气候变化下乌伦古河流域农业面源污染负荷响应

doi:10.13866/j.azr.2022.02.29

摘要/Abstract

摘要：

以新疆乌伦古河流域为研究区,基于SWAT模型构建适用于该流域的面源污染分布式水文模型,采用情景设置法设定9种气候变化情景。分析不同气候变化情景对流域径流及面源污染负荷的影响,识别重点影响区域,为流域面源污染防控提供科学依据。结果表明：（1）径流、总氮和总磷在率定期和验证期的决定系数R²均在0.75以上,纳什系数E_ns均在0.55以上,建立的SWAT模型能够用于乌伦古河流域农业面源污染负荷模拟;（2）增加20%的降水量,径流量增加48.01%、总氮负荷增加23.19%、总磷负荷增加29.65%,气温变化对径流量的影响小于降水量变化对径流量的影响;（3）各子流域年均单位面积总氮负荷为0.01~164.79 kg·hm^-2·a^-1,单位面积总磷负荷为0.01~45.10 kg·hm^-2·a^-1,气候变化对面源污染影响较大区域主要分布在福海县的阿尔达乡、富蕴县的吐尔洪乡、库尔特乡、恰库尔图镇,及青河县的阿热勒乡、阿热勒托别镇、塔克什肯镇等。说明降水增多是乌伦古河流域农业面源污染负荷的主要影响因素之一。未来在农业面源污染防治中应重点控制畜禽养殖和农田径流这两类污染源。

关键词: 气候变化, SWAT模型, 乌伦古河流域, 面源污染

Abstract:

Taking the Ulungur River basin in Xinjiang as study area, a nonpoint source pollution distributed hydrological model suitable for the basin was constructed based on the soil and water assessment tool (SWAT), and nine climate change scenarios were set using the scenario setting method. The impact of different climate change scenarios on the river basin runoff and nonpoint source pollution load was analyzed, key affected areas were identified, and a scientific basis for the prevention and control of nonpoint source pollution in the river basin was provided. The results show that (1) the values of the determination coefficient R² for runoff, total nitrogen and total phosphorus in the regular rate and verification period were all above 0.75, and the Nash coefficient E_ns was above 0.55. The established SWAT model could be used for agricultural nonpoint sources in the pollution load simulation applied to the Ulungur watershed. (2) Precipitation, runoff, total nitrogen load, and total phosphorus increased by 20%, 48.01%, 23.19%, and 29.65%, respectively, and the impact on runoff caused by temperature variation was less than that caused by precipitation variation. (3) The average annual total nitrogen load per unit area of each sub-catchment was between 0.01-164.79 kg·hm^-2·a^-1, and the total phosphorus load per unit area was between 0.01-45.10 kg·hm^-2·a^-1. The areas where climate change had a greater impact on nonpoint source pollution were mainly distributed in Arda Township in Fuhai County, Turhong Township, Kuerte Township, and Chakurtu Township in Fuyun County, Arele Township, Altobe Town, and Takshken Town, etc. in Qinghe County. Studies have shown that increased precipitation is one of the main factors affecting the agricultural nonpoint source pollution load in the basin. In the future, the prevention and control of this type of pollution should focus on controlling two types of pollution sources, livestock and poultry breeding and farmland runoff.

Key words: climate change, SWAT model, Ulungur River basin, nonpoint source pollution

邹凯波,张玉虎,刘晓伟,薛淑慧,杨博文,崔艳欣. 气候变化下乌伦古河流域农业面源污染负荷响应[J]. 干旱区研究, 2022, 39(2): 625-637.

ZOU Kaibo,ZHANG Yuhu,LIU Xiaowei,XUE Shuhui,YANG Bowen,CUI Yanxin. Response of agricultural nonpoint source pollution load in the Ulungur River basin under climate change[J]. Arid Zone Research, 2022, 39(2): 625-637.

