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    资源环境与城乡规划管理外文资料翻译--陆面过程模式CLM的稳定同位素的季节变化仿真

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    资源环境与城乡规划管理外文资料翻译--陆面过程模式CLM的稳定同位素的季节变化仿真

    1、4430字    毕业设计 外文资料翻译    题     目   陆面过程模式 CLM的稳定同位素的季节变化仿真        学     院       资源与环境学院       专     业       资源环境与城乡规划管理   班     级          资源

    2、 0702 班          学     生          包芳         学     号          20072102002      指导教师          王永森           二一  一 年

    3、 三 月   二十五  日 济南大学毕业设计 外文资料翻译  - 1 -  Simulations of seasonal variations of stable water isotopes in land surface process model CLM  ZHANG XinPing1, WANG XiaoYun2, YANG ZongLiang3, NIU GuoYue3 & Xie ZiChu1 1 College of Resources and Environmental Sciences, Hunan Nor

    4、mal University, Changsha 410081, China; 2 Qingdao Meteorological Bureau, Qingdao 266003, China; 3 Department of Geological Sciences, the University of Texas at Austin, Texas 78721-0254, USA  Abstract: In this study, we simulated and analyzed the monthly variations of stable water isotopes in di

    5、fferent reservoirs at Manus, Brazil, using the Community Land Model (CLM) that incorporates stable isotopic effects as a diagnostic tool for understanding stable water isotopic processes, filling the observational data gaps and predicting hydro meteorological processes. The simulation results show t

    6、hat the 18O values in precipitation, vapor and surface runoff have distinct seasonality with the marked negative correlations with corresponding water amount. Compared with the survey results by the International Atomic Energy Agency (IAEA) in co-operation with the World Meteorological Organization

    7、(WMO), the simulations by CLM reveal the similar temporal distributions of the 18O in precipitation. Moreover, the simulated amount effect between monthly 18O and monthly precipitation amount, and MWL (meteoric water line) are all close to the measured values. However, the simulated seasonal differe

    8、nce in the 18O in precipitation is distinctly smaller than observed one, and the simulated temporal distribution of the 18O in precipitation displays the ideal bimodal seasonality rather than the observed single one. These mismatches are possibly related to the simulation capacity and the veracity i

    9、n forcing data.  Key word : stable water isotope, CLM, simulation, amount effect, seasonal variation    The modeling of the land surface and soil moisture is increasingly seen as an important component in understanding hydrological cycles 1 . Stable water isotopes, for example18O and

    10、D, are superlative tracers for the hydrological cycles because their abundance in water reflects the accumulated record of physical phase change 3,2 . Using the features of stable isotopes can accurately determine the partitioning of precipitation into transpiration, evaporation and runoff 5,4 , whi

    11、ch cannot be detected with mass balance alone 6 . Recently, the conducting iPILPS (isotopes in the Project for Intercomparison of Land-surface Parameterization Schemes) incorporates stable water isotopes into land surface parameterization schemes. The iPILPS experiment aims to identify and test diff

    12、erent land-surface schemes that incorporate stable water isotopes, appraise the applicability of stable isotopic data in hydro-climatic study and water resources survey, identify observational data gaps required for evaluating the land-surface schemes with isotopes and apply stable water isotopic da

    13、ta to specific prediction of hydro meteorological processes 8,7 .     This study, a part of iPILPS, incorporates stable water isotopes in CLM as a diagnostic tool, 济南大学毕业设计 外文资料翻译  - 2 -  simulates and analyses variations of stable water isotopes in different reservoirs on monthl

    14、y time scales at Manaus, Brazil. The simulated behaviors of stable isotopes in precipitation on monthly time scale have good consistency with actual survey result at Manaus station set up by IAEA/WMO, howing that the simulation by the CLM incorporating stable water isotopes is reasonable.  1 Mo

    15、del description 1.1 CLM Earths biosphere is an important part of the Earths climate system. Relatedly, the dynamic, thermodynamic and physiological processes of vegetation coverage are the key factors impacted climate change. These numerical models including physical process parameterizations are ca

    16、lled as the land surface process model. Land surface model is composed of different physical process modes including the parameterization of dynamics characteristics, the longwave and shortwave radiation transfer and rainfall interception, etc. in canopy associated with vegetation shape; photosynthe

    17、sis, transpiration and evaporation related to plant physiology; and physical process of water-heat conduction, soil chemical processes, freezing and thawing of permafrost within soil, and so on. The Community Land Model (CLM) is currently one of well-developed and potential land surface models. CLM

    18、is developed from the Biosphere-Atmosphere Transfer Scheme (BATS), the Institute of Atmospheric Physics, Chinese Academy of Sciences land model (IAP94) and the NCAR land surface model (LSM) 9 . The model takes into account ecological differences among vegetation types, hydraulic and thermal differen

    19、ces among soil types, and allows for multiple land cover types within a grid cell. Strictly speaking, CLM is a single point model. According to different physical processes, the model structure can be separated into two parts, the biogeophysical processes relating to vegetation cover at surface and

    20、physical processes relating to hydraulic and thermal transfer in soil, mainly including radiation transfer, turbulence diffusion and thermal conduction in soil and so on. Detailed descriptions about CLM may refer to relational references and technical notes 10,9 . 1.2 Stable water isotope parameteri

    21、zation The stable isotopic ratio incorporated into CLM is noted as                                  RW =OH OH 162182                            

    22、  (1)  The subscription w stands for reservoir water, for example precipitation, runoff or vapor, etc. There are two possible ways of mixing the reservoir water with input, “total mixing” scheme and “partial mixing” scheme:  Rw(t) = N1Rw (t 1) + N2Rinputs (t) N                 (2)


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