A novel generalized combinative procedure for Multi-Scalar standardized drought Indices-The long average weighted joint aggregative criterion

References

• Akyuz, D. E., Bayazit, M. and Onoz, B. 2012. Markov chain models for hydrological drought characteristics. J. Hydrometeor. 13, 298–309. doi:10.1175/JHM-D-11-019.1
   Google Scholar
• Ali, Z., Hussain, I., Faisal, M., Almanjahie, I. M., Ahmad, I. and co-authors. 2019a. A Probabilistic Weighted Joint Aggregative Drought Index (PWJADI) criterion for drought monitoring systems. Tellus A: Dynamic Meteorology and Oceanography 1–21.
   Google Scholar
• Ali, Z., Hussain, I., Faisal, M., Nazir, H. M., Abd-el Moemen, M. and co-authors. 2017. A novel multi-scalar drought index for monitoring drought: the standardized precipitation temperature index. Water Resour. Manage. 31, 4957–4969. doi:10.1007/s11269-017-1788-1
   Google Scholar
• Ali, Z., Hussain, I., Faisal, M., Shoukry, A. M., Gani, S. and co-authors. 2019b. A framework to identify homogeneous drought characterization regions. Theor. Appl. Climatol. 137, 3161–3172.
   Google Scholar
• Allen, H. 1914. The storage to be provided in impounding reservoirs for municipal water supply. Transactions of the American Society of Civil Engineers 77, 1539–1669.
   Google Scholar
• Avilés, A., Célleri, R., Solera, A. and Paredes, J. 2016. Probabilistic forecasting of drought events using markov chain-and bayesian network-based models: A case study of an andean regulated river basin. Water 8, 37. doi:10.3390/w8020037
   Google Scholar
• Balint, Z., Mutua, F., Muchiri, P. and Omuto, C. T. ( 2013. ). Monitoring drought with the combined drought index in Kenya. In: Developments in Earth Surface Processes, Vol. 16, Elsevier, pp. 341–356.
   Google Scholar
• Bayissa, Y., Maskey, S., Tadesse, T., van Andel, S., Moges, S. and co-authors. 2018. Comparison of the performance of six drought índices in characterizing historical drought for the Upper Blue Nile Basin, Ethiopia. Geosciences 8, 81. doi:10.3390/geosciences8030081
   Google Scholar
• Bhalme, H. N. and Mooley, D. A. 1979. On the performance of modified Palmer index. Arch. Met. Geoph. Biokl. B 27, 281–295. doi:10.1007/BF02246650
   Google Scholar
• Chen, S., Chung, G., Kim, B. S. and Kim, T. W. 2019. Modified analogue forecasting in the hidden Markov framework for meteorological droughts. Sci. China Technol. Sci. 62, 151–162. doi:10.1007/s11431-017-9203-9
   Google Scholar
• Dai, A., Trenberth, K. E. and Qian, T. 2004. A global dataset of Palmer Drought Severity Index for 1870–2002: Relationship with soil moisture and effects of surface warming. J. Hydrometeor. 5, 1117–1130. doi:10.1175/JHM-386.1
   Google Scholar
• Eder, B. K., Davis, J. M. and Monahan, J. F. 1987. Spatial and temporal analysis of the palmer drought severity index over the south‐eastern United States. J. Climatol. 7, 31–56. doi:10.1002/joc.3370070105
   Google Scholar
• El Kharraz, J., El-Sadek, A., Ghaffour, N. and Mino, E. 2012. Water scarcity and drought in WANA countries. Procedia Eng. 33, 14–29. doi:10.1016/j.proeng.2012.01.1172
   Google Scholar
• El-Shanshoury, G. I. and El-Hemamy, S. T. 2013. On selection of the probability distribution for representing the maximum annual wind speed in East Cairo. Egypt. Arab Journal of Nuclear Science and Applications 46, 359–373.
   Google Scholar
• Erhardt, T. M. and Czado, C. 2018. Standardized drought indices: a novel univariate and multivariate approach. J. R Stat. Soc. C 67, 643–664. doi:10.1111/rssc.12242
   Google Scholar
• Farahmand, A. and AghaKouchak, A. 2015. A generalized framework for deriving nonparametric standardized drought indicators. Adv. Water Resour. 76, 140–145. doi:10.1016/j.advwatres.2014.11.012
   Google Scholar
• Farooqi, A. B., Khan, A. H. and Mir, H. 2005. Climate change perspective in Pakistan. Pakistan J. Meteorol. 2, 11–21.
