辽宁省精细化暴雨洪涝灾害风险评估与预评估

基于辽宁省61个国家气象站1961—2020年和998个区域自动气象观测站建站至2020年逐小时、逐日降水资料,分析了辽宁省暴雨洪涝灾害主要致灾因子,计算了暴雨洪涝孕灾环境指标,完成了辽宁省暴雨洪涝灾害危险性评估。结果表明：暴雨洪涝高危险性地区主要位于丹东;暴雨洪涝灾害人口高风险区主要位于沈阳和大连市区;经济高风险区主要位于大连和盘锦市区;水稻、玉米高风险区主要位于锦州、盘锦和丹东。利用辽宁省无缝隙智能网格预报数据对2022年7月28—29日的暴雨过程灾害风险进行了预评估,发现暴雨灾害危险性高值区域主要分布在朝阳、葫芦岛以及辽宁中部。暴雨灾害可能造成的人口、经济高风险区域主要位于辽宁西部和中部地区;暴雨灾害可能造成的水稻和玉米高风险区主要位于沈阳、铁岭和朝阳北部等地区。预计高风险区主要影响人口约为449万人,经济损失约为1432万元,受影响的水稻面积约为1.028万公顷、玉米面积约为1.798万公顷。通过灾后效果检验,发现预评估模型效果良好,可在实际的暴雨洪涝灾害风险评估业务中使用。     

太湖表层沉积物多环芳烃的空间分布、来源及其风险

随着太湖流域社会与经济的发展,多环芳烃(PAHs)在各种环境介质中逐渐累积,污染日益严重,可能对太湖生态环境及周边人体健康构成威胁。为探究太湖沉积物PAHs的来源及生态风险,于2021年12月在太湖采集30个表层沉积物样品,利用气相色谱-质谱联用仪(GC-MS)检测样品中16种PAHs含量;利用受体模型和苯并[a]芘(BaP)毒性当量法进行来源解析及生态风险评估,并将各来源贡献与毒性当量浓度相结合,量化源风险。结果表明,太湖表层沉积物中16种PAHs总含量介于124~592 ng/g之间,平均值为294 ng/g,中值为279 ng/g;高环多环芳烃(HMW PAHs)为主要组分,占∑PAHs的67%。高含量区域位于竺山湾、梅梁湾、贡湖湾和西太湖,与国内外其他湖泊沉积物相比,太湖沉积物PAHs含量处于较低水平。源解析的结果表明,太湖表层沉积物中PAHs交通排放源贡献率为29.1%、煤炭燃烧源贡献率为26.7%、生物质燃烧源贡献率为28.7%、石油源贡献率为15.6%。生态风险评价结果表明,交通排放源、生物质燃烧源、煤炭燃烧源和石油源的BaP毒性当量含量(TEQ_BaP)均值分别为19.34、17.81、16.33和9.1 ng/g,均小于70 ng/g,几乎处于无风险水平。西太湖、贡湖湾和梅梁湾的部分区域ΣTEQ_BaP大于70 ng/g属于潜在风险区,具有一定潜在毒性。在后续的污染治理中应重点关注太湖西北部地区污染物的排放。本研究可为沉积物中PAHs污染的研究提供数据支撑,为地方政府精准、高效地管控PAHs污染提供理论依据。     

一种新型降水预报评分方法

在目前已有的降水评分办法的基础上，对原有办法存在的弊端进行了有效的改进，使得降水的预报评估与预报服务业务结合更紧密，也更贴近公众的感受。主要介绍了新方法的基本思路、关键技术、实现方法等。评分系统已投入业务运行，成为考核预报员业务水平的重要依据。     

桥梁气象专题研究与服务

根据实际开展的桥梁气象专题研究，分析桥梁气象研究与服务如何面对国内大型桥梁建设发展趋势所带来的机遇和挑战，提出开展桥梁气象研究与服务的必要性，并从桥位气候背景、桥位气象参数取值、桥梁气象研究方法以及桥梁施工及营运期气象服务与保障等方面介绍桥梁气象专题研究及服务应开展的主要内容，同时，给出一个已投入实际使用的桥梁施工期气象环境监测与预警系统的建设思路。     

云南省Internet网络天气预报会商系统

云南省基于Internet网络的天气预报会商系统覆盖云南省、地 (州 )、县三级。系统采用了Internet网络技术、数据库技术 ,实现了省、地 (州 )、县天气预报发布及浏览查询、预报讨论BBS、天气预报会商室、系统管理 ,在线短消息互发等功能。     

An internal-external optimized convolutional neural network for arbitrary orientated object detection from optical remote sensing images

Due to the bird's eye view of remote sensing sensors, the orientational information of an object is a key factor that has to be considered in object detection. ...     

