Flood Warning System Research Articles

Highwater Mark Collection after Post Tropical Storm Dorian and Implications for Prince Edward Island, Canada

Prince Edward Island (PEI), Canada has been experiencing the consequences of a rising sea level and intense storms on its coasts in recent years. The most recent severe event, Post Tropical Storm Dorian (Dorian), began impacting Prince Edward Island on 7 September 2019 and lasted for over 20 h until the morning of 8 September 2019. The measurement of highwater marks (HWM) from the storm was conducted between 25 September and 25 October 2019 using a high precision, survey grade methodology. The HWM measured included vegetation lines, wrack lines, beach, cliff, and dune morphological features, and tide gauge data at 53 locations in the Province along coastal areas that are exposed to high tides, storm surge, high winds, and wave runup. Photos were taken to provide evidence on the nature of the HWM data locations. The data reveal that Dorian caused extensive coastal floods in many areas along the North and South Coast of Prince, Queens and Western Kings Counties of Prince Edward Island. The floods reached elevations in excess of 3.4 m at some locations, posing threats to local infrastructure and causing damage to natural features such as sand dunes in these areas. The HWM data can provide useful information for community and emergency response organizations as plans are developed to cope with the rising sea level and increased frequency of highwater events as predicted by researchers. As Dorian has caused significant damage in several coastal areas in PEI, better planning using an enhanced storm forecasting and coastal flood warning system, in conjunction with flood stage values, could possibly have reduced the impacts of the storm in the impacted areas. This could help enhance public understanding of the potential impacts in local areas and how they can prepare and adapt for these events in the future.

Prediction models for urban flood evolution for satellite remote sensing

Accurate and timely flood forecasts are critical for protecting people and infrastructure in a changing climate. Satellite remote sensing provides the necessary wide area coverage and period revisits to measure episodic heavy precipitation and resultant urban floods. We propose two methods to assimilate satellite-observed precipitation into hydrologic models in real time to update flood forecasts, bypassing two previous barriers in this technology: infrequent satellite overpasses and long model run times. Constellations of small satellites overcomes the first barrier by providing frequent flights over an area of interest; however, these constellations require coordination and planning to capture precipitation data where it is most needed to inform flood forecasts. The primary purpose of this paper is to address the second barrier – that is high computational costs that make it infeasible to run flood forecast models on-board these satellites so that they can re-orient to measure where most needed. We develop a simple regression-based approach and a machine learning framework (Long Short-Term Memory (LSTM) models) to provide reliable flood forecasts using satellite-observed precipitation at a fraction of the computational cost of physics-based hydrologic models. We apply these approaches to a test case for the Atlanta metropolitan region using the Weather Research and Forecasting model hydrologic modeling system (WRF-Hydro) to simulate flooding across the model domain for several precipitation events. We compare the accuracy of our proposed approaches to the WRF-Hydro model using different spatial extents and temporal frequencies of precipitation observations to examine different plausible satellite constellation scenarios. The LSTM approach trades performance accuracy and adaptability for computational efficiency, which can be important in a time and resource constrained scenario. The LSTM model reduces total error up to 38% from an initial flood forecast. Additionally, this approach correctly classified flooding to within one flood magnitude category in ∼90% of cases. These new forecasting algorithms can be used onboard constellations of small satellites to observe ongoing flood events, update short term predictions, and schedule observations to maximize useful measurements and thereby improve flood warning systems for protecting residents and properties.

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

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.

A Propagation Study of LoRa P2P Links for IoT Applications: The Case of Near-Surface Measurements over Semitropical Rivers.

Internet of Things (IoT) radio networks are becoming popular in several scenarios for short-range applications (e.g., wearables and home security) and medium-range applications (e.g., shipping container tracking and autonomous farming). They have also been proposed for water monitoring in flood warning systems. IoT communications may use long range (LoRa) radios working in the 915 MHz industrial, scientific and medical (ISM) band. In this research, we study the propagation characteristics of LoRa chirp radio signals close to and over water in a tropical meadow region. We use as a case study the Colima River in Mexico. We develop a novel point-to-point IoT measurement sounding system that does not require decoding of LoRa propriety bursts and provides accurate power versus distance profiles along the riparian zone of a steeply dropping mountain river. We used this system to obtain the measurements reported in this work, which are also analyzed and modeled. The results show that the LoRa signal propagation over water exhibits a log-normal distribution. As a result of the chirp signal processing, two new experimental path loss models are presented. The path loss results show a considerable degradation of the received signal power over water within vegetation and less signal degradation at antenna heights closer to the water surface.

