Evaluation of Drought Severity with a Bayesian Network Analysis of Multiple Drought Indices

Drought is a natural phenomenon caused by the variability of climate. This study was conducted in the Songhua River Basin of China. The drought events were estimated by using the Reconnaissance Drought Index (RDI) and Standardized Precipitation Index (SPI) which are based on precipitation (P) and potential evapotranspiration (PET) data. Furthermore, drought characteristics were identified for the assessment of drought trends in the study area. Short term (3 months) and long term (12 months) projected meteorological droughts were identified by using these drought indices. Future climate precipitation and temperature time series data (2021–2099) of various Representative Concentration Pathways (RCPs) were estimated by using outputs of the Global Circulation Model downscaled with a statistical methodology. The results showed that RCP 4.5 have a greater number of moderate drought events as compared to RCP 2.6 and RCP 8.5. Moreover, it was also noted that RCP 8.5 (40 events) and RCP 4.5 (38 events) showed a higher number of severe droughts on 12-month drought analysis in the study area. A severe drought conditions projected between 2073 and 2076 with drought severity (DS-1.66) and drought intensity (DI-0.42) while extreme drying trends were projected between 2097 and 2099 with drought severity (DS-1.85) and drought intensity (DI-0.62). It was also observed that Precipitation Decile predicted a greater number of years under deficit conditions under RCP 2.6. Overall results revealed that more severe droughts are expected to occur during the late phase (2050–2099) by using RDI and SPI. A comparative analysis of 3- and 12-month drying trends showed that RDI is prevailing during the 12-month drought analysis while almost both drought indices (RDI and SPI) indicated same behavior of drought identification at 3-month drought analysis between 2021 and 2099 in the research area. The results of study will help to evaluate the risk of future drought in the study area and be beneficial for the researcher to make an appropriate mitigation strategy.

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Bayesian Network Structure and Predictability of Autistic Traits

Psychological Reports ◽

10.1177/0033294120978159 ◽

2020 ◽

pp. 003329412097815

Author(s):

Giovanni Briganti ◽

Donald R. Williams ◽

Joris Mulder ◽

Paul Linkowski

Keyword(s):

Sex Differences ◽

Network Analysis ◽

Bayesian Network ◽

Network Structure ◽

Network Connectivity ◽

Autistic Traits ◽

Data Set ◽

Conditional Dependence ◽

Bayesian Network Structure ◽

Male Subjects

The aim of this work is to explore the construct of autistic traits through the lens of network analysis with recently introduced Bayesian methods. A conditional dependence network structure was estimated from a data set composed of 649 university students that completed an autistic traits questionnaire. The connectedness of the network is also explored, as well as sex differences among female and male subjects in regard to network connectivity. The strongest connections in the network are found between items that measure similar autistic traits. Traits related to social skills are the most interconnected items in the network. Sex differences are found between female and male subjects. The Bayesian network analysis offers new insight on the connectivity of autistic traits as well as confirms several findings in the autism literature.

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Assessment of the Dissimilarities of EDI and SPI Measures for Drought Determination in South Africa

Water ◽

10.3390/w13010082 ◽

2021 ◽

Vol 13 (1) ◽

pp. 82

Author(s):

Omolola M. Adisa ◽

Muthoni Masinde ◽

Joel O. Botai

Keyword(s):

