Articles | Volume 26, issue 17
https://doi.org/10.5194/hess-26-4553-2022
https://doi.org/10.5194/hess-26-4553-2022
Research article
 | 
13 Sep 2022
Research article |  | 13 Sep 2022

Low-flow estimation beyond the mean – expectile loss and extreme gradient boosting for spatiotemporal low-flow prediction in Austria

Johannes Laimighofer, Michael Melcher, and Gregor Laaha

Related authors

Parsimonious statistical learning models for low-flow estimation
Johannes Laimighofer, Michael Melcher, and Gregor Laaha
Hydrol. Earth Syst. Sci., 26, 129–148, https://doi.org/10.5194/hess-26-129-2022,https://doi.org/10.5194/hess-26-129-2022, 2022
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Stochastic approaches
Technical note: Two-component electrical-conductivity-based hydrograph separation employing an exponential mixing model (EXPECT) provides reliable high-temporal-resolution young water fraction estimates in three small Swiss catchments
Alessio Gentile, Jana von Freyberg, Davide Gisolo, Davide Canone, and Stefano Ferraris
Hydrol. Earth Syst. Sci., 28, 1915–1934, https://doi.org/10.5194/hess-28-1915-2024,https://doi.org/10.5194/hess-28-1915-2024, 2024
Short summary
Flood frequency analysis using mean daily flows vs. instantaneous peak flows
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
On the regional-scale variability in flow duration curves in Peninsular India
Pankaj Dey, Jeenu Mathai, Murugesu Sivapalan, and Pradeep P. Mujumdar
Hydrol. Earth Syst. Sci., 28, 1493–1514, https://doi.org/10.5194/hess-28-1493-2024,https://doi.org/10.5194/hess-28-1493-2024, 2024
Short summary
Towards a conceptualization of the hydrological processes behind changes of young water fraction with elevation: a focus on mountainous alpine catchments
Alessio Gentile, Davide Canone, Natalie Ceperley, Davide Gisolo, Maurizio Previati, Giulia Zuecco, Bettina Schaefli, and Stefano Ferraris
Hydrol. Earth Syst. Sci., 27, 2301–2323, https://doi.org/10.5194/hess-27-2301-2023,https://doi.org/10.5194/hess-27-2301-2023, 2023
Short summary
A mixed distribution approach for low-flow frequency analysis – Part 2: Comparative assessment of a mixed probability vs. copula-based dependence framework
Gregor Laaha
Hydrol. Earth Syst. Sci., 27, 2019–2034, https://doi.org/10.5194/hess-27-2019-2023,https://doi.org/10.5194/hess-27-2019-2023, 2023
Short summary

Cited articles

Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., Mount, N. J., See, L. M., Shamseldin, A. Y., Solomatine, D. P., Toth, E., and Wilby, R. L.: Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting, Prog. Phys. Geog., 36, 480–513, https://doi.org/10.1177/0309133312444943, 2012. a
Aigner, D. J., Amemiya, T., and Poirier, D. J.: On the Estimation of Production Frontiers: Maximum Likelihood Estimation of the Parameters of a Discontinuous Density Function, Int. Econ. Rev., 17, 377–396, https://doi.org/10.2307/2525708, 1976. a, b
Arnold, J. B.: ggthemes: Extra Themes, Scales and Geoms for “ggplot2”, r package version 4.2.4, https://CRAN.R-project.org/package=ggthemes (last access: 8 April 2022), 2021. a
Beguería, S., Vicente-Serrano, S. M., Reig, F., and Latorre, B.: Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring, Int. J. Climatol., 34, 3001–3023, https://doi.org/10.1002/joc.3887, 2014. a
Blöschl, G., Sivapalan, M., Wagener, T., Savenije, H., and Viglione, A.: Runoff prediction in ungauged basins: synthesis across processes, places and scales, Cambridge University Press, https://doi.org/10.1017/CBO9781139235761, 2013. a, b
Download
Short summary
Our study uses a statistical boosting model for estimating low flows on a monthly basis, which can be applied to estimate low flows at sites without measurements. We use an extensive dataset of 260 stream gauges in Austria for model development. As we are specifically interested in low-flow events, our method gives specific weight to such events. We found that our method can considerably improve the predictions of low-flow events and yields accurate estimates of the seasonal low-flow variation.