Deriving Rain Threshold for Early Warning Based on a Coupled Hydrological-Hydraulic Model

Abstract Flash floods are highly variable phenomena in both time and space. Therefore, tools with the potential to provide early warning are needed to analyse them. In Europe, flash floods often occur on small catchments; it has already been shown that the spatial variability of rainfall has a great impact on the catchment response. The aim of this paper is to use a coupled hydrological-hydraulic model (MIKE SHE/MIKE 11) to determine the rainfall thresholds and transformation coefficients from hourly rain to other durations, which will lead to flooding of the inhabited areas to the ungauged Ungureni catchment. The model was calibrated and validated using a reference discharge previously obtained by UTCB at the downstream gauge section of Teleorman River (Tatarastii de Sus) using MIKE 11 UHM module. Once the rainfall thresholds are determined, they can be used in flood forecasting and issuing warning with lead time for the inhabitants of the two villages located in Ungureni watershed. The method proposed in this paper can be used for other watersheds prone to flooding, so warnings can be issued with lead time.

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Increasing Early Warning Lead Time Through Improved Transboundary Flood Forecasting in the Gash River Basin, Horn of Africa

Flood Forecasting ◽

10.1016/b978-0-12-801884-2.00008-6 ◽

2016 ◽

pp. 183-200 ◽

Cited By ~ 2

Author(s):

G. Amarnath ◽

N. Alahacoon ◽

Y. Gismalla ◽

Y. Mohammed ◽

B.R. Sharma ◽

...

Keyword(s):

River Basin ◽

Early Warning ◽

Lead Time ◽

Flood Forecasting ◽

Horn Of Africa

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Flood forecasting using quantitative precipitation forecasts and hydrological modeling in the Sebeya catchment, Rwanda

H2Open Journal ◽

10.2166/h2oj.2021.094 ◽

2021 ◽

Author(s):

Mukakarangwa Assoumpta ◽

Daniel Aja

Keyword(s):

Early Warning ◽

Early Warning System ◽

Lead Time ◽

Hydrological Modeling ◽

Model Performance ◽

Warning System ◽

Flood Forecasting ◽

False Alarms ◽

Time Flow ◽

Quantitative Precipitation Forecasts

Abstract The absence of a viable flood early warning system for the Sebeya River catchment continues to impede government efforts toward improving community preparedness, the reduction of flood impacts and relief. This paper reports on a recent study that used satellite data, quantitative precipitation forecasts and the rainfall–runoff model for short-term flood forecasting in the Sebeya catchment. The global precipitation measurement product was used as a satellite rainfall product for model calibration and validation and forecasted European Centre Medium-Range Weather Forecasts (ECMWF) rainfall products were evaluated to forecast flood. Model performance was evaluated by the visual examination of simulated hydrographs, observed hydrographs and a number of performance indicators. The real-time flow forecast assessment was conducted with respect to three different flood warning threshold levels for a 3–24-h lead time. The result for a 3-h lead time showed 72% of hits, 7.5% of false alarms and 9.5% of missed forecasts. The number of hits decreased, as the lead time increased. This study did not consider the uncertainties in observed data, and this can influence the model performance. This work provides a base for future studies to establish a viable flood early warning system in the study area and beyond.

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Verification of two hydrological models for real-time flood forecasting in the Hindu Kush Himalaya (HKH) region

Natural Hazards ◽

10.1007/s11069-021-05014-y ◽

2021 ◽

Author(s):

Karma Tsering ◽

Manish Shrestha ◽

Kiran Shakya ◽

Birendra Bajracharya ◽

Mir Matin ◽

...

Keyword(s):

Early Warning ◽

Lead Time ◽

Model Performance ◽

Forecast Accuracy ◽

Early Warning Systems ◽

Flood Forecasting ◽

Hydrological Models ◽

Good Decision ◽

Hindu Kush ◽

Forecast Validation

AbstractThe Hindu Kush Himalayan region is extremely susceptible to periodic monsoon floods. Early warning systems with the ability to predict floods in advance can benefit tens of millions of people living in the region. Two web-based flood forecasting tools (ECMWF-SPT and HIWAT-SPT) are therefore developed and deployed jointly by SERVIR-HKH and NASA-AST to provide early warning to Bangladesh, Bhutan, and Nepal. ECMWF-SPT provides ensemble forecast up to 15-day lead time, whereas HIWAT-SPT provides deterministic forecast up to 3-day lead time covering almost 100% of the rivers. Hydrological models in conjunction with forecast validation contribute not only to advancing the processes of a forecasting system, but also objectively assess the joint distribution of forecasts and observations in quantifying forecast accuracy. The validation of forecast products has emerged as a priority need to evaluate the worth of the predictive information in terms of quality and consistency. This paper describes the effort made in developing the hydrological forecast systems, the current state of the flood forecast services, and the performance of the forecast evaluation. Both tools are validated using a selection of appropriate metrics in measurement in both probabilistic and deterministic space. The numerical metrics are further complemented by graphical representations of scores and probabilities. It was found that the models had a good performance in capturing high flood events. The evaluation across multiple locations indicates that the model performance and forecast goodness are variable on spatiotemporal scale. The resulting information is used to support good decision-making in risk and resource management.

