The modeling ability of a stand-alone version of the Simple Biosphere Model 2（SiB2） was tested mainly through diagnosing the simulated latent heat（LE）,sensible heat（H）,CO2 flux,and air temperature at the Tongyu ...The modeling ability of a stand-alone version of the Simple Biosphere Model 2（SiB2） was tested mainly through diagnosing the simulated latent heat（LE）,sensible heat（H）,CO2 flux,and air temperature at the Tongyu field observation station（44°25＇N,122°52＇E,184 m elevation） of Coordinated Enhanced Observing Period（CEOP）,where the land cover is cropland and grassland.In the whole year of 2003,the canopy height and the leaf area index was variable.During non-growth period,the surface would become bare,while during the growth period,the canopy height could reach 2.0 m high over cropland and 0.8 m high over grassland,respectively,and max leaf area index could reach 4.2 and 2.4,respectively.The model was initialized with measurement and driven by half-hourly atmospheric observations.The simulation values for 2003 were compared against measurements.Results show that the model is of a good ability of simulating the hourly latent heat（LE）,sensible heat（H）,CO2 flux and temperature during the growth period.Moreover,the daily LE,H and CO2 flux simulated by SiB2 could reflect their yearly change reasonably.However,the model may overestimate the H generally.展开更多
Soil moisture is an important variable in the fields of hydrology, meteorology, and agriculture, and has been used for numerous applications and forecasts. Accurate soil moisture predictions on both a large scale and ...Soil moisture is an important variable in the fields of hydrology, meteorology, and agriculture, and has been used for numerous applications and forecasts. Accurate soil moisture predictions on both a large scale and local scale for different soil depths are needed. In this study, a soil moisture assimilation and prediction based on the Ensemble Kalman Filter(EnKF) and Simple Biosphere Model(SiB2) have been performed in Meilin watershed, eastern China, to evaluate the initial state values with different assimilation frequencies and precipitation influences on soil moisture predictions. The assimilated results at the end of the assimilation period with different assimilation frequencies were set to be the initial values for the prediction period. The measured precipitation, randomly generated precipitation,and zero precipitation were used to force the land surface model in the prediction period. Ten cases were considered based on the initial value and precipitation. The results indicate that, for the summer prediction period with the deeper water table depth, the assimilation results with different assimilation frequencies influence soil moisture predictions significantly. The higher assimilation frequency gives better soil moisture predictions for a long lead-time. The soil moisture predictions are affected by precipitation within the prediction period. For a short lead-time, the soil moisture predictions are better for the case with precipitation, but for a long lead-time, they are better without precipitation. For the winter prediction period with a lower water table depth, there are better soil moisture predictions for the whole prediction period. Unlike the summer prediction period, the soil moisture predictions of winter prediction period are not significantly influenced by precipitation. Overall, it is shown that soil moisture assimilations improve its predictions.展开更多
SiB2(simple biosphere model Version 2)是用来模拟生态系统通量较为理想的国外模型,为了探讨其在我国黄河灌区的适用性及利用遥感数据驱动模型的可行性,并用其来研究该地区农田能量收支情况,以位山灌区为研究试点,利用位山实验站1a左...SiB2(simple biosphere model Version 2)是用来模拟生态系统通量较为理想的国外模型,为了探讨其在我国黄河灌区的适用性及利用遥感数据驱动模型的可行性,并用其来研究该地区农田能量收支情况,以位山灌区为研究试点,利用位山实验站1a左右的观测数据对模型进行了验证分析,模拟结果表明:SiB2模型能够较好地模拟位山试验站农田的能量通量、CO2通量及地表温度,净辐射、潜热通量、感热通量、CO2通量与地表温度的模拟值与观测值吻合较好,线性相关系数R分别为0.988,0.714,0.607,0.677与0.933,其中净辐射模拟效果最好,感热通量偏差较大。另外,利用遥感MODIS LAI数据驱动SiB2模型表明,除净辐射外,模拟效果很差,因此在站点尺度遥感LAI(叶面积指数,leaf area index)产品不适合驱动SiB2模型。展开更多
以位山试验站典型农田为对象,利用位山站2005-10-10～2006-06-10日的实验观测数据,探讨了冬小麦整个生长期农田的热、碳通量特征,并运用SiB2(simple biosphere model Version2)模型对热、碳通量进行了模拟分析,结果表明,农田的热、碳通...以位山试验站典型农田为对象,利用位山站2005-10-10～2006-06-10日的实验观测数据,探讨了冬小麦整个生长期农田的热、碳通量特征,并运用SiB2(simple biosphere model Version2)模型对热、碳通量进行了模拟分析,结果表明,农田的热、碳通量在冬小麦生长过程中表现出明显的日变化,这些通量的最大值基本出现在正午前后;热、碳通量的日际变化也较明显,其中净辐射与潜热通量在冬小麦不同生长期表现为:越冬期<拔节抽穗期<灌浆成熟期;感热通量表现为:拔节抽穗期<灌浆成熟期<越冬期;而CO2通量为:越冬期<灌浆成熟期<拔节抽穗期.对以上通量及地表温度的模拟表明,SiB2模型能较好地模拟冬小麦生长期中农田热、碳通量及地表温度,净辐射、潜热通量、感热通量、CO2通量与地表温度的模拟值与观测值的一致性较好,线性相关系数R2分别达0.985、0.637、0.481、0.725、0.499与0.877,其中感热通量与CO2通量模拟偏差较大.另外,按冬小麦生长期分阶段对农田以上分量模拟结果表明,SiB2模型在冬小麦拔节抽穗期模拟效果最好,并发现模型对叶面积指数敏感.展开更多
