Application of the Hidden Markov Bayesian Classifier and Propagation Concept for Probabilistic Assessment of Meteorological and Hydrological Droughts in South Korea

Muhammad Nouman Sattar,Tae-Woong Kim,Hyun-Han Kwon,Muhammad Jehanzaib,Ji Eun Kim

doi:10.3390/atmos11091000

Muhammad Nouman Sattar, Tae-Woong Kim + Show 3 more

Open Access

https://doi.org/10.3390/atmos11091000

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Abstract

Drought is one of the most destructive natural hazards and results in negative effects on the environment, agriculture, economics, and society. A meteorological drought originates from atmospheric components, while a hydrological drought is influenced by properties of the hydrological cycle and generally induced by a continuous meteorological drought. Several studies have attempted to explain the cross dependencies between meteorological and hydrological droughts. However, these previous studies did not consider the propagation of drought classes. Therefore, in this study, to consider the drought propagation concept and to probabilistically assess the meteorological and hydrological drought classes, characterized by the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI), respectively, we employed the Markov Bayesian Classifier (MBC) model that combines the procedure of iteration of feature extraction, classification, and application for assessment of drought classes for both SPI and SRI. The classification results were compared using the observed SPI and SRI, as well as with previous findings, which demonstrated that the MBC was able to reasonably determine drought classes. The accuracy of the MBC model in predicting all the classes of meteorological drought varies from 36 to 76% and in predicting all the classes of hydrological drought varies from 33 to 70%. The advantage of the MBC-based classification is that it considers drought propagation, which is very useful for planning, monitoring, and mitigation of hydrological drought in areas having problems related to hydrological data availability.

Highlights

Drought is one of the most destructive natural hazards because it has negative impacts on the environment, agriculture, economics, and society, and occurs in most climatic zones over the world [1,2,3].Many efforts have been made worldwide in the planning, monitoring, and mitigation of drought due to its impacts and complex nature
In some years, the Markov Bayesian Classifier (MBC) failed to predict the expected classes of drought, and another shortcoming is that the start and end time of the classes vary in comparison to Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI)
We utilized the concept of propagation of drought classes to probabilistically assess the meteorological and hydrological drought classes represented by the standardized precipitation index (SPI) and standardized runoff index (SRI), respectively

Summary

Introduction

Drought is one of the most destructive natural hazards because it has negative impacts on the environment, agriculture, economics, and society, and occurs in most climatic zones over the world [1,2,3]. Many efforts have been made worldwide in the planning, monitoring, and mitigation of drought due to its impacts and complex nature. Droughts can be generally classified into four closely related categories: meteorological, agricultural, hydrological, and socioeconomic [4]. Among these drought types, meteorological drought is mainly dependent on deficient precipitation, while agricultural. Evaluation of prolonged and severe hydrological drought can directly influence irrigation/residential water supply and hydropower generation, which can affect the country’s agriculture and economy. It is necessary to develop an effective technique for accurate assessment and characterization of drought

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