Analysis of Forecast and Early Warning of Flood in Medium and Small Rivers

Flooding of small and medium rivers is caused by environmental factors like rainfall and soil loosening.With the development and application of technologies such as the Internet of Things and big data,the disaster supervision and management of large river basins in China has improved over the years.However,due to the frequent floods in small and medium-sized rivers in our country,the current prediction and early warning of small and medium-sized rivers is not accurate enough;it is difficult to realize real-time monitoring of small and medium-sized rivers,and it is also impossible to obtain corresponding data and information in time.Therefore,the construction and application of small and medium-sized river prediction and early warning systems should be further improved.This paper presents an analysis and discussion on flood forecasting and early warning systems for small and medium-sized rivers in detail,and corresponding strategies to improve the effect of forecasting and early warning systems are proposed.

Construction of Forecast and Early Warning System of Meteorological and Geological Disasters in Qinghai Province

Based on the meteorological and geological disaster data, ground observation data set, CLDAS grid point data set, and EC, BJ and other model product data during 2008-2020, the temporal and spatial distribution characteristics of meteorological and geological disasters and precipitation were analyzed, and the causes of the occurrence of meteorological geological disasters and the deviation of model precipitation forecast were revealed. Besides, an objective precipitation forecast system and a forecast and early warning system of meteorological and geological disasters were established. The results show that meteorological and geological disasters and precipitation were mainly concentrated from May to October, of which continuous precipitation appeared frequently in June and September, and convective precipitation was mainly distributed in July-August;the occurrence frequency of meteorological and geological disasters was basically consistent with the distribution of accumulated precipitation and short-term heavy precipitation, and they were mainly concentrated in the southern and eastern parts of Qinghai. Meteorological and geological disasters were basically caused by heavy rain and above, and meteorological and geological disasters were divided into three types: continuous precipitation(type I), short-term heavy precipitation(type II) and mixed precipitation(type III). For type I, the early warning conditions of meteorological and geological disasters in Qinghai are as follows: if the soil volumetric water content difference between 0-10 and 10-40 cm is ≤0.03 mm^(3)/mm^(3), or the soil volumetric water content at one of the depths is ≥0.25 mm^(3)/mm^(3), the future effective precipitation reaches 8.4 mm in 1 h, 10.2 mm in 2 h, 11.5 mm in 3 h, 14.2 mm in 6 h, 17.7 mm in 12 h, and 18.2 mm in 24 h, and such warning conditions are mainly used in Yushu, Guoluo, southern Hainan, southern Huangnan and other places. For type II, when the future effective precipitation is up to 11.5 mm in 1 h, 14.9 mm in 2 h, 16.2 mm in 3 h, 19.9 mm in 6 h, 25.3 mm in 12 h, and 26.3 mm in 24 h, such precipitation thresholds are mainly used in Hainan, Huangnan, and eastern Guoluo;as it is up to 13.3 mm in 1 h, 15.5 mm in 2 h, 16.6 mm in 3 h, 19.9 mm in 6 h, 31.1 mm in 12 h, and 34.0 mm in 24 h, such precipitation thresholds are mainly used in Hehuang valley. The precipitation thresholds of type III are between type I and type II, and closer to that of type II;such precipitation thresholds are mainly used in Hainan, Huangnan, and northern Guoluo. The forecasting ability of global models for heavy rain and above was not as good as that of mesoscale numerical prediction model, and global models had a wet bias for small-scale precipitation and a dry bias for large-scale precipitation;meso-scale models had a significantly larger precipitation bias. The forecast ability of precipitation objective forecast system constructed by frequency matching and multi-model integration has improved. At the same time, the constructed grid forecast and early warning system of meteorological and geological disasters is more precise and accurate, and is of instructive significance for the forecast and early warning of meteorological and geological disasters.

Classified Early Warning and Forecast of Severe Convective Weather Based on LightGBM Algorithm

Severe convective weather can lead to a variety of disasters, but they are still difficult to be pre-warned and forecasted in the meteorological operation. This study generates a model based on the light gradient boosting machine (LightGBM) algorithm using C-band radar echo products and ground observations, to identify and classify three major types of severe convective weather (i.e., hail, short-term heavy rain (STHR), convective gust (CG)). The model evaluations show the LightGBM model performs well in the training set (2011-2017) and the testing set (2018) with the overall false identification ratio (FIR) of only 4.9% and 7.0%, respectively. Furthermore, the average probability of detection (POD), critical success index (CSI) and false alarm ratio (FAR) for the three types of severe convective weather in two sample sets are over 85%, 65% and lower than 30%, respectively. The LightGBM model and the storm cell identification and tracking (SCIT) product are then used to forecast the severe convective weather 15 - 60 minutes in advance. The average POD, CSI and FAR for the forecasts of the three types of severe convective weather are 57.4%, 54.7% and 38.4%, respectively, which are significantly higher than those of the manual work. Among the three types of severe convective weather, the STHR has the highest POD and CSI and the lowest FAR, while the skill scores for the hail and CG are similar. Therefore, the LightGBM model constructed in this paper is able to identify, classify and forecast the three major types of severe convective weather automatically with relatively high accuracy, and has a broad application prospect in the future automatic meteorological operation.

