地表反照率是影响地–气相互作用的关键因子,而准确描述地表反照率是改进陆面模型水热模拟能力的关键。当前Noah-MP (the Noah land surface model with Multiple Parameterizations)土壤反照率估算主要依赖于查找表方法,该方法基于土...地表反照率是影响地–气相互作用的关键因子,而准确描述地表反照率是改进陆面模型水热模拟能力的关键。当前Noah-MP (the Noah land surface model with Multiple Parameterizations)土壤反照率估算主要依赖于查找表方法,该方法基于土壤颜色获得不同土壤类型的反照率,但在区域尺度上土壤颜色等级尚未得到有效率定,直接影响了区域反照率模拟水平。此外,裸土反照率的计算还高度依赖于土壤水分。针对这一问题,以同化得到的土壤水分数据作为输入,计算得到不同土壤颜色等级对应的反照率时间序列。在此基础上,以MODIS反照率为参照,同时排除高植被覆盖和积雪的影响,逐步筛选得到青藏高原区域0.25°格点尺度下最优的土壤颜色等级。评估结果表明,优化得到的土壤颜色等级空间分布规律符合土壤质地与反照率之间的物理规律,且改进了研究区域70%空间网格内的Noah-MP模型反照率估计。展开更多
陆面模式Noah-MP(Noah land surface model with Multi-Parameterizations)为陆面物理过程提供了大量复杂的参数化方案,往往受限于计算量和计算能力,使得利用全组合方案实验,来确定适用的参数化方案难以实现,从而给模式模拟带来不确定...陆面模式Noah-MP(Noah land surface model with Multi-Parameterizations)为陆面物理过程提供了大量复杂的参数化方案,往往受限于计算量和计算能力,使得利用全组合方案实验,来确定适用的参数化方案难以实现,从而给模式模拟带来不确定性。为科学减少实验次数,本文引入正交试验法,选择了影响地表温度模拟较大的5个主要过程,包括动态植被、气孔阻抗过程、控制气孔阻抗的土壤湿度参数过程和表面热交换系数过程以及辐射传输过程等,设计了9次正交试验,利用CLDAS-V2.0(中国气象局陆面数据同化系统)驱动Noah-MP陆面模式对我国东南地区地表温度进行模拟,对参数化方案寻优,以确定其适用的参数化方案组合。结果表明,参数化方案的选择对林地区域的模拟,以及在7月和8月的模拟影响较大。物理过程的敏感性和最优参数化方案同时受到下垫面和季节的影响,多数情况中,动态植被与冠层气孔阻抗过程对模拟影响显著,是较敏感的物理过程。通过对不同下垫面不同季节的最优参数化方案组合的时空尺度分析,经模拟验证,东南地区地表温度模拟的较优方案组合为开启动态植被,Ball-Berry的冠层气孔阻抗方案,Noah的土壤湿度参数方案,M-O的地表热交换系数方案和GAPFVEG的辐射传输方案。展开更多
We evaluate water budget components-namely,soil moisture,runoff,evapotranspiration,and terrestrial water storage (TWS)-simulated by the Noah land surface model with multi-parameterization options (Noah-MP) in China,a ...We evaluate water budget components-namely,soil moisture,runoff,evapotranspiration,and terrestrial water storage (TWS)-simulated by the Noah land surface model with multi-parameterization options (Noah-MP) in China,a large geographic domain challenging for hydrological modeling due to poor observational data and a lack of one single parameterization that can fit for complex hydrological processes.By comparing the model simulations with multi-source reference data,we show that Noah-MP can generally reproduce the overall spatiotemporal patterns of runoff and evapotranspiration over six major river basins,with the annual correlation coefficients generally greater than 0.8 and the Nash-Sutcliffe model efficiency coefficient exceeding 0.5.Among the six basins evaluated,the best model performance is seen over the Huaihe River basin.The temporal trend of the modeled TWS anomalies agrees well with GRACE (Gravity Recovery and Climate Experiment) observations,capturing major flood and drought events in different basins.Experiments with 12 selected physical parameterization options show that the runoff parameterization has a stronger impact on the simulated soil moisture-runoff-evapotranspiration relationships than the soil moisture factor for stomatal resistance schemes,a result consistent with previous studies.Overall,Noah-MP driven by GLDAS forcing simulates the hydrological variables well,except for the Songliao basin in northeastern China,likely because this is a transitional region with extensive freeze-thaw activity,while representations of human activities may also help improve the model performance.展开更多
