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.

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.

Estimation of Rainfall Threshold for Flood Warning for Small Urban Watersheds Based on the 1D-2D Drainage Model Simulation

This study proposed an equation for Rainfall Threshold for Flood Warning (RTFW) for small urban watersheds based on computer simulations. First, a coupled 1D-2D dual-drainage model was developed for nine watersheds in Seoul, Korea. Next, the model simulation was repeated for a total of 540 combinations of the synthetic rainfall events and watershed imperviousness (9 watersheds x 4 NRCS Curve Number (CN) values x 15 rainfall events). Then, the results of the 101 simulations that caused the critical flooded depth (0.25m-0.35m) 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 Curve Number. The results suggest that (1) RTFW exponentially decreases as the rainfall CV increases; (2) RTFW linearly decreases as the watershed CV 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 the hit, false and missed alarm rates at 69% (48%), 31% (52%) and 6.7% (4.5%), respectively for the 2010 (2011) event.

Primarily Results of a Real-Time Flash Flood Warning System in Vietnam

In recent years, losses and damages from flash floods have been steadily increasing worldwide as well as in Vietnam, due to physical factors, human activities, especially under a changing climate. This is a hotspot issue which requires immediate response from scientists and policy-makers to monitor and mitigate the negative impacts of flash floods. This study presents a way to reduce losses through increasing the accuracy of real-time flash flood warning systems in Vietnam, a case study developed for Ha Giang province where the topography is relatively complex with severe flash floods observed. The objective of this paper is to generate the real-time flash flood system based on bankfull discharge threshold. To do this, HEC-HMS model is applied to calibrate and validate observer inflow to the reservoir with nine automatic rain gauges installed. More importantly, on the basic of measured discharge at 35 locations from the fieldtrips, an empirical equation constructed is to identify the bankful discharge values. It bases on the relationship between basin characteristics of river length, basin area and bankfull discharge. The results indicate an effective approach to determine bankfull threshold with the established-empirical equation. On the scale of a small basin, it depicts the consistency of flood status and warning time with the reality. Doi: 10.28991/cej-2021-03091687 Full Text: PDF

Advances and challenges of the French operational flash flood warning system, Vigicrues Flash

&lt;p align=&quot;justify&quot;&gt;&lt;span&gt;Since March 2017, t&lt;/span&gt;&lt;span&gt;he French flash flood warning system, Vigicrues Flash, provides warnings for small-to-medium ungauged basins for about 10,000 municipalities to help emergency services better mitigate potential impacts of ongoing and upcoming flash flood events. Set up by the Ministry in charge of Environment, this system complements flood warnings produced by the Vigicrues procedure for French monitored rivers. Based on a discharge-threshold flood warning method called AIGA, Vigicrues Flash currently ingests radar-gauge rainfall grids at a 1-km resolution into a conceptual distributed rainfall-runoff model. Real-time peak discharge estimated on any river cell are then compared to regionalized flood quantiles (estimated with the same hydrological model). Automated warnings are issued for rivers exceeding the high flood and very high flood thresholds (defined as years of return periods) and for the associated municipalities that might be impacted. This service shares a web platform for the dissemination and communication of early warnings and hazard map displays with the APIC heavy rainfall warning service from M&amp;#233;t&amp;#233;o-France. &lt;/span&gt;&lt;/p&gt;&lt;p align=&quot;justify&quot;&gt;&lt;span&gt;To better anticipate flash flood events and extend the coverage of the Vigicrues Flash service, the hydrological modeling is being enhanced within the SMASH &lt;/span&gt;&lt;span&gt;(&lt;/span&gt;&lt;span&gt;S&lt;/span&gt;&lt;span&gt;patially-distributed &lt;/span&gt;&lt;span&gt;M&lt;/span&gt;&lt;span&gt;odelling and &lt;/span&gt;&lt;span&gt;AS&lt;/span&gt;&lt;span&gt;similation for &lt;/span&gt;&lt;span&gt;H&lt;/span&gt;&lt;span&gt;ydrology) &lt;/span&gt;&lt;span&gt;platform developed by INRAE (formerly Irstea). For the upcoming operational update of Vigicrues Flash, a simplified distributed hydrologic model is continuously run at a 15-minute time step and a 1-km resolution. It includes only 2 parameters per cell, controlling respectively a production reservoir and a transfer reservoir from the G&amp;#233;nie Rural (GR) conceptual models. Cross-validation and regionalization of these two parameters have been improved to better account for basins spatial heterogeneities while optimizing flash flood warning performance. Evaluation results for 921 French basins on the 2007-2019 period show improvements in terms of flash flood event detection and effective warning lead time. Current developments aim to integrate a cell-to-cell routing component and improve parameters estimation at the national scale with the variational calibration schemes recently developed on the SMASH platform by Jay-Allemand et al. (2020). Challenges of including high-resolution precipitation nowcasts and accounting for the hydrometeorological uncertainties via data assimilation and ensemble forecasting are also discussed based on ongoing SMASH research.&lt;/span&gt;&lt;/p&gt;&lt;p align=&quot;justify&quot;&gt;&amp;#160;&lt;/p&gt;&lt;p align=&quot;justify&quot;&gt;Jay-Allemand, M., Javelle, P., Gejadze, I., Arnaud, P., Malaterre, P.-O., Fine, J.-A., and Organde, D.: On the potential of variational calibration for a fully distributed hydrological model: application on a Mediterranean catchment, Hydrol. Earth Syst. Sci., 24, 5519&amp;#8211;5538, https://doi.org/10.5194/hess-24-5519-2020, 2020.&lt;/p&gt;