   Google Scholar
• Gringorten, I. I. 1963. A plotting rule for extreme probability paper. J. Geophys. Res. 68, 813–814. doi:10.1029/JZ068i003p00813
   Google Scholar
• Guha-Sapir, D., Vos, F., Below, R. and Ponserre, S. 2011. Annual Disaster Statistical Review 2010. Centre for Research on the Epidemiology of Disasters, Brussels, Belgium.
   Google Scholar
• Guttman, N. B. 1999. Accepting the standardized precipitation index: a calculation algorithm 1. JAWRA Journal of the American Water Resources Association 35, 311–322. doi:10.1111/j.1752-1688.1999.tb03592.x
   Google Scholar
• Hagman, G. (1984). Prevention Before Cure: Report on Human and Natural Disasters in the Third World. Swedish Red Cross, Stockholm.
   Google Scholar
• Hao, Z. and AghaKouchak, A. 2013. Multivariate standardized drought index: a parametric multi-index model. Adv. Water Resour. 57, 12–18. doi:10.1016/j.advwatres.2013.03.009
   Google Scholar
• Hao, Z. and Singh, V. P. 2015. Drought characterization from a multivariate perspective: A review. J. Hydrol. 527, 668–678. doi:10.1016/j.jhydrol.2015.05.031
   Google Scholar
• Jensen, M. E., Burman, R. D. and Allen, R. G. 1990. Evapotranspiration and irrigation water requirements. ASCE.70, 1–360.
   Google Scholar
• Karl, T. R. and Koscielny, A. J. 1982. Drought in the United States: 1895–1981. J. Climatol. 2, 313–329. doi:10.1002/joc.3370020402
   Google Scholar
• Karl, T. R. and Quayle, R. G. 1981. The 1980 summer heat wave and drought in historical perspective. Mon. Wea. Rev. 109, 2055–2073. doi:10.1175/1520-0493(1981)109<2055:TSHWAD>2.0.CO;2
   Google Scholar
• Khadr, M. 2016. Forecasting of meteorological drought using Hidden Markov Model (case study: The upper Blue Nile river basin, Ethiopia). Ain Shams Eng. J. 7, 47–56. doi:10.1016/j.asej.2015.11.005
   Google Scholar
• Klugman, M. R. 1978. Drought in the upper midwest, 1931–1969. J. Appl. Meteor. 17, 1425–1431. doi:10.1175/1520-0450(1978)017<1425:DITUM>2.0.CO;2
   Google Scholar
• Lal, R. 2018. Managing agricultural soils of Pakistan for food and climate. SE. 37, 1–10. doi:10.25252/SE/18/61527
   Google Scholar
• Lawler, G. F. ( 2018). Introduction to Stochastic Processes. London, New Yark: Chapman and Hall/CRC.
   Google Scholar
• Lennartsson, J., Baxevani, A. and Chen, D. 2008. Modelling precipitation in Sweden using multiple step Markov chains and a composite model. J. Hydrol. 363, 42–59. doi:10.1016/j.jhydrol.2008.10.003
   Google Scholar
• Looney, S. W. and Gulledge, T. R. Jr, 1985. Probability plotting positions and goodness of fit for the normal distribution. Journal of the Royal Statistical Society: Series D (the Statistician) 34, 297–303.
   Google Scholar
• Luetkemeier, R., Stein, L., Drees, L. and Liehr, S. 2017. Blended drought index: integrated drought hazard assessment in the Cuvelai-Basin. Climate 5, 51. doi:10.3390/cli5030051
   Google Scholar
• Mallya, G., Tripathi, S. and Govindaraju, R. S. 2015. Probabilistic drought classification using gamma mixture models. J. Hydrol. 526, 116–126. doi:10.1016/j.jhydrol.2014.11.008
   Google Scholar
• Mallya, G., Tripathi, S., Kirshner, S. and Govindaraju, R. S. 2013. Probabilistic assessment of drought characteristics using hidden Markov model. J. Hydrol. Eng. 18, 834–845. doi:10.1061/(ASCE)HE.1943-5584.0000699
   Google Scholar
• Massey, F. J. Jr, 1951. The Kolmogorov-Smirnov test for goodness of fit. J. Am. Stat. Assoc. 46, 68–78. doi:10.1080/01621459.1951.10500769
   Google Scholar
• McKee, T. B., Doesken, N. J. and Kleist, J. (1993, January). The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology (Vol. 17, No. 22, pp. 179–183). Boston, MA: American Meteorological Society.
