Bayesian network based procedure for regional drought monitoring: The Seasonally Combinative Regional Drought Indicator

Ali, Zulfiqar; Hussain, Ijaz; Grzegorczyk, Marco; Ni, Guangheng; Faisal, Muhammad; Qamar, Sadia; Shoukry, Alaa Mohamd; Sharkawy, Mohammed Abdel Wahab; Gani, Showkat; Al-Deek, Fares Fawzi

doi:10.1016/j.jenvman.2020.111296

Cited by 21 publications

(7 citation statements)

References 61 publications

(71 reference statements)

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“…For example, Adnan et al (2018) used various drought indices to monitor and assess the drought status. Similarly, Ali et al (2020) have suggested an ensemble procedure for regional drought pattern recognition.…”

Section: Applicationmentioning

confidence: 99%

“…In past research, numerous authors have provided various methods for regional drought monitoring. For example, Ali et al (2020) developed Seasonally Combinative Regional Drought Indicator (SCRDI) under the Bayesian Network approach. In SCRDI, time‐series data of SPTI index of various meteorological stations located in homogenous regions are combined systematically.…”

Section: Introductionmentioning

confidence: 99%

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The Spatiotemporal Weighted Efficient Drought Index—A new generalized procedure of regional drought indicator

et al. 2022

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Drought is a multifaceted climate phenomenon. Due to recent climate warming, the risk of drought has increased. Regional drought management requires accurate regional drought characterization to prepare early warning drought mitigation policies. Therefore, a more flexible and efficient procedure is necessary for characterizing drought at a regional level. This study provides a new spatiotemporal weighting scheme for integrating precipitation data from various meteorological stations located in a specific region. Consequently, this article presents a new generalized regional drought indicator-The Spatiotemporal Weighted Efficient Drought Index (STWEDI). The methodology of STWEDI consists of two stages: (1) The first stage combines the meteorological data of various stations located in a certain region under the proposed weighting scheme, and (2) the second stage standardizes the cumulative distribution function (CDF) of mixture probability models. In application, this research estimated the time series data of STWEDI of seven regions containing a varying number of meteorological stations. To assess the performance of STWEDI, we compared the time series data of STWEDI with the most commonly used Standardized Precipitation Index (SPI) using the Pearson correlation. Results show that STWEDI is strongly correlated with individual SPIs in all the clusters and scales.Moreover, we found that STWEDI is strongly associated with SPI at all time scales. These statistical measures show that STWEDI is the more effective drought indicator for regional drought monitoring.

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Section: Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Spatiotemporal Weighted Efficient Drought Index—A new generalized procedure of regional drought indicator

et al. 2022

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“…(c) Furthermore, let the grand average be called Average Marginal Posterior Probability (AMPP) (Ali et al, 2020c) for each scale by the following equation:…”

Section: Phase IV the Choice Of Time Scalementioning

confidence: 99%

On the Bayesian network based data mining framework for the choice of appropriate time scale for regional analysis of drought Hazard

Qamar

Khalique

Grzegorczyk

2021

Theor Appl Climatol

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Data mining has a significant role in hyrdrologic research. Among several methods of data mining, Bayesian network theory has great importance and wide applications as well. The drought indices are very useful tools for drought monitoring and forecasting. However, the multi-scaling nature of standardized type drought indices creates several problems in data analysis and reanalysis at regional level. This paper presents a novel framework of data mining for hydrological research-the Bayesian Integrated Regional Drought Time Scale (BIRDts). The mechanism of BIRDts gives effective and sufficient time scales by considering dependency/interdependency probabilities from Bayesian network algorithm. The resultant time scales are proposed for further investigation and research related to the hydrological process. Application of the proposed method consists of 46 meteorological stations of Pakistan. In this research, we have employed Standardized Precipitation Temperature Index (SPTI) drought index for 1-, 3-, 6-, 9-, 12-, 24-, and ()month time scales. Outcomes associated with this research show that the proposed method has rationale to aggregate time scales at regional level by configuring marginal posterior probability as weights in the selection process of effective drought time scales.

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“…In later studies Ali et al . (2020) proposed a combinative procedure for regional drought monitoring based on a single drought index. A few types of composite or hybrid indices have been observed (Zargar et al ., 2011; Waseem et al ., 2015).…”

Section: Introductionmentioning

confidence: 99%

A seasonally blended and regionally integrated drought index using Bayesian network theory

Raza

Hussain

Ali

et al. 2021

Meteorological Applications

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Among the list of all‐natural hazards, the unique characteristic of drought is its multiplex nature. Besides, the inherited regional characteristics and seasonal variation of drought make it more complicated. Conventional drought indices are inadequate to integrate seasonal and local elements. Therefore, to integrate seasonal components and regional factors, the present study emphasizes the following features: the existence of multiple drought monitoring indicators, the regional broadcasting of drought‐related statistics, and seasonal fluctuations. The seasonally blended regionally integrated drought index (SBRIDI) based on two‐stage Bayesian network theory comprehensively captures seasonal and regional variability embedded in individual drought indicators and regionally scattered homogeneous meteorological stations. It extracts information from a widely accepted standardized precipitation index and other multiscalar variants (standardized precipitation evapotranspiration index, standardized precipitation temperature index). It offers a better estimate of drought severity and pattern in the whole study region. The application of the SBRIDI is based on six meteorological stations located in the Gilgit Baltistan region of Pakistan. Among 216 time series datasets, the stage I Bayesian network selects 72 seasonally relevant standardized drought indices datasets, and the stage II Bayesian network selects 12 of the seasonally most prominent meteorological station datasets. Temporal graphical evidence shows that the SBRIDI can assess the drought risk and depicts the spatial extent of drought conditions at the regional level. The SBRIDI can highlight hotspots of drought‐prone regions, which could help freshwater management agencies and stakeholders.

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Bayesian network based procedure for regional drought monitoring: The Seasonally Combinative Regional Drought Indicator

Cited by 21 publications

References 61 publications

The Spatiotemporal Weighted Efficient Drought Index—A new generalized procedure of regional drought indicator

The Spatiotemporal Weighted Efficient Drought Index—A new generalized procedure of regional drought indicator

On the Bayesian network based data mining framework for the choice of appropriate time scale for regional analysis of drought Hazard

A seasonally blended and regionally integrated drought index using Bayesian network theory

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