scholarly journals The benefit of high-resolution operational weather forecasts for flash flood warning

2008 ◽  
Vol 5 (1) ◽  
pp. 345-377 ◽  
Author(s):  
J. Younis ◽  
S. Anquetin ◽  
J. Thielen
Keyword(s):  
High Resolution ◽  
Weather Forecasting ◽  
Flash Flood ◽  
Flood Forecasting ◽  
Flash Floods ◽  
Rainfall Runoff ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. In Mediterranean Europe, flash flooding is one of the most devastating hazards in terms of human life loss and infrastructures. Over the last two decades, flash floods brought losses of a billion Euros of damage in France alone. One of the problems of flash floods is that warning times are very short, leaving typically only a few hours for civil protection services to act. This study investigates if operationally available shortrange numerical weather forecasts together with a rainfall-runoff model can be used as early indication for the occurrence of flash floods. One of the challenges in flash flood forecasting is that the watersheds are typically small and good observational networks of both rainfall and discharge are rare. Therefore, hydrological models are difficult to calibrate and the simulated river discharges cannot always be compared with ground "truth". The lack of observations in most flash flood prone basins, therefore, lead to develop a method where the excess of the simulated discharge above a critical threshold can provide the forecaster with an indication of potential flood hazard in the area with leadtimes of the order of the weather forecasts. This study is focused on the Cévennes-Vivarais region in the Southeast of the Massif Central in France, a region known for devastating flash floods. The critical aspects of using numerical weather forecasting for flash flood forecasting are being described together with a threshold – exceedance. As case study the severe flash flood event which took place on 8–9 September 2002 has been chosen. The short-range weather forecasts, from the Lokalmodell of the German national weather service, are driving the LISFLOOD model, a hybrid between conceptual and physically based rainfall-runoff model. Results of the study indicate that high resolution operational weather forecasting combined with a rainfall-runoff model could be useful to determine flash floods more than 24 hours in advance.

scholarly journals The benefit of high-resolution operational weather forecasts for flash flood warning

2008 ◽  
Vol 12 (4) ◽  
pp. 1039-1051 ◽  
Author(s):  
J. Younis ◽  
S. Anquetin ◽  
J. Thielen
Keyword(s):  
High Resolution ◽  
Short Range ◽  
Weather Forecasting ◽  
Flash Flood ◽  
Flash Floods ◽  
Rainfall Runoff ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. In Mediterranean Europe, flash flooding is one of the most devastating hazards in terms of loss of human life and infrastructures. Over the last two decades, flash floods have caused damage costing a billion Euros in France alone. One of the problems of flash floods is that warning times are very short, leaving typically only a few hours for civil protection services to act. This study investigates if operationally available short-range numerical weather forecasts together with a rainfall-runoff model can be used for early indication of the occurrence of flash floods. One of the challenges in flash flood forecasting is that the watersheds are typically small, and good observational networks of both rainfall and discharge are rare. Therefore, hydrological models are difficult to calibrate and the simulated river discharges cannot always be compared with ground measurements. The lack of observations in most flash flood prone basins, therefore, necessitates the development of a method where the excess of the simulated discharge above a critical threshold can provide the forecaster with an indication of potential flood hazard in the area, with lead times of the order of weather forecasts. This study is focused on the Cévennes-Vivarais region in the Southeast of the Massif Central in France, a region known for devastating flash floods. This paper describes the main aspects of using numerical weather forecasting for flash flood forecasting, together with a threshold – exceedance. As a case study the severe flash flood event which took place on 8–9 September 2002 has been chosen. Short-range weather forecasts, from the Lokalmodell of the German national weather service, are used as input for the LISFLOOD model, a hybrid between a conceptual and physically based rainfall-runoff model. Results of the study indicate that high resolution operational weather forecasting combined with a rainfall-runoff model could be useful to determine flash floods more than 24 h in advance.

scholarly journals Ensemble Flash Flood Predictions Using a High-Resolution Nationwide Distributed Rainfall-Runoff Model: Case Study of the Heavy Rain Event of July 2018 and Typhoon Hagibis in 2019

