Ensemble flood forecasting: Current status and future opportunities

Abstract

Ensemble flood forecasting has gained significant momentum over the past decade due to the growth of ensemble numerical weather and climate prediction, expansion in high performance computing, growing interest in shifting from deterministic to risk-based decision-making that accounts for forecast uncertainty, and the efforts of communities such as the international Hydrologic Ensemble Prediction Experiment (HEPEX), which focuses on advancing relevant ensemble forecasting capabilities and fostering its adoption. With this shift, comes the need to understand the current state of ensemble flood forecasting, in order to provide insights into current capabilities and areas for improvement, thus identifying future research opportunities to allow for better allocation of research resources. In this article, we provide an overview of current research activities in ensemble flood forecasting and discuss knowledge gaps and future research opportunities, based on a review of 70 papers focusing on various aspects of ensemble flood forecasting around the globe. Future research directions include opportunities to improve technical aspects of ensemble flood forecasting, such as data assimilation techniques and methods to account for more sources of uncertainty, and developing ensemble forecasts for more variables, for example, flood inundation, by applying techniques such as machine learning. Further to this, we conclude that there is a need to not only improve technical aspects of flood forecasting, but also to bridge the gap between scientific research and hydrometeorological model development, and real-world flood management using probabilistic ensemble forecasts, especially through effective communication.

LakeEnsemblR: An R package that facilitates ensemble modelling of lakes

“…Ensemble modeling involves either running the same model multiple times with different settings or running multiple models on the same study site. One of the main advantages of model ensembles is that the uncertainty in the model predictions can be estimated (Trolle et al, 2014;Wu et al, 2020). This allows the modeller to assess the likelihood of occurrence of certain model predictions.…”

“…Model ensembles are now widely used in meteorological forecasting (Gneiting and Raftery, 2005;Leutbecher and Palmer, 2008), flood forecasting (Wu et al, 2020), and climate studies (Mu et al, 2017;Parker, 2010). Ensemble models have gained momentum in large-scale water quality studies (Van Vliet et al, 2019), but their adoption in limnology has been slow.…”

Using ensemble reforecasts to generate flood thresholds for improved global flood forecasting

“…The state‐of‐the‐art systems in use today provide an ensemble of equally likely solutions that can be used to define occurrence probabilities for certain flood events (Cloke & Pappenberger, 2009; Wu et al, 2020). These flood events are defined by comparing the forecast time series with flood thresholds, usually based on a return period magnitude or a quantile.…”

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

“…Instead of deterministic forecasting, probabilistic forecasting with EPS has advanced in the last decade (Cloke and Pappenberger 2009;Wu et al 2020). When the possibility of a severe storm becomes high because a typhoon is approaching or because a frontal line is stagnant, if we can predict the occurrence probability of flash floods leading to local damage, we could prepare for the extreme weather as a society (Terti et al 2019).…”