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Casestudie - Miljørisiko ved rydding
Get PDFSom del av prosjektet Rydderisk - Beslutningsmatrise for effektiv og skånsom rydding av ulike miljøer, har vi undersøkt mulige negative virkninger av rydding av plast. I denne rapporten beskriver vi casestudier fra Troms og Finnmark som utgangspunkt for testing av en vektet matrise for vurdering av rydderisiko. Vi testet den vektede vurderingsmatrisen på 8 lokaliteter på Arnøya og tre lokaliteter i Kvænangen og Loppa basert på bildedata tilgjengelig i Rent hav portalen. Casestudiene viser at det selv med bakgrunn i fotografier er mulig å gjennomføre en vektet vurdering av mulig miljørisiko forbundet med rydding basert på avfallstype, degraderingsgrad og grad av infiltrering i naturen. Den største utfordringen med bildedata som utgangspunkt var vurdering av degraderingsgrad, noe som vil være lettere å vurdere i felt. Selv om det er etablert veiledere til bruk under ryddeaksjoner hos flere aktører, mangler det en systematisk tilnærming til vektet vurdering av rydderelatert risiko knyttet til avfallstype, degraderingsgrad og grad av infiltrering i naturen.Casestudie - Miljørisiko ved ryddingpublishedVersio
Physical Activity and School Achievement and School Absence in Norwegian Adolescents
Get PDFThere is emerging evidence of an association between physical activity and school functioning, but the nature of the association in late adolescence is uncertain. This study investigates the association between self-reported physical activity and register-based school achievement and school absence in 7,730 Norwegian adolescents aged 16–18. There was a graded positive association between physical activity and school achievement, and a graded negative association between physical activity and school absence. The association was strongest at lower levels until reaching a point of saturation at four days of physical activity, indicating the importance of promoting physical activity for the least active adolescents.publishedVersio
Sensitivity analysis for multi-measurement points based SHM in the mooring lines of floating offshore wind turbines
Get PDFStructural health monitoring in floating offshore wind turbines' mooring lines is vital for detecting early faults and preventing disruptions. Currently, sporadic and expensive monitoring is conducted via remote-operating vehicles. Efficient, automated, economical monitoring methods based on a continuous stream of data acquired via multiple measurement points in the wind turbine, are required. Such methods based on vibration signals have been investigated limitedly for steel chains. Recently, faulty synthetic mooring lines of a simulated 10MW semi-submersible wind turbine have been detected under varying environmental conditions and via the Functional Model Based Method (FMBM) equipped with functional models. The uncertainty due to the conditions' stochasticity has been addressed by training the models using ten signal realizations per condition (wind) under the healthy wind turbine and from each of two measurement points. The current study presents a preliminary sensitivity analysis of the FMBM's capability in detecting the simulated wind turbine's faulty mooring lines when the functional models are trained using one signal realization per varying condition (wind) from each measurement point. The method's effectiveness is evaluated with acceleration signals from 11 healthy/ 66 faulty (reduced stifness in one mooring line) cases. The FMBM detects all cases even when trained using one signal realization.publishedVersio
Sedimentary ancient DNA sequences reveal marine ecosystem shifts and indicator taxa for glacial-interglacial sea ice conditions
Get PDFSedimentary ancient DNA (sedaDNA) analysis is a promising new approach for reconstructing the impact of past climate and environmental changes on marine paleobiodiversity. By recovering, amplifying, and sequencing taxonomically informative sedaDNA fragments preserved in sediments, it is possible to assess the response of a broad range of eukaryote taxa, including non-fossilizing lineages, to past climate change. Here we present a unique marine derived sedaDNA metabarcoding record, spanning the penultimate glacial-interglacial transition across Marine Isotope Stages 6 to 5d (>135–107 ka) from an Eirik Drift core-site in the Labrador Sea. We identified a range of marine groups including dinoflagellates, diatoms, coccolithophores, chlorophytes, and copepods. There were representatives of primary/secondary producers, grazers, and parasites, which may represent remnants of complex ecosystems and ancient food webs. There were significant biodiversity shifts following the penultimate deglaciation and changing sea ice conditions throughout the Last Interglacial. These shifts reflected the striking increase in community richness during periods of seasonal sea ice and reduction under extensive perennial sea ice cover and open ocean. We identify two potential sedaDNA indicator taxa sequences associated with past seasonal sea ice which are most likely pico-eukaryote representatives of Micromonas and Pyramimonas, both green algae with known sea ice associations in modern ecosystems. Our work demonstrates the importance of high resolution marine sedaDNA metabarcoding for unravelling climate-ecosystem linkages and strengthens the potential of sedaDNA signals for past sea ice reconstructions through indicator sequences.publishedVersio