Alpine grassland is the main ecosystem of the Tibetan Plateau(TP),thus accurate simulation of water and heat exchange in the grassland will significantly enhance the understanding of the land-atmosphere interaction pr...Alpine grassland is the main ecosystem of the Tibetan Plateau(TP),thus accurate simulation of water and heat exchange in the grassland will significantly enhance the understanding of the land-atmosphere interaction process on the TP.In this study,we assessed and improved the ensemble numerical simulations of the community Noah land surface model with multiparameterization options(Noah-MP)by using observations collected from four alpine grassland observation sites.The four observation sites belong to the upper Heihe River Basin Integrated Observatory Network located in the northeastern part of the TP.First,an ensemble of 1008 numerical simulation experiments,based on multiparameterization options of seven physical processes/variables in the Noah-MP,was carried out for the vegetation growing season.The Taylor skill score was then used to assess the model performance and select the optimal combination of parameterization options for a more exact simulation of the water and heat exchange in alpine grassland.The accuracy of Noah-MP simulation was further improved by introducing new parameterizations of thermal roughness length,soil hydraulic properties,and vertical root distribution.It was found that:(1)Simulation of water and heat exchange over alpine grassland in the growing season was mainly affected by the parameterizations of dynamic vegetation,canopy stomatal resistance,runoff and groundwater dynamics,and surface exchange coefficient for heat transfer.Selection of different parameterization options for these four physical processes/variables led to large differences in the simulation of water and heat fluxes.(2)The optimal combination of parameterization options selected in the current Noah-MP framework suffered from significant overestimation of sensible heat flux(H)and underestimation of soil moisture(θ)at all observation sites.(3)The overestimation of H was significantly improved by introducing a new parameterization of thermal roughness length.Furthermore,the underestimation ofθwas resolved by introducing a new parameterization of soil hydraulic properties that considered the organic matter effect and a new vertical distribution function for the vegetation root system.The results of this study provide an important reference for further improving the simulation of water and heat exchange by using the land surface model in alpine grassland.展开更多
为研究不同陆面模式对中国区域土壤温度的模拟效果,基于中国气象局陆面数据同化系统(CMA Land Data Assimilation System,CLDAS)大气驱动数据分别驱动Noah和Noah-MP陆面模式进行中国区域土壤温度的模拟(简称:CLDAS_Noah和CLDAS_Noah-MP...为研究不同陆面模式对中国区域土壤温度的模拟效果,基于中国气象局陆面数据同化系统(CMA Land Data Assimilation System,CLDAS)大气驱动数据分别驱动Noah和Noah-MP陆面模式进行中国区域土壤温度的模拟(简称:CLDAS_Noah和CLDAS_Noah-MP试验),使用2010—2018年中国气象局2380个土壤温度观测站点10和40 cm观测数据以及美国全球陆面数据同化系统(The Global Land Data Assimilation System,GLDAS)驱动的Noah模式(GLDAS_Noah试验)模拟的土壤温度结果,从空间分布、季节、分区等角度进行了评估,实现了不同驱动数据相同陆面模式和相同驱动数据不同陆面模式的对比分析。结果表明:GLDAS_Noah、CLDAS_Noah和CLDAS_Noah-MP试验均能合理模拟出中国区域土壤温度空间分布,但在量级上有一定差异,主要表现在中国东北、新疆、青藏高原等积雪区。对于相同陆面模式不同驱动数据,均方根误差显示CLDAS_Noah试验在季节与分区上均优于GLDAS_Noah试验,间接表明CLDAS大气驱动数据优于GLDAS大气驱动数据,且大气驱动数据是提高土壤温度模拟精度的重要因素之一;对于相同驱动数据不同陆面模式,总体上CLDAS_Noah-MP试验棋拟效果优于CLDAS_Noah试验,其中CLDAS_Noah试验模拟的10和40 cm深度土壤温度在冬季积雪区误差明显大于CLDAS_Noah-MP试验,可能与Noah-MP模式改进了积雪方案有关,但10和40 cm深度下CLDAS_Noah-MP试验在东北、华北、青藏高原地区对春季土壤温度模拟误差明显大于CLDAS_Noah试验,可能与Noah-MP模式融雪方案有关。总之,本研究对于后续开展土壤温度多模式集成、土壤温度站点资料同化,最终研制中国区域高质量土壤温度数据集具有一定的参考意义。展开更多
