| 1. |
- Sikorska, A. E., et al.
(author)
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Effective precipitation duration for runoff peaks based on catchment modelling
- 2018
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In: Journal of Hydrology. - : Elsevier BV. - 0022-1694 .- 1879-2707. ; 556, s. 510-522
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Journal article (peer-reviewed)abstract
- Despite precipitation intensities may greatly vary during one flood event, detailed information about these intensities may not be required to accurately simulate floods with a hydrological model which rather reacts to cumulative precipitation sums. This raises two questions: to which extent is it important to preserve sub-daily precipitation intensities and how long does it effectively rain from the hydrological point of view? Both questions might seem straightforward to answer with a direct analysis of past precipitation events but require some arbitrary choices regarding the length of a precipitation event. To avoid these arbitrary decisions, here we present an alternative approach to characterize the effective length of precipitation event which is based on runoff simulations with respect to large floods. More precisely, we quantify the fraction of a day over which the daily precipitation has to be distributed to faithfully reproduce the large annual and seasonal floods which were generated by the hourly precipitation rate time series. New precipitation time series were generated by first aggregating the hourly observed data into daily totals and then evenly distributing them over sub-daily periods (n hours). These simulated time series were used as input to a hydrological bucket-type model and the resulting runoff flood peaks were compared to those obtained when using the original precipitation time series. We define then the effective daily precipitation duration as the number of hours n, for which the largest peaks are simulated best. For nine mesoscale Swiss catchments this effective daily precipitation duration was about half a day, which indicates that detailed information on precipitation intensities is not necessarily required to accurately estimate peaks of the largest annual and seasonal floods. These findings support the use of simple disaggregation approaches to make usage of past daily precipitation observations or daily precipitation simulations (e.g. from climate models) for hydrological modeling at an hourly time step.
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| 2. |
- Sikorska, A. E., et al.
(author)
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Value of different precipitation data for flood prediction in an alpine catchment : A Bayesian approach
- 2018
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In: Journal of Hydrology. - : Elsevier BV. - 0022-1694 .- 1879-2707. ; 556, s. 961-971
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Journal article (peer-reviewed)abstract
- Flooding induced by heavy precipitation is one of the most severe natural hazards in alpine catchments. To accurately predict such events, accurate and representative precipitation data are required. Estimating catchment precipitation is, however, difficult due to its high spatial, and, in the mountains, elevation-dependent variability. These inaccuracies, together with runoff model limitations, translate into uncertainty in runoff estimates. Thus, in this study, we investigate the value of three precipitation datasets, commonly used in hydrological studies, i.e., station network precipitation (SNP), interpolated grid precipitation (IGP) and radar-based precipitation (RBP), for flood predictions in an alpine catchment. To quantify their effects on runoff simulations, we perform a Bayesian uncertainty analysis with an improved description of model systematic errors. By using periods of different lengths for model calibration, we explore the information content of these three datasets for runoff predictions. Our results from an alpine catchment showed that using SNP resulted in the largest predictive uncertainty and the lowest model performance evaluated by the Nash–Sutcliffe efficiency. This performance improved from 0.674 to 0.774 with IGP, and to 0.829 with RBP. The latter two datasets were also much more informative than SNP, as half as many calibration data points were required to obtain a good model performance. Thus, our results show that the various types of precipitation data differ in their value for flood predictions in an alpine catchment and indicate RBP as the most useful dataset.
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| 3. |
- Westerberg, Ida, et al.
(author)
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A comparison of methods for streamflow uncertainty estimation.
- 2018
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In: Water resources research. - 0043-1397 .- 1944-7973. ; :54, s. 7149–7176-
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Journal article (peer-reviewed)abstract
- Streamflow time series are commonly derived from stage‐discharge rating curves, but the uncertainty of the rating curve and resulting streamflow series are poorly understood. While different methods to quantify uncertainty in the stage‐discharge relationship exist, there is limited understanding of how uncertainty estimates differ between methods due to different assumptions and methodological choices. We compared uncertainty estimates and stage‐discharge rating curves from seven methods at three river locations of varying hydraulic complexity. Comparison of the estimated uncertainties revealed a wide range of estimates, particularly for high and low flows. At the simplest site on the Isère River (France), full width 95% uncertainties for the different methods ranged from 3 to 17% for median flows. In contrast, uncertainties were much higher and ranged from 41 to 200% for high flows in an extrapolated section of the rating curve at the Mahurangi River (New Zealand) and 28 to 101% for low flows at the Taf River (United Kingdom), where the hydraulic control is unstable at low flows. Differences between methods result from differences in the sources of uncertainty considered, differences in the handling of the time‐varying nature of rating curves, differences in the extent of hydraulic knowledge assumed, and differences in assumptions when extrapolating rating curves above or below the observed gaugings. Ultimately, the selection of an uncertainty method requires a match between user requirements and the assumptions made by the uncertainty method. Given the significant differences in uncertainty estimates between methods, we suggest that a clear statement of uncertainty assumptions be presented alongside streamflow uncertainty estimates.
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