Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data
- 363 Downloads
Meteorological drought is a natural hazard that can occur under all climatic regimes. Monitoring the drought is a vital and important part of predicting and analyzing drought impacts. Because no single index can represent all facets of meteorological drought, we took a multi-index approach for drought monitoring in this study. We assessed the ability of eight precipitation-based drought indices (SPI (Standardized Precipitation Index), PNI (Percent of Normal Index), DI (Deciles index), EDI (Effective drought index), CZI (China-Z index), MCZI (Modified CZI), RAI (Rainfall Anomaly Index), and ZSI (Z-score Index)) calculated from the station-observed precipitation data and the AgMERRA gridded precipitation data to assess historical drought events during the period 1987–2010 for the Kashafrood Basin of Iran. We also presented the Degree of Dryness Index (DDI) for comparing the intensities of different drought categories in each year of the study period (1987–2010). In general, the correlations among drought indices calculated from the AgMERRA precipitation data were higher than those derived from the station-observed precipitation data. All indices indicated the most severe droughts for the study period occurred in 2001 and 2008. Regardless of data input source, SPI, PNI, and DI were highly inter-correlated (R2=0.99). Furthermore, the higher correlations (R2=0.99) were also found between CZI and MCZI, and between ZSI and RAI. All indices were able to track drought intensity, but EDI and RAI showed higher DDI values compared with the other indices. Based on the strong correlation among drought indices derived from the AgMERRA precipitation data and from the station-observed precipitation data, we suggest that the AgMERRA precipitation data can be accepted to fill the gaps existed in the station-observed precipitation data in future studies in Iran. In addition, if tested by station-observed precipitation data, the AgMERRA precipitation data may be used for the data-lacking areas.
Keywordssevere drought degree of dryness MDM (Meteorological Drought Monitoring) software precipitation intensity Middle East
Unable to display preview. Download preview PDF.
We would like to thank Dr. Carol WILKERSON (Independent Consultant, Gainesville, Florida, USA) and K. Grace CRUMMER (Institute for Sustainable Food Systems, University of Florida, USA) for editing and improving the language of the manuscript.
- Asefjah B, Fanian F, Feizi Z, et al. 2014. Meteorological drought monitoring using several drought indices (case study: Salt Lake Basin in Iran). Desert, 19(2): 155–165.Google Scholar
- Bannayan M, Lashkari A, Zare H, et al. 2015. Applicability of AgMERRA forcing dataset to fill gaps in historical in-situ meteorological data. In: American Geophysical Union, Fall Meeting 2015. Abstract #GC13D-1180. 2015AGUFMGC13D1180B. Washington DC: American Geophysical Union.Google Scholar
- Bărbulescu A, Deguenon J. 2014. Models for trend of precipitation in Dobrudja. Environmental Engineering & Management Journal, 13(4): 873–880.Google Scholar
- Edwards D C, McKee T B. 1997. Characteristics of 20th century drought in the United States at multiple time scales. In: Department of Atmospheric Sciences, Colorado State University. Atmospheric Science Paper No. 634, Climatology Report No.97-2. Fort Collins, CO.Google Scholar
- FAO (Food and Agriculture Organization of the United Nations). 2008. Statistics. [2010-08-12]. http://faostat.fao.org.Google Scholar
- Gibbs W, Maher J. 1967. Rainfall Deciles as Drought Indicators. Melbourne: Bureau of Meteorology, 117.Google Scholar
- Hayes M J. 2006. Drought Indices. Van Nostrand’s Scientific Encyclopedia. Hoboken: John Wiley & Sons, Inc. Doi: 10.1002/0471743984.vse8593. http://onlinelibrary.wiley.com/doi/10.1002/0471743984.vse8593/full.Google Scholar
- Ju X S, Yang X W, Chen L J, et al. 1997. Research on determination of station indexes and division of regional flood/drought grades in China. Quarterly Journal of Applied Meteorology, 8(1): 26–33. (in Chinese)Google Scholar
- McKee T B, Doesken N J, Kleist J. 1993. The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology. Anaheim, CA: American Meteorological Society, 179–184.Google Scholar
- McKee T B, Doesken N J, Kleist J. 1995. Drought monitoring with Multiple Time scales. In: Proceeding of the 9th Conference on Applied Climatology. Dallas, TX: American Meteorological Society, 233–236.Google Scholar
- Ministry of Jihad-e-Agriculture (Iran). 2009. Crop statistics. [2009-04-03]. http://dpe.agri-jahad.ir/portal/File/ShowFile.aspx? ID=bd799699-4e89-437f-8a30-5e15a014d332. (in Persian)Google Scholar
- USDA Foreign Agricultural Service. 2010. Iran: crop progress report. FAS—Office of Global Analysis (OGA), United States Department of Agriculture (USDA). International Operational Agriculture Monitoring Program. [2009-12-28]. https://www.pecad.fas.usda.gov/pdfs/Iran/Iran_December_28_2009.pdf.Google Scholar
- Willeke G, Hosking J R M, Wallis J R, et al. 1994. The national drought atlas. In: Institute for Water Resources Report 94-NDS-4. U.S Army Corp of Engineers, CD-ROM.Google Scholar
- Norfolk, VA. WMO (World Meteorological Organization). 2013. High-level Meeting on National Drought Policy. Geneva: International Conference Center (CICG). [2013-03-11]. http://www.wmo.int/pages/prog/wcp/agm/meetings/hmndp13/.Google Scholar