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.

Introduction to marine emergency forecasting and early-warning system(MEFES)

Marine emergencies especially oil spill may bring irreversible harm to the marine environment,and will cause immeasurable economic losses.In recent years,the demand for crude oil is increasing year by year in China with the high-speed economic development,leading to the high risk of marine oil spill.Therefore,it is necessary that promoting emergency response on marine oil spill in China and improving oil spill forecasting and early-warning techniques.This paper introduces the Marine Emergency Forecasting and Early-warning System(MEFES)developed by National Marine Data and Information Service(NMDIS).The system consists of one database,two modelling subsystems and a GIS platform.The database is the marine emergency database,and two subsystems include the marine environmental forecasting subsystem and the oil spill behaviour forecasting subsystem.MEFES has been applied in the emergency response of some major oil spill accidents occurred in recent years.The operational applications of the system can provide some theoretical basis and reference for marine oil spill emergency response.

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 modem 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.

Characteristics Analysis and the Early-warning Service System of Heavy Fog in Chizhou City

By analyzing the heavy fog data in Chizhou City in recent 50 years(1959-2007),the general rules of meteorological elements variations were found when the heavy fog happened.The meteorological elements included the temperature,humidity,wind direction,wind speed,air pressure and so on.The conceptual models of high-altitude and ground situation were established when the heavy fog happened in Chizhou City.Based on considering sufficiently the special geographical environment in Chizhou City,we found the key factors which affected the local heavy fog via the relative analyses.By using the statistical forecast methods which included the second-level judgment method and regression method of event probability and so on,the forecast mode equation of heavy fog was established.Moreover,the objective forecast system of heavy fog in Chizhou City was also manufactured.It provided the basis and platform which could be referred for the heavy fog forecast,service and the release of early-warning signal.

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.

Overview of an early warning system for Glacial Lake outburst flood risk mitigation in Dudh-Koshi Basin, Nepal

Natural disasters inflict severe damage on almost the entire spectrum of social and natural habitats. This ranges from housing and shelter, water, food, health, sanitation to information and communication networks, supply of power and energy,transportation infrastructure, and others. Nepal is a risk prone country for Glacial Lake Outburst Flood(GLOF). GLOFs exist as major challenges as they repeatedly cause a heavy toll of life and property. During such a disaster, major challenges are indeed the protection of life, property and vital life-supporting infrastructure. Any delay or laxity in disaster relief can escalate the magnitude of distress for the victims. Thus, rather than trying to take curative measures, it is better to minimize the impacts of GLOF. These measures subsequently help in reducing the magnitude of death and casualties due to a GLOF event. This reduction of impact is often achieved by optimizing preventive measures. For applying necessary deterrent measures, it is essential to disseminate information about the danger beforehand. Early Warning System(EWS) is an important step for such information dissemination for GLOF disaster management and helps to anticipate the risk of disaster and disseminate information to lives at risk. It is impossible and impractical to reduce all GLOF risks, but it is possible to reduce several impacts of a GLOF through the implementation of the EWS. This paper presents the design and implementation of an EWS for monitoring potential outbursts of a glacier lake in the Dudh-Koshi Basin, Nepal.

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.

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.

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.

Energy-Efficient Scheduling for a Cognitive IoT-Based Early Warning System

Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response.Cognitive Internet of things(CIoT)technologies including inherent characteristics of cognitive radio(CR)are potential candidates to develop a monitoring and early warning system(MEWS)that helps in efficiently utilizing the short response time to save lives during flash floods.However,most CIoT devices are battery-limited and thus,it reduces the lifetime of the MEWS.To tackle these problems,we propose a CIoTbased MEWS to slash the fatalities of flash floods.To extend the lifetime of the MEWS by conserving the limited battery energy of CIoT sensors,we formulate a resource assignment problem for maximizing energy efficiency.To solve the problem,at first,we devise a polynomial-time heuristic energyefficient scheduler(EES-1).However,its performance can be unsatisfactory since it requires an exhaustive search to find local optimum values without consideration of the overall network energy efficiency.To enhance the energy efficiency of the proposed EES-1 scheme,we additionally formulate an optimization problem based on a maximum weight matching bipartite graph.Then,we additionally propose a Hungarian algorithm-based energy-efficient scheduler(EES-2),solvable in polynomial time.The simulation results show that the proposed EES-2 scheme achieves considerably high energy efficiency in the CIoT-based MEWS,leading to the extended lifetime of the MEWS without loss of throughput performance.

