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Roughness length and zero-plane displacement over three typical surfaces were calculated iteratively by least-square method, which are Yucheng Experimental Station for agriculture surfaces, Qianyanzhou Experimental Station for complex and undulant surfaces, and Changbai Mountains Experimental Station for forest surfaces. On the basis of roughness length dynamic, the effects of roughness length dynamic on fluxes were analyzed with SEBS model. The results indicate that, aerodynamic roughness length changes with vegetation conditions (such as vegetation height, LAI), wind speed, friction velocity and some other factors. In Yucheng and Changbai Mountains Experimental Station, aerodynamic roughness length over the fetch of flux tower changes with vegetation height and LAI obviously, that is, with the increase of LAI, roughness length increases to the peak value firstly, and then decreases. In Qianyanzhou Experimental Station, LAI changes slightly, so the relationship between roughness length and LAI is not obvious. The aerodynamic roughness length of Yucheng and Changbai Mountains Experimental Station changes slightly with wind direction, while aerodynamic roughness length of Qianyanzhou Experimental Station changes obviously with wind direction. The reason for that is the terrain in Yucheng and Changbai Mountains Experimental Station is relatively flat, while in Qianyanzhou Experimental Station the terrain is very undulant and heterogeneous. With the increase of wind speed, aerodynamic roughness length of Yucheng Experimental Station changes slightly, while it decreases obviously in Qianyanzhou Experimental Station and Changbai Mountains Experimental Station. Roughness length dynamic takes great effects on fluxes calculation, and the effects are analyzed by SEBS model. By comparing 1 day averaged roughness length in Yucheng Experimental Station and 5 day averaged roughness length of Qianyanzhou and Changbai Mountains Experimental Station with roughness length parameter chosen by the model, the effects of roughness length dynamic on flux calculation is analyzed. The maximum effect of roughness length dynamic on sensible heat flux is 2.726%, 33.802% and 18.105%, in Yucheng, Qianyanzhou, and Changbai Mountains experimental stations, respectively.
相似文献At present, using Eddy Covariance (EC) method to estimate the “true value” of carbon sequestration in terrestrial ecosystem arrests more attention. However, one issue is how to solve the uncertainty of observations (especially the nighttime CO2 flux data) appearing in post-processing CO2 flux data. The ratio of effective and reliable nighttime EC CO2 flux data to all nighttime data is relatively low (commonly, less than 50%) for all the long-term and continuous observation stations in the world. Thus, the processing method of nighttime CO2 flux data and its effect analysis on estimating CO2 flux annual sums are very important. In this paper, the authors analyze and discuss the reasons for underestimating nighttime CO2 flux using EC method, and introduce the general theory and method for processing nighttime CO2 flux data. By analyzing the relationship between nighttime CO2 flux and air fraction velocity u*, we present an alternate method, Average Values Test (AVT), to determine the thresholds of fraction velocity (u*c) for screening the effective nighttime CO2 flux data. Meanwhile, taking the data observed in Yucheng and Changbai Mountains stations for an example, we analyze and discuss the effects of different methods or parameters on nighttime CO2 flux estimations. Finally, based on the data of part ChinaFLUX stations and related literatures, empirical models of nighttime respiration at different sites in ChinaFLUX are summarized.
相似文献Knowledge of seasonal variation of net ecosystem CO2 exchange (NEE) and its biotic and abiotic controllers will further our understanding of carbon cycling process, mechanism and large-scale modelling. Eddy covariance technique was used to measure NEE, biotic and abiotic factors for nearly 3 years in the hinterland alpine steppe—Korbresia meadow grassland on the Tibetan Plateau, the present highest fluxnet station in the world. The main objectives are to investigate dynamics of NEE and its components and to determine the major controlling factors. Maximum carbon assimilation took place in August and maximum carbon loss occurred in November. In June, rainfall amount due to monsoon climate played a great role in grass greening and consequently influenced interannual variation of ecosystem carbon gain. From July through September, monthly NEE presented net carbon assimilation. In other months, ecosystem exhibited carbon loss. In growing season, daytime NEE was mainly controlled by photosynthetically active radiation (PAR). In addition, leaf area index (LAI) interacted with PAR and together modulated NEE rates. Ecosystem respiration was controlled mainly by soil temperature and simultaneously by soil moisture. Q 10 was negatively correlated with soil temperature but positively correlated with soil moisture. Large daily range of air temperature is not necessary to enhance carbon gain. Standard respiration rate at referenced 10°C (R 10) was positively correlated with soil moisture, soil temperature, LAI and aboveground biomass. Rainfall patterns in growing season markedly influenced soil moisture and therefore soil moisture controlled seasonal change of ecosystem respiration. Pulse rainfall in the beginning and at the end of growing season induced great ecosystem respiration and consequently a great amount of carbon was lost. Short growing season and relative low temperature restrained alpine grass vegetation development. The results suggested that LAI be usually in a low level and carbon uptake be relatively low. Rainfall patterns in the growing season and pulse rainfall in the beginning and at end of growing season control ecosystem respiration and consequently influence carbon balance of ecosystem.
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