Exploring the performance of riparian zones in reducing non-point source pollution by coupling remote sensing with the SWAT model
LIU Yiyao1(), WU Taixia1(), WANG Shudong2, JU Maosen3
1. School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China 2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100000, China 3. Research and Training Center for River Chief System, Hohai University, Nanjing 210098, China
摘要岸边带正广泛应用于世界各地的面源污染治理项目,遥感也逐渐成为面源污染研究的重要手段,但如何将遥感技术与岸边带结合使截污效果更佳仍然是一个挑战。该文以云南省星云湖流域为例,耦合遥感建立土壤水分评估模型(soil and water assessment tool, SWAT),通过改变土地利用类型的方式建立岸边带进行情景模拟,研究不同宽度和植被类型对污染物消减效果的差异。结果发现,设置岸边带对氮元素的截留效果好于磷元素; 当岸边带植被类型不同时,林地的截污效果明显好于草地,并随着岸边带宽度的增加污染物消减率逐渐变大。设置30 m林地加30 m草地的岸边带可减少5.20%的总氮产量和6.03%的总磷产量,且可截留19.83%的有机氮入湖量和21.30%有机磷入湖量,在所有岸边带中截污效果最好。
Riparian zones have been extensively used in non-point source pollution control projects worldwide, and remote sensing has gradually become a significant means of non-point source pollution research. However, combining remote sensing technology with riparian zones for efficient pollution interception effects is still a challenge. With the Xingyun Lake basin in Yunnan Province as the study area, this study established a soil and water assessment tool (SWAT) model by coupling with remote sensing. It created a riparian zone by changing the land use type for scenario simulation, investigating the different effects of various widths and vegetation types on pollutant reduction. The key findings are as follows: ①The created riparian zone exhibited better interception effects for nitrogen compared to phosphorus; ② Concerning different vegetation types in the riparian zone, forest land manifested significantly better pollution interception effects than grassland. Moreover, the pollutant reduction rate gradually increased with an increase in the width of the riparian zone; ③A riparian zone consisting of 30-m-wide forest land and 30-m-wide grassland can reduce total nitrogen production by 5.20% and total phosphorus production by 6.03% while intercepting 19.83% of organic nitrogen and 21.30% of organic phosphorus into the lake, demonstrating the optimal pollution interception effects.
刘翼遥, 吴太夏, 王树东, 鞠茂森. 耦合遥感与SWAT模型的岸边植被带消减面源污染效能研究[J]. 自然资源遥感, 2025, 37(2): 256-264.
LIU Yiyao, WU Taixia, WANG Shudong, JU Maosen. Exploring the performance of riparian zones in reducing non-point source pollution by coupling remote sensing with the SWAT model. Remote Sensing for Natural Resources, 2025, 37(2): 256-264.
Zhu Z L, Sun B, Yang L Z, et al. Policy and countermeasures to control non-point pollution of agriculture in China[J]. Science & Technology Review, 2005, 23(4):47-51.
Ye Z M, Peng Y, Zhao F, et al. Investigation of water environment status and analysis of assessment methods[J]. Environmental Impact Assessment, 2020, 42(3):92-96.
[3]
国家统计局. 第一次全国污染源普查公报[R]. 北京, 2010.
National Bureau of Statistics. Bulletin of the first national pollution source survey[R]. Beijing, 2010.
National Development and Reform Commission, Ministry of Environmental Protection, Ministry of Housing and Urban Rural Development, et al. Overall plan for comprehensive treatment of water environment in Taihu Lake basin (revised in 2013) [EB/OL]. https://www.ndrc.gov.cn/fzggw/jgsj/dqs/sjdt/201401/W020190909483338371074.pdf.
[5]
Tu J. Spatially varying relationships between land use and water quality across an urbanization gradient explored by geographically weighted regression[J]. Applied Geography, 2011, 31(1):376-392.
[6]
Sweeney B W, Bott T L, Jackson J K, et al. Riparian deforestation,stream narrowing,and loss of stream ecosystem services[J]. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101(39):14132-14137.
[7]
MacFarlane W W, Gilbert J T, Jensen M L, et al. Riparian vegetation as an indicator of riparian condition:Detecting departures from historic condition across the North American West[J]. Journal of Environmental Management, 2017, 202(Pt 2):447-460.
[8]
Hoang L, van Griensven A, Mynett A. Enhancing the SWAT model for simulating denitrification in riparian zones at the river basin scale[J]. Environmental Modelling & Software, 2017, 93(C):163-179.
[9]
Krysanova V, White M. Advances in water resources assessment with SWAT:An overview[J]. Hydrological Sciences Journal, 2015:1-13.
[10]
Malagó A, Bouraoui F, Vigiak O, et al. Modelling water and nutrient fluxes in the Danube River basin with SWAT[J]. The Science of the Total Environment, 2017, 603/604:196-218.
