Characterisation of seasonal flood types according to timescales in mixed probability distributions

Fischer, Svenja; Schumann, Andreas; Schulte, Markus

doi:10.1016/j.jhydrol.2016.05.005

Cited by 49 publications

(30 citation statements)

References 25 publications

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“…Also, as we focused only on large annual floods (annual maxima), we did not represent the flood seasonality in our analysis. Yet, some recent works emphasise the need to include such information on the flood type (Brunner et al, 2017) or on flood seasonality (Brunner et al, 2018b) into bivariate analysis of floods, or to represent a mixture of both flood type and flood seasonality in flood frequency analysis (Fischer et al, 2016;Fischer, 2018). Thus, the proposed selection methods could potentially be extended to account for different flood types during representative parameter selection, using e.g.…”

Section: Limitations and Perspectivesmentioning

confidence: 99%

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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Section: Limitations and Perspectivesmentioning

confidence: 99%

Downsizing parameter ensembles for simulations of extreme floods

Sikorska‐Senoner¹,

Schaefli²,

Seibert³

2020

Preprint

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“…The results show that the maximum floods in summer as well as in winter in general exhibit a local behaviour, that can be explained by the climatic conditions, especially the different types of flood events (see [6]). Similar results were also detected by e.g.…”

Section: Flood Seriesmentioning

confidence: 92%

Ordinal pattern dependence between hydrological time series

Fischer

Schumann

Schnurr

2017

Journal of Hydrology

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Ordinal patterns provide a method to measure correlation between time series. In contrast to classical correlation measures like the Pearson correlation coefficient they are able to measure not only linear correlation but also non-linear correlation even in the presence of non-stationarity. Hence, they are a noteworthy alternative to the classical approaches when considering discharge series. Discharge series naturally show a high variation as well as single extraordinary extreme events and, caused by anthropogenic and climatic impacts, non-stationary behaviour. Here, the method of ordinal patterns is used to compare pairwise discharge series derived from macroand mesoscale catchments in Germany. Differences of coincident groups were detected for winter and summer annual maxima. Hydrological series, which are mainly driven by annual climatic conditions (yearly discharges and low water discharges) showed other and in some cases surprising interdependencies between macroscale catchments. Anthropogenic impacts as the construction of a reservoir or different flood conditions caused by urbanization could be detected.

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“…Among the different systematic approaches to identify seasons from a sample of historical flood hydrographs, the contributions of [24,25] are particularly interesting. Following its lines, and for the present the present research, the ratio α = Q/V is analyzed for all available historical flood events, in such a way that two main seasons (winter/summer) can be effectively identified, depending on the prevailing values of the ratio.…”

Section: Seasons Identificationmentioning

confidence: 99%

“…The same rule would apply for the winter season and its representative ratio value α w , with a coefficient of determination R 2 W . The best assignment of months to each season, and thus, splitting of the year into two adequate subperiods, can be attained in practice by maximizing [R 2 W + R 2 S ], regarding each of the seasons contains a minimum of 25% of the total number of historical events considered in the analysis [24].…”

Section: Seasons Identificationmentioning

confidence: 99%

“…The annual cumulative distribution can be obtained as the product of the seasonal cumulative distribution functions, under the assumption of statistical independence among different seasons [15,57]. This assumption has been adopted in related recent studies [23,24]. Following those lines, it is possible to infer the MWRL and MO CDFs for the annual flood frequency regime, based on the seasonal stochastic approaches (Figures 14 and 15).…”

Section: Reservoir Dam System Routingmentioning

confidence: 99%

See 1 more Smart Citation

Effect of Seasonality on the Quantiles Estimation of Maximum Floodwater Levels in a Reservoir and Maximum Outflows

Aranda

García-Bartual

2020

Water

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Certain relevant variables for dam safety and downstream safety assessments are analyzed using a stochastic approach. In particular, a method to estimate quantiles of maximum outflow in a dam spillway and maximum water level reached in the reservoir during a flood event is presented. The hydrological system analyzed herein is a small mountain catchment in north Spain, whose main river is a tributary of Ebro river. The ancient Foradada dam is located in this catchment. This dam has no gates, so that flood routing operation results from simple consideration of fixed crest spillway hydraulics. In such case, both mentioned variables (maximum outflow and maximum reservoir water level) are basically derived variables that depend on flood hydrograph characteristics and the reservoir’s initial water level. A Monte Carlo approach is performed to generate very large samples of synthetic hydrographs and previous reservoir levels. The use of extreme value copulas allows the ensembles to preserve statistical properties of historical samples and the observed empirical correlations. Apart from the classical approach based on annual periods, the modelling strategy is also applied differentiating two subperiods or seasons (i.e., summer and winter). This allows to quantify the return period distortion introduced when seasonality is ignored in the statistical analysis of the two relevant variables selected for hydrological risk assessment. Results indicate significant deviations for return periods over 125 years. For the analyzed case study, ignoring seasonal statistics and trends, yields to maximum outflows underestimation of 18% for T = 500 years and 29% for T = 1000 years were obtained.

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Characterisation of seasonal flood types according to timescales in mixed probability distributions

Cited by 49 publications

References 25 publications

Downsizing parameter ensembles for simulations of extreme floods

Downsizing parameter ensembles for simulations of extreme floods

Ordinal pattern dependence between hydrological time series

Effect of Seasonality on the Quantiles Estimation of Maximum Floodwater Levels in a Reservoir and Maximum Outflows

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