Coupling analysis of surface runoff variation with atmospheric teleconnection indices in the middle reaches of the Yangtze River

Yang, Peng; Xia, Jun; Zhang, Shengqing; Cai, Wei

doi:10.1007/s00704-022-04013-8

Cited by 3 publications

(3 citation statements)

References 34 publications

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“…The coupling analysis of variations In runoff with Iic teleconnection Indices showed significant correlations between runoff and the selected teleconnection indices [29]. This correlation highlights the important role that these indices play in runoff and its dynamics.…”

Section: Introductionmentioning

confidence: 76%

“…Pandžić et al [27] analysed the relationships between large-scale atmospheric circulation and monthly precipitation and river discharge. Some studies have assessed the impact of teleconnections on runoff [28][29][30]. The results of Lorenzo-Lacruz et al [30] showed the dynamic interplay between runoff and atmospheric circulation in recent decades.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Teleconnection Patterns on the Formation of Potential Ecological Flow Variables in Lowland Rivers

Gurjazkaitė,

Akstinas,

Meilutytė-Lukauskienė

2023

Water

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Climate is probably the most important factor affecting river discharge and flow dynamics. Low flows in rivers during the warm period cause stress to aquatic ecosystems and pose a challenge to sustainable water management. Previous research has shown that the average minimum discharge of the 30 driest continuous days, known as Q30, is a suitable measure for ecological flow estimation in Lithuania. This study aims to examine whether large-scale atmospheric processes, so-called teleconnections, can have an impact on Q30 during the warm period. Hydrological data for 1961–2020 from 25 water gauging stations were used to search for hydrological response signals with five selected climate indices (NAO, SCA, POL, EA/WR, and EA). Pearson correlation and Wilcoxon–Mann–Whitney test approaches were applied. The results suggested that the EA/WR and NAO had the strongest influence on Q30 in the studied region during the warm period. The positive phases of the indices tended to cause a greater decrease in Q30 values due to the prevailing easterly edge of the anticyclonic circulation over the studied region determined by the EA/WR and NAO indices, while the negative phases of the mentioned indices caused an increase and greater dispersion of Q30.

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Section: Introductionmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Effect of Teleconnection Patterns on the Formation of Potential Ecological Flow Variables in Lowland Rivers

Gurjazkaitė,

Akstinas,

Meilutytė-Lukauskienė

2023

Water

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show abstract

“…Variations of runoff are closely related to large-scale climate factors in hydrometeorological teleconnection analysis (Lima and Lall, 2010;Peters et al, 2013;Steinschneider and Lall, 2016;Wang et al, 2022). Therefore, many studies combining teleconnection analysis have been proposed to strengthen the physical meaning of runoff prediction.…”

Section: Introductionmentioning

confidence: 99%

Climate-informed monthly runoff prediction model using machine learning and feature importance analysis

Yan

Lei

Jiang

et al. 2022

Front. Environ. Sci.

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Accurate runoff prediction can provide a reliable decision-making basis for flood and drought disaster prevention and scientific allocation of water resources. Selecting appropriate predictors is an effective way to improve the accuracy of runoff prediction. However, the runoff process is influenced by numerous local and global hydrometeorological factors, and there is still no universal approach about the selection of suitable predictors from these factors. To address this problem, we proposed a runoff prediction model by combining machine learning (ML) and feature importance analysis (FIA-ML). Specifically, take the monthly runoff prediction of Yingluoxia, China as an example, the FIA-ML model uses mutual information (MI) and feature importance ranking method based on random forest (RF) to screen suitable predictors, from 130 global climate factors and several local hydrometeorological information, as the input of ML models, namely the hybrid kernel support vector machine (HKSVM), extreme learning machine (ELM), generalized regression neural network (GRNN), and multiple linear regression (MLR). An improved particle swarm optimization (IPSO) is used to estimate model parameters of ML. The results indicated that the performance of the FIA-ML is better than widely-used long short-term memory neural network (LSTM) and seasonal autoregressive integrated moving average (SARIMA). Particularly, the Nash-Sutcliffe Efficiency coefficients of the FIA-ML models with HKSVM and ELM were both greater than 0.9. More importantly, the FIA-ML models can explicitly explain which physical factors have significant impacts on runoff, thus strengthening the physical meaning of the runoff prediction model.

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Forecasting precipitation based on teleconnections using machine learning approaches across different precipitation regimes

Helali,

Nouri,

Mohammadi Ghaleni

et al. 2023

Environ Earth Sci

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Coupling analysis of surface runoff variation with atmospheric teleconnection indices in the middle reaches of the Yangtze River

Cited by 3 publications

References 34 publications

Effect of Teleconnection Patterns on the Formation of Potential Ecological Flow Variables in Lowland Rivers

Effect of Teleconnection Patterns on the Formation of Potential Ecological Flow Variables in Lowland Rivers

Climate-informed monthly runoff prediction model using machine learning and feature importance analysis

Forecasting precipitation based on teleconnections using machine learning approaches across different precipitation regimes

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