Composite Agrometeorological Drought Index Accounting for Seasonality and Autocorrelation
Publication: Journal of Hydrologic Engineering
Volume 23, Issue 6
Abstract
Drought indices are statistical tools used for monitoring the departure from normal conditions of water availability. Recently, the multivariate nature of droughts was addressed through composite indices capable of including different factors contributing to the occurrence of a drought. However, some issues (like the autocorrelation or the proper definition of the multivariate index) are still open and need to be addressed to make these indices applicable in current practice. Here, a composite agrometeorological drought index (AMDI-SA) has been introduced, accounting for meteorological and agricultural droughts, considering specifically seasonality and autocorrelation. The AMDI-SA combines, through the copula concept and the Kendall function, two drought indices [namely multivariate standardized precipitation index (MSPI) and the multivariate standardized soil moisture index (MSSI)] in a statistically consistent (normal distributed) drought indicator. Nonparametric distributions have been used for the variables of interest and the calculation of MSPI and MSSI, whereas parametric and nonparametric (empirical) copulas are used to build the AMDI-SA. A prewhitening procedure has been applied to the MSPI and MSSI to remove the autocorrelation. An application to the Urmia lake basin in Iran has been presented, drought indices compared, and their spatial variability investigated. Results showed that MSPI and MSSI are able to justify 72 and 89% of the variability throughout the year. The AMDI-SA reflects the combined effect of soil moisture and precipitation, and has a behavior in between whitened MSPI and MSSI. In addition, having no memory and being a composite index, the AMDI-SA is able to clearly detect the temporal variability of recorded droughts to a greater extent than the MSPI and MSSI.
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References
Adeloye, A. J., and Montaseri, M. (2002). “Preliminary streamflow data analyses prior to water resources planning study.” Hydrol. Sci. J., 47(5), 679–692.
AghaKouchak, A. (2014). “A baseline probabilistic drought forecasting framework using standardized soil moisture index: Application to the 2012 United States drought.” Hydrol. Earth Syst. Sci., 18(7), 2485–2492.
Ashouri, H., et al. (2015). “PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies.” Bull. Am. Meteorol. Soc., 96(1), 69–83.
Azmi, M., Rüdiger, C., and Walker, J. P. (2016). “A data fusion-based drought index.” Water Resour. Res., 52(3), 2222–2239.
Balsamo, G., et al. (2015). “ERA-Interim/land: A global land surface reanalysis data set.” Hydrol. Earth Syst. Sci., 19(1), 389–407.
Bazrafshan, J., Hejabi, S., and Rahimi, J. (2014). “Drought monitoring using the multivariate standardized precipitation index (MSPI).” Water Resour. Manage., 28(4), 1045–1060.
Behrangi, A., Loikith, P., Fetzer, E., Nguyen, H., and Granger, S. (2015). “Utilizing humidity and temperature data to advance monitoring and prediction of meteorological drought.” Climate, 3(4), 999–1017.
Bodagh-Jamli, J. (2015). “Validation of satellite-based PERSIANN rainfall estimates using surface-based APHRODITE data over Iran.” Earth Sci., 4(5), 11.
Box, G. E. P., and Jenkins, G. M. (1970). Time series analysis: Forecasting and control, Holden-Day, San Francisco.
Dracup, J. A., Lee, K. S., and Paulson, E. G. (1980). “On the definition of droughts.” Water Resour. Res., 16(2), 297–302.
Entekhabi, D., Rodriguez-Iturbe, I., and Castelli, F. (1996). “Mutual interaction of soil moisture state and atmospheric processes.” J. Hydrol., 184(1–2), 3–17.
Farahmand, A., and AghaKouchak, A. (2015). “A generalized framework for deriving nonparametric standardized drought indicators.” Adv. Water Resour., 76, 140–145.
Farahmand, A., AghaKouchak, A., and Teixeira, J. (2015). “A vantage from space can detect earlier drought onset: An approach using relative humidity.” Sci. Rep., 5, 8553.
Genest, C., Rémillard, B., and Beaudoin, D. (2009). “Goodness-of-fit tests for copulas: A review and a power study.” Insurance Math. Econ., 44(2), 199–213.
Ghaheri, M., Baghal-Vayjooee, M. H., and Naziri, J. (1999). “Lake Urmia, Iran: A summary review.” Int. J. Salt Lake Res., 8(1), 19–22.
Ghajarnia, N., Liaghat, A., and DaneshkarArasteh, P. (2015). “Comparison and evaluation of high resolution precipitation estimation products in Urmia basin-Iran.” Atmos. Res., 158–159, 50–65.
Hamed, K. H. (2014). “Significance of statistical tests and persistence in hydrologic processes.” Handbook of engineering hydrology: Modeling, climate change, and variability, S. Eslamian, ed., CRC Press, Boca Raton, FL.
Hao, Z., and AghaKouchak, A. (2013). “Multivariate standardized drought index: A parametric multi-index model.” Adv. Water Resour., 57, 12–18.
Hao, Z., and AghaKouchak, A. (2014). “A nonparametric multivariate multi-index drought monitoring framework.” J. Hydrometeorol., 15(1), 89–101.
Hao, Z., Hao, F., Singh, V. P., Xia, Y., Ouyang, W., and Shen, X. (2016). “A theoretical drought classification method for the multivariate drought index based on distribution properties of standardized drought indices.” Adv. Water Resour., 92, 240–247.