2020 ◽  
Author(s):  
Takahiro Sayama ◽  
Masafumi Yamada ◽  
Yoshito Sugawara ◽  
Dai Yamazaki
Keyword(s):  
Lead Time ◽  
Flash Flood ◽  
Heavy Rain ◽  
Flash Floods ◽  
Rain Event ◽  
Rainfall Runoff ◽  
Heavy Rain Event ◽  
Peak Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract The heavy rain event of July 2018 and Typhoon Hagibis in October 2019 caused severe flash flood disasters in numerous parts of western and eastern Japan. Flash floods need to be predicted over a wide range with long forecasting lead time for effective evacuation. The predictability of flash floods caused by the two extreme events are investigated by using a high-resolution (~150 m) nationwide distributed rainfall-runoff model forced by ensemble precipitation forecasts with 39-h lead time. Results of the deterministic simulation at nowcasting mode with radar and gauge composite rainfall could reasonably simulate the storm runoff hydrographs at many dam reservoirs over western Japan for the case of heavy rainfall in 2018 (F18) with the default parameter setting. For the case of Typhoon Hagibis in 2019 (T19), a similar performance was obtained by incorporating unsaturated flow effect in the model applied to Kanto region. The performance of the ensemble forecast was evaluated based on the bias ratios and the relative operating characteristic curves, which suggested the higher predictability in peak runoff for T19. For the F18, the uncertainty arises due to the difficulty in accurately forecasting the storm positions by the frontal zone; as a result, the actual distribution of the peak runoff could not be well forecasted. Overall, this study showed that the predictability of flash floods was different between the two extreme events. The ensemble spreads contain quantitative information of predictive uncertainty, which can be utilized for the decision making of emergency responses against flash floods.

scholarly journals Ensemble Flash Flood Predictions Using a High-Resolution Nationwide Distributed Rainfall-Runoff Model: Case Study of the Heavy Rain Event of July 2018 and Typhoon Hagibis in 2019

2020 ◽  
Author(s):  
Takahiro Sayama ◽  
Masafumi Yamada ◽  
Yoshito Sugawara ◽  
Dai Yamazaki
Keyword(s):  
Lead Time ◽  
Flash Flood ◽  
Heavy Rain ◽  
Flash Floods ◽  
Rain Event ◽  
Rainfall Runoff ◽  
Heavy Rain Event ◽  
Peak Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract The heavy rain event of July 2018 and Typhoon Hagibis in October 2019 caused severe flash flood disasters in numerous parts of western and eastern Japan. Flash floods need to be predicted over a wide range with long forecasting lead time for effective evacuation. The predictability of flash floods caused by the two extreme events are investigated by using a high-resolution (~ 150 m) nationwide distributed rainfall-runoff model forced by ensemble precipitation forecasts with 39-h lead time. Results of the deterministic simulation at nowcasting mode with radar and gauge composite rainfall could reasonably simulate the storm runoff hydrographs at many dam reservoirs over western Japan for the case of heavy rainfall in 2018 (F18) with the default parameter setting. For the case of Typhoon Hagibis in 2019 (T19), a similar performance was obtained by incorporating unsaturated flow effect in the model applied to Kanto region. The performance of the ensemble forecast was evaluated based on the bias scores and the relative operating characteristic curves, which suggested the higher predictability in peak runoff for T19. For the F18, the uncertainty arises due to the difficulty in accurately forecasting the storm positions by the frontal zone; as a result, the actual distribution of the peak runoff could not be well forecasted. Overall, this study showed that the predictability of flash floods was different between the two extreme events. The ensemble spreads contain quantitative information of predictive uncertainty, which can be utilized for the decision making of emergency responses against flash floods.

scholarly journals Ensemble flash flood predictions using a high-resolution nationwide distributed rainfall-runoff model: case study of the heavy rain event of July 2018 and Typhoon Hagibis in 2019

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Takahiro Sayama ◽  
Masafumi Yamada ◽  
Yoshito Sugawara ◽  
Dai Yamazaki
Keyword(s):  
Lead Time ◽  
Flash Flood ◽  
Heavy Rain ◽  
Flash Floods ◽  
Rain Event ◽  
Rainfall Runoff ◽  
Heavy Rain Event ◽  
Peak Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

