Ensemble flood forecasting: a review.

By Hannah L. Cloke, Florian Pappenberger. 2009

1,065 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/e5aff7dfd1c5cabb88f48379f5662f907961fe16

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In Atmospheric Science

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Citations in the corpus

A review on regional convection‐permitting climate modeling: Demonstrations, prospects, and challenges (2015, background): “Several studies highlight the potential benefits of CPMs applied to flash flood predictions [Cloke and Pappenberger, 2009; Renner et al., 2009]; however, very little is known about the real potentials of CPMs on the climate time scale.”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “Ensemble learning methods have been prominent in climate extreme problems such as flood forecasting [337, 338].”
Functional peaks‐over‐threshold analysis (2020, background): “Cloke and Pappenberger (2009) review similar approaches based on climate models.”
Discrimination ability of the Energy score (2013, background): “This phenomenon touches a wide range of applications, from economics and finance [1, 2], to earthquake prediction [3], while it also has wide appeal in meteorology [4], and for weather-related processes like renewable energy production [5, 6] and floods [7].”
Very‐short‐term probabilistic forecasting of wind power with generalized logit–normal distributions (2012, background): “…(Tay and Wallis, 2000; Timmermann, 2000), earthquake prediction (Kagan and Jackson, 2000) and meteorology (Leutbecher and Palmer, 2008), as well as for weather-related processes like renewable energy production (Bacher et al., 2009; Pinson et al., 2007) or floods (Cloke and Pappenberger, 2009).”
Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications (2015, positive): “…to distribution models) and their diverse uses in many applications (Nossent et al., 2011), such as land use (Park et al., 2013) and climate change scenario analysis (Ntegeka et al., 2014), flood prediction (Cloke and Pappenberger, 2009), and rainfall–runoff modeling (Modarres and Ouarda, 2013).”
A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework (2018, background): “Therefore, accurate and reliable long-term forecasts play increasingly important roles in flood and drought control, reservoir operation, and water resource management and utilization (Cloke and Pappenberger 2009; Yang et al. 2013).”
Balance between calibration objectives in a conceptual hydrological model (2010, methodology): “Hydrological models have been applied extensively in water resources management for purposes such as flood forecasting (for an overview, see Cloke & Pappenberger, 2009 ), low-flow simulation (e.”
Evaluation of a nonparametric post‐processor for bias correction and uncertainty estimation of hydrologic predictions (2013, background): “INTRODUCTION Ensemble prediction systems (EPS) are increasingly used to quantify and propagate uncertainties through atmospheric and hydrologic models (Liu and Gupta, 2007; Cloke and Pappenberger, 2009;Matott et al., 2009).”
The Monetary Benefit of Early Flood Warnings in Europe (2015, positive): “…in weather prediction and make many forecasts, known as ensemble members, by making alterations to the forecasting model or to the starting conditions (Buizza, 2003, 2015; Cloke and Pappenberger, 2009; Hagedorn et al., 2012; Vitart et al., 2008 (Bogner and Pappenberger, 2011; Bogner et al., 2012).”
The Annual Maximum Flood Peak Discharge Forecasting Using Hermite Projection Pursuit Regression with SSO and LS Method (2016, background): “For such purpose, the flood forecasting has always received tremendous attention of researchers and been a particular interested issue in operational hydrology (Cloke and Pappenberger 2009).”
Alternative configurations of quantile regression for estimating predictive uncertainty in water level forecasts for the upper Severn River: A comparison (2014, methodology): “An overview of applications and best practices was given by Cloke and Pappenberger (2009).”
Use of a nonstationary copula to predict future bivariate low flow frequency in the Connecticut river basin (2016, background): “Improvements in hydrologic forecasting have occurred because of recent advances in hydrological modelling, weather forecasting, and the incorporation understanding of hydro-climatic teleconnections (Cloke and Pappenberger, 2009; Wood et al., 2011).”