Uncertainties of flood frequency estimation approaches based on continuous simulation using data resampling

Arnaud, Patrick; Cantet, Philippe; Odry, Jean

doi:10.1016/j.jhydrol.2017.09.011

Cited by 24 publications

(26 citation statements)

References 54 publications

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“…Beven, 2002, 2004;Cameron et al, 1999). In addition to the uncertainties from seven hydrological model parameters, Arnaud et al (2017) investigated how the uncertainty related to six rainfall generator parameters propagates through the simulation framework, using more than 1000 French basins with hydrologic observation series of 40 years (median over all basins) and several hundreds of replicats. In their study they found that the uncertainty of the rainfall generator dominates the uncertainty in the simulated extreme flood quantiles.…”

mentioning

confidence: 99%

“…In their study they found that the uncertainty of the rainfall generator dominates the uncertainty in the simulated extreme flood quantiles. With the exception of the work of Arnaud et al (2017) using a simplified hydrologic model, studies that deal with meteorological and hydrological modeling uncertainty in fully continuous simulation frameworks are currently missing. This is despite the fact that recent improvements in computational power with cluster and cloud computing theoretically open up the unlimited possibility of analyzing different combinations of meteorological scenarios and parameter sets of a hydrological model within such ensemble-based simulation frameworks.…”

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confidence: 99%

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Downsizing parameter ensembles for simulations of extreme floods

Sikorska‐Senoner¹,

Schaefli²,

Seibert³

2020

Preprint

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Abstract. For extreme flood estimation, simulation-based approaches represent an interesting alternative to purely statistical approaches, particularly if hydrograph shapes are required. Such simulation-based methods are adapted within continuous simulation frameworks that rely on statistical analyses of continuous streamflow time series derived from a hydrologic model fed with long precipitation time series. These frameworks are, however, affected by high computational demands, particularly if floods with return periods > 1000 years are of interest or if modelling uncertainty due to different sources (meteorological input or hydrologic model) is to be quantified. Here, we propose three methods for reducing the computational requirements for the hydrological simulations for extreme flood estimation, so that long streamflow time series can be analysed at a reduced computational cost. These methods rely on simulation of annual maxima and on analyzing their simulated range to downsize the hydrological parameter ensemble to a small number suitable for continuous simulation frameworks. The methods are tested in a Swiss catchment with 10 000 years of synthetic streamflow data simulated with a weather generator. Our results demonstrate the reliability of the proposed downsizing methods for robust simulations of extreme floods with uncertainty. The methods are readily transferable to other situations where ensemble simulations are needed.

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confidence: 99%

mentioning

confidence: 99%

Downsizing parameter ensembles for simulations of extreme floods

Sikorska‐Senoner¹,

Schaefli²,

Seibert³

2020

Preprint

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“…For this reason stochastic rainfall generators are commonly used with CS models, as well as many other rainfall-runoff simulation-based approaches, to generate or extended rainfall records (e.g. Beven, 1987;Smithers et al, 2000;Clothier and Pegram, 2002;Frezghi, 2005;Sivapalan et al, 2005;Rogger et al, 2012;Sharma et al, 2016;Arnaud et al, 2017;Odry and Arnaud, 2017). Similarly, rainfall disaggregation models, or simple disaggregation techniques, are also commonly applied to generate short duration data from longer time-steps, e.g., daily to hourly (Calver et al, 2005;Knoesen, 2005;Knoesen and Smithers, 2009;Grimaldi et al, 2012;Haberlandt and Radtke, 2014;Nathan and Ling, 2016).…”

Section: Generalised Framework For Continuous Simulation Modellingmentioning

confidence: 99%

“…This includes the GR4H model, as implemented in ARR Revision Project 8, which is an hourly version of the GR4J daily rainfall-runoff model developed by Perrin et al (2003) with 2 storages and 4 parameters (Van Esse et al, 2013;Bennett et al, 2014). Although not a CSM approach, another noteworthy event-based simulation approach -SHYREG, developed over several years, also by IRSTEA, has recently been established as a national DFE method in France (Arnaud et al, 2016;Arnaud et al, 2017;Odry and Arnaud, 2017). Arnaud et al (2016);Arnaud et al (2017) and Odry and Arnaud (2017) have compared the SHYREG approach to several other FFA methods applied in the country and highlight several advantages of the approach, including greater stability of the regionalised rainfall-runoff model parameter for different regionalisation methods, in comparison to a regional FFA for example.…”

Section: The United Statesmentioning

confidence: 99%

“…Although not a CSM approach, another noteworthy event-based simulation approach -SHYREG, developed over several years, also by IRSTEA, has recently been established as a national DFE method in France (Arnaud et al, 2016;Arnaud et al, 2017;Odry and Arnaud, 2017). Arnaud et al (2016);Arnaud et al (2017) and Odry and Arnaud (2017) have compared the SHYREG approach to several other FFA methods applied in the country and highlight several advantages of the approach, including greater stability of the regionalised rainfall-runoff model parameter for different regionalisation methods, in comparison to a regional FFA for example. The method also provides more adequate flood quantiles at higher return periods compared to regionalised statistical approaches including the regional FFA approach: however, it overestimates the lower return period events (Odry and Arnaud, 2017).…”

Section: The United Statesmentioning

confidence: 99%

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Review: Continuous simulation modelling for design flood estimation – a South African perspective and recommendations

Rowe

Smithers

2018

WSA

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A number of severe flooding events have occurred both in South Africa and internationally in recent years. Furthermore, changes in both the intensity and frequency of extreme rainfall events have been documented, both locally and internationally, associated with climate change. The recent loss of life, destruction of infrastructure, and associated economic losses caused by flooding, compounded by the probability of increased rainfall variability in the future, highlight that design flood estimation (DFE) techniques within South Africa are outdated and in need of revision. A National Flood Studies Programme (NFSP) has recently been initiated to overhaul DFE procedures in South Africa. One of the recommendations in the NFSP is the further development of a continuous simulation modelling (CSM) system for DFE in South Africa. The focus of this paper is a review of CSM techniques for DFE, to guide further development for application in South Africa. An introduction to DFE, and particularly the CSM approach, is presented, followed by a brief overview of DFE techniques used in South Africa, leading into a more detailed summary of CSM for DFE within South Africa to date. This is followed by a review of the development and application of CSM methods for DFE internationally, with a focus on the United Kingdom and Australia, where methods have been developed with the intention of national scale implementation. It is important to highlight that there is a plethora of CSM methods available internationally and this review is not exhaustive; it focuses on and identifies some of the strengths and weaknesses of several popular methods, particularly those intended for national scale application, as the intended outcome from this review is to identify a path towards the development of a usable national scale CSM system for DFE in South Africa. Emphasis on a usable method is important, considering the reality that, despite promising results, numerous benefits, and national scale methods being developed, it appears that the CSM method for DFE is rarely used in practice.

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