annual or water year (Willeke et al. 1994). PNI (Percent of Normal Index) has been found to be rather effective for describing drought for a single region or/and for a single season (Hayes, 2006).

Boughton (2009) calculated streamflow drought characteristics using percent of normal index (PNI) in eastern Australia and found that the severity of droughts increased with average recurrence interval to the limit of the generated data. Salehnia et al. (2017) revealed that the trends of SPI, DI, and PNI indices were very similar in the study area.





References


Boughton W., 2009. Multi-year streamflow drought in Eastern Australia. Aust J Water Resour 13(1):31-42

Hayes M.J. 2006. Drought Indices. Van Nostrand's Scientific Encyclopedia. Hoboken: John Wiley and Sons, Inc. Doi: 10.1002/0471743984.vse8593. http://onlinelibrary.wiley.com/doi/10.1002/0471743984.vse8593/full.

Smakhtin V U, Hughes D A. 2007. Automated estimation and analyses of meteorological drought characteristics from monthly rainfall data. Environmental Modelling and Software, 22(6): 880-890.

Salehnia, N., Alizadeh, A., Sanaeinejad,H.,Bannayan, M., Zarrin, A., Hoogenboom, G., 2017. Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data. Journal of Arid Land, 9(6): 797-809. https://doi.org/10.1007/s40333-017-0070-y

Willeke G, Hosking JRM, Wallis JR, Guttman NB, 1994. The national drought atlas. Institute for Water Resources Report 94-NDS-4,U.S. Army Corps of Engineers.


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