scholarly journals A dynamic river network method for the prediction of floods using a parsimonious rainfall-runoff model

2019 ◽  
Vol 51 (2) ◽  
pp. 146-168 ◽  
Author(s):  
Aynalem Tassachew Tsegaw ◽  
Thomas Skaugen ◽  
Knut Alfredsen ◽  
Tone M. Muthanna
Keyword(s):  
Relative Error ◽  
Flood Forecasting ◽  
River Network ◽  
Rainfall Runoff ◽  
Design Flood ◽  
Study Results ◽  
Network Method ◽  
Flood Peaks ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract Floods are one of the major climate-related hazards and cause casualties and substantial damage. Accurate and timely flood forecasting and design flood estimation are important to protect lives and property. The Distance Distribution Dynamic (DDD) is a parsimonious rainfall-runoff model which is being used for flood forecasting at the Norwegian flood forecasting service. The model, like many other models, underestimates floods in many cases. To improve the flood peak prediction, we propose a dynamic river network method into the model. The method is applied for 15 catchments in Norway and tested on 91 flood peaks. The performance of DDD in terms of KGE and BIAS is identical with and without dynamic river network, but the relative error (RE) and mean absolute relative error (MARE) of the simulated flood peaks are improved significantly with the method. The 0.75 and 0.25 quantiles of the RE are reduced from 41% to 23% and from 22% to 1%, respectively. The MARE is reduced from 32.9% to 15.7%. The study results also show that the critical support area is smaller in steep and bare mountain catchments than flat and forested catchments.

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.

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.

Fuzzy computing based rainfall-runoff model for real time flood forecasting

2005 ◽  
Vol 19 (4) ◽  
pp. 955-968 ◽  
Author(s):  
P. C. Nayak ◽  
K. P. Sudheer ◽  
K. S. Ramasastri
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Fuzzy Computing

scholarly journals A systematic comparison of statistical and hydrological methods for design flood estimation

2019 ◽  
Vol 50 (6) ◽  
pp. 1665-1678 ◽  
Author(s):  
Kenechukwu Okoli ◽  
Maurizio Mazzoleni ◽  
Korbinian Breinl ◽  
Giuliano Di Baldassarre
Keyword(s):  
Model Selection ◽  
Statistical Methods ◽  
Model Averaging ◽  
Return Periods ◽  
Rainfall Runoff ◽  
Design Flood ◽  
High Return ◽  
Flood Estimation ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract We compare statistical and hydrological methods to estimate design floods by proposing a framework that is based on assuming a synthetic scenario considered as ‘truth’ and use it as a benchmark for analysing results. To illustrate the framework, we used probability model selection and model averaging as statistical methods, while continuous simulations made with a simple and relatively complex rainfall–runoff model are used as hydrological methods. The results of our numerical exercise show that design floods estimated by using a simple rainfall–runoff model have small parameter uncertainty and limited errors, even for high return periods. Statistical methods perform better than the linear reservoir model in terms of median errors for high return periods, but their uncertainty (i.e., variance of the error) is larger. Moreover, selecting the best fitting probability distribution is associated with numerous outliers. On the contrary, using multiple probability distributions, regardless of their capability in fitting the data, leads to significantly fewer outliers, while keeping a similar accuracy. Thus, we find that, among the statistical methods, model averaging is a better option than model selection. Our results also show the relevance of the precautionary principle in design flood estimation, and thus help develop general recommendations for practitioners and experts involved in flood risk reduction.

scholarly journals Ensemble flood forecasting to support dam water release operation using 10 and 2 km-resolution JMA Nonhydrostatic Model ensemble rainfalls

2015 ◽  
Vol 3 (12) ◽  
pp. 7411-7456
Author(s):  
K. Kobayashi ◽  
S. Otsuka ◽  
K. Saito ◽  
Keyword(s):  
Flood Control ◽  
Flood Forecasting ◽  
Ensemble Member ◽  
Japan Meteorological Agency ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Water Release ◽  
Nonhydrostatic Model ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency Nonhydrostatic Model are used as the input data to a rainfall–runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a high-resolution 2 km spatial resolution. A distributed rainfall–runoff model is constructed for the Kasahori dam catchment (approx. 70 km2) and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km-resolution JMA-NHM ensemble simulation are more appropriate than the 10 km-resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m3 s−1; a threshold value for flood control. The inflows with the 10 km-resolution ensemble rainfall are all considerably smaller than the observations, while, at least one simulated discharge out of 11 ensemble members with the 2 km-resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the position-shifted rainfalls show much better results than the original ensemble discharge simulations.

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.

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