不同精度的土壤数据对水质和水量模拟的影响

doi:10.3864/j.issn.0578-1752.2020.16.010

中国农业科学 ›› 2020, Vol. 53 ›› Issue (16): 3319-3332.doi: 10.3864/j.issn.0578-1752.2020.16.010

• 土壤肥料·节水灌溉·农业生态环境 • 上一篇下一篇

不同精度的土壤数据对水质和水量模拟的影响

李影^1,²(),雷秋良¹(),秦丽欢¹,朱阿兴^3,⁴,李晓虹¹,翟丽梅¹,王洪媛¹,武淑霞¹,闫铁柱¹,李文超¹,胡万里⁵,任天志⁶,刘宏斌¹

¹中国农业科学院农业资源与农业区划研究所/农业农村部面源污染控制重点实验室,中国北京100081
²中国科学院地理科学与资源研究所,中国北京100101
³南京师范大学/江苏省地理信息资源开发与利用协同创新中心,中国南京 210023
⁴Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
⁵云南省农业科学院农业环境资源研究所,中国昆明 50205
⁶中国农业科学院,中国北京100081

收稿日期:2019-09-23 接受日期:2019-12-08 出版日期:2020-08-16 发布日期:2020-08-27
通讯作者: 雷秋良
作者简介:李影,Tel：18810031557;E-mail：liying9391@126.com
基金资助:
国家自然科学基金(31572208);国家公益性行业(农业科研专项201303089);国家重点研发计划(2018YFD0200200);国家重点研发计划(2016YFC0500205);国家重点基础研究发展计划(2015CB954103)

Impact of Soil Data with Different Precision on Water Quality and Flow Simulation

LI Ying^1,²(),LEI QiuLiang¹(),QIN LiHuan¹,ZHU AXing^3,⁴,LI XiaoHong¹,ZHAI LiMei¹,WANG HongYuan¹,WU ShuXia¹,YAN TieZhu¹,LI WenChao¹,HU WanLi⁵,REN TianZhi⁶,LIU HongBin¹

¹Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/Key Laboratory of Nonpoint Source Pollution Control, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
² Institute of Geographical Sciences and Natural Resources Research, Chinese of Academy of Sciences, Beijing 100101, China
³Nanjing Normal University/Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
⁴Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
⁵Institute of Agricultural Resources & Environment, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
⁶Chinese Academy of Agricultural Sciences, Beijing 100081, China

Received:2019-09-23 Accepted:2019-12-08 Online:2020-08-16 Published:2020-08-27
Contact: QiuLiang LEI

摘要/Abstract

摘要：

【背景】模型模拟是研究面源污染的重要手段,建模过程中输入数据的质量是影响模型准确度的重要因素,其中土壤数据作为流域模型的重要输入数据之一,对模型的产流过程有重要的影响。然而,以往的研究多集中于土壤数据精度对水量和水文过程的影响,对水质的研究还比较欠缺。【目的】为丰富该领域建模的先验知识,为流域模型建立过程中的数据选择提供帮助。【方法】采用SWAT（soil & water assessment tool）模型,利用不同精度（1:5万、1:50万和1:100万）的土壤数据进行建模,对凤羽河流域的水量、泥沙、总氮和总磷含量进行了模拟。并采用SWAT-CUP软件进行参数的率定,得到基于3种不同土壤数据的最佳模拟结果。在此基础上,研究不同精度土壤数据对水文响应单元划分、模型参数、水质和水量模拟的影响。【结果】（1）土壤数据对水文响应单元（HRU,hydrologic response unit）的划分数量有明显影响,HRU划分数量的敏感性与划分阈值及土壤图详细程度有关;（2）进行参数率定后模型的表现效果有明显的提高,不同精度的土壤数据对于不同指标（流量、泥沙、总氮和总磷）的模拟效果存在差异,但并非土壤数据精度越高模拟效果越好;（3）随着子流域面积的增大,不同土壤数据提取的土壤属性的平均值趋于一致,且校准过程会对面积较小的子流域产生较大的影响。【结论】因此,在实际的模型模拟中应根据流域的大小和模拟的指标选择土壤数据的精度,同时在模型校准过程中要注意空间尺度的影响。

