地基GNSS-R在复杂地形条件下的积雪探测

GNSS-R技术是一种以GNSS为信号源的全新遥感技术, 可用于监测海洋和陆地.在地基GNSS-R积雪探测研究中, 反射区域内的复杂地形不仅会引起积雪的不均匀分布, 还使得接收机能在同一时刻接收到来自多个反射界面的反射信号, 从而增加了积雪探测的难度.针对该问题, 本文利用北极圈内斯匹次卑尔根岛上布设的GNSS-R测站, 开展了复杂地形条件下的积雪探测研究.根据不同界面上的反射信号具有不同多普勒频移的特点, 本文提出了基于傅里叶变换的谱分析方法, 将不同界面的反射信号从混合信号中分离出来, 并根据特定多普勒频率段的功率来监测地表积雪变化.基于该方法, 本文分析了反射路径位于Schetelig山地区域的8颗卫星在2014年间的反射信号, 计算结果表明: 卫星PRN 2、12和28与积雪地面介电常数之间的相关系数分别为0.02、0.20和0.05, 表明这三颗卫星的反射信号与地面介电常数之间不相关; 卫星PRN 14和20与地面介电常数之间的相关系数分别为0.29和0.26, 为低度相关; PRN 17、18和25与地面介电常数之间的相关系数分别为0.43、0.54和0.44, 为中等程度相关.研究证明本文提出的方法, 可用于探测复杂地形下的地表积雪.

     

湖北省荆江大堤沿堤地基土的分类特征

利用脉动测振方法对荆江大堤堤面沿线土层进行了实测研究，并对实测记录进行了傅氏谱分析。结果表明，沿堤土层绝大部分属于二类土地基，但有若干堤段土层属于由松散的粉、细砂所构成的三类土地基，且饱和程度非常高，有产生管涌和地震动液化的可能，应予特别注意。     

Rayleigh波对浅地表地基震害的影响

基于场地砂土液化的宏观现场证据和简要分析,指出Rayleigh波对距震中中远距离的浅层饱和土层的砂土液化有着很大的影响。通过单相和双相介质模型分析,阐述了Rayleigh波在土中传播的特性以及对土壤液化的影响,指出在浅源地震中Rayleigh波将是中远场地区土壤液化的主要动力影响因素,提出了一些在分析土壤液化时应考虑的新因素和方法。最后提出了在考虑Rayleigh波对土壤液化评判和小区划时应注意的一些问题。     

考虑次网格变异性和土壤冻融过程的土壤湿度同化方案

集合Kalman滤波以其简单有效的特点在陆面数据同化中广泛应用,通常作为预报模型的陆面过程模式往往要考虑模式次网格变异性和土壤冻融过程,若对此不加考虑而直接对土壤湿度进行同化可能会使得同化结果发生偏差．将双集合Kalman滤波应用于土壤湿度的同化,基于NCAR／CLM陆面过程模式建立了一个考虑次网格变异性和土壤冻融过程的土壤湿度同化方案：在同一个时间步内用状态滤波对模式网格内某片上液态水分含量进行优化,用参数滤波对该片上的固态水分含量和其他片上的液态／固态水分含量进行优化,由此考虑模式次网格变异性和土壤冻融过程的影响,从而实现对整个模式网格上土壤湿度的同化．初步的同化试验表明：其同化效果在有、无土壤冻融阶段都优于一般的不考虑次网格变异性和土壤冻融变化的同化方案;该同化方案不仅能够提高那些有直接观测信息的土壤层的土壤湿度模拟精度,还能在一定程度上改善那些没有任何观测信息的土壤层的模拟效果;另外,土壤湿度同化结果的改善还能在一定程度上提高陆面模式对于土壤温度的模拟精度．     

中国东部春季土壤湿度的时空变化特征

利用中国气象局提供的土壤湿度观测资料和欧洲中期天气预报中心（ECMWF）的ERA-40土壤湿度再分析资料,在仔细比较分析两套资料的基础上,研究了100°E以东中国春季土壤湿度的空间分布特征及其在不同时间尺度上的变化特征,结果表明ERA-40资料能很好的再现中国东部春季土壤湿度的时空变化特征,较好地反映出了中国春季土壤存在东北和西南湿、华北和内蒙古干的地理分布及其年际变化．在整个中国东部地区,春季土壤存在不同程度上的干旱化现象;其中西南地区土壤从浅层到深层都存在—致的变干趋势,20世纪80年代后这种变干趋势变得显著;在东部中纬度地区,浅层土壤湿度具有明显的年际变化特征,没有明显的干化趋势,但深层土壤湿度从1988年以后存在较为明显的干化现象;东北地区浅层和深层土壤也存在较明显的变干趋势,其中浅层土壤在20世纪70年代初以后变干趋势减缓,而深层土壤在70年代末以后的变干趋势加剧．     

