Articles | Volume 27, issue 10
https://doi.org/10.5194/hess-27-2075-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-2075-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Jun 2023
Research article |
| 01 Jun 2023
| 01 Jun 2023
Uncertainty estimation of regionalised depth–duration–frequency curves in Germany
Institute of Hydrology and Water Resources Management, Leibniz University of Hannover, Hannover, Germany
Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Uwe Haberlandt
Institute of Hydrology and Water Resources Management, Leibniz University of Hannover, Hannover, Germany
Related authors
Anne Bartens, Bora Shehu, and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 28, 1687–1709, https://doi.org/10.5194/hess-28-1687-2024, https://doi.org/10.5194/hess-28-1687-2024, 2024
Short summary
Short summary
River flow data are often provided as mean daily flows (MDF), in which a lot of information is lost about the actual maximum flow or instantaneous peak flows (IPF) within a day. We investigate the error of using MDF instead of IPF and identify means to predict IPF when only MDF data are available. We find that the average ratio of daily flood peaks and volumes is a good predictor, which is easily and universally applicable and requires a minimum amount of data.
Bora Shehu, Winfried Willems, Henrike Stockel, Luisa-Bianca Thiele, and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 1109–1132, https://doi.org/10.5194/hess-27-1109-2023, https://doi.org/10.5194/hess-27-1109-2023, 2023
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Rainfall volumes at varying duration and frequencies are required for many engineering water works. These design volumes have been provided by KOSTRA-DWD in Germany. However, a revision of the KOSTRA-DWD is required, in order to consider the recent state-of-the-art and additional data. For this purpose, in our study, we investigate different methods and data available to achieve the best procedure that will serve as a basis for the development of the new KOSTRA-DWD product.
Ross Pidoto, Nejc Bezak, Hannes Müller-Thomy, Bora Shehu, Ana Claudia Callau-Beyer, Katarina Zabret, and Uwe Haberlandt
Earth Surf. Dynam., 10, 851–863, https://doi.org/10.5194/esurf-10-851-2022, https://doi.org/10.5194/esurf-10-851-2022, 2022
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Erosion is a threat for soils with rainfall as the driving force. The annual rainfall erosivity factor quantifies rainfall impact by analysing high-resolution rainfall time series (~ 5 min). Due to a lack of measuring stations, alternatives for its estimation are analysed in this study. The best results are obtained for regionalisation of the erosivity factor itself. However, the identified minimum of 60-year time series length suggests using rainfall generators as in this study as well.
Bora Shehu and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 26, 1631–1658, https://doi.org/10.5194/hess-26-1631-2022, https://doi.org/10.5194/hess-26-1631-2022, 2022
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In this paper we investigate whether similar storms behave similarly and whether the information obtained from past similar storms can improve storm nowcast based on radar data. Here a nearest-neighbour approach is employed to first identify similar storms and later to issue either a single or an ensemble nowcast based on k most similar past storms. The results indicate that the information obtained from similar storms can reduce the errors considerably, especially for convective storm nowcast.
Anne Bartens, Bora Shehu, and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 28, 1687–1709, https://doi.org/10.5194/hess-28-1687-2024, https://doi.org/10.5194/hess-28-1687-2024, 2024
Short summary
Short summary
River flow data are often provided as mean daily flows (MDF), in which a lot of information is lost about the actual maximum flow or instantaneous peak flows (IPF) within a day. We investigate the error of using MDF instead of IPF and identify means to predict IPF when only MDF data are available. We find that the average ratio of daily flood peaks and volumes is a good predictor, which is easily and universally applicable and requires a minimum amount of data.
Ross Pidoto and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 3957–3975, https://doi.org/10.5194/hess-27-3957-2023, https://doi.org/10.5194/hess-27-3957-2023, 2023
Short summary
Short summary
Long continuous time series of meteorological variables (i.e. rainfall, temperature) are required for the modelling of floods. Observed time series are generally too short or not available. Weather generators are models that reproduce observed weather time series. This study extends an existing station-based rainfall model into space by enforcing observed spatial rainfall characteristics. To model other variables (i.e. temperature) the model is then coupled to a simple resampling approach.
