基于GPS反射信号技术的渤海海冰实验

传统的海冰检测手段都存在着受天气影响或实时检测成本太高的缺点,为了更实时有效的检测海冰,减轻海冰灾害对于我国的影响,并达到防灾减灾的目的,本文研究了GPS反射信号(GPS-R)技术检测海冰的可能性。通过对2013年2月下旬在中国天津渤海湾的岸基架设GPS信号接收机所接收的观测数据进行分析,使用不同特性的反射海域(几乎全为海水或是海冰)的数据进行比对分析,其结果显示极化比值(反射左旋信号与直射右旋信号的比值)能够明显反演海冰密集度。本文首次将GPS-R技术应用于国内自主设计的渤海海冰检测实验中。     

洱海四种沉水植物对弱光环境的适应性比较

水下弱光环境是水生态系统沉水植被恢复面临的常见问题,因此研究沉水植物在弱光胁迫下的适应性是必要的.本研究以云南大理州洱海南部重度退化的湖区(惯称湖心平台,曾分布有大面积沉水植物)作为实验地点,选取4种沉水植物——苦草(Vallisneria natans)、轮叶黑藻(Hydrilla verticillata)、穗花狐...     

Fifteen Years of Ocean Observations with China Argo

For 15 years since the beginning of China Argo project, China has deployed over 350 profiling floats in Pacific and Indian ocean, and constructed China Argo ocean observing network. Moreover, we have setup the Argo data receiving, processing and distributing system, and developed various Argo data products using Argo observations, which has promoted the progress of ocean data sharing in China. The abundant Argo data have become a main data source in oceanic and atmospheric basic researches and operational applications. A batch of important achievements in basic research and operational application have been brought, e.g. in aspects of tropical cyclone (typhoon), ocean circulation, meso-scale eddy, turbulence, heat/salt storage and transport and water mass, as well as in ocean, atmosphere/climate operational forecasting and predicting. With the extension of the international Argo program from “Core Argo” to “Global Argo”, we are faced with great challenges in the long-term maintaining and sustained developing of our Argo ocean observing network. It is suggested that we should take the opportunity to construct China regional Argo ocean observing network as soon as possible in adjacent northwestern Pacific and Indian ocean using Chinese BeiDou profiling floats, which will make us to take responsibility and obligation of a big country for addressing global climate changes and preventing natural disasters.     

中国地面相对湿度非均一性检验及订正

利用加拿大环境部气候研究中心研发的PMTred和PMFT方法,选取均一的邻近站为参考站,使用相关系数权重平均构建参考序列,结合元数据信息,对1951-2014年中国2400多个国家级地面站月平均相对湿度进行了非均一性检验与订正,并分析了造成相对湿度序列非均一的主要原因。结果表明,中国地面相对湿度资料存在较严重的非均一问题,68%的台站存在断点,人工观测转自动观测、迁站和时次变化是造成序列非均一的主要原因。整套资料负订正量所占比例较高,订正范围主要集中在-5%～0之间,这种负订正量与人工转自动观测后相对湿度观测值偏低有密切关系。这也使得订正后中国平均相对湿度趋势与订正前存在明显差异,订正前中国平均相对湿度呈下降趋势,订正后相对湿度没有趋势性变化。     

挤密条件下U型地埋管换热器换热效率的理论分析及数值模拟

土层的导热系数是影响地源热泵地埋管换热器换热效率的重要因素之一,与土样的密度密切相关。为了提高地源热泵换热器换热效率,本文基于柱热源理论,建立了挤密钻进条件下换热器的传热理论模型,对换热器的换热效率进行了计算,并利用Ansys有限元软件对土层挤密条件下换热器的换热效果进行了数值分析。结果表明,与传统方法相比,土层挤密可有效降低钻孔周围的热阻,U型管换热器的换热效率可提高17%~20%。     

惠民县土地利用总体规划实施中存在的问题及规划修编建议

惠民县上一轮规划是根据《中华人民共和国土地管理法》和中共中央国务院《关于加强土地管理切实保护耕地》的要求,在县政府统一领导下,于1997年12月至1998年10月完成的县、乡（镇）两级土地利用总体规划的编制任务。规划是以1996年为基期,以2000年为近期目标年,2010年为规划目标年。该规划以控制建设用地保护耕地为重点,以耕地保有量和基本农田面积不低于上级下达的控制指标,建设占用耕地和城镇规模不突破上级控制指标,补充耕地面积不少于上级下达的控制指标为总原则,规划指标落实方法是在专题研究的基础上,实行逐级控制,上级控制下级,层层分解落实。     

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.     

海气耦合气侯模式的依时解

运用量子力学方法,精确地求出了海气耦合随机－动力模式的依时解．对依时解的分析表明：当气候系统处于基态时,系统的行为呈布朗运动形式,由此从理论上证明了Ｈａｓｓｅｌｍａｎｎ关于随机气候模式建立的立足点．当系统处于第一激发态时,系统运动呈随时间衰减的形式;在一定条件下则为周期振荡,主周期长度为２．３年．最后将结果用来讨论二氧化碳浓度倍增时的气候影响．     

Forecasting Monsoon Precipitation Using Artificial Neural Networks

This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It pres ents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corre sponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a ＇new＇ data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.