scholarly journals Sensitivity Analysis of Flash Flood Hazard on Sediment Load Characteristics

2021 ◽  
Vol 9 ◽  
Author(s):  
Hongxi Liu ◽  
Yujun Yi ◽  
Zhongwu Jin
Keyword(s):  
Flash Flood ◽  
Flood Hazard ◽  
Warning System ◽  
Sediment Characteristics ◽  
Hazard Management ◽  
Flood Warning ◽  
Flooding Hazard ◽  
Medium Particle ◽  
Sediment Particles ◽  
Flood Warning System

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.

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 Bias Correction Method for Rainfall Forecasts Using Backward Storm Tracking

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1728 ◽  
Author(s):  
Wooyoung Na ◽  
Chulsang Yoo
Keyword(s):  
Conventional Method ◽  
Bias Correction ◽  
Flash Flood ◽  
Warning System ◽  
Correction Method ◽  
Storm Events ◽  
Flood Warning ◽  
Backward Tracking ◽  
Flood Warning System ◽  
Study Sites

This study proposes a new method to estimate the bias correction ratio for the rainfall forecast to be used as input for a flash flood warning system. This method requires a backward tracking to locate where the forecasted storm is at the present time, and the bias correction ratio is estimated at the tracked location, not at the warning site. The proposed method was applied to the rainfall forecasts provided by the Korea Meteorological Administration. A total of 300 warning sites considered in the flash flood warning system for mountain regions in Korea (FFWS-MR) were considered as study sites, along with four different storm events in 2016. As a result, it was confirmed that the proposed method provided more reasonable results, even in the case where the number of rain gauges was small. Comparison between the observed rain rate and the corrected rainfall forecasts by applying the conventional method and the proposed method also showed that the proposed method was superior to the conventional method.

scholarly journals Optimize Short-Term Rainfall Forecast with Combination of Ensemble Precipitation Nowcasts by Lagrangian Extrapolation

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1752 ◽  
Author(s):  
Na ◽  
Yoo
Keyword(s):  
Flash Flood ◽  
Weighted Average ◽  
Warning System ◽  
Error Variance ◽  
Ensemble Forecast ◽  
Urban Flood ◽  
Target Time ◽  
Flood Warning ◽  
Flood Warning System ◽  
Better Than

The rainfall forecasts currently available in Korea are not sufficiently accurate to bedirectly applied to the flash flood warning system or urban flood warning system. As the lead timeincreases, the quality becomes even lower. In order to overcome this problem, this study proposesan ensemble forecasting method. The proposed method considers all available rainfall forecasts asensemble members at the target time. The ensemble members are combined based on the weightedaverage method, where the weights are determined by applying the two conditions of theunbiasedness and minimum error variance. The proposed method is tested with McGill Algorithmfor Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) rainfall forecasts for four stormevents that occurred during the summers of 2016 and 2017 in Korea. In Korea, rainfall forecasts aregenerated every 10 min up to six hours, i.e., there are always a total of 36 sets of rainfall forecasts.As a result, it is found that just six ensemble members is sufficient to make the ensemble forecast.Considering additional ensemble members beyond six does not significantly improve the quality ofthe ensemble forecast. The quality of the ensemble forecast is also found to be better than that of thesingle forecast, and the weighted average method is found to be better than the simple arithmeticaverage method.

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

2021 ◽  
Author(s):  
Julie Demargne ◽  
Catherine Fouchier ◽  
Didier Organde ◽  
Olivier Piotte ◽  
Anne Belleudy
Keyword(s):  
Hydrological Model ◽  
Flash Flood ◽  
Warning System ◽  
Hydrologic Model ◽  
Parameters Estimation ◽  
Flood Events ◽  
Time Step ◽  
Flood Warning ◽  
Flood Warning System ◽  
High Flood

<p align="justify"><span>Since March 2017, t</span><span>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 Météo-France. </span></p><p align="justify"><span>To better anticipate flash flood events and extend the coverage of the Vigicrues Flash service, the hydrological modeling is being enhanced within the SMASH </span><span>(</span><span>S</span><span>patially-distributed </span><span>M</span><span>odelling and </span><span>AS</span><span>similation for </span><span>H</span><span>ydrology) </span><span>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 Génie 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.</span></p><p align="justify"> </p><p align="justify">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, 5519–5538, https://doi.org/10.5194/hess-24-5519-2020, 2020.</p>

scholarly journals Application of the HBV hydrological model in a flash flood case in Slovenia

2010 ◽  
Vol 10 (12) ◽  
pp. 2713-2725 ◽  
Author(s):  
M. G. Grillakis ◽  
I. K. Tsanis ◽  
A. G. Koutroulis
Keyword(s):  
Hydrological Model ◽  
Flash Flood ◽  
Warning System ◽  
Weather Forecast ◽  
Radar Data ◽  
Added Value ◽  
Distributed Hydrological Model ◽  
Flood Warning ◽  
Forecast Models ◽  
Flood Warning System

Abstract. An atmospheric depression passed over northwest Slovenia on 18 September 2007 producing precipitation that exceeded 300 mm/d and a 100-year return period runoff in Zelezniki tributary. The resultant flash flood in the study area, which consisted of five basins, was simulated with the conceptual distributed hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning). The model was calibrated and validated with past rainfall – runoff events with satisfactory results producing values of Nash – Sutcliffe coefficient between 0.82 and 0.96. The validated model was applied to the flash flood case with stream gauge failure, driven by spatiotemporal precipitation produced by a set of rain gauges and radar data. The model delivered satisfactory results on three out of five basin outlets while the other two had stream gauge failure during the event. The internal basin dynamics of the most affected area in Zelezniki, was successfully tested in eight of its sub-basins by comparing the peak discharges with the ones evaluated by the slope-conveyance method during a detailed intensive post event campaign. The added value of this method is in the reduced uncertainty in peak discharge estimation and event interpretation and in an effective flash flood warning system for the study area when it is combined with radar nowcasts and operational high resolution short range weather forecast models.

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