Discussion on Earthquake Forecasting and Early Warning

Through analysis of natural and social attributes of earthquake forecasting,the relationship between the natural and social attributes of earthquake forecasting(early warning)has been discussed.Regarding the natural attributes of earthquake forecasting,it only attempts to forecast the magnitude,location and occurrence time of future earthquake based on the analysis of observational data and relevant theories and taking into consideration the present understanding of seismogeny and earthquake generation.It need not consider the consequences an earthquake forecast involves,and its purpose is to check out the level of scientific understanding of earthquakes.In respect of the social aspect of earthquake forecasting,people also focus on the consequence that the forecasting involves,in addition to its natural aspect,such as the uncertainty of earthquake prediction itself,the impact of earthquake prediction,and the earthquake resistant capability of structures(buildings),lifeline works,etc.In a word,it highlights the risk of earthquake forecasting and tries to mitigate the earthquake hazard as much as possible.In this paper,the authors also discuss the scientific and social challenges faced in earthquake prediction and analyze preliminarily the meanings and content of earthquake early warning.

Study on the Analysis System of Meteorological and Geological Disaster Grades Early Warning of WebGIS

The method of realizing meteorological and geological disaster forecast and early warning information system in Qixian County of Shanxi Province was studied. According to meteorological factor parameter of the geological disasters for many years in Qixian County Land Bureau, and data of average meteorological element parameters that corresponded by evolution of geological disaster region each year, evaluation algorithm model of the forecast and eady warning of geological disasters in Qixian County was obtained, and the database of meteorological factor of geological disaster was established. On this basis, WebGIS geographic information system based on C/S and B/S which could real-time monitor geological disaster region was studied and designed. After data analysis system could evaluate the geological disaster forecast and early warning and make the real-time and accurate early warning, which greatly improved the timeliness of meteorological geological disasters forecast and early warning in Qixian County. System set of data processing and forecast and early warning issued in one, to realize the dynamic information release function of grade of geological disaster early warning, and had the characteristics of easy sharing, real-time dynamic update.

Current status and challenges of typhoon forecasting and warning systems in China

China is one of the countries most severely suffering from tropical cyclones. The exact and timely forecasting and warning is of significant importance in fighting against tropical cyclones and mitigating their impacts on China. The numerical weather prediction(NWP)system for tropical cyclone rainfall and strong wind is going to play a more and more important role. There is also a need for timely and user friendly modern warning services in order to provide the governments and relevant authorities at all levels and general public with typhoon forecasts and information about the associated disasters and response strategy services.

Development and Application of Meteorological Disaster Monitoring and Early Warning Platform for Characteristic Agriculture in Huzhou City Based on GIS

Based on the needs of characteristic agricultural production for meteorological services in Huzhou City,we use C# programming language to develop the meteorological disaster monitoring and early warning platform for characteristic agriculture in Huzhou City. This platform integrates the functions of meteorological and agricultural information monitoring,disaster identification and early warning,fine weather forecast product display,and data query and management,which effectively enhances the capacity of meteorological disaster monitoring and early warning for characteristic agriculture in Huzhou City,and provides strong technical support for the meteorological and agricultural departments in the agricultural meteorological services.

城市内涝预警预报系统研发及应用

城市内涝问题具有成因多元、影响面广的特点,是基础性社会问题。分析了城市内涝的成因,提出通过信息化手段提升城市内涝灾害应对能力。构建基于内涝实时物联感知、数据汇聚、数学模型预报、大数据分析、水利数字孪生的城市内涝预警预报系统,并在宁波市进行了应用。通过搭建“天基”监测预警、“落地雨”监测预警、内涝预报预警、内涝成因分析、内涝影响评估等功能模块,实现了城区内涝的精准模拟、风险图的在线绘制以及内涝损失的高效评估,有力支撑了城市暴雨内涝灾害预报预警,提升了城市内涝风险管控能力。