Design of an automatic hydro-meteorological observation network for real-time flood warning system: a case study of Vu Gia-Thu Bon river basin, Vietnam

This paper presents an interdisciplinary approach, along with Vietnam's legal frameworks, to design an automatic hydro-meteorological (HM) observation network for a real-time flood warning system in Vu Gia-Thu Bon (VGTB) river basin, Vietnam. The automatic HM monitoring network consists of weather-proof enclosures containing data loggers, rechargeable batteries, sensors for air temperature, air humidity, solar radiation, wind speed, water level with attached solar panels and mounted upon masts located at fixed ground stations. A total of 20 meteorological stations and five hydrological stations have been built in VGTB river basin. To capture changes in weather and stream flow in the basin, the 5-minute and half-hour recording frequency options were set for meteorological and hydrological variables, respectively. All HM data was transmitted every 30 minutes to the data server at the data processing centre via Global System for Mobile Communications (GSM)/General Packet Radio Service (GPRS) network. These data were then input into hydrological-hydraulic models for inundation simulation in the basin. The results showed that the performance of flood simulation at hourly time step has significantly improved during flood events in September and November 2015. Overall, near-real-time HM data recording from automatic monitoring network proved beneficial for an flood early warning system.

Determination of Deep Learning Model and Optimum Length of Training Data in the River with Large Fluctuations in Flow Rates

Recently, developing countries have steadily been pushing for the construction of stream-oriented smart cities, breaking away from the existing old-town-centered development in the past. Due to the accelerating effects of climate change along with such urbanization, it is imperative for urban rivers to establish a flood warning system that can predict the amount of high flow rates of accuracy in engineering, compared to using the existing Computational Fluid Dynamics (CFD) models for disaster prevention. In this study, in the case of streams where missing data existed or only small observations were obtained, the variation in flow rates could be predicted with only the appropriate deep learning models, using only limited time series flow data. In addition, the selected deep learning model allowed the minimum number of input learning data to be determined. In this study, the time series flow rates were predicted by applying the deep learning models to the Han River, which is a highly urbanized stream that flows through the capital of Korea, Seoul and has a large seasonal variation in the flow rate. The deep learning models used are Convolution Neural Network (CNN), Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Gated Recurrent Unit (GRU). Sequence lengths for time series runoff data were determined first to assess the accuracy and applicability of the deep learning models. By analyzing the forecast results of the outflow data of the Han River, sequence length for 14 days was appropriate in terms of the predicted accuracy of the model. In addition, the GRU model is effective for deep learning models that use time series data of the region with large fluctuations in flow rates, such as the Han River. Furthermore, through this study, it was possible to propose the minimum number of training data that could provide flood warning system with an effective flood forecasting system although the number of input data such as flow rates secured in new towns developed around rivers was insufficient.