Bora Shehu, Winfried Willems, Henrike Stockel, Luisa-Bianca Thiele, and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 1109–1132, https://doi.org/10.5194/hess-27-1109-2023, https://doi.org/10.5194/hess-27-1109-2023, 2023
Short summary
Short summary
Rainfall volumes at varying duration and frequencies are required for many engineering water works. These design volumes have been provided by KOSTRA-DWD in Germany. However, a revision of the KOSTRA-DWD is required, in order to consider the recent state-of-the-art and additional data. For this purpose, in our study, we investigate different methods and data available to achieve the best procedure that will serve as a basis for the development of the new KOSTRA-DWD product.
Ross Pidoto, Nejc Bezak, Hannes Müller-Thomy, Bora Shehu, Ana Claudia Callau-Beyer, Katarina Zabret, and Uwe Haberlandt
Earth Surf. Dynam., 10, 851–863, https://doi.org/10.5194/esurf-10-851-2022, https://doi.org/10.5194/esurf-10-851-2022, 2022
Short summary
Short summary
Erosion is a threat for soils with rainfall as the driving force. The annual rainfall erosivity factor quantifies rainfall impact by analysing high-resolution rainfall time series (~ 5 min). Due to a lack of measuring stations, alternatives for its estimation are analysed in this study. The best results are obtained for regionalisation of the erosivity factor itself. However, the identified minimum of 60-year time series length suggests using rainfall generators as in this study as well.
Bora Shehu and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 26, 1631–1658, https://doi.org/10.5194/hess-26-1631-2022, https://doi.org/10.5194/hess-26-1631-2022, 2022
Short summary
Short summary
In this paper we investigate whether similar storms behave similarly and whether the information obtained from past similar storms can improve storm nowcast based on radar data. Here a nearest-neighbour approach is employed to first identify similar storms and later to issue either a single or an ensemble nowcast based on k most similar past storms. The results indicate that the information obtained from similar storms can reduce the errors considerably, especially for convective storm nowcast.
Anne Bartens and Uwe Haberlandt
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-466, https://doi.org/10.5194/hess-2021-466, 2021
Preprint withdrawn
Short summary
Short summary
River flow data is often provided as mean daily flow (MDF), in which a lot of information is lost about the actual maximum flow or instantaneous peak flow (IPF) within a day. We investigate the error of using MDFs instead of IPFs and identify means to predict IPFs when only MDF data is available. We find that the average ratio of daily flood peaks and volumes is a good predictor, which is easily and universally applicable and requires a minimum amount of data.
Anne Fangmann and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 23, 447–463, https://doi.org/10.5194/hess-23-447-2019, https://doi.org/10.5194/hess-23-447-2019, 2019
Short summary
Short summary
Low-flow events are little dynamic in space and time. Thus, it is hypothesized that models can be found, based on simple statistical relationships between low-flow metrics and meteorological states, that can help identify potential low-flow drivers. In this study we assess whether such relationships exist and whether they can be applied to predict future low flow within regional climate change impact assessment in the northwestern part of Germany.
Ehsan Rabiei, Uwe Haberlandt, Monika Sester, Daniel Fitzner, and Markus Wallner
Hydrol. Earth Syst. Sci., 20, 3907–3922, https://doi.org/10.5194/hess-20-3907-2016, https://doi.org/10.5194/hess-20-3907-2016, 2016
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The value of using moving cars for rainfall measurement purposes (RCs) was investigated with laboratory experiments by Rabiei et al. (2013). They analyzed the Hydreon and Xanonex optical sensors against different rainfall intensities. A continuous investigation of using RCs with the derived uncertainties from laboratory experiments for areal rainfall estimation as well as implementing the data in a hydrological model are addressed in this study.