基于简化参数方法的蒙古干旱区土壤湿度被动微波遥感

卫星被动微波遥感土壤湿度,是准确分析大空间尺度上陆表水分变化信息的有效手段.美国航天局(NASA)发布的基于AMSR-E观测亮温资料的全球土壤湿度反演产品,在蒙古干旱区的实际精度并不令人满意.本文基于对地表微波辐射传输中地表粗糙度和植被层影响的简化处理方法,采用AMSR-E的6.9 GHz,10.7 GHz和18.7 GHz之V极化亮温资料,应用多频率反演算法,并以国际能量和水循环协同观测计划(The Coordinated Energy and Water Cycle Observations Project)即CEOP实验在蒙古国东部荒漠地区的地面实验资料作为先验知识,获取被动微波遥感模型的优化参数,以期获得蒙古干旱区精度更高的土壤湿度遥感估算结果.分析表明,本文方法反演的白天和夜间土壤湿度结果与地面验证值之间的均方根误差(RMSE)接近0.030 cm³/cm³, 证明所用方法在不需要其他辅助资料或参数帮助下,可较精确地反演干旱区表层土壤湿度信息,能够全天候、动态监测大空间尺度的土壤湿度变化,可为干旱区气候变化研究及陆面过程模拟和数据同化研究提供高精度的表层土壤湿度初始场资料.     

条形基础砂土地基的破坏模型试验

为了研究条形基础砂土地基的剪切破坏形式和承载机理,在相似理论的指导下,进行了一组条形基础砂土地基的模型试验,模拟了砂土地基受荷后产生变形、扩展直到破坏的全过程;测量并分析了地基中不同点处的土压力大小及其分布规律,观测了剪切带的形成和发展过程。结果表明,模型试验能够很好地体现砂土地基变形、破坏的整个过程及其沉降变形特性、地基中各点的真实应力状态及其变化规律,对于研究深基坑变形过程和破坏形态具有应用价值。     

用动力测量值确定基础—地基系统的频变参数

提出了基础－地基系统具有频变参数的质－弹－阻模型，在这个模型中，系统的刚度Ｋ＝Ｋ０－Ｋ１ω＾２和阻尼系数Ｃ＝Ｃ０＋Ｃ１ω随系统振动频率而变化。文章以竖向振动为例，给出了用稳态激振下的动力反应测量值确定系统参数Ｋ０，Ｋ１，Ｃ０，Ｃ１的方法；讨论了基础频变刚度系统与附加质量系统的等效范围和差别。     

复合地基与天然地基在地震作用下附加沉降的数值分析

通过大型通用有限元程序ANSYS建立模型,研究了复合地基和天然地基在地震作用下的附加沉降,计算结果表明,采用复合地基技术,对减少附加沉降有很好的效果。同时对群桩复合地基不同桩位特点进行分析,表明群桩复合地基中桩附加沉降大于边、角桩。提出了附加沉降系数的概念及计算公式,可对复合地基的设计提供技术支持。     

小波基在GPS-IR反演土壤湿度中优选方法的研究

全球定位系统多径干涉遥感技术(Global Positioning System-Interference Reflection,GPS-IR)是一种基于卫星反射信号的新遥感技术,可实现土壤湿度、雪深、海平面等方面的监测.针对卫星反射信号分离问题,本文利用小波分析对信噪比(Signal-to-Noise Ratio,SNR)中的卫星反射信号进行分离.小波基的选择直接影响到卫星反射信号分离的效果,本文通过对比低阶多项式、sym10、bior6.8、dmey和coif5这5种方法的实际消趋和反演效果来优选.经实验表明:利用coif5小波基分解6层能够有效提高卫星反射信号的分离精度和信号的分辨率,获取的相对延迟相位与土壤湿度之间的线性关系显著增强.     

探地雷达测定土壤含水量的研究进展

结合国内外关于探地雷达测定土壤含水量的最新研究成果,阐述了探地雷达测定土壤含水量的基本原理和计算方法,总结和评价了土壤含水量与土壤介电常数的不同计算模型,针对在实际应用中存在的不足,提出了相关改进措施,并展望发展方向.     

