Articles | Volume 28, issue 7
https://doi.org/10.5194/hess-28-1687-2024
© Author(s) 2024. 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-28-1687-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Apr 2024
Research article |
| 12 Apr 2024
| 12 Apr 2024
Flood frequency analysis using mean daily flows vs. instantaneous peak flows
Anne Bartens
Institute of Hydrology and Water Resources Management, Leibniz University of Hanover, Hanover, Germany
Bora Shehu
Institute for Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Uwe Haberlandt
CORRESPONDING AUTHOR
Institute of Hydrology and Water Resources Management, Leibniz University of Hanover, Hanover, Germany
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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Preprint withdrawn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
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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.
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A method for estimating extreme rainfall from radar observations is provided. Extreme value statistics are applied on merged radar rainfall product covering different area sizes from a single point up to about 1000 km2. The rainfall extremes are supposed to decrease as the area increases. This behavior could not be confirmed by the radar observations. The reason is the limited single-point sampling approach for extreme value analysis. New multiple-point sampling strategies are proposed to mitigate this problem.
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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 and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 2075–2097, https://doi.org/10.5194/hess-27-2075-2023, https://doi.org/10.5194/hess-27-2075-2023, 2023
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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.
Bora Shehu, Winfried Willems, Henrike Stockel, Luisa-Bianca Thiele, and Uwe Haberlandt
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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.
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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 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.
Cited articles
Acharya, A. and Ryu Jae, H.: Simple Method for Streamflow Disaggregation, J. Hydrol. Eng., 19, 509–519, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000818, 2014. a
Canuti, P. and Moisello, U.: Relationship between the yearly maxima of peak and daily discharge for some basins in tuscany, Hydrolog. Sci. J., 27, 111–128, https://doi.org/10.1080/02626668209491094, 1982. a, b, c
Dastorani, M. T., Koochi, J. S., Darani, H. S., Talebi, A., and Rahimian, M. H.: River instantaneous peak flow estimation using daily flow data and machine-learning-based models, J. Hydroinform., 15, 1089–1098, https://doi.org/10.2166/hydro.2013.245, 2013. a
Ding, J. and Haberlandt, U.: Estimation of instantaneous peak flow from maximum mean daily flow by regionalization of catchment model parameters, Hydrol. Process., 31, 612–626, https://doi.org/10.1002/hyp.11053, 2017. a, b
Ding, J., Haberlandt, U., and Dietrich, J.: Estimation of the instantaneous peak flow from maximum daily flow: A comparison of three methods, Hydrol. Res., 46, 671–688, https://doi.org/10.2166/nh.2014.085, 2015. a, b
Ding, J., Wallner, M., Müller, H., and Haberlandt, U.: Estimation of instantaneous peak flows from maximum mean daily flows using the HBV hydrological model, Hydrol. Process., 30, 1431–1448, https://doi.org/10.1002/hyp.10725, 2016. a, b
DWD – Deutscher Wetterdienst: Vieljährige Mittelwerte, https://www.dwd.de/DE/leistungen/klimadatendeutschland/vielj_mittelwerte.html (last access: 17 September 2021), 2021. a
Fill, H. and Steiner, A.: Estimating Instantaneous Peak Flow from Mean Daily Flow Data, J. Hydrol. Eng., 8, 365–369, https://doi.org/10.1061/(ASCE)1084-0699(2003)8:6(365), 2003. a, b
Fischer, S.: A seasonal mixed-POT model to estimate high flood quantiles from different event types and seasons, J. Appl. Stat., 45, 2831–2847, https://doi.org/10.1080/02664763.2018.1441385, 2018. a, b, c
Fischer, S., Schumann, A., and Schulte, M.: Characterisation of seasonal flood types according to timescales in mixed probability distributions, J. Hydrol, 539, 38–56, https://doi.org/10.1016/j.jhydrol.2016.05.005, 2016. a, b
Fuller, W. E.: Flood Flows, Trans. Am. Soc. Civ. Eng., 77, 564–617, https://doi.org/10.1061/taceat.0002552, 1914. a, b, c, d
Gaál, L., Szolgay, J., Kohnová, S., Hlavčová, K., Parajka, J., Viglione, A., Merz, R., and Blöschl, G.: Relation entre pics et volumes de crues: étude des déterminants climatiques et hydrologiques, Hydrolog. Sci. J., 60, 968–984, https://doi.org/10.1080/02626667.2014.951361, 2015. a, b, c, d
