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Journal of Arid Land
Journal of Arid Land  2017, Vol. 9 Issue (6): 797-809    DOI: 10.1007/s40333-017-0070-y
Orginal Article     
Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data
SALEHNIA Nasrin1, ALIZADEH Amin1,*(), SANAEINEJAD Hossein1, BANNAYAN Mohammad1, ZARRIN Azar2, HOOGENBOOM Gerrit3
1 Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 9177948944, Iran
2 Department of Geography, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
3 Institute for Sustainable Food System, University of Florida, Gainesville 110570, USA
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Abstract  

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.

Key words： severe drought      degree of dryness      MDM (Meteorological Drought Monitoring) software      precipitation      intensity      Middle East     
Received: 25 March 2017      Published: 20 December 2017
Corresponding Authors: ALIZADEH Amin     E-mail: alizadeh@um.ac.ir;alizadeh@gmail.com
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SALEHNIA Nasrin
ALIZADEH Amin
SANAEINEJAD Hossein
BANNAYAN Mohammad
ZARRIN Azar
HOOGENBOOM Gerrit
Cite this article:

SALEHNIA Nasrin, ALIZADEH Amin, SANAEINEJAD Hossein, BANNAYAN Mohammad, ZARRIN Azar, HOOGENBOOM Gerrit. Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data. Journal of Arid Land, 2017, 9(6): 797-809.

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http://jal.xjegi.com/10.1007/s40333-017-0070-y     OR     http://jal.xjegi.com/Y2017/V9/I6/797

[1] Alley W M.1984. The Palmer drought severity index: limitations and assumptions. Journal of Applied Meteorology, 23(7): 1100-1109.
[2] 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.
[3] 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.
[4] B?rbulescu A, Deguenon J.2014. Models for trend of precipitation in Dobrudja. Environmental Engineering & Management Journal, 13(4): 873-880.
[5] Byun H R, Wilhite D A.1999. Objective quanti?cation of drought severity and duration. Journal of Climate, 12(9): 2747-2756.
[6] Ceglar A, Toreti A, Balsamo G, et al.2017. Precipitation over Monsoon Asia: a comparison of reanalyses and observations. Journal of Climate, 30(2): 465-476.
[7] Chang T J, Kleopa X A.1991. A proposed method for drought monitoring. Journal of the American Water Resources Association, 27(2): 275-281.
[8] Deo R C.2011. On meteorological droughts in tropical Pacific Islands: time-series analysis of observed rainfall using Fiji as a case study. Meteorological Applications, 18(2): 171-180.
[9] Deo R C, Byun H R, Adamowski J F, et al.2017. Application of effective drought index for quantification of meteorological drought events: a case study in Australia. Theoretical and Applied Climatology, 128(1-2): 359-379.
[10] Dogan S, Berktay A, Singh V P.2012. Comparison of multi-monthly rainfall-based drought severity indices, with application to semi-arid Konya closed basin, Turkey. Journal of Hydrology, 470-471: 255-268.
[11] 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.
[12] Escalante-Sandoval C, Nu?ez-Garcia P.2017. Meteorological drought features in northern and northwestern parts of Mexico under different climate change scenarios. Journal of Arid Land, 9(1): 65-75.
[13] FAO (Food and Agriculture Organization of the United Nations). 2008. Statistics. [2010-08-12]. .
[14] Gibbs W, Maher J.1967. Rainfall Deciles as Drought Indicators. Melbourne: Bureau of Meteorology, 117.
[15] Hayes M J, Svoboda M D, Wilhite D A, et al.1999. Monitoring the 1996 drought using the standardized precipitation index. Bulletin of the American Meteorological Society, 80(3): 429-438.
[16] Hayes M J.2006. Drought Indices. Van Nostrand’s Scienti?c Encyclopedia. Hoboken: John Wiley & Sons, Inc. Doi: 10.1002/0471743984.vse8593. .
[17] Heim Jr R R.2002. A review of twentieth-century drought indices used in the United States. Bulletin of the American Meteorological Society, 83(3): 1149-1165.
[18] IPCC (Intergovernmental Panel on Climate Change). 2014. Working Group III Contribution to the IPCC Fifth Assessment Report Climate Change 2014: Mitigation of Climate Change. Cambridge, UK, and New York, NY, USA: Cambridge University Press, 895-896.
[19] 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)
[20] Karabulut M.2015. Drought analysis in Antakya-Kahramanmara? Graben, Turkey. Journal of Arid Land, 7(6): 741-754.
[21] Keyantash J, Dracup J.2002. The quantification of drought: an evaluation of drought indices. Bulletin of the American Meteorological Society, 83(8): 1167-1180.
[22] Kim D W, Byun H R, Choi K S.2009. Evaluation, modification, and application of the Effective Drought Index to 200-Year drought climatology of Seoul, Korea. Journal of Hydrology, 378(1-2): 1-12.
[23] Li R, Tsunekawa A, Tsubo M.2014. Index-based assessment of agricultural drought in a semi-arid region of Inner Mongolia, China. Journal of Arid Land, 6(1): 3-15.
[24] Li Y, Yao N, Sahin S, et al.2017. Spatiotemporal variability of four precipitation-based drought indices in Xinjiang, China. Theoretical and Applied Climatology, 129(3-4): 1017-1034.
[25] 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.
[26] 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.
[27] Ministry of Jihad-e-Agriculture (Iran). 2009. Crop statistics. [2009-04-03]. . (in Persian)
[28] Morid S, Smakhtin V, Moghaddasi M.2006. Comparison of seven meteorological indices for drought monitoring in Iran. International Journal of Climatology, 26(7): 971-985.
[29] Potop V, Boronean? C, Mo?ny M, et al.2014. Observed spatiotemporal characteristics of drought on various time scales over the Czech Republic. Theoretical and Applied Climatology, 115(3-4): 563-581.
[30] Quiring S M.2009. Monitoring drought: an evaluation of meteorological drought indices. Geography Compass, 3(1): 64-88.
[31] Rienecker M M, Suarez M J, Gelaro R, et al.2011. MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of Climate, 24(14): 3624-3648.
[32] Ruane A C, Goldberg R, Chryssanthacopoulos J.2015. Climate forcing datasets for agricultural modeling: merged products for gap-filling and historical climate series estimation. Agricultural and Forest Meteorology, 200: 233-248.
[33] Shahabfar A, Eitzinger J.2013. Spatio-temporal analysis of droughts in semi-arid regions by using meteorological drought indices. Atmosphere, 4(2): 94-112.
[34] Smakhtin V U, Hughes D A.2007. Automated estimation and analyses of meteorological drought characteristics from monthly rainfall data. Environmental Modelling & Software, 22(6): 880-890.
[35] 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]. .
[36] White J W, Hoogenboom G, Stackhouse Jr P W, et al.2008. Evaluation of NASA satellite- and assimilation model-derived long-term daily temperature data over the continental US. Agricultural and Forest Meteorology, 148(10): 1574-1584.
[37] 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. Norfolk, VA.
[38] WMO (World Meteorological Organization). 2013. High-level Meeting on National Drought Policy. Geneva: International Conference Center (CICG). [2013-03-11]. .
[39] Wu H, Hayes M J, Weiss A, et al.2001. An evaluation of the Standardized Precipitation Index, the China-Z Index and the statistical Z-Score. International Journal of Climatology, 21(6): 745-758.
[40] Yu G, Sauchyn D, Li Y F.2013. Drought changes and the mechanism analysis for the North American Prairie. Journal of Arid Land, 5(1): 1-14.
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