Hao, Z., and Singh, V. P. (2015). “Drought characterization from a multivariate perspective: A review.” J. Hydrol., 527, 668–678.
Heim, R. R. (2002). “A review of twentieth-century drought indices used in the United States.” Bull. Am. Meteorol. Soc., 83(8), 1149–1165.
Hesami, A., and Amini, A. (2016). “Changes in irrigated land and agricultural water use in the Lake Urmia basin.” Lake Reservoir Manage., 32(3), 288–296.
Hipel, K. W., and McLeod, A. I. (1994). Time series modelling of water resources and environmental systems, Elsevier, Amsterdam, Netherlands.
IRIMO (Iran Meteorological Organization). (2009). Iranian meteorological office, data processing center, Tehran, Iran.
Joe, H. (1997). Multivariate models and multivariate dependence concepts, Chapman & Hall, London.
Kao, S.-C., and Govindaraju, R. S. (2010). “A copula-based joint deficit index for droughts.” J. Hydrol., 380(1–2), 121–134.
Katiraie-Boroujerdy, P.-S., Nasrollahi, N., Hsu, K.-l., and Sorooshian, S. (2013). “Evaluation of satellite-based precipitation estimation over Iran.” J. Arid Environ., 97, 205–219.
Keyantash, J. A., and Dracup, J. A. (2004). “An aggregate drought index: Assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage.” Water Resour. Res., 40(9), W09304.
Lall, U., Devineni, N., and Kaheil, Y. (2016). “An empirical, nonparametric simulator for multivariate random variables with differing marginal densities and nonlinear dependence with hydroclimatic applications.” Risk Anal., 36(1), 57–73.
Ljung, G. M., and Box, G. E. (1978). “On a measure of lack of fit in time series models.” Biometrika, 65(2), 297–303.
McKee, T. B., Doesken, N. J., and Kleist, J. (1993). “The relationship of drought frequency and duration to time scales.” Proc., 8th Conf. on Applied Climatology, American Meteorological Society Publications, Boston.
Mishra, A. K., and Singh, V. P. (2010). “A review of drought concepts.” J. Hydrol., 391(1–2), 202–216.
Moazami, S., Golian, S., Kavianpour, M. R., and Hong, Y. (2013). “Comparison of PERSIANN and V7 TRMM multi-satellite precipitation analysis (TMPA) products with rain gauge data over Iran.” Int. J. Remote Sens., 34(22), 8156–8171.
Nelsen, R. B. (2013). An introduction to copulas, Vol. 139, Springer, New York.
Nelsen, R. B., Quesada-Molina, J. J., Rodríguez-Lallena, J. A., and Úbeda-Flores, M. (2003). “Kendall distribution functions.” Stat. Probab. Lett., 65(3), 263–268.
Rajsekhar, D., Singh, V. P., and Mishra, A. K. (2014). “Multivariate drought index: An information theory based approach for integrated drought assessment.” J. Hydrol., 526, 164–182.
Salvadori, G., and De Michele, C. (2010). “Multivariate multi-parameter extreme value models and return periods: A copula approach.” Water Resour. Res., 46(10), W10501.
Salvadori, G., De Michele, C., Kottegoda, N., and Rosso, R. (2007). Extremes in nature: An approach using copulas, Springer, Berlin.
Sklar, M. (1959). “Fonctions de repartition an dimensions et leurs marges.” Publ. Inst. Statist. Univ. Paris, 8, 229–231.
Soláková, T., De Michele, C., and Vezzoli, R. (2014). “Comparison between parametric and nonparametric approaches for the calculation of two drought indices: SPI and SSI.” J. Hydrol. Eng., 04014010.
Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Van Loon, A. F., and Stahl, K. (2015). “Candidate distributions for climatological drought indices (SPI and SPEI).” Int. J. Climatol., 35(13), 4027–4040.
Steinemann, A. C., and Cavalcanti, L. F. (2006). “Developing multiple indicators and triggers for drought plans.” J. Water Resour. Plann. Manage., 164–174.
Svoboda, M., et al. (2002). “The drought monitor.” Bull. Am. Meteorol. Soc., 83(8), 1181–1190.
Wang, H., Gao, X., Qian, L., and Yu, S. (2012). “Uncertainty analysis of hydrological processes based on ARMA-GARCH model.” Sci. China Technol. Sci., 55(8), 1–11.
Waseem, M., Ajmal, M., and Kim, T.-W. (2015). “Development of a new composite drought index for multivariate drought assessment.” J. Hydrol., 527, 30–37.
Wilks, D. S. (2011). Statistical methods in the atmospheric sciences, Vol. 100, Academic Press, Cambridge, MA.
Yekom, C. C. (2005). “Environmental impacts (qualitative and quantitative) of water resources development projects in Urmia Lake basin.”, Publications of Ministry of Energy, Iran (in Persian).
Zargar, A., Sadiq, R., Naser, B., and Khan, F. I. (2011). “A review of drought indices.” Environ. Rev., 19(1), 333–349.
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Published In
Journal of Hydrologic Engineering
Volume 23 • Issue 6 • June 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Jul 11, 2017
Accepted: Nov 16, 2017
Published online: Mar 30, 2018
Published in print: Jun 1, 2018
Discussion open until: Aug 30, 2018
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