关键词: 土壤数据, 不确定性, SWAT（Soil & Water Assessment Tool）模型;, 水质, 流量, 水文响应单元

Abstract:

【Background】 Watershed model is an important tool to study non-point source pollution, and the reliability of input data is an important factor to ensure the accuracy of the model estimation. Soil data, as one of the important input data of the watershed model, has a significant influence on the runoff process. The previous studies mainly focused on the water quantity and hydrological process, while the research on water quality needs more attention. Furthermore, the disagreements over appropriate resolution of the soil maps also existed in the previous studies. 【Objective】 The purpose of this study was to enrich the prior knowledge of modeling and to provide useful suggestion for data selection in watershed simulation. 【Method】 This paper employed the widely used SWAT (soil & water assessment tool) model as an example and simulated flow, sediment, TN (total nitrogen) and TP (total phosphorus) of Fengyu river basin by three types of soil data with different scales. The scales were used for soil data as follows: 1﹕50 000 (soil-1), 1﹕500 000 (soil-2) and 1﹕1 000 000 (soil-3). SWAT-CUP software was used to adjust parameters. Based on the above methods, the effects of soil data with different scales (1﹕50 000, 1﹕500 000 and 1﹕1 000 000) on HRU (hydrologic response unit) division, model parameters and water quality and quantity were studied. 【Result】 (1) Soil data with different scales had great influence on the result of the HRU division, and the sensitivity of the number of HRU division was related to the division threshold and soil map precision. (2) The calibration of model parameters could remarkably improve the simulation performance of the model. The simulation performance associated with simulated substance, and the highest precision soil map not had the best simulation performance. (3) The attributes of soil tended to be consistent with the increase of sub-watershed area. The variation of simulation results caused by different soils gradually tends to be steady with the increase of confluence area, and the calibration process could have a great effect when confluence area was small. 【Conclusion】 In summary, the precision of soil data should be selected according to the basin size and simulated substance, and the impact of spatial scale need to be considered.

Key words: soil data, uncertainty, SWAT model, water quality, flow, hydrologic response unit

李影,雷秋良,秦丽欢,朱阿兴,李晓虹,翟丽梅,王洪媛,武淑霞,闫铁柱,李文超,胡万里,任天志,刘宏斌. 不同精度的土壤数据对水质和水量模拟的影响[J]. 中国农业科学, 2020, 53(16): 3319-3332.

LI Ying,LEI QiuLiang,QIN LiHuan,ZHU AXing,LI XiaoHong,ZHAI LiMei,WANG HongYuan,WU ShuXia,YAN TieZhu,LI WenChao,HU WanLi,REN TianZhi,LIU HongBin. Impact of Soil Data with Different Precision on Water Quality and Flow Simulation[J]. Scientia Agricultura Sinica, 2020, 53(16): 3319-3332.