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参考文献 25

[1]	李秀芬, 朱金兆, 顾晓君, 等. 农业面源污染现状与防治进展[J]. 中国人口·资源与环境, 2010, 20(4):81-84.
	[ Li Xiufen, Zhu Jinzhao, Gu Xiaojun, et al. Current situation and control of agricultural non-point source pollution[J]. China Population, Resources and Environment, 2010, 20(4):81-84. ]
[2]	Xia T, Chen Z, Jin S. New normal control of agricultural non-point source pollution in the Dianchi Lake basin[J]. Meteorological and Environmental Research, 2017, 8(2):63-72.
[3]	丘雯文, 钟涨宝, 李兆亮, 等. 中国农业面源污染排放格局的时空特征[J]. 中国农业资源与区划, 2019, 40(1):26-34.
	[ Qiu Wenwen, Zhong Zhangbao, Li Zhaoliang, et al. Spatial-temporal variations of agricultural non-point source pollution in China[J]. China Journal Agricultural Resources and Regional Planning, 2019, 40(1):26-34. ]
[4]	付磊, 李德山. 中国农业面源污染与绿色全要素生产率的区域差异[J]. 西南科技大学学报(哲学社会科学版), 2021, 38(3):37-48.
	[ Fu Lei, Li Deshan. Regional differences between of agricultural non-point source pollution and growth of Green TFP in China[J]. Journal of Southwest University of Science and Technology (Philosophy and Social Science Edition), 2021, 38(3):37-48. ]
[5]	周子渭, 马琼, 侯玉龙, 等. 新疆农业面源污染及环境规制分析[J]. 安徽农业科学, 2021, 49(7):74-76.
	[ Zhou Ziwei, Ma Qiong, Hou Yulong, et al. Analysis of agricultural non-point source pollution and environmental regulation in Xinjiang[J]. Journal of Anhui Agricultural Sciences, 2021, 49(7):74-76. ]
[6]	陈亚宁, 杨青, 罗毅, 等. 西北干旱区水资源问题研究思考[J]. 干旱区地理, 2012, 35(1):1-9.
	[ Chen Yaning, Yang Qing, Luo Yi, et al. Ponder on the issues of water resources in the arid region of Northwest China[J]. Arid Land Geography, 2012, 35(1):1-9. ]
[7]	李哲, 丁永建, 陈艾姣, 等. 1960—2019年西北地区气候变化中的Hiatus现象及特征[J]. 地理学报, 2020, 75(9):1845-1859. doi: 10.11821/dlxb202009003
	[ Li Zhe, Ding Yongjian, Chen Aijiao, et al. Characteristics of warming hiatus of the climate change in Northwest China from 1960 to 2019[J]. Acta Geographica Sinica, 2020, 75(9):1845-1859. ] doi: 10.11821/dlxb202009003
[8]	施雅风, 沈永平, 胡汝骥. 西北气候由暖干向暖湿转型的信号、影响和前景初步探讨[J]. 冰川冻土, 2002, 24(3):219-226.
	[ Shi Yafeng, Shen Yongping, Hu Ruji. Preliminary study on the signals, impact and foreground of climatic shift from warm-dry to warm-humid in Northwest China[J]. Journal of Glaciology and Geocryology, 2002, 24(3):219-226. ]
[9]	李江, 龙爱华. 近60 a新疆水资源变化及可持续利用思考[J]. 水利规划与设计, 2021, 34(7):1-5.
	[ Li Jiang, Long Aihua. Changes in Xinjiang water resources in the past 60 years and considerations on sustainable utilization[J]. Water Resources Planning and Design, 2021, 34(7):1-5. ]
[10]	李慧菁, 贾尔恒·阿哈提, 程艳. 乌伦古湖流域污染负荷估算[J]. 环境工程技术学报, 2015, 5(2):121-128.
	[ Li Huijing, Jiaerheng Ahati, Cheng Yan. Estimation of pollution load in Ulungur Lake Basin[J]. Journal of Environmental Engineering Technology, 2015, 5(2):121-128. ]
[11]	徐伟. 乌伦古河流域非点源污染负荷估算探讨[J]. 能源与节能, 2021, 2(1):91-92.
	[ Xu Wei. Discussion on estimation of non-point source pollution load in Ulungur river basin[J]. Energy and Energy Conservation, 2021, 2(1):91-92. ]
[12]	Romagnoli M, Portapila M, Rigalli A, et al. Assessment of the SWAT model to simulate a watershed with limited available data in the Pampas region, Argentina[J]. Science of the Total Environment, 2017, 596(597):437-450.
[13]	Grizzetti B, Bouraoui F, Granlund K, et al. Modelling diffuse emission and retention of nutrients in the Vantaanjoki watershed (Finland) using the SWAT model[J]. Ecological Modelling, 2003, 169(1):25-38. doi: 10.1016/S0304-3800(03)00198-4