Assessment of Flood Risks in Ifo Local Government Area of Ogun State

The most frequent natural hazards and the most costly in terms of property damage in study area is Flood. The purpose of this work is use Geographic Information Systems (GIS) Hydrological tools to model and map flood event in Ifo Local Government Area of Ogun state, Nigeria using Shuttle Radar Topographic Mission ( SRTM) data and land use maps as input data layers. In this study, flood inundation depth is calculated with GIS in order to understand the temporal, spatial scale and transition. Flood inundation map and 3D views of the hydrological modelling results are provided for a better understanding of the severity of flooding in the location. It is recommended that the processed data from this study be used as input for future research and the development of flood warning systems for the area. It will also be a base study for future study when rainfall data will be available.

Development of a flood warning system for the Isle of Man

A storm surge event in the Irish Sea on 1 February 2002 caused significant coastal flooding around the Isle of Man. Following this event, a group was formed to develop a flood warning system for the Island. A network of tide gauges was installed around the Island and a method was developed to improve the short-term prediction of high water levels. The flood warning system was later extended to include rainfall and river flooding, and flood warning pages were added to the Island's government website.

Sensitivity Analysis of Flash Flood Hazard on Sediment Load Characteristics

Changing climate has raised attention toward weather-driven natural hazards, such as rain-induced flash floods. The flooding model is an efficient tool used in flash flood warning and hazard management. More and more evidence showed significant impacts of sediment on hydrodynamics and flooding hazard of flash flood. But little information is available regarding flooding hazard sensitivity to sediment characteristics, which hampers the inclusion of sediment characteristics into the flash flood warning system and hazard management. This study used a 1D model to simulate flood hazards. After calibrating and validating the hydrodynamic model, we carried out simulations to test the sensitivity of flood hazard to sediment characteristics like inflow point, size distribution, and concentration. Our results showed that sediment from highly erosive slopes affects the flooding hazard more than sediment from watershed. This is particularly true when sediment particles are fine particles with a medium size of 0.06 mm. When medium particle size of sediment increased above 1 mm, most of the sediment particles are deposited in the river and we see little effect on flooding hazard downstream. Sediment concentration significantly influenced the flooding hazard but was less important than sediment inflow point and medium particle size. Our study suggested considering more characteristics than concentration when including sediment particles into the flash flood warning system.

LANGKIWA RIVER WATER DETECTION SYSTEM IMPLEMENTING INTERNET OF EVERYTHING

The goal of this study is to establish a structure and test model that will provide communities with information and early flood warning and notification system and information dissemination for the municipalities of Biñan City, Laguna particularly in Command Control and Communication Centre (City Disaster Risk Reduction and Management Office). Since the study focuses on the design, development and evaluation of instructional research methods, The study focuses on the design, production and evaluation of teaching systems, procedures and products. With the introduction of the IOE, the Langkiwa river water level detection system is a significant aid to the disaster rescue team of the Biñan City local government. It could be a replacement for the municipality's existing manual control scheme. It uses ultrasonic sensor to detect the current situation of the river, and by the power of Arduino microcontroller that serves as the brain of the system that initiates to produce reliable and accurate information, and delivering of messages through GSM modem in a most convenient and fastest way of communication. The proposed project can also be a useful tool for monitoring the impact of global warning, the sudden increase and heavy rain water per year. It keeps the data gathered by the system from the river in database for future study and reference

Automated Flood Depth Estimates from Online Traffic Sign Images: Explorations of a Convolutional Neural Network-Based Method.