South Africa ◽

South African ◽

Correlation Coefficient ◽

Pearson Correlation ◽

Standardized Precipitation Index ◽

Drought Monitoring ◽

Drought Indices ◽

Drought Severity ◽

Pearson Correlation Coefficient ◽

Drought Duration

This study examines the (dis)similarity of two commonly used indices Standardized Precipitation Index (SPI) computed over accumulation periods 1-month, 3-month, 6-month, and 12-month (hereafter SPI-1, SPI-3, SPI-6, and SPI-12, respectively) and Effective Drought Index (EDI). The analysis is based on two drought monitoring indicators (derived from SPI and EDI), namely, the Drought Duration (DD) and Drought Severity (DS) across the 93 South African Weather Service’s delineated rainfall districts over South Africa from 1980 to 2019. In the study, the Pearson correlation coefficient dissimilarity and periodogram dissimilarity estimates were used. The results indicate a positive correlation for the Pearson correlation coefficient dissimilarity and a positive value for periodogram of dissimilarity in both the DD and DS. With the Pearson correlation coefficient dissimilarity, the study demonstrates that the values of the SPI-1/EDI pair and the SPI-3/EDI pair exhibit the highest similar values for DD, while the SPI-6/EDI pair shows the highest similar values for DS. Moreover, dissimilarities are more obvious in SPI-12/EDI pair for DD and DS. When a periodogram of dissimilarity is used, the values of the SPI-1/EDI pair and SPI-6/EDI pair exhibit the highest similar values for DD, while SPI-1/EDI displayed the highest similar values for DS. Overall, the two measures show that the highest similarity is obtained in the SPI-1/EDI pair for DS. The results obtainable in this study contribute towards an in-depth knowledge of deviation between the EDI and SPI values for South Africa, depicting that these two drought indices values are replaceable in some rainfall districts of South Africa for drought monitoring and prediction, and this is a step towards the selection of the appropriate drought indices.

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A Bayesian network analysis of workplace accidents caused by falls from a height

Safety Science ◽

10.1016/j.ssci.2008.03.004 ◽

2009 ◽

Vol 47 (2) ◽

pp. 206-214 ◽

Cited By ~ 75

Author(s):

J.E. Martín ◽

T. Rivas ◽

J.M. Matías ◽

J. Taboada ◽

A. Argüelles

Keyword(s):

Network Analysis ◽

Bayesian Network ◽

Workplace Accidents

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Efficacy of locally-delivered statins adjunct to non-surgical periodontal therapy for chronic periodontitis: a Bayesian network analysis

BMC Oral Health ◽

10.1186/s12903-019-0789-2 ◽

2019 ◽

Vol 19 (1) ◽

Author(s):

Ruoyan Cao ◽

Qiulan Li ◽

Yu Chen ◽

Mianfeng Yao ◽

Qiqi Wu ◽

...

Keyword(s):

Network Analysis ◽

Bayesian Network ◽

Chronic Periodontitis ◽

Periodontal Therapy

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Effects of different reference periods on drought index (SPEI) estimations from 1901 to 2014

Hydrology and Earth System Sciences ◽

10.5194/hess-21-4989-2017 ◽

2017 ◽

Vol 21 (10) ◽

pp. 4989-5007 ◽

Cited By ~ 11

Author(s):

Myoung-Jin Um ◽

Yeonjoo Kim ◽

Daeryong Park ◽

Jeongbin Kim

Keyword(s):

West Africa ◽

East Asia ◽

The United States ◽

Spatial Extent ◽

Drought Indices ◽

Drought Severity ◽

Climate Change Scenarios ◽

Reference Period ◽

Drought Characteristics ◽

Regional Drought

Abstract. This study aims to understand how different reference periods (i.e., calibration periods) of climate data used to estimate drought indices influence regional drought assessments. Specifically, we investigate the influences of different reference periods on historical drought characteristics, such as the trend, frequency, intensity and spatial extent, using the standardized precipitation evapotranspiration index (SPEI) with a 12-month lag (SPEI-12), which was estimated from the datasets of the Climate Research Unit (CRU) and the University of Delaware (UDEL). For the 1901–1957 (P1) and 1958–2014 (P2) estimation periods, three different types of reference periods are used to compute the SPEI: P1 and P2 together, P1 and P2 separately and P1 only. Focusing on East Asia, Europe, the United States and West Africa, we find that the influence of the reference period is significant in East Asia and West Africa, with dominant drying trends from P1 to P2. The reference period influenced the assessment of drought characteristics, particularly the severity and spatial extent, whereas the influence on the frequency was relatively small. Finally, self-calibration, which is the most common practice for indices such as the SPEI, tends to underestimate the drought severity and spatial extent relative to the other approaches used in this study. Although the conclusions drawn in this study are limited by the use of two global datasets, they highlight the need for clarification of the reference period in drought assessments to better understand regional drought characteristics and the associated temporal changes, particularly under climate change scenarios.

