Articles | Volume 26, issue 20
Hydrol. Earth Syst. Sci., 26, 5391–5410, 2022
https://doi.org/10.5194/hess-26-5391-2022
Hydrol. Earth Syst. Sci., 26, 5391–5410, 2022
https://doi.org/10.5194/hess-26-5391-2022
Research article
27 Oct 2022
Research article | 27 Oct 2022

A geostatistical spatially varying coefficient model for mean annual runoff that incorporates process-based simulations and short records

Thea Roksvåg et al.

Related authors

Estimation of annual runoff by exploiting long-term spatial patterns and short records within a geostatistical framework
Thea Roksvåg, Ingelin Steinsland, and Kolbjørn Engeland
Hydrol. Earth Syst. Sci., 24, 4109–4133, https://doi.org/10.5194/hess-24-4109-2020,https://doi.org/10.5194/hess-24-4109-2020, 2020
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Stochastic approaches
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
A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow
Álvaro Ossandón, Manuela I. Brunner, Balaji Rajagopalan, and William Kleiber
Hydrol. Earth Syst. Sci., 26, 149–166, https://doi.org/10.5194/hess-26-149-2022,https://doi.org/10.5194/hess-26-149-2022, 2022
Short summary
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
Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling
Kailong Li, Guohe Huang, and Brian Baetz
Hydrol. Earth Syst. Sci., 25, 4947–4966, https://doi.org/10.5194/hess-25-4947-2021,https://doi.org/10.5194/hess-25-4947-2021, 2021
Short summary

Cited articles

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, ISBN 978-1584884101, 2003. a
Beldring, S., Roald, L. A., and Voksø, A.: Arenningskart for Norge. Årsmiddelverdier for avrenning 1961–1990, Tech. Rep. Oslo: NVE, ISSN 1501-2840, http://publikasjoner.nve.no/dokument/2002/dokument2002_02.pdf (last access: last access: 19 October 2022), 2002. a, b, c
Beldring, S., Engeland, K., Roald, L. A., Sælthun, N. R., and Voksø, A.: Estimation of parameters in a distributed precipitation-runoff model for Norway, Hydrol. Earth Syst. Sci., 7, 304–316, https://doi.org/10.5194/hess-7-304-2003, 2003. a
Bergström, S.: Development and Application of a Conceptual Runoff Model for Scandinavian Catchments, SMHI, Norrköping, Sweden, RHO 7, 134 pp., 1976. a, b
Download
Short summary
The goal of this work was to make a map of the mean annual runoff for Norway for a 30-year period. We first simulated runoff by using a process-based model that models the relationship between runoff, precipitation, temperature, and land use. Next, we corrected the map based on runoff observations from streams by using a statistical method. We were also able to use data from rivers that only had a few annual observations. We find that the statistical correction improves the runoff estimates.