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参考文献 40

[1]	中华人民共和国生态环境部. 中国环境状况公报. 环保工作资料选, 2017: 17-31.
	Ministry of Ecology and Environment of the People's Republic of China. Bulletin on the state of China's environment. Environmental Performance Information, 2017: 17-31. (in Chinese)
[2]	赵永宏, 邓祥征, 战金艳, 席北斗, 鲁奇. 我国湖泊富营养化防治与控制策略研究进展. 环境科学与技术, 2010,33(3):92-98.
	ZHAO Y H, DENG X Z, ZHAN J Y, XI B D, LU Q. Progress on preventing and controlling strategies of lake eutrophication in China. Environmental Science & Technology, 2010,33(3):92-98. (in Chinese)
[3]	HOWARTH R, CHAN F, CONLEY D J, GARNIER J, DONEY S C, MARINO R, BILLEN G. Coupled biogeochemical cycles: eutrophication and hypoxia in temperate estuaries and coastal marine ecosystems. Frontiers in Ecology & the Environment, 2011,9(1):18-26.
[4]	ROY J W, BICKERTON G. Elevated dissolved phosphorus in riparian groundwater along gaining urban streams. Environmental Science & Technology, 2014,48(3):1492-1498. pmid: 24422427
[5]	MORSE N B, WOLLHEIM W M. Climate variability masks the impacts of land use change on nutrient export in a suburbanizing watershed. Biogeochemistry, 2014,121(1):45-59.
[6]	陈亮, 董晓华, 李英海, 李中华, 刘冀, 薄会娟, 万浩, 蔡莉莉. 基于SWAT模型的黄柏河东支流域气候变化的水文响应研究. 三峡大学学报(自然科学版), 2019(2):1-5.
	CHEN L, DONG X H, LI Y H, LI Z H, LIU J, BO H J, WAN H, CAI L L. Hydrological response of climate change in east branch of Huangbaihe river based on SWAT model. Journal of China Three Gorges University(Natural Sciences), 2019(2):1-5. (in Chinese)
[7]	娄永才, 郭青霞. 岔口小流域非点源污染模型AnnAGNPS不确定性分析. 农业环境科学学报, 2018,37(5):956-964.
	LOU Y C, GUO Q X. Uncertainty analysis of an AnnAGNPS model used in the Chakou watershed. Journal of Agro-Environment Science, 2018,37(5):956-964. (in Chinese)
[8]	罗娜, 李华, 樊霆, 郭彬, 李凝玉, 傅庆林, 马洁, 金跃群. HSPF模型在流域面源污染模拟中的应用. 浙江农业科学, 2019(1):141-145.
	LUO N, LI H, FAN T, GUO B, LI N Y, FU Q L, MA J, JIN Y Q. Application of HSPF model in simulation of non-point source pollution. Journal of Zhejiang Agricultural Sciences, 2019(1):141-145. (in Chinese)
[9]	ZHANG X, HAO F, CHENG H, LI D. Application of swat model in the upstream watershed of the Luohe River. Chinese Geographical Science, 2003,13(4):334-339.
[10]	余红, 沈珍瑶. 非点源污染不确定性研究进展. 水资源保护, 2008,24(1):1-5.
	YU H, SHEN Z Y. Uncertainty of non-point source pollution. Water Resources Protection, 2008,24(1):1-5. (in Chinese)
[11]	廖谦, 沈珍瑶. 农业非点源污染模拟不确定性研究进展. 生态学杂志, 2011,30(7):1542-1550.
	LIAO Q, SHEN Z Y. Uncertainties in agricultural non-point source pollution simulation: Research progress. Chinese Journal of Ecology, 2011,30(7):1542-1550. (in Chinese)
[12]	LI W, ZHAI L, LEI Q, WOLLHEIM W M, LIU J, LIU H, HU W, REN T, WANG H, LIU S. Influences of agricultural land use composition and distribution on nitrogen export from a subtropical watershed in China. Science of the Total Environment, 2018,642:21-32. doi: 10.1016/j.scitotenv.2018.06.048 pmid: 29894879
[13]	崔超. 三峡库区香溪河流域氮磷入库负荷及迁移特征研究[D]. 北京: 中国农业科学院, 2016.
	CUI C. Characteristics of nitrogen and phosphorus loadings into receiving water body and migration in Xiangxi river basin, Three Gorges Reservoir region[D]. Beijing: Chinese Academy of Agricultural Sciences, 2016. (in Chinese)
[14]	李润奎, 朱阿兴, 陈腊娇, 刘军志, 宋现锋, 林耀明. SCS-CN模型中土壤参数的作用机制研究. 自然资源学报, 2013,28(10):1778-1787.
