Output risk evolution analysis of agricultural non-point source pollution under different scenarios based on multi-model

The Fuling District is located in central Chongqing, China, and characterized by a high ecological status, a high ecological risk, a high agricultural proportion in economy, and a high agricultural non-point source pollution (AGNPS) risk, and represents the ecological security barrier of the Yangtze...

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Main Authors: Kang-Wen Zhu, Yu-Cheng Chen, Sheng Zhang, Zhi-Min Yang, Lei Huang, Lei Li, Bo Lei, Zhong-Bo Zhou, Hai-Ling Xiong, Xi-Xi Li, Yue-Chen Li, Shahidul Islam
Format: Article
Language:English
Published: Elsevier 2020-09-01
Series:Global Ecology and Conservation
Subjects:
GIS
Online Access:http://www.sciencedirect.com/science/article/pii/S2351989420306855
id doaj-ec9b48d2695e4fe19e1ea3d4f13b65d5
record_format Article
collection DOAJ
language English
format Article
sources DOAJ
author Kang-Wen Zhu
Yu-Cheng Chen
Sheng Zhang
Zhi-Min Yang
Lei Huang
Lei Li
Bo Lei
Zhong-Bo Zhou
Hai-Ling Xiong
Xi-Xi Li
Yue-Chen Li
Shahidul Islam
spellingShingle Kang-Wen Zhu
Yu-Cheng Chen
Sheng Zhang
Zhi-Min Yang
Lei Huang
Lei Li
Bo Lei
Zhong-Bo Zhou
Hai-Ling Xiong
Xi-Xi Li
Yue-Chen Li
Shahidul Islam
Output risk evolution analysis of agricultural non-point source pollution under different scenarios based on multi-model
Global Ecology and Conservation
CLUE-S model
Markov model
SWAT model
GIS
Output coefficient
Agricultural non-point source pollution
author_facet Kang-Wen Zhu
Yu-Cheng Chen
Sheng Zhang
Zhi-Min Yang
Lei Huang
Lei Li
Bo Lei
Zhong-Bo Zhou
Hai-Ling Xiong
Xi-Xi Li
Yue-Chen Li
Shahidul Islam
author_sort Kang-Wen Zhu
title Output risk evolution analysis of agricultural non-point source pollution under different scenarios based on multi-model
title_short Output risk evolution analysis of agricultural non-point source pollution under different scenarios based on multi-model
title_full Output risk evolution analysis of agricultural non-point source pollution under different scenarios based on multi-model
title_fullStr Output risk evolution analysis of agricultural non-point source pollution under different scenarios based on multi-model
title_full_unstemmed Output risk evolution analysis of agricultural non-point source pollution under different scenarios based on multi-model
title_sort output risk evolution analysis of agricultural non-point source pollution under different scenarios based on multi-model
publisher Elsevier
series Global Ecology and Conservation
issn 2351-9894
publishDate 2020-09-01
description The Fuling District is located in central Chongqing, China, and characterized by a high ecological status, a high ecological risk, a high agricultural proportion in economy, and a high agricultural non-point source pollution (AGNPS) risk, and represents the ecological security barrier of the Yangtze River and the Three Gorges Reservoir area. To analyze the output risk response of AGNPS under different land use scenarios in the future, we combined the advantages of various models and techniques such as the CLUE-S model, the Markov model, the SWAT model, and GIS technology and selected 12 driving factors for land use changes as well as one limiting factor of the ecological protection redline based on land use data of the Fuling District in 2009 and 2017, with the aim to perform an output risk probability evolution analysis of regional AGNPS. The three scenarios were natural development (ND), ecological priority (EP), and agricultural development (AD). The results were as follows: (1) from 2009 to 2017, land use change mainly consisted of the conversion from paddy field and dry land into construction land, accounting for 41.25% of the total area increase in construction land in 2017; (2) the simulation results of land use changes in Fuling District by combination of the CLUE-S model and the Markov model showed a high consistency; (3) from 2009 to 2017, the numbers of sub-basins where the TN risk level declined, remained unchanged, and increased were 36, 425, and 9, respectively, while in terms of the TP risk levels, the numbers were 16, 443, and 11, respectively; (4) under the three development scenarios of ND, EP, and AD, paddy field, dry land, forest land, and construction land were the main types of land use conversion; (5) the output risk levels of total nitrogen (TN) and total phosphorus (TP) both presented a declining trend at present and in the future, and the number of sub-basins where the risk level declined was highest under the EP scenario; (6) under the ND scenario, adjustments of ±5% or ±10% on the output coefficients of TN and TP could lead to an obvious response of the output risk probability level of sub-basins. Therefore, the sub-basins that were the most sensitive to changes in land use or output coefficients deserve considerable attention. Our results also indicate that the output risk levels of sub-basins and regional TN and TP could be reduced through land use optimization or fertilizer control, thereby minimizing regional AGNPS.