AbstractThe heavy rain event of July 2018 and Typhoon Hagibis in October 2019 caused severe flash flood disasters in numerous parts of western and eastern Japan. Flash floods need to be predicted over a wide range with long forecasting lead time for effective evacuation. The predictability of flash floods caused by the two extreme events is investigated by using a high-resolution (~ 150 m) nationwide distributed rainfall-runoff model forced by ensemble precipitation forecasts with 39 h lead time. Results of the deterministic simulation at nowcasting mode with radar and gauge composite rainfall could reasonably simulate the storm runoff hydrographs at many dam reservoirs over western Japan for the case of heavy rainfall in 2018 (F18) with the default parameter setting. For the case of Typhoon Hagibis in 2019 (T19), a similar performance was obtained by incorporating unsaturated flow effect in the model applied to Kanto Region. The performance of the ensemble forecast was evaluated based on the bias ratios and the relative operating characteristic curves, which suggested the higher predictability in peak runoff for T19. For the F18, the uncertainty arises due to the difficulty in accurately forecasting the storm positions by the frontal zone; as a result, the actual distribution of the peak runoff could not be well forecasted. Overall, this study showed that the predictability of flash floods was different between the two extreme events. The ensemble spreads contain quantitative information of predictive uncertainty, which can be utilized for the decision making of emergency responses against flash floods.

scholarly journals Technical Note: Updating procedure for flood forecasting with conceptual HBV-type models

2006 ◽  
Vol 10 (6) ◽  
pp. 783-788 ◽  
Author(s):  
Th. Wöhling ◽  
F. Lennartz ◽  
M. Zappa
Keyword(s):  
Flood Forecasting ◽  
Technical Note ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Mountainous Catchments ◽  
Rainfall Runoff Model ◽  
Updating Procedure ◽  
Global And Local ◽  
Hbv Model ◽  
Runoff Model

Abstract. Flood forecasting is of increasing importance as it comes to an increasing variability in global and local climates. But rainfall-runoff models are far from being perfect. In order to achieve a better prediction for emerging flood events, the model outputs have to be continuously updated. This contribution introduces a rather simple, yet effective updating procedure for the conceptual semi-distributed rainfall-runoff model PREVAH, whose runoff generation module relies on similar algorithms as the HBV-Model. The current conditions of the system, i.e. the contents of the upper soil reservoirs, are updated by the proposed method. The testing of the updating procedure on data from two mountainous catchments in Switzerland reveals a significant increase in prediction accuracy with regards to peak flow.

Uncertainty Evaluation in a Flood Forecasting Model Using JMA Numerical Weather Prediction

2009 ◽  
Vol 4 (4) ◽  
pp. 600-605 ◽  
Author(s):  
Hadi Kardhana ◽  
◽  
Akira Mano ◽  
Keyword(s):  
Numerical Weather Prediction ◽  
Lead Time ◽  
Weather Prediction ◽  
Japan Meteorological Agency ◽  
Rainfall Runoff ◽  
Flood Prediction ◽  
Numerical Weather ◽  
Forecast Lead Time ◽  
Rainfall Runoff Model ◽  
Runoff Model

Numerical weather prediction (NWP) is useful in flood prediction using a rainfall-runoff model. Uncertainty occurring in the forecast, however, adversely affects flood prediction accuracy, in addition to uncertainty inherent in the rainfall-runoff model. Clarifying this uncertainty and its magnitude is expected to lead to wider forecast applications. Taking the case of Japan’s Shichikashuku Dam, 6 flood events between 2002 and 2007 were analyzed. NWP was based on short-range forecasts by the Japan Meteorological Agency (JMA). The rainfall-runoff model is based on a distributed tank model. This research calculates uncertainty by identifying and quantifying the relative error of forecasts by a) NWP and b) the runoff model. Results showed that NAP is the main cause of flood forecast uncertainty. They also showed the correlation between forecast lead time and uncertainty. Uncertainty rises with longer lead time, corresponding to the magnitude of observed discharge and precipitation.

scholarly journals Enhancing Automated Water Level Control at Navigable Waterways by High-Resolution Weather Predictions

