ATHYS模型及其在巢湖流域丰乐河水文过程模拟中的应用
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摘要
以巢湖典型支流丰乐河流域为例,利用分布式水文模型ATHYS对流域降雨—径流过程进行模拟,以期为丰乐河流域水土保持、防洪、水资源利用和非点源污染防治等提供科学依据。ATHYS模型将流域分为大小相同的空间小单元,以库模型进行产流计算,以滞后演算法(lag and route)方法进行汇流计算,并以Nash and Sutcliffe指数作为检验模拟结果的标准,模拟流域出口断面日径流量,初步分析流域径流实测值与模拟值的年际变化。模型模拟结果表明,在率定期和验证期模拟与实测径流量的总Nash and Sutcliffe值(Nash值)都可以达到0.80以上,相关系数均可达0.89以上。除了部分峰值和基流,模型能较好地模拟丰乐河的径流过程。模型模拟径流总量的相对误差在率定期为23.1%,验证期为34.6%。流域降雨量、径流量的年内、年际变化均比较明显,径流实测值和模拟值的年际变化也都比较大。综合来看,模型比较适用于所研究流域的日径流模拟。
The rainfall-runoff on the Fengle catchment in the Lake Chao basin was simulated using ATHYS model to provide a scientific basis for the control of erosion, flooding, non-point source pollution, and water utilization in the Fengle catchment area. The model is based on Digital Elevation Model(DEM), land use, soil and climate data of the catchment and the daily discharge data from the outlet Taoxi station. ATHYS model is a distributed hydrological model. The catchment was divided into the same size(90 ×90 m) meshes that describe DEM, landuse and soil properties. The rainfall at the location of the mesh was interpolated using the Thiessen method and the rainfall data was based on the daily rainfall measurement from eight rain gauge stations. Fengle basin is one of the biggest tributaries of Lake Chao. The basin area is 1500 km2, The mainstream of the river is about 50 km long and its elevation is from 6 to 463 m. Fengle catchment belongs to the humid subtropical monsoon climate with an average of annual rainfall about 1000 meters. ATHYS model was developed based on a variety of production and transfer computing models. The runoff from each mesh was calculated using a reservoir model and the runoff volume was routed to the outlet using a lag and route method to get the hydrograph and to obtain the discharge volume. STO, INF, ω, ds, the primary parameters in production model, represent the maximum volume of the reservoir(mm), coefficient of infiltration(mm·h-1), coefficient of subsurface runoff(no dimension), coefficient of soil infiltration(1·d-1), respectively. There were two parameters in the transfer function: V0 and K0. V0 is the speed of propagation(m·s-1), and K0 is a diffusion coefficient without dimension. V0 and K0 are assumed here to be identical for each mesh and must be calibrated from rainfall and discharge data. There are various types of landuse in the catchment, such as rice, agricultural, nature, urban and so on. Two landuse types, rice and other, were used in this model. The model was run in a continuous way at the daily timescale. The discharge at the outlet was available for ten years duration in which five years' s discharge data were randomly selected for the calibration purpose. The comparison observed and simulated discharge was based on Nash and Sutcliffe index. Data from other five years were used for validation. The parameter was calibrated using trial and error method. The model demonstrated a good calibration and validation result with the total Nash coefficients being 0.86 and 0.80, respectively. The maximum Nash coefficient in calibration was 0.92, while the minimum value was 0.68. In validation, the maximum and minimum of Nash coefficient were 0.92 and 0.68. Except for one value, the correlation coefficient between observed and simulated discharge was over 0.9 for both calibration and validation periods. The model can generally well reproduce the hydrograph except for some peaks and some baseflow periods. The relative error of the annul runoff volumes between observed and simulated runoff was about 23.1% in calibration period and 34.6% in validation period. The rainfall and runoff both had a great variation by year, while a corresponding relationship was observed between them. The eigenvalues of rainfall and runoff volume had a great inter-annual variation and the average value of annual rainfall was 1238 m and the variation coefficient was 20.2%. The average value of annual runoff depth was 0.52 and the variation coefficient of annual runoff depth reached to 47.0%. There is no big difference in the average of annual runoff volume and daily peak runoff volume between observation and simulation values, but the observed and simulated runoff volume both showed a great inter-annual variation in total runoff volume and daily peak runoff volume. ATHYS model has proven to be able to simulate runoff from Fengle catchment in a continuous way.
