Thea Roksvåg

Norwegian Computing Center · Department of SAMBA

Agricultural food production and natural ecological systems depend on a range of seasonal climate indicators that describe seasonal patterns in climatological conditions. This article proposes a probabilistic forecasting framework for predicting the end of the freeze‐free season, or the time to a mean daily near‐surface air temperature below 0°C (r...

We present a Bayesian geostatistical model for mean annual runoff that incorporates simulations from a process-based hydrological model. The simulations are treated as a covariate and the regression coefficient is modeled as a spatial field. This way the relationship between the covariate (simulations from a hydrological model) and the response var...

Agricultural food production and natural ecological systems depend on a range of seasonal climate indicators that describe seasonal patterns in climatological conditions. This paper proposes a probabilistic forecasting framework for predicting the end of the freeze-free season, or the time to a mean daily near-surface air temperature below 0 $^\cir...

As a warming climate leads to more frequent heavy rainfall, the importance of accurate rainfall statistics is increasing. Rainfall statistics are often presented as intensity-duration-frequency (IDF) curves showing the rainfall intensity (return level) that can be expected at a location for a duration, and the frequency of this intensity (return pe...

We present a Bayesian geostatistical model for mean annual runoff that incorporates simulations from a process-based hydrological model by treating the simulations as a covariate in the statistical model. The regression coefficient of the covariate is modeled as a spatial field such that the relationship between the covariate (simulations from a hy...

We estimate annual runoff by using a Bayesian geostatistical model for interpolation of hydrological data of different spatial support: streamflow observations from catchments (areal data), and precipitation and evaporation data (point data). The model contains one climatic spatial effect that is common for all years under study, and 1 year specifi...

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...

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 fi...

In this study, annual runoff is estimated by using a Bayesian geostatistical model for interpolating hydrological data of different spatial support. That is, streamflow observations from catchments (areal data), and precipitation and evaporation data (point data). The model contains one climatic spatial effect that is common for all years under stu...