scholarly journals Evolving Threshold of Flood-Leading Precipitation in a User-Oriented Forecast System Based on the TIGGE Dataset

2021 ◽  
Vol 9 ◽  
Author(s):  
Ziyan Zheng ◽  
Zhongwei Yan ◽  
Jing Chen ◽  
Jiarui Han ◽  
Jiangjiang Xia ◽  
...  
Keyword(s):  
Water Level ◽  
Real Time ◽  
Flood Hazard ◽  
Precipitation Forecast ◽  
Hazard Evaluation ◽  
Forecast System ◽  
Inverse Simulation ◽  
Flood Warning ◽  
Huai River ◽  
Probability Of Precipitation

Specific users play a key role in interactive forecast systems through user-oriented information (UOI). For hydrological users, a key component of the user-oriented forecast system (UOFS) is to determine the threshold of flood-leading precipitation (TFLP) as a target of the forecast by considering the decision-making information at the user end. This study demonstrates a novel way of simulating TFLP via the inverse simulation of a hydrological model, combined with the flood hazard assessment in the upper reaches of the Huai River Basin controlled by the Wang Jiaba (WJB) hydrological station. The flood hazard, defined as the probability of precipitation beyond the daily evolving TFLP for the next day, was evaluated by using the THORPEX Interactive Global Grand Ensemble (TIGGE) datasets, including 162 members retrieved from 5 TIGGE archive centers. Having integrated the real-time monitored water level (as the UOI) into the UOFS, we applied it to the flood season of 2008 as a case study to evaluate the flood hazard generated by the UOFS for the WJB sub-basin. The simulated TFLP corresponded well with the gap between the monitored and warning water level. The predicted flood hazard probability showed good agreement with the first two flood peaks at 100% accuracy, while exceeding 60% accuracy for the third flood event in that season. Thus, the flood hazard could be better quantified via integration of the forecasted flood-leading precipitation. Overall, this study highlights the usefulness of a UOFS coupled with interactive UOI of real-time water level to determine the dynamical TFLP for flood hazard evaluation with ensemble precipitation forecast. The early flood warning which resulted from such integrated UOFS is directly applicable to operational flood prevention and mitigation.

scholarly journals Design and Implementation of Real-Time Flood Early Warning System (FEWS) Based on IoT Blynk Application

ELKHA ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 113
Author(s):  
Hasbi Nur Prasetyo Wisudawan
Keyword(s):  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Disaster Risk ◽  
Disaster Mitigation ◽  
Average Temperature ◽  
Flood Warning ◽  
Flood Warning System

Disaster occurrence in Indonesia needs attention and role from all parties including the community to reduce the risks.  Disaster mitigation is one of the ways to reduce the disaster risk through awareness, capacity building, and the development of physical facilities, for example by applying disaster mitigation technology (early warning system, EWS). EWS is one of the effective methods to minimize losses due to disasters by providing warning based on certain parameters for disasters which usually occur such as floods. This research promotes a real-time IoT-based EWS flood warning system (Flood Early Warning System, FEWS) using Arduino and Blynk as well as Global System for Mobile Communication network (GSM) as the communication medium. The steps for implementing FEWS system in real locations are also discussed in this paper. Parameters such as water level, temperature, and humidity as well as rain conditions that are read by the EWS sensor can be accessed in real-time by using android based Blynk application that has been created. The result of the measurement of average temperature, humidity, and water level were 28.6 oC, 63.7 %, and 54.5 cm. Based on this analysis, the parameters indicated that the water level is in normal condition and there are no signs indicating that there will be flooding in the 30 days observation.  Based on the data collected by the sensor, FEWS can report four conditions, namely Normal, Waspada Banjir (Advisory), Siaga Banjir (Watch), and Awas Banjir (Warning) that will be sent immediately to the Blynk FEWS application user that has been created.

Development and Testing of a Low-cost Water Level Sensor and a Mobile based Real-time Flood Warning System for the Flash Flood Prone North Eastern part of Bangladesh

2020 ◽  
Vol 10 ◽  
Author(s):  
Nova Ahmed ◽  
Md. Sirajul Islam ◽  
Sifat Kalam ◽  
Farzana Islam ◽  
Nabila Chowdhury ◽  
...  
Keyword(s):  
Water Level ◽  
Real Time ◽  
Flash Flood ◽  
Low Cost ◽  
Warning System ◽  
Outdoor Environment ◽  
Flood Warning ◽  
North Eastern ◽  
Flood Warning System ◽  
North Eastern Part

Background: The North-Eastern part of Bangladesh is suffering from flash flood very frequently, causing colossal damage to life and properties, especially the vast croplands. A distributed sensing system can monitor the water level on a continuous basis to warn people near the riverbank beforehand and reduce the damage largely. However, the required communication infrastructure is not available in most of the remote rural areas in a developing country like Bangladesh. Objective: This study intends to develop a low-cost sensor based warning system, customizing to the Bangladesh context. Method: The system utilizes a low-cost ultrasound based sensor device, a lightweight mobile phone based server, low-cost IoT sensing nodes, and a central server for continuous monitoring of river stage data along with the provision of storage and long-term data analytics. Results: A flash flood warning system developed afterward with the sensors, mobile-based server, and appropriate webbased interfaces. The device was tested for some environmental conditions in the lab and deployed it later in the outdoor conditions for short-term periods. Conclusion: Overall, the warning system performed well in the lab as well as the outdoor environment, with the ability to detect water level at reasonable accuracy and transmit data to the server in real time. Some minor shortcomings still noted with the scope for improvements, which are in the way to improve further.

