LAPS在冰雹云模拟中的应用

LAPS是中国气象局武汉暴雨研究所引进的中尺度分析系统,能融合区域内多种非常规观测资料,提供高分辨率中尺度分析场。该文对探空资料和LAPS分析场两种初始场的大气层结和环境风场信息进行了比较,并应用探空资料和不同时次的降雹点的LAPS分析场作为三维对流云模式的初始场对2008年7月27日、28日湖北西部山区冰雹天气过程进行数值模拟,并将模拟结果与观测实况进行了对比分析,最后对27日降雹过程应用临近降雹时次的LAPS分析场作为云模式初始场模拟的回波、风场和垂直速度等特征进行了分析,以探讨LAPS用于云数值模式的适用性、优越性以及冰雹云发生发展特点。结果表明: LAPS输出场用于云模式初始场进行冰雹云数值模拟具有时空上的优势,能更好地模拟出午后局地降雹,可以弥补探空资料作为云模式初始场的不足,应用临近时次的LAPS分析场作为云模式初始场的数值模拟能体现出冰雹云发展过程中多次增强等细节,有利于人工防雹作业预警和催化方案的确定。     

甘孜地区古地震遗迹的初步研究

本文所指的甘孜地区是指我国四川省西北部甘孜高原西部的甘孜—马尼干戈断裂带、甘孜—理塘断裂带北段以及鲜水河断裂的北西段所展布的范围。作者于1982年对该断裂的近代构造活动性进行了调查,在调查中发现了一些古地震遗迹,现报导如下: 1.甘孜地区地震地质构造背景该区处于青藏高原的东北缘,区内发育一些近于平行和相互斜交的北西向断裂构造(图     

TiO2柱撑蒙脱石的X射线衍射和扫描电镜研究

通过TiCL4在HCL溶液中的水解制备了TiO2柱撑蒙脱石，用X射线粉末衍射（XRD）、扫描电镜（SEM）及N2吸附－脱附实验对所得材料进行表征。结果表明，煅烧能有效地改善柱撑蒙脱石层间TiO2柱体的结晶程度，但同时也会引起TiO2晶粒长大、比表面积下降以及硅酸盐片层之间平行有序分布的程度降低。     

非均质条件下珠江口潮汐湿地地下水流动特征

开展非均质条件下潮汐湿地中地下水流动特征研究,成为深入揭示潮汐湿地水循环及物质循环过程的重要前提。以广州南沙区的河畔潮汐湿地为对象,基于放射性氡同位素示踪技术考察含水层非均质情况下地下水的流动特征。结果表明：1）研究区介质干容重、介质孔隙度和介质氡释放（E_{rn}）呈现空间异质性,介质氡释放是导致介质氡平衡能力（[R_n{]}_{gw}）呈现显著空间异质性（2 210.15~7 700.33 Bq/m³）的主要因素。2）非均质条件下地下水流动呈现显著的分区特征,根据氡的比活度可以划分为快速流动区和滞留区。其中,快速流动区地下水氡比活度较小,观测期内平均值为1 522.39 Bq/m³,地下水呈现由陆向海近似“U”型的流动模式。滞留区地下水氡比活度显著增大,观测期内平均值为3 858.40 Bq/m³,地下水受潮汐作用而滞留更久。滞留区地下水流动状态受潮高差影响,潮高差越大,潮汐对湿地地下水的整体驱动力越大,滞留区地下水渗出相对容易,反之滞留区地下水渗出较难。由此可见,潮汐湿地含水层介质的空间异质性是影响地下水流动特征的关键因素。     

