[Research advances in model simulation and data assimilation of water and carbon fluxes in land surface vegetation].

Accurately estimating water and carbon fluxes is of great significance for the research in land surface water and carbon cycles. However, it is very challenging. The estimation accuracy needs further improvement. Both traditional model simulation and site observation methods have advantages and disadvantages, which need to be examined in combination. Data assimilation integrates observations into models based on physics laws to obtain the optimal estimates of model state variables and parameters as much as possible, and provides an effective way for their combination. In this review, we traced the research progress for process models assimilated with multi-source observational data of land surface water carbon fluxes and analyzed the domestic and foreign research status of land surface process models focused on water carbon fluxes, data assimilation algorithms, and assimilation of land surface carbon flux data. We summaried problems in this research area, including insufficient coordination of multi-source observation data, relatively simple assimilation strategy, lacking fusion of assimilation models, and limited assimilation scale. The future development directions and trends were analyzed and prospected from five aspects, including assimilation strategy, model selection, data expansion, scale effect, and scientific calculation. This work would provide more comprehensive background information for scholars in this field, and arouse common concerns.

[Temporal and spatial heterogeneity analysis of optimal value of sensitive parameters in ecological process model: The BIOME-BGC model as an example.] / 生态过程模型敏感参数最优取值的时空异质性分析­­ä»¥BIOME-BGC模型为例.

The ecological process models are powerful tools for studying terrestrial ecosystem water and carbon cycle at present. However, there are many parameters for these models, and weather the reasonable values of these parameters were taken, have important impact on the models simulation results. In the past, the sensitivity and the optimization of model parameters were analyzed and discussed in many researches. But the temporal and spatial heterogeneity of the optimal parameters is less concerned. In this paper, the BIOME-BGC model was used as an example. In the evergreen broad-leaved forest, deciduous broad-leaved forest and C3 grassland, the sensitive parameters of the model were selected by constructing the sensitivity judgment index with two experimental sites selected under each vegetation type. The objective function was constructed by using the simulated annealing algorithm combined with the flux data to obtain the monthly optimal values of the sensitive parameters at each site. Then we constructed the temporal heterogeneity judgment index, the spatial heterogeneity judgment index and the temporal and spatial heterogeneity judgment index to quantitatively analyze the temporal and spatial heterogeneity of the optimal values of the model sensitive parameters. The results showed that the sensitivity of BIOME-BGC model parameters was different under different vegetation types, but the selected sensitive parameters were mostly consistent. The optimal values of the sensitive parameters of BIOME-BGC model mostly presented time-space heterogeneity to different degrees which varied with vegetation types. The sensitive parameters related to vegetation physiology and ecology had relatively little temporal and spatial heterogeneity while those related to environment and phenology had generally larger temporal and spatial heterogeneity. In addition, the temporal heterogeneity of the optimal values of the model sensitive parameters showed a significant linear correlation with the spatial heterogeneity under the three vegetation types. According to the temporal and spatial heterogeneity of the optimal values, the parameters of the BIOME-BGC model could be classified in order to adopt different parameter strategies in practical application. The conclusion could help to deeply understand the parameters and the optimal values of the ecological process models, and provide a way or reference for obtaining the reasonable values of parameters in models application.

[Study on the conductivity properties of lightning channel by spectroscopy].

Combining the spectra of cloud-to-ground lightning return obtained by a slit-less spectrograph with the transport theory of air plasma, the electrical conductivity in one discharge channel was calculated with different methods. The results show that the conductivity of the lightning channel core is of the order of 10(4) S m-1; the conductivity goes down with the increase in the channel height in the same channel; the contributions of the collisions between electron and first or second degree ionized atoms, and electron-electron as well as ion-ion collisions to the electrical conductivity of the lightning channel core can not be neglected; the collision integrals method is more reasonable for calculating the conductivity of the lightning channel core. Based on the conductivity, the discharge current was estimated and compared with the peak current of every return stroke, and the results are in the reasonable range, further, the correlation between the channel temperature and the discharge characteristics is discussed, which provides a practical way for this aspect.

[Simulation of water and carbon fluxes in harvard forest area based on data assimilation method].

Model simulation and in situ observation are the two most important means in studying the water and carbon cycles of terrestrial ecosystems, but have their own advantages and shortcomings. To combine these two means would help to reflect the dynamic changes of ecosystem water and carbon fluxes more accurately. Data assimilation provides an effective way to integrate the model simulation and in situ observation. Based on the observation data from the Harvard Forest Environmental Monitoring Site (EMS), and by using ensemble Kalman Filter algorithm, this paper assimilated the field measured LAI and remote sensing LAI into the Biome-BGC model to simulate the water and carbon fluxes in Harvard forest area. As compared with the original model simulated without data assimilation, the improved Biome-BGC model with the assimilation of the field measured LAI in 1998, 1999, and 2006 increased the coefficient of determination R2 between model simulation and flux observation for the net ecosystem exchange (NEE) and evapotranspiration by 8.4% and 10.6%, decreased the sum of absolute error (SAE) and root mean square error (RMSE) of NEE by 17.7% and 21.2%, and decreased the SAE and RMSE of the evapotranspiration by 26. 8% and 28.3%, respectively. After assimilated the MODIS LAI products of 2000-2004 into the improved Biome-BGC model, the R2 between simulated and observed results of NEE and evapotranspiration was increased by 7.8% and 4.7%, the SAE and RMSE of NEE were decreased by 21.9% and 26.3%, and the SAE and RMSE of evapotranspiration were decreased by 24.5% and 25.5%, respectively. It was suggested that the simulation accuracy of ecosystem water and carbon fluxes could be effectively improved if the field measured LAI or remote sensing LAI was integrated into the model.