   Google Scholar
• Mechler, R., Bouwer, L., Schinko, T., Surminski, S., & Linnerooth-Bayer, J. (Eds.). (2018). Loss and Damage from Climate Change: Concepts, Methods and Policy Options. Springer.
   Google Scholar
• Mehdi, F. and Mehdi, J. 2011. Determination of plotting position formula for the normal, log-normal, Pearson (III), log-Pearson (III) and Gumble distributional hypotheses using the probability plot correlation coefficient test. World Applied Sciences Journal 15, 1181–1185.
   Google Scholar
• Memon, M. H., Aamir, N. and Ahmed, N. 2018. Climate Change and Drought: Impact of Food Insecurity on Gender Based Vulnerability in District Tharparkar. Pdr. 57, 307–321. doi:10.30541/v57i3pp.307-321
   Google Scholar
• Moreira, E. E., Coelho, C. A., Paulo, A. A., Pereira, L. S. and Mexia, J. T. 2008. SPI-based drought category prediction using loglinear models. J. Hydrol. 354, 116–130. doi:10.1016/j.jhydrol.2008.03.002
   Google Scholar
• Nalbantis, I. and Tsakiris, G. 2009. Assessment of hydrological drought revisited. Water Resour. Manage. 23, 881–897. doi:10.1007/s11269-008-9305-1
   Google Scholar
• Palmer, W. C. 1965. Meteorological drought. RUS Weather Bureau Research Paper 45, 58. https://www.fws.gov/southwest/es/documents/R2ES/LitCited/LPC_2012/Palmer_1965.pdf
   Google Scholar
• Payab, A. H. and Türker, U. 2019. Comparison of standardized meteorological indices for drought monitoring at northern part of Cyprus. Environ. Earth Sci. 78, 309. doi:10.1007/s12665-019-8309-x
   Google Scholar
• Poggi, P., Notton, G., Muselli, M. and Louche, A. 2000. Stochastic study of hourly total solar radiation in Corsica using a Markov model. Int. J. Climatol. 20, 1843–1860. doi:10.1002/1097-0088(20001130)20:14<1843::AID-JOC561>3.0.CO;2-O
   Google Scholar
• Quiring, S. M. 2009a. Developing objective operational definitions for monitoring drought. J. Appl. Meteor. Climatol. 48, 1217–1229. doi:10.1175/2009JAMC2088.1
   Google Scholar
• Quiring, S. M. 2009b. Monitoring drought: an evaluation of meteorological drought indices. Geography Compass 3, 64–88. doi:10.1111/j.1749-8198.2008.00207.x
   Google Scholar
• Rana, R. S. and Naim, H. 2014. Tharparkar: a forgotten disaster. Lancet. Glob. Health. 2, e508 doi:10.1016/S2214-109X(14)70254-2
   Google Scholar
• Rind, D., Goldberg, R., Hansen, J., Rosenzweig, C. and Ruedy, R. 1990. Potential evapotranspiration and the likelihood of future drought. J. Geophys. Res. 95, 9983–10004. doi:10.1029/JD095iD07p09983
   Google Scholar
• Runger, G. and Wasserman, S. 1979. Longitudinal analysis of friendship networks. Social Networks 2, 143–154. doi:10.1016/0378-8733(79)90003-0
   Google Scholar
• Scholz, F. W. and Stephens, M. A. 1987. K-sample Anderson–Darling tests. J. Am. Stat. Assoc. 82, 918–924.