10.29007/tfbm ◽  
2018 ◽  
Author(s):  
Julia Kasper ◽  
Georg Pranner ◽  
Franz Simons ◽  
Michael Denhard ◽  
Carsten Thorenz
Keyword(s):  
High Resolution ◽  
Water Level ◽  
Extreme Weather Events ◽  
Rainfall Runoff ◽  
Level Control ◽  
Weather Events ◽  
Rainfall Runoff Model ◽  
Feed Forward Controller ◽  
Runoff Model ◽  
The Ideal

Heavy rainfall can cause large variations in the water level of navigable waterways when a lot of urban runoff is generated on sealed surfaces and discharged into the river. Due to climate change, extreme weather events will increase in intensity and frequency demanding a better automated water level control at impounded waterways. High- resolution forecasts of catchment rainfall are intended to serve as input to a rainfall- runoff model. Based on the resulting discharge forecasts, a model predictive feed forward controller calculates the ideal water level and discharge across the barrage. The control system is completed by a PI control loop. In this way water level deviations and discharge peaks resulting from stormwater overflow events can be reduced, which enhances the safety of shipping. Regarding the uncertainties of weather predictions, the consequences of an underestimated or overestimated overflow discharge are investigated.

scholarly journals The challenge of forecasting impacts of flash floods: test of a simplified hydraulic approach and validation based on insurance claim data

2017 ◽  
Author(s):  
Guillaume Le Bihan ◽  
Olivier Payrastre ◽  
Eric Gaume ◽  
David Moncoulon ◽  
Frederic Pons
Keyword(s):  
Cross Sections ◽  
Flash Flood ◽  
Claim Data ◽  
Flash Floods ◽  
Water Levels ◽  
Claim Database ◽  
Rainfall Runoff ◽  
Insurance Claim ◽  
Local Reference ◽  
Rainfall Runoff Model

Abstract. Up to now, flash flood monitoring and forecasting systems, based on rainfall radar measurements and distributed rainfall-runoff models, generally aimed at estimating flood magnitudes - typically discharges or return periods – at selected river cross-sections. The approach presented here goes one step ahead by proposing an integrated forecasting chain for the direct assessment of flash flood possible impacts on inhabited areas (number of buildings at risk in the presented case studies). The proposed approach includes, in addition to a distributed rainfall-runoff model, an automatic hydraulic method suited for the computation of flood extent maps on a dense river network and over large territories. The resulting catalogue of flood extent maps is then combined with land use data to build a flood impact curve for each considered river reach: i.e. number of inundated buildings versus discharge. Theses curves are finally used to compute estimated impacts based on forecasted discharges. The approach has been extensively tested in the regions of Alès and Draguignan, located in the South of France, where well documented major flash floods recently occurred. The article presents two types of validation results. First, the automatically computed flood extent maps and corresponding water levels are tested against rating curves at available river gauging stations as well as against local reference or observed flood extent maps. Second, a rich and comprehensive insurance claim database is used to evaluate the relevance of the estimated impacts for some recent major floods.

scholarly journals Investigation of satellite rainfall-driven rainfall–runoff model using deep learning approaches in two different catchments in India

2021 ◽  
Author(s):  
Pavan Kumar Yeditha ◽  
Maheswaran Rathinasamy ◽  
Sai Sumanth Neelamsetty ◽  
Biswa Bhattacharya ◽  
Ankit Agarwal
Keyword(s):  
Deep Learning ◽  
River Basin ◽  
Flood Forecasting ◽  
Precipitation Data ◽  
Rainfall Runoff ◽  
Data Set ◽  
Satellite Precipitation ◽  
Satellite Rainfall ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and >50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's Eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in NSC values of 0.68 and 0.67, while ELM models for Mahanadhi river basin (MRB) with the same input resulted in NSC values of 0.86 and 0.87, respectively, during training and validation stages. At the same time, the LSTM model with CHIRPS inputs for the VRB resulted in NSC values of 0.68 and 0.65, and the ELM model with CHIRPS inputs for the MRB resulted in NSC values of 0.89 and 0.88, respectively, in training and validation stages. These results indicated that both the SPPs could reliably be used with LSTM and ELM models for rainfall–runoff modelling and streamflow prediction. This paper highlights that deep learning models, such as ELM and LSTM, with the GPM-IMERG products can lead to a new horizon to provide flood forecasting in flood-prone catchments.

Sign in / Sign up

Export Citation Format

Share Document