Potential of soil moisture observations in flood modelling: Estimating initial conditions and correcting rainfall

Rainfall runoff (RR) models are fundamental tools for reducing flood hazards. Although several studies have highlighted the potential of soil moisture (SM) observations to improve flood modelling, much research has still to be done for fully exploiting the evident connection between SM and runoff. As a way of example, improving the quality of forcing data, i.e. rainfall observations, may have a great benefit in flood simulation. Such data are the main hydrological forcing of classical RR models but may suffer from poor quality and record interruption issues. This study explores the potential of using SM observations to improve rainfall observations and set a reliable initial wetness condition of the catchment for improving the capability in flood modelling. In particular, a RR model, which incorporates SM for its initialization, and an algorithm for rainfall estimation from SM observations are coupled using a simple integration method. The study carried out at the Valescure experimental catchment (France) demonstrates the high information content retained by SM for RR transformation, thus giving new possibilities for improving hydrological applications. Results show that an appropriate configuration of the two models allows obtaining improvement in flood simulation up to 15% in mean and 34% in median Nash Sutcliffe performances as well as a reduction of the median error in volume and on peak discharge of about 30% and 15%, respectively.     

Assessment of drought vulnerability based on the soil moisture PDF

This paper studies the statistics of the soil moisture condition and its monthly variation for the purpose of evaluating drought vulnerability. A zero-dimensional soil moisture dynamics model with the rainfall forcing by the rectangular pulses Poisson process model are used to simulate the soil moisture time series for three sites in Korea: Seoul, Daegu, and Jeonju. These sites are located in the central, south-eastern, and south-western parts of the Korean Peninsular, respectively. The model parameters are estimated on a monthly basis using hourly rainfall data and monthly potential evaporation rates obtained by the Penmann method. The resulting soil moisture simulations are summarized on a monthly basis. In brief, the conclusions of our study are as follows. (1) Strong seasonality is observed in the simulations of soil moisture. The soil moisture mean is less than 0.5 during the dry spring season (March, April, and June), but other months exceed the 0.5 value. (2) The spring season is characterized by a low mean value, a high standard deviation and a positive skewness of the soil moisture content. On the other hand, the wet season is characterized by a high mean value, low standard deviation, and negative skewness of the soil moisture content. Thus, in the spring season, much drier soil moisture conditions are apparent due to the higher variability and positive skewness of the soil moisture probability density function (PDF), which also indicates more vulnerability to severe drought occurrence. (3) Seoul, Daegue, and Jeonju show very similar overall trends of soil moisture variation; however, Daegue shows the least soil moisture contents all through the year, which implies that the south-eastern part of the Korean Peninsula is most vulnerable to drought. On the other hand, the central part and the south-western part of the Korean peninsula are found to be less vulnerable to the risk of drought. The conclusions of the study are in agreement with the climatology of the Korean Peninsula.     

Multivariate analysis of soil moisture history for a hillslope

In this study, the spatial distribution of measured soil moisture was analyzed on the platform of multivariate modeling. Soil moisture time series for two seasons were selected and used for analysis to reveal similarities and differences in soil moisture responses for a few rainfall events. The development of a soil moisture transport process that considers the representative element volume and uncertainty of soil media provides the hydrological basis for time series modeling. The systematic procedure of Box–Jenkins with noise modeling was used to delineate the final models for all monitoring points. The physical basis of mass balance and the continuity in inflow contribution, as well as statistical criteria, were used in the model selection procedure. Heuristic approaches provide the spatial distribution of selected models along the transect of a hillside. Comparative analysis for two different depths and seasons provide an understanding of the variation in soil moisture transfer processes at the hillslope scale. Differences in soil moisture models for both depths and seasons are associated with eco-hydrological processes. The relationships between distributed topographic features and modeling results were explored to configure dominant hydrological processes for each season.     

A stochastic differential equation approach to soil moisture

The dynamics of water within the unsaturated root zone of the soil are represented by a pair of stochastic differential equations (SDE's), one representing the so-called surplus state of the moisture and the other the deficit condition. The inputs to the model are the climatically controlled random infiltration events and evapotranspiration which are modeled as a compound Poisson process and a Wiener (Brownian motion) process, respectively.The solutions to these SDE's are not in close-form but sample functions are obtained by numerical integration. The moment properties of the soil moisture evolution process have also been derived analytically including the mean, variance, covariance and autocorrelation functions.To illustrate the model, climatic parameters representing the surplus and deficit cases and properties of clay loam soil have been used to numerically derived the corresponding sample functions. With proper selection of all the parameters, physically realistic sample trajectories can be obtained for the model.     