文摘地表反照率是影响地–气相互作用的关键因子,而准确描述地表反照率是改进陆面模型水热模拟能力的关键。当前Noah-MP (the Noah land surface model with Multiple Parameterizations)土壤反照率估算主要依赖于查找表方法,该方法基于土壤颜色获得不同土壤类型的反照率,但在区域尺度上土壤颜色等级尚未得到有效率定,直接影响了区域反照率模拟水平。此外,裸土反照率的计算还高度依赖于土壤水分。针对这一问题,以同化得到的土壤水分数据作为输入,计算得到不同土壤颜色等级对应的反照率时间序列。在此基础上,以MODIS反照率为参照,同时排除高植被覆盖和积雪的影响,逐步筛选得到青藏高原区域0.25°格点尺度下最优的土壤颜色等级。评估结果表明,优化得到的土壤颜色等级空间分布规律符合土壤质地与反照率之间的物理规律,且改进了研究区域70%空间网格内的Noah-MP模型反照率估计。
文摘陆面模式Noah-MP(Noah land surface model with Multi-Parameterizations)为陆面物理过程提供了大量复杂的参数化方案,往往受限于计算量和计算能力,使得利用全组合方案实验,来确定适用的参数化方案难以实现,从而给模式模拟带来不确定性。为科学减少实验次数,本文引入正交试验法,选择了影响地表温度模拟较大的5个主要过程,包括动态植被、气孔阻抗过程、控制气孔阻抗的土壤湿度参数过程和表面热交换系数过程以及辐射传输过程等,设计了9次正交试验,利用CLDAS-V2.0(中国气象局陆面数据同化系统)驱动Noah-MP陆面模式对我国东南地区地表温度进行模拟,对参数化方案寻优,以确定其适用的参数化方案组合。结果表明,参数化方案的选择对林地区域的模拟,以及在7月和8月的模拟影响较大。物理过程的敏感性和最优参数化方案同时受到下垫面和季节的影响,多数情况中,动态植被与冠层气孔阻抗过程对模拟影响显著,是较敏感的物理过程。通过对不同下垫面不同季节的最优参数化方案组合的时空尺度分析,经模拟验证,东南地区地表温度模拟的较优方案组合为开启动态植被,Ball-Berry的冠层气孔阻抗方案,Noah的土壤湿度参数方案,M-O的地表热交换系数方案和GAPFVEG的辐射传输方案。
基金supported by the National Key Research and Development Program of China (Grant No. 2018YFA0606004)the National Natural Science Foundation of China (Grant Nos. 91337217 and 41375088)
文摘We evaluate water budget components-namely,soil moisture,runoff,evapotranspiration,and terrestrial water storage (TWS)-simulated by the Noah land surface model with multi-parameterization options (Noah-MP) in China,a large geographic domain challenging for hydrological modeling due to poor observational data and a lack of one single parameterization that can fit for complex hydrological processes.By comparing the model simulations with multi-source reference data,we show that Noah-MP can generally reproduce the overall spatiotemporal patterns of runoff and evapotranspiration over six major river basins,with the annual correlation coefficients generally greater than 0.8 and the Nash-Sutcliffe model efficiency coefficient exceeding 0.5.Among the six basins evaluated,the best model performance is seen over the Huaihe River basin.The temporal trend of the modeled TWS anomalies agrees well with GRACE (Gravity Recovery and Climate Experiment) observations,capturing major flood and drought events in different basins.Experiments with 12 selected physical parameterization options show that the runoff parameterization has a stronger impact on the simulated soil moisture-runoff-evapotranspiration relationships than the soil moisture factor for stomatal resistance schemes,a result consistent with previous studies.Overall,Noah-MP driven by GLDAS forcing simulates the hydrological variables well,except for the Songliao basin in northeastern China,likely because this is a transitional region with extensive freeze-thaw activity,while representations of human activities may also help improve the model performance.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant Nos.XDA20100101,XDA20100103)。
