Simulating CO2 flux of three different ecosystems in ChinaFLUX based on artificial neural networks

The nonlinearity of the relationship between CO₂ flux and other micrometeorological variables flux parameters limits the applicability of carbon flux models to accurately estimate the flux dynamics. However, the need for carbon dioxide (CO₂) estimations covering larger areas and the limitations of the point eddy covariance technique to address this requirement necessitates the modeling of CO₂ flux from other micrometeorological variables. Artificial neural networks (ANN) are used because of their power to fit highly nonlinear relations between input and output variables without explaining the nature of the phenomena. This paper applied a multilayer perception ANN technique with error back propagation algorithm to simulate CO₂ flux on three different ecosystems (forest, grassland and cropland) in ChinaFLUX. Energy flux (net radiation, latent heat, sensible heat and soil heat flux) and temperature (air and soil) and soil moisture were used to train the ANN and predict the CO₂ flux. Diurnal half-hourly fluxes data of observations from June to August in 2003 were divided into training, validating and testing. Results of the CO₂ flux simulation show that the technique can successfully predict the observed values with R ² value between 0.75 and 0.866. It is also found that the soil moisture could not improve the simulative accuracy without water stress. The analysis of the contribution of input variables in ANN shows that the ANN is not a black box model, it can tell us about the controlling parameters of NEE in different ecosystems and micrometeorological environment. The results indicate the ANN is not only a reliable, efficient technique to estimate regional or global CO₂ flux from point measurements and understand the spatiotemporal budget of the CO₂ fluxes, but also can identify the relations between the CO₂ flux and micrometeorological variables.

     

Spatial Variation of Dissolved Organic Carbon in Soils of Riparian Wetlands and Responses to Hydro-geomorphologic Changes in Sanjiang Plain,China

Spatial variation of dissolved organic carbon(DOC) in soils of riparian wetlands and responses to hydro-geomorphologic changes in the Sanjiang Plain were analyzed through in situ collecting soil samples in the Naoli River and the Bielahong River. The results showed that the average contents of DOC for soil layer of 0–100 cm were 730.6 mg/kg, 250.9 mg/kg, 423.0 mg/kg and 333.1 mg/kg respectively from riverbed to river terrace along the transverse directions of the Naoli watershed. The content of the soil DOC was the highest in the riverbed, lower in the high floodplain and much lower in the river terrace, and it was the lowest in the low floodplain. The difference in the content and vertical distribution of DOC between the riverbed and the three riparian wetlands was significant, while it was not significant among the low floodplain, the high floodplain and the river terrace. The variability of soil DOC was related to the hydrological connectivity between different landscape position of the riparian wetlands and the adjacent stream. Extremely significant correlations were observed between DOC and total organic carbon(TOC), total iron(TFe), ferrous iron(Fe(II)) whose correlation coefficients were 0.819, –0.544 and –0.709 in riparian wetlands of the Naoli River. With the increase of wetland destruction, soil p H increased and soil DOC content changed. The correlation coefficients between soil DOC and TOC, TFe, Fe(II) also changed into 0.759, –0.686 and –0.575 respectively in the Bielahong River. Under the impact of drainage ditches, the correlations between soil DOC and TFe, Fe(II) were not obvious, while the soil p H was weakly alkaline and was negatively correlated with soil DOC in the previous high floodplain. It indicates that riparian hydro-geomorphology is the main factor that could well explain this spatial variability of soil DOC, and the agricultural environmental hydraulic works like ditching also must be considered.     

中国东海海平面长期变化与趋势研究

From the analyses of the satellite altimeter Maps of Sea Level Anomaly(MSLA) data, tidal gauge sea level data and historical sea level data, this paper investigates the long-term sea level variability in the East China Sea(ECS).Based on the correlation analysis, we calculate the correlation coefficient between tidal gauge and the closest MSLA grid point, then generate the map of correlation coefficient of the entire ECS. The results show that the satellite altimeter MSLA data is effective to observe coastal sea level variability. An important finding is that from map of correlation coefficient we can identify the Kuroshio. The existence of Kuroshio decreases the correlation between coastal and the Pacific sea level. Kurishio likes a barrier or a wall, which blocks the effect of the Pacific and the global change. Moreover, coastal sea level in the ECS is mainly associated with local systems rather than global change. In order to calculate the long-term sea level variability trend, the empirical mode decomposition(EMD) method is applied to derive the trend on each MSLA grid point in the entire ECS. According to the 2-D distribution of the trend and rising rate, the sea level on the right side of the axis of Kuroshio rise faster than in its left side. This result supports the barrier effect of Kuroshio in the ECS. For the entire ECS, the average sea level rose 45.0 mm between 1993 and 2010, with a rising rate of(2.5±0.4) mm/a which is slower than global average.The relatively slower sea level rising rate further proves that sea level rise in the ECS has less response to global change due to its own local system effect.     