Uwe Haberlandt and Christian Berndt
Proc. IAHS, 373, 81–85, https://doi.org/10.5194/piahs-373-81-2016, https://doi.org/10.5194/piahs-373-81-2016, 2016
U. Haberlandt and I. Radtke
Hydrol. Earth Syst. Sci., 18, 353–365, https://doi.org/10.5194/hess-18-353-2014, https://doi.org/10.5194/hess-18-353-2014, 2014
E. Rabiei, U. Haberlandt, M. Sester, and D. Fitzner
Hydrol. Earth Syst. Sci., 17, 4701–4712, https://doi.org/10.5194/hess-17-4701-2013, https://doi.org/10.5194/hess-17-4701-2013, 2013
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Subject: Engineering Hydrology | Techniques and Approaches: Stochastic approaches
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Hossein Foroozand and Steven V. Weijs
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Vincenzo Totaro, Andrea Gioia, and Vito Iacobellis
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Manuela I. Brunner, András Bárdossy, and Reinhard Furrer
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Cong Jiang, Lihua Xiong, Lei Yan, Jianfan Dong, and Chong-Yu Xu
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Alain Dib and M. Levent Kavvas
Hydrol. Earth Syst. Sci., 22, 1993–2005, https://doi.org/10.5194/hess-22-1993-2018, https://doi.org/10.5194/hess-22-1993-2018, 2018
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A new method is proposed to solve the stochastic unsteady open-channel flow system in only one single simulation, as opposed to the many simulations usually done in the popular Monte Carlo approach. The derivation of this new method gave a deterministic and linear Fokker–Planck equation whose solution provided a powerful and effective approach for quantifying the ensemble behavior and variability of such a stochastic system, regardless of the number of parameters causing its uncertainty.
Alain Dib and M. Levent Kavvas
Hydrol. Earth Syst. Sci., 22, 2007–2021, https://doi.org/10.5194/hess-22-2007-2018, https://doi.org/10.5194/hess-22-2007-2018, 2018
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Liang Gao, Limin Zhang, and Mengqian Lu
Hydrol. Earth Syst. Sci., 21, 4573–4589, https://doi.org/10.5194/hess-21-4573-2017, https://doi.org/10.5194/hess-21-4573-2017, 2017
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Elena Shevnina, Ekaterina Kourzeneva, Viktor Kovalenko, and Timo Vihma
Hydrol. Earth Syst. Sci., 21, 2559–2578, https://doi.org/10.5194/hess-21-2559-2017, https://doi.org/10.5194/hess-21-2559-2017, 2017
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Eleni Maria Michailidi and Baldassare Bacchi
Hydrol. Earth Syst. Sci., 21, 2497–2507, https://doi.org/10.5194/hess-21-2497-2017, https://doi.org/10.5194/hess-21-2497-2017, 2017
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In this research, we explored how the sampling uncertainty of flood variables (flood peak, volume, etc.) can reflect on a structural variable, which in our case was the maximum water level (MWL) of a reservoir controlled by a dam. Next, we incorporated additional information from different sources for a better estimation of the uncertainty in the probability of exceedance of the MWL. Results showed the importance of providing confidence intervals in the risk assessment of a structure.
M. J. Machado, B. A. Botero, J. López, F. Francés, A. Díez-Herrero, and G. Benito
Hydrol. Earth Syst. Sci., 19, 2561–2576, https://doi.org/10.5194/hess-19-2561-2015, https://doi.org/10.5194/hess-19-2561-2015, 2015
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A flood frequency analysis using a 400-year historical flood record was carried out using a stationary model (based on maximum likelihood estimators) and a non-stationary model that incorporates external covariates (climatic and environmental). The stationary model was successful in providing an average discharge around which value flood quantiles estimated by non-stationary models fluctuate through time.
