基于GIS和SWAT模型的清江流域面源污染模拟研究_英文_

　 第 2 7卷第 1期 长 　江 　科 　学 　院 　院 　报 Vol. 27　No. 1　　 　　2 0 1 0 年 1 月 Journa l of Yangtze R iver Sc ien tif ic Research Institute Jan . 2 0 1 0　 　 Rece ived da te: 2009207206 Correspond ing author:WANG Zhao2hui(19782) , male, Master D. , graduated from Institute of Subtrop ical Agriculture, Chinese Academy of Sci2 ences, China, in 2004. H ismain interests are involved in research of water resource p rotection and app lication of“3S”tech2 nology. ( Tel. ) 027282826895 ( E2mail) wangzh@mail. crsri. cn 　　Article ID : 1001 - 5485 (2010) 01 - 0057 - 05 Research on Simulation of Non2point Source Pollution in Q ingjiang R iver Basin Based on SWATModel and GIS W ANG Zhao2hu i1, 2 , ZHAO D eng2zhong1 , CAO Bo1 , L IANG D ong2ye1 (1. Yangtze R iver Scientific Research Institute, W uhan　430010, China; 2. School of Resource and Environmental Science, W uhan University, W uhan　430010, China) Abstract:A ssessment of pollution of water bodies from non2point sources (NPS) is a comp lex, requisite long series data and time2consum ing task. The accuracy of NPS pollution models depends to a great extent on how well a model selects input pa2 rameters describing the relevant characteristics of the watershed. It is certain that the p romoting p recision of input parameters affects simulation results of runoff, sediment and nutrients yield for the entire watershed. In this study, a basic database, which includes DEM, soil sort and landuse map, climate data, and land management data, is established using GIS. The generation and formation of non2point source pollution involves a great uncertainty which makes pollution monitoring and con2 trolling very difficult. Understanding the main parameters that affect NPS pollution uncertainty are necessary for p lanning and design of control measures. On the basis of the results of parameter sensitivity analysis, the sensitive parameters of soil and water assessment tool ( SWAT) model are identified, and then model parameters related to stream flow and nutrient loadings are calibrated and validated by observed values. The results show that simulated values are reasonably compared with observed data. Spatial2temporal distribution features of NPS pollution in the Q ingjiang R iver basin are revealed. NPS pollution mainly takes p lace in flood season. The critical risk areas of soil erosion are identified. Stream flow and nutrient loadings ( including total nitrogen ( TN ) and total phosphorus ( TP) ) in Q ingjiang R iver Basin are simulated. The surface runoff and nutrient yield results indicate that average annual runoff and the output of TN and TP p rovide better understanding on stream flow and nutrient loadings corresponding to various variation conditions of land use mode, agricultural tillage operation and natural rain2 fall etc. Key words: SWAT model and GIS; sensitivity analysis; stream flow; sediment; nutrient loadings ; Q ingjiang R iver Basin CLC num ber:X522　　　D ocum en t code:A 1　 I NTROD UCT IO N W ith rap id change and econom ic development, particularly in the densely populated and econom ically developed regions of the m iddle reach of Yangtze R iver Basin, dramatic pollutant loadings have deteriorated river water quality. Though point source pollution con2 trolling has achieved outstanding results, there are still serious p roblem s because the non2point source (NPS) pollution accounts for a large part of water pollution. Many researches in different countries have p roved that NPS pollution was one of the main reasons deteriorating water quality. Finding out the effect of NPS pollution has instructive significance on sustainable water re2 source development and water quality management in Yangtze R iver Basin. Hydrologic models are useful tools for understand2 ing and analyzing watershed hydrologic parameters’ change p rocesses, interactions, testing research hypot2 heses, and assessing management scenarios[ 1 ] . The soil and water assessment tool ( SWAT) is a model de2 veloped by D r. Jeff A rnold for the USDA2ARS ( agri2 cultural research service) , which is based on physics and p resents continuous time. It is mostly used to p re2 dict impact of land management p ractices on water, sediment, and nutrient yields over long periods of time[ 2 ] . 　　　　　　　　　　　　　　　　　　　　　　