莒南县路网中心性对建设用地时空格局的影响

针对道路网络复杂性如何影响城市建设用地时空演变这一问题,该文以莒南县为例,计算道路网络中心性指标,并应用地理探测器定量分析其对建设用地时空格局的影响.结果表明:①莒南县道路网络中,全局临近中心性单中心特征强烈,全局介数中心性、直达中心性则表现出多中心的空间态势.②道路网络中心性特征与建设用地空间分布显著相关,全局路网通达效率、通达度对建设用地空间布局影响强烈.③道路网络中心性指标对建设用地扩张均有显著影响,其中又以全局直达中心性对建设用地扩张的影响力最强.     

"3S"技术在城市"两违"动态监测中的应用

为解决城市违法建设和违法占地人工巡查耗时费力,难以及时、动态地进行监测的问题,本文提出了基于遥感技术、地理信息技术和全球定位系统进行"两违"动态监测的新方法.基于国产高景卫星数据进行一月一周期的监测,发现并提取可疑图斑;结合行政区划、土地利用等数据进行图斑筛选;开发"两违"执法监测系统进行建库归档并提供精准的定位信息,辅助执法队员开展外业核查和执法工作.在多个市、县开展了应用,能够快速、动态发现"两违"线索,辅助外业执法,为城市管理提供可行、有效的技术手段.     

城市环境下智能手机GNSS/视觉组合导航算法和性能评估

通过将卫星定位和计算机视觉定位组合,可以使智能手机在城市、峡谷等卫星定位结果跳变或中断的情况下,仍为用户提供连续、高精度的位置服务.因此,为改善手机端卫星信号不理想区域的定位精度,设计和实现了GNSS与单目相机组合导航的实验.通过数据平滑、旋转矩阵求解等过程完成系统间的轨迹转换,并分析了坐标转换误差.使用结合贝叶斯估计的卡尔曼滤波对两种轨迹进行融合处理.最终结果表明,在城市环境下,GNSS与单目相机组合后,定位误差小于3 m的概率由21.21％提升至48.15％,小于5 m的概率由48.82％提升至70.03％.     

基于多尺度有限元的海上升压站脚靴式桩套筒连接结构分析研究

脚靴式海上升压站导管架的桩套筒与主导管的连接结构是关键荷载传递部位。整体梁系有限元模型无法反应局部板型结构的失效状态;截断板系有限元模型则受制于边界条件的合理性,结果不够精确。文中基于某工程海上升压站,建立不同精细化范围的多尺度有限元模型及不同边界条件的截断模型,对结构屈服失效及屈曲失效复核的同时论证了局部网格对计算精细程度的影响及截断模型对边界条件的敏感性;此外,研究了轭板板厚及应力比对屈曲失效的敏感程度,为今后海上升压站脚靴结构不同阶段的设计、优化提供参考和借鉴。     

不同雨强对杉木和米槠林地表径流和可溶性有机碳的影响

采用径流小区法,对中亚热带地区的杉木人工林和米槠次生林在不同雨强下径流量和可溶性有机碳（DOC）流失浓度进行比较研究。结果表明：在小雨、中雨、大雨和暴雨4场降雨过程中,米槠次生林和杉木人工林的地表径流量和DOC流失浓度变化趋势一致,即降雨强度增加,地表径流量也随之增加。DOC浓度均呈现了随着雨强的增加而逐渐降低的趋势。说明降雨强度对径流量和DOC浓度均产生了较为明显的影响;米槠次生林和杉木人工林的地表径流量之间差异不显著,米槠次生林和杉木人工林的DOC流失浓度除大雨外均达到显著性水平差异（P〈0.05）,说明植被类型对地表径流量影响较小,对DOC浓度具有较明显的影响。     

基于ROMS模式利用集合最优插值同化沿轨海表面高度异常数据的研究

The ensemble optimal interpolation （EnOI） is applied to the regional ocean modeling system （ROMS） with the ability to assimilate the along-track sea level anomaly （TSLA）. This system is tested with an eddy-resolving system of the South China Sea （SCS）. Background errors are derived from a running seasonal ensemble to account for the seasonal variability within the SCS. A fifth-order localization function with a 250 km localization radius is chosen to reduce the negative effects of sampling errors. The data assimilation system is tested from January 2004 to December 2006. The results show that the root mean square deviation （RMSD） of the sea level anomaly decreased from 10.57 to 6.70 cm, which represents a 36.6% reduction of error. The data assimilation reduces error for temperature within the upper 800 m and for salinity within the upper 200 m, although error degrades slightly at deeper depths. Surface currents are in better agreement with trajectories of surface drifters after data assimilation. The variance of sea level improves significantly in terms of both the amplitude and position of the strong and weak variance regions after assimilating TSLA. Results with AGE error （AGE） perform better than no AGE error （NoAGE） when considering the improvements of the temperature and the salinity. Furthermore, reasons for the extremely strong variability in the northern SCS in high resolution models are investigated. The results demonstrate that the strong variability of sea level in the high resolution model is caused by an extremely strong Kuroshio intrusion. Therefore, it is demonstrated that it is necessary to assimilate the TSLA in order to better simulate the SCS with high resolution models.