	LI R K, ZHU A X, CHEN L J, LIU J Z, SONG X F, LIN Y M. Effects of soil parameters in SCS-CN runoff model. Journal of Natural Resources, 2013,28(10):1778-1787. (in Chinese)
[15]	DIEK S, TEMME A J A M, TEULING A J. The effect of spatial soil variation on the hydrology of a semi-arid Rocky Mountains catchment. Geoderma, 2014,235-236(4):113-126.
[16]	WORQLUL A W, AYANA E K, YEN H, JEONG J, MACALISTER C, TAYLOR R, GERIK T J, STEENHUIS T S. Evaluating hydrologic responses to soil characteristics using SWAT model in a paired- watersheds in the Upper Blue Nile Basin. Catena, 2018,163:332-341.
[17]	CHEN L, WANG G, ZHONG Y, SHEN Z. Evaluating the impacts of soil data on hydrological and nonpoint source pollution prediction. Science of the Total Environment, 2016,563-564:19-28. doi: 10.1016/j.scitotenv.2009.10.027 pmid: 19896162
[18]	叶许春, 张奇, 刘健, 李丽娇, 左海军. 土壤数据空间分辨率对水文过程模拟的影响. 地理科学进展, 2009,28(4):575-583.
	YE X C, ZHANG Q, LIU J, LI L J, ZUO H J. Effects of spatial resolution of soil data on hydrological processes. Modeling Progress in Geography, 2009,28(4):575-583. (in Chinese)
[19]	LI R K, RUI X P, ZHU A X, LIU J Z, Band L E, Song X F. Increasing detail of distributed runoff modeling using fuzzy logic in curve number. Environmental Earth Sciences, 2015,73(7):3197-3205.
[20]	李润奎, 朱阿兴, 李宝林, 裴韬, 秦承志. 流域水文模型对土壤数据响应的多尺度分析. 地理科学进展, 2011,30(1):80-86. doi: 10.11820/dlkxjz.2011.01.010
	LI R K, ZHU A X, LI B L, PEI T, QIN C Z. Response of simulated stream flow to soil data spatial detail across different routing areas. Progress in Geography, 2011,30(1):80-86. (in Chinese) doi: 10.11820/dlkxjz.2011.01.010
[21]	KUMAR S, MERWADE V. Impact of watershed subdivision and soil data resolution on SWAT model calibration and parameter uncertainty. Jawra Journal of the American Water Resources Association, 2009,45(5):1179-1196.
[22]	MENGISTU G, MCCRAY J E. Effects of soil data resolution on SWAT model stream flow and water quality predictions. Journal of Environmental Management, 2008,88(3):393-406. doi: 10.1016/j.jenvman.2007.03.016 pmid: 17475392
[23]	WANG X, MELESSE A M. Effects of STATSGO and SSURGO as inputs on SWAT Model's Snowmelt Simulation. Jawra Journal of the American Water Resources Association, 2006,42(5):1217-1236.
[24]	庞燕, 项颂, 储昭升, 薛力强, 叶碧碧. 洱海流域农业用地与入湖河流水质的关系研究. 环境科学, 2015,36(11):4005-4012. doi: 10.1021/es0200903
	PANG Y, XIANG S, CHU Z S, XUE L Q, YE B B. Relationship between agricultural land and water quality of inflow river in Erhai Lake basin. Environmental Science, 2015,36(11):4005-4012. (in Chinese) doi: 10.1021/es0200903
[25]	张辰, 陆建忠, 陈晓玲. 基于输出系数模型的云南洱海流域农业非点源污染研究. 华中师范大学学报(自然科学版), 2017,51(1):108-114.
	ZHANG C, LU J Z, CHEN X L. Study of pollution from agricultural non-point sources in Lake Erhai watershed in Yunnan Province based on export coefficient model. Journal of Central China Normal University (Natural Sciences), 2017,51(1):108-114. (in Chinese)
[26]	张召喜. 基于SWAT模型的凤羽河流域农业面源污染特征研究[D]. 北京: 中国农业科学院, 2013.
	ZHANG Z X. Study on charateristics of agricultural non-point source pollution in Fengyu river basin on SWAT model[D]. Beijing: Chinese Academy of Agricultural Sciences, 2013. (in Chinese)
[27]	李文超. 凤羽河流域农业面源污染负荷估算及关键区识别研究[D]. 北京: 中国农业科学院, 2014.
