基于AnnAGNPS模型的山美水库流域非点源氮控制研究
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摘要
探索研究流域非点源氮污染控制的有效措施,对于治理改善水环境恶化具有重要的现实和长远意义。以山美水库流域为研究区域,建立AnnAGNPS氮污染模型,通过情景模拟技术分别模拟了河岸缓冲带、适量施肥、免耕、少耕、梯田和退耕还林等最佳管理措施的非点源氮污染削减效率。结果表明:(1)梯田与退耕还林对氮的削减率较高,均高于15%;免耕较低,为13%;少耕、合理施肥、河岸缓冲带削减效果有限,低于10%。(2)河岸缓冲带、少耕、免耕、梯田等措施的总氮削减率在不同月份的变化趋势与泥沙削减变化趋势一致,在7月和8月有较高的削减率;合理施肥与退耕还林的总氮削减率则与流域施肥状况相关性更高,在2—4月份有较高的削减率,因此山美水库流域水环境改善应结合非点源氮污染治理和流域水土流失治理。
Exploring effective measures to control non-point source nitrogen pollution is of great practical and long-term significance for the protection of water environment. AnnAGNPS model was utilized to simulate the nitrogen reduction of Best Management Practices(BMPs) for non-point source pollution in Shanmei reservoir basin. The results showed that:(1) The nitrogen reduction of parallel terraces and returning farmland to forests were relatively high(larger than 15%), followed by the no-tillage(13%), and then other BMPs(less than 10%).(2) The nitrogen reduction of vegetation filtering belt strips, reduced tillage, no-tillage and parallel terraces in different months were consistent with those of sediment reduction, and higher reduction rates of those BMPs occurred in July and August. Moreover, the nitrogen reduction of fertilizer reduction and returning farmland to forests were more correlated with the fertilization situation, and higher reduction rate occurred during February to April. The results of the paper indicate that the treatments of non-point source nitrogen pollution and soil erosion should be combined in the improvement of water environment change in Shanmei reservoir basin.
Exploring effective measures to control non-point source nitrogen pollution is of great practical and long-term significance for the protection of water environment. AnnAGNPS model was utilized to simulate the nitrogen reduction of Best Management Practices(BMPs) for non-point source pollution in Shanmei reservoir basin. The results showed that:(1) The nitrogen reduction of parallel terraces and returning farmland to forests were relatively high(larger than 15%), followed by the no-tillage(13%), and then other BMPs(less than 10%).(2) The nitrogen reduction of vegetation filtering belt strips, reduced tillage, no-tillage and parallel terraces in different months were consistent with those of sediment reduction, and higher reduction rates of those BMPs occurred in July and August. Moreover, the nitrogen reduction of fertilizer reduction and returning farmland to forests were more correlated with the fertilization situation, and higher reduction rate occurred during February to April. The results of the paper indicate that the treatments of non-point source nitrogen pollution and soil erosion should be combined in the improvement of water environment change in Shanmei reservoir basin.
引文
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[25] PARKER G T, DROSTE R L, KENNEDT K J. Modeling the effect of agricultural best management practices on water quality under various climatic scenarios [J]. Journal of Environmental Engineering and Science, 2008, 7(1): 9-19.
[26] MIKOLAJ P,PAWEL M,IGNACY K, et al. Spatial quantification of non-point source pollution in a meso-scale catchment for an assessment of buffer zones efficiency [J]. Water, 2015, 7(5): 1889-1920.
[27] 查恩爽. 伊通河流域农业非点源污染模拟及最佳管理措施的应用[D]. 长春:吉林大学, 2011:51.
[28] 马放,姜晓峰,王立,等. 基于SWAT模型的阿什河流域非点源污染控制措施[J]. 中国环境科学, 2016, 36(2): 610-618.
[29] TASDIGHI A, ARABI M, HARMEL D, et al. A Bayesian total uncertainty analysis framework for assessment of management practices using watershed models [J]. Environmental Modeling & Software, 2018, 108: 240-252.
[2] ABDELWAHAB O M M,BINGNER R L, MILILLO F, et al. Evaluation of alternative management practices with the AnnAGNPS Model in the Carapelle Watershed [J]. Soil Science, 2016: 1.
[3] HAO L, RICHARD M, CRUSE, R L, et al. Evaluating ephemeral gully erosion impact on Zea mays L. yield and economics using AnnAGNPS [J]. Soil & Tillage Research, 2016, 155: 157-165.
[4] MARTHA L V,COLIN D B. Modelling triazines in the valley of the River Cauca, Colombia, using the annualized agricultural non-point source pollution model [J]. Agricultural Water Management, 2016, 177: 24-36.
[5] KARKI R, TAGERT M L M,PAZ J O, et al. Application of AnnAGNPS to model an agricultural watershed in East-Central Mississippi for the evaluation of an on-farm water storage (OFWS) system [J]. Agricultural Water Management, 2017, 192: 103-114.