Response of Flash Flood Early Warning Critical Rainfall to Storm Pattern in South Branch of Censhui Watershed

Critical rainfall estimation for early warning of rainstorm-induced flash flood is an inverse rainstorm-runoff process based on warning discharge threshold for a warning station of interest in a watershed. The key aspects of critical rainfall include rainfall amount and rainfall duration. Storm pattern affects highly the estimation of critical rainfall. Using hydrological modeling technique with detailed sub-basin delineation and manual for design rainstorm-runoff computation, this study first introduced basic concept and analysis methods on critical rainfall for flash flood early warning, then, investigated the responses of flash flood warning critical rainfall to storm pattern. Taking south branch of Censhui watershed in China as an example, critical rainfall in case of typical storm patterns for early warning of rainstorm-induced flash flood were estimated at 3 warning stations. This research illustrates that storm pattern plays important role in the estimation of critical rainfall and enough attention should also be paid to storm pattern when making a decision on whether a warning to be issued or not.

Critical Rainfall Analysis on Flash Flood Early Warning for South Branch of Censhui Watershed

Critical rainfall for flash flood early warning is a converse result of precipitation-runoffprocess based on warning discharge threshold for a warning station of interest in a watershed; the key aspects of critical rainfall include rainfall amount and rainfall duration Using hydrological modeling technique with detailed sub-basin delineation and manual for design precipitation-runoff computation, this study introduces basic concept and methods of analyzing critical rainfall for flash flood early warning. Taking South Branch of Censhui watershed in China as an example, typical critical rainfalls for flash flood dynamic early warning were estimated for 3 warning stations located in the watershed. This research illustrates that detailed watershed characteristics in the context of several warning stations can be modeled in-depth by further delineating the watershed into smaller sub-basins to simulate spatial distribution of various basin parameters. It further confirms that time of concentration of a watershed is an important factor to rainfall duration determination, and the antecedent soil moisture condition of a watershed has significant impact on critical rainfall for same rainfall duration.

小型水库预报预警系统研发与应用

针对小型水库洪水预报预警研究不足问题,以构建的分布式水文模型、缺资料地区参数移植技术,以及小型水库防洪预警方式与指标体系、预警方案编制等为支撑,利用Java语言、Vue技术、ElementUI组件、EChart组件、Spring Boot、JSON数据交互等技术,设计开发了一套基于B/S架构的标准化、通用型小型水库预报预警系统。介绍了系统的设计模式及功能结构,阐述了基础信息展示、洪水预报、防洪调度、洪水预警、数据管理、基础空间地理分析等主要系统模块的设计,以及系统开发关键技术。以在安徽省的应用为例,展示了系统具备的可靠性、灵活性与实用性。

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。

数字孪生驱动的长江流域干旱防御平台设计与开发

在全球气候变化的背景下,长江流域发生了多次严重的高温干旱灾害,流域抗旱管理面临着旱情监测告警效率较低、旱灾预报预警精度不高、抗旱预案推演能力不足等瓶颈,迫切需要开展数字化转型。从长江流域抗旱减灾业务管理和“四预”应用需求出发,基于智慧水利和数字孪生建设的总体要求,综合运用WebGL、GIS等技术,建立了干旱防御数字孪生平台,研发了遥感干旱监测评估、干旱专业模型动态加载、旱警水位超限预警、抗旱预案可视化等关键技术,初步实现了“预报-预警-预演-预案”全链条贯通业务应用,切实提升了长江流域抗旱管理智能化、精细化水平,为流域干旱防灾减灾提供了技术支撑。

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。

数字孪生水网建设应着力解决的几个关键问题

数字孪生水网建设涉及多学科交叉、新技术融合,有诸多重点和难点,应着力把握好坚持问题导向、夯实算据基础、优化算法核心、强化算力支撑、着力业务应用、加强网络安全等关键问题。结合数字孪生水网建设先行先试经验,围绕工程安全、供水安全、水质安全,提出了数字孪生水网建设的关键问题及解决思路:围绕建设目标确定建设内容和重点,以问题为导向开展需求分析;打造“天空地”一体化监测感知体系,完善地面监测站网、提升新型监测感知能力、强化数据治理与运用、推进数据资源共享夯实算据基础;完善模型平台,开发知识平台,优化算法核心;建设信创云平台和高性能计算集群,扩充计算资源、升级通信网络强化算力支撑;提升水网智慧调控能力,着力实现业务“四预”;构建网络安全体系,保障网络和数据安全;理清数字孪生流域、数字孪生水网、数字孪生工程的关系,提升整体效能。

基于注意力机制的CNN-LSTM-XGBoost台风暴雨电力气象混合预测模型

极端台风暴雨灾害具有非线性、极差大以及多峰值等特点。为使电网及时获取预警信息,提出一种基于注意力机制的CNN-LSTM-XGBoost台风暴雨电力气象混合预测模型。首先,利用基于注意力机制的卷积神经网络(CNN)辨识关键台风暴雨灾害特征;然后,利用长短期记忆网络(LSTM)训练时间序列预测模型以挖掘台风暴雨时序特征,使用极限梯度提升算法替换模型输出层以缓解过拟合问题;最后,以2023年台风泰利为例验证所提方法的有效性。算例分析表明,所提模型具有较高的准确性,对预测精度的提升可达40.84%以上。