	LI W C. Evaluating the loads of agricultural non-point source pollution and identifying critical source areas in Fengyu basin[D]. Beijing: Chinese Academy of Agricultural Sciences, 2014. (in Chinese)
[28]	龙爱华. SWAT 2009理论基础. 郑州: 黄河水利出版社, 2012.
	LONG A H. Theoretical Documentation Version 2009 Soil&Water Assessment Tool. Zhengzhou: The Yellow River Water Conservancy Press, 2012. (in Chinese)
[29]	SHARIFI A, LANG M W, MCCARTY G W, SADEGHI A M, LEE S, YEN H, RABENHORST M C, JEONG J, YEO I Y. Improving model prediction reliability through enhanced representation of wetland soil processes and constrained model auto calibration - A paired watershed study. Journal of Hydrology, 2016,541:1088-1103.
[30]	MORIASI D N, ARNOLD J G, LIEW M W V, BINGNER R L, HARMEL R D, VEITH T L. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 2007,50(3):885-900. doi: 10.13031/2013.23153
[31]	GOWDA P H, MULLA D J, NANGIA V, ALE S. Scale effects of STATSGO and SSURGO on flow and water quality predictions. Journal of Water Resource and Protection, 2013,5(3):266-274. doi: 10.4236/jwarp.2013.53027
[32]	CHAPLOT V. Impact of DEM mesh size and soil map scale on SWAT runoff, sediment, and NO₃--N loads predictions. Journal of Hydrology, 2005,312(1/4):207-222. doi: 10.1016/j.jhydrol.2005.02.017
[33]	BARONI G, ZINK M, KUMAR R, SAMANIEGO L, ATTINGER S. Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales. Hydrology & Earth System Sciences, 2017,21(5):1-37.
[34]	LI R, ZHU A, SONG X, LI B, PEI T, QIN C. Effects of Spatial Aggregation of Soil Spatial Information on Watershed Hydrological Modeling. Hydrological Processes, 2012,26:1390-1404. doi: 10.1002/hyp.v26.9
[35]	李润奎, 朱阿兴, 秦承志, 陈腊娇, 刘军志. 土壤数据对分布式流域水文模型模拟效果的影响. 水科学进展, 2011,22(2):168-174.
	LI R K, ZHU A X, QIN C Z, CHEN L J, LIU J Z. Effects of spatial detail of soil data on distributed hydrological modeling. Advances in Water Science, 2011,22(2):168-174. (in Chinese)
[36]	朱阿兴, 杨琳, 樊乃卿, 曾灿英, 张甘霖. 数字土壤制图研究综述与展望. 地理科学进展, 2018,37(1):66-78. doi: 10.18306/dlkxjz.2018.01.008
	ZHU A X, YANG L, FAN N Q, ZENG C Y, ZHANG G L. The review and outlook of digital soil mapping. Progress in Geography, 2018,37(1):66-78. (in Chinese) doi: 10.18306/dlkxjz.2018.01.008
[37]	BOSSA A Y, DIEKKRUGER B, IGUE A M, GAISER T. Analyzing the effects of different soil databases on modeling of hydrological processes and sediment yield in Benin (West Africa). Geoderma, 2012, 173-174(2):61-74.
[38]	李润奎, 朱阿兴, Peter C. Augello, James E. Burt. SWAT模型对高精度土壤信息的敏感性研究. 地球信息科学学报, 2007,9(3):72-78.
	LI R K, ZHU A X, PETER C A, JAMES E B. Sensitivity of SWAT model to detailed soil information. Geo-Information Science, 2007,9(3):72-78. (in Chinese)
[39]	WAHREN F T, JULICH S, NUNES J P, GONZALEZ P O, HAWTREE D, FEGER K H, KEIZER J J. Combining digital soil mapping and hydrological modeling in a data scarce watershed in north-central Portugal. Geoderma, 2016,264:350-362. doi: 10.1016/j.geoderma.2015.08.023
[40]	ZIADAT F M, DHANESH Y, SHOEMATE D, SRINIVASAN R, NARASIMHAN B, TECH J. Soil-Landscape Estimation and Evaluation Program (SLEEP) to predict spatial distribution of soil attributes for environmental modeling. International Journal of Agricultural & Biological Engineering, 2015,8(3):1-15.