Xia J, Zhai X Y, Zhang Y Y. Progress in the research of water environmental nonpoint source pollution models[J]. Progress in Geography, 2012, 31(7):941-952.
doi: 10.11820/dlkxjz.2012.07.013
[12]
Turpin N, Bontems P, Rotillon G, et al. AgriBMPWater:Systems approach to environmentally acceptable farming[J]. Environmental Modelling & Software, 2005, 20(2):187-196.
Chang J. Evaluation for the effectiveness of best management practices(BMPs) based on SWAT model[D]. Hangzhou: Zhejiang University, 2017.
[14]
Ryu J, Cho J, Kim I J, et al. Technical note:Enhancement of SWAT-REMM to simulate reduction of total nitrogen with riparian buffer[J]. Transactions of the ASABE, 2011, 54(5):1791-1798.
[15]
Jang S S, Ahn S R, Kim S J. Evaluation of executable best management practices in Haean highland agricultural catchment of South Korea using SWAT[J]. Agricultural Water Management, 2017, 180:224-234.
Wang M, Zhang Y T, Li Q C, et al. Simulation and optimal management of non-point source pollution in Zhaohe small watershed in Anhui Province[J]. Bulletin of Soil and Water Conservation, 2021, 41(5):127-136,153.
[17]
云南省统计局. 云南统计年鉴-2019(总第35期)[M]. 北京: 中国统计出版社, 2019.
Yunnan Provincial Bureau of Statistics. Yunnan statistical yearbook-2019[M]. Beijing: China Statistics Press, 2019.
[18]
陈杨. 星云湖水体环境与氮磷空间分布特征研究[D]. 昆明: 云南师范大学, 2020.
Chen Y. Characteristics of water environments and spatial distribution of nitrogen and phosphorus in Xingyun Lake[D]. Kunming: Yunnan Normal University, 2020.
Yunnan Provincial Department of Environmental Protection. 1998-2018Yunnan environmental status bulletin[EB/OL]. http://sthjt.yn.gov.cn/hjzl/hjzkgb/.
[20]
Gong P, Liu H, Zhang M, et al. Stable classification with limited sample:Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017[J]. Science Bulletin, 2019, 64(6):370-373.
[21]
Harrigan S, Zsoter E, Barnard C, et al. River discharge and related historical data from the Global Flood Awareness System,v3.1,Copernicus Climate Change Service (C3S) Climate Data Store (CDS)[Z]. 2021.
[22]
Arnold J G, Srinivasan R, Muttiah R S, et al. Large area hydrologic modeling and assessment part Ⅰ:Model development1[J]. Journal of the American Water Resources Association, 1998, 34(1):73-89.
Guo W, Chen X W, Lin B Q. Response of SWAT model parameters to land use change and its effects on the simulation of runoff with different time scales[J]. Acta Ecologica Sinica, 2021, 41(16):6373-6383.
Zhang Z Z, Cheng J R, Bi J P, et al. Impact of land use on non-point sources of phosphorus in the Yong River basin:SWAT model analysis[J]. Journal of Agro-Environment Science, 2019, 38(3):650-658.
[25]
Neitsch S, Arnold J, Kiniry J, et al. Soil and water assessment tool theoretical documentation,version 2009[Z]. Texas Water Resources Institute Technical Report, 2011.
Chen H T, Cao X Z. Comparative analysis of the results of parameter optimization methods based on the optimal confidence interval of SUFI-2 parameters[J]. Water Saving Irrigation, 2021(6):24-30.
Li Z W, Yu X X, Yao X Y, et al. Identification approaches of critical source area of agricultural non-point source pollution:A review[J]. Chinese Journal of Ecology, 2011, 30(12):2907-2914.
[28]
朱鹤健, 陈健飞, 陈松林, 等. 土壤地理学[M].2版. 北京: 高等教育出版社, 2010.
Zhu H J, Chen J F, Chen S L, et al. Pedogeography[M].2nd ed. Beijing: Higher Education Press, 2010.
[29]
Haycock N E, Pinay G. Groundwater nitrate dynamics in grass and poplar vegetated riparian buffer strips during the winter[J]. Journal of Environmental Quality, 1993, 22(2):273-278.
[30]
Osborne L L, Kovacic D A. Riparian vegetated buffer strips in water-quality restoration and stream management[J]. Freshwater Biology, 1993, 29(2):243-258.
[31]
Sabater S, Butturini A, Clement J C, et al. Nitrogen removal by riparian buffers along a European climatic gradient:Patterns and factors of variation[J]. Ecosystems, 2003, 6(1):20-30.
Tang J X, Sun L N, Sun T H, et al. Research advances on retaining and transformation of N and P and ecological restoration of riparian buffer zone[J]. Ecology and Environmental Sciences, 2012, 21(8):1514-1520.
Shen X B. Study on interception effects of the main agricultural non-point source pollution by grass vegetative filter strips[D]. Changsha: Central South University of Forestry & Technology, 2014.
[34]
何聪. 混播草皮缓冲带农业面源污染拦截效果的试验研究[D]. 扬州: 扬州大学, 2012.
He C. Mixture sowing turf buffer zone for agricultural non-point source pollution interception effect experimental study[D]. Yangzhou: Yangzhou University, 2012.
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