长江科学院院报 　　　　　　　　　　　　　　　　　　2010年 　　 This model has been widely used. The SWAT is p robably attributed to the comp rehensive considerations of hydrologic, biological and environmental p rocesses, the incorporation of management scenarios, the availa2 bility of parameter databases, and its robustness, flexi2 bility, as well as user friendliness. Heuvelmans p res2 ented a discussion in model parameter transferability for simulating the impact of land use on catchments hy2 drology[ 3 ] . W hile the SWAT is widely app lied to a broad range of conditions, however, a few studies have reported on the variability and transferability of the model parameters, and on evaluation of its crop growth, soil water, and groundwater modules using ex2 tensive field experimental data at the p rocess scale[ 4 ] . In this paper, the Q ingjiang R iver Basin, a main branch of m iddle Yangtze R iver Basin, is selected as a research area. W ater discharge and pollutant loadings are simulated based on SWAT model to reconstruct riv2 er nutrients evolution. The critical risk areas of soil e2 rosion are identified. According to the simulation re2 sults, soil erosion moduli are calculated in each sub2 basin and soil erosion intensities are classified. Stream flow and nutrient loadings ( including total nitrogen ( TN) and total phosphorus ( TP) ) in Q ingjiang R iver Basin are simulated. F ig. 1　Geograph ic loca tion and wa ter system of Q ingjiang R iver Ba sin 2　STUDY AREA D ESCR IPT IO NS Q ingjiang R iver Basin lies in the m iddle reach of Yangtze R iver Basin, near the Three Gorges Project, app roximately between 29°33′- 30°50′N and 108°35′ - 111°35′E. It is a mother river with long history of peop le living and agricultural development. The famous Tujia Nationality lives in this area. The terrain of Q ingjiang R iver Basin is intensely fluctuated, from the west high mountains and hills to the east low altitude ( Fig. 1). Climate iswarm and wet, it belongs typ ical2 ly subtrop ical monsoon climate. The average annual temperature is 15216℃, and average annual p recip ita2 tion and evaporation are 1460 mm and 600 - 800 mm respectively. The basin is covered naturally by ever2 green and deciduously broad2leaved, m ixed forest. Three major soil types are found in the basin including red2yellow soil, yellow cinnamon soil and paddy soil. 3　DATA AND M ETHOD S 3. 1　Da ta descr iption The data for SWAT include a land use database, a soil map, digital elevation model data (DEM) , a digital river network and a climate database. The scale of DEM data is 1 /50 000, and mesh size is 25 m ×25 m. In or2 der to elim inate the errors of river network extracted from DEM data , the digital river network is revised by water system map s in scale of 1 /10 000[ 5 ] . Besides the DEM data, all the other data are p rojected to a 25 m × 25 m grid data under the same reference frame. 3. 2　S im ula tion m ethods The first level of sub2division in SWAT is sub2ba2 sin which contains at least one hydrologic response unit (HRU ) , a tributary channel and a main channel or reach. HRU s possess unique land2use, management and soil attributes. During the simulation based on SWAT, the whole Q ingjiang R iver Basin was divided into fifteen sub2ba2 sins, and every sub2basin was composed of many HRU s. Before simulation, parameter sensitivity analy2 sis were conducted, the sensitive parameters of SWAT model were identified, and then model parameters re2 lated to stream flow and nutrient loadings were calibra2 ted and validated by observed values. In order to research the impacts of nutrient load2 ings caused by human activities, nutrient loading were simulated from year 1980 to 2005. NPS pollution dis2 tribution and critical risk areas of soil erosion and nu2 trient loss were identified. The spatial2temporal distri2 bution features of NPS pollution in the Q ingjiang R iver basin were revealed. 