scholarly journals Influence of Topographic Characteristics on the Adaptive Time Interval for Diffusion Wave Simulation

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 431
Author(s):  
Pin-Chun Huang ◽  
Kwan Lee ◽  
Boris Gartsman
Keyword(s):  
Flood Control ◽  
Temporal Distribution ◽  
Living Environment ◽  
Mitigation Measures ◽  
Computational Time ◽  
Time Interval ◽  
Flood Warning ◽  
Urgent Task ◽  
Research Findings ◽  
Flood Warning Systems

Frequent flash floods in recent years have resulted in a major impact on the living environment, urban planning, economic system and flood control facilities of residents around the world; therefore, the establishment of disaster management and flood warning systems is an urgent task, required for government units to propose flood mitigation measures. To conserve the numerical accuracy and maintain stability for explicit scheme, the Courant–Friedrich–Lewy (CFL) condition is necessarily enforced, and it is conducted to regulate the relation between the numerical marching speed and wave celerity. On the other hand, to avoid the problem of flow reflux between adjacent grids in executing 2D floodplain simulation, another restriction on time intervals, known as the Hunter condition, was devised in an earlier study. The objective of this study was to analyze the spatial and temporal distribution of these two time-interval restrictions during runoff simulations. Via a case study of the Komarovsky River Basin in Russia, the results show that at the beginning of a storm, the computational time interval is restricted by the CFL condition along the upstream steep hillsides, and the time interval is subject to the Hunter condition in the mainstream during the occurrence of the main storm. The reason of a reduction in computational efficiency, which is a common problem in conducting distributed routing, was clearly explained. To relax the time-interval restrictions for efficient flood forecasting, the research findings also indicate the importance of integrating modified hydrological models proposed in recent studies.

scholarly journals An Application of Mathematical Programming to Floodplain Land use Planning

1974 ◽  
Vol 3 (2) ◽  
pp. 217-228 ◽  
Author(s):  
Joseph Smiarowski ◽  
Cleve Willis ◽  
John Foster
Keyword(s):  
Land Use ◽  
Mathematical Programming ◽  
Land Use Planning ◽  
Flood Control ◽  
Control Measures ◽  
Floodplain Management ◽  
Warning Systems ◽  
Flood Warning ◽  
Programming Techniques ◽  
Flood Warning Systems

The motivation of this study is to provide and solve a decision model for land use planning for an existing regional situation. The chosen framework views the task of floodplain management as a constrained optimization problem capable of solution by standard mathematical programming techniques. The framework is sufficiently flexible to permit incorporation of, in addition to floodplain zoning, such non-structural flood control measures as flood proofing, insurance, and flood warning systems. Further, the framework can be modeled so as to include political restraints and a broad range of socially desirable goals. In a larger regional context, the framework permits the internalization of the value of the externality commonly associated with development of floodplain lands.

Simulation of karst floods with a hydrological model improved by meteorological model coupling

2021 ◽  
Keyword(s):  
Flood Control ◽  
Underground Structure ◽  
Wrf Model ◽  
Precipitation Forecast ◽  
Karst Area ◽  
Peak Time ◽  
Flood Prediction ◽  
Meteorological Model ◽  
Flood Peak ◽  
Flood Warning