topic CLUE-S model
Markov model
SWAT model
GIS
Output coefficient
Agricultural non-point source pollution
url http://www.sciencedirect.com/science/article/pii/S2351989420306855
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spelling doaj-ec9b48d2695e4fe19e1ea3d4f13b65d52020-11-25T02:39:56ZengElsevierGlobal Ecology and Conservation2351-98942020-09-0123e01144Output risk evolution analysis of agricultural non-point source pollution under different scenarios based on multi-modelKang-Wen Zhu0Yu-Cheng Chen1Sheng Zhang2Zhi-Min Yang3Lei Huang4Lei Li5Bo Lei6Zhong-Bo Zhou7Hai-Ling Xiong8Xi-Xi Li9Yue-Chen Li10Shahidul Islam11College of Resources and Environment, Southwest University, 400716, ChinaCollege of Resources and Environment, Southwest University, 400716, China; Chongqing Engineering Research Center of Rural Cleaning, Chongqing, 400716, China; Corresponding author. College of Resources and Environment, Southwest University, 400716, China.Chongqing Academy of Ecology and Environmental Sciences, Chongqing, 401147, China; Corresponding author. Chongqing Academy of Ecology and Environmental Sciences, 401147, China.College of Resources and Environment, Southwest University, 400716, China; Chongqing Engineering Research Center of Rural Cleaning, Chongqing, 400716, ChinaCollege of Resources and Environment, Southwest University, 400716, China; Chongqing Engineering Research Center of Rural Cleaning, Chongqing, 400716, ChinaCollege of Resources and Environment, Southwest University, 400716, China; Chongqing Engineering Research Center of Rural Cleaning, Chongqing, 400716, ChinaChongqing Academy of Ecology and Environmental Sciences, Chongqing, 401147, ChinaCollege of Resources and Environment, Southwest University, 400716, China; Chongqing Engineering Research Center of Rural Cleaning, Chongqing, 400716, ChinaCollege of Computer & Information Science, Southwest University, 400716, ChinaChongqing Chemical Industry Vocational College, Chongqing, 400020, ChinaSchool of geographical Sciences, Southwest University, 400716, ChinaSchool of geographical Sciences, Southwest University, 400716, ChinaThe Fuling District is located in central Chongqing, China, and characterized by a high ecological status, a high ecological risk, a high agricultural proportion in economy, and a high agricultural non-point source pollution (AGNPS) risk, and represents the ecological security barrier of the Yangtze River and the Three Gorges Reservoir area. To analyze the output risk response of AGNPS under different land use scenarios in the future, we combined the advantages of various models and techniques such as the CLUE-S model, the Markov model, the SWAT model, and GIS technology and selected 12 driving factors for land use changes as well as one limiting factor of the ecological protection redline based on land use data of the Fuling District in 2009 and 2017, with the aim to perform an output risk probability evolution analysis of regional AGNPS. The three scenarios were natural development (ND), ecological priority (EP), and agricultural development (AD). The results were as follows: (1) from 2009 to 2017, land use change mainly consisted of the conversion from paddy field and dry land into construction land, accounting for 41.25% of the total area increase in construction land in 2017; (2) the simulation results of land use changes in Fuling District by combination of the CLUE-S model and the Markov model showed a high consistency; (3) from 2009 to 2017, the numbers of sub-basins where the TN risk level declined, remained unchanged, and increased were 36, 425, and 9, respectively, while in terms of the TP risk levels, the numbers were 16, 443, and 11, respectively; (4) under the three development scenarios of ND, EP, and AD, paddy field, dry land, forest land, and construction land were the main types of land use conversion; (5) the output risk levels of total nitrogen (TN) and total phosphorus (TP) both presented a declining trend at present and in the future, and the number of sub-basins where the risk level declined was highest under the EP scenario; (6) under the ND scenario, adjustments of ±5% or ±10% on the output coefficients of TN and TP could lead to an obvious response of the output risk probability level of sub-basins. Therefore, the sub-basins that were the most sensitive to changes in land use or output coefficients deserve considerable attention. Our results also indicate that the output risk levels of sub-basins and regional TN and TP could be reduced through land use optimization or fertilizer control, thereby minimizing regional AGNPS.http://www.sciencedirect.com/science/article/pii/S2351989420306855CLUE-S modelMarkov modelSWAT modelGISOutput coefficientAgricultural non-point source pollution