scholarly journals A Bayesian decision approach to rainfall thresholds based flood warning

2006 ◽  
Vol 10 (3) ◽  
pp. 413-426 ◽  
Author(s):  
M. L. V. Martina ◽  
E. Todini ◽  
A. Libralon
Keyword(s):  
Real Time ◽  
Initial Conditions ◽  
Flood Forecasting ◽  
Rainfall Threshold ◽  
Precipitation Forecast ◽  
Bayesian Decision ◽  
Threshold Values ◽  
Flood Warning ◽  
Critical Rainfall ◽  
Forecasting System

Abstract. Operational real time flood forecasting systems generally require a hydrological model to run in real time as well as a series of hydro-informatics tools to transform the flood forecast into relatively simple and clear messages to the decision makers involved in flood defense. The scope of this paper is to set forth the possibility of providing flood warnings at given river sections based on the direct comparison of the quantitative precipitation forecast with critical rainfall threshold values, without the need of an on-line real time forecasting system. This approach leads to an extremely simplified alert system to be used by non technical stakeholders and could also be used to supplement the traditional flood forecasting systems in case of system failures. The critical rainfall threshold values, incorporating the soil moisture initial conditions, result from statistical analyses using long hydrological time series combined with a Bayesian utility function minimization. In the paper, results of an application of the proposed methodology to the Sieve river, a tributary of the Arno river in Italy, are given to exemplify its practical applicability.

scholarly journals Hydrodynamic and Wave Responses During Storm Surges on the Southern Brazilian Coast: A Real-Time Forecast System

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3397
Author(s):  
Arslaan Khalid ◽  
Andre de Souza de Lima ◽  
Felicio Cassalho ◽  
Tyler Miesse ◽  
Celso Ferreira
Keyword(s):  
Water Level ◽  
Real Time ◽  
Extreme Weather ◽  
Coastal Areas ◽  
Water Levels ◽  
Coastal Communities ◽  
Economic Losses ◽  
Forecast System ◽  
The Real ◽  
Daily Weather

Coastal flooding is a global phenomenon that results in severe economic losses, threatens lives, and impacts coastal communities worldwide. While recent developments in real-time flood forecasting systems provide crucial information to support coastal communities during coastal disasters, there remains a challenge to implement such systems in data-poor regions. This study demonstrates an operational real-time coupled surge wave guidance system for the coastal areas of Southern Brazil. This system is based on the recently developed integrated flood (iFLOOD) model, which utilizes the coupled hydrodynamic and phase-averaged ADCIRC–SWAN wave numerical model, driven by astronomical tides and atmospheric forcing from the Global Forecast System (GFS). This numerical modeling framework can simulate water levels and waves with a lead time of 84 h. A version of the coupled ADCIRC–SWAN model calibrated for Brazil, i.e., iFLOOD-Brazil, was operationally implemented (i.e., twice a day) over a period of 4 months (April to September 2020) for normal daily weather validation, as well as during a recent “bomb” cyclone that strongly impacted the southern coast of the country in June 2020. The real-time water levels and waves forecasted by iFLOOD-Brazil showed promising results against observations, with root mean square error (RMSE) values of 0.32 m and 0.68 m, respectively, for normal daily weather. Additionally, the RMSE values were 0.23 m for water levels and 1.55 m for waves during extreme weather, averaged over eight water level and two wave recording stations. In order to improve real-time predictions, a bias correction scheme was introduced and was shown to improve the water level and wave forecasts by removing the known systematic errors resulting from underestimation of astronomical tides and inadequate initial boundary conditions. The bias-corrected forecasts showed significant improvements in forecasted wave heights (0.47 m, 0.35 m) and water levels (0.17 m, 0.28 m) during daily and extreme weather conditions. The real-time iFLOOD-Brazil forecast system is the first step toward developing an accurate prediction model to support effective emergency management actions, storm mitigation, and planning in order to protect these economically valuable and socially vulnerable coastal areas.

scholarly journals Evaluation and Benchmarking of Operational Short-Range Ensemble Mean and Median Streamflow Forecasts for the Ohio River Basin

2018 ◽  
Vol 19 (10) ◽  
pp. 1689-1706 ◽  
Author(s):  
Thomas E. Adams III ◽  
Randel Dymond
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Geographic Location ◽  
Ensemble Forecast ◽  
Precipitation Forecast ◽  
Ohio River ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Meteorological Model ◽  
Ensemble Mean

