灰色预测模型GM(1.1)在水文预测中的应用——以玛纳斯河为例

由于灰色预测模型理论较时间序列预测具有很大的优势,所以在许多领域得到广泛的应用.为将该方法应用于水文与水资源预测,本文以玛纳斯河流量预测为例,应用GM(1.1)灰色预测模型对水文水资源要素进行预测,并分析该预测模型在水文水资源中的应用.经分析认为,GM(1.1)灰色预测模型对水文水资源要素预测具有重要的意义.     

The use of digital technologies for landslide disaster risk research and disaster risk management: progress and prospects

Landslide is a serious natural disaster next only to earthquake and flood, which will cause a great threat to people’s lives and property safety. The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective , difficult to quantify, and no pertinence. As a new research method for landslide susceptibility assessment, machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models. Taking Western Henan for example, the study selected 16 landslide influencing factors such as topography, geological environment, hydrological conditions, and human activities, and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination (RFE) method. Five machine learning methods [Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)] were used to construct the spatial distribution model of landslide susceptibility. The models were evaluated by the receiver operating characteristic curve and statistical index. After analysis and comparison, the XGBoost model (AUC 0.8759) performed the best and was suitable for dealing with regression problems. The model had a high adaptability to landslide data. According to the landslide susceptibility map of the five models, the overall distribution can be observed. The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest, the Xiaoshan Mountain range in the west, and the Yellow River Basin in the north. These areas have large terrain fluctuations, complicated geological structural environments and frequent human engineering activities. The extremely high and highly prone areas were 12043.3 km² and 3087.45 km², accounting for 47.61% and 12.20% of the total area of the study area, respectively. Our study reflects the distribution of landslide susceptibility in western Henan Province, which provides a scientific basis for regional disaster warning, prediction, and resource protection. The study has important practical significance for subsequent landslide disaster management.     

秦皇岛市青龙县城工程场地动力特征及分区评价

工程场地动力分区评价（即地震影响小区划），是在工程场地地震危险性分析的基础上，根据场地岩土动力特征及地形地貌、地质构造条件等对地震动影响的反应程度，从而对不同类型场地的地震效应作出评价．其成果可做为建筑抗震设计、工程加固、震害预测与对策的制定及土地合理利用和总体规划布局的依据．本文以秦皇岛市青龙县城工程场地为例，进行了工程场地动力特征研究与分区评价及震害预测（即工程场地地震影响小区划）。     

辣椒新品种“洪杂3号”引种与栽培研究

针对粤西地区生态条件和北运辣椒的栽培特点，进行辣椒引种适应性研究。经品比试验筛选出的新品种“洪杂3号”．较对照“保椒”增产30％以上哈方差分析差异达极显著水平。分期播种试验结果表明，在8月下旬至9月中旬播种．产量可达46845～74100kg·hm-2。     

Damming of large river by debris flow: Dynamic process and particle composition

The frequency and extent of debris flows have increased tremendously due to the extreme weather and the Wenchuan earthquake on May 12, 2008. Previous studies focused on the debris flow from gullies damming the mountain streams. In this paper, an equation for the run-out distance of debris flow in the main river is proposed based on the dynamic equation of debris flow at different slopes given by Takahashi. By undertaking field investigations and flume experiments, a new calculation method of the volume of debris flow damming large river is obtained. Using the percolation theory and the renormalization group theory it was deduced that the large particles should comprise more than 50% for forming a stable debris flow dam. Hence, the criteria of damming large river by debris flow is presented in terms of run-out distance and grain composition which was then validated through the event of damming river by debris flow at Gaojia gully, the upper reaches of the Minjiang River, Sichuan, China, on July 3, 2011.     

Detecting Arctic snow and ice cover with FY-1D global data

Chinese meteorological satellite FY-1D can obtain global data from four spectral channels which include visible channel(0.58-0.68 μm) and infrared channels(0.84-0.89 μm,10.3-11.3 μm,11.5-12.5 μm).2366 snow and ice samples,2024 cloud samples,1602 land samples and 1648 water samples were selected randomly from Arctic imageries.Land and water can be detected by spectral features.Snow-ice and cloud can be classified by textural features.The classifier is Bayes classifier.By synthesizing five d ays classifying result of Arctic snow and ice cover area,complete Arctic snow and ice cover area can be obtained.The result agrees with NOAA/NESDIS IMS products up to 70%.     