4　PARAM ETERS SENS IT IV ITY A2 NALY S IS 　　 In this study, parameter sensitivity was analyzed 85 第 1期　　　　WANG Zhao2hui, et al　Research on Simulation of Non2point Source Pollution on W ater Quality of Q ingjiang R iver Basin Based on SWATModel and GIS by LH2OAT method p roposed byMorris in SWAT mod2 el. The advantages of method one2factor2at2a2time (OAT) and the method Latin2Hypercube are extracted and adop ted by the method LH2OAT[ 6 ] . The merits of LH2OAT method are that it lessens the range of param2 eters, decreases the number of parameters being adjus2 ted and imp roves the efficiency of simulation. The purpose of conducting sensitivity analysis for all parameters is to understand the influences of the ten top parameters on stream flow, sediment, total phos2 phorous and total nitrogen in order ( seen in Tbale 1). Because model calibration and validation depend on the actual physical p rocess, these listed parameters were acted as reference from observed values. Table 1　The results of param eter sen sitiv ity ana lyses Stream flow Sediment loading Total phosphorus Total nitrogen CN2 CN2 SOL2DRCP SOL2ORGN ESCO SPCON SOL2Z SOL2Z SOL2Z SOL2Z CN2 CN2 SOL2AWC B IOM IX AWC SOL2AWC CANMX SLOPE ESCO ESCO B IOM IX SOL2AWC SOL2LABP SOL2LABP SOL2K USLE2P SLOPE SLOPE GWQMN ESCO ALPHA2BF ALPHA2BF RCHRG2DP SURLAG USLE2P USLE2P ALPHA2BF CANMX CANMX CANMX 5 　MOD EL CAL IBRAT IO N AND VAL IDAT IO N 　　The aim of model calibration and validation is to find out the parameter value which coincides with sim2 ulated datum and observed value. It is indispensable for simulation, which is used to assess model p redicted results[ 7 ] . Observed data from three stations including Shuibuya Station, Geheyan Station and Changyang Sta2 tion were used in this research. Only data of Changy2 ang Station were used to calibrate and validate sedi2 ment and nutrition because of observed data in the ab2 sence of former two stations. In this study Nash2Sutcliffe coefficient ( ENS) and correlation coefficient were adop ted in model calibra2 tion and validation. In comparson simulated values with observed values, if a reasonable stream flow was achieved, same parameters were used for calibration of the sediment and nutrient yield. The Nash2Sutcliffe co2 efficient and correlation coefficient reached 0. 81 and 0. 991 respectively in flow simulation. The Nash2Sut2 cliffe coefficient and correlation coefficient reached 0. 752 and 0. 964 respectively in sediment simulation. The Nash2Sutcliffe coefficient and correlation coeffi2 cient of nutrient calibration both reached satisfied value using the same method . 6　ANALY S IS AND D ISCUSS IO N 6. 1　 Iden tif ica tion of the cr itica l r isk area s of so il erosion 　　Soil erosion quantity was calculated in each sub2 basin. According to the simulated results of the soil ero2 sion based on SWAT model during 198022005, soil ero2 sion intensities were classified, and spatial distribution characters were revealed. The simulation results showed that the area of low2grade soil erosion accounted for 68. 87% , that of m iddle2grade soil erosion 26. 74% and that of drastic soil erosion 4. 39%. (Fig. 2). F ig. 2　The d istr ibution of so il erosion cr itica l r isk area The result shows that there are intimate relation2 ship s between soil erosion, p recip itation, slope length and slope gradient[ 8 ] , that the relative coefficient be2 tween soil erosion and p recip itation is 0. 59, and that the relative coefficient between soil erosion and slope length is 0. 23, while the coefficient between soil ero2 sion and slope gradient is 0. 39. 6. 2　S im ula tion on stream flow The simulated average annual stream flow was 1. 57 ×1010 m3 ·yr- 1 from 1980 to 2005 ( Fig. 3 ). The maximum runoff occurred in 1983 , following with 1998, viz. 2. 46 ×1010 m3 and 2. 39 ×1010 m3 respec2 tively. The m inimum stream flow was in 1997, viz. 1. 03 ×1010 m3. 95 　　　　　　　　　　　　　　　　　　　　　　长江科学院院报 　　　　　　　　　　　　　　　　　　2010年 　　 F ig. 3　S im ula ted stream flow dur ing 1980 - 2005 The annual stream flow mainly occured from May to Sep tember, which was 61. 