Hydrologie und Wasserwirtschaft

Maniak, Ulrich

doi:10.1007/978-3-662-22018-4

Cited by 14 publications

(14 citation statements)

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“…This ordered list was compared with the observed ordered list by dividing the latter into bins of size 0.6 standard deviations of the actual variable, calculating the averages obs i within each bin, and calculating the corresponding averages cal i of the generated list. The binning was required to keep the MRE value from being dominated entirely by the numerous small values in the generally strongly skewed distributions; 0.6 standard deviations is a recommended bin size (Maniak, 1993). MRE of each validation variable was calculated as (2) and to obtain an overall measure of the accuracy of a certain disaggregation experiment, MRE was averaged over all seven variables (Mn(MRE)).…”

Section: Fig 1(a) Typical Location Of Mean and Quantiles In An Ordementioning

confidence: 99%

Cascade-based disaggregation of continuous rainfall time series: the influence of climate

Güntner¹,

Olsson

Calver

et al. 2001

Hydrol. Earth Syst. Sci.

120

123

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Rainfall data of high temporal resolution are required in a multitude of hydrological applications. In the present paper, a temporal rainfall disaggregation model is applied to convert daily time series into an hourly resolution. The model is based on the principles of random multiplicative cascade processes. Its parameters are dependent on (1) the volume and (2) the position in the rainfall sequence of the time interval with rainfall to be disaggregated. The aim is to compare parameters and performance of the model between two contrasting climates with different rainfall generating mechanisms, a semi-arid tropical (Brazil) and a temperate (United Kingdom) climate. In the range of time scales studied, the scale-invariant assumptions of the model are approximately equally well fulfilled for both climates. The model parameters differ distinctly between climates, reflecting the dominance of convective processes in the Brazilian rainfall and of advective processes associated with frontal passages in the British rainfall. In the British case, the parameters exhibit a slight seasonal variation consistent with the higher frequency of convection during summer. When applied for disaggregation, the model reproduces a range of hourly rainfall characteristics with a high accuracy in both climates. However, the overall model performance is somewhat better for the semi-arid tropical rainfall. In particular, extreme rainfall in the UK is overestimated whereas extreme rainfall in Brazil is well reproduced. Transferability of parameters in time is associated with larger uncertainty in the semi-arid climate due to its higher interannual variability and lower percentage of rainy intervals. For parameter transferability in space, no restrictions are found between the Brazilian stations whereas in the UK regional differences are more pronounced. The overall high accuracy of disaggregated data supports the potential usefulness of the model in hydrological applications.

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Section: Fig 1(a) Typical Location Of Mean and Quantiles In An Ordementioning

confidence: 99%

Cascade-based disaggregation of continuous rainfall time series: the influence of climate

Güntner¹,

Olsson

Calver

et al. 2001

Hydrol. Earth Syst. Sci.

120

123

View full text Add to dashboard Cite

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“…However, the extrapolation is limited by the length of the simulated runoff time 5 series. As per Maniak (2005), a maximum return period of three times the runoff time series length should be used to avoid too high statistical uncertainties caused by extrapolation. This results in 75 years for Pionierbrücke, 21 years for Tetendorf and 45 years for Reckershausen.…”

Section: A) Hbv-simulation Results With Calibration Using Three Rain mentioning

confidence: 99%

“…Therefore, the maximum observed value of each half year were extracted from both data sources to generate periods as long as possible. 20001929-20061952-2005Tetendorf 20041986-20001986-2000Reckershausen 20041964-20061974-2005 …”

Section: Runoff Datamentioning

confidence: 99%

Rainfall disaggregation for hydrological modeling: Is there a need for spatial consistence?

Müller¹,

Wallner²,

Förster³

et al. 2017

Preprint

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Abstract.In this investigation, the influence of disaggregated rainfall data sets with different degrees of spatial consistence on rainfall runoff modeling results is analyzed for three meso-scale catchments in Lower Saxony, Germany. For the disaggregation of daily rainfall time series into hourly values a multiplicative random cascade model is applied. The disaggregation is applied on a per station basis without consideration of surrounding stations, hence subsequent steps are then required to implement 15 spatial consistence. Spatial consistence is here represented by three bivariate spatial rainfall characteristics, complementing each other. A resampling algorithm and a parallelization approach are evaluated against the disaggregated time series without any subsequent steps. With respect to rainfall, clear differences between these three approaches can be identified regarding bivariate spatial rainfall characteristics, areal rainfall intensities and extreme values. The resampled time series lead to the best agreement with the observed ones. Using these different rainfall data sets as input to hydrological modeling, 20 we hypothesize that derived runoff statistics are subject to similar differences as well. However, an impact on the runoff statistics summer and winter peak flows, monthly average discharge and flow duration curve of the simulated runoff time series cannot be detected. Several modifications of the investigation using rainfall runoff models with and without parameter calibration or using different rain gauge densities lead to similar results in runoff statistics. Only if the spatially highly resolved rainfall-runoff WaSiM-model is applied instead of the semi-distributed HBV-IWW-model, slight differences 25 regarding the seasonal peak flows can be identified. Hence, the hypothesis formulated before is rejected in this case study. These findings suggest that (i) simple model structures might compensate for deficiencies in spatial representativeness through parameterization and (ii) highly resolved hydrological models benefit from improved spatial modeling of rainfall.Hydrol. Earth Syst. Sci. Discuss., https://doi

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“…Both methods have a severe drawback. Counting the frequency of critical events is only reasonable if the length of the time series is at least three times longer than the considered return period (Maniak 1992), which is typically not available. Using a pre-defined probability distribution can lead to wrong extrapolations, especially for high return periods, if the theoretical distribution does not comply with the real distribution of the extremes data.…”

Section: Lammersenmentioning

confidence: 99%

Extreme value statistics for river quality simulations

Schindler

Tränckner

Krebs

2010

Water Science and Technology

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Various methods have been proposed to assess intermittent pollution loads and impacts on rivers in urban areas. Although the variables to describe the impact are mainly the same, the standards show significant differences in the assessment of permitted concentration level, duration and return period. The probability of an event is derived using either frequencies of occurrence or predefined extreme value distributions. Both methods have drawbacks. To bypass these, an a posteriori estimation of the statistical distribution of data based on the peak-over-threshold method is proposed. The method is exemplarily demonstrated using a semi-virtual case study.

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Hydrologie und Wasserwirtschaft

Cited by 14 publications

References 0 publications

Cascade-based disaggregation of continuous rainfall time series: the influence of climate

Cascade-based disaggregation of continuous rainfall time series: the influence of climate

Rainfall disaggregation for hydrological modeling: Is there a need for spatial consistence?

Extreme value statistics for river quality simulations

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