Abstract Karst basins are prone to rapid flooding because of their geomorphic complexity and exposed karst landforms with low infiltration rates. Accordingly, simulating and forecasting floods in karst regions can provide important technical support for local flood control. The study area, the Liujiang karst river basin, is the most well-developed karst area in South China, and its many mountainous areas lack rainfall gauges, limiting the availability of precipitation information. Quantitative precipitation forecast (QPF) from the Weather Research and Forecasting model (WRF) and quantitative precipitation estimation (QPE) from remote sensing information by an artificial neural network cloud classification system (PERSIANN-CCS) can offer reliable precipitation estimates. Here, the distributed Karst-Liuxihe (KL) model was successfully developed from the terrestrial Liuxihe model, as reflected in improvements to its underground structure and confluence algorithm. Compared with other karst distributed models, the KL model has a relatively simple structure and small modeling data requirements, which are advantageous for flood prediction in karst areas lacking hydrogeological data. Our flood process simulation results suggested that the KL model agrees well with observations and outperforms the Liuxihe model. The average Nash coefficient, correlation coefficient, and water balance coefficient increased by 0.24, 0.19, and 0.20, respectively, and the average flood process error, flood peak error, and peak time error decreased by 13%, 11%, and 2 hours, respectively. Coupling the WRF model and PERSIANN-CCS with the KL model yielded a good performance in karst flood simulation and prediction. Notably, coupling the WRF and KL models effectively predicted the karst flood processes and provided flood prediction results with a lead time of 96 hours, which is important for flood warning and control.

Street Level Flood Monitoring and Warning System, a conceptual model using IoT: A Case of Surat City

2021 ◽  
Vol 14 (12) ◽  
pp. 55-65
Author(s):  
Anant Patel ◽  
Sanjay Yadav
Keyword(s):  
Developing Countries ◽  
Water Level ◽  
Warning System ◽  
Water Levels ◽  
Mitigation Measures ◽  
Developed Country ◽  
River Bank ◽  
Street Level ◽  
Flood Warning ◽  
Flood Monitoring

Most of the natural disasters are unpredictable, but the most frequent occurring catastrophic event over the globe is flood. Developing countries are severely affected by the floods because of the high frequencies of floods. The developing countries do not have good forecasting system compared to the developed country. The metro cities are also settled near the coast or river bank which are the most vulnerable places to floods. This study proposes plan for street level flood monitoring and warning system for the Surat city, India. Waterlogging happens in the low lying area of the Surat city due to heavy storm and heavy releases from the Ukai dam. The high releases from upstream Ukai dam and heavy rainfall resulted into flooding in the low lying area of the Surat city. This research proposed a wireless water level sensor network system for the street water level flood monitoring. The system is proposed to monitor the water levels of different areas of city through the wireless water level sensors as well as to capture live photos using CCTV camera. This will help authority not only to issue flood warning but also to plan flood mitigation measures and evacuation of people.

The East Coast and London tidal flood warning systems

1972 ◽  
Vol 272 (1221) ◽  
pp. 173-178 ◽  
Keyword(s):  
At Risk ◽  
East Coast ◽  
Warning System ◽  
Warning Systems ◽  
Tidal Flooding ◽  
Thames Estuary ◽  
Flood Warning ◽  
Physical Setting ◽  
Flood Warning System ◽  
Flood Warning Systems

Disastrous tidal flooding on the East Coast of England in 1953 was followed by the setting up of a flood warning system for the East Coast, and led to consideration being given to the feasibility of excluding dangerous surges from London by the construction of a tidal barrier across the Thames. Frequency estimates in connexion with the latter led in turn to the introduction of an improved warning system for London in 1968. This paper describes the physical setting and the nature of surges on the East Coast and in the Thames estuary, and the means used to forecast them; and refers to supporting investigational work. It discusses the means of disseminating warnings to those at risk and concludes by attempting to foresee how the system might develop.

scholarly journals Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine

2020 ◽  
Vol 9 (10) ◽  
pp. 580 ◽  
Author(s):  
Maria Antonia Brovelli ◽  
Yaru Sun ◽  
Vasil Yordanov
Keyword(s):  
Machine Learning ◽  
Forest Dynamics ◽  
Google Earth ◽  
Classification Model ◽  
Mitigation Measures ◽  
Computational Time ◽  
Forest Change ◽  
Machine Learning Classification ◽  
Cloud Processing ◽  
Google Earth Engine

