Climate change and tectonic implications during the Pliocene climate transition interval from lacustrine records in western Wei River Basin, central China

2019 ◽  
Vol 55 (11) ◽  
pp. 7385-7399
Author(s):  
Tianyu Zhang ◽  
Shuanghu Fan ◽  
Shue Chen ◽  
Yudong Lu
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Central China ◽  
Tectonic Implications ◽  
Climate Transition ◽  
Wei River Basin ◽  
Wei River

scholarly journals Quantitative contribution of climate change and human activities to runoff changes in the Wei River basin, China

2014 ◽  
Vol 18 (8) ◽  
pp. 3069-3077 ◽  
Author(s):  
C. S. Zhan ◽  
S. S. Jiang ◽  
F. B. Sun ◽  
Y. W. Jia ◽  
C. W. Niu ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Human Activities ◽  
Yellow River ◽  
Climate Elasticity ◽  
The Wei River Basin ◽  
Runoff Decrease ◽  
The Impact ◽  
Wei River Basin ◽  
Wei River

Abstract. Surface runoff from the Wei River basin, the largest tributary of the Yellow River in China, has dramatically decreased over last 51 years from 1958 to 2008. Climate change and human activities have been identified as the two main reasons for the decrease in runoff. The study period is split into two sub-periods (1958–1989 and 1990–2008) using the Mann–Kendall jump test. This study develops an improved climate elasticity method based on the original climate elasticity method, and conducts a quantitative assessment of the impact of climate change and human activities on the runoff decrease in the Wei River basin. The results from the original climate elasticity method show that climatic impacts contribute 37–40% to the decrease in runoff, while human impacts contribute 60–63%. In contrast, the results from the improved climate elasticity method yield a climatic contribution to runoff decrease of 22–29% and a human contribution of 71–78%. A discussion of the simulation reliability and uncertainty concludes that the improved climate elasticity method has a better mechanism and can provide more reasonable results.

scholarly journals Response of runoff to climate change in the Wei River basin, China

2015 ◽  
Vol 60 (3) ◽  
pp. 508-522 ◽  
Author(s):  
Depeng Zuo ◽  
Zongxue Xu ◽  
Jie Zhao ◽  
Karim C. Abbaspour ◽  
Hong Yang
Keyword(s):  
Climate Change ◽  
River Basin ◽  
The Wei River Basin ◽  
Wei River Basin ◽  
Wei River

scholarly journals Quantitative contribution of climate change and human activities to runoff changes in the Wei River basin, China

2014 ◽  
Vol 11 (2) ◽  
pp. 2149-2175 ◽  
Author(s):  
C. S. Zhan ◽  
S. S. Jiang ◽  
F. B. Sun ◽  
Y. W. Jia ◽  
W. F. Yue ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Human Activities ◽  
Yellow River ◽  
Climate Elasticity ◽  
The Wei River Basin ◽  
Runoff Decrease ◽  
The Impact ◽  
Wei River Basin ◽  
Wei River

Abstract. Surface runoff from the Wei River basin, the largest tributary of the Yellow River in China, has dramatically decreased over last 51 yr from 1958 to 2008. Climate change and human activities have been identified as the two main reasons for the decrease in runoff. The study period is split into two sub-periods (1958–1989 and 1990–2008) using the Mann–Kendall jump test. This study develops an improved climate elasticity method based on the original climate elasticity method, and conducts a quantitative assessment of the impact of climate change and human activities on the runoff decrease in the Wei River basin. The results from the original climate elasticity method show that climatic impacts contribute 37% ~ 40% to the decrease in runoff, while human impacts contribute 60% ~ 63%. In contrast, the results from the improved climate elasticity method yield a climatic contribution to runoff decrease of 22% ~ 29% and a human contribution of 71% ~ 78%. A discussion of the simulation reliability and uncertainty concludes that the improved climate elasticity method has better mechanism and can provide more reasonable results.

Assessing the impact of human interventions on floods and low flows in the Wei River Basin in China using the LISFLOOD model

2019 ◽  
Vol 653 ◽  
pp. 1077-1094 ◽  
Author(s):  
Lingtong Gai ◽  
João P. Nunes ◽  
Jantiene E.M. Baartman ◽  
Hongming Zhang ◽  
Fei Wang ◽  
...  
Keyword(s):  
River Basin ◽  
Low Flows ◽  
The Wei River Basin ◽  
Human Interventions ◽  
The Impact ◽  
Wei River Basin ◽  
Wei River

scholarly journals Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3532
Author(s):  
Qianyang Wang ◽  
Yuan Liu ◽  
Qimeng Yue ◽  
Yuexin Zheng ◽  
Xiaolei Yao ◽  
...  
Keyword(s):  
River Basin ◽  
Prediction Accuracy ◽  
Principal Component ◽  
Time Step ◽  
Flood Peak ◽  
Runoff Forecasting ◽  
The Wei River Basin ◽  
The Impact ◽  
Wei River Basin ◽  
Wei River

A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.

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