Non-Gaussian Ensemble Optimization
Get PDFEnsemble-based optimization (EnOpt), commonly used in reservoir management, can be seen as a special case of a natural evolution algorithm. Stein’s lemma gives a new interpretation of EnOpt. This interpretation enables us to study EnOpt in the context of general mutation distributions. In this paper, a non-Gaussian generalization of EnOpt (GenOpt) is proposed, where the control gradient is estimated using Stein’s lemma, and the mutation distribution is updated separately via natural evolution. For the multivariate case, a Gaussian copula is used to represent dependencies between the marginals. The correlation matrix is also iteratively optimized. It is shown that using beta distributions as marginals in the GenOpt algorithm addresses the truncation problem that sometimes arises when applying EnOpt on bounded optimization problems. The performance of the proposed optimization algorithm is evaluated on several test cases. The experiments indicate that GenOpt is less dependent on the chosen hyperparameters, and it is able to converge more quickly than EnOpt on a reservoir management test case.publishedVersio
The role of social attraction and social avoidance in shaping modular networks
Get PDFHow interactions between individuals contribute to the emergence of complex societies is a major question in behavioural ecology. Nonetheless, little remains known about the type of immediate social structure (i.e. social network) that emerges from relationships that maximize beneficial interactions (e.g. social attraction towards informed individuals) and minimize costly relationships (e.g. social avoidance of infected group mates). We developed an agent-based model where individuals vary in the degree to which individuals signal benefits versus costs to others and, on this basis, choose with whom to interact depending on simple rules of social attraction (e.g. access to the highest benefits) and social avoidance (e.g. avoiding the highest costs). Our main findings demonstrate that the accumulation of individual decisions to avoid interactions with highly costly individuals, but that are to some extent homogeneously beneficial, leads to more modular networks. On the contrary, individuals favouring interactions with highly beneficial individuals, but that are to some extent homogeneously costly, lead to less modular networks. Interestingly, statistical models also indicate that when individuals have multiple potentially beneficial partners to interact with, and no interaction cost exists, this also leads to more modular networks. Yet, the degree of modularity is contingent upon the variability in benefit levels held by individuals. We discuss the emergence of modularity in the systems and their consequences for understanding social trade-offs.publishedVersio
Iceberg Detection With RADARSAT-2 Quad-Polarimetric C-Band SAR in Kongsfjorden, Svalbard-Comparison With a Ground-Based Radar
Get PDFSatellite monitoring of icebergs in the Arctic region is paramount for the safety of shipping and maritime activities. The potential of polarimetric synthetic aperture radar data in enhancing detection capabilities of icebergs under interchangeable and challenging conditions is explored in this work. We introduce RADARSAT-2 quad-pol C -band data to detect icebergs in Kongsfjorden, Svalbard. The location contains two tidewater glaciers and is chosen because multiple processes are present in this region, such as ice formation and its relationship with the glaciers, freshwater discharge. Six state-of-the-art detectors are tested for detection performance. These are the dual-intensity polarization ratio anomaly detector, polarimetric notch filter, polarimetric match filter, symmetry, polarimetric whitening filter (PWF), and optimal polarimetric detector (OPD). In addition, we also tested the parameters of the Cloude–Pottier decomposition. In this study, we make use of a ground-based radar for validation and comparison with satellite images. We show that in calm sea-state conditions, the OPD and PWF detectors give high probability of detection ( PD ) values of 0.7–0.8 when the probability of false alarm ( PF ) value is 0.01–0.05, compared with choppy sea conditions where the same detectors have degraded performance ( PD = 0.5–0.7). Target-to-clutter ratio (TCR) values for each polarization channel is also extracted and compared to the icebergs’ dimensions. The ground-based radar shows higher values in TCR, compared with satellite images. These findings corroborate previous work and show that sea-ice activity, surface roughness, incidence angle, weather, and sea-state conditions all affect the sensitivity of the detectors for this task.publishedVersio