辽宁锦州市地质遗迹资源禀赋及保护性利用

地质遗迹资源是提高人类当前和将来生活质量最珍贵的、不可再生的自然遗产,其保护、利用与管理是地质遗迹可持续性发展的前提和基础。辽宁省锦州市地跨燕山余脉和辽西中生代盆地,长期的地质演化留下了丰富的地质遗迹资源,其中尤以典型地层剖面、古生物化石、花岗岩地貌、河流地貌景观和海岸线最具特色,典型地质遗迹特征是对重要地质事件与人类活动历史的响应。通过锦州地质遗迹资源专项调查,从遗迹特征、成因等角度,将锦州市地质遗迹资源分为两大类、七小类、十一亚类,共计238处,整体上类型多样、内涵独特、分布范围广。然而,由于基础数据不清、保护意识淡薄、投入资金有限、管理机制不健全等原因,锦州市内的地质遗迹正遭受或面临严重破坏。针对锦州市"山、海、城"旅游产业空间布局、打造精品旅游目的地、宜居城市、生态文明、乡村振兴建设与地质遗迹保护性利用中存在的问题,建立地质遗迹资源保护名录、科普基地、特色风景廊道、风景旅游区、特色小镇及地质文化村,形成多层次、多样化的保护性利用路径。     

四川九寨黄龙机场高填方地基变形三维动力分析

四川九寨黄龙机场具有高填方、高地震烈度、高海拔等三高的特点,高填方体地基在地震作用下的动力稳定与变形问题成为该机场需着重考虑的主要工程地质问题之一。在对目前各种动力分析方法研究的基础上,选用快速拉格郎日差分分析(FLAC)程序,对该机场元山子沟段高填方地基在地震作用下的变形规律进行了系统地研究,获得了一些有实际意义的认识。     

Measures of Parameter Uncertainty in Geostatistical Estimation and Geostatistical Optimal Design

Studies of site exploration, data assimilation, or geostatistical inversion measure parameter uncertainty in order to assess the optimality of a suggested scheme. This study reviews and discusses measures for parameter uncertainty in spatial estimation. Most measures originate from alphabetic criteria in optimal design and were transferred to geostatistical estimation. Further rather intuitive measures can be found in the geostatistical literature, and some new measures will be suggested in this study. It is shown how these measures relate to the optimality alphabet and to relative entropy. Issues of physical and statistical significance are addressed whenever they arise. Computational feasibility and efficient ways to evaluate the above measures are discussed in this paper, and an illustrative synthetic case study is provided. A major conclusion is that the mean estimation variance and the averaged conditional integral scale are a powerful duo for characterizing conditional parameter uncertainty, with direct correspondence to the well-understood optimality alphabet. This study is based on cokriging generalized to uncertain mean and trends because it is the most general representative of linear spatial estimation within the Bayesian framework. Generalization to kriging and quasi-linear schemes is straightforward. Options for application to non-Gaussian and non-linear problems are discussed.     

Formation Mechanism of Mud Bank Along the Southwest Coast of India

Mud bank formation during the southwest monsoon along the southwest coast of India remains an enigma to the researchers and coastal community in spite of several earlier studies. The present study attempts to unravel the mystery through a high-frequency, season-long time-series observation at Alappuzha, located at the southern part of the west coast of India, a region of frequent occurrence of mud bank. Using 7-month-long weekly time-series observation, we identified strong winds and high waves associated with onset of the southwest monsoon and subsequent three episodic atmospheric low-pressure events (LPEs).With the help of in situ time-series data, we show that the strong winds and high waves associated with southwest monsoon pre-conditions the near shore bottom sediment to bring it into suspension. The high amplitude waves associated with the southwest monsoon, while propagating from the deep water to shallow water region, interact with the bottom initiating bottom-sediment movement and its suspension due to wave refraction and shoaling. The sporadic occurrence of the atmospheric LPEs enhances the process of suspension of bottom sediment in the near shore region leading to the formation of fluid mud. Simulations with a cohesive sediment transport model yielded realistic estimates of sediment transport, in the presence of an onshore current, a pre-requisite for transporting the fluid mud toward the coast. The prevailing onshore upwelling current during the southwest monsoon provides the favorable pre-requisite conditions for transporting the fluid mud through depression channel network towards the coast. Once sufficient quantity and thickness of fluid mud is accumulated in the near shore region, it acts as a wave damper for subsequent high monsoon waves, as indicated by the time-series wave data, leading to the formation of tranquil mud bank region. Depression channel networks extending from the shelf to the coast off Alappuzha, Kochi, Ponnani, Beypore, and Ullal were found in the bathymetric charts, thus explaining why mud banks occur only at few locations in spite of the prevalence of similar monsoon conditions.