Abstract This study presents findings from a real-time forecast experiment that compares legacy deterministic hydrologic stage forecasts to ensemble mean and median stage forecasts from the NOAA/NWS Meteorological Model-Based Ensemble Forecast System (MMEFS). The NOAA/NWS Ohio River Forecast Center (OHRFC) area of responsibility defines the experimental region. Real-time forecasts from subbasins at 54 forecast point locations, ranging in drainage area, geographic location within the Ohio River valley, and watershed response time serve as the basis for analyses. In the experiment, operational hydrologic forecasts, with a 24-h quantitative precipitation forecast (QPF) and forecast temperatures, are compared to MMEFS hydrologic ensemble mean and median forecasts, with model forcings from the NOAA/NWS National Centers for Environmental Prediction (NCEP) North American Ensemble Forecast System (NAEFS), over the period from 30 November 2010 through 24 May 2012. Experiments indicate that MMEFS ensemble mean and median forecasts exhibit lower errors beginning at about lead time 90 h when forecasts at all locations are aggregated. With fast response basins that peak at ≤24 h, ensemble mean and median forecasts exhibit lower errors much earlier, beginning at about lead time 36 h, which suggests the viability of using MMEFS ensemble forecasts as an alternative to OHRFC legacy forecasts. Analyses show that ensemble median forecasts generally exhibit smaller errors than ensemble mean forecasts for all stage ranges. Verification results suggest that OHRFC MMEFS NAEFS ensemble forecasts are reasonable, but needed improvements are identified.

scholarly journals A Bayesian decision approach to rainfall thresholds based flood warning

2005 ◽  
Vol 2 (6) ◽  
pp. 2663-2706 ◽  
Author(s):  
M. L. V. Martina ◽  
E. Todini ◽  
A. Libralon
Keyword(s):  
Real Time ◽  
Initial Conditions ◽  
Flood Forecasting ◽  
Rainfall Threshold ◽  
Precipitation Forecast ◽  
Bayesian Decision ◽  
Threshold Values ◽  
Flood Warning ◽  
Critical Rainfall ◽  
Forecasting System

Abstract. Operational real time flood forecasting systems generally require a hydrological model to run in real time as well as a series of hydro-informatics tools to transform the flood forecast into relatively simple and clear messages to the decision makers involved in flood defense. The scope of this paper is to set forth the possibility of providing flood warnings at given river sections based on the direct comparison of the quantitative precipitation forecast with critical rainfall threshold values, without the need of an on-line real time forecasting system. This approach leads to an extremely simplified alert system to be used by non technical stakeholders and could also be used to supplement the traditional flood forecasting systems in case of system failures. The critical rainfall threshold values, incorporating the soil moisture initial conditions, result from statistical analyzes using long hydrological time series combined with a Bayesian utility function minimization. In the paper, results of an application of the proposed methodology to the Sieve river, a tributary of the Arno river in Italy, are given to exemplify its practical applicability.

scholarly journals A Wireless Smart Sensor Network for Flood Management Optimization

2017 ◽  
Vol 4 (1) ◽  
pp. 36-42
Author(s):  
Hossam Adden Alfarra ◽  
Mohammed Hayyan Alsibai
Keyword(s):  
Water Level ◽  
Real Time ◽  
Sensor Network ◽  
Warning System ◽  
Flood Management ◽  
Base Station ◽  
Risk Level ◽  
Size Analysis ◽  
Smart Sensor ◽  
Flood Warning

Recent development in the sensing technologies and wireless sensor networks has encouraged many innovative applications in disaster management and forecasting. Flood is one of the most dangerous natural disasters that occurs frequently in south Asia. Therefore, water level monitoring and flood early prediction, play an important role in lives and properties saving. In this paper, an optimized Flood Warning System (FWS) is presented. The system is based on multi parameters Wireless Smart Sensor Network (WSSN) for early flood warning. WSSN performs pre-processing procedures at sensor level before sending the data to a base data analyzing station. This pre-processing step is to improve the reliability, data quality and transmission quality. For the purpose of validation, the proposed method is applied using two parameters: The water level (L) and streamflow of water (R) in rivers. The proposed system provides early flood detection by continuously measuring R and L in real-time. The collected data is to be used to predict flood time and place. Data is exchanged among sensors in real-time. Pre-analyzing is performed and reports are sent to the base station only if the analysis gave a high risk level. The main purpose is to cut down the data size. Analysis and simulation showed that the data size is improved significantly using this method. Calculations considers real cases on a part of Pahang River (Sungai Pahang).

AUTOMATIC WATER LEVEL MONITORING WITH IOT AND LPWAN

2019 ◽  
pp. 95-103
Author(s):  
Krum Videnov ◽  
Vanya Stoykova
Keyword(s):  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Water Sources ◽  
Water Levels ◽  
Water Level Fluctuations ◽  
Level Monitoring ◽  
Water Level Monitoring

Monitoring water levels of lakes, streams, rivers and other water basins is of essential importance and is a popular measurement for a number of different industries and organisations. Remote water level monitoring helps to provide an early warning feature by sending advance alerts when the water level is increased (reaches a certain threshold). The purpose of this report is to present an affordable solution for measuring water levels in water sources using IoT and LPWAN. The assembled system enables recording of water level fluctuations in real time and storing the collected data on a remote database through LoRaWAN for further processing and analysis.

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