The rainfall-runoff on the Fengle catchment in the Lake Chao basin was simulated using ATHYS model to provide a scientific basis for the control of erosion, flooding, non-point source pollution, and water utilization in the Fengle catchment area. The model is based on Digital Elevation Model(DEM), land use, soil and climate data of the catchment and the daily discharge data from the outlet Taoxi station. ATHYS model is a distributed hydrological model. The catchment was divided into the same size(90 ×90 m) meshes that describe DEM, landuse and soil properties. The rainfall at the location of the mesh was interpolated using the Thiessen method and the rainfall data was based on the daily rainfall measurement from eight rain gauge stations. Fengle basin is one of the biggest tributaries of Lake Chao. The basin area is 1500 km2, The mainstream of the river is about 50 km long and its elevation is from 6 to 463 m. Fengle catchment belongs to the humid subtropical monsoon climate with an average of annual rainfall about 1000 meters. ATHYS model was developed based on a variety of production and transfer computing models. The runoff from each mesh was calculated using a reservoir model and the runoff volume was routed to the outlet using a lag and route method to get the hydrograph and to obtain the discharge volume. STO, INF, ω, ds, the primary parameters in production model, represent the maximum volume of the reservoir(mm), coefficient of infiltration(mm·h-1), coefficient of subsurface runoff(no dimension), coefficient of soil infiltration(1·d-1), respectively. There were two parameters in the transfer function: V0 and K0. V0 is the speed of propagation(m·s-1), and K0 is a diffusion coefficient without dimension. V0 and K0 are assumed here to be identical for each mesh and must be calibrated from rainfall and discharge data. There are various types of landuse in the catchment, such as rice, agricultural, nature, urban and so on. Two landuse types, rice and other, were used in this model. The model was run in a continuous way at the daily timescale. The discharge at the outlet was available for ten years duration in which five years' s discharge data were randomly selected for the calibration purpose. The comparison observed and simulated discharge was based on Nash and Sutcliffe index. Data from other five years were used for validation. The parameter was calibrated using trial and error method. The model demonstrated a good calibration and validation result with the total Nash coefficients being 0.86 and 0.80, respectively. The maximum Nash coefficient in calibration was 0.92, while the minimum value was 0.68. In validation, the maximum and minimum of Nash coefficient were 0.92 and 0.68. Except for one value, the correlation coefficient between observed and simulated discharge was over 0.9 for both calibration and validation periods. The model can generally well reproduce the hydrograph except for some peaks and some baseflow periods. The relative error of the annul runoff volumes between observed and simulated runoff was about 23.1% in calibration period and 34.6% in validation period. The rainfall and runoff both had a great variation by year, while a corresponding relationship was observed between them. The eigenvalues of rainfall and runoff volume had a great inter-annual variation and the average value of annual rainfall was 1238 m and the variation coefficient was 20.2%. The average value of annual runoff depth was 0.52 and the variation coefficient of annual runoff depth reached to 47.0%. There is no big difference in the average of annual runoff volume and daily peak runoff volume between observation and simulation values, but the observed and simulated runoff volume both showed a great inter-annual variation in total runoff volume and daily peak runoff volume. ATHYS model has proven to be able to simulate runoff from Fengle catchment in a continuous way.
引文
[1]杨涛,陆桂华,李会会,等.气候变化下水文极端事件变化预测研究进展[J].水科学进展,2011,22(2):279-286.
[2]赵晓慎,周海,王文川.气候变化对区域水循环系统影响的研究进展[J].华北水利水电学院学报,2012,33(2):46-49.
