A Smart Arctic Future - Technology Part I

Abstracts

  • 09:00 Integrating new technologies in reindeer herding and indignous communities
    Authors: Niklas Labba ( UIT/Sesam ); Bård Kårtveit ( Norut ); Greg Poelzer ( University of Saskatchewan )

    NB: Please consider this submission for the session(s) organised by the Arctic Research Centre (ARC), UiT.

    Historically, reindeer herders, along with other circumpolar indigenous peoples, have incorporated and adapted