[14]	薛亚婷, 孙文锦, 邹长武, 等. 基于模型的赤水河流域面源污染研究[J]. 亚热带资源与环境学报, 2020, 15(3):17-23.
	[ Xue Yating, Sun Wenjin, Zou Changwu, et al. Study on non-point source pollution in Chishui River Watershed based on SWAT model[J]. Journal of Subtropical Resources and Environment, 2020, 15(3):17-23. ]
[15]	付意成, 臧文斌, 董飞, 等. 基于模型的浑太河流域农业面源污染物产生量估算[J]. 农业工程学报, 2016, 32(8):1-8.
	[ Fu Yicheng, Zang Wenbin, Dong Fei, et al. Yield calculation of agricultural non-point source pollutants in Huntai river basin based on SWAT model[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(8):1-8. ]
[16]	王晓燕, 秦福来, 欧洋, 等. 基于SWAT模型的流域非点源污染模拟——以密云水库北部流域为例[J]. 农业环境科学学报, 2008, 27(3):1098-1105.
	[ Wang Xiaoyan, Qin Fulai, Ou Yang, et al. SWAT-based simulation on non-point source pollution in the northern watershed of Miyun reservoi[J]. Journal of Agro-Environmental Sciences, 2008, 27(3):1098-1105. ]
[17]	马睿, 程凯, 郭莹莹, 等. 基于SWAT模型的石汶河流域农业非点源氮污染时空分布特征研究[J]. 中国水土保持, 2020, 15(7):61-64.
	[ Ma Rui, Cheng Kai, Guo Yingying, et al. Research on temporal and spatial distribution characteristics of agricultural non-point source nitrogen pollution in Shiwen River Basin based on SWAT model[J]. Soil and Water Conservation in China, 2020, 15(7):61-64. ]
[18]	Wang Y, Bian J, Zhao Y, et al. Assessment of future climate change impacts on nonpoint source pollution in snowmelt period for a cold area using SWAT[J]. Scientific Reports, 2018, 8(1):1-13.
[19]	刘吉开, 万甜, 程文, 等. 未来气候情境下渭河流域陕西段非点源污染负荷响应[J]. 水土保持通报, 2018, 38(4):82-86.
	[ Liu Jikai, Wan Tian, Cheng Wen, et al. Effects of climate scenarios on non-point source pollution load on Shaanxi section of Weihe river basin[J]. Bulletin of Soil and Water Conservation, 2018, 38(4):82-86. ]
[20]	赵雪松. 气候变化对营口地区农业面源污染影响的定量评估研究[J]. 水土保持应用技术, 2019, 39(4):16-18.
	[ Zhao Xuesong. Quantitative assessment of the impact of climate change on agricultural non-point source pollution in Yingkou area[J]. Technology of Soil and Water Conservation, 2019, 39(4):16-18. ]
[21]	李家科, 刘健, 秦耀民, 等. 基于SWAT模型的渭河流域非点源氮污染分布式模拟[J]. 西安理工大学学报, 2008, 31(3):278-285.
	[ Li Jiake, Liu Jian, Qin Yaomin, et al. Distributed simulation on nitrogen non-point source pollution in the Weihe river watershed based on SWAT model[J]. Journal of Xi’an University of Technology, 2008, 31(3):278-285. ]
[22]	宋玉鑫, 左其亭, 马军霞. 基于SWAT模型的开都河流域水文干旱变化特征及驱动因子分析[J]. 干旱区研究, 2021, 38(3):610-617.
	[ Song Yuxin, Zuo Qiting, Ma Junxia. Hydrological drought characteristics and driving factors analysis of the Kaidu River Basin based on SWAT model[J]. Arid Zone Research, 2021, 38(3):610-617. ]
[23]	矫桂丽, 刘洪林, 孙秀玲, 等. 基于SWAT模型的尼山水库流域面源污染特征分析[J]. 山东农业大学学报(自然科学版), 2021, 52(3):470-474.
	[ Jiao Guili, Liu Honglin, Sun Xiuling, et al. Analysis of non-point source pollution characteristics of Nishan Reservoir based on SWAT model[J]. Journal of Shandong Agricultural University (Natural Science Edition), 2021, 52(3):470-474. ]
[24]	杨宝林, 崔远来, 赵树君, 等. 基于SWAT模型的莲塘口流域农业面源污染模拟[J]. 武汉大学学报(工学版), 2016, 49(3):359-364.
	[ Yang Baolin, Cui Yuanlai, Zhao Shujun, et al. Simulation and application of agricultural non-point source pollution in Liantangkou watershed based on SWAT model[J]. Journal of Wuhan University(Engineering and Technology Edition), 2016, 49(3):359-364. ]
[25]	张鹏飞. 不同气候条件下密云水库流域非点源污染评价[D]. 北京: 首都师范大学, 2013.
	[ Zhang Pengfei. Non-point Source Pollution Assessment of the Miyun Reservoir Basin under Different Climatic Conditions[D]. Beijing: Capital Normal University, 2013. ]

