| 1. |
- Peltola, Olli, et al.
(författare)
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Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations
- 2019
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Ingår i: Earth System Science Data. - : Copernicus GmbH. - 1866-3508 .- 1866-3516. ; 11:3, s. 1263-1289
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Tidskriftsartikel (refereegranskat)abstract
- Natural wetlands constitute the largest and most uncertain source of methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process ("bottom-up") or inversion ("top-down") models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45° N). Eddy covariance data from 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash-Sutcliffe model efficiency D 0:47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3-41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4-39.9) or 38 (25.9-49.5) Tg(CH4) yr-1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available at https://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019).
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| 2. |
- Tootoonchi, Faranak, et al.
(författare)
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Copulas for hydroclimatic analysis : A practice‐oriented overview
- 2022
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Ingår i: WIREs Water. - : Wiley. - 2049-1948.
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Tidskriftsartikel (refereegranskat)abstract
- A warming climate is associated with increasing hydroclimatic extremes, which are often interconnected through complex processes, prompting their concurrence and/or succession, and causing compound extreme events. It is critical to analyze the risks of compound events, given their disproportionately high adverse impacts. To account for the variability in two or more hydroclimatic variables (e.g., temperature and precipitation) and their dependence, a rising number of publications focuses on multivariate analysis, among which the notion of copula-based probability distribution has attracted tremendous interest. Copula is a mathematical function that expresses the joint cumulative probability distribution of multiple variables. Our focus is to re-emphasize the fundamental requirements and limitations of applying copulas. Confusion about these requirements may lead to misconceptions and pitfalls, which can potentially compromise the robustness of risk analyses for environmental processes and natural hazards. We conducted a systematic literature review of copulas, as a prominent tool in the arsenal of multivariate methods used for compound event analysis, and underpinned them with a hydroclimatic case study in Sweden to illustrate a practical approach to copula-based modeling. Here, we (1) provide end-users with a didactic overview of necessary requirements, statistical assumptions and consequential limitations of copulas, (2) synthesize common perceptions and practices, and (3) offer a user-friendly decision support framework to employ copulas, thereby support researchers and practitioners in addressing hydroclimatic hazards, hence demystify what can be an area of confusion.
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| 3. |
- Tootoonchi, Faranak
(författare)
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Reducing uncertainties in climate change impact studies through uni- and multivariate methods : A Nordic perspective
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Climate change poses undeniable impacts on hydroclimatic processes due to simultaneous effects of rising temperature and changing precipitation patterns. To quantify these impacts, simulations of climate variables are typically retrieved from climate models, which are then downscaled and bias-adjusted for a particular study site.The literature holds various methods for bias adjustment, ranging from simple univariate methods that only adjust one variable at a time, to more advanced multivariate methods that additionally consider the dependence between variables. There is, however, still no guidance for choosing appropriate bias adjustment methods for a study at hand. In particular, the question whether the benefits of potentially improved adjustments outweigh the cost of increased complexity, remains unanswered.This thesis primarily sought to provide an answer to this question by offering practical guidelines for the application of uni- and multivariate bias-adjustment methods in hydrological climate-change impact studies. To this end, the thesis includes a practice-oriented overview of copulas, one of the most widely used multivariate methods in climate-change studies. Furthermore, it presents an evaluation of two commonly used parsimonious univariate and two advanced multivariate methods. The assessment focused on their ability to reproduce numerous statistical properties of precipitation and temperature series, and on the cascading effects on simulated hydrologic signatures. The thesis culminates in a practical application of one bias adjustment method as part of a modeling chain to quantify future droughts. The results elucidate that all bias adjustment methods generally improved the raw climate model simulations, but not a single method consistently outperformed all other methods. Univariate methods generally adjusted the simulations reasonably well, while multivariate methods were favorable only for particular flow regimes. Thus, other practical aspects such as computational time and theoretical requirements should also be taken into consideration when choosing an appropriate bias adjustment method.
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| 4. |
- Tootoonchi, Faranak, et al.
(författare)
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Uni- and multivariate bias adjustment methods in Nordic catchments : Complexity and performance in a changing climate
- 2022
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Ingår i: Science of the Total Environment. - : Elsevier. - 0048-9697 .- 1879-1026. ; 853
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Tidskriftsartikel (refereegranskat)abstract
- For climate-change impact studies at the catchment scale, meteorological variables are typically extracted from ensemble simulations provided by global and regional climate models, which are then downscaled and bias-adjusted for each study site. For bias adjustment, different statistical methods that re-scale climate model outputs have been suggested in the scientific literature. They range from simple univariate methods that adjust each meteorological variable individually, to more complex and more demanding multivariate methods that take existing relationships between meteorological variables into consideration. Over the past decade, several attempts have been made to evaluate such methods in various regions. There is, however, still no guidance for choosing appropriate bias adjustment methods for a study at hand. In particular, the question whether the benefits of potentially improved adjustments outweigh the cost of increased complexity, remains unanswered.This paper presents a comprehensive evaluation of the performance of two commonly used univariate and two multivariate bias adjustment methods in reproducing numerous univariate, multivariate and temporal features of precipitation and temperature series in different catchments in Sweden. The paper culminates in a discussion on trade-offs between the potential benefits (i.e., skills and added value) and disadvantages (complexity and computational demand) of each method to offer plausible, defensible and actionable insights from the standpoint of climate-change impact studies in high latitudes.We concluded that all selected bias adjustment methods generally improved the raw climate model simulations, but that not a single method consistently outperformed the other methods. There were, however, differences in the methods' performance for particular statistical features, indicating that other practical aspects such as computational time and heavy theoretical requirements should also be taken into consideration when choosing an appropriate bias adjustment method.
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