Uncertainty analysis of CO_2 flux components in subtropical evergreen coniferous plantation

We present an uncertainty analysis of ecological process parameters and CO2 flux components (Reco, NEE and gross ecosystem exchange (GEE)) derived from 3 years’ continuous eddy covariance meas-urements of CO2 fluxes at subtropical evergreen coniferous plantation, Qianyanzhou of ChinaFlux. Daily-differencing approach was used to analyze the random error of CO2 fluxes measurements and bootstrapping method was used to quantify the uncertainties of three CO2 flux components. In addition, we evaluated different ...     

GM（1,1）模型在铁路路基冻胀监测中的应用

通过对季节性冻土区铁路路基冻胀量与地层温度的监测,可以了解监测段的冻胀变化过程,但无法对未来的发展趋势作出有效预测。本文采用GM（1,1）模型对监测段落路基的冻胀变形及冻结深度的变化情况进行预测。结果表明,此预测方法可以在现有监测数据的基础上有效预测短期内路基冻胀的发展趋势。根据模型预测结果,可以提前采取有效整治措施,便于决策者对轨道检验周期、维修周期、限速周期等作出合理调整。     

Robustness of Precipitation Projections in China:Comparison between CMIP5 and CMIP3 Models

Three sources of uncertainty in model projections of precipitation change in China for the 21st century were separated and quantified: internal variability,inter-model variability,and scenario uncertainty.Simulations from models involved in the third phase and the fifth phase of the Coupled Model Intercomparison Project(CMIP3 and CMIP5) were compared to identify improvements in the robustness of projections from the latest generation of models.No significant differences were found between CMIP3 and CMIP5 in terms of future precipitation projections over China,with the two datasets both showing future increases.The uncertainty can be attributed firstly to internal variability,and then to both inter-model and internal variability.Quantification analysis revealed that the uncertainty in CMIP5 models has increased by about 10%–60% with respect to CMIP3,despite significant improvements in the latest generation of models.The increase is mainly due to the increase of internal variability in the initial decades,and then mainly due to the increase of inter-model variability thereafter,especially by the end of this century.The change in scenario uncertainty shows no major role,but makes a negative contribution to begin with,and then an increase later.     

双通道能见度自动测量仪研究

从透射和散射的原理出发,推导了基于红外测量的双通道能见度仪的工程测量方程,比较了单通道和双通道能见度测量仪的性能,结果表明,双通道能见度测量仪具有精度高、性能稳定、使用可靠、维护量小的特点。     

地图概括中空间目标几何信息传递模型研究

地图概括是实现多比例尺地图生产和更新的一种重要理论方法。目前,国际国内制图学界对地图概括的研究主要集中在发展地图概括算法方面,在地图概括的质量评价方面的研究还很少。为此,本文以信息论为理论基础,以地图概括中空间目标几何信息的传递问题为研究对象,以相应空间目标几何信息的传递效率来度量地图概括中单个空间目标的概括质量。首先,建立了地图概括的空间信息传递模型,描述不同比例尺地图之间空间信息的传递过程。进而提出了地图概括过程中空间目标(即:线和面目标)几何信息的传递模型,并且对地图概括中空间目标几何信息的传递状况进行了评估,给出了具体的计算和实现方法。最后,通过实验初步验证了地图概括中空间目标几何信息传递模型的可行性。     

倾斜实景模型和LiDAR点云数据相融合的不动产测量新方法

不动产地籍测量是一项基础性的测绘工作,地籍测量技术与方法的良性发展不仅能够最大限度地提升我国土地利用率,还能为获取高精度的空间数据提供高效便捷的手段.针对传统全站仪与GPS RTK组合地籍测量作业效率低、人力财力耗费大的缺点,本文依托陕南某地的地籍和不动产权籍调查项目,提出了一种地面三维激光扫描技术和倾斜摄影测量技术相...     

测氡在滑坡研究中的应用

测氡是一种放射性地球物理方法,利用滑坡体与其周边岩土体的结构差异,应用测氡可对滑坡进行勘探,对滑坡的结构特征进行研究,达到节约投资,缩短勘探时间的目的,并可与地质分析相互验证,为工程应用提供依据。     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

Measurement of stream cross section using ground penetration radar with Hilbert–Huang transform

This study presents a new method to measure stream cross section without having contact with water. Compared with conventional measurement methods which apply instruments such as sounding weight, ground penetration radar (GPR), used in this study, is a non‐contact measurement method. This non‐contact measurement method can reduce the risk to hydrologists when they are conducting measurements, particularly in high flow period. However, the original signals obtained by using GPR are very complex, different from studies in the past where the measured data were mostly interpreted by experts with special skill or knowledge of GPR so that the results obtained were less objective. This study employs Hilbert–Huang transform (HHT) to process GPR signals which are difficult to interpret by hydrologists. HHT is a newly developed signal processing method that can not only process the nonlinear and non‐stationary complex signals, but also maintain the physical significance of the signal itself. Using GPR with HHT, this study establishes a non‐contact stream cross‐section measurement method with the ability to measure stream cross‐sectional areas precisely and quickly. Also, in comparison with the conventional method, no significant difference in results is found to exist between the two methods, but the new method can considerably reduce risk, measurement time, and manpower. It is proven that the non‐contact method combining GPR with HHT is applicable to quickly and accurately measure stream cross section. Copyright © 2013 John Wiley & Sons, Ltd.     

Balancing trade‐off issues in land use change and the impact on streamflow and salinity management

The south‐west region of the Goulburn–Broken catchment in the south‐eastern Murray–Darling Basin in Australia faces a range of natural resource challenges. A balanced strategy is required to achieve the contrasting objectives of remediation of land salinization and reducing salt export, while maintaining water supply security to satisfy human consumption and support ecosystems. This study linked the Catchment Analysis Tool (CAT), comprising a suite of farming system models, to the catchment‐scale CATNode hydrological model to investigate the effects of land use change and climate variation on catchment streamflow and salt export. The modelling explored and contrasted the impacts of a series of different revegetation and climate scenarios. The results indicated that targeted revegetation to only satisfy biodiversity outcomes within a catchment is unlikely to have much greater impact on streamflow and salt load in comparison with simple random plantings. Additionally, the results also indicated that revegetation to achieve salt export reduction can effectively reduce salt export while having a disproportionately smaller affect on streamflows. Furthermore, streamflow declines can be minimized by targeting revegetation activities without significantly altering salt export. The study also found that climate change scenarios will have an equal if not more significant impact on these issues over the next 70 years. Uncertainty in CATNode streamflow predictions was investigated because of the effect of parameter uncertainty. Copyright © 2012 John Wiley & Sons, Ltd.