3% of the whole year. Simulation results indicate that the maximum flow oc2 curred in August ( Fig. 4). F ig. 4　S im ula ted m on thly output changes of stream flow dur ing 1980 - 2005 6. 3　S im ula tion on changes of TP and TN Average annual nutrient outputs of TN and TP were 1. 559 ×106 kg and 1. 56 ×105 kg respectively. Seasonal variations of the nutrient output were differ2 ent. The result of 1980 is not included in Table 2 be2 cause its values are abnormal. Table 2　S im ula ted changes of TN and TP from 1981 - 2005 ( 105 kg) Year N P Year N P Year N P 1981 19. 98 2. 44 1990 12. 65 1. 52 1999 12. 11 1. 41 1982 21. 49 2. 63 1991 16. 74 2. 01 2000 15. 6 1. 77 1983 22. 38 2. 72 1992 10. 83 1. 26 2001 9. 92 1. 16 1984 18. 02 2. 20 1993 17. 55 1. 98 2002 14. 23 1. 65 1985 15. 76 1. 90 1994 13. 32 1. 55 2003 13. 07 1. 48 1986 15. 51 1. 86 1995 14. 00 1. 59 2004 14. 06 1. 59 1987 17. 51 2. 08 1996 16. 32 1. 89 2005 13. 70 1. 56 1988 15. 43 1. 85 1997 12. 37 1. 43 Average 15. 59 1. 83 1989 18. 69 2. 24 1998 18. 55 2. 10 　　 The concentration and output of TN and TP changed according to season transformation. The maxi2 mum output of TN and TP occured in late sp ring and summer ( from Ap ril to Sep tember) ( Fig. 5 ) , the m inimum output occured in winter. There are two rea2 sons for this phenomenon. The most important one is agricultural tillage operation, such as cultivation and fertilizing in sp ring[ 9 ] . The other one is temperature. Grass and leaves are rotted rap idly in higher tempera2 ture, which makes water carry p lenty of pollutants and enhances nutrient loadings[ 10 ] . F ig. 5　S im ula ted m on thly output changes of TN and TP dur ing 1980 - 2005 7　SUMM ARY AND CO NCL US IO N A lthough the SWAT model is commonly used in simulation on stream flow, sediment and nutrient load2 ings, it is influenced not only by natural conditions such as p recip itation, soil type and topography but also human activities. Land use, tillage operation and p re2 cip itation are the main reasons influencing surface run2 off and nutrient concentration and output. The outputs of nutrient loadings are in accordance with tillage oper2 ation and fertilizer app lication seasons. In Q ingjiang R iver basin the area of low2grade soil erosion accounted for 68. 87% , that of m iddle2grade soil erosion 26. 74% , and that of drastic soil erosion 4. 39% respectively. The average annual stream flow was 1. 57 ×1010 m3 ·yr- 1 from 1980 to 2005. Stream flow mainly distributed from May to Sep tember, which owns 61. 3% of the whole year. The average annual output of TN and TP were 1. 559 ×105 kg and 1. 56 × 106 kg respectively. The surface runoff and nutrient yield result indicates that the average annual runoff and 06 第 1期　　　　WANG Zhao2hui, et al　Research on Simulation of Non2point Source Pollution on W ater Quality of Q ingjiang R iver Basin Based on SWATModel and GIS output of TN and TP p rovides better understanding in stream flow and nutrient loadings corresponding to vari2 ations of land use conditions, agricultural tillage opera2 tion and natural rainfall etc. REFERENCES: [ 1 ] 　LUO Yi, HE Chan2sheng, SOPHOCLEOUS Marios, et a l. A ssessment of crop growth and soil water modules in SWAT2000 using extensive field experiment data in an irrigation district of the Yellow R iver basin [ J ]. Journal of Hydrology, 2008, 352: 139 - 156. [ 2 ]　GEZA Mengistu and McCray J E. 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( Edited by L IU Yun2fei, YI Xin2hua) 基于 GIS和 SWAT模型的清江流域 面源污染模拟研究 汪朝辉 1, 2 ,赵登忠 1 ,曹 　波 1 ,梁东业 1 (1. 长江科学院 空间信息技术应用研究所 ,武汉 　430010; 2.武汉大学 资源与环境科学学院 ,武汉 　430079) 摘要 :水体面源污染评价研究是一项复杂的工作 ,涉及面广和要求有较长时间序列的数据。面源污染模型很大程 度上依赖于对于流域的特征描述的相关参数 ,因此提高输入模型参数的精度 ,有利于提升流域面源污染的径流、泥 沙和营养物质的产出模拟效果。GIS和面源污染模型的有机结合是当前面源污染模拟研究最有效的方法。本研究 建立了基于 GIS的基础数据库 ,其中包括 DEM、土壤类型、土地利用、气象数据以及农业耕作管理数据等。面源污 染模拟的产生和形成具有很大的不确定性 ,这更加增加了监测和控制面源污染的难度。探索影响面源污染的主要 因素 ,研究其不确定性对于提出和制定污染控制措施至关重要。本研究进行了清江流域面源污染的参数敏感性分 析 ,根据观测数据对 SWAT模型进行验证和率定 ,并利用 SWAT模型进行模拟 ,揭示了清江流域面源污染的时空分 布特征 ,确定了清江流域水土流失风险区。结果表明清江流域的面源污染主要发生在丰水期 ,不同的土地利用方 式、农业耕作以及降水等是影响径流量和营养物质产生的主要因素。 关 　键 　词 : SWAT模型和 GIS;敏感性分析 ;径流模拟 ;泥沙模拟 ;营养物载荷 ;清江流域 16