Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.936 IF 4.936
  • IF 5-year value: 5.615 IF 5-year
    5.615
  • CiteScore value: 4.94 CiteScore
    4.94
  • SNIP value: 1.612 SNIP 1.612
  • IPP value: 4.70 IPP 4.70
  • SJR value: 2.134 SJR 2.134
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 107 Scimago H
    index 107
  • h5-index value: 63 h5-index 63
HESS | Articles | Volume 23, issue 1
Hydrol. Earth Syst. Sci., 23, 447–463, 2019
https://doi.org/10.5194/hess-23-447-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 23, 447–463, 2019
https://doi.org/10.5194/hess-23-447-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 25 Jan 2019

Research article | 25 Jan 2019

Statistical approaches for identification of low-flow drivers: temporal aspects

Anne Fangmann and Uwe Haberlandt
Related authors  
Areal rainfall estimation using moving cars – computer experiments including hydrological modeling
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
Short summary
The value of weather radar data for the estimation of design storms – an analysis for the Hannover region
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
Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows
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
Rainfall estimation using moving cars as rain gauges – laboratory experiments
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
Related subject area  
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Assessing the impacts of hydrologic and land use alterations on water temperature in the Farmington River basin in Connecticut
John R. Yearsley, Ning Sun, Marisa Baptiste, and Bart Nijssen
Hydrol. Earth Syst. Sci., 23, 4491–4508, https://doi.org/10.5194/hess-23-4491-2019,https://doi.org/10.5194/hess-23-4491-2019, 2019
Short summary
Future shifts in extreme flow regimes in Alpine regions
Manuela I. Brunner, Daniel Farinotti, Harry Zekollari, Matthias Huss, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 23, 4471–4489, https://doi.org/10.5194/hess-23-4471-2019,https://doi.org/10.5194/hess-23-4471-2019, 2019
Short summary
Time variability and uncertainty in the fraction of young water in a small headwater catchment
Michael Paul Stockinger, Heye Reemt Bogena, Andreas Lücke, Christine Stumpp, and Harry Vereecken
Hydrol. Earth Syst. Sci., 23, 4333–4347, https://doi.org/10.5194/hess-23-4333-2019,https://doi.org/10.5194/hess-23-4333-2019, 2019
Short summary
Hydrodynamic simulation of the effects of stable in-channel large wood on the flood hydrographs of a low mountain range creek, Ore Mountains, Germany
Daniel Rasche, Christian Reinhardt-Imjela, Achim Schulte, and Robert Wenzel
Hydrol. Earth Syst. Sci., 23, 4349–4365, https://doi.org/10.5194/hess-23-4349-2019,https://doi.org/10.5194/hess-23-4349-2019, 2019
Short summary
Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores
Wouter J. M. Knoben, Jim E. Freer, and Ross A. Woods
Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019,https://doi.org/10.5194/hess-23-4323-2019, 2019
Short summary
Cited articles  
Akaike, H.: New Look at Statistical-Model Identification, IEEE T. Automat. Contr., 19, 716–723, https://doi.org/10.1109/Tac.1974.1100705, 1974. 
Coles, S.: An introduction to statistical modeling of extreme values, Springer, London, 2001. 
Dai, A. G.: Increasing drought under global warming in observations and models, Nat. Clim. Change, 3, 52–58, https://doi.org/10.1038/Nclimate1633, 2013. 
de Wit, M. J. M., van den Hurk, B., Warmerdam, P. M. M., Torfs, P. J. J. F., Roulin, E., and van Deursen, W. P. A.: Impact of climate change on low-flows in the river Meuse, Climatic Change, 82, 351–372, https://doi.org/10.1007/s10584-006-9195-2, 2007. 
Fangmann, A.: Low flow prediction in time and space: An adaptive statistical scheme for regional climate change impact assessment, Mitteilungen, Heft 106, Inst. of Hydrology, Leibniz University of Hannover, Hannover, 160 pp., 2017. 
Publications Copernicus
Download
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.
Low-flow events are little dynamic in space and time. Thus, it is hypothesized that models can...
Citation