以国产高分一号(GF-1)宽幅数据(wide field of view,WFV)为数据源,采用简单生物圈模型2(simple biosphere model2,SiB2)对黑龙江省漠河县森林植被叶面积指数(leaf area index,LAI)进行估算,并与增强植被指数(enhanced vegetation index,...以国产高分一号(GF-1)宽幅数据(wide field of view,WFV)为数据源,采用简单生物圈模型2(simple biosphere model2,SiB2)对黑龙江省漠河县森林植被叶面积指数(leaf area index,LAI)进行估算,并与增强植被指数(enhanced vegetation index,EVI)线性模型的估算结果进行对比,结合地面实测LAI数据分别对这2种模型估算结果进行精度评价。结果表明,采用EVI线性模型估算LAI,决定系数R 2为0.582,均方根误差(root mean square error,RMSE)为0.701;而采用SiB2模型估算LAI,R 2为0.798,RMSE为0.358,均比EVI线性模型有所改善。该研究发现,结合中高空间分辨率的GF-1 WFV数据,SiB2模型更适宜于该研究区森林植被的LAI反演。展开更多
基金supported by the National Basic Research Program of China (2006CB400506)
文摘The modeling ability of a stand-alone version of the Simple Biosphere Model 2（SiB2） was tested mainly through diagnosing the simulated latent heat（LE）,sensible heat（H）,CO2 flux,and air temperature at the Tongyu field observation station（44°25＇N,122°52＇E,184 m elevation） of Coordinated Enhanced Observing Period（CEOP）,where the land cover is cropland and grassland.In the whole year of 2003,the canopy height and the leaf area index was variable.During non-growth period,the surface would become bare,while during the growth period,the canopy height could reach 2.0 m high over cropland and 0.8 m high over grassland,respectively,and max leaf area index could reach 4.2 and 2.4,respectively.The model was initialized with measurement and driven by half-hourly atmospheric observations.The simulation values for 2003 were compared against measurements.Results show that the model is of a good ability of simulating the hourly latent heat（LE）,sensible heat（H）,CO2 flux and temperature during the growth period.Moreover,the daily LE,H and CO2 flux simulated by SiB2 could reflect their yearly change reasonably.However,the model may overestimate the H generally.
基金Supported by the National Natural Science Foundation of China(51709046,41323001,and 41130638)National(Key)Basic Research and Development(973)Program of China(2016YFC0402706)+2 种基金National Science Funds for Creative Research Groups of China(51421006)Program of Dual Innovative Talents Plan and Innovative Research Team in Jiangsu ProvinceOpen Foundation of State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering,Hohai University(2015490311)
文摘Soil moisture is an important variable in the fields of hydrology, meteorology, and agriculture, and has been used for numerous applications and forecasts. Accurate soil moisture predictions on both a large scale and local scale for different soil depths are needed. In this study, a soil moisture assimilation and prediction based on the Ensemble Kalman Filter(EnKF) and Simple Biosphere Model(SiB2) have been performed in Meilin watershed, eastern China, to evaluate the initial state values with different assimilation frequencies and precipitation influences on soil moisture predictions. The assimilated results at the end of the assimilation period with different assimilation frequencies were set to be the initial values for the prediction period. The measured precipitation, randomly generated precipitation,and zero precipitation were used to force the land surface model in the prediction period. Ten cases were considered based on the initial value and precipitation. The results indicate that, for the summer prediction period with the deeper water table depth, the assimilation results with different assimilation frequencies influence soil moisture predictions significantly. The higher assimilation frequency gives better soil moisture predictions for a long lead-time. The soil moisture predictions are affected by precipitation within the prediction period. For a short lead-time, the soil moisture predictions are better for the case with precipitation, but for a long lead-time, they are better without precipitation. For the winter prediction period with a lower water table depth, there are better soil moisture predictions for the whole prediction period. Unlike the summer prediction period, the soil moisture predictions of winter prediction period are not significantly influenced by precipitation. Overall, it is shown that soil moisture assimilations improve its predictions.