L. Gaál, P. Molnar, and J. Szolgay
Hydrol. Earth Syst. Sci., 18, 1561–1573, https://doi.org/10.5194/hess-18-1561-2014, https://doi.org/10.5194/hess-18-1561-2014, 2014
P. E. O'Connell and G. O'Donnell
Hydrol. Earth Syst. Sci., 18, 155–171, https://doi.org/10.5194/hess-18-155-2014, https://doi.org/10.5194/hess-18-155-2014, 2014
A. I. Requena, L. Mediero, and L. Garrote
Hydrol. Earth Syst. Sci., 17, 3023–3038, https://doi.org/10.5194/hess-17-3023-2013, https://doi.org/10.5194/hess-17-3023-2013, 2013
S. Das and C. Cunnane
Hydrol. Earth Syst. Sci., 15, 819–830, https://doi.org/10.5194/hess-15-819-2011, https://doi.org/10.5194/hess-15-819-2011, 2011
B. A. Botero and F. Francés
Hydrol. Earth Syst. Sci., 14, 2617–2628, https://doi.org/10.5194/hess-14-2617-2010, https://doi.org/10.5194/hess-14-2617-2010, 2010
L. Mediero, A. Jiménez-Álvarez, and L. Garrote
Hydrol. Earth Syst. Sci., 14, 2495–2505, https://doi.org/10.5194/hess-14-2495-2010, https://doi.org/10.5194/hess-14-2495-2010, 2010
B. Guse, Th. Hofherr, and B. Merz
Hydrol. Earth Syst. Sci., 14, 2465–2478, https://doi.org/10.5194/hess-14-2465-2010, https://doi.org/10.5194/hess-14-2465-2010, 2010
D. Koutsoyiannis
Hydrol. Earth Syst. Sci., 14, 585–601, https://doi.org/10.5194/hess-14-585-2010, https://doi.org/10.5194/hess-14-585-2010, 2010
Cited articles
Bárdossy, A. and Hörning, S.: Random Mixing: An Approach to
Inverse Modeling for Groundwater Flow and Transport Problems,
Transport Porous Med., 114, 241–259, https://doi.org/10.1007/s11242-015-0608-4, 2016. a
Bastante, F. G., Ordóñez, C., Taboada, J., and Matías, J. M.:
Comparison of indicator kriging, conditional indicator simulation and
multiple-point statistics used to model slate deposits, Eng. Geol.,
98, 50–59, https://doi.org/10.1016/j.enggeo.2008.01.006, 2008. a
Bourennane, H., King, D., Couturier, A., Nicoullaud, B., Mary, B., and Richard,
G.: Uncertainty assessment of soil water content spatial patterns using
geostatistical simulations: An emperical comparison of a simulation
accounting for single attribute and a simulation accounting for secondary
information, Ecol. Model., 205, 323–335,
https://doi.org/10.1016/j.ecolmodel.2007.02.034, 2007. a
Burn, D. H.: A framework for regional estimation of
intensity-duration-frequency (IDF) curves, Hydrol. Process., 28, 4209–4218, https://doi.org/10.1002/hyp.10231, 2014. a, b
Ceresetti, D., Ursu, E., Carreau, J., Anquetin, S., Creutin, J. D., Gardes, L., Girard, S., and Molinié, G.: Evaluation of classical spatial-analysis schemes of extreme rainfall, Nat. Hazards Earth Syst. Sci., 12, 3229–3240, https://doi.org/10.5194/nhess-12-3229-2012, 2012. a
Chaudhuri, R. R. and Sharma, P.: Addressing uncertainty in extreme rainfall
intensity for semi-arid urban regions: case study of Delhi, India, Nat.
Hazards, 104, 2307–2324, https://doi.org/10.1007/s11069-020-04273-5, 2020. a
Cinnirella, S., Buttafouco, G., and Pirrone, N.: Stochastic analysis to assess
the spatial distribution of groundwater nitrate concentrations in the Po
catchment (Italy), Environ. Pollut., 133, 569–580,
https://doi.org/10.1016/j.envpol.2004.06.020, 2005. a, b
Coles, S.: An Introduction to Statistical Modeling of Extreme Values,
vol. 53, Springer-Verlag London Berlin Heidelberg, London,
https://doi.org/10.1007/978-1-4471-3675-0, 2001. a
CSöRgő, S. and Faraway, J. J.: The Exact and Asymptotic Distributions
of Cramér-Von Mises Statistics, J. Roy. Stat. Soc. B Met., 58, 221–234,
https://doi.org/10.1111/j.2517-6161.1996.tb02077.x, 1996. a
DVWK: Statistische Analyse von Hochwasserabflüssen, Deutscher Verband für Wasserwirtschaft und Kulturbau, Tech. Rep. H. 251, Bonn, Germany, p. 62, 1999. a