数据类别 Data item	数据来源 Data source	比例尺 Scale	数据用途 Purpose
数字高程图 Digital elevation model	国家基础地理信息中心 National geomatic center of China	1﹕50000	坡度、河网的提取及流域的划分 Extracting slope, river network and watershed division
土地利用数据 Land use data	大理州洱源县土地局（2003年） Land Resources Bureau	1﹕10000	获得流域内土地利用类型及比例 Land use information
水系图 Drainage map	国家基础地理信息中心 National geomatic center of China	1﹕250000	获得流域内水系分布情况 Distribution of river systems
土壤图（soil-1） Soil map	全国第二次土壤普查土壤图 The 2nd national soil survey	1﹕50000	获得土壤类型及分布 Soil type and distribution
土壤图（soil-2） Soil map	全国第二次土壤普查土壤图 The 2nd national soil survey	1﹕500000	获得土壤类型及分布 Soil type and distribution
土壤图（soil-3） Soil map	中国科学院南京土壤研究所 Institute of soil science, CAS	1﹕1000000	获得土壤类型及分布 Soil type and distribution

数据类别 Data item	数据来源 Data source	数据用途 Purpose
气象数据 Weather data	气象观测站 Weather station	降水、气温、太阳辐射、风速等数据 Precipitation, temperature and so on
土壤属性数据 Soil properties data	土种志,野外挖掘土壤剖面及采样实测 Annals of soil classification, soil profiles and samples	获得土壤物理及化学属性数据 Soil physical and chemical properties
农田管理措施 Management information	农户调查、统计资料 Survey and statistical data	作物种植模式、施肥、灌溉和耕作情况 Field management data
农村生活污染及畜禽养殖 Living pollution and livestock farming	农户调查、统计资料 Survey and statistical data	农村污水、固废垃圾产生量及处理方式,畜禽养殖规模及其粪便处理方式等 Waste water, solid waste and livestock data

参数 Parameter	参数说明 Parameter description	初始阈值 Initial threshold
R_CN2.mgt	水分条件Ⅱ时的初始SCS径流曲线数 Initial SCS runoff curve number for moisture condition II	-1	0.5
V_ALPHA_BF.gw	基流α因子 Baseflow alpha factor	0	1
V_RCHRG_DP.gw	深层含水层的渗透系数 Deep aquifer percolation fraction	0	1
V_GW_REVAP.gw	地下水的revap系数 Groundwater “revap” coefficient	0.02	0.2
V_GW_DELAY.gw	地下水的时间延迟 Groundwater delay time	0	500
V_GWQMN.gw	发生回归流的浅层含水层的水位阈值 Threshold depth of water in the shallow aquifer required for return flow to occur	0	5000
V_SHALLST.gw	浅层含水层的初始水深 Initial depth of water in the shallow aquifer	0	50000
V_DEEPST.gw	深层含水层的初始水深 Initial depth of water in the deep aquifer	0	50000
V_SLSUBBSN.hru	平均坡长 Average slope length	10	150
V_OV_N.hru	坡面漫流的曼宁系数n值 Manning’s “n” value for overland flow	0.01	30
V_ESCO.hru	土壤蒸发补偿因子 Soil evaporation compensation factor	0.01	1
V_LAT_TTIME.hru	侧向流的运动时间 Lateral flow travel time	0	180
V_EPCO.hru	植物吸收补偿因子 Plant uptake compensation factor	0	1
V_HRU_SLP.hru	平均比降 Average slope steepness	0	1
V_CH_N2.rte	主河道的曼宁系数n值 Manning’s “n” value for the main channel	-0.01	0.3
V_FFCB.bsn	初始土壤蓄水量 Initial soil water storage	0	1
V_SURLAG.bsn	地表径流滞后系数 Surface runoff lag coefficient	0.05	24

参数 Parameter	参数说明 Parameter description	初始阈值 Initial threshold
V_BIOMIX.mgt	生物混合效率 Biological mixing efficiency	0	1
V_FRT_SURFACE.mgt	表层10 mm土壤中的施肥量占施肥总量的分数 Fraction of fertilizer applied to top 10 mm of soil	0	1
V_FILTERW.mgt	过滤带宽度 Width of edge-of-field filter strip	0	100
V_GWSOLP.gw	向子流域河流输入的地下水中可溶性磷浓度 Concentration of soluble phosphorus in groundwater contribution to streamflow from subbasin	0	1000
V_ERORGN.hru	泥沙运移中有机氮的富集比 Organic N enrichment ratio for loading with sediment	0	5
V_ERORGP.hru	泥沙运移中有机磷的富集比 Phosphorus enrichment ratio for loading with sediment	0	5
V_CDN.bsn	反硝化指数速率系数 Denitrification exponential rate coefficient	0	3
V_SDNCO.bsn	发生反硝化作用的土壤含水量阈值 Denitrification threshold water content	0	1
V_RSDCO.bsn	残留物的分解系数 Residue decomposition coefficient	0.02	0.1
V_PPERCO.bsn	磷的渗流系数 Phosphorus percolation coefficient in soil layer	10	17.5
V_PHOSKD.bsn	磷的土壤分配系数 Phosphorus soil partitioning coefficient	100	200
V_NPERCO.bsn	硝酸盐的渗流系数 Nitrate percolation coefficient	0	1
V_BC1_BSN.bsn	NH₃生物氧化的速率常数 Rate constant for biological oxidation of NH₃	0.1	1
V_BC2_BSN.bsn	从NO₂到NO₃的生物氧化速率常数 Rate constant for biological oxidation NO₂ to NO₃	0.1	1
V_BC3_BSN.bsn	从有机氮到氨基的水解速率常数 Rate constant for hydrolysis of organic nitrogen to ammonia	0.02	0.4
V_BC4_BSN.bsn	从有机磷到可溶性磷的腐化速率常数 Rate constant for decay of organic phosphorus to dissolved phosphorus	0.01	0.7

