The floods in the piedmont plain city with complex underlying surface conditions exhibits characteristics of both mountain and urban floods,posing challenges for hydrological simulation and flood forecasting.In this study,we developed several flood forecasting models based on recurrent neural network variations in the Xiaoqing River Watershed above the Huangtaiqiao Hydrological Station in Jinan City,and assessed its predictive performance.The research findings demonstrate that the constructed flood forecasting model is suitable for forecasting both single flood events and providing continuous predictions for long series of processes.It has the capability to flexibly generate discharge and water level processes,while maintaining a high level of prediction accuracy within a specific forecast step.Among them,the model based on BiGRU(Bidirectional Gate Recurrent Unit)network exhibits the best prediction performance,with the weakest performance degradation as the length of the prediction step increases.Therefore,it can be regarded as a novel approach for riverine flood forecasting in piedmont plain cities.
Real-time and high-precision water level monitoring is crucial for the fields of hydrology, hydraulic engineering, and disaster prevention and control. The most prevalent method for measuring water level is through the use of water level gauges, which can be costly and have limited coverage. In recent years, Global Navigation Satellite System Reflectometry (GNSS-R) technology has emerged as a promising approach for water level monitoring due to its low cost and high coverage. However, a limitation of current GNSS-R technology is the extended time required to record signals, which hinders its potential for real-time application. This paper introduces a novel real-time water level monitoring method based on GNSS dual-antenna attitude measurement and develops a model to invert water level based on baseline vector. This method uses double-difference observations to eliminate errors caused by various factors, such as satellite and receiver clock, and ionospheric and tropospheric delay. To avoid the impact of detecting and correcting cycle slips during real-time operations, a single-epoch calculation method is introduced. In order to verify the stability and reliability of our method, field tests were carried out at Dongshahe Station in Beijing. We obtained water level data with a time resolution of 1 Hz through field experiments. Experimental data collected from 12 May to 8 June 2022 and from 4 July to 8 August 2022 showed good agreement with on-site water gauge measurements, with root mean square errors of 2.77 cm and 2.54 cm, respectively. Experimental results demonstrate that this method can achieve high-precision, high-temporal-resolution water level monitoring.
为提高相关部门针对广州市中心城区暴雨条件下应急管理能力,采用SWMM(Storm Water Management Model)模型,通过现状排水管网等资料建立模型并根据实测降雨及液位监测数据进行参数率定.率定后再通过另一场实测降雨对模型进行验证,证明了所建模型能对该研究区域的排水情况进行较准确地模拟.采用Python语言将建立的模型嵌入ArcGIS,实现城市径流在线实时模拟,建立内涝预警系统,模拟结果直接在ArcGIS中展示,便于制定预警方案.
在系统性全域推进海绵城市建设的背景下,城市内涝的综合治理已经成为我国城市更新和建设的重中之重.以秦皇岛市河东片区为例,提出在高密度老城区采用基于自然的内涝综合解决方案解并同时解决水环境问题,主要内容包括内涝成因分析、分散式绿色雨水基础设施的设计原则及海绵骨干网络构建.
降雨预报在农业、水资源管理、城市规划和自然灾害预警等方面有着至关重要的作用,短时临近预报可以为实时决策提供更为有效的参考信息.对目前降雨短时临近预报的主要研究方法进行了总结,阐述了基于探测数据外推、数值模式、统计学习3种常用方法的最新进展.基于探测数据外推的算法对于短时间和小范围内的降雨预报效果较好.数值模式强调物理过程,能全面分析和模拟大气环流场和降雨系统演变过程.机器学习技术的发展推动了基于统计学习的算法在降雨短时临近预报中的应用,具有广阔的应用前景.
