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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2019-415
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-415
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 24 Sep 2019

Submitted as: research article | 24 Sep 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

A geostatistical framework for estimating flow indices by exploiting short records and long-term spatial averages – Application to annual and monthly runoff

Thea Roksvåg1, Ingelin Steinsland1, and Kolbjørn Engeland2 Thea Roksvåg et al.
  • 1Norwegian University of Science and Technology, NTNU, Department of Mathematical Sciences
  • 2The Norwegian Water Resources and Energy Directorate, NVE

Abstract. In this article, we present a Bayesian geostatistical framework that is particularly suitable for interpolation of hydrological data when the available dataset is sparse and includes missing values and short records of data. A key feature of the proposed framework is that several years of runoff is modeled simultaneously with two Gaussian random fields (GRFs): One that is common for all years under study and represents the runoff generation due to long-term climatic conditions, and one that is year specific. The climatic GRF learns how short records of runoff from partially gauged catchments vary relatively to longer time series from other catchments, and transfers this information across years. Another property, is that the model takes the nested structure of catchments into account such that the water balance is preserved for any point in the landscape. The framework is demonstrated by interpolation of annual and monthly runoff from around 200 catchments in Norway, and we compare it to Top-Kriging (interpolation method) and simple linear regression (method for exploiting short records). The results show that if the correlation between neighboring catchments is high, a model that considers several years of runoff simultaneously is considerably better at capturing large spatial variability than a model that treats each year of data separately.

Thea Roksvåg et al.
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Short summary
We present a geostatistical model for annual and monthly runoff interpolation that is particularly useful when the available dataset is sparse, i.e there are several missing values. The key feature of the model is that we model several years of runoff simultaneously such that information from short records can be transferred across years. We demonstrate that the potential gain of using this framework is large when the correlation between neighbouring catchments is high.
We present a geostatistical model for annual and monthly runoff interpolation that is...
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