Bayesian network model for flood forecasting based on atmospheric ensemble forecasts

Leila Goodarzi,Abbas Roozbahani,Mohammad E. Banihabib,Jörg Dietrich

doi:10.5194/nhess-19-2513-2019

Abstract

Abstract. The purpose of this study is to propose the Bayesian network (BN) model to estimate flood peaks from atmospheric ensemble forecasts (AEFs). The Weather Research and Forecasting (WRF) model was used to simulate historic storms using five cumulus parameterization schemes. The BN model was trained to compute flood peak forecasts from AEFs and hydrological pre-conditions. The mean absolute relative error was calculated as 0.076 for validation data. An artificial neural network (ANN) was applied for the same problem but showed inferior performance with a mean absolute relative error of 0.39. It seems that BN is less sensitive to small data sets, thus it is more suited for flood peak forecasting than ANN.

Highlights

Floods are the most threatening natural disaster across the world (Hénonin et al, 2010)
This study proposed a probabilistic model to address the uncertainties of flood forecasts using the Bayesian networks (BNs) and to estimate the flood peak in an ensemble flood forecasting
Results of the BN are compared with the results obtained from an artificial neural network as a widely used model to show the performance of BN

Summary

Introduction

Floods are the most threatening natural disaster across the world (Hénonin et al, 2010). Many flood forecasting systems in the world rely on observed rainfall, and the lead time of these systems is often short for small basins (Banihabib and Arabi, 2016). Numerical weather prediction (NWP) models can be used to increase the lead time of flood warning by using in advance forecasts of rainfall. The combination of NWP and hydrological models can significantly increase the flood warning lead time rather than using observed rainfall, the deterministic weather prediction does not reflect the existing uncertainties. Ensemble methods are considered to be an effective way to estimate the probability of future states of the atmosphere by addressing uncertainties present in initial conditions and in model approximations (Tennant et al, 2007). Various approaches have been developed to produce atmospheric ensemble forecasts including perturbing the initial conditions, perturbing the input parameters of the model, using multi-model ensembles and using different parameterization schemes (Yang et al, 2012)

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Conclusion