黑龙江哈尔滨白渔泡国家湿地公园沼泽、林地和农田土壤物理、化学和生物性质差异

为了揭示黑龙江哈尔滨白渔泡国家湿地公园沼泽、林地和农田土壤物理、化学和生物性质的差异,于2018年7月25日~8月2日,在湿地公园内,在天然芦苇(Phragmites australis)沼泽、林地、旱田和水田中设置采样地,采集不同深度(0~10 cm、10~20 cm和20~30 cm)的土壤样品,测定土壤样品的物理、化学和生物指标。研究结果表明,白渔泡国家湿地公园不同采样地土壤指标存在差异;与天然芦苇沼泽土壤相比,其它采样地土壤的含水量明显偏低,土壤全氮、全磷、碱解氮和有机质含量都明显偏小,水田土壤速效磷含量偏大;天然芦苇沼泽土壤脲酶、硝酸还原酶、纤维素酶、蛋白酶和β-葡萄糖苷酶活性都高于林地和农田土壤,水田0~10 cm和10~20 cm深度土壤的硝酸还原酶活性显著高于旱田和林地;与天然芦苇沼泽土壤相比,旱田土壤小于0.25 mm的小团聚体含量偏大,而其它采样地土壤的各粒级团聚体的比例变化较小,水田土壤团聚体平均重量直径比天然芦苇沼泽和旱田土壤低。     

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.     

黑河中游夏季昼夜水汽同位素特征及水汽来源分析

基于2012年6~8月的实测水汽同位素数据及相关气象数据，对黑河中游夏季昼夜的同位素基本特征、水汽来源方向及潜在蒸发源地进行了研究。结果表明：空气水汽线斜率白天大于夜晚和水汽过量氘值白天大于夜晚，综合说明白天局地蒸发较夜晚强烈；夏季受西风水汽影响显著。其中，6月主要受西风水汽和北冰洋水汽影响，7、8月主要受西风水汽和东南方向水汽影响，且8月受东南方向水汽影响最为明显；水汽运移路径上下垫面地形和气压带移动会影响水汽后向轨迹高度，西北方向上水汽输送通道较顺畅，风速较大，有利于水汽的输送；水汽蒸发源地主要集中在研究区周围及以东、以北部，其次是西北部。绿洲是主要的水汽蒸发源地，其次是城市和河流，白天较夜晚局地蒸发强烈且面积大。     

全球盐湖卤水锂矿床的分布特征及其控制因素

盐湖卤水锂资源占全球锂矿总储量的78%,是全球最重要的一种锂矿床类型。研究并总结了全球盐湖卤水型锂矿床的分布特征、物质来源以及成矿规律,并提出了盐湖卤水型锂矿床成矿模型。研究发现,卤水型锂矿床的形成均受控于新构造运动、封闭-半封闭的低洼汇水盆地以及极端干旱的气候条件等因素,所有卤水型锂矿床均与新生代地质水热活动有密切关联,成矿物质特别是锂元素主要来源于火山喷发岩,经热泉水、温泉水以及大气降水等的水-岩作用后迁移富集。     

空间分析与生物地球化学循环:西藏林周县大骨节病患病村与非患病村的比较研究

西藏林周县是我国大骨节病(KBD)患病较为严重的地区之一,本文将林周县作为研究区,通过使用地理探测器(GeoDetector)量化分析KBD患病率风险因子的影响,并使用环境化学方法验证空间分析结果。通过对10个潜在影响因子的分析以及对当地KBD患病村和非患病村的土壤-水-粮食-人这一生物地球化学循环的环境化学分析,结果表明:(1)林周县KBD由一组多重且交互作用的环境影响因子共同作用影响,其中最重要的控制因子是地层因子;(2)所有环境介质(土壤、水、谷物)及人体组织中的硒元素浓度在KBD患病区均低于非患病区;(3)当地居民对硒和铬的摄入严重不足,尤其是KBD患病村中居民硒元素平均日摄入量(ADD)大约仅为世界卫生组织(WHO)建议的成人基本摄入量下限的4%;(4)我们推测,当地居民患病主要是由于地层这一影响因子,这是由于通过生态系统的迁移转化导致当地人口严重硒缺乏,最终导致地方性生物地球化学硒缺乏。     

An approximate method for evaluating the shear band thickness in saturated soils

A formula for the thickness of a shear band formed in saturated soils under a simple shear or a combined stress state has been proposed. It is shown that the shear band thickness is dependent on the pore pressure properties of the material and the dilatancy rate, but is independent of the details of the combined stress state. This is in accordance with some separate experimental observations. Copyright © 2004 John Wiley & Sons, Ltd.     

利用MapObjects的空间分析功能判断空间线面拓扑关系

MapObjects是广泛应用于GIS软件开发的组件,但它不能够独立支持空间拓扑关系,从而限制了它在空间分析方面的应用。为拓展MapObjects的空间分析功能,本文利用它提供的对图形对象的操作方法,实现了对九交模型中的空间线面拓扑关系的判断,且结果表明本文的实现方法正确可靠,具有一定的实用价值。     

GPS/LEO掩星观测的变分同化技术

在简单介绍GPS/LEO掩星探测大气的发展历史和科学意义之后,详细阐述了反演的基本原理;分析了标准反演中存在的问题,并说明一维变分同化(1DVAR)在反演方法中的重要性;给出了一维变分同化中价值函数的求解,以及各种同化因子;简单介绍了对当前气象学中普遍使用的四维变分同化(4DVAR);重点讨论了各种同化方法,以及使用各种同化因子的优缺点。最后,通过CHAMP卫星的观测实例分析,验证了GPS数据在数值天气预报(NWP)中的作用,以及相对于标准反演法一维变分对气象要素的改进。     

Approach to Mountain Hazards in Tibet, China

Tibet is located at the southwest boundary of China. It is the main body of the Qinghai-Tibet Plateau, the highest and the youngest plateau in the world. Owing to complicated geology, Neo-tectonic movements, geomorphology, climate and plateau environment, various mountain hazards, such as debris flow, flash flood, landslide, collapse, snow avalanche and snow drifts, are widely distributed along the Jinsha River (the upper reaches of the Yangtze River), the Nu River and the Lancang River in the east, and the Yarlungzangbo River, the Pumqu River and the Poiqu River in the south and southeast of Tibet. The distribution area of mountain hazards in Tibet is about 589,000 km^2, 49.3% of its total territory. In comparison to other mountain regions in China, mountain hazards in Tibet break out unexpectedly with tremendously large scale and endanger the traffic lines, cities and towns, farmland, grassland, mountain environment, and make more dangers to the neighboring countries, such as Nepal, India, Myanmar and Bhutan. To mitigate mountain hazards, some suggestions are proposed in this paper, such as strengthening scientific research, enhancing joint studies, hazards mitigation planning, hazards warning and forecasting, controlling the most disastrous hazards and forbidding unreasonable human exploring activities in mountain areas.     

γ射线暴的logN(>P)～logP分布

本文用简单的宇宙学模型 ,在标准烛光和均匀分布的假设下计算了γ射线暴的logN(>P)～logP分布 (大小谱 ) .在考虑了探测效率修正和死时间修正后 ,由宇宙学模型计算的理论结果和BATSE实测的大小谱没有显著的偏离