A continuous modelling approach for design flood estimation on sub-daily time scale

Winter, Benjamin; Schneeberger, Klaus; Dũng, Nguyễn Việt; Huttenlau, Matthias; Achleitner, Stefan; Stötter, Johann; Merz, Bruno; Vorogushyn, Sergiy

doi:10.1080/02626667.2019.1593419

Cited by 50 publications

(39 citation statements)

References 78 publications

(125 reference statements)

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“…The WeGen approach seems to overestimate the overall spatial dependence in the study area in comparison to the observed values. This was also found in a previous study, comparing a different set of gauging stations (Winter et al, 2019). One possible reason could, the spatial interpolation of the meteorological data by the rather simple IDW-approach, without consideration of shading effects etc.…”

Section: Discussionsupporting

confidence: 78%

“…PRAMo was previously driven by the synthetic flood event series of coherent peak discharges generated by the HT-model (Schneeberger and Steinberger, 2018). A second event generation approach based on a multi-site, multi-variate weather generator and continuous rainfall-runoff modelling was recently introduced by Winter et al (2019) and is used for comparison with the HT-model based approach and the assumption of homogeneous return periods. Figure 2 provides an overview of the modules and the simulation steps, which are described in more details in the following.…”

Section: Study Areamentioning

confidence: 99%

“…A non-parametric k-nearest neighbour algorithm based on the method of fragments is applied to disaggregate the generated daily values to hourly time steps (Winter et al, 2019). For a day to disaggregate, the generated daily values of temperature and precipitation from the weather generator are compared against observed daily data at all stations.…”

Section: Hazard Module Ii: Wegenmentioning

confidence: 99%

“…Next, one matching day is randomly sampled from the selected neighbours and the corresponding relative temporal patterns from the match day are transferred to the input day (method of fragments). In contrast to the previous study (Winter et al, 2019), a centred moving window of 30 days is applied instead of the identical months in order to restrict the search of possible matching days. The modification increases the variability between the disaggregated days and reduces the maximum search distance on a temporal scale, especially for days at the beginning and end of a month.…”

Section: Hazard Module Ii: Wegenmentioning

confidence: 99%

“…Analogously to the HT-model approach, a threshold of ≥ 30 year return period, at least at one site across the study area is applied to define relevant flood events. A more detailed description of the modelling chain, including the disaggregation procedure, is given in Winter et al (2019).…”

mentioning

confidence: 99%

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Event generation for probabilistic flood risk modelling: multi-site peak flow dependence model vs weather generator based approach

Winter¹,

Schneeberger²,

Vorogushyn³

2020

Preprint

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Abstract. Flood risk assessment is an important prerequisite for risk management decisions. To estimate the risk, flood damages need to be either systematically recorded over long period or they need to be modelled for a series of synthetically generated flood events. Since damage records are typically rare, time series of plausible, spatially coherent event precipitation or peak discharges need to be generated to drive the chain of process models. In the present study, synthetic flood events are generated by two different approaches to model flood risk in a meso-scale alpine study area (Vorarlberg, Austria). The first approach is based on the semi-conditional multi-variate dependence model applied to discharge series. The second approach is based on the continuous hydrological modelling of synthetic meteorological fields generated by a multi-site weather generator and using an hourly disaggregation scheme. The results of the two approaches are compared in terms of simulated spatial patterns and overall flood risk estimates. It could be demonstrated that both methods are valid approaches for risk assessment with specific advantages and disadvantages. Both methods are superior to the traditional assumption of a uniform return period, where risk is computed by assuming a homogeneous return period (e.g. 100-year flood) across the entire study area.

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Section: Discussionsupporting

confidence: 78%

Section: Study Areamentioning

confidence: 99%

Section: Hazard Module Ii: Wegenmentioning

confidence: 99%

Section: Hazard Module Ii: Wegenmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Event generation for probabilistic flood risk modelling: multi-site peak flow dependence model vs weather generator based approach

Winter¹,

Schneeberger²,

Vorogushyn³

2020

Preprint

Self Cite

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Comprehensive evaluation of an improved large‐scale multi‐site weather generator for Germany

Merz

Hundecha

Haberlandt

et al. 2021

Intl Journal of Climatology

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In this work, we present a comprehensive evaluation of a stochastic multi-site, multi-variate weather generator at the scale of entire Germany and parts of the neighbouring countries covering the major German river basins Elbe, Upper Danube, Rhine, Weser and Ems with a total area of approximately 580,000 km 2 . The regional weather generator, which is based on a first-order multi-variate auto-regressive model, is setup using 53-year long daily observational data at 528 locations. The performance is evaluated by investigating the ability of the weather generator to replicate various important statistical properties of the observed variables including precipitation occurrence and dry/wet transition probabilities, mean daily and extreme precipitation, multi-day precipitation sums, spatial correlation structure, areal precipitation, mean daily and extreme temperature and solar radiation. We explore two marginal distributions for daily precipitation amount: mixed Gamma-Generalized Pareto and extended Generalized Pareto. Furthermore, we introduce a new procedure to estimate the spatial correlation matrix and model mean daily temperature and solar radiation. The extensive evaluation reveals that the weather generator is greatly capable of capturing most of the crucial properties of the weather variables, particularly of extreme precipitation at individual locations. Some deficiencies are detected in capturing spatial precipitation correlation structure that leads to an overestimation of areal precipitation extremes. Further improvement of the spatial correlation structure is envisaged for future research. The mixed marginal model found to outperform the extended Generalized Pareto in our case. The use of power transformation in combination with normal distribution significantly improves the performance for nonprecipitation variables. The weather generator can be used to generate synthetic event footprints for large-scale trans-basin flood risk assessment.

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Evaluation of the climate change impact on the extreme rainfall amounts using modified method of fragments for sub‐daily rainfall disaggregation

Rafatnejad

Tavakolifar

Nazif

2021

Intl Journal of Climatology

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Climate change has a dramatic effect on the hydrologic variables including extreme rainfall amounts. To evaluate the climate change effects, general circulation models (GCMs) have been developed. However, due to the daily temporal scale of GCM outputs which could be insufficient for some hydrological studies, disaggregation models are introduced. The available disaggregation models which are almost useful in producing time series of finer scale than a day, cannot accurately estimate some statistical characteristics such as extreme events. The method of fragments (MOF) is one of the disaggregation models which considers daily rainfall as the only input. In the present study, in addition to daily rainfall, other influential factors on the rainfall distribution during a day such as weather variables and sub‐daily characteristics have been considered to improve the disaggregation results especially extreme events estimation in the MOF model. The two introduced approaches have been examined for a case study in Tehran, Iran and indicated that weather variables and sub‐daily characteristics are effective in the daily rainfall disaggregation during the dry and wet seasons, respectively. These approaches seem to be much better than the basic MOF in sub‐daily rainfall disaggregation. Hence, the modified disaggregation approaches have been used to evaluate the climate change impacts on the sub‐daily rainfall distribution. The obtained results indicated an increase in the extreme value statistics such as mean and standard deviation of the 95th percentile data compared with the historical ones.

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A continuous modelling approach for design flood estimation on sub-daily time scale

Cited by 50 publications

References 78 publications

Event generation for probabilistic flood risk modelling: multi-site peak flow dependence model vs weather generator based approach

Event generation for probabilistic flood risk modelling: multi-site peak flow dependence model vs weather generator based approach

Comprehensive evaluation of an improved large‐scale multi‐site weather generator for Germany

Evaluation of the climate change impact on the extreme rainfall amounts using modified method of fragments for sub‐daily rainfall disaggregation

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