scholarly journals Microplastics in surface waters of the Wei River, China

2021 ◽  
Vol 251 ◽  
pp. 02090
Author(s):  
Jie Jiao ◽  
Hong Hu ◽  
Gang Chen ◽  
Zechuan Yang
Keyword(s):  
Surface Waters ◽  
Pollution Level ◽  
Shandong Peninsula ◽  
Dominant Type ◽  
The Wei River Basin ◽  
Average Abundance ◽  
New Type ◽  
Wei River Basin ◽  
Wei River

Microplastics are a new type of persistent organic pollutants, usually on the micron scale. In this study, we investigated the abundance, distribution, and other characterization of microplastics in surface waters of the Wei River Basin in the Shandong peninsula. The results showed that the abundance of microplastics in the surface water of Wei River varied from 0.40 to 1.20 items/L, and the average abundance was 0.81 items/L, which was at a moderate pollution level compared with other rivers. It was found that the abundance of microplastics was higher in densely populated areas, and hydrodynamic conditions such as river inflow and seawater scour also affected the abundance of microplastics. Fiber (83.4%) was the dominant type. Colorless (93.6%) was the dominant color type. The particle size (0.06 - 0.5mm) (47.9%) was the main size of microplastics in the Wei River. This study provides data for the further study of microplastics in rivers and provides a warning for the management and prevention of microplastics in freshwater.

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.

Spatio-Temporal Changes in Potential Evaporation Based on Entropy Across the Wei River Basin

2014 ◽  
Vol 28 (13) ◽  
pp. 4599-4613 ◽  
Author(s):  
Shengzhi Huang ◽  
Jianxia Chang ◽  
Qiang Huang ◽  
Yimin Wang ◽  
Yutong Chen
Keyword(s):  
River Basin ◽  
Temporal Changes ◽  
Potential Evaporation ◽  
The Wei River Basin ◽  
Spatio Temporal ◽  
Wei River Basin ◽  
Wei River

Spatial-temporal change in precipitation patterns based on the cloud model across the Wei River Basin, China

2014 ◽  
Vol 120 (1-2) ◽  
pp. 391-401 ◽  
Author(s):  
Shengzhi Huang ◽  
Beibei Hou ◽  
Jianxia Chang ◽  
Qiang Huang ◽  
Yutong Chen
Keyword(s):  
River Basin ◽  
Temporal Change ◽  
Cloud Model ◽  
Precipitation Patterns ◽  
The Wei River Basin ◽  
Wei River Basin ◽  
Wei River

Multi-objective design of rainfall network based on information theory for streamflow simulation

2020 ◽  
Author(s):  
Wenqi Wang ◽  
Dong Wang ◽  
Vijay P. Singh ◽  
Yuankun Wang
Keyword(s):  
Information Theory ◽  
Network Design ◽  
Information Transfer ◽  
Rainfall Runoff ◽  
Multi Objective ◽  
The Wei River Basin ◽  
Hydrometric Station ◽  
Wei River Basin ◽  
Wei River ◽  
Streamflow Simulation

<p><span lang="EN-US">Rainfall networks provide rainfall data needed for water resource management and decision-making. These data are especially important for runoff simulation and forecast when intense rainfall occurs in the flood season. Rainfall networks should, therefore, be carefully designed and evaluated. Information theory-based methods have lately received significant attention for rainfall network design. This study focuses on the integrated design of a rainfall network, especially for streamflow simulation. We proposed a multi-objective rainfall network design method based on information theory and applied it to the Wei River basin in China. The rainfall network design can be viewed as the input for a rainfall-runoff model, as it was intended to consider streamflow data at the outlet hydrometric station. We use the total correlation as an indicator of information redundancy and multivariate transinformation as an indicator of information transfer. Information redundancy refers to the overlapped information between rainfall stations, and information transfer refers to the rainfall-runoff relationship. The outlet hydrometric station (Huaxian station in the Wei River basin) is used as the target station for the streamflow simulation. A non-dominated sorting genetic algorithm (NSGA-II) was used for the multi-objective optimization of the rainfall network design. We compared the proposed multi-objective design with two other methods using an artificial neural network (ANN) model. The optimized rainfall network from the proposed method led to reasonable outlet streamflow forecasts with a balance between network efficiency and streamflow simulation. Our results indicate that the multi-objective strategy provides an effective design by which the rainfall network can consider the rainfall-runoff process and benefit streamflow prediction on a catchment scale.</span></p>

scholarly journals Impact of LUCC on Streamflow using the SWAT Model over the Wei River Basin on the Loess Plateau of China

2016 ◽  
Author(s):  
Hong Wang ◽  
Fubao Sun
Keyword(s):  
Land Use ◽  
River Basin ◽  
Loess Plateau ◽  
Agricultural Land ◽  
Yellow River ◽  
Swat Model ◽  
The Wei River Basin ◽  
Soil Flow ◽  
Wei River Basin ◽  
Wei River

Abstract. Under the Grain for Green project in China, vegetation recovery constructions have been widely implemented on the Loess Plateau for the purpose of soil and water conservation. Now it becomes controversial whether the recovery constructions of vegetation, particularly forest, is reducing streamflow in rivers of the Yellow River Basin. In this study, we choose the Wei River, the largest branch of the Yellow River and implemented with revegetation constructions, as the study area. To do that, we apply the widely used Soil and Water Assessment Tool (SWAT) model for the upper and middle reaches of the – Wei River basin. The SWAT model was forced with daily observed meteorological forcings (1960–2009), calibrated against daily streamflow for 1960–1969, validated for the period of 1970–1979 and used for analysis for 1980–2009. To investigate the impact of the LUCC (Land Use and land Cover Change) on the streamflow, we firstly use two observed land use maps of 1980 and 2005 that are based on national land survey statistics emerged with satellite observations. We found that the mean streamflow generated by using the 2005 land use map decreased in comparison with that using the 1980 one, with the same meteorological forcings. Of particular interest here, we found the streamflow decreased in agricultural land but increased in forest area. More specifically, the surface runoff, soil flow and baseflow all decreased in agricultural land, while the soil flow and baseflow of forest were increased. To investigate that, we then designed five scenarios including (S1) the present land use (1980), (S2) 10 %, (S3) 20 %, (S4) 40 % and (S5) 100 % of agricultural land was converted into forest. We found that the streamflow consistently increased with agricultural land converted into forest by about 7.4 mm per 10 %. Our modeling results suggest that forest recovery constructions have positive impact on both soil flow and base flow compensating reduced surface runoff, which leads to a slight increase in streamflow in the Wei River with mixed landscapes of Loess Plateau and earth-rock mountain.

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