Flood depth monitoring is crucial for flood warning systems and damage control, especially in the event of an urban flood. Existing gauge station data and remote sensing data still has limited spatial and temporal resolution and coverage. Therefore, to expand flood depth data source taking use of online image resources in an efficient manner, an automated, low-cost, and real-time working frame called FloodMask was developed to obtain flood depth from online images containing flooded traffic signs. The method was built on the deep learning framework of Mask R-CNN (regional convolutional neural network), trained by collected and manually annotated traffic sign images. Following further the proposed image processing frame, flood depth data were retrieved more efficiently than manual estimations. As the main results, the flood depth estimates from images (without any mirror reflection and other inference problems) have an average error of 0.11 m, when compared to human visual inspection measurements. This developed method can be further coupled with street CCTV cameras, social media photos, and on-board vehicle cameras to facilitate the development of a smart city with a prompt and efficient flood monitoring system. In future studies, distortion and mirror reflection should be tackled properly to increase the quality of the flood depth estimates.

A Sustainable Early Warning System Using Rolling Forecasts Based on ANN and Golden Ratio Optimization Methods to Accurately Predict Real-Time Water Levels and Flash Flood.

Remote monitoring sensor systems play a significant role in the evaluation and minimization of natural disasters and risk. This article presents a sustainable and real-time early warning system of sensors employed in flash flood prediction by using a rolling forecast model based on Artificial Neural Network (ANN) and Golden Ratio Optimization (GROM) methods. This Early Flood Warning System (EFWS) aims to support decision makers by providing reliable and accurate information and warning about any possible flood events within an efficient lead-time to reduce any damages due to flash floods. In this work, to improve the performance of the EFWS, an ANN forecast model based on a new optimization method, GROM, is developed and compared to the traditional ANN model. Furthermore, due to the lack of literature regarding the optimal ANN structural model for forecasting the flash flood, this paper is one of the first extensive investigations into the impact of using different exogenous variables and parameters on the ANN structure. The effect of using a rolling forecast model compared to fixed model on the accuracy of the forecasts is investigated as well. The results indicate that the rolling ANN forecast model based on GROM successfully improved the model accuracy by 40% compared to the traditional ANN model and by 93.5% compared to the fixed forecast model.

Flood Stage Forecasting at the Gurye-Gyo Station in Sumjin River Using LSTM-Based Deep Learning Models

Instances of flood damage caused by extreme storm rainfall due to climate change and variability have been showing an increasing trend. Particularly, a flood forecasting and warning system has been recognized as an important nonstructural measure for flood damage reduction, including loss of life. Flood forecasting and warning have been performed by the forecasts of flood discharge and flood stage using the physically based rainfall-runoff models. However, recently, studies involving the application of a machine learning-based flood forecasting models, which addresses the limitations of extant physically based flood stage forecasting models, have been performed. We may require various case studies to determine more accurate methods. Therefore, this study performed the real-time forecasting of the river water level or stage at the Gurye station of the Sumjin river with lead times of 1, 3, and 6 h by applying a long short-term memory (LSTM)-based deep learning model. In addition, the applicability of the LSTM model was evaluated by comparing the results with those from widely used models based on support vector machine and multilayer perceptron. Consequently, we noted that the LSTM model exhibited a relatively better forecasting performance. Therefore, the applicability of the LSTM model should be extensively studied for flood forecasting applications.

Flood Monitoring and Warning Systems: A Brief Review

Floods and excessive rainfall are unavoidable phenomena that can cause massive loss of people's lives and destruction of infrastructure. Flash floods rise rapidly in flood-prone areas, resulting in property damage, but the impact on human lives is relatively preventable by the presence of monitoring systems. Although there are many systems widely in practice by disaster management agencies in monitoring flood levels, most of these systems are limited range and sophisticated to be used and maintained. Furthermore, in most developing countries, the conventional flood gates in water canals are manually operated and suffer from the lack of real-time monitoring of water levels, leading to an overflow in the channels and flash floods. On top of that, the lacking accurate data analysis in the system that can be accessed is one of the limitations of the conventional flood monitoring and warning systems (FMWS). Therefore, in this paper, we have explored and reviewed the existing methods of flood monitoring and emphasizing their structure and sensing techniques. We have also classified and compared their advantages and limitations and accordingly suggested new solutions and improvements by utilizing new technologies based on the Internet of Things. This paper introduces a detailed mini-review of sensing methods in the existing flood systems as reported in previous studies to serve as a quick guide to researchers who are engaging in this field. Based on the review, conclusions have been drawn.