DWA: Arbeitsblatt DWA-A 531: Starkregen in Abhängigkeit von Wiederkehrzeit und Dauer, DWA Arbeitsgruppe HW 1.1e, Hennef, Deutschland, https://de.dwa.de (last access: 20 March 2022), 2012. a
Emery, X.: Multi-gaussian kriging and simulation in the presence of an
uncertain mean value, Stochastic Enviornmental Research and Risk Assessment,
24, 211–219, https://doi.org/10.1007/s00477-009-0311-5, 2010. a
Ersoy, A. and Yünsel, T. Y.: Assessment of lignite quality variables: A
practical approach with sequential Gaussian simulation,
Energ. Source Part A, 31, 175–190,
https://doi.org/10.1080/15567030701522260, 2009. a
Fischer, S. and Schumann, A. H.: Berücksichtigung von Starkregen in der Niederschlagsstatistik, Hydrol. Wasserbewirts., 62, 248–256, https://doi.org/10.5675/HyWa_2018,4_2, 2018 a
Forestieri, A., Lo Conti, F., Blenkinsop, S., Cannarozzo, M., Fowler, H. J.,
and Noto, L. V.: Regional frequency analysis of extreme rainfall in Sicily
(Italy), Int. J. Climatol., 38, e698–e716,
https://doi.org/10.1002/joc.5400, 2018. a
German Weather Service: Climate Data Center (CDC), https://opendata.dwd.de/climate_environment/CDC/, German Weather Service [data set], last access: 24 May 2023. a
Goovaerts, P.: Geostatistical tools for deriving block-averaged values of
environmental attributes, Lect. Notes Comput. Sc., 5, 88–96,
https://doi.org/10.1080/10824009909480518, 1999a. a
Goovaerts, P.: Geostatistics in soil science: state-of-the-art and
perspectives, Geoderma, 89, 1–45, https://doi.org/10.1016/S0016-7061(98)00078-0,
1999b. a
Goovaerts, P.: Estimation or simulation of soil properties? An optimization
problem with conflicting criteria, Geoderma, 97, 165–186,
https://doi.org/10.1016/S0016-7061(00)00037-9, 2000. a, b
Goovaerts, P.: Geostatistical modelling of uncertainty in soil science,
Geoderma, 103, 3–26, https://doi.org/10.1016/S0016-7061(01)00067-2, 2001. a
Gyasi-Agyei, Y. and Pegram, G.: Interpolation of daily rainfall networks using
simulated radar fields for realistic hydrological modelling of spatial rain
field ensembles, J. Hydrol., 519, 777–791,
https://doi.org/10.1016/j.jhydrol.2014.08.006, 2014. a
Haese, B., Hörning, S., Chwala, C., Bardossy, A., Schalge, B., and
Kunstmann, H.: Stochastic reconstruction and interpolation of precipitation
fields using combined information of commercial microwave links and rain
gauges, Water Resour. Res., 53, 10740–10756,
https://doi.org/10.1002/2017WR021015, 2017. a
Hofmann, T., Darsow, A., and Schafmeister, M. T.: Importance of the nugget
effect in variography on modeling zinc leaching from a contaminated site
using simulated annealing, J. Hydrol., 389, 78–89,
https://doi.org/10.1016/j.jhydrol.2010.05.024, 2010. a
Hosking, J. R. M. and Wallis, J. R.: Regional Frequency Analysis, Cambridge
University Press, https://doi.org/10.1017/CBO9780511529443, 1997. a, b, c
Jang, C. S.: Geostatistical analysis for spatially characterizing
hydrochemical features of springs in Taiwan, Environ. Earth Sci.,
73, 7517–7531, https://doi.org/10.1007/s12665-014-3924-z, 2015. a
Jang, C. S. and Huang, H. C.: Applying spatial analysis techniques to assess the suitability of multipurpose uses of spring water in the Jiaosi Hot Spring Region, Taiwan, Environ. Monit. Assess., 189, 328, https://doi.org/10.1007/s10661-017-6029-9, 2017. a
Journel, A. G. and Posa, D.: Characteristic behavior and order relations for
indicator variograms, Math. Geol., 22, 1011–1025,
https://doi.org/10.1007/BF00890121, 1990. a, b