Measurement of near surface soil water content using a capacitance probe

The portable surface capacitance insertion probe was used to measure the in situ water content of near surface soil layers. The probe readings were calibrated against gravimetric samples collected over a wide range of water contents, and were found to be very closely correlated. The capacitance probe was used repeatedly in time and space at a field site where ridges and furrows ensured a regular pattern of soil water variations. The observed spatial variations of the water content were related to topography and land mangement. The degree of variability was dependent on the average water content.     

III. Measuring surface soil moisture using passive microwave remote sensing

Soil moisture is one of the few directly observable hydrological variables that has an important role in water and energy budgets necessary for climate studies. At the present time there is no practical approach to measuring and monitoring soil moisture at the frequency and scale necessary for these large scale analyses. Current and developing satellite systems have not addressed this important question. A solution utilizing passive microwave remote sensing is presented here and an optimum system, soil moisture estimation algorithms and a microwave simulation model are described.     

NIR-red spectral space based new method for soil moisture monitoring

Drought is a complex natural disaster that occurs frequently. Soil moisture has been the main issue in remote monitoring of drought events as the most direct and important variable describing the drought. Spatio-temporal distribution and variation of soil moisture evidently affect surface evapotranspiration, agricultural water demand, etc. In this paper, a new simple method for soil moisture monitoring is de- veloped using near-infrared versus red (NIR-red) spectral reflectance space. First, NIR-red spectral reflectance space is established using atmospheric and geometric corrected ETM data, which is manifested by a triangle shape, in which different surface covers have similar spatial distribution rules. Next, the model of soil moisture monitoring by remote sensing (SMMRS) is developed on the basis of the distribution characteristics of soil moisture in the NIR-red spectral reflectance space. Then, the SMMRS model is validated by comparison with field measured soil moisture data at different depths. The results showed that satellite estimated soil moisture by SMMRS is highly accordant with field measured data at 5 cm soil depth and average soil moisture at 0―20 cm soil depths, correlation coef- ficients are 0.80 and 0.87, respectively. This paper concludes that, being simple and effective, the SMMRS model has great potential to estimate surface moisture conditions.     

Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals

The upcoming deployment of satellite-based microwave sensors designed specifically to retrieve surface soil moisture represents an important milestone in efforts to develop hydrologic applications for remote sensing observations. However, typical measurement depths of microwave-based soil moisture retrievals are generally considered too shallow (top 2–5 cm of the soil column) for many important water cycle and agricultural applications. Recent work has demonstrated that thermal remote sensing estimates of surface radiometric temperature provide a complementary source of land surface information that can be used to define a robust proxy for root-zone (top 1 m of the soil column) soil moisture availability. In this analysis, we examine the potential benefits of simultaneously assimilating both microwave-based surface soil moisture retrievals and thermal infrared-based root-zone soil moisture estimates into a soil water balance model using a series of synthetic twin data assimilation experiments conducted at the USDA Optimizing Production Inputs for Economic and Environmental Enhancements (OPE³) site. Results from these experiments illustrate that, relative to a baseline case of assimilating only surface soil moisture retrievals, the assimilation of both root- and surface-zone soil moisture estimates reduces the root-mean-square difference between estimated and true root-zone soil moisture by 50% to 35% (assuming instantaneous root-zone soil moisture retrievals are obtained at an accuracy of between 0.020 and 0.030 m³ m⁻³). Most significantly, improvements in root-zone soil moisture accuracy are seen even for cases in which root-zone soil moisture retrievals are assumed to be relatively inaccurate (i.e. retrievals errors of up to 0.070 m³ m⁻³) or limited to only very sparse sampling (i.e. one instantaneous measurement every eight days). Preliminary real data results demonstrate a clear increase in the R² correlation coefficient with ground-based root-zone observations (from 0.51 to 0.73) upon assimilation of actual surface soil moisture and tower-based thermal infrared temperature observations made at the OPE³ study site.     

基于探地雷达回波信号获取污染土壤中污染物含量的研究进展

从探地雷达GPR(Ground Penetrating Radar)在土壤污染探测中的应用、污染土壤介电常数模型的演化和土壤介电常数求取三方面综述了目前国内外学者利用探地雷达技术开展的土壤污染探测研究.研究认为以土壤介电常数为纽带,采用适宜的方法从雷达回波信号求得介电常数,基于合理的土壤介电常数模型,通过神经网络技术,可驯化建立介电常数和污染土壤多个参数之间联系,从而实现污染土壤中多种污染物含量的监测.