推动长江经济带高质量发展的水文实践与思考

长江流域经济社会高质量发展离不开水文的强有力支撑。近年来,长江流域水文站网布局与功能日趋完善,水文监测自动化水平不断提高,水文预报能力进一步提升,水文信息处理智慧化水平逐步提高,对水旱灾害防御、水资源配置调度、水生态保护修复等支撑作用更加凸显,水文现代化发展成效较为显著。当前,推动长江经济带高质量发展对水文提出新的更高要求,通过分析研判当前面临的新形势新要求,提出了下一步工作重点。

输电线路气象风险精细化建模及气象灾害的在线预警防御策略

气象因素在输电线路稳定运行中起着重要作用,在评估风险时应予以考虑。为此,提出一种输电线路气象风险预警及防护方法。该方法考虑时空发电预测、设备健康和可靠性评估以及概率负荷预测等方面对气象风险进行精细化建模,并基于气象危害、电网脆弱性和灾后恢复成本提出一种新的风险度量标准。此外,针对负荷中断恢复以及缓解用电拥堵,提出一种气象灾害的在线预警防御策略。最后,在案例中对所提出的方法和策略进行测试分析,其结果可以验证方法和策略的有效性。

煤矿冲击危险预测与监测预警技术发展现状

随着矿井生产强度与开采深度的日益加大,煤炭资源开采逐步向深部、西部转移,致使冲击地压灾害普遍化,成为威胁中国井工煤矿开采的主要灾害之一。从预测预报与监测预警两方面,重点阐述5种预测预报技术、7种监测预警技术的发展现状,以及冲击危险指标构建以及综合监测体系与平台构建,论述冲击危险识别技术在实际应用中存在的问题,提出新技术创新发展思路,并展望其总体发展趋势。

基于LSTM神经网络的基坑工程智能预警系统研发与应用

基坑开挖过程中伴随有支护结构及周围岩土体的受力和形变状态的改变,因此在工程建设中对基坑进行监测十分必要。为解决基坑监测智能化程度低、可视化程度低、预警精确度较低导致频繁报警、监测数据更新共享速度慢等问题,采用B/S模式、Vue前端、C#语言后端、SQLServer2012数据库等并嵌入python语言编写长短期记忆(Long Short-Term Memory,LSTM)神经网络算法模型开发一套基坑智能预测预警系统。该系统实现了信息集中管理、数据存储与查看、数据算法自动计算、自动绘制图表、自动报警预警、快速生成报警报告等功能。通过在苏州某地铁基坑开挖过程的应用,证明了本系统能够综合利用监测预警与基于LSTM神经网络模型的超前预测预警两种预警模式为施工人员准确掌握基坑开挖过程中支护结构及周围土体变形情况提供技术支持与保障,具有很强的现实使用意义。

浙江省地质灾害气象风险预警一体化建设的探索与实践

地质灾害预报预警是有效防范地质灾害、减少人员伤亡的重要手段。自2003年开始实施以来,全国有80%的山区省、市、县开展了地质灾害气象预警工作。但这种省、市、县独立建设模式难以满足部省协同、市县联动、全域贯通的业务需求。文章介绍了浙江省地质灾害气象风险预警一体化建设:按照“省级预报到县、市级预报到乡、县级预警到村(点)”的工作原则,在趋势预测、每日预报、短临预报和实时预警与地质灾害防治业务一体化融合方面开展探索,初步建成了以“地灾智治”应用驾驶舱为可视化监管,以“地灾智防”APP为闭环手段的地质灾害全生命周期管控防治业务体系,形成了具有浙江特色的地质灾害预报预警数字化产品体系。

基于时序数据库的海洋观测预报平台研发

为满足信息化时代海洋观测预报和防灾减灾工作需求,利用实时数据采集、时序数据库、三维可视化等技术研发了海洋观测预报预警三维可视化平台。该平台可实时掌握潮位、海浪、海流等观测和预报信息,实现海洋数据的三维动态渲染,并利用可视化技术进行展示。将发布的风暴潮、海浪专报和预警报与台风等海洋自然灾害历史数据进行对比分析,为海洋风险分析与灾害预警提供决策依据。该平台已在深圳市成功落地,实时接入和管理海洋观测预报数据近20亿条,为深圳市水文、气象、应急等部门提供了海洋预警报与灾害防治信息化支撑。