[3]吴险峰,刘昌明.流域水文模型研究的若干进展[J].地理科学进展,2002,21(4):341-348.
[4]徐宗学.水文模型:回顾与展望[J].北京师范大学学报:自然科学版,2010,46(3):278-289.
[5]傅春,张强.流域水文模型综述[J].江西科学,2008,26(4):588-592;638.
[6]芮孝芳,朱庆平.分布式流域水文模型研究中的几个问题[J].水利水电科技进展,2002,22(3):56-58;70.
[7]Bouvier C&Delclaux F.ATHYS:a hydrological environment for spatial modeling and coupling with a GIS[J].Geographic Information Systems in Hydrology and Water Resources Management,1996,235:19-28.
[8]刘兆存,金生,韩丽华.国内流域产汇流模型与应用分析[J].地球信息科学,2007,9(3):96-103.
[9]刘国华,钱镜林,汪树玉.产汇流模型参数综合率定在洪水预报中的应用[J].浙江大学学报:工学版,2003,37(5):576-581.
[10]王纲胜,夏军,牛存稳.分布式水文模拟汇流方法及应用[J].地理研究,2004,23(2):175-182.
[11]芮孝芳.Muskingum法及其分段连续演算的若干理论探讨[J].水科学进展,2002,13(6):682-688.
[12]Thompson S A.Hydrology for water management[J].Rotterdam:A A Balkema,1999:212-216;272-275.
[13]Beven K J.Rainfall-runoff modeling[M].John Wiley&Sons Ltd,2001:67-68;124-178.
[14]谢平,梁瑞驹.扩散模拟型流域地貌汇流模型[J].地理学报,1997,52(4):316-323.
[15]黄国如,芮孝芳.基于运动波数值扩散的洪水演算方法[J].河海大学学报,2001,29(2):110-113.
[16]龙海峰,熊立华,万民.基于DEM的分布式水文模型在清江流域的应用[J].长江流域资源与环境,2012,21(1):71-78.
[17]郝玉伟,王兴菊,陆小蕾.流域汇流计算方法的对比分析[C]//中国水利学会水资源专业委员会、中国水利水电科学研究院、大连理工大学.变化环境下的水资源响应与可持续利用—中国水利学会水资源专业委员会2009学术年会论文集.2009:670-675.
[18]Tramblay Y,Bouvier C,Ayral P A,et al.Impact of rainfall spatial distribution on rainfall-runoff modeling efficiency and initial soil moisture conditions estimation[J].Nat Hazards Earth Syst Sci,2011,11:157-170.
[19]王中根,夏军,刘昌明,等.分布式水文模型的参数率定及敏感性分析探讨[J].自然资源学报,2007,22(4):649-655.
[20]Nash J E and Sutcliffe J V:River flow forecasting through conceptual models part I:A discussion of principles,J Hydrol,1970,10(3):282-290.
[21]盛春淑,罗定贵.基于AVSWAT丰乐河流域水文预测[J].中国农学通报,2006,22(9):493-496.
[22]储茵,潮洪武,马友华,等.巢湖流域丰乐河洪水事件营养盐输出动态研究[J].长江流域资源与环境,2013,22(8):1072-1080.
[23]袁艺,史培军.土地利用对流域降雨-径流关系的影响—SCS模型在深圳市的应用[J].北京师范大学学报:自然科学版,2001,37(1):131-136.
[24]王杰,黄英,段琪彩,等.土地利用变化情景下牧羊河水文响应研究[J].长江流域资源与环境,2013,22(6):756-762.
[25]陈实,高超,黄银兰.不同季节划分尺度下巢湖流域气候变化趋势分析[J].长江流域资源与环境,2013,22(5):582-587.
[26]闫红飞,王船海,文鹏.分布式水文模型研究综述[J].水电能源科学,2008,26(6):1-4.
