Investigating meteorological/groundwater droughts by copula to study anthropogenic impacts

Sadeghfam, Sina; Mirahmadi, Rasa; Khatibi, Rahman; Mirabbasi, Rasoul; Nadiri, Ata Allah

doi:10.1038/s41598-022-11768-7

Cited by 12 publications

(3 citation statements)

References 53 publications

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“…Current studies on drought propagation can be divided into drought propagation probability and drought propagation threshold (such as drought propagation time, duration, and severity). Drought propagation probability model is generally constructed by copula function (Zhu et al 2021, Sadeghfam et al 2022, Bai et al 2023. Drought propagation thresholds, such as duration and severity, are mainly determined by constructing nonlinear equations for different types of drought characteristics (Wu et al 2017, Han et al 2021.…”

Section: Extension and Limitationmentioning

confidence: 99%

A new perspective for assessing hydro-meteorological drought relationships at large scale based on causality analysis

Zhou,

Ding,

Zhao

et al. 2023

Environ. Res. Lett.

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Correlation analysis is the common method to evaluate the relationship between two variables; however, it may sometimes cause spurious correlations. Specifically, in the field of hydrometeorology, with the impacts of climate change and human activities, correlation analysis is difficult to identify the true relationship between variables, and thus, causality analysis should be adopted instead. This study analyzed the causal relationship between meteorological drought and hydrological drought in different climatic regions of China by using convergent cross mapping (CCM). We improved the identification of CCM convergence by using the coefficient of variation and applied it in the field of large-scale hydrometeorology. The results of correlation analysis were compared, and the applicability of causality analysis was explored. The results revealed that: In Southeast China, the correlation and causality between meteorological drought and hydrological drought were both large. In Northeast China and central Qinghai–Tibet Plateau, the correlation between meteorological drought and hydrological drought was small, but the causality was large. In view of the spurious correlation, introducing causality analysis can better explain the relationship between meteorological drought and hydrological drought, especially in areas with snowmelt runoff. Overall, CCM can provide valuable causal information from common time series in the field of large-scale hydrometeorology and has a wide range of application values. However, causality analysis cannot explain the positive or negative relationship between variables. Therefore, when analyzing the relationship between variables, the advantages of the two methods should be given full play.

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Section: Extension and Limitationmentioning

confidence: 99%

A new perspective for assessing hydro-meteorological drought relationships at large scale based on causality analysis

Zhou,

Ding,

Zhao

et al. 2023

Environ. Res. Lett.

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“…The more objective way to use this method is to apply the procedures described in [32,33], which incorporate the decision theory.…”

Section: Data Fusionmentioning

confidence: 99%

Developing a Data-Fused Water Quality Index Based on Artificial Intelligence Models to Mitigate Conflicts between GQI and GWQI

Nadiri

Barzegar

Sadeghfam

et al. 2022

Water

Self Cite

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The study of groundwater quality is typically conducted using water quality indices such as the Groundwater Quality Index (GQI) or the GroundWater Quality Index (GWQI). The indices are calculated using field data and a scoring system that uses ratios of the constituents to the prescribed standards and weights based on each constituent’s relative importance. The results obtained by this procedure suffer from inherent subjectivity, and consequently may have some conflicts between different water quality indices. An innovative feature drives this research to mitigate the conflicts in the results of GQI and GWQI by using the predictive power of artificial intelligence (AI) models and the integration of multiple water quality indicators into one representative index using the concept of data fusion through the catastrophe theory. This study employed a two-level AI modeling strategy. In Level 1, three indices were calculated: GQI, GWQI, and a data-fusion index based on four pollutants including manganese (Mn), arsenic (As), lead (Pb), and iron (Fe). Further data fusion was applied at Level 2 using supervised learning methods, including Mamdani fuzzy logic (MFL), support vector machine (SVM), artificial neural network (ANN), and random forest (RF), with calculated GQI and GWQI indices at Level 1 as inputs, and data-fused indices target values derived from Level 1 fusion as targets. We applied these methods to the Gulfepe-Zarinabad subbasin in northwest Iran. The results show that all AI models performed reasonably well, and the difference between models was negligible based on the root mean square errors (RMSE), and the coefficient of determination (r2) metrics. RF (r2 = 0.995 and RMSE = 0.006 in the test phase) and MFL (r = 0.921 and RMSE = 0.022 in the test phase) had the best and worst performances, respectively. The results indicate that AI models mitigate the conflicts between GQI and GWQI results. The method presented in this study can also be applied to modeling other aquifers.

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“…Using local meteorological, regional atmospheric, and climatic variables, the Effective Drought Index (EDI) [12], Standardized Precipitation Index (SPI) [13], Standardized Precipitation Evaporation Index (SPEI) [14], Standardized Streamflow Index (SSI) [15], and Standardized Groundwater Index (SGI) [16] have been used to analyze meteorological and hydrological droughts at various spatial-scales covering different climatic regimes. SPI and SGI were used together to estimate the duration and severity of meteorological and groundwater droughts [17,18]. However, due to complex nonlinear relationships among hydroclimatic variables controlling droughts [19], drought indices may not perform well at all locations and hydroclimatic conditions, and hence, cannot be used universally as a robust tool for drought risk assessment and forecast [20,21].…”

Section: Introductionmentioning

confidence: 99%

Heuristic Data-inspired Scheme to Characterize Meteorological and Groundwater Droughts in Semi-arid Karstic Region under Warming Climate

Basagaoglu,

Sharma,

Chakraborty

et al. 2023

Preprint

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Study regionThe Edwards Aquifer Region is located in south-central Texas, United States.Study focusThe paper focuses on the development and implementation of a data-inspired heuristic drought identification scheme to (i) quantify the intensity, duration, and frequency of precipitation deficit- and high temperature-driven meteorological droughts (PMet- and TMet-droughts), and (ii) link their propagation to groundwater droughts (GW-droughts) using baseline hydroclimatic measures and prevailing drought conditions derived from historical climate data and regional mitigation strategies.New hydrological insights for the regionBased on the intensity, duration, and timing of PMet- and TMet-droughts in the semi-arid karstic region, we identified three distinct GW-droughts, including persistence-driven, preconditions-driven, and intensity-driven droughts. The analysis revealed that successive heavy precipitation events are needed to end GW-droughts in the region. The scheme also identified TMet-droughts with the longest dry spells, TMet- and PMet-droughts with the highest intensity, and GW-drought with the second-highest intensity on record all occurred over the past 15 years. These findings provide evidence for a warming climate, intensified meteorological droughts, and increasing stress on the aquifer. Among the artificial intelligence models used, Extremely Randomized Trees (ERT) predicted time series of intensity & duration of GWdroughts from hydroclimatic features with high accuracy. Moreover, the ERT classifier revealed that the duration of PMet droughts and the intensity of TMet droughts are the topmost decisive features in predicting GW-drought intensity in the region.

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Investigating meteorological/groundwater droughts by copula to study anthropogenic impacts

Cited by 12 publications

References 53 publications

A new perspective for assessing hydro-meteorological drought relationships at large scale based on causality analysis

A new perspective for assessing hydro-meteorological drought relationships at large scale based on causality analysis

Developing a Data-Fused Water Quality Index Based on Artificial Intelligence Models to Mitigate Conflicts between GQI and GWQI

Heuristic Data-inspired Scheme to Characterize Meteorological and Groundwater Droughts in Semi-arid Karstic Region under Warming Climate

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