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Hydrological and hydraulic model for flood forecasting in Rwanda

Modeling Earth Systems and Environment ◽

10.1007/s40808-021-01146-z ◽

2021 ◽

Author(s):

Gisele Icyimpaye ◽

Chérifa Abdelbaki ◽

Khaldoon A. Mourad

Keyword(s):

Flood Forecasting ◽

Hydraulic Model

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Flood Forecasting and Warning System Structures: Procedure and Application to a Small Urban Stream in South Korea

Water ◽

10.3390/w11081571 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1571 ◽

Cited By ~ 4

Author(s):

Song ◽

Park ◽

Lee ◽

Park ◽

Song

Keyword(s):

Decision Making ◽

Water Level ◽

Real Time ◽

Lead Time ◽

Flash Flood ◽

Warning System ◽

Flood Forecasting ◽

Urban Stream ◽

Forecasting And Warning ◽

Flood Warnings

The runoff from heavy rainfall reaches urban streams quickly, causing them to rise rapidly. It is therefore of great importance to provide sufficient lead time for evacuation planning and decision making. An efficient flood forecasting and warning method is crucial for ensuring adequate lead time. With this objective, this paper proposes an analysis method for a flood forecasting and warning system, and establishes the criteria for issuing urban-stream flash flood warnings based on the amount of rainfall to allow sufficient lead time. The proposed methodology is a nonstructural approach to flood prediction and risk reduction. It considers water level fluctuations during a rainfall event and estimates the upstream (alert point) and downstream (confluence) water levels for water level analysis based on the rainfall intensity and duration. We also investigate the rainfall/runoff and flow rate/water level relationships using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and the HEC’s River Analysis System (HEC-RAS) models, respectively, and estimate the rainfall threshold for issuing flash flood warnings depending on the backwater state based on actual watershed conditions. We present a methodology for issuing flash flood warnings at a critical point by considering the effects of fluctuations in various backwater conditions in real time, which will provide practical support for decision making by disaster protection workers. The results are compared with real-time water level observations of the Dorim Stream. Finally, we verify the validity of the flash flood warning criteria by comparing the predicted values with the observed values and performing validity analysis.

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2D hydraulic model integration to real-time flash flood forecasting chain

Comprehensive Flood Risk Management ◽

10.1201/b13715-224 ◽

2012 ◽

Author(s):

C Girard ◽

T Godfroy ◽

M Erlich ◽

E David ◽

C Sorbet ◽

...

Keyword(s):

Real Time ◽

Flash Flood ◽

Flood Forecasting ◽

Hydraulic Model ◽

Model Integration ◽

Flash Flood Forecasting

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Exploring community resilience and early warning solution for flash floods, debris flow and landslides in conflict prone villages of Badakhshan, Afghanistan

International Journal of Disaster Risk Reduction ◽

10.1016/j.ijdrr.2018.07.012 ◽

2019 ◽

Vol 33 ◽

pp. 5-15 ◽

Cited By ~ 11

Author(s):

Ashutosh Mohanty ◽

Mujahid Hussain ◽

M. Mishra ◽

D.B. Kattel ◽

Indrajit Pal

Keyword(s):

Debris Flow ◽

Early Warning ◽

Community Resilience ◽

Flash Floods

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Assessing the Potential of Upcoming Satellite Altimeter Missions in Operational Flood Forecasting Systems

Remote Sensing ◽

10.3390/rs13214459 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4459

Author(s):

Aline Falck ◽

Javier Tomasella ◽

Fabrice Papa

Keyword(s):

Data Collection ◽

Lead Time ◽

Flood Forecasting ◽

Lead Times ◽

Latency Time ◽

Distributed Hydrological Model ◽

Satellite Altimeter ◽

Operational Characteristics ◽

Potential Benefits ◽

Forecasting System

This study investigates the potential of observations with improved frequency and latency time of upcoming altimetry missions on the accuracy of flood forecasting and early warnings. To achieve this, we assessed the skill of the forecasts of a distributed hydrological model by assimilating different historical discharge time frequencies and latencies in a framework that mimics an operational forecast system, using the European Ensemble Forecasting system as the forcing. Numerical experiments were performed in 22 sub-basins of the Tocantins-Araguaia Basin. Forecast skills were evaluated in terms of the Relative Operational Characteristics (ROC) as a function of the drainage area and the forecasts’ lead time. The results showed that increasing the frequency of data collection and reducing the latency time (especially 1 d update and low latency) had a significant impact on steep headwater sub-basins, where floods are usually more destructive. In larger basins, although the increased frequency of data collection improved the accuracy of the forecasts, the potential benefits were limited to the earlier lead times.

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