scholarly journals Spatiotemporal variation of nitrogen and phosphorus and its main influencing factors in Huangshui River basin

2021 ◽  
Vol 193 (5) ◽  
Author(s):  
Biqiong Dong ◽  
Tianling Qin ◽  
Yu Wang ◽  
Yan Zhao ◽  
Shanshan Liu ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Influencing Factors ◽  
River Basin ◽  
Temporal Change ◽  
Yellow River ◽  
Landscape Patterns ◽  
Runoff Coefficient ◽  
Rainfall Runoff ◽  
Nitrogen And Phosphorus ◽  
Water Contaminants

AbstractThe foundation of managing excess nutrients in river is the identification of key physical processes and the control of decisive influencing factors. The existing studies seldom consider the influence of rainfall-runoff relationship and only focus on a few anthropogenic activities and natural attributes factors. To address this issue, a comprehensive set of influencing factors including rainfall-runoff relationship (represented by runoff coefficient), basic physical and chemical parameters of water quality, land use types, landscape patterns, topography, and socioeconomic development was constructed in this study. M–K test and cluster analysis were conducted to identify the temporal mutation and spatial clustering characteristics of NH3-N and TP in Huangshui River basin, respectively. Partial least squares regression was used to elucidate the linkages between water contaminants and the factors. As shown in the results, the temporal mutations of NH3-N and TP were obvious in the middle reaches, with 4 out of 7 catchments in the middle reaches have a larger number of mutations of NH3-N than other catchments. The cluster analysis results of NH3-N and TP among catchments were similar. This study also indicated that although the Huangshui River basin was located in the upper reaches of the Yellow River, the influences of rainfall-runoff relationship on spatiotemporal changes of NH3-N and TP in its sub-basins were limited. Only the temporal change of NH3-N in Jintan catchment in the upstream area was significantly affected by runoff coefficient. The indexes of proportion of water area (PWA), proportion of impervious area (PIA), and proportion of primary industry (PPI) were the top three influencing factors of temporal variation of NH3-N and TP for most catchments in the middle reaches. The temporal change of NH3-N in Jintan catchment in the upstream area was obviously affected by runoff coefficient. The spatial variation of NH3-N and TP were all affected by PWA and proportion of secondary industry significantly. The results of this study can provide theoretical basis and technical support for the control and management of nitrogen and phosphorus pollution in upper reaches of rivers.

scholarly journals Rainfall-runoff processes in the Loess Plateau, China: Temporal dynamics of event rainfall-runoff characteristics and diagnostic analysis of runoff generation patterns

2020 ◽  
Author(s):  
Qiang Wu ◽  
Zhaoxi Zhang ◽  
Guodong Zhang ◽  
Shengqi Jian ◽  
Li Zhang ◽  
...  
Keyword(s):  
Loess Plateau ◽  
Time Scale ◽  
Overland Flow ◽  
Yellow River ◽  
Separation Procedure ◽  
Runoff Coefficient ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
The Loess Plateau

Abstract. The Loess Plateau is the most erosion-prone area in China, while under large-scale ecological restoration runoff and sediments continue to decrease. This study examined the runoff generation mechanism at the catchment scale to understand the change in runoff generation. Six baseflow used to separation method were tested and the nonparametric simple smoothing method was seperating base flow. With the event runoff separation procedure, 340 rainfall–runoff events are selected in five typical catchments affected by significant human intervention in the Loess Plateau. Runoff characteristics, such as the event runoff coefficient, time scale, rise time, and peak discharge are studied on monthly and long-term scales. In catchments of Jialuhe, Chabagou and Gushanchuan with poor vegetation runoff response is strongly decided by rainfall intensity and is produced by Horton overland flow (HOF). While the mountainous catchments of Jingle and Zulihe runoff response is controlled by rainfall volume. The relation between runoff event characteristics and rainfall is complicated in Loess Plateau, where rainfall and underlying surface is significantly changing. The monthly of event characteristics is mostly controlled by rainfall characteristics. Long-term runoff coefficient experiences decreasing trend, while time scale trend is increasing. Land use changes lead to increasing catchment wetness display mostly strong reason in event characteristic response. According to our proposed framework for classifying dominant runoff generation patterns considering of hydrograph response time, discharge source, and flow paths, HOF runoff is still the dominant mechanism, but gradually shifts to Dunne overland flow (DOF) and combination runoff. We speculate that the reduction in runoff in the Yellow River is likely to be the dominant runoff mechanism changing.

