Assessing water quality by ratio of the number of dominant bacterium species between surface/subsurface sediments in Haihe River Basin

2015 ◽  
Vol 98 (1-2) ◽  
pp. 267-273 ◽  
Author(s):  
Xin Ke ◽  
Chunyong Wang ◽  
Debing Jing ◽  
Yun Zhang ◽  
Haijun Zhang
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Haihe River Basin ◽  
Haihe River ◽  
Subsurface Sediments ◽  
Bacterium Species

scholarly journals Study on the Causes of Water Environmental Pollution of Important Rivers in Haihe River Basin and Countermeasures

2021 ◽  
Vol 261 ◽  
pp. 04023
Author(s):  
Xu He ◽  
Hou Siyan
Keyword(s):  
Water Quality ◽  
Point Source ◽  
Environmental Pollution ◽  
River Basin ◽  
River Regulation ◽  
Haihe River Basin ◽  
Point Source Pollution ◽  
Haihe River ◽  
The Impact

The water quality of six important rivers in Haihe River Basin, including Yongding River, Luanhe River, North Canal, Daqing River, South Canal and Chaobai River, was evaluated. The influence of point source and non-point source on water quality was analyzed. The causes of water environmental pollution in the major rivers were preliminarily revealed. The results show that the water quality of Chaobai River is good, and the impact of point source and non-point source discharge on the water body is small. Other rivers are affected by different degrees of point source and non-point source pollution. Based on the analysis results, the engineering measures and management countermeasures for river regulation are put forward.

Use of PCA-RBF model for prediction of chlorophyll-a in Yuqiao Reservoir in the Haihe River Basin, China

2013 ◽  
Vol 14 (1) ◽  
pp. 73-80 ◽  
Author(s):  
Liu Xiaobo ◽  
Dong Fei ◽  
He Guojian ◽  
Liu Jingling
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Chlorophyll A ◽  
River Basin ◽  
Forecast Error ◽  
Principal Component ◽  
Haihe River Basin ◽  
Haihe River ◽  
Yuqiao Reservoir ◽  
The Haihe River Basin

Chlorophyll-a is a well-accepted index for phytoplankton abundance and population of primary producers in an aquatic environment. The relationships between chlorophyll-a and 18 chemical, physical and biological water quality variables in YuQiao Reservoir (YQR) in the Haihe River Basin in P.R. China were studied by using principal component analysis (PCA) coupled with a radial basis function network (RBF) model to predict chlorophyll-a levels. Principal component analysis was used to simplify the complexity of relations between water quality variables. Score values obtained by PC scores were used as independent variables in the RBF models. In the forecast, only five selected score values obtained by PC analysis were used for the prediction of chlorophyll-a levels. Correlative analysis between the modeled results and observed data indicates that the correlative coefficient is 0.61, and analysis of the forecast error rate shows that the average forecast error is 32.9%, proving the viability of the forecast model.

A Coupled Model of Hydrodynamics and Water Quality for Yuqiao Reservoir in Haihe River Basin

2008 ◽  
Vol 20 (5) ◽  
pp. 574-582 ◽  
Author(s):  
Xiao-bo Liu ◽  
Wen-qi Peng ◽  
Guo-jian He ◽  
Jing-ling Liu ◽  
Yu-chun Wang
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Coupled Model ◽  
Haihe River Basin ◽  
Haihe River ◽  
Yuqiao Reservoir

Long-term variation of water vapor content and precipitation in the Haihe river basin

2014 ◽  
Vol 6 (2) ◽  
pp. 341-351 ◽  
Author(s):  
Chun Chang ◽  
Ping Feng ◽  
Fawen Li ◽  
Yunming Gao
Keyword(s):  
Water Vapor ◽  
River Basin ◽  
Reanalysis Data ◽  
Water Vapor Content ◽  
Haihe River Basin ◽  
Haihe River ◽  
Annual Water ◽  
Environmental Prediction ◽  
High Consistency ◽  
The Haihe River Basin

Based on the Haihe river basin National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data from 1948 to 2010 and the precipitation data of 53 hydrological stations during 1957–2010, this study analyzed the variation of water vapor content and precipitation, and investigated the correlation between them using several statistical methods. The results showed that the annual water vapor content decreased drastically from 1948 to 2010. It was comparatively high from the late 1940s to the late 1960s and depreciated from the early 1970s. From the southeast to the northwest of the Haihe river basin, there was a decrease in water vapor content. For vertical distribution, water vapor content from the ground to 700 hPa pressure level accounted for 72.9% of the whole atmospheric layer, which indicated that the water vapor of the Haihe river basin was mainly in the air close to the ground. The precipitation in the Haihe river basin during 1957–2010 decreased very slightly. According to the correlation analysis, the precipitation and water vapor content changes showed statistically positive correlation, in addition, their break points were both in the 1970s. Furthermore, the high consistency between the precipitation efficiency and precipitation demonstrates that water vapor content is one of the important factors in the formation of precipitation.

scholarly journals Spatial and Temporal Variation Characteristics of Snowfall in the Haihe River Basin from 1960 to 2016

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1798
Author(s):  
Xu Wu ◽  
Su Li ◽  
Bin Liu ◽  
Dan Xu
Keyword(s):  
Temporal Variation ◽  
River Basin ◽  
Spatial And Temporal Variation ◽  
Haihe River Basin ◽  
Temperature Threshold ◽  
Threshold Method ◽  
Exponential Equation ◽  
Haihe River ◽  
The Haihe River Basin ◽  
Different Levels

The spatio-temporal variation of precipitation under global warming had been a research hotspot. Snowfall is an important part of precipitation, and its variabilities and trends in different regions have received great attention. In this paper, the Haihe River Basin is used as a case, and we employ the K-means clustering method to divide the basin into four sub-regions. The double temperature threshold method in the form of the exponential equation is used in this study to identify precipitation phase states, based on daily temperature, snowfall, and precipitation data from 43 meteorological stations in and around the Haihe River Basin from 1960 to 1979. Then, daily snowfall data from 1960 to 2016 are established, and the spatial and temporal variation of snowfall in the Haihe River Basin are analyzed according to the snowfall levels as determined by the national meteorological department. The results evalueted in four different zones show that (1) the snowfall at each meteorological station can be effectively estimated at an annual scale through the exponential equation, for which the correlation coefficient of each division is above 0.95, and the relative error is within 5%. (2) Except for the average snowfall and light snowfall, the snowfall and snowfall days of moderate snow, heavy snow, and snowstorm in each division are in the order of Zones III > IV > I > II. (3) The snowfall and the number of snowfall days at different levels both show a decreasing trend, except for the increasing trend of snowfall in Zone I. (4) The interannual variation trend in the snowfall at the different levels are not obvious, except for Zone III, which shows a significant decreasing trend.

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