复杂山区地形条件下ERA5再分析地表气温降尺度方法

高时空分辨率的气温栅格数据是多种地学模型和气候模型的重要输入。山区地形复杂,气温空间异质性强,如何获取高时空分辨率的山区地表气温数据一直是研究热点与难点。本文选择地形复杂的河北省张家口市作为试验区,基于局部薄盘样条函数对ERA5再分析日均近地表气温（2 m高度）进行空间插值,并利用随机森林算法,结合少量气象站观测气温数据、地形地表参数数据构建日均气温订正模型和气温逐时化模型,实现空间分辨率由0.1°（约11 km）到30 m的逐时气温降尺度,最后将该模型拓展应用于其他时间与区域,检验本文发展的降尺度方法在没有站点观测数据条件下的时空移植性。结果显示,本文降尺度方法得到的高时空分辨率山区气温数据精度较高,1月均方根误差（RMSE）平均值为2.4℃,明显优于气象站点插值结果,且气温相对高低的空间分布更为合理、纹理更加丰富;将该方法应用到其他时间与区域的RMSE平均值分别为2.9℃与2.5℃,均小于再分析资料直接插值所产生的误差。研究结果总体表明,在气象站点较少甚至没有时,可利用本文方法通过ERA5再分析气温准确获取复杂地形条件下的山区高时空分辨率气温数据。     

Neural network prediction of the reliability of heterogeneous cohesive slopes

The reliability of heterogeneous slopes can be evaluated using a wide range of available probabilistic methods. One of these methods is the random finite element method (RFEM), which combines random field theory with the non‐linear elasto‐plastic finite element slope stability analysis method. The RFEM computes the probability of failure of a slope using the Monte Carlo simulation process. The major drawback of this approach is the intensive computational time required, mainly due to the finite element analysis and the Monte Carlo simulation process. Therefore, a simplified model or solution, which can bypass the computationally intensive and time‐consuming numerical analyses, is desirable. The present study investigates the feasibility of using artificial neural networks (ANNs) to develop such a simplified model. ANNs are well known for their strong capability in mapping the input and output relationship of complex non‐linear systems. The RFEM is used to generate possible solutions and to establish a large database that is used to develop and verify the ANN model. In this paper, multi‐layer perceptrons, which are trained with the back‐propagation algorithm, are used. The results of various performance measures indicate that the developed ANN model has a high degree of accuracy in predicting the reliability of heterogeneous slopes. The developed ANN model is then transformed into relatively simple formulae for direct application in practice. Copyright © 2016 John Wiley & Sons, Ltd.     

地形扫描数据TIN模型生成DEM的误差计算方法

由地表三维空间数据生产的数字高程模型(DEM)的误差包括从数据误差传递来的误差和因数据数量不足以反映地形起伏造成的信息损失误差。本文给出了由三维空间数据的不规则三角网(TIN)模型栅格化为DEM过程中,通过线性插值将数据随机误差传递到DEM栅格上的随机误差的解析解,同时利用地基激光扫描仪测量的冲沟地形数据点云,分析得出因信息损失产生的DEM系统和随机误差的估算方法。结果显示信息损失产生的DEM平均高程系统误差和随机误差都与有效测点密度有关,其中有效测点由TIN中包含DEM栅格中心点的所有三角面的3个顶点组成;信息损失产生的DEM平均高程系统误差和随机误差除与有效测点密度相关外,还与地形特征有关,平均高程系统误差与地形整体凹度参数有关,随机误差与地形整体起伏程度参数有关。建立了分辨率为0.1 m×0.1 m的DEM误差估算模型。     

再论DEM精度评定的基本理论问题

数字高程模型(DEM)应用十分广阔。本文通过实际存在的对地面两种不同认识论观点的分析,阐明地面是多个单元曲面,它们是在复杂的地形结构线上拼接而成的确定表面,那种把地面或其局部视为随机曲面的观点不符合人类生活和生产实践;通过对系统误差和偶然误差性质概念和DEM生产过程的分析,指出DEM的总体误差以偶然误差概而论之,完全忽视了内插过程并错判了内插误差的性质;同时,对DEM的偶然误差计算中前提的设置、计算推理链的问题进行一定分析,揭示偶然误差论在实践中难予解脱的困境,并进一步阐述了它对DEM误差检核方法、标准的不良影响和对质量的危害。因此希望重视这一重要理论问题,开展讨论,澄清疑虑,促进问题的解决。     