scholarly journals Study on the Spatiotemporal Evolution and Influencing Factors of Urban Resilience in the Yellow River Basin

2021 ◽  
Vol 18 (19) ◽  
pp. 10231
Author(s):  
Yu Chen ◽  
Xuyang Su ◽  
Qian Zhou
Keyword(s):  
Influencing Factors ◽  
River Basin ◽  
Yellow River ◽  
Yellow River Basin ◽  
Indicator System ◽  
Theil Index ◽  
The Yellow River ◽  
Urban Resilience ◽  
The Yellow River Basin ◽  
The Impact

The outbreak of COVID-19 has prompted consideration of the importance of urban resilience. Based on a multidimensional perspective, the authors of this paper established a comprehensive evaluation indicator system for evaluating urban resilience in the Yellow River basin (YRB), and various methods such as the entropy value method, Theil index, exploratory spatial data analysis (ESDA) model, and geographical detector model were used to measure the spatiotemporal characteristics and influencing factors of urban resilience in the YRB from 2011 to 2018. The results are as follows. (1) From 2011 to 2018, the urban resilience index (URI) of the YRB showed a “V”-shaped dynamic evolution in the time series, and the URI increased by 13.4% overall. The resilience of each subsystem showed the following hierarchical structure: economic resilience > social resilience > ecological resilience > infrastructure resilience. (2) The URI of the three major regions—upstream, midstream, and downstream—increased, and the resilience of each subsystem in the region showed obvious regional characteristics. The comprehensive difference in URI values within the basin was found to be shrinking, and intraregional differences have contributed most to the comprehensive difference. (3) There were obvious zonal differences in the URI from 2011 to 2018. Shandong Peninsula and Hohhot–Baotou–Ordos showed a “High–High” agglomeration, while the southern and southwestern regions showed a “Low–Low” agglomeration. (4) Among the humanist and social factors, economic, fiscal, market, urbanization, openness, and innovation were found to be the factors that exert a high impact on the URI, while the impacts of natural factors were found to be low. The impact of the interaction of each factor is greater than that of a single factor.

scholarly journals What Factors Affect the Level of Green Urbanization in the Yellow River Basin in the Context of New-Type Urbanization?

2020 ◽  
Vol 12 (6) ◽  
pp. 2488 ◽  
Author(s):  
Luping Shi ◽  
Zhongyao Cai ◽  
Xuhui Ding ◽  
Rong Di ◽  
Qianqian Xiao
Keyword(s):  
Influencing Factors ◽  
River Basin ◽  
Environmental Governance ◽  
Yellow River ◽  
Generalized Method Of Moments ◽  
Yellow River Basin ◽  
The Yellow River ◽  
Urbanization Level ◽  
New Type ◽  
The Yellow River Basin

Promoting new-type urbanization with the concept of green development has become an inevitable requirement for high-quality development in the Yellow River Basin. Grasping the development trend and influencing factors of green urbanization level in the Yellow River Basin is of great significance for implementing the international conventions on environmental protection and participating in global environmental governance. This paper selects the green urbanization level panel data of nine provinces in the Yellow River Basin from 2006 to 2018. Then, principal component analysis and factor analysis are applied to measure and evaluate the green urbanization level of each province. Furthermore, this paper constructs a dynamic panel estimation model and uses differential generalized method of moments (DIF-GMM) model and system generalized method of moments (SYS-GMM) model to explore the influencing factors. The results show that the overall level of green urbanization in the Yellow River Basin has steadily and rapidly increased, and there are significant spatial differences. The green urbanization level of eastern provinces is significantly higher than that of central and western provinces. In addition, the overall level of green urbanization shows a convergence trend. From the perspective of influencing factors, the factors that have significant positive effects on the level of green urbanization include economic development level, technological innovation level, and urban size. Industrial structure, foreign direct investment (FDI), and education level counteract the level of green urbanization. However, environmental regulation strength and opening degree fail to pass the significance test. Therefore, it is necessary to promote and upgrade industrial transformation, improve the quality of opening up, and strengthen cooperation in technological innovation and environmental governance. There are requirements that the government control the urban size and population scientifically and implement the environmental access system strictly in order to improve the level of green urbanization in the Yellow River Basin. It is more possible to achieve harmonious economic and ecological environment development.

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