Junghänel, T., Bär, F., Deutschländer, T., Haberlandt, U., Otte, I., Shehu, B., Stockel, H., Stricker, K., Thiele, L.-B., and Willems, W.: Methodische Untersuchungen zur Novellierung der Starkregenstatistik für Deutschland (MUNSTAR), Tech. rep., Synthesebericht, p. 95, https://www.dwd.de/DE/leistungen/kostra_dwd_rasterwerte/download/Synthesebericht_MUNSTAR_pdf.pdf?__blob=publicationFile&v=3 (last access: 24 May 2023), 2022. a
Koutsoyiannis, D.: Statistics of extremes and estimation of extreme rainfall:
I. Theoretical investigation, Hydrolog. Sci. J., 49, 575–590,
https://doi.org/10.1623/hysj.49.4.575.54430, 2004a. a
Koutsoyiannis, D.: Statistics of extremes and estimation of extreme rainfall:
II. Empirical investigation of long rainfall records, Hydrolog. Sci. J., 49, 591–610, https://doi.org/10.1623/hysj.49.4.591.54424, 2004b. a
Koutsoyiannis, D., Kozonis, D., and Manetas, A.: A mathematical framework for
studying rainfall intensity-duration-frequency relationships, J. Hydrol., 206, 118–135, https://doi.org/10.1016/S0022-1694(98)00097-3, 1998. a, b, c
Liao, K., Lai, X., Lv, L., and Zhu, Q.: Uncertainty in predicting the spatial
pattern of soil water temporal stability at the hillslope scale, Soil
Res., 54, 739–748, https://doi.org/10.1071/SR15059, 2016. a, b
Lin, Y.-P. and Chang, T.-K.: Simulated annealing and kriging method for
identifying the spatial patterns and variability of soil heavy metal,
J. Environ. Sci. Heal. A, 35, 1089–115,
https://doi.org/10.1080/10934520009377022, 2000. a, b
Luca, C., Si, B. C., and Farrell, R. E.: Assessing spatial distribution and
joint uncertainty of TPH-fractions: Indicator kriging and sequential
indicator simulation, Can. J. Soil Sci., 87, 551–563,
https://doi.org/10.4141/CJSS07003, 2007. a, b, c
Marra, F., Morin, E., Peleg, N., Mei, Y., and Anagnostou, E. N.: Intensity–duration–frequency curves from remote sensing rainfall estimates: comparing satellite and weather radar over the eastern Mediterranean, Hydrol. Earth Syst. Sci., 21, 2389–2404, https://doi.org/10.5194/hess-21-2389-2017, 2017. a, b
Marra, F., Nikolopoulos, E. I., Anagnostou, E. N., Bárdossy, A., and
Morin, E.: Precipitation frequency analysis from remotely sensed datasets: A
focused review, J. Hydrol., 574, 699–705,
https://doi.org/10.1016/j.jhydrol.2019.04.081, 2019a. a, b, c
Marra, F., Zoccatelli, D., Armon, M., and Morin, E.: A simplified MEV
formulation to model extremes emerging from multiple nonstationary underlying
processes, Adv. Water Resour., 127, 280–290,
https://doi.org/10.1016/j.advwatres.2019.04.002, 2019b. a
Miniussi, A. and Marra, F.: Estimation of extreme daily precipitation return
levels at-site and in ungauged locations using the simplified MEV approach,
J. Hydrol., 603, 126946, https://doi.org/10.1016/j.jhydrol.2021.126946,
2021. a
Namysłowska-Wilczyńska, B.: Application of turning bands technique to
simulate values of copper ore deposit parameters in Rudna mine
(Lubin-Sieroszowice region in SW part of Poland), Georisk, 9, 224–241,
https://doi.org/10.1080/17499518.2015.1104363, 2015. a
NASA Shuttle Radar Topography Mission (SRTM): Shuttle Radar Topography Mission (SRTM) Global, Distributed by OpenTopography, https://doi.org/10.5069/G9445JDF, 2013. a
Notaro, V., Liuzzo, L., Freni, G., and Loggia, G. L.: Uncertainty analysis in
the evaluation of extreme rainfall trends and its implications on urban
drainage system design, Water, 7, 6931–6945, https://doi.org/10.3390/w7126667,
2015. a
Overeem, A., Buishand, A., and Holleman, I.: Rainfall depth-duration-frequency
curves and their uncertainties, J. Hydrol., 348, 124–134,
https://doi.org/10.1016/j.jhydrol.2007.09.044, 2008. a
Overeem, A., Buishand, T. A., and Holleman, I.: Extreme rainfall analysis and
estimation of depth-duration-frequency curves using weather radar, Water
Resour. Res., 45, W10424, https://doi.org/10.1029/2009WR007869, 2009. a
