Articles | Volume 24, issue 8
https://doi.org/10.5194/hess-24-4109-2020
https://doi.org/10.5194/hess-24-4109-2020
Research article
 | 
21 Aug 2020
Research article |  | 21 Aug 2020

Estimation of annual runoff by exploiting long-term spatial patterns and short records within a geostatistical framework

Thea Roksvåg, Ingelin Steinsland, and Kolbjørn Engeland

Related authors

A geostatistical spatially varying coefficient model for mean annual runoff that incorporates process-based simulations and short records
Thea Roksvåg, Ingelin Steinsland, and Kolbjørn Engeland
Hydrol. Earth Syst. Sci., 26, 5391–5410, https://doi.org/10.5194/hess-26-5391-2022,https://doi.org/10.5194/hess-26-5391-2022, 2022
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Stochastic approaches
A mixed distribution approach for low-flow frequency analysis – Part 1: Concept, performance, and effect of seasonality
Gregor Laaha
Hydrol. Earth Syst. Sci., 27, 689–701, https://doi.org/10.5194/hess-27-689-2023,https://doi.org/10.5194/hess-27-689-2023, 2023
Short summary
Significant regime shifts in historical water yield in the Upper Brahmaputra River basin
Hao Li, Baoying Shan, Liu Liu, Lei Wang, Akash Koppa, Feng Zhong, Dongfeng Li, Xuanxuan Wang, Wenfeng Liu, Xiuping Li, and Zongxue Xu
Hydrol. Earth Syst. Sci., 26, 6399–6412, https://doi.org/10.5194/hess-26-6399-2022,https://doi.org/10.5194/hess-26-6399-2022, 2022
Short summary
A geostatistical spatially varying coefficient model for mean annual runoff that incorporates process-based simulations and short records
Thea Roksvåg, Ingelin Steinsland, and Kolbjørn Engeland
Hydrol. Earth Syst. Sci., 26, 5391–5410, https://doi.org/10.5194/hess-26-5391-2022,https://doi.org/10.5194/hess-26-5391-2022, 2022
Short summary
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
Hydrol. Earth Syst. Sci., 26, 4553–4574, https://doi.org/10.5194/hess-26-4553-2022,https://doi.org/10.5194/hess-26-4553-2022, 2022
Short summary
Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: a case study on data from Uccle, Belgium
Jorn Van de Velde, Matthias Demuzere, Bernard De Baets, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 26, 2319–2344, https://doi.org/10.5194/hess-26-2319-2022,https://doi.org/10.5194/hess-26-2319-2022, 2022
Short summary

Cited articles

Adamowski, K. and Bocci, C.: Geostatistical regional trend detection in river flow data, Hydrol. Process., 15, 3331–3341, https://doi.org/10.1002/hyp.1045, 2001. a
Bakka, H., Rue, H., Fuglstad, G.-A., Riebler, A., Bolin, D., Illian, J., Krainski, E., Simpson, D., and Lindgren, F.: Spatial modeling with R-INLA: A review, WIREs Computational Statistics, 10, e1443, https://doi.org/10.1002/wics.1443, 2018. a
Banerjee, S., Gelfand, A., and Carlin, B.: Hierarchical Modeling and Analysis for Spatial Data, vol. 101 of Monographs on Statistics and Applied Probability, Chapman & Hall, Boca Raton, Florida, 2004. a
Blöschl, G., Sivapalan, M., Wagener, T., Viglione, A., and Savenije, H.: Runoff Prediction in Ungauged Basins: Synthesis across Processes, Places and Scales, Camebridge University Press, Cambridge, 2013. a, b, c, d, e, f, g, h, i, j
Brenner, S. and Scott, L.: The Mathematical Theory of Finite Element Methods, 3rd Edition. Vol. 15 of Texts in Applied Mathematics, Springer, New York, 2008. a
Download
Short summary
Annual runoff is a measure of how much water flows through a river during a year and is an important quantity, e.g. when planning infrastructure. In this paper, we suggest a new statistical model for annual runoff estimation. The model exploits correlation between rivers and is able to detect whether the annual runoff in the target river follows repeated patterns over time relative to neighbouring rivers. In our work we show for what cases the latter represents a benefit over comparable methods.