[2]赵晓慎,周海,王文川.气候变化对区域水循环系统影响的研究进展[J].华北水利水电学院学报,2012,33(2):46-49.
[3]吴险峰,刘昌明.流域水文模型研究的若干进展[J].地理科学进展,2002,21(4):341-348.
[4]徐宗学.水文模型:回顾与展望[J].北京师范大学学报:自然科学版,2010,46(3):278-289.
[5]傅春,张强.流域水文模型综述[J].江西科学,2008,26(4):588-592;638.
[6]芮孝芳,朱庆平.分布式流域水文模型研究中的几个问题[J].水利水电科技进展,2002,22(3):56-58;70.
[7]Bouvier C&Delclaux F.ATHYS:a hydrological environment for spatial modeling and coupling with a GIS[J].Geographic Information Systems in Hydrology and Water Resources Management,1996,235:19-28.
[8]刘兆存,金生,韩丽华.国内流域产汇流模型与应用分析[J].地球信息科学,2007,9(3):96-103.
[9]刘国华,钱镜林,汪树玉.产汇流模型参数综合率定在洪水预报中的应用[J].浙江大学学报:工学版,2003,37(5):576-581.
[10]王纲胜,夏军,牛存稳.分布式水文模拟汇流方法及应用[J].地理研究,2004,23(2):175-182.
[11]芮孝芳.Muskingum法及其分段连续演算的若干理论探讨[J].水科学进展,2002,13(6):682-688.
[12]Thompson S A.Hydrology for water management[J].Rotterdam:A A Balkema,1999:212-216;272-275.
[13]Beven K J.Rainfall-runoff modeling[M].John Wiley&Sons Ltd,2001:67-68;124-178.
[14]谢平,梁瑞驹.扩散模拟型流域地貌汇流模型[J].地理学报,1997,52(4):316-323.
[15]黄国如,芮孝芳.基于运动波数值扩散的洪水演算方法[J].河海大学学报,2001,29(2):110-113.
[16]龙海峰,熊立华,万民.基于DEM的分布式水文模型在清江流域的应用[J].长江流域资源与环境,2012,21(1):71-78.
[17]郝玉伟,王兴菊,陆小蕾.流域汇流计算方法的对比分析[C]//中国水利学会水资源专业委员会、中国水利水电科学研究院、大连理工大学.变化环境下的水资源响应与可持续利用—中国水利学会水资源专业委员会2009学术年会论文集.2009:670-675.
[18]Tramblay Y,Bouvier C,Ayral P A,et al.Impact of rainfall spatial distribution on rainfall-runoff modeling efficiency and initial soil moisture conditions estimation[J].Nat Hazards Earth Syst Sci,2011,11:157-170.
[19]王中根,夏军,刘昌明,等.分布式水文模型的参数率定及敏感性分析探讨[J].自然资源学报,2007,22(4):649-655.
[20]Nash J E and Sutcliffe J V:River flow forecasting through conceptual models part I:A discussion of principles,J Hydrol,1970,10(3):282-290.
[21]盛春淑,罗定贵.基于AVSWAT丰乐河流域水文预测[J].中国农学通报,2006,22(9):493-496.
[22]储茵,潮洪武,马友华,等.巢湖流域丰乐河洪水事件营养盐输出动态研究[J].长江流域资源与环境,2013,22(8):1072-1080.
[23]袁艺,史培军.土地利用对流域降雨-径流关系的影响—SCS模型在深圳市的应用[J].北京师范大学学报:自然科学版,2001,37(1):131-136.
[24]王杰,黄英,段琪彩,等.土地利用变化情景下牧羊河水文响应研究[J].长江流域资源与环境,2013,22(6):756-762.
[25]陈实,高超,黄银兰.不同季节划分尺度下巢湖流域气候变化趋势分析[J].长江流域资源与环境,2013,22(5):582-587.
[26]闫红飞,王船海,文鹏.分布式水文模型研究综述[J].水电能源科学,2008,26(6):1-4.
目录
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