数据类型		分辨率	年份	格式	数据来源
空间数据	流域DEM	30 m	2009年	栅格	地理空间数据云（https://www.gscloud.cn）
	土地利用	1:250 000	2015年	栅格	清华大学地球系统科学系（http://data.ess.tsinghua.edu.cn/）
	土壤类型	1:1000 000		栅格	HWSD（世界土壤数据库）
属性数据	气象数据	1/3°×1/3°	2008—2018年	Txt	CMADS V 1.0大气同化驱动数据集（http://www.cmads.org/）
	土壤属性	-	-	Excel	HWSD、SPAW
	水文、水质	-	-	Excel	阿勒泰水文局

土地利用类型编号	面积占比/%	SWAT土地利用分类	土地类别
1	3.60	AGRL	耕地
2	2.20	FRST	林地
3	28.67	PAST	草地
4	0.03	WETL	湿地
5	1.13	WATR	水域
6	64.07	BARR	裸地
7	0.30	URHD	城镇用地

土壤类型编号	土壤名称	面积比例/%	土壤类型编号	土壤名称	面积比例/%
1	简育灰色土	1.20	18	黏化砂性土	1.59
2	简育黑钙土	0.51	19	钙积石膏土	3.09
3	钙积黑钙土	5.11	20	钙积变性土	0.48
4	黏化栗钙土1	2.82	21	石灰性黑土	4.25
5	黏化栗钙土2	0.03	22	潜育黑土	0.15
6	石灰性冲积土	1.05	23	盐化冲积土	0.13
7	简育栗钙土1	5.58	24	松软潜育土	0.34
8	钙积栗钙土1	5.60	25	松软盐土1	0.08
9	钙积栗钙土2	0.09	26	松软盐土2	0.19
10	黏化钙积土1	17.43	27	石膏盐土	0.11
11	黏化钙积土2	0.20	28	钙积盐土	0.90
12	过渡性红砂土	4.13	29	永冻薄层土	2.88
13	干旱土1	2.24	30	饱和薄层土	5.84
14	简育砂性土	20.27	31	水体	3.71
15	冰冻潜育土	4.88	32	简育栗钙土2	0.08
16	干旱土2	0.08	33	永冻雏形土	0.01
17	黏化石膏土	4.93	34	简育钙积土	0.02

参数名称	含义	注释
NLAYERS	土壤分层数	HWSD
HYDGRP	土壤水文学分组	公式计算
SOL_ZMX	土壤剖面最大根系深度/mm	HWSD
ANION_EXCL	阴离子交换孔隙度	模型默认值为0.5
SOL_CRK	土壤最大可压缩量	模型默认值为0.5, 可选
TEXTURE	土壤层结构	SPAW计算
SOL_Z	各土壤层底层到土壤表层的深度/mm	HWSD
SOL_BD	土壤湿密度/（mg·m^-3或g·cm^-3）	SPAW计算
SOL_AWC	土壤层有效持水量/mm	SPAW计算
SOL_K	饱和导水率、饱和水力传导系数/（mm·h^-1）	SPAW计算
SOL_CBN	土壤层中有机碳含量	HWSD
CLAY	黏土含量（直径<0.002 mm的土壤颗粒）	HWSD
SILT	壤土含量（直径0.002~ 0.05 mm的土壤颗粒）	HWSD
SAND	砂土含量（直径0.05~2.0 mm的土壤颗粒）	HWSD
ROCK	砾石含量（直径>2.0 mm的土壤颗粒）	HWSD
SOL_ALB	地表反射率	模型默认值0.01
USLE_K	USLE方程中土壤侵蚀力因子	公式计算
SOL_EC	土壤电导率/（dS·m^-1）	默认为0

	模拟时段	决定系数R²	纳什系数E_ns
径流	率定期（2010-05—2016-07）	0.87	0.77
径流	验证期（2017-04—2018-07）	0.85	0.76
总氮	率定期（2017-04—2017-07）	0.86	0.86
总氮	验证期（2018-03—2018-07）	0.85	0.87
总磷	率定期（2017-04—2017-07）	0.91	0.84
总磷	验证期（2018-03—2018-07）	0.83	0.78