Calibration and Validation of GLEAMS for Predicting Non-Point Source Pollution from Agricultural Lands

2013 ◽  
Author(s):  
A. Bakhsh and R. S. Kanwar
Keyword(s):  
Point Source ◽  
Agricultural Lands ◽  
Point Source Pollution ◽  
Calibration And Validation ◽  
Non Point Source Pollution

scholarly journals Spatial-Temporal Change of Land Use and Its Impact on Water Quality of East-Liao River Basin from 2000 to 2020

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1955
Author(s):  
Mingxi Zhang ◽  
Guangzhi Rong ◽  
Aru Han ◽  
Dao Riao ◽  
Xingpeng Liu ◽  
...  
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Land Use Change ◽  
Point Source ◽  
Water Body ◽  
Forest Land ◽  
Point Source Pollution ◽  
Construction Land ◽  
Unused Land ◽  
Non Point Source Pollution

Land use change is an important driving force factor affecting the river water environment and directly affecting water quality. To analyze the impact of land use change on water quality change, this study first analyzed the land use change index of the study area. Then, the study area was divided into three subzones based on surface runoff. The relationship between the characteristics of land use change and the water quality grade was obtained by grey correlation analysis. The results showed that the land use types changed significantly in the study area since 2000, and water body and forest land were the two land types with the most significant changes. The transfer rate is cultivated field > forest land > construction land > grassland > unused land > water body. The entropy value of land use information is represented as Area I > Area III > Area II. The shift range of gravity center is forest land > grassland > water body > unused land > construction land > cultivated field. There is a strong correlation between land use change index and water quality, which can be improved and managed by changing the land use type. It is necessary to establish ecological protection areas or functional areas in Area I, artificial lawns or plantations shall be built in the river around the water body to intercept pollutants from non-point source pollution in Area II, and scientific and rational farming in the lower reaches of rivers can reduce non-point source pollution caused by farming.

scholarly journals Coupling ITO3dE model and GIS for spatiotemporal evolution analysis of agricultural non-point source pollution risks in Chongqing in China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kang-wen Zhu ◽  
Zhi-min Yang ◽  
Lei Huang ◽  
Yu-cheng Chen ◽  
Sheng Zhang ◽  
...  
Keyword(s):  
High Risk ◽  
Point Source ◽  
Kernel Density ◽  
Point Source Pollution ◽  
Spatiotemporal Evolution ◽  
Matrix Kernel ◽  
Medium Risk ◽  
Density Analysis ◽  
Kernel Density Analysis ◽  
Non Point Source Pollution

AbstractTo determine the risk state distribution, risk level, and risk evolution situation of agricultural non-point source pollution (AGNPS), we built an ‘Input-Translate-Output’ three-dimensional evaluation (ITO3dE) model that involved 12 factors under the support of GIS and analyzed the spatiotemporal evolution characteristics of AGNPS risks from 2005 to 2015 in Chongqing by using GIS space matrix, kernel density analysis, and Getis-Ord Gi* analysis. Land use changes during the 10 years had a certain influence on the AGNPS risk. The risk values in 2005, 2010, and 2015 were in the ranges of 0.40–2.28, 0.41–2.57, and 0.41–2.28, respectively, with the main distribution regions being the western regions of Chongqing (Dazu, Jiangjin, etc.) and other counties such as Dianjiang, Liangping, Kaizhou, Wanzhou, and Zhongxian. The spatiotemporal transition matrix could well exhibit the risk transition situation, and the risks generally showed no changes over time. The proportions of ‘no-risk no-change’, ‘low-risk no-change’, and ‘medium-risk no-change’ were 10.86%, 33.42%, and 17.25%, respectively, accounting for 61.53% of the coverage area of Chongqing. The proportions of risk increase, risk decline, and risk fluctuation were 13.45%, 17.66%, and 7.36%, respectively. Kernel density analysis was suitable to explore high-risk gathering areas. The peak values of kernel density in the three periods were around 1110, suggesting that the maximum gathering degree of medium-risk pattern spots basically showed no changes, but the spatial positions of high-risk gathering areas somehow changed. Getis-Ord Gi* analysis was suitable to explore the relationships between hot and cold spots. Counties with high pollution risks were Yongchuan, Shapingba, Dianjiang, Liangping, northwestern Fengdu, and Zhongxian, while counties with low risks were Chengkou, Wuxi, Wushan, Pengshui, and Rongchang. High-value hot spot zones gradually dominated in the northeast of Chongqing, while low-value cold spot zones gradually dominated in the Midwest. Our results provide a scientific base for the development of strategies to prevent and control AGNPS in Chongqing.

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