面向寒区水文模拟的区域气候模式降水订正方法对比
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摘要
使用区域气候模式降水驱动水文模型是提高站点稀少地区水文模拟精度的一种重要思路。如何根据地面台站观测对区域气候模式降水数据进行误差订正,一直都是寒区水文研究的热点问题。然而,目前尚无系统研究对比不同降水订正方案并评估其对寒区水文模拟的影响。因此,对比了两种主流的区域气候模式降水订正方法:概率密度匹配法和最优插值法,以2004～2009年为研究时间段,评估了两者在玛纳斯河流域的表现。结果发现:在统计意义上两种方法各有优劣。概率密度匹配法对中低值降水的订正结果良好,年平均降水的空间分布也更合理,但对高降水值的订正结果不稳定,且相关系数和均方根误差没有改善。订正前,模式降水与台站观测降水的相关系数为0.37,均方根误差2.80 mm/d,订正后的相关系数为0.36,均方根误差为2.70 mm/d。最优插值法对相关系数和均方根误差改善显著,订正后分别为0.85和1.46 mm/d,但空间分布相比订正前改善不明显,且会得到更多的微小降水。将订正前后的降水数据分别驱动水文模型,在使用同一套率定参数的情况下,概率密度匹配法订正的降水对径流模拟的改善微弱,纳什效率系数仅从0.63提高至0.65,而最优插值法则对降水的订正更加有效,订正后的降水模拟的径流纳什效率系数提高至0.71。本研究有助于解决寒区水文模拟中降水数据的质量优化问题,提高寒区水文的模拟精度。
It is a significant way to improve the accuracy of hydrologic simulation in the area having sparse observation sites by using the regional climate model precipitation driving hydrological model.As a result,a critical issue arises,which is how to correct the precipitation data coming from Regional Climate Model(RCM) based on the observation in the hydrology research in cold region.However,no systematic studies have been conducted to compare different precipitation correction methods and evaluate their impact on the hydrologic simulation in the cold region yet.Due to this,two kinds of mainstream regional climate model precipitation correction methods,Quantile Mapping(QM) and the Optimal Interpolation(OI),have been compared and evaluated in Manas River basin between 2004 to 2009.The results show that both methods have their own advantages and disadvantages in statistical significance.The correction result of QM is good in low precipitation value and the average annual precipitation is more reasonable at spatial distribution.But when comes the high precipitation value,the result is not stable.Moreover,the correlation coefficient(R) and Root Mean Square Error(RMSE) doesn't improve.Compared to R 0.37 and RMSE 2.80 mm/d,the modified R is 0.36 and RMSE is 2.70 mm/d;OI can improve R and RMSE significantly,one increases to 0.85 and the other reduces to 1.46 mm/d after the correction.Despite that,OI also has its limitations.It gets more tiny precipitation relative to observation and the improvement of spatial distribution is not obvious.Using precipitation data before and after the correction to drive the hydrological models in a same set of model parameters.The results show that QM improves the simulation slightly,Nash-Sutcliffe efficiency coefficient(NSE) changes from 0.63 to 0.65 compared before,while OI is comparatively better,NSE increases to 0.71.This study is helpful to solve the problem of quality optimization in the preparation of hydrological simulation precipitation data and improve the precision of hydrologic simulation in cold region.
It is a significant way to improve the accuracy of hydrologic simulation in the area having sparse observation sites by using the regional climate model precipitation driving hydrological model.As a result,a critical issue arises,which is how to correct the precipitation data coming from Regional Climate Model(RCM) based on the observation in the hydrology research in cold region.However,no systematic studies have been conducted to compare different precipitation correction methods and evaluate their impact on the hydrologic simulation in the cold region yet.Due to this,two kinds of mainstream regional climate model precipitation correction methods,Quantile Mapping(QM) and the Optimal Interpolation(OI),have been compared and evaluated in Manas River basin between 2004 to 2009.The results show that both methods have their own advantages and disadvantages in statistical significance.The correction result of QM is good in low precipitation value and the average annual precipitation is more reasonable at spatial distribution.But when comes the high precipitation value,the result is not stable.Moreover,the correlation coefficient(R) and Root Mean Square Error(RMSE) doesn't improve.Compared to R 0.37 and RMSE 2.80 mm/d,the modified R is 0.36 and RMSE is 2.70 mm/d;OI can improve R and RMSE significantly,one increases to 0.85 and the other reduces to 1.46 mm/d after the correction.Despite that,OI also has its limitations.It gets more tiny precipitation relative to observation and the improvement of spatial distribution is not obvious.Using precipitation data before and after the correction to drive the hydrological models in a same set of model parameters.The results show that QM improves the simulation slightly,Nash-Sutcliffe efficiency coefficient(NSE) changes from 0.63 to 0.65 compared before,while OI is comparatively better,NSE increases to 0.71.This study is helpful to solve the problem of quality optimization in the preparation of hydrological simulation precipitation data and improve the precision of hydrologic simulation in cold region.