情感地理视角下脱贫村农户情感重构——以湖南省十八洞村为例

以湖南省花垣县十八洞村为案例地,以“留守”农户和“返乡”农户为研究对象,基于情感地理视角,通过深度访谈法和参与式观察法探究了精准扶贫政策影响下农户情感重构及其重构机制。结果表明：1）脱贫村农户情感重构主要包括归属感、幸福感、风险厌恶感、相对剥夺感和失落感5个维度的重构,这些维度相互影响、相互制约。2）精准扶贫政策的实施使得脱贫村农户地方归属感上升,幸福感增强,风险厌恶感下降。然而,扶贫力度的不均衡导致农户间的相对剥夺感增强,传统文化的消解和社会关系的重构等使得农户产生失落感。3）在脱贫村农户情感重构过程中,农户认知是核心力量,政府行动起主导作用,企业行动发挥辅助作用。政府和企业行动通过重构乡村地域系统的物质空间、经济空间和社会空间刺激乡村地域主体即农户认知转变,继而引发情感的重构。     

A two-layer model for studying 2D dissolved pollutant runoff over impermeable surfaces

Dissolved pollutants in stormwater are a main contributor to water pollution in urban environments. However, many existing transport models are semi-empirical and only consider one-dimensional flows, which limit their predictive capacity. Combining the shallow water and the advection–diffusion equations, a two-dimensional physically based model is developed for dissolved pollutant transport by adopting the concept of a ‘control layer’. A series of laboratory experiments has been conducted to validate the proposed model, taking into account the effects of buildings and intermittent rainfalls. The predictions are found to be in good agreement with experimental observations, which supports the assumption that the depth of the control layer is constant. Based on the validated model, a parametric study is conducted, focusing on the characteristics of the pollutant distribution and transport rate over the depth. The hyetograph, including the intensity, duration and intermittency, of rainfall event has a significant influence on the pollutant transport rates. The depth of the control layer, rainfall intensity, surface roughness and area length are dominant factors that affect the dissolved pollutant transport. Finally, several perspectives of the new pollutant transport model are discussed. This study contributes to an in-depth understanding of the dissolved pollutant transport processes on impermeable surfaces and urban stormwater management.     

Meteorological disaster frequency at prefecture-level city scale and induced losses in mainland China during 2011–2019

Natural Hazards - Meteorological disasters (MDs), including drought, flood, hail, low temperature and frost (LTF), are causing severe damage to human life and economic development in China. Mapping...     

Damage and Acoustic Emission Characteristics of Combined Rock Mass with Anchor Under Different Inclination Angles and Combination Modes

Geotechnical and Geological Engineering - Soft and hard rock assemblages and anchor solids have important effects on rock mechanics properties under different inclinations and assemblages, so it is...     

Intraguild predation by polyps of three scyphozoan jellyfish: Nemopilema nomurai,Aurelia coerulea,and Rhopilema esculentum

Journal of Oceanology and Limnology - Jellyfish blooms occur worldwide and have resulted in serious problems in tourism, fisheries, coastal industries, and the marine ecosystem. The life cycle of...     

A hybrid ensemble method for improved prediction of slope stability

Accurate prediction of slope stability is a significant issue in geomechanics with many artificial intelligence (AI) techniques being utilised. However, the application of AI has not reached its full potential because of the lack of more robust algorithms. In this paper, we proposed a hybrid ensemble method for the improved prediction of slope stability using classifier ensembles and genetic algorithm. Gaussian process classification, quadratic discriminant analysis, support vector machine, artificial neural networks, adaptive boosted decision trees, and k‐nearest neighbours were chosen to be individual AI techniques, and the weighted majority voting was used as the combination method. Validation method was chosen to be the 10‐fold cross‐validation, and performance measures were selected to be the accuracy, the receiver operating characteristic curve, and the area under the receiver operating characteristic curve (AUC). Grid search and genetic algorithm were used for the hyperparameter tuning and weight tuning respectively. The results show that the proposed hybrid ensemble method has great potential in improving the prediction of slope stability. Compared with individual classifiers, the optimum ensemble classifier achieved the highest AUC value (0.943) and the highest accuracy (0.902) on the testing set, denoting that the predictive performance has been improved. The optimum ensemble classifier with the Youden's cut‐off was recommended for slope stability prediction with respect to the AUC value, the accuracy, the true positive rate, and the true negative rate. This research indicates that the use of the classifier ensembles, rather than the search for the ideal individual classifiers, might help for the slope stability prediction.