Modeling of surface flux in Tongyu using the Simple Biosphere Model 2 (SiB2)

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.

SiB2模型在黄灌区的应用探讨

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。

Evaluating Soil Moisture Predictions Based on Ensemble Kalman Filter and SiB2 Model

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.