Deforestation causes diverse and profound consequences for the environment and species. Direct or indirect effects can be related to climate change, biodiversity loss, soil erosion, floods, landslides, etc. As such a significant process, timely and continuous monitoring of forest dynamics is important, to constantly follow existing policies and develop new mitigation measures. The present work had the aim of mapping and monitoring the forest change from 2000 to 2019 and of simulating the future forest development of a rainforest region located in the Pará state, Brazil. The land cover dynamics were mapped at five-year intervals based on a supervised classification model deployed on the cloud processing platform Google Earth Engine. Besides the benefits of reduced computational time, the service is coupled with a vast data catalogue providing useful access to global products, such as multispectral images of the missions Landsat five, seven, eight and Sentinel-2. The validation procedures were done through photointerpretation of high-resolution panchromatic images obtained from CBERS (China–Brazil Earth Resources Satellite). The more than satisfactory results allowed an estimation of peak deforestation rates for the period 2000–2006; for the period 2006–2015, a significant decrease and stabilization, followed by a slight increase till 2019. Based on the derived trends a forest dynamics was simulated for the period 2019–2028, estimating a decrease in the deforestation rate. These results demonstrate that such a fusion of satellite observations, machine learning, and cloud processing, benefits the analysis of the forest dynamics and can provide useful information for the development of forest policies.

Lessons Learned From the Higher Risk Perception and the Efficient Flood Mitigation in Haji-Abad, Golestan Province, Iran, 2019

2020 ◽  
pp. 1-5
Author(s):  
Mohammad Heidari ◽  
Nasrin Sayfouri ◽  
Samaneh Heidari
Keyword(s):  
Risk Perception ◽  
Thematic Analysis ◽  
Flood Control ◽  
Geographical Location ◽  
Lessons Learned ◽  
Mitigation Measures ◽  
Healthcare Center ◽  
Golestan Province ◽  
Authentic Data ◽  
Appropriate Education

ABSTRACT Objective: In the present study, the factors inducing the successful immediate mitigation measures and other activities at Haji-Abad village in Golestan Province, Iran, were scrutinized. Methods: To find authentic data, information was gathered from a variety of sources, including mass media documents and interviews with the related Health House attendant (Behvarz) at Haji-Abad and the disaster liaison at the Rural-Urban Healthcare Center, both of whom were among the residents. A thematic analysis was performed on the transcriptions. Results: The findings showed that apart from the favorable geographical location of the area, appropriate education as well as vast family kinship among the residents were the major causes that induced high-risk perception, adequate collaboration and coordination among the residents and between them and the local authorities, and the women's active participation; these major effects, in turn, helped provide all of the efficient mitigation measures leading to the flood control. Conclusion: It is recommended that opportunities for people's collaboration in preparedness, mitigation measures, and resilience during the occurrence of disasters be arranged by means of providing related inclusive operational education prior to the incidents. This can simultaneously generate risk perception and help people assume themselves as the owners of the disasters.

Developing a variable time-interval continuous verification method for ensemble flood forecasts

2020 ◽  
Author(s):  
Schalk Jan van Andel
Keyword(s):  
Flood Event ◽  
False Alarms ◽  
Time Interval ◽  
Forecast Verification ◽  
Time Intervals ◽  
Verification Method ◽  
Variable Time ◽  
Equal Interval ◽  
Flood Warning ◽  
Variable Duration

<p>Most continuous verification metrics for hydrometeorological forecasts are based on equal interval forecasts and observations (e.g. daily, 6-hourly, etc.). For some purposes of verification, however, it might be more beneficial to have variable time intervals that take into account the duration of events, e.g. rainfall or flood event (or discharge exceeding a flood warning threshold). Such verification, however, is challenged by defining the length of the non-event intervals for scoring correct rejections and false alarms, needed for continuous verification.  The work presented here suggests how to approach this challenge and presents verification results of a continuous forecast verification method that take into account variable duration of events.</p>

Probability distributions for flood warning systems

1994 ◽  
Vol 30 (4) ◽  
pp. 1145-1152 ◽  
Author(s):  
Karen S. Kelly ◽  
Roman Krzysztofowicz
Keyword(s):  
Probability Distributions ◽  
Warning Systems ◽  
Flood Warning ◽  
Flood Warning Systems
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