Probability Distribution of the Hull Motion and Mooring Line Tension of Two Floating Systems

The statistical and distribution characteristics of the responses of a floater and its mooring lines are essential in designing floating/mooring systems.In general,the dynamic responses of offshore structures obey a Gaussian distribution,assuming that the structural system,and sea loads are linear or weakly nonlinear.However,mooring systems and wave loads are considerably nonlinear,and the dynamic responses of hull/mooring systems are non-Gaussian.In this study,the dynamic responses of two types of floaters,semi-submersible and spar platforms,and their mooring lines are computed using coupled dynamic analysis in the time domain.Herein,the statistical characteristics and distributions of the hull motion and mooring line tension are discussed and compared.The statistical distributions of the dynamic responses have strong non-Gaussianity and are unreasonably fitted by a Gaussian distribution for the two floating and mooring systems.Then,the effects of water depth,wave parameters,and low-frequency and wave-frequency components on the non-Gaussianity of the hull motion,and mooring line tension are investigated and discussed.A comparison of the statistical distributions of the responses with various probability density functions,including the Gamma,Gaussian,General Extreme Value,Weibull,and Gaussian Mixture Model(GMM)distributions,shows that the GMM distribution is better than the others for characterizing the statistical distributions of the hull motion,and mooring line tension responses.Furthermore,the GMM distribution has the best accuracy of response prediction.     

九江市蓼花剖面末次冰期中晚期沉积的粒度端元特征与气候变化

鄱阳湖湖滨地区广泛分布着晚第四纪风沙沉积序列。在星子县沙岭沙山进行野外调查后选择蓼花剖面开展工作,测试了地质时代和粒度,对粒度结果使用端元分析模型进行研究,探讨该区域末次冰期中晚期的气候变化规律。结果显示：该剖面由湖相-古土壤-沙丘砂等沉积相叠覆堆积组成,形成于末次冰期中晚期（48.8—17.1 ka）。端元分析模型将粒度数据分解出3个不同的粒度端元,不同端元组分在垂向上呈峰谷交替的旋回变化,EM1代表粉砂端元组分,峰值对应湖相和古土壤发育时期;EM2和EM3代表中砂—粗砂端元组分,峰值对应沙丘砂发育期,这些峰谷交替变化的规律指示了末次冰期的季风演变以及气候波动变化,万年尺度上表现为LH10 (48.8—39.9 ka)和LH3～LH5 (28.1—17.1 ka)的冬季风强盛期,分别对应深海氧同位素的MIS3b和MIS2阶段。LH6～LH9 (39.9—28.1 ka)为温暖的夏季风时期,对应深海氧同位素的MIS3a阶段。这些变化与YZ洞石笋氧同位素以及格陵兰冰心有良好的对应,与全球气候变化基本一致。     

基于NPP数据和样区对比法的青藏高原自然保护区保护成效分析

在青藏高原选择11个代表性自然保护区,基于高寒草地植被净初级生产力（Net Primary Production,简写NPP）变化过程数据,比较分析了自然保护区与其相邻等面积区域的NPP变化差异;采用样区对比法,在自然保护区内外选取21组对比样区,比较自然保护区建立前后及其内外的生态状况,评估了自然保护区的保护成效。研究表明：1. 1982-2009年间,82%的代表性自然保护区NPP比保护区周邻区域及青藏高原的平均水平低,反映了自然保护区的生态系统状况更为脆弱;2. 在代表性自然保护区中,曼则塘自然保护区的NPP增长趋势最为明显,塔什库尔干野生动物自然保护区的NPP增长趋势最弱;除色林错自然保护区外,以草甸和湿地为主的自然保护区NPP增速明显高于以草原与荒漠草地为主的自然保护区;3. 代表性样区的研究发现：① 自然保护区内76%以上的样区和国家级保护区内82%以上的样区NPP增加幅度明显高于保护区外对应样区的增幅;② 取得明显保护效果的有中昆仑、长沙贡玛、若尔盖和色林错等自然保护区;曼则塘自然保护区的东南部边缘地区和塔什库尔干野生动物自然保护区的北部边缘地区的效果不明显,可能与保护区及其周邻地区人类扰动增强密切相关;③ 高寒草甸类型自然保护区的保护效果最为显著,高寒草原类型自然保护区的保护效果较差。本研究展示了样区对比法在评估大区域生态变化中所具有的独特优势,其关键在于科学设计样区并进行合理的空间抽样。     

当前国内酸雨研究进展

回顾了我国在酸雨研究方面所获得的成果,包括酸雨的时空分布特征、影响因素、生态影响模拟以及控制对策等4个方面的内容。大量的研究表明,近些年来酸雨污染的范围有所增加、程度有所加重。酸雨的形成是多种自然和人为因素共同作用的结果,与特定的气象条件、天气类型和大气环流等有密切的关系。针对酸雨污染有所加重的趋势,必须采取有效的控制对策。