   Google Scholar
• Sedlmeier, K., Mieruch, S., Schädler, G. and Kottmeier, C. 2016. Compound extremes in a changing climate–a Markov chain approach. Nonlin. Processes Geophys. 23, 375–390. doi:10.5194/npg-23-375-2016
   Google Scholar
• Sen, Z. 1990. Critical drought analysis by second-order Markov chain. Journal of Hydrology (Amsterdam) 120, 183–202. doi:10.1016/0022-1694(90)90149-R
   Google Scholar
• Sharma, T. C. and Panu, U. S. 2012. Prediction of hydrological drought durations based on Markov chains: case of the Canadian prairies. Hydrol. Sci. J. 57, 705–722. doi:10.1080/02626667.2012.672741
   Google Scholar
• Shen, Z., Zhang, Q., Singh, V. P., Sun, P., Song, C. and co-authors. 2019. Agricultural drought monitoring across Inner Mongolia, China: Model development, spatiotemporal patterns and impacts. J. Hydrol. 571, 793–804. doi:10.1016/j.jhydrol.2019.02.028
   Google Scholar
• Spedicato, G. A., Kang, T. S., Yalamanchi, S. B., Yadav, D. and Cordón, I. 2016. The markovchain package: a package for easily handling Discrete Markov Chains in R.
   Google Scholar
• 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, 4027–4040. doi:10.1002/joc.4267
   Google Scholar
• Stewart, W. J. ( 2009. ). Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling. Princeton University Press.
   Google Scholar
• Svoboda, M. and Fuchs, B. (2016). Handbook of Drought Indicators and Indices.
   Google Scholar
• Tan, C., Yang, J. and Li, M. 2015. Temporal-spatial variation of drought indicated by SPI and SPEI in Ningxia Hui Autonomous Region, China. Atmosphere 6, 1399–1421. doi:10.3390/atmos6101399
   Google Scholar
• Thornthwaite, C. W. 1948. An approach toward a rational classification of climate. Geographical Review 38, 55–94. doi:10.2307/210739
   Google Scholar
• Tsakiris, G. and Vangelis, H. 2004. Towards a drought watch system based on spatial SPI. Water Resour. Manage. 18, 1–12. doi:10.1023/B:WARM.0000015410.47014.a4
   Google Scholar
• Vicente-Serrano, S. M., Beguería, S. and López-Moreno, J. I. 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Climate 23, 1696–1718. doi:10.1175/2009JCLI2909.1
   Google Scholar
• Vogel, R. M. 1986. The probability plot correlation coefficient test for the normal, lognormal, and Gumbel distributional hypotheses. Water Resour. Res. 22, 587–590. doi:10.1029/WR022i004p00587
   Google Scholar
• Watanabe, S. 2013. A widely applicable Bayesian information criterion. Journal of Machine Learning Research 14, 867–897.
   Google Scholar
• Wilhite, D. 2006. Drought monitoring and early warning: Concepts, progress and future challenges. World Meteorological Organization (WMO), 1006.
   Google Scholar
• Wilhite, D. A., Sivakumar, M. V. and Pulwarty, R. 2014. Managing drought risk in a changing climate: The role of national drought policy. Weather Clim. Extremes 3, 4–13. doi:10.1016/j.wace.2014.01.002
   Google Scholar
• Yamaoka, K., Nakagawa, T. and Uno, T. 1978. Application of Akaike’s information criterion (AIC) in the evaluation of linear pharmacokinetic equations. Journal of Pharmacokinetics and Biopharmaceutics 6, 165–175. doi:10.1007/BF01117450
   Google Scholar
• Yu, H., Zhang, Q., Xu, C. Y., Du, J., Sun, P. and co-authors. 2019. Modified Palmer Drought Severity Index: Model improvement and application. Environ. Int. 130, 104951. doi:10.1016/j.envint.2019.104951
   Google Scholar
• Zhang, Q., Li, J., Singh, V. P. and Bai, Y. 2012. SPI-based evaluation of drought events in Xinjiang, China. Nat. Hazards 64, 481–492. doi:10.1007/s11069-012-0251-0
   Google Scholar
• Zhu, J., Zhou, L. and Huang, S. 2018. A hybrid drought index combining meteorological, hydrological, and agricultural information based on the entropy weight theory. Arab. J. Geosci. 11, 91. doi:10.1007/s12517-018-3438-1
   Google Scholar