Pebesma, E. J.: Multivariable geostatistics in S: The gstat package,
Comput. Geosci., 30, 683–691, https://doi.org/10.1016/j.cageo.2004.03.012,
2004. a
Pebesma, E. J. and Wesseling, C. G.: Gstat: A program for geostatistical
modelling, prediction and simulation, Comput. Geosci., 24, 17–31,
https://doi.org/10.1016/S0098-3004(97)00082-4, 1998. a
Perica, S., Pavlovic, S., St. Laurent, M., Trypaluk, C., Unruh, D., Martin, D., and Wilhite, O.: NOAA Atlas 14, Volume 10: Precipitation-Frequency Atlas of the United States, NOAA, National Weather Service, Silver Spring, MD, 1, https://www.weather.gov/media/owp/hdsc_documents/Atlas14_Volume10.pdf (last access: 24 May 2023), 2019. a
Poggio, L., Gimona, A., Brown, I., and Castellazzi, M.: Soil available water
capacity interpolation and spatial uncertainty modelling at multiple
geographical extents, Geoderma, 160, 175–188,
https://doi.org/10.1016/j.geoderma.2010.09.015, 2010. a
Poschlod, B.: Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria, Nat. Hazards Earth Syst. Sci., 21, 3573–3598, https://doi.org/10.5194/nhess-21-3573-2021, 2021. a, b
Requena, A. I., Burn, D. H., and Coulibaly, P.: Pooled frequency analysis for intensity–duration–frequency curve estimation, Hydrol. Process., 33, 2080/2094, https://doi.org/10.1002/hyp.13456, 2019. a
Ribeiro, M. C. and Pereira, M. J.: Modelling local uncertainty in relations
between birth weight and air quality within an urban area: combining
geographically weighted regression with geostatistical simulation,
Environ. Sci. Pollt. R., 25, 25942–25954,
https://doi.org/10.1007/s11356-018-2614-x, 2018. a
Szatmári, G. and Pásztor, L.: Comparison of various uncertainty
modelling approaches based on geostatistics and machine learning algorithms,
Geoderma, 337, 1329–1340, https://doi.org/10.1016/j.geoderma.2018.09.008, 2019. a
Tfwala, C. M., van Rensburg, L. D., Schall, R., Mosia, S. M., and Dlamini, P.:
Precipitation intensity-duration-frequency curves and their uncertainties
for Ghaap plateau, Climate Risk Management, 16, 1–9,
https://doi.org/10.1016/j.crm.2017.04.004, 2017. a
Uboldi, F., Sulis, A. N., Lussana, C., Cislaghi, M., and Russo, M.: A spatial bootstrap technique for parameter estimation of rainfall annual maxima distribution, Hydrol. Earth Syst. Sci., 18, 981–995, https://doi.org/10.5194/hess-18-981-2014, 2014. a, b
Van de Vyver, H.: Bayesian estimation of rainfall intensity-duration-frequency relationships, J. Hydrol., 529, 1451/1463, https://doi.org/10.1016/j.jhydrol.2015.08.036, 2015. a
Varouchakis, E. A.: Median polish kriging and sequential gaussian simulation for the spatial analysis of source rock data, Journal of Marine Science and Engineering, 9, 717, https://doi.org/10.3390/jmse9070717, 2021.
a
Yang, Y., Tian, Q., Yang, K., Meng, C., and Luo, Y.: Using Sequential Gaussian Simulation to Assess the Spatial Uncertainty of PM2.5 in China, International Conference on Geoinformatics, June 2018, 1–5, Kunming, China, https://doi.org/10.1109/GEOINFORMATICS.2018.8557167, 2018. a
Short summary
Design rainfall volumes at different duration and frequencies are necessary for the planning of water-related systems and facilities. As the procedure for deriving these values is subjected to different sources of uncertainty, here we explore different methods to estimate how precise these values are for different duration, locations and frequencies in Germany. Combining local and spatial simulations, we estimate tolerance ranges from approx. 10–60% for design rainfall volumes in Germany.
Design rainfall volumes at different duration and frequencies are necessary for the planning of...
