Development of an integrated PCA-SCA-ANOVA framework for assessing multi-factor effects on water flow: A case study of the Aral Sea

Su, Y.Y.; Li, Y.P.; Liu, Y.R.; Fan, Yurui; Gao, Pan

doi:10.1016/j.catena.2020.104954

Cited by 10 publications

(1 citation statement)

References 39 publications

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“…Hydrological models and copula are unable to quantify the individual and interactive effects of these uncertainty sources [37]. Factorial analysis (FA), an efficient statistical tool, can be utilized to identify the dominant uncertainty source that has the most significant impact on the system response [38,39]. For instance, Duan et al [40] developed a factorialanalysis-based method to investigate the population exposure to drought over the Pearl River Basin under climate change, revealing the GCM and RCP are the major uncertainty sources of drought exposure in different periods.…”

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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