Thordis Thorarinsdottir

• Lutz, Julia; Roksvåg, Thea Julie Thømt; Dyrrdal, Anita Verpe; Lussana, Cristian & Thorarinsdottir, Thordis Linda (2024). Areal reduction factors from gridded data products. Journal of Hydrology. ISSN 0022-1694. 635, s. 1–12. doi: 10.1016/j.jhydrol.2024.131177.
• Scheuerer, Michael; Bahaga, Titike Kassa; Segele, Z.T. & Thorarinsdottir, Thordis Linda (2024). Probabilistic rainy season onset prediction over the greater horn of africa based on long-range multi-model ensemble forecasts. Climate Dynamics. ISSN 0930-7575. doi: 10.1007/s00382-023-07085-y.
• Heinrich-Mertsching, Claudio Constantin; Wahl, Jens Christian; Ordonez, Alba; Stien, Marita; Elvsborg, John & Haug, Ola [Vis alle 7 forfattere av denne artikkelen] (2023). Assessing present and future risk of water damage using building attributes, meteorology, and topography. The Journal of the Royal Statistical Society, Series C (Applied Statistics). ISSN 0035-9254. 72(4), s. 809–828. doi: 10.1093/jrsssc/qlad043.
• Hellton, Kristoffer Herland; Amdahl, Helga; Thorarinsdottir, Thordis; Alsheikh, Muath K; Aamlid, Trygve S. & Jørgensen, Marit [Vis alle 8 forfattere av denne artikkelen] (2023). Yield predictions of timothy (Phleum pratense L.) in Norway under future climate scenarios. Agricultural and Food Science. ISSN 1459-6067. 32(2), s. 80–93. doi: 10.23986/afsci.127935. Fulltekst i vitenarkiv Vis sammendrag

  The perennial forage grass timothy (Phleum pratense L.) is the most important forage crop in Norway. Future changes in the climate will affect growing conditions and hence the yield output. We used data from the Norwegian Value for Cultivation and Use testing to find a statistical prediction model for total dry matter yield (DMY) based on agro-climatic variables. The statistical model selection found that the predictors with the highest predictive power were growing degree days (GDD) in July and the number of days with rain (>1mm) in June–July. These predictors together explained 43% of the variability in total DMY. Further, the prediction model was combined with a range of climate ensembles (RCP4.5) to project DMY of timothy for the decades 2050–2059 and 2090–2099 at 8 locations in Norway. Our projections forecast that DMY of today’s timothy varieties may decrease substantially in South-Eastern Norway, but increase in Northern Norway, by the middle of the century, due to increased temperatures and changing precipitation patterns.

• Barna, Danielle Marie; Engeland, Kolbjørn; Thorarinsdottir, Thordis Linda & Xu, Chong-Yu (2023). Flexible and consistent Flood–Duration–Frequency modeling: A Bayesian approach. Journal of Hydrology. ISSN 0022-1694. 620. doi: 10.1016/j.jhydrol.2023.129448. Fulltekst i vitenarkiv Vis sammendrag

  Design flood values give estimates of flood magnitude within a given return period and are essential to making adaptive decisions around land use planning, infrastructure design, and disaster mitigation. Many hydrologic applications where flood retention is important, e.g. floodplain management and reservoir design, need design flood values for different durations. Flood–Duration–Frequency (QDF) models extend the standard statistical flood frequency analysis framework to multiple flood durations and are analogous to intensity–duration–frequency models for precipitation. Implementations of QDF models commonly assume simple scaling, where only the magnitude of the index flood is assumed to change with duration, despite empirical analyses showing a more complex dependence structure. We propose a multiscaling extension to existing QDF models where the magnitude of the index flood and the slope of the growth curve may scale independently with duration. In an application to 12 locations in Norway, we assess how three different QDF models capture relationships between floods of different duration. Incorporating duration dependency independently in both the index flood and the growth curve (extended QDF model) improves modeling of both short-duration events and events with long return periods. This model extension further expands the models’ ability to simultaneously model a wide range of durations. As measured by the integrated quadratic distance, the extended QDF model performs better than the original QDF model in 83% of the out of sample subdaily durations studied. Additionally, we find that the choice of durations used to fit QDF models is a highly influential aspect of the modeling process.