引文
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[3] Wang Jian,Li Wenjun.Establishing Simulated Model of Snowmelt Runoff for Large Scale Basin in Western China[J].Journal of Glacio Logy and Geocryology,1999,21(3):264-268.[王建,李文君.中国西部大尺度流域建立分带式融雪径流模拟模型[J].冰川冻土,1999,21(3):264-268.]
[4] Li Hongyi,Wang Jian.The Snowmelt Runoff Model Applied in the Upper Heihe River Basin[J].Journal of Glaciology and Geocryology,2008,30(5):769-775.[李弘毅,王建.SRM融雪径流模型在黑河流域上游的模拟研究[J].冰川冻土,2008,30(5):769-775.]
[5] Yang Chuanguo,Lin Chaohui,Hao Zhenchun,et al.Review of Coupling Atmospheric and Hydrologic Models[J].Advances in Earth Science,2007,22(8):810-817.[杨传国,林朝晖,郝振纯,等.大气水文模式耦合研究综述[J].地球科学进展,2007,22(8):810-817.]
[6] Zhou Lin,Pan Jie,Zhang Lei,et al.Analysis on Statistical Bias Correction of Daily Precipitation Simulated by Regional Climate Model[J].Journal of Tropical Meteorology,2014,30(1):137-144.[周林,潘婕,张镭,等.气候模拟日降水量的统计误差订正分析——以上海为例[J].热带气象学报,2014,30(1):137-144.]
[7] Goodess C M,Anagnostopoulou C,Bárdossy A.,et al.An Intercomparison of Statistical Downscaling Methods for Europe and European Regions:Assessing Their Performance With Respect to Extreme Temperature and Precipitation Events[J].Climatic Change,2012:1-67.
[8] Gudmundsson L,Bremnes J B,Haugen J E,et al.Technical Note:Downscaling RCM Precipitation to the Station Scale Using Statistical Transformations:A Comparison of Methods[J].Hydrology and Earth System Sciences,2012,16(9):3383-3390.
[9] Schmidli J,Frei C,Vidale P L.Downscaling from GCM Precipitation:A Benchmark for Dynamical and Statistical Downscaling Methods[J].International Journal of Climatology,2006,26(5):679-689.
[10] Liu Luliu,Ren Guoyu.Percentile Statistical Downscaling Method and Its Application in the Correction of GCMs Daily Precipitation in China[J].Plateau Meteorology,2012(3):715-722.[刘绿柳,任国玉.百分位统计降尺度方法及在GCMs日降水订正中的应用[J].高原气象,2012(3):715-722.]
[11] Tian Lin,Meng Fanhao,Liu Tie,et al.Comparison of Bias-Correction Methods for CFSR Reanalysis Precipitation Data in Typical Arid Mountains Regions:A Case Study in Kaikong River Basin[J].Journal of Arid Meteorology,2017,35(2):313-320.[田霖,孟凡浩,刘铁,等.干旱典型山区CFSR降水数据的偏差校正方法研究——以新疆开孔河流域为例[J].干旱气象,2017,35(2):313-320.]
[12] Hay L E,Wilby R L,Leavesley G H.A Comparison of Delta Change and Downscaled GCM Scenarios for Three Mountainous Basins in the United States[J].Journal of the American Water Resources Association,2000,36(2):387-397.
