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

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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
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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.