• Yuan, Qifen; Thorarinsdottir, Thordis L.; Beldring, Stein; Wong, Wai Kwok & Xu, Chong-Yu (2023). Assessing uncertainty in hydrological projections arising from local-scale internal variability of climate. Journal of Hydrology. ISSN 0022-1694. 620. doi: 10.1016/j.jhydrol.2023.129415. Fulltekst i vitenarkiv Vis sammendrag

  Hydrological impact assessments are increasingly performed at fine spatial and temporal resolutions in order to resolve local-scale changes under a future climate. Apart from the uncertainty represented by different climate models, emission scenarios and post-processing methods, the local-scale internal variability of the climate can be a major source of uncertainty for hydrological projections. To assess the latter at the catchment scale, this paper presents a methodology which is particularly suitable for spatially distributed hydrological models. An ensemble of daily precipitation and daily mean temperature realizations on a high-resolution grid is simulated from stochastic weather generators (WGs) trained on historical data and equipped with climate change information obtained from a regional climate model. Based on the resulting simulated daily runoff data, the significance of changes in the runoff regime is assessed using a statistical hypothesis test, and the variability contributed by the two input variables is quantified using the analysis of variance (ANOVA). As a proof of concept, simulations on a 1-km grid over a period of 19 years are carried out for nine catchments in central Norway. Significant changes in runoff regimes are found, indicating that the trends introduced in the WGs are not overwhelmed by the local-scale internal variability. Variability in the runoff simulations varies substantially throughout the year; it is highest in periods with potential snowmelt and lowest during winter. Temperature is the dominant source of variability in the colder months (November–March) due to its influence on rainfall and snowmelt. High variability in May–June is contributed comparably by both temperature and precipitation. In summer and early autumn the runoff variability is precipitation dominated. The results are in line with findings in the literature where the runoff variability is driven by the large-scale internal climate variability. This indicates that ignoring the local-scale internal variability may yield an underestimation of the overall variability in runoff projections and projected changes.

Se alle arbeider i Cristin

• Aastveit, Marthe Elisabeth; Thorarinsdottir, Thordis Linda; Lenkoski, Frank Alexander & Pensar, Johan (2024). Demand changes over time in the short-term rental markets.
• Rognli, Odd Arne; Aamlid, Trygve S.; Alsheikh, Muath K; Amdahl, Helga; Dalmannsdottir, Sigridur & Hellton, Kristoffer Herland [Vis alle 13 forfattere av denne artikkelen] (2023). Securing adaptation of timothy cultivars under climate change and during seed multiplication .
• Rognli, Odd Arne; Reddy Pashapu, Akhil; Kovi, Mallikarjuna Rao; Jørgensen, Marit; Dalmannsdottir, Sigridur & Aamlid, Trygve S. [Vis alle 13 forfattere av denne artikkelen] (2023). Genetiske endringer i nord-norske timoteisorter over tid og ved oppformering på ulike breddegrader.
• Haug, Ola; Heinrich-Mertsching, Claudio & Thorarinsdottir, Thordis (2023). Assessing risk of water damage to buildings under current and future climates.
• Roksvåg, Thea Julie Thømt; Lutz, Julia; Dyrrdal, Anita Verpe; Lussana, Cristian & Thorarinsdottir, Thordis (2023). Estimating extreme areal precipitation from gridded data products.
• Thorarinsdottir, Thordis (2023). From weather to climate predictions .
• Thorarinsdottir, Thordis (2023). Statistical modelling of environmental extremes.
• Lutz, Julia; Grinde, Lars; Dyrrdal, Anita Verpe; Roksvåg, Thea & Thorarinsdottir, Thordis Linda (2022). Estimating consistent rainfall design values for Norway using Bayesian inference and post-processing of posterior quantiles.
• Thorarinsdottir, Thordis Linda; Solberg, Rune; Lenkoski, Alex & Roksvåg, Thea (2022). Potensialet i data .
• Thorarinsdottir, Thordis Linda; Haugen, Marion & Guttorp, Peter (2022). Extracting robust information from data .
• Thorarinsdottir, Thordis Linda (2022). Climate Futures: Navigating climate risk .
• Thorarinsdottir, Thordis Linda; Roksvåg, Thea; Engeland, Kolbjørn; Barna, Danielle; Xu, Chong-Yu & Lutz, Julia [Vis alle 8 forfattere av denne artikkelen] (2022). Consistent estimation of extreme precipitation and flooding across multiple durations .
• Wahl, Jens Christian; Heinrich-Mertsching, Claudio; Liu, Izzie Yi; Thorarinsdottir, Thordis & Haug, Ola (2023). Gjensidige Denmark: Water damage risk model and preliminary analysis of storm damages. Norsk Regnesentral.
• Hellton, Kristoffer Herland & Thorarinsdottir, Thordis (2022). Analysis of variety crossings for improved yield in timothy. Norsk Regnesentral Oslo.

Se alle arbeider i Cristin