scholarly journals Changes in turbidity and human activities along Haihe River Basin during lockdown of COVID-19 using satellite data

2021 ◽  
Author(s):  
Xu Chen ◽  
Wei Chen ◽  
Yanbing Bai ◽  
Xiaole Wen
Keyword(s):  
River Basin ◽  
Satellite Data ◽  
Human Activities ◽  
Haihe River Basin ◽  
Haihe River

Evaluation of changes in streamflow and the underlying causes: a perspective of an upstream catchment in Haihe River basin, China

2018 ◽  
Vol 11 (1) ◽  
pp. 241-257 ◽  
Author(s):  
Sicheng Wan ◽  
Jianyun Zhang ◽  
Guoqing Wang ◽  
Lu Zhang ◽  
Lei Cheng ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Human Activities ◽  
Hydrological Cycle ◽  
Kendall Test ◽  
Haihe River Basin ◽  
Changing Environment ◽  
Haihe River ◽  
Hydrological Simulation

Abstract Investigating long-term streamflow changes pattern and its response to climate and human factors is of crucial significance to understand the hydrological cycle under a changing environment. Caijiazhuang catchment located within Haihe River basin, north China was selected as the study area. To detect the trend and changes in streamflow, Mann–Kendall test was used. Elasticity and hydrological simulation methods were applied to assess the relative contribution of climate change and human activities on streamflow variability under three periods (baseline (1958–1977), impact I (1978–1997), and impact II (1998–2012)). The long-term hydro-climatic variables experienced substantial changes during the whole study period, and 1977 was the breaking year of streamflow change. Attribution analysis using the two methods showed consistent results: for impact I, climate change impacts explained 65% and 68% of streamflow reduction; however for impact II, it only represented 49% and 56% of streamflow reduction. This result indicated that human activities were intensifying over time. Various types of human activities presented significant effects on streamflow regimes including volumes and hydrographs. The findings of this paper could provide better insights of hydrological evolution and would thus assist water managers in sustainably managing and providing water use strategies under a changing environment.

Attribution for decreasing streamflow of the Haihe River basin, northern China: Climate variability or human activities?

2012 ◽  
Vol 460-461 ◽  
pp. 117-129 ◽  
Author(s):  
Zhenxin Bao ◽  
Jianyun Zhang ◽  
Guoqing Wang ◽  
Guobin Fu ◽  
Ruimin He ◽  
...  
Keyword(s):  
Climate Variability ◽  
River Basin ◽  
Human Activities ◽  
Northern China ◽  
Haihe River Basin ◽  
Haihe River ◽  
The Haihe River Basin

scholarly journals Climate, CO2, and Anthropogenic Drivers of Accelerated Vegetation Greening in the Haihe River Basin

2022 ◽  
Vol 14 (2) ◽  
pp. 268
Author(s):  
Wenjing Yang ◽  
Yong Zhao ◽  
Qingming Wang ◽  
Buliao Guan
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Human Activities ◽  
Co2 Concentration ◽  
Haihe River Basin ◽  
Mountainous Areas ◽  
Haihe River ◽  
The Haihe River Basin ◽  
The Impact ◽  
Anthropogenic Drivers

Vegetation regulates the exchange of terrestrial carbon and water fluxes and connects the biosphere, hydrosphere, and atmosphere. Over the last four decades, vegetation greening has been observed worldwide using satellite technology. China has also experienced a notably widespread greening trend. However, the responsiveness of vegetation dynamics to elevated CO2 concentration, climate change, and human activities remains unclear. In this study, we attempted to explore the impact of natural (precipitation, air temperature), biogeochemical (CO2), and anthropogenic drivers (nighttime light, afforestation area) on changes in vegetation greenness in the Haihe River Basin (HRB) during 2002–2018 at the county-level. We further determined the major factors affecting the variation in satellite-derived normalized difference vegetation index (NDVI) from moderate resolution imaging spectroradiometer (MODIS) for each county. The results indicated that over 85% of the counties had a significantly increased NDVI trend, and the average linear trend of annual NDVI across the study region was 0.0037 per year. The largest contributor to the NDVI trend was CO2 (mean contribution 45%), followed by human activities (mean contribution of 27%). Additionally, afforestation was a pronounced driving force for NDVI changes in mountainous areas, resulting from ecosystem restoration efforts. Our findings emphasize the crucial role of CO2 fertilization in vegetation cover change, while considering CO2 concentration, climate change, and human activities, and shed light on the significant influences of afforestation programs on water resources, especially in mountainous areas.

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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