文摘Alpine grassland is the main ecosystem of the Tibetan Plateau(TP),thus accurate simulation of water and heat exchange in the grassland will significantly enhance the understanding of the land-atmosphere interaction process on the TP.In this study,we assessed and improved the ensemble numerical simulations of the community Noah land surface model with multiparameterization options(Noah-MP)by using observations collected from four alpine grassland observation sites.The four observation sites belong to the upper Heihe River Basin Integrated Observatory Network located in the northeastern part of the TP.First,an ensemble of 1008 numerical simulation experiments,based on multiparameterization options of seven physical processes/variables in the Noah-MP,was carried out for the vegetation growing season.The Taylor skill score was then used to assess the model performance and select the optimal combination of parameterization options for a more exact simulation of the water and heat exchange in alpine grassland.The accuracy of Noah-MP simulation was further improved by introducing new parameterizations of thermal roughness length,soil hydraulic properties,and vertical root distribution.It was found that:(1)Simulation of water and heat exchange over alpine grassland in the growing season was mainly affected by the parameterizations of dynamic vegetation,canopy stomatal resistance,runoff and groundwater dynamics,and surface exchange coefficient for heat transfer.Selection of different parameterization options for these four physical processes/variables led to large differences in the simulation of water and heat fluxes.(2)The optimal combination of parameterization options selected in the current Noah-MP framework suffered from significant overestimation of sensible heat flux(H)and underestimation of soil moisture(θ)at all observation sites.(3)The overestimation of H was significantly improved by introducing a new parameterization of thermal roughness length.Furthermore,the underestimation ofθwas resolved by introducing a new parameterization of soil hydraulic properties that considered the organic matter effect and a new vertical distribution function for the vegetation root system.The results of this study provide an important reference for further improving the simulation of water and heat exchange by using the land surface model in alpine grassland.
文摘为研究不同陆面模式对中国区域土壤温度的模拟效果,基于中国气象局陆面数据同化系统(CMA Land Data Assimilation System,CLDAS)大气驱动数据分别驱动Noah和Noah-MP陆面模式进行中国区域土壤温度的模拟(简称:CLDAS_Noah和CLDAS_Noah-MP试验),使用2010—2018年中国气象局2380个土壤温度观测站点10和40 cm观测数据以及美国全球陆面数据同化系统(The Global Land Data Assimilation System,GLDAS)驱动的Noah模式(GLDAS_Noah试验)模拟的土壤温度结果,从空间分布、季节、分区等角度进行了评估,实现了不同驱动数据相同陆面模式和相同驱动数据不同陆面模式的对比分析。结果表明:GLDAS_Noah、CLDAS_Noah和CLDAS_Noah-MP试验均能合理模拟出中国区域土壤温度空间分布,但在量级上有一定差异,主要表现在中国东北、新疆、青藏高原等积雪区。对于相同陆面模式不同驱动数据,均方根误差显示CLDAS_Noah试验在季节与分区上均优于GLDAS_Noah试验,间接表明CLDAS大气驱动数据优于GLDAS大气驱动数据,且大气驱动数据是提高土壤温度模拟精度的重要因素之一;对于相同驱动数据不同陆面模式,总体上CLDAS_Noah-MP试验棋拟效果优于CLDAS_Noah试验,其中CLDAS_Noah试验模拟的10和40 cm深度土壤温度在冬季积雪区误差明显大于CLDAS_Noah-MP试验,可能与Noah-MP模式改进了积雪方案有关,但10和40 cm深度下CLDAS_Noah-MP试验在东北、华北、青藏高原地区对春季土壤温度模拟误差明显大于CLDAS_Noah试验,可能与Noah-MP模式融雪方案有关。总之,本研究对于后续开展土壤温度多模式集成、土壤温度站点资料同化,最终研制中国区域高质量土壤温度数据集具有一定的参考意义。
