| 雪热力模型(SNTHERM)在冰沟流域的模拟和敏感性试验 |
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| 引用本文： | 刘誉,蒋玲梅,施建成,张立新,张生雷,潘金梅,王培.雪热力模型(SNTHERM)在冰沟流域的模拟和敏感性试验[J].遥感学报,2011,15(4):792-810. |
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| 作者姓名： | 刘誉 蒋玲梅 施建成 张立新 张生雷 潘金梅 王培 |
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| 作者单位： | 遥感科学国家重点实验室 北京师范大学,北京师范大学 地理学与遥感科学学院,北京 100875;遥感科学国家重点实验室 北京师范大学,北京师范大学 地理学与遥感科学学院,北京 100875;遥感科学国家重点实验室 中国科学院遥感应用研究所,北京 100101;遥感科学国家重点实验室 北京师范大学,北京师范大学 地理学与遥感科学学院,北京 100875;遥感科学国家重点实验室 中国科学院遥感应用研究所,北京 100101;遥感科学国家重点实验室 北京师范大学,北京师范大学 地理学与遥感科学学院,北京 100875;遥感科学国家重点实验室 北京师范大学,北京师范大学 地理学与遥感科学学院,北京 100875 |
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| 基金项目： | 国家重点基础研究发展计划(973计划）(编号：2007CB714403）；中国科学院西部行动计划(二期）项目“黑河流域遥感－地面观测同步试验与综合模拟平台建设”(编号：KZCX2-XB2-09）；国家自然科学基金(编号：40701115）；国家高技术研究发展计划(863计划）专项经费(编号：2008AA12Z110) |
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| 摘 要： | 采用基于质能平衡的积雪过程模型—雪热力模型(Snow Thermal Model, SNTHERM.89）来描述和模拟2008年中国甘肃省黑河实验冰沟流域的积雪过程与积雪特性参数，并将模型模拟的雪水当量与高级微波扫描辐射计(AMSR-E）的雪水当量产品进行了对比。模型验证结果表明，SNTHERM模型能准确模拟黑河冰沟流域的积雪变化过程和积雪特性，对积雪的演变特征作出合理的描述，表明SNTHERM在中国黑河冰沟流域有较好的适用性。对SNTHERM模型进行不同驱动气象参数和初始输入参数的敏感性分析的结果表明，积雪特性参数对辐射通量最敏感；各积雪特性参数对各自的初始输入比较敏感，密度则对初始输入的雪深、密度和颗粒大小都比较敏感，表明在需要准确模拟密度的情况下以及进行雪水当量同化工作时，初始输入的雪层深度、密度和颗粒都必须比较准确。
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| 关 键 词： | SNTHERM 黑河冰沟流域 积雪特性参数 敏感性分析 AMSR-E |
| 收稿时间： | 2010/5/12 0:00:00 |
| 修稿时间： | 2010/12/16 0:00:00 |
Validation and sensitivity analysis of the snow thermal model (SNTHERM) at Binggou basin, Gansu |
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| LIU Yu,JIANG Lingmei,SHI Jiancheng,ZHANG Lixin,ZHANG Shenglei,PAN Jinmei and WANG Pei.Validation and sensitivity analysis of the snow thermal model (SNTHERM) at Binggou basin, Gansu[J].Journal of Remote Sensing,2011,15(4):792-810. |
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| Authors: | LIU Yu JIANG Lingmei SHI Jiancheng ZHANG Lixin ZHANG Shenglei PAN Jinmei and WANG Pei |
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| Institution: | State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote SensingApplications of Chinese Academy of Sciences, School of Geography and Remote Sensing Science,Beijing Normal University, Beijin;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote SensingApplications of Chinese Academy of Sciences, School of Geography and Remote Sensing Science,Beijing Normal University, Beijin;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of ChineseAcademy of Sciences and Beijing Normal University, Beijing 100101, China;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote SensingApplications of Chinese Academy of Sciences, School of Geography and Remote Sensing Science,Beijing Normal University, Beijin;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of ChineseAcademy of Sciences and Beijing Normal University, Beijing 100101, China;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote SensingApplications of Chinese Academy of Sciences, School of Geography and Remote Sensing Science,Beijing Normal University, Beijin;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote SensingApplications of Chinese Academy of Sciences, School of Geography and Remote Sensing Science,Beijing Normal University, Beijin |
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| Abstract: | In this paper, we evaluate the capability of a mass and energy balance computer modelsnow thermal model (SNTHERM.89) by simulating the snow properties with experimental data measured at Binggou basin, Gansu Province, China, 2008. We also compare the simulated snow water equivalence (SWE) with the product from the advanced microwave scanning radiometer (AMSR-E).The results show that SNTHERM model could accurately predict the snow evolution process and snow properties at Binggou , and reasonably describe the characteristics of snow evolution, indicating that SNTHERM applied at Binggou successfully. In addition, we conduct the sensitivity analysis of snow properties to the meteorological forcing data and initial input parameters. It has been found that the solar radiation energy fl ux is the most sensitive parameter to the snow properties simulated by SNTHERM, such as the snow depth, density and the grain size. The snow properties are sensitive to their respective perturbation, while the snow density is sensitive to all the other properties. Hence, in the case that snow density need to predict accurately, or SWE assimilation work is conducting, the initial snow depth, density and grain size should be accurate. |
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| Keywords: | SNTHERM Binggou snow properties sensitive analysis AMSR-E |
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