太湖富营养化条件下影响蓝藻水华的主导气象因子

利用2004-2018年卫星遥感解译的太湖蓝藻水华信息构建蓝藻综合指数,采用随机森林机器学习算法分析同期气象因子与蓝藻水华综合指数的关系,定量评估影响蓝藻水华的主要气象因子特征变量的重要性度量和贡献率.结果表明,在光、温、水、风等主要气象要素中,气温对蓝藻水华综合指数起着主导的作用,其次是风速和降水,日照时间的影响或可忽略.其中气温条件中重要性度量最大的是年平均气温,其次是冬、春季节的平均气温;风速因子中影响较大的是7月份的平均风速;水分条件中主导因子是9月累计降水量.优选的随机森林模型模拟值与实际蓝藻水华综合指数的变化趋势基本一致,拟合优度为0.91,通过0.01显著性检验,随机森林模型模拟效果较好.用随机森林模型模拟值对太湖蓝藻水华分等级评估,模型模拟精度达到了86.7%,其中5个重度等级年份模型模拟结果完全一致,中度等级的6个年份模型模拟值有5年与之相符,中度以上等级的模拟精度达90.9%,模型能够反映气象因子对蓝藻水华综合指数的综合影响,对中、重度蓝藻水华的模拟效果更好.随机森林模型有助于理解富营养化状态下影响蓝藻水华的主导气象因子,利用气象因子的可预测性可以促进蓝藻水华预测预警能力的提升.     

Non‐parametric–parametric model for random uncertainties in non‐linear structural dynamics: application to earthquake engineering

This paper deals with the transient response of a non‐linear dynamical system with random uncertainties. The non‐parametric probabilistic model of random uncertainties recently published and extended to non‐linear dynamical system analysis is used in order to model random uncertainties related to the linear part of the finite element model. The non‐linearities are due to restoring forces whose parameters are uncertain and are modeled by the parametric approach. Jayne's maximum entropy principle with the constraints defined by the available information allows the probabilistic model of such random variables to be constructed. Therefore, a non‐parametric–parametric formulation is developed in order to model all the sources of uncertainties in such a non‐linear dynamical system. Finally, a numerical application for earthquake engineering analysis is proposed concerning a reactor cooling system under seismic loads. Copyright © 2003 John Wiley & Sons, Ltd.     

资源型城市长时间序列土壤含水量变化分析——以锡林浩特市为例

大规模的煤炭开采活动将对生态环境产生扰动,而土壤含水量是受扰动的生态参数之一,且具有重要意义.现有的土壤含水量产品分辨率较低,不适用于区域尺度的研究,而高精度的微波反演由于数据的局限性无法进行长时问序列的研究.本文以我国的重要产煤基地锡林浩特市为研究区,以2004—2020年的AMSR-E与AMSR-2土壤含水量产品及...     

石头坪景区边坡几何形态对稳定可靠度的影响分析

兰州金城公园石头坪景区提升改造工程存在大量的自然边坡和人工边坡。因边坡土体物理力学参数存在变异性,采用了蒙特卡洛随机抽样法和极限平衡法按不同坡高及坡度计算分析了边坡稳定可靠性指标及其变化规律,结果表明在景区实际坡高、坡度范围内边坡是稳定可靠的,适宜景区建设,但当坡高、坡度达到实际范围的最大值或超范围时,边坡处于基本稳定或欠稳定状态,建议采取加固措施。     

Stochastic modelling of a diffusive wave for flood propagation using the random walk particle tracking method in a hypothetical city

Deterministic numerical schemes have been widely used for the solution of the diffusive wave (DW) equation, however, these schemes are computationally costly and suffer instability issues. This paper presents a stochastic random walk particle tracking (RWPT) method to solve such an equation for a dam‐break flow problem. Three different wave duration scenarios are presented for simulations of the DW for flood flows in a hypothetical city. The hypothetical city is represented by a domain of size 2,000 m by 500 m in x and y directions, respectively. The domain is divided into 25 m by 25 m cells. A dam is located at the upstream of the hypothetical city. Each scenario has a distinct propagation pattern after the dam is breached. Analysed and presented are 18 different simulations, which are composed of three different building configurations, two different bed slopes, and three different shapes of hydrographs. In this method, the flood volume is divided into a large number of particles where each particle carries a fixed amount of the flood volume. These particles undergo convective and diffusive movements, and their superposition represents propagation of the DW in the flow domain. The solution algorithm of the RWPT‐based equations is used to compute flood inundation depths in the hypothetical city. Comparison is drawn among the simulated results from three different shapes of the inflow hydrographs. The proposed stochastic method has two major advantages over traditional deterministic schemes: (a) greater efficiency, thus lesser computational costs, and (b) no instability issues.     

基于EIFS和P-M的海底管道腐蚀疲劳寿命预测

鉴于腐蚀疲劳损伤的特殊性,研究了点蚀过程和腐蚀疲劳裂纹扩展过程。基于等效初始缺陷尺寸(EIFS)和线性累积损伤理论(P-M)方法,消除了点蚀形核、蚀坑生长及腐蚀疲劳短裂纹扩展对腐蚀疲劳寿命预测的影响;避免了基于单点蚀坑建立的腐蚀疲劳寿命预测表达式的弊端;合理地简化了随机荷载下腐蚀疲劳寿命的预测流程。利用现有试验数据,对基于EIFS和P-M方法建立的腐蚀疲劳寿命预测表达式进行了模型验证。结果显示,所提模型的有效性和合理性得到了验证,为工程实际中海底管道的腐蚀疲劳寿命预测提供了一种可行方法。