Rainfall intensity affects runoff responses in a semi-arid catchment

The source and hydrochemical makeup of a stream reflects the connectivity between rainfall, groundwater, the stream, and is reflected to water quantity and quality of the catchment. However, in a semi-arid, thick, loess covered catchment, temporal variation of stream source and event associated behaviours are lesser known. Thus, the isotopic and chemical hydrographs in a widely distributed, deep loess, semi-arid catchment of the northern Chinese Loess Plateau were characterized to determine the source and hydrochemical behaviours of the stream during intra-rainfall events. Rainfall and streamflow were sampled during six hydrologic events coupled with measurements of stream baseflow and groundwater. The deuterium isotope (²H), major ions (Cl⁻, SO₄²⁻, NO₃⁻, Ca²⁺, K⁺, Mg²⁺, and Na⁺) were evaluated in water samples obtained during rainfall events. Temporal variation of ²H and Cl⁻ measured in the groundwater and stream baseflow prior to rainfall was similar; however, the isotope compositions of the streamflow fluctuated significantly and responded quickly to rainfall events, likely due to an infiltration excess, overland dominated surface runoff during torrential rainfall events. Time source separation using ²H demonstrated greater than 72% on average, the stream composition was event water during torrential rainfall events, with the proportion increasing with rainfall intensity. Solutes concentrations in the stream had loglinear relationships with stream discharge, with an outling anomaly with an example of an intra-rainfall event on Oct. 24, 2015. Stream Cl⁻ behaved nonconservative during rainfall events, temporal variation of Cl⁻ indicated a flush and washout at the onset of small rainfall events, a dilution but still high concentration pattern in high discharge and old water dominated in regression flow period. This study indicates rainfall intensity affects runoff responses in a semi-arid catchment, and the stored water in the thick, loess covered areas was less connected with stream runoff. Solute transport may threaten water quality in the area, requiring further analysis of the performance of the eco-restoration project.     

基于PSR-WSVM模型的边坡位移预测

为建立高精度的边坡位移预测模型，采用相空间重构（PSR）将边坡位移时间序列数据转换为多维数据，同时构造小波核函数改进的支持向量机模型，建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机（SVM）模型、小波支持向量机（WSVM）模型和基于相空间重构的支持向量机（PSR-SVM）模型预测结果进行对比，通过平均绝对误差（MAE）、平均绝对误差百分比（MAPE）和均方根误差（RMSE）3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明，PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型，边坡位移预测的精度明显提升。     

基于噪声谱比法的内蒙古西部地区台站场地响应研究

利用内蒙古西部12个台站的地脉动噪声数据，采用噪声谱比法研究台站的场地响应情况。分析表明，台站场地响应按曲线形态可分为3类，且可能受地形地貌、局部构造和台基状况等条件影响。对比分析噪声谱比法与Moya方法的场地响应结果发现，场地响应曲线形态基本一致，只有极少数台站存在明显差异。结果表明，内蒙古西部区域大部分台站的台基状况总体较好，场地响应曲线较为平坦，无明显频率放大点。     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

基于GPIB接口的计数器数据采集与控制软件

在Windows平台上 ,使用VC 6 .0开发了一套基于GPIB接口的计数器数据采集与控制软件 ,其特点是采用了多线程机制 ,可实现对计数器的控制 ,以及数据的采集与实时显示。     

一种有发展前景的隧道及井巷超前预报技术——电磁导弹探测技术

对于隧道和煤矿井巷的安全掘进 ,超前预报显得特别重要。但由于掌子面前的特殊环境限制及多种干扰的严重影响 ,给隧道及井巷超前预报技术的研究带来许多困难。在瞬变电磁理论的基础上 ,引入新的电磁传播理论———电磁导弹技术 ,作为对该技术的初步探索 ,文章论证了该技术在隧道超前预报中应用的可能性 ,并提出了研究的主要内容和发展方向以及可能取得的新的技术突破和技术支撑     

从商务日语课程设置谈高职教育课程改革

本科院校办高职学院是我国 2 0世纪末出现的一个新生事物 ,如何设置专业课程 ,提高教学质量 ,使学生一出校门就能容纳到直接为经济服务当中去 ,结合商务日语专业的具体情况对这个问题进行了思考。     

青藏高原隆升的非线性动态有限元仿真研究

根据青藏高原的地质特征建立分析模型，采用3维动态有限元方法，在计算仿真板块速度场的基础上，计算在青藏高原的隆升过程中该地区地壳岩石的等效应力和位移随时间的变化，计算仿真得到的速度场与1998年GPS观测的速度场吻合良好；与过去一贯的假设相反，计算结果反映出地壳应力场不是静态的，而是此起彼伏，不断变化的，应力值最大且变化最剧烈的地区在克什米尔地区、鄂尔多斯地区和鲜水河－小江断裂带，与地震多发区域吻合。     

大同—阳高Ms6.1地震前兆综合加权信息熵的变化

利用“地震预报计算机专家系统”的思想对大同 -阳高 Ms6 .1地震前每一前兆异常事件进行综合评估 ,以每一异常的最可能发震时间来计算发震概率 ,利用地震前兆综合加权信息熵研究了系统熵值与地震的关系。对华北地区的地震前兆综合加权信息熵研究表明 ,在大同 -阳高 Ms6 .1地震前 ,信息熵出现了明显的减熵有序变化     

对青藏东北缘现今块体划分、运动及变形的初步研究

利用2维非连续变形分析方法（DDA），以位移代替围压作为边界约束力，研究青藏东北缘现今块体划分及其运动变形。根据该地区地质构造及地震活动，以GPS点测量位移作为模拟结果约束点，得出了较合理的块体划分模型和随时间演化的主应变分布图，并把应变高值区与近几年来发生的5级以上地震作对比，得出了研究区内地震危险性可能较大的区域。另外，对模拟的甘青块体与阿拉善块体的边缘带断裂左旋运动做了大概计算。