Estimation of rainfall threshold for flood warning for small urban watersheds based on the 1D–2D drainage model simulation

Flood warning systems can provide an effective and low-cost solution to protect urban residents especially in developing countries suffering from severe flood damages caused by weak capacity of flood defense structures. Rainfall Threshold for Flood Warning (RTFW) is an essential key of accurate flood warning system. This study proposed an easy-to-use mathematical equation for RTFW for small urban watersheds based on computer simulations. First, a coupled 1D–2D dual-drainage model was used to simulate the flooded area in nine watersheds of Seoul, Korea corresponding to 540 scenarios of various synthetic rainfall events and watershed imperviousness. Then, the results of the 101 simulations that caused the critical flooded depth (0.25 m–0.35 m) were used to develop the equation that relates the value of RTFW to the rainfall event temporal variability (represented as coefficient of variation or CV) and the watershed imperviousness (represented as NRCS Curve Number or CN). The results suggest that (1) RTFW exponentially decreases as the rainfall CV increases; (2) RTFW linearly decreases as the watershed CN increases; and that (3) RTFW is dominated by CV when the rainfall has low temporal variability (e.g., CV < 0.2) while RTFW is dominated by CN when the rainfall has high temporal variability (e.g., CV > 0.4). For validation, the proposed equation was applied for the flood warning system with two storm events occurred in 2010 and 2011 over 239 watersheds in Seoul. The system showed the hit, false and missed alarm rates at 69% (48%), 31% (52%) and 6.7% (4.5%), respectively for the 2010 (2011) event.

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

Background: The North-Eastern part of Bangladesh suffers from frequent flash flood, 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.

Changes in precipitation patterns in Houston, Texas

There has been an alarming increase in the frequency of major flooding events along the Gulf Coast over the last three decades, primarily due to events of unprecedented, or extreme, rainfall. Using data from 63 rain gauges maintained by the Harris County Flood Control District's Flood Warning System (HCFCD FWS), this study examines the changes in daily precipitation amounts in the highly urbanized city of Houston, Texas, USA. The potential shift in annual precipitation patterns over a period of three decades (1989-2018) was examined by investigating the numbers of dry and wet days as well as annual precipitation totals over the study period. Wet days were then further scrutinized based on daily rainfall amounts (e.g., R10, R20, R30, R40, R50, R100) to determine if extreme events are beginning to dominate annual rainfall amounts. Trends were analyzed for statistical significance temporally using the Mann-Kendall and Sen's slope methods and for spatial trends using GIS applications. The results indicate a statistically significant increase in extreme rainfall at the expense of light, moderate, and heavy rainfall over time. The only negative relationship is found in dry days. The most statistically significant trends exist in the 99th percentile, maximum, and R100 parameters with p-values of 0.07, 0.08, and 0.11, respectively. There has been rapid growth and intensive development in the Houston area in recent decades that continues to this day, and land cover change has been significant as 12.6% of Harris County changed to one of four National Land Cover Database (NLCD) Urban classes (e.g., developed: barren, low intensity, medium intensity, high intensity). This confirms that urbanization has continued to increase while total vegetative and wetland coverage has decreased. The findings of this study provide essential guidance for city and state planners and engineers.

Communication protocols evaluation for a wireless rainfall monitoring network in an urban area

Rainfall monitoring networks are key elements for the development of alerts and prediction models for communities at risk of flooding during high intensity rainfall events. Currently, most of these networks send the precipitation measurement to a data center in real-time using wireless communication protocols, avoiding travel to the measurement site. An Early Warning System (EWS) for pluvial flash floods developed in Barranquilla (Colombia), used the GPRS protocol to send rain gauge data in real-time to a web server for further processing; however, this protocol has a high consumption of energy and also high maintenance costs. This article carried out an evaluation in terms of link budget, link profile, energy consumption and devices costs of three low-power wireless communication protocols, Zigbee, LoRaWAN and Sigfox, to determine which one is the most suitable for the EWS of the city of Barranquilla. To perform the evaluation, a wireless sensor network was designed and characterized for Zigbee and LoRaWAN with Radio Mobile tool taking into account the measurement points implemented with GPRS network. The evaluation included the power consumption of Zigbee, LoRaWAN and Sigfox. From the results of simulations, LoRaWAN and Zigbee network has similar radio signal received and the LoRaWAN network obtains the least losses per path. As for power consumption, the LoRaWAN devices has the lowest energy consumption, as well as, the LoRaWAN network sensor nodes are cheaper. Finally, the protocol with the best general performance was LoRAWAN, since complies with the communication, consumption and cost requirements.

