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 both long and short records of runoff. A key feature of the proposed framework is that several years of runoff are modelled simultaneously with two spatial fields: one that is common for all years under study that represents the runoff generation due to long-Term (climatic) conditions and one that is year-specific. The climatic spatial field captures how short records of runoff from partially gauged catchments vary relative to longer time series from other catchments, and transfers this information across years. To make the Bayesian model computationally feasible and fast, we use integrated nested Laplace approximations (INLAs) and the stochastic partial differential equation (SPDE) approach to spatial modelling.
Roksväg, T., Norwegian University of Science and Technology, Ntnu, Department of Mathematical Sciences, Trondheim, Norway; Steinsland, I., Norwegian University of Science and Technology, Ntnu, Department of Mathematical Sciences, Trondheim, Norway; Engeland, K., Norwegian Water Resources and Energy Directorate, Nve, Oslo, Norway
Recommended Citation:
Roksväg T.,Steinsland I.,Engeland K.. Estimation of annual runoff by exploiting long-Term spatial patterns and short records within a geostatistical framework[J]. Hydrology and Earth System Sciences,2020-01-01,24(8)