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Analysis of drought characteristics for improved understanding of a water resource system

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-364-404-2014 ◽

2014 ◽

Vol 364 ◽

pp. 404-409 ◽

Cited By ~ 3

Author(s):

A. T. Lennard ◽

N. Macdonald ◽

J. Hooke

Keyword(s):

Resource Management ◽

Water Supply ◽

Water Resource ◽

Water Resource Management ◽

Drought Indices ◽

Drought Severity ◽

Small Scale ◽

Water Resource System ◽

Drought Characteristics ◽

Standardised Precipitation Index

Abstract. Droughts are a reoccurring feature of the UK climate; recent drought events (2004–2006 and 2010–2012) have highlighted the UK’s continued vulnerability to this hazard. There is a need for further understanding of extreme events, particularly from a water resource perspective. A number of drought indices are available, which can help to improve our understanding of drought characteristics such as frequency, severity and duration. However, at present little of this is applied to water resource management in the water supply sector. Improved understanding of drought characteristics using indices can inform water resource management plans and enhance future drought resilience. This study applies the standardised precipitation index (SPI) to a series of rainfall records (1962–2012) across the water supply region of a single utility provider. Key droughts within this period are analysed to develop an understanding of the meteorological characteristics that lead to, exist during and terminate drought events. The results of this analysis highlight how drought severity and duration can vary across a small-scale water supply region, indicating that the spatial coherence of drought events cannot be assumed.

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Predictors of Outcome in Traumatic Brain Injury: New Insight Using Receiver Operating Curve Indices and Bayesian Network Analysis

PLoS ONE ◽

10.1371/journal.pone.0158762 ◽

2016 ◽

Vol 11 (7) ◽

pp. e0158762 ◽

Cited By ~ 23

Author(s):

Zsolt Zador ◽

Matthew Sperrin ◽

Andrew T. King

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Network Analysis ◽

Bayesian Network ◽

Receiver Operating Curve ◽

Predictors Of Outcome ◽

Receiver Operating

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A novel index for ecological drought monitoring based on ecological water deficit: a case study of Northwestern China

10.5194/egusphere-egu21-5439 ◽

2021 ◽

Author(s):

Tianliang Jiang ◽

Xiaoling Su

Keyword(s):

Water Deficit ◽

Time Scales ◽

Drought Index ◽

Vegetation Index ◽

Standardized Precipitation Index ◽

Northwestern China ◽

Drought Monitoring ◽

Drought Indices ◽

Drought Severity ◽

Different Time Scales

Although the concept of ecological drought was first defined by the Science for Nature and People Partnership (SNAPP) in 2016, there remains no widely accepted drought index for monitoring ecological drought. Therefore, this study constructed a new ecological drought monitoring index, the standardized ecological water deficit index (SEWDI). The SEWDI is based on the difference between ecological water requirements and consumption, referred to as the standardized precipitation index (SPI) method, which was used to monitor ecological drought in Northwestern China (NWRC). The performances of the SEWDI and four widely-used drought indices [standardized root soil moisture index (SSI), self-calibrated Palmer drought index (scPDSI), standardized precipitation-evaporation drought index (SPEI), and SPI) in monitoring ecological drought were evaluated through comparing the Pearson correlations between these indices and the standardized normalized difference vegetation index (SNDVI) under different time scales, wetness, and water use efficiencies (WUEs) of vegetation. Finally, the rotational empirical orthogonal function (REOF) was used to decompose the SEWDI at a 12-month scale in the NWRC during 1982&#8211;2015 to obtain five ecological drought regions. The characteristics of ecological drought in the NWRC, including intensity, duration, and frequency, were extracted using run theory. The results showed that the performance of the SEWDI in monitoring ecological drought was highest among the commonly-used drought indices evaluated under different time scales [average correlation coefficient values (r) between SNDVI and drought indices: SEWDI= 0.34, SSI= 0.24, scPDSI= 0.23, SPI= 0.20, SPEI= 0.18), and the 12-month-scale SEWDI was largely unaffected by wetness and WUE. In addition, the results of the monitoring indicated that serious ecological droughts in the NWRC mainly occurred in 1982&#8211;1986, 1990&#8211;1996, and 2005&#8211;2010, primarily in regions I, II, and V, regions II, and IV, and in region III, IV, and V, respectively. This study provides a robust approach for quantifying ecological drought severity across natural vegetation areas and scientific evidence for governmental decision makers.

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