Habitatkartlegging i Ørskogelva i 2021
Get PDFDenne rapporten sammenstiller resultatene av habitatkartlegging utført av NORCE LFI i Ørskogelva våren 2021 og høsten 2023. Habitat for laks og sjøørret, og menneskelige inngrep, ble kartlagt på hele anadrom strekning. Ørskogelva er i moderat grad påvirket av fysiske inngrep, med forbygninger i nedre del og langs jordbruksområder øverst som de største inngrepene. Forbygningene har innsnevret elvebredden og avstengt sideløp og flomsletter i disse områdene. Det er likevel totalt sett relativt gode habitatforhold for laks og sjøørret i elven. Det er relativt mange gyteområder med god spredning, og moderat mengde skjul for ungfisk. Elven kategoriseres dermed som høyproduktiv. Det foreslås å åpne et avstengt sideløp i øvre del av elven, samt å reetablere kantvegetasjon der det mangler trær langs elvebredden. Elvekraftverket Valgermo Giskemo reduserer vannføringen på en kort og bratt strekning helt øverst i Ørskogelva. Dette har i seg selv trolig liten betydning for anadrom fisk, men det anbefales å utrede risikoen for stranding av ungfisk på strekningen nedstrøms i forbindelse med driftsstans i kraftverket. I tillegg anbefales det å flytte grus fra kraftverkets inntaksdam til elven nedenfor, ettersom inntaksdammen reduserer transporten av gytegrus ned til anadrom strekning.Habitatkartlegging i Ørskogelva i 2021publishedVersio
Multisensor data fusion of operational sea ice observations
Get PDFMultisensor data fusion (MDF) is a process/technique of combining observations from multiple sensors to provide a more robust, accurate and complete description of the concerned object, environment or process. In this paper we introduce a new MDF method, multisensor optimal data fusion (MODF), to fuse different operational sea ice observations around Svalbard. The overall MODF includes regridding, univariate multisensor optimal data merging (MODM), multivariate check of consistency, and generation of new variables. For MODF of operational sea ice observations around Svalbard, the AMSR2 sea ice concentration (SIC) is firstly merged with the Norwegian Meteorological Institute ice chart. Then the daily SMOS sea ice thickness (SIT) is merged with the weekly CS2SMOS SIT to form a daily CS2SMOS SIT, which is further refined to be consistent with the SIC through consistency check. Finally sea ice volume (SIV) and its uncertainty are calculated based on the merged SIC and fused SIT. The fused products provide an improved, united, consistent and multifaceted description for the operational sea ice observations, they also provide consistent descriptions of sea ice edge and marginal ice zone. We note that uncertainties may vary during the regridding process, and therefore correct determination of the observation uncertainties is critically important for MDF. This study provides a basic framework for managing multivariate multisensor observations.publishedVersio
Importance Weighting in Hybrid Iterative Ensemble Smoothers for Data Assimilation
Get PDFBecause it is generally impossible to completely characterize the uncertainty in com- plex model variables after assimilation of data, it is common to approximate the uncertainty by sampling from approximations of the posterior distribution for model variables. When minimization methods are used for the sampling, the weights on each of the samples depend on the magnitude of the data mismatch at the critical points and on the Jacobian of the transformation from the prior density to the sample proposal density. For standard iterative ensemble smoothers, the Jacobian is identical for all samples, and the weights depend only on the data mismatch. In this paper, a hybrid data assimilation method is proposed which makes it possible for each ensemble member to have a distinct Jacobian and for the approximation to the posterior density to be multimodal. For the proposed hybrid iterative ensemble smoother, it is necessary that a part of the mapping from the prior Gaussian random variable to the data be analytic. Examples might include analytic transformation from a latent Gaussian random variable to permeability followed by a black-box transformation from permeability to state variables in porous media flow, or a Gaussian hierarchical model for variables followed by a similar black-box transformation from permeability to state variables. In this paper, the application of weighting to both hybrid and standard iterative ensemble smoothers is investigated using a two-dimensional, two-phase flow problem in porous media with various degrees of nonlinearity. As expected, the weights in a standard iterative ensemble smoother become degenerate for problems with large amounts of data. In the examples, however, the weights for the hybrid iterative ensemble smoother were useful for improving forecast reliability.Importance Weighting in Hybrid Iterative Ensemble Smoothers for Data AssimilationpublishedVersio