禽流感病毒检测方法及致病机理研究进展

为了解掌握禽流感最新致病机理研究进程及检测技术研究成果,该文对2020—2023年国内外禽流感基因芯片检测技术、多重实时RT-PCR检测技术等病原学检测技术和竞争性酶联免疫吸附法、阻断ELISA抗体检测方法、胶体金检测方法等血清学检测方法以及对最新的禽流感致病机制机理进行了综述,旨在为快速准确诊断禽流感疫病,预警预报,有效防控禽流感疫情的发生提供可靠方法。

下水道管网有毒有害气体智能监测系统设计研究

多种有害气体监测分析系统,实现了对下水道管网多组分有毒有害气体浓度的实时监测,对浓度进行智能分级预警预报并可实现联动应急处置。同时,该智能系统实现了监测数据采集后的分析与应用,解决了下水道管网有毒有害气体的监管问题。为此,文章研究的系统设计主要在于开发设计多种有害气体的浓度实时监测、分级预报警及报警应急处置等智能功能,指导管网清掏及风险预防工作,从而提升环境质量。

生态环境监测在生态环境保护中的作用探析

本文探讨了生态环境监测在生态环境保护中的重要作用。首先,介绍了生态环境监测的定义和目标,详细阐述了它在监测与评估、预警与预报、政策制定与实施以及公众参与等方面的作用。然后,列举了一些生态环境监测的实际案例,并分析了其对生态环境保护的贡献。最后,总结了生态环境监测的意义,并提出了未来发展的建议。

Review of big-data and AI application in typhoon-related disaster risk early warning in Typhoon Committee region

ESCAP/WMO Typhoon Committee Members are directly or indirectly affected by typhoons every year.Members have accumulated rich experiences dealing with typhoons'negative impact and developed the technologies and measures on typhoon-related disaster risk forecasting and early warning in various ways to reduce the damage caused by typhoon.However,it is still facing many difficulties and challenges to accurately forecast the occurrence of typhoons and warning the potential impacts in an early stage due to the continuously changing weather conditions.With the development of information technology(IT)and computing science,and increasing accumulated hydro-meteorological data in recent decades,scientists,researchers and operationers keep trying to improve forecasting models based on the application of big data and artificial intelligent(AI)technology to promote the capacity of typhoon-related disaster risk forecasting and early warning.This paper reviewed the current status of application of big data and AI technology in the aspect of typhoon-related disaster risk forecasting and early warning,and discussed the challenges and limitations that must be addressed to effectively harness the power of big data and AI technology application in typhoon-related disaster risk reduction in the future.

Applicability of Galway River Flow Forecasting and Modeling System (GFFMS) for Lake Tana Basin, Ethiopia

Flow forecasting is used in activities requiring stream flow data such as irrigation development, water supply, and flood control and hydropower development. Real time flow forecasting with special interest to flooding is one of the most important applications of hydrology for decision making in water resources. In order to meet flood and flow forecasts using hydrological models may be used and subsequently be updated in accordance with residuals. Therefore in this study, different flood forecasting methods are evaluated for their potential of stream flow forecasting using Galway River Flow Forecasting and Modeling System (GFFMS) in Lake Tana basin, upper Blue Nile basin, Ethiopia. The areal rainfall and temperature data was used for the model input. Three forecast updating methods, i.e., autoregressive (AR), linear transfer function (LTF) and neuron network updating (NNU) methods were compared for stream flow forecasting, at one to six days lead time. The most sensitive parameters were fine-tuned first and modeled for a calibration period of 1994-2004 for three selected watersheds of the Tana basin. The results indicate that with the exception of the simple linear model, an acceptable result could be obtained using models embedded in the software. Artificial neural network model performed well for Gilgel Abay (NSE = 0.87) and Gumara (NSE = 0.9) watersheds but for Megech watershed, SMAR model (NSE = 0.78) gave a better forecast result. In capturing the peak flows LTF and NNU in forecast updating mode performed better for Gilgel Abay and Megech watersheds, respectively. The results of this study implied that GFFMS can be used as a useful tool to forecast peak stream flows for flood early warning in the upper Blue Nile basin.

Forecast Service of An Extreme Rainstorm Process in Bayannur City,Inner Mongolia in 2022

On August 1,2022,a rainstorm process occurred in Bayannur City,Inner Mongolia.An extreme precipitation event occurred in Wuyuan County,Urat Middle Banner and Urat Front Banner,causing rainstorm,flood,strong convective wind and other disasters,thereby resulting in crop damage,livestock death and other losses.Meteorological departments made a series of forecast and early warning,meteorological service and basin joint prevention for the rainstorm process,set an example for dealing with the rainstorm disaster,and accumulated experience for the forecast service of rainstorm in future.