scholarly journals High-Arsenic Groundwater from Huaihe River Basin, China：Geochemistry and Hydrogeochemical Processes

2021 ◽  
Author(s):  
Naizheng Xu ◽  
Jianshi Gong ◽  
Yonghong Ye
Keyword(s):  
Drinking Water ◽  
River Basin ◽  
Total Alkalinity ◽  
World Health ◽  
Huaihe River Basin ◽  
Exposure Risk ◽  
Huaihe River ◽  
High Exposure ◽  
High Arsenic ◽  
High Arsenic Groundwater

Abstract Arsenic (As) poses a danger to environmental health, and drinking arsenic-rich groundwater is a key exposure risk for humans. The distribution, migration, and enrichment of As in groundwater is an important worldwide environmental and public health problem that requires research. Huaihe River Basin has been newly identified as a region of high-arsenic groundwater in China. This study aims to analyze the hydrogeochemical data of high-arsenic groundwater, trace its formation and evolution, and evaluate its potential pollution risks. The results showed that As and F were the main inorganic chemical substances affecting the water quality in the study area, with concentrations of 5.75±5.42 μg L-1 and 1.29±0.40 mg L-1, respectively, exceeding the recommended drinking water standards of the World Health Organization by 23% and 31%, respectively. The proportion of groundwater with a high As content presents a high exposure risk. According to the hydrochemical diagram and the calculation of mineral saturation indices, the groundwater in the study area underwent evaporation, halite dissolution, and water-rock interaction. The total alkalinity of high-arsenic groundwater ranges mainly between 400–700 mg L-1, and the chemical type is mainly of HCO3-Na. High-arsenic groundwater is largely affected by evaporation and cation exchange. In an alkaline environment, As in high-arsenic groundwater derives from the dissolution and release of arsenic sulfide in aquifer sediments and poses a potential threat to human health through food and drinking water.

scholarly journals Responses of Hydrological Processes under Different Shared Socioeconomic Pathway Scenarios in the Huaihe River Basin, China

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1053
Author(s):  
Yuan Yao ◽  
Wei Qu ◽  
Jingxuan Lu ◽  
Hui Cheng ◽  
Zhiguo Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Climate Models ◽  
Global Climate Models ◽  
Hydrological Response ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Infiltration Capacity ◽  
Change Analysis ◽  
Average Annual Runoff

The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides more scenarios and reliable climate change results for improving the accuracy of future hydrological parameter change analysis. This study uses five CMIP6 global climate models (GCMs) to drive the variable infiltration capacity (VIC) model, and then simulates the hydrological response of the upper and middle Huaihe River Basin (UMHRB) under future shared socioeconomic pathway scenarios (SSPs). The results show that the five-GCM ensemble improves the simulation accuracy compared to a single model. The climate over the UMHRB likely becomes warmer. The general trend of future precipitation is projected to increase, and the increased rates are higher in spring and winter than in summer and autumn. Changes in annual evapotranspiration are basically consistent with precipitation, but seasonal evapotranspiration shows different changes (0–18%). The average annual runoff will increase in a wavelike manner, and the change patterns of runoff follow that of seasonal precipitation. Changes in soil moisture are not obvious, and the annual soil moisture increases slightly. In the intrayear process, soil moisture decreases slightly in autumn. The research results will enhance a more realistic understanding of the future hydrological response of the UMHRB and assist decision-makers in developing watershed flood risk-management measures and water and soil conservation plans.

Changing of flood risk due to climate and development in Huaihe River basin, China

2016 ◽  
Vol 31 (4) ◽  
pp. 935-948 ◽  
Author(s):  
Yenan Wu ◽  
Ping-an Zhong ◽  
Bin Xu ◽  
Feilin Zhu ◽  
Biao Ma
Keyword(s):  
River Basin ◽  
Flood Risk ◽  
Huaihe River Basin ◽  
Huaihe River

Hydrological simulation using multi-sources precipitation estimates in the Huaihe River Basin

2021 ◽  
Vol 14 (18) ◽  
Author(s):  
Mohammad Ilyas Abro ◽  
Dehua Zhu ◽  
Ehsan Elahi ◽  
Asghar Ali Majidano ◽  
Bhai Khan Solangi
Keyword(s):  
River Basin ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Hydrological Simulation ◽  
Precipitation Estimates ◽  
The Huaihe River

Atmospheric-hydrological modeling of severe precipitation and floods in the Huaihe River Basin, China

2006 ◽  
Vol 330 (1-2) ◽  
pp. 249-259 ◽  
Author(s):  
Charles A. Lin ◽  
Lei Wen ◽  
Guihua Lu ◽  
Zhiyong Wu ◽  
Jianyun Zhang ◽  
...  
Keyword(s):  
River Basin ◽  
Hydrological Modeling ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
The Huaihe River

scholarly journals Improving TIGGE Precipitation Forecasts Using an SVR Ensemble Approach in the Huaihe River Basin

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Chenkai Cai ◽  
Jianqun Wang ◽  
Zhijia Li
Keyword(s):  
River Basin ◽  
Lead Time ◽  
Flood Control ◽  
Ensemble Methods ◽  
New Method ◽  
Support Vector ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Reservoir Regulation ◽  
The Huaihe River

Recently, the use of the numerical rainfall forecast has become a common approach to improve the lead time of streamflow forecasts for flood control and reservoir regulation. The control forecasts of five operational global prediction systems from different centers were evaluated against the observed data by a series of area-weighted verification and classification metrics during May to September 2015–2017 in six subcatchments of the Xixian Catchment in the Huaihe River Basin. According to the demand of flood control safety, four different ensemble methods were adopted to reduce the forecast errors of the datasets, especially the errors of missing alarm (MA), which may be detrimental to reservoir regulation and flood control. The results indicate that the raw forecast datasets have large missing alarm errors (MEs) and cannot be directly applied to the extension of flood forecasting lead time. Although the ensemble methods can improve the performance of rainfall forecasts, the missing alarm error is still large, leading to a huge hazard in flood control. To improve the lead time of the flood forecast, as well as avert the risk from rainfall prediction, a new ensemble method was proposed on the basis of support vector regression (SVR). Compared to the other methods, the new method has a better ability in reducing the ME of the forecasts. More specifically, with the use of the new method, the lead time of flood forecasts can be prolonged to at least 3 d without great risk in flood control, which corresponds to the aim of flood prevention and disaster reduction.

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