[13] Piani C,Haerter J O,Coppola E.Statistical Bias Correction for Daily Precipitation in Regional Climate Models over Europe[J].Theoretical and Applied Climatology,2010,99(1-2):187-192.
[14] Piani C,Weedon G P,Best M,et al.Statistical Bias Correction of Global Simulated Daily Precipitation and Temperature for the Application of Hydrological Models[J].Journal of Hydrology,2010,395(3):199-215.
[15] Reynolds Richard W,Smith Thomas M.Improved Global Sea Surface Temperature Analyses Using Optimum Interpolation[J].Journal of Climate,1994,7(6):929-948.
[16] Li Yun,Liu Qinzheng,Zhang Jianhua,et al.Optimal Interpolation and Its Application to Assimilation of Sea Temperature in Northwest Pacific Region[J].Marine Forecasts,2008(2):25-32.[李云,刘钦政,张建华,等.最优插值方法在西北太平洋海温同化中的应用研究[J].海洋预报,2008,(2):25-32.]
[17] Huffman G J,Adler R F,Arkin P,et al.The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset[J].1997,78(1):5-20.
[18] Seo D J.Real-Time Estimation of Rainfall Fields Using Radar Rainfall and Rain Gage Data[J].Journal of Hydrology,1998,208(1):37-52.
[19] Seo D J,Breidenbach J P.Real-Time Correction of Spatially Nonuniform Bias in Radar Rainfall Data Using Rain Gauge Measurements[J].Journal of Hydrometeorology,2002,3(2):93-111.
[20] Sinclair S,Pegram G.Combining Radar and Rain Gauge Rainfall Estimates Using Conditional Merging[J].Atmospheric Science Letters,2005,6(1):19-22.
[21] Xie P P,Xiong A Y.A Conceptual Model for Constructing High-Resolution Gauge-Satellite Merged Precipitation Analyses[J].Journal of Geophysical Research Atmospheres,2011,116(D21):1-14.
[22] Feng Xuezhi,Li Wenjun,Shi Zhengtao,et al.Satellite Snowcover Monitoring and Snowmelt Runoff Simulation of Manas River in Tianshan Region[J].Remote Sensing Technology and Application,2000,15(1):18-21.[冯学智,李文君,史正涛,等.卫星雪盖监测与玛纳斯河融雪径流模拟[J].遥感技术与应用,2000,15(1):18-21.]
[23] Li Jianqin,Li Yongqiang.Hydrological Analysis of Manas River Basin.[J].Western China Technology,2008(14):28-29.[李建琴,李永强.玛纳斯河流域水文特征分析[J].中国西部科技,2008(14):28-29.]
[24] Zhang Pu,Wang Jian,Liu Yan,et al.Application of SRM to Flood Forecast and Forwarning of Manasi River Basin in Spring[J].Remete Sensing Technology and Application,2009,24(4):456-461.[张璞,王建,刘艳,等.SRM模型在玛纳斯河流域春季洪水预警中的应用研究[J].遥感技术与应用,2009,24(4):456-461.]
[25] Ran Youhua,Li Xin,Lu Ling.Land Cover Products of China[DB].Cold and Arid Regions Science Data Center at Lanzhou,2010.[冉有华,李新,卢玲.中国地区土地覆盖综合数据集[DB].寒区旱区科学数据中心,2010.]
[26] Fao I,Isric I.Harmonized World Soil Database (version 1.1)[DB/OL].FAO,Rome,Italy and IIASA,Laxenburg,Austria,2009.westdc.westgis.ac.cn/heihe/view/uuid/708bae73-9455-43ab-b944-f4702f70c2a3.
[27] Lin Y L,Farley Richard D,Orville Harold D.Bulk Parameterization of the Snow Field in A Cloud Model[J].Journal of Climate and Applied Meteorology,1983,22(6):1065-1092.
[28] Chen F,Dudhia J.Coupling an Advanced Land Surface-Hydrology Model with the Penn State-NCAR MM5 Modeling System.Part I:Model Implementation and Sensitivity[J].Monthly Weather Review,2001,129(4):569-585.
[29] Hong S Y,Noh Y,Dudhia J.A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes[J].Monthly Weather Review,2006,134(9):2318-2341.
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