[6] 赵中华. 基于AnnAGNPS模型的桃江流域农业非点源污染研究[D]. 南昌:南昌大学, 2012:95.
[7] 白静. 基于AnnAGNPS模型的小流域土地利用最佳管理措施研究[D]. 太原:山西大学, 2014:35.
[8] MAX D M. Factorial sampling plans for preliminary computational experiments [J]. Technometrics, 1991, 33(2): 161-174.
[9] 席庆,李兆富,罗川. 基于扰动分析方法的 AnnAGNPS 模型水文水质参数敏感性分析[J]. 资源科学 2014, 35(5): 1773-1780.
[10] YOUSSEF C,JAVIER C,RAFAEL G, et al. Evaluation of the AnnAGNPS model for predicting runoff and sediment yield in a small Mediterranean agricultural watershed in Navarre (Spain) [J]. Agricultural Water Management, 2014, 134: 24-37.
[11] BISANTINO T, BINGNER R, CHOUAIB W, et al. Estimation of runoff, peak discharge and sediment load at the event scale in a medium-size Mediterranean watershed using the AnnAGNPS model [J]. Land Degradation & Development, 2015, 26(4): 340-355.
[12] CHUAN L,ZHAO F L,HENG P L, et al. Evaluation of the AnnAGNPS model for predicting runoff and nutrient export in a typical small watershed in the hilly region of Taihu Lake [J]. International Journal of Environmental Research and Public Health, 2015, 12(9): 10955-10973.
[13] HOTLOS H. Variations in water consumption observed in some municipalities in the time span of 1990 to 2008 [J]. Ochrona Srodowiska,2010, 32(3): 39-42.
[14] NASH J E, SUTCLIFFE J V. River flow forecasting through conceptual models part I: A discussion of principles [J]. Journal of Hydrology, 1970, 10(3): 282-290.
[15] 李怀恩,邓娜,杨寅群,等. 植被过滤带对地表径流中污染物的净化效果[J]. 农业工程学报, 2010, 26(7): 81-86.
[16] LEE K. Sediment and nutrient removal in an established multi-species riparian buffer [J]. Journal of Soil and Water Conservation, 2003, 58(1): 1-7.
[17] HAYCOCK N E. Groundwater nitrate dynamics in grass and poplar vegetated riparian buffer strips during the winter [J]. Journal of Environmental Quality, 1993, 22(2): 273-278.
[18] XING G X, ZHU Z L. Regional nitrogen budgets for China and its major watersheds [J]. Biogeochemistry, 2002, 57-58(1): 405-427.
[19] MAZDAK A, JANE R F, BERNIE A E, et al. Representation of agricultural conservation practices with SWAT [J]. Hydrological Processes, 2008, 22(16): 3042-3055.
[20] HAYTHEM B T, ROMAIN L, VINCENT B, et al. Screening parameters in the Pasture Simulation model using the Morris method [J]. Ecological Modeling, 2013, 266: 42-57.
[21] DEJONGE K C, LI J C A, AHMADI M, et al. Global sensitivity and uncertainty analysis of a dynamic agroecosystem model under different irrigation treatments [J]. Ecological Modeling, 2012, 231(4): 113-125.
[22] MORIASI D N, ARNOLD J G, VAN LIEW M W, et al. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations [J]. Transactions of the Asabe, 2007, 50(3): 885-900.
[23] STEVEN W,THOMAS S,TREVOR F, et al. A modeling approach to water quality management of an agriculturally dominated watershed, Kansas, USA [J]. Water Air and Soil Pollution, 2009, 203(1/4): 193-206.
[24] SHAMSHAD A, LEOW C S, RAMLAH A, et al. Applications of AnnAGNPS model for soil loss estimation and nutrient loading for Malaysian conditions [J]. International Journal of Applied Earth Observation and Geoinformation, 2008, 10(3): 239-252.
[25] PARKER G T, DROSTE R L, KENNEDT K J. Modeling the effect of agricultural best management practices on water quality under various climatic scenarios [J]. Journal of Environmental Engineering and Science, 2008, 7(1): 9-19.
[26] MIKOLAJ P,PAWEL M,IGNACY K, et al. Spatial quantification of non-point source pollution in a meso-scale catchment for an assessment of buffer zones efficiency [J]. Water, 2015, 7(5): 1889-1920.
[27] 查恩爽. 伊通河流域农业非点源污染模拟及最佳管理措施的应用[D]. 长春:吉林大学, 2011:51.
[28] 马放,姜晓峰,王立,等. 基于SWAT模型的阿什河流域非点源污染控制措施[J]. 中国环境科学, 2016, 36(2): 610-618.
[29] TASDIGHI A, ARABI M, HARMEL D, et al. A Bayesian total uncertainty analysis framework for assessment of management practices using watershed models [J]. Environmental Modeling & Software, 2018, 108: 240-252.
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