Probabilistic drought classification using gamma mixture models

Mallya, Ganeshchandra; Tripathi, Shivam; Govindaraju, Rao S.

doi:10.1016/j.jhydrol.2014.11.008

Cited by 19 publications

(7 citation statements)

References 34 publications

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“…Temporal plots of SPI index for six stations and SWADI at scale-1. while some are working with mixture distribution functions (Mallya et al, 2015). However, yet the issue is not appropriately addressed.…”

Section: Resultsmentioning

confidence: 99%

A novel spatially weighted accumulative procedure for regional drought monitoring

Niaz

Hussain

Ali

et al. 2020

Tellus A: Dynamic Meteorology and Oceanography

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Section: Resultsmentioning

confidence: 99%

A novel spatially weighted accumulative procedure for regional drought monitoring

Niaz

Hussain

Ali

et al. 2020

Tellus A: Dynamic Meteorology and Oceanography

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“…Therefore, by the rationale of our criterion, the use of long-term behavior of drought classes can overcome the effect of extreme values in reporting accurate drought class. In recent years, some authors are working with mixture probability distribution functions (Mallya et al, 2015), while some authors have recommended non-parametric functions-based standardization (Farahmand and AghaKouchak, 2015). Although these Fig.…”

Section: Resultsmentioning

confidence: 99%

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

Ali

Almanjahie

Hussain

et al. 2020

Tellus A: Dynamic Meteorology and Oceanography

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Drought hazards have complex climatic and spatio-temporal features. Therefore, its accurate monitoring is a challenging task in hydrological research. In recent, the use of standardized drought indices for drought monitoring is common in practice. However, the existence of several drought indices creates chaotic problems in data mining and decision making. This article presents a new weighting scheme for combining multiple drought indices. We propagated steady-state probabilities of Markov chain as weights in the Probabilistic Weighted Joint Aggregative Index (PWJADI) criterion. Hence, to aggregate drought characterization two or more indices, averaged long term behavior of drought classification states observed in the individual drought index is considered as a weighting characteristic. The proposed algorithm is rather general and can be used for any standardized type of drought indices. The new procedure is named as Long Averaged Weighted Joint Aggregative Criterion (LAWJAC). In this research, we focused on the three multi-scalar drought indices namely, Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), Standardized Precipitation Temperature Index (SPTI). The selection of these indices is due to their similar computational procedures. In the evolution of LAWJAC, three meteorological stations of the Northern Area of Pakistan are considered. A comparison of LAWJAC is made with PWJADI. Results show significant deviations between existing and proposed methods. By the rationale of the proposed algorithm, these deviations strongly advocate the use of LAWJAC for more accuracy in drought characterization.

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“…In recent years, a considerable number of researchers have attempted to investigate the drought frequency. In a similar vein, Markov chains have been commonly used to evaluate predictability characteristics of meteorological droughts (Kim et al ., ; Ochola and Kerkides, ; Steineman, ; Paulo and Pereira, , ; Cancelliere et al ., ; Mishra et al ., ; Nalbantis and Tsakiris, ; Cancelliere and Salas, ; Akyuz et al ., ; Mallya et al ., ; ; Sanusi et al ., ). Cancelliere et al .…”

Section: Introductionmentioning

confidence: 99%

Long‐term probability of drought characteristics based on Monte Carlo simulation approach

Montaseri

Amirataee

Yasi

2018

Intl Journal of Climatology

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In this study, an advanced stochastic procedure was applied to establish long‐term probability of different drought characteristics in 10 different parts of the world with various climatic conditions. Such purpose cannot be accomplished with a single historical data record, because the historical data record, no matter its length, represents only a realization of all such possible sequences of similar length. For this reason, a stochastic procedure was extensively used to generate alternative sequences of rainfall data in the selected 10 rainfall stations. Then, the generated synthetic rainfall sequences were served as a basis for drought monitoring using the Standardized Precipitation Index (SPI), which produce a set of realizations of possible drought characteristics. The long‐term probability distribution of non‐exceedance of drought duration, intensity, inter‐arrival time, density function of wet and dry periods, empirical joint probability of drought duration and severity, and transition probabilities of drought events were considered and developed as very useful guide on drought management and investigation. The results indicated the essential advantages of proposed methodology (Monte Carlo procedure) in long‐term drought characteristics investigations.

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Probabilistic drought classification using gamma mixture models

Cited by 19 publications

References 34 publications

A novel spatially weighted accumulative procedure for regional drought monitoring

A novel spatially weighted accumulative procedure for regional drought monitoring

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

Long‐term probability of drought characteristics based on Monte Carlo simulation approach

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