Development of a multi-GCMs Bayesian copula method for assessing multivariate drought risk under climate change: A case study of the Aral Sea basin

Yang, Xiaohua; Li, Y.P.; Huang, Guohe; Liu, Y.R.; Zhou, Xiong

doi:10.1016/j.catena.2022.106048

Cited by 16 publications

(8 citation statements)

References 46 publications

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“…However, the copulas can be classified into two categories, such as Archimedean and elliptical method families. Because of their simplicity and proprieties, Archimedean methods are powerful to simulate bivariate distributions [ 12 , 17 , 19 ]. Meanwhile, the MVD-VSG method adopted elliptical copulas, as we are studying several groundwater quality parameters and they can generate random variables with high dimensions.…”

Section: Methods Descriptionmentioning

confidence: 99%

An approach based on multivariate distribution and Gaussian copulas to predict groundwater quality using DNN models in a data scarce environment

Nafii¹,

Lamane²,

Taleb³

et al. 2023

MethodsX

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Section: Methods Descriptionmentioning

confidence: 99%

An approach based on multivariate distribution and Gaussian copulas to predict groundwater quality using DNN models in a data scarce environment

Nafii¹,

Lamane²,

Taleb³

et al. 2023

MethodsX

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Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, the introduction of copula in drought research has advanced the field of probabilistic drought modeling [21][22][23][24][25][26][27][28][29][30][31][32]. For instance, Yang et al [21] developed a Bayesian copula method using multiple GCMs to generate reliable ensemble projections of drought risk. Yue et al [22] integrated copula and Bayesian network techniques to investigate the spatiotemporal dynamics and meteorological triggering conditions of hydrological drought.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a random-forest-copula-factorial analysis (RFCFA) method for predicting propagation between meteorological and hydrological drought

Wang,

Li,

Huang

et al. 2024

NSO

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In the context of global climate warming, the propagation of meteorological drought (MD) may aggravate the devastating impact of hydrological drought (HD) on water security and sustainable development. There are challenges in accurately predicting the propagation of drought and effectively quantifying the effects of uncertainty, especially in datadeficient regions. In this study, a novel method called RFCFA is developed through integrating random forest (RF), copula, and factorial analysis (FA) into a general framework as well as applied to the Aral Sea Basin (a typical arid and data-scarce basin in Central Asia) under considering the impact of climate change. Several findings can be summarized: (1) the projected future drought propagation probability of ASB is 39.2%, which is about 8% higher than historical level; (2) drought propagation is mainly affected by mean climate condition, catchment characteristics (i.e., elevation, LUCC, and slope), and human activities (i.e., irrigation and reservoir operation); (3) the lower propagation probability in spring is expected under SSP1-2.6 due to increased snow meltwater, and the drought propagation probability in autumn is the highest (reaching 45.4%) under the influence of reservoir operation; (4) the combined effects of meteorological conditions and agricultural irrigation can lead to a higher probability of future propagation in the upper river basin in summer. Findings are valuable for predicting drought propagation risk, revealing main factors and inherent uncertainties, as well as providing support for drought management and disaster prevention.

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“…Compared to rule-based classification methods, Bayesian methods are non-rule-based classification methods that achieve optimal statistical classification results based on class priors and class probability density functions. In recent years, theoretical and applied research on Bayesian statistics both nationally and in other countries has increased, and their application in research on extreme climate risk [15][16][17], sediment fingerprinting identification [18][19][20], soil properties [21,22], and many other fields has become widespread and demonstrated significant application potential.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Soil Salinity Classes through Remote Sensing-Based Ensemble Learning Concept: Considering Scale Effects

Chen,

Wu,

2024

Remote Sensing

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Remote sensing (RS) technology can rapidly obtain spatial distribution information on soil salinization. However, (1) the scale effects resulting from the mismatch between ground-based “point” salinity data and remote sensing pixel-based “spatial” data often limit the accuracy of remote sensing monitoring of soil salinity, and (2) the same salinity RS monitoring model usually provides inconsistent or sometimes conflicting explanations for different data. Therefore, based on Landsat 8 imagery and synchronously collected ground-sampling data of two typical study regions (denoted as N and S, respectively) of the Yichang Irrigation Area in the Hetao Irrigation District for May 2013, this study used geostatistical methods to obtain “relative truth values” of salinity corresponding to the Landsat 8 pixel scale. Additionally, based on Landsat 8 multispectral data, 14 salinity indices were constructed. Subsequently, the Correlation-based Feature Selection (CFS) method was used to select sensitive features, and a strategy similar to the concept of ensemble learning (EL) was adopted to integrate the single-feature-sensitive Bayesian classification (BC) model in order to construct an RS monitoring model for soil salinization (Nonsaline, Slightly saline, Moderately saline, Strongly saline, and Solonchak). The research results indicated that (1) soil salinity exhibits moderate to strong variability within a 30 m scale, and the spatial heterogeneity of soil salinity needs to be considered when developing remote sensing models; (2) the theoretical models of salinity variance functions in the N and S regions conform to the exponential model and the spherical model, with R2 values of 0.817 and 0.967, respectively, indicating a good fit for the variance characteristics of salinity and suitability for Kriging interpolation; and (3) compared to a single-feature BC model, the soil salinization identification model constructed using the concept of EL demonstrated better potential for robustness and effectiveness.

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Development of a multi-GCMs Bayesian copula method for assessing multivariate drought risk under climate change: A case study of the Aral Sea basin

Cited by 16 publications

References 46 publications

An approach based on multivariate distribution and Gaussian copulas to predict groundwater quality using DNN models in a data scarce environment

An approach based on multivariate distribution and Gaussian copulas to predict groundwater quality using DNN models in a data scarce environment

Development of a random-forest-copula-factorial analysis (RFCFA) method for predicting propagation between meteorological and hydrological drought

Monitoring Soil Salinity Classes through Remote Sensing-Based Ensemble Learning Concept: Considering Scale Effects

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