Establishment of flood forecasting and warning system in the un-gauged small and medium watershed through ODA

Establishment of flood forecasting and warning system in the un-gauged small and medium watershed through ODA

An early warning system for wave-driven coastal flooding at Imperial Beach, CA

Waves overtop berms and seawalls along the shoreline of Imperial Beach (IB), CA when energetic winter swell and high tide coincide. These intermittent, few-hour long events flood low-lying areas and pose a growing inundation risk as sea levels rise. To support city flood response and management, an IB flood warning system was developed. Total water level (TWL) forecasts combine predictions of tides and sea-level anomalies with wave runup estimates based on incident wave forecasts and the nonlinear wave model SWASH. In contrast to widely used empirical runup formulas that rely on significant wave height and peak period, and use only a foreshore slope for bathymetry, the SWASH model incorporates spectral incident wave forcing and uses the cross-shore depth profile. TWL forecasts using a SWASH emulator demonstrate skill several days in advance. Observations set TWL thresholds for minor and moderate flooding. The specific wave and water level conditions that lead to flooding, and key contributors to TWL uncertainty, are identified. TWL forecast skill is reduced by errors in the incident wave forecast and the one-dimensional runup model, and lack of information of variable beach morphology (e.g., protective sand berms can erode during storms). Model errors are largest for the most extreme events. Without mitigation, projected sea-level rise will substantially increase the duration and severity of street flooding. Application of the warning system approach to other locations requires incident wave hindcasts and forecasts, numerical simulation of the runup associated with local storms and beach morphology, and model calibration with flood observations.

Assessment and mitigation of tangible flood damages driven by climate change in a tropical city: Hat Yai Municipality, southern Thailand

Climate change-induced floods in tropical urban areas have presented a serious global challenge because of failed conventional stormwater management practices. This research aims to develop a comprehensive methodological framework for flood damage estimation and mitigation, particularly in a tropical urban city. In this study, interdisciplinary fields were integrated through statistical downscaling, hydrologic-hydraulic modeling, and the development of flood damage curves. Relationships between tangible flood damage and flood-borne outbreak with flood depths were elucidated to predict future damage. Various flood mitigation strategies were evaluated. Herein, Hat Yai Municipality in Southern Thailand was selected as the study area. The flood simulation was conducted for 2010 and the highest flood damage sensitivity was exhibited by non-commercial buildings due to significant commercial stock damage, which was followed by that observed for detached houses. There was a strong linear relationship between the number of patients infected with leptospirosis and flood depth (R2 = 0.85). For climate change studies, flood maps for storms with 20-, 50-, and 100-year return periods under the A2/RCP8.5 scenario were generated using hydrological-hydraulic 1D/2D model; these maps were applied with the developed flood damage curves for damage estimation. It was found that reducing flood damage by implementing agroforestry and expanding the main bypass channel provides comparable damage reductions of −25.5% and − 27.5%, respectively, under the worst-case scenario of a 100-year return period in 2040–2059. Therefore, to deal with uncertain climate change situations, the incorporation of structural and non-structural measures is recommended. Such a combination when coupled with an eight-hour flood awareness time can result in a damage reduction of −59.9%. A flood warning system was in high demand by residents in the area; however, damage reduction from this measure alone was not high (approximately −17.0%) when compared to that obtained with other measures; consequently, additional measures were needed.

Comparison of the flow estimation methods through GIUH rainfall-runoff model for flood warning system on Banseong stream

Comparison of the flow estimation methods through GIUH rainfall-runoff model for flood warning system on Banseong stream