文摘SiB2(simple biosphere model Version 2)是用来模拟生态系统通量较为理想的国外模型,为了探讨其在我国黄河灌区的适用性及利用遥感数据驱动模型的可行性,并用其来研究该地区农田能量收支情况,以位山灌区为研究试点,利用位山实验站1a左右的观测数据对模型进行了验证分析,模拟结果表明:SiB2模型能够较好地模拟位山试验站农田的能量通量、CO2通量及地表温度,净辐射、潜热通量、感热通量、CO2通量与地表温度的模拟值与观测值吻合较好,线性相关系数R分别为0.988,0.714,0.607,0.677与0.933,其中净辐射模拟效果最好,感热通量偏差较大。另外,利用遥感MODIS LAI数据驱动SiB2模型表明,除净辐射外,模拟效果很差,因此在站点尺度遥感LAI(叶面积指数,leaf area index)产品不适合驱动SiB2模型。
文摘以位山试验站典型农田为对象,利用位山站2005-10-10～2006-06-10日的实验观测数据,探讨了冬小麦整个生长期农田的热、碳通量特征,并运用SiB2(simple biosphere model Version2)模型对热、碳通量进行了模拟分析,结果表明,农田的热、碳通量在冬小麦生长过程中表现出明显的日变化,这些通量的最大值基本出现在正午前后;热、碳通量的日际变化也较明显,其中净辐射与潜热通量在冬小麦不同生长期表现为:越冬期<拔节抽穗期<灌浆成熟期;感热通量表现为:拔节抽穗期<灌浆成熟期<越冬期;而CO2通量为:越冬期<灌浆成熟期<拔节抽穗期.对以上通量及地表温度的模拟表明,SiB2模型能较好地模拟冬小麦生长期中农田热、碳通量及地表温度,净辐射、潜热通量、感热通量、CO2通量与地表温度的模拟值与观测值的一致性较好,线性相关系数R2分别达0.985、0.637、0.481、0.725、0.499与0.877,其中感热通量与CO2通量模拟偏差较大.另外,按冬小麦生长期分阶段对农田以上分量模拟结果表明,SiB2模型在冬小麦拔节抽穗期模拟效果最好,并发现模型对叶面积指数敏感.
基金公益性行业(气象)科研专项“大尺度水热通量观测系统的研制与应用研究”(编号:GYHY200706046)国家自然科学基金项目“遥感数据与植被生态系统碳循环模型的同化研究”(编号:40971221)欧盟FP7CEOP-AEGIS项目“Coordinated Asia-European long-term observing system of Qinhai-Tibet plateau hydro-meteorological processes and the Asian-monsoon systEm with ground satellite image data and numerical simulations”(编号:FP7-ENV2007-1 Grant no.212921)资助
文摘以国产高分一号(GF-1)宽幅数据(wide field of view,WFV)为数据源,采用简单生物圈模型2(simple biosphere model2,SiB2)对黑龙江省漠河县森林植被叶面积指数(leaf area index,LAI)进行估算,并与增强植被指数(enhanced vegetation index,EVI)线性模型的估算结果进行对比,结合地面实测LAI数据分别对这2种模型估算结果进行精度评价。结果表明,采用EVI线性模型估算LAI,决定系数R 2为0.582,均方根误差(root mean square error,RMSE)为0.701;而采用SiB2模型估算LAI,R 2为0.798,RMSE为0.358,均比EVI线性模型有所改善。该研究发现,结合中高空间分辨率的GF-1 WFV数据,SiB2模型更适宜于该研究区森林植被的LAI反演。
