2018
DOI: 10.1002/hyp.11502
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Investigation of the relationship between runoff and atmospheric oscillations, sea surface temperature, and local‐scale climate variables in theYellow Riverheadwaters region

Abstract: The Yellow River headwaters region (YRHR) contributes nearly 40% of total flow in the Yellow River basin, which is suffering from a serious water shortage problem. Investigation of the relationship between runoff and climate variables is important for understanding the variation trend of runoff in the YRHR under global climate change. Global and local climate variables, including the West Pacific subtropical high; northern hemisphere polar vortex (NH); Tibetan Plateau Index B (TPI‐B); southern oscillation inde… Show more

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Cited by 14 publications
(5 citation statements)
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References 41 publications
(51 reference statements)
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“…Pandhiani and Shabri (2015) developed new hybrid models by integrating the discrete wavelet transform with an artificial neural network (WANN) model and discrete wavelet transform with least square support vector machine (WLSSVM) model to measure monthly streamflow forecasting for two rivers in Pakistan (Pandhiani and Shabri, 2015). Chu et al (2018) forecasted runoff for the Yellow River in China by using multiple linear regressions (MLR), radial basis functions neural network (RBFNN) and supports vector regression (SVR) models (Chu et al, 2018). Zhou et al (2018) forecasted the streamflow of the Jinsha River by using three (ANN) architectures: a radial basis function network, an extreme learning machine, and the Elman network (Zhou et al, 2018).…”
Section: Introductionmentioning
confidence: 99%
“…Pandhiani and Shabri (2015) developed new hybrid models by integrating the discrete wavelet transform with an artificial neural network (WANN) model and discrete wavelet transform with least square support vector machine (WLSSVM) model to measure monthly streamflow forecasting for two rivers in Pakistan (Pandhiani and Shabri, 2015). Chu et al (2018) forecasted runoff for the Yellow River in China by using multiple linear regressions (MLR), radial basis functions neural network (RBFNN) and supports vector regression (SVR) models (Chu et al, 2018). Zhou et al (2018) forecasted the streamflow of the Jinsha River by using three (ANN) architectures: a radial basis function network, an extreme learning machine, and the Elman network (Zhou et al, 2018).…”
Section: Introductionmentioning
confidence: 99%
“…Even for plants in different locations in the same region, there are differences of soil moisture and temperature ( Minami et al., 2021 ). In addition, Tibet’s water reserves are also affected by global warming, and water resources are becoming more abundant ( Chu et al., 2018 ; Fu et al., 2019 ). The shifts in temperature and soil moisture cause the same plant to produce divergent metabolism ( Xiao et al., 2014 ).…”
Section: Discussionmentioning
confidence: 99%
“…TP's runoff is double influenced by large-scale atmospheric circulations and local climate variables (Chu et al 2018). Based on the significantly positive correlation between precipitation and runoff, we examine the influence of ISM characteristics on potential water availability, mirrored in the runoff (Wang et al 2021b).…”
Section: Effects Of Ism Characteristics On Potential Water Availabilitymentioning
confidence: 99%