参数 Parameter	参数说明 Parameter description	初始阈值 Initial threshold
R_USLE_C.plant.dat	USLE方程中的C因子的最小值 Minimum value of USLE C factor	-10	10
V_USLE_P.mgt	USLE方程中的P因子 USLE equation support practice factor	0	1
V_LAT_SED.hru	侧向流与地下径流中的泥沙含量 Sediment concentration in lateral and groundwater flow	0	5000
V_ADJ_PKR.bsn	子流域（支流）泥沙演算的最大流速调节因子 Peak rate adjustment factor for sediment routing in the subbasin	0.5	2

不同精度的土壤数据对水质和水量模拟的影响

Impact of Soil Data with Different Precision on Water Quality and Flow Simulation

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参数 Parameter	参数说明 Parameter description	初始阈值 Initial threshold
V_SPCON.bsn	最大泥沙量的线性参数 Linear parameter for calculating the maximum amount of sediment	0.0001	0.01
V_SPEXP.bsn	最大泥沙量的指数参数 Exponent parameter for calculating sediment restrained in channel sediment routing	1	2
V_CH_K2.rte	主河道冲积物的有效渗透系数 Effective hydraulic conductivity in main channel alluvium	-0.01	500
V_CH_COV1.rte	河道侵蚀因子 Channel erodibility factor	-0.05	0.6
V_CH_COV2.rte	河道覆盖因子 Channel cover factor	-0.001	1
V_ALPHA_BNK.rte	河岸调蓄的基流α因子 Baseflow alpha factor for bank storage	0	1

土壤数据 Soil data	SCS径流曲线数CN2	最大根系深度 SOL_ZMX (mm)	土层埋深 SOL_Z (mm)	湿容重 SOL_BD (g·cm^-3)	有效含水量SOL_AWC (mm·mm^-1)	饱和渗透系数SOL_K (mm·h^-1)	土壤侵蚀K因子USLE_K
soil-1	72.93	722.73	211.24	1.19	0.16	21.61	0.27
soil-2	77.43	879.93	160.66	1.15	0.36	42.56	0.29
soil-3	66.23	526.07	201.79	1.27	0.18	21.18	0.24

土壤数据 Soil data	项目 Item	校准后 After calibration		校准前 Before calibration
土壤数据 Soil data	项目 Item	R²	NS	R²	NS
soil-1	流量Flow	0.81	0.68	0.84	-5.29
	泥沙 Sediment	0.44	0.43	0.49	0.31
	总氮Total nitrogen	0.58	0.57	0.50	-605.73
	总磷Total phosphorus	0.53	0.53	0.30	-5007.4
soil-2	流量Flow	0.86	0.79	0.77	-6.86
	泥沙 Sediment	0.46	0.41	0.50	0.1
	总氮Total nitrogen	0.71	0.59	0.53	-710.97
	总磷Total phosphorus	0.75	0.75	0.31	-1539.57
soil-3	流量Flow	0.75	0.64	0.86	-5.27
	泥沙 Sediment	0.43	0.38	0.45	0.28
	总氮Total nitrogen	0.85	0.69	0.49	-1097.61
	总磷Total phosphorus	0.63	0.62	0.29	-5717.73

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