The SCHADEX method: A semi-continuous rainfall–runoff simulation for extreme flood estimation

Paquet, Emmanuel; Garavaglia, Federico; Garçon, Rémy; Gailhard, Joël

doi:10.1016/j.jhydrol.2013.04.045

Cited by 131 publications

(88 citation statements)

References 21 publications

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“…The MOD10 satellite product (Hall et al, 2002) provides gridded snow cover time series. This product has been available since 2000 at a 500 m 1 day −1 resolution, and is widely used for hydrological applications (e.g., Rodell and Houser, 2004;Parajka and Blöschl, 2006;Thirel et al, 2013). In this study we average the gridded values on catchment scale in order to compute a fractional snow cover.…”

Section: Mordor V0 Mordor V1 Mordor Sdmentioning

confidence: 99%

“…The triple focus on time series, seasonal streamflow and flow duration curve can properly identify the different components of the model. Numerous industrial applications of this OF, within a wide range of hydroclimatic conditions, showed that it was well designed to calibrate the MORDOR model (e.g., Paquet et al, 2013).…”

Section: Model Calibrationmentioning

confidence: 99%

“…Moreover, when focusing only on runoff simulation, we often fail to discriminate different model structures. However, interesting conclusions may be3938 F. Garavaglia et al: Impact of model structure on flow simulation and hydrological realism used in the calibration process, such as low flows (Staudinger et al, 2011) or high flows (Vansteenkiste et al, 2014), or on other hydrological variables, such as soil moisture (Orth et al, 2015), snow (Parajka and Blöschl, 2006) and groundwater (Motovilov et al, 1999;Beldring, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

Garavaglia

Lay

Gottardi

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Model intercomparison experiments are widely used to investigate and improve hydrological model performance. However, a study based only on runoff simulation is not sufficient to discriminate between different model structures. Hence, there is a need to improve hydrological models for specific streamflow signatures (e.g., low and high flow) and multi-variable predictions (e.g., soil moisture, snow and groundwater). This study assesses the impact of model structure on flow simulation and hydrological realism using three versions of a hydrological model called MOR-DOR: the historical lumped structure and a revisited formulation available in both lumped and semi-distributed structures. In particular, the main goal of this paper is to investigate the relative impact of model equations and spatial discretization on flow simulation, snowpack representation and evapotranspiration estimation. Comparison of the models is based on an extensive dataset composed of 50 catchments located in French mountainous regions. The evaluation framework is founded on a multi-criterion split-sample strategy. All models were calibrated using an automatic optimization method based on an efficient genetic algorithm. The evaluation framework is enriched by the assessment of snow and evapotranspiration modeling against in situ and satellite data. The results showed that the new model formulations perform significantly better than the initial one in terms of the various streamflow signatures, snow and evapotranspiration predictions. The semi-distributed approach provides better calibration-validation performance for the snow cover area, snow water equivalent and runoff simulation, especially for nival catchments.

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Section: Mordor V0 Mordor V1 Mordor Sdmentioning

confidence: 99%

Section: Model Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

Garavaglia

Lay

Gottardi

et al. 2017

Hydrol. Earth Syst. Sci.

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“…The main advantage of this method over resampling techniques (Paquet et al 2013) and statistical description of storm structures (Salvadori andDe Michele 2006, Langousis et al 2013) is that the two steps of finding the worst-case flood causing precipitation distributions and implementing them in rainfall-runoff models are clearly separated. Therefore, the method ensures realistic conditions for the model input.…”

Section: Discussionmentioning

confidence: 99%

An approach for the determination of precipitation input for worst-case flood modelling

Felder

Weingartner

2016

Hydrological Sciences Journal

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There is a lack of suitable methods for creating precipitation scenarios that can be used to realistically estimate peak discharges with very low probabilities. On the one hand, existing methods are methodically questionable when it comes to physical system boundaries. On the other hand, the spatio-temporal representativeness of precipitation patterns as system input is limited. In response, this paper proposes a method of deriving spatio-temporal precipitation patterns and presents a step towards making methodically correct estimations of infrequent floods by using a worst-case approach. A Monte Carlo approach allows for the generation of a wide range of different spatio-temporal distributions of an extreme precipitation event that can be tested with a rainfall-runoff model that generates a hydrograph for each of these distributions. Out of these numerous hydrographs and their corresponding peak discharges, the physically plausible spatio-temporal distributions that lead to the highest peak discharges are identified and can eventually be used for further investigations.

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“…In addition, models such as the modified Barlett-Lewis rectangular pulse model have limited capacity to simulate extreme rainfall depths, which can lead to an underestimation of runoff (Kim et al, 2017). In order to avoid the limitations of the continuous weather generators, the semi-continuous method SCHADEX (Paquet et al, 2013) uses a probabilistic model for centered rainfall events (MEWP; Garavaglia et al (2010)), identified as over threshold 5 values that are larger than the adjacent rainfall values. Using this approach, millions of rainfall events can be sampled from the MEWP model and inserted directly into the historic precipitation series to replace observed rainfall events.…”

mentioning

confidence: 99%

A stochastic event-based approach for flood estimation in catchments with mixed rainfall/snowmelt flood regimes

Filipova¹,

Lawrence²,

Skaugen³

2018

Preprint

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Abstract. The estimation of extreme floods is associated with high uncertainty, in part due to the limited length of streamflow records. Traditionally, flood frequency analysis or event-based model using a single design storm have been applied. We propose here an alternative, stochastic event-based modelling approach. The stochastic PQRUT method involves Monte Carlo procedure to simulate different combinations of initial conditions, rainfall and snowmelt, from which a distribution of flood peaks can be constructed. The stochastic PQRUT was applied for 20 small and medium-sized catchments in Norway and the 5 results show good fit to the observations. A sensitivity analysis of the method indicates that the soil saturation level is less important than the rainfall input and the parameters of the PQRUT model for flood peaks with return periods higher than 100 years, and that excluding the snow routine can change the seasonality of the flood peaks. Estimates for the 100-and 1000-year return level based on the stochastic PQRUT model are compared with results for a) statistical frequency analysis, and b) a standard implementation of the event-based PQRUT method. The differences between the estimates can be up to 200% for 10 some catchments, which highlights the uncertainty in these methods.

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The SCHADEX method: A semi-continuous rainfall–runoff simulation for extreme flood estimation

Cited by 131 publications

References 21 publications

Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

An approach for the determination of precipitation input for worst-case flood modelling

A stochastic event-based approach for flood estimation in catchments with mixed rainfall/snowmelt flood regimes

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