Haktanir, T. and Horlacher, H. B.: Evaluation of various distributions for flood frequency analysis, Hydrolog. Sci. J., 38, 15–32, https://doi.org/10.1080/02626669309492637, 1993. a
Hosking, J. R. M.: L-Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics, J. Roy. Stat. Soc. B, 52, 105–124, https://doi.org/10.1111/j.2517-6161.1990.tb01775.x, 1990. a
Hosking, J. R. M. and Wallis, J. R.: Regional Frequency Analysis, Cambridge University Press, ISBN 9780521430456, https://doi.org/10.1017/CBO9780511529443, 1997. a
Institute of Hydology: Low flow studies, Report 1, Wallingford, UK, 1980. a
Jarvis, A., Guevara, E., Reuter, H. I., and Nelson, A. D.: Hole-filled SRTM for the globe: version 4: data grid, Web publication/site, CGIAR Consortium for Spatial Information, http://srtm.csi.cgiar.org/ (last access: 21 June 2021), 2008. a
Jimeno-Sáez, P., Senent-Aparicio, J., Pérez-Sánchez, J., Pulido-Velazquez, D., and María Cecilia, J.: Estimation of instantaneous peak flow using machine-learning models and empirical formula in Peninsular Spain, Water, 9, 347, https://doi.org/10.3390/w9050347, 2017. a
Kumar, D. N., Lall, U., and Petersen, M. R.: Multisite disaggregation of monthly to daily streamflow, Water Resour. Res., 36, 1823–1833, https://doi.org/10.1029/2000WR900049, 2000. a
Langbein, W. B.: Peak Discharges from Daily Records, US Geol. Survey Bull., p. 145, 1944. a
LFULG – Sächsisches Landesamt für Umwelt, Landwirtschaft und Geologie: Wasserstand und Durchfluss, https://www.umwelt.sachsen.de/umwelt/infosysteme/ida/ (last access: 21 June 2021), 2021. a
LfU – Bayerisches Landesamt für Umwelt: Abfluss, https://www.gkd.bayern.de/de (last access: 21 June 2021), 2021. a
LHW – Landesbetrieb für Hochwasserschutz und Wasserwirtschaft Sachsen-Anhalt: Wasserstand und Durchfluss, https://gld.lhw-sachsen-anhalt.de (last access: 21 June 2021), 2021. a
LUBW – Landesanstalt für Umwelt Baden-Württemberg: Hydrologische Landespegel, https://udo.lubw.baden-wuerttemberg.de/public/ (last access: 21 June 2021), 2021. a
Muñoz, E., Arumí, J. L., and Vargas, J.: A Design Peak Flow Estimation Method for Medium-Large and Data-Scarce Watersheds With Frontal Rainfall, J. Am. Water Resour. Assoc., 48, 439–448, https://doi.org/10.1111/j.1752-1688.2011.00622.x, 2012. a
NLWKN – Niedersächsischer Landesbetrieb für Wasserwirtschaft, Küsten- und Naturschutz: Wasserstände und Abflüsse Messwerte, http://www.wasserdaten.niedersachsen.de/cadenza/ (last access: 21 June 2021), 2021. a
Sangal, B. P.: Practical method of estimating peak flow, J. Hydraul. Eng., 109, 549–563, 1983. a
Shabani, M. and Shabani, N.: Application of Artificial Neural Networks in Instantaneous Peak Flow Estimation for Kharestan Watershed, Iran, J. Resour. Ecol., 3, 379–383, https://doi.org/10.5814/j.issn.1674-764x.2012.04.012, 2012. a
Stedinger, J. R. and Vogel, R. M.: Disaggregation Procedures for Generating Serially Correlated Flow Vectors, Water Resour. Res., 20, 47–56, https://doi.org/10.1029/WR020i001p00047, 1984. a
Taguas, E. V., Ayuso, J. L., Pena, A., Yuan, Y., Sanchez, M. C., Giraldez, J. V., and Pérez, R.: Testing the relationship between instantaneous peak flow and mean daily flow in a Mediterranean Area Southeast Spain, Catena, 75, 129–137, https://doi.org/10.1016/j.catena.2008.04.015, 2008. a
Tan, K.-S., Chiew, F. H., and Grayson, R. B.: A steepness index unit volume flood hydrograph approach for sub-daily, Hydrol. Process., 21, 2807–2816, https://doi.org/10.1002/hyp.6501, 2007. a, b
Tarasova, L., Basso, S., Zink, M., and Merz, R.: Exploring Controls on Rainfall-Runoff Events: 1. Time Series-Based Event Separation and Temporal Dynamics of Event Runoff Response in Germany, Water Resour. Res., 54, 7711–7732, https://doi.org/10.1029/2018WR022587, 2018. a, b, c, d
Tarboton, D. G., A, S., and U, L.: Disaggregation procedures for stochastic hydrology based on nonparametric density estimation, Water Resour. Res., 34, 107–119, 1998. a
Viglione, A. and Blöschl, G.: On the role of storm duration in the mapping of rainfall to flood return periods, Hydrol. Earth Syst. Sci., 13, 205–216, https://doi.org/10.5194/hess-13-205-2009, 2009. a
Villarini, G., Smith, J. A., Serinaldi, F., and Ntelekos, A. A.: Analyses of seasonal and annual maximum daily discharge records for central Europe, J. Hydrol., 399, 299–312, https://doi.org/10.1016/j.jhydrol.2011.01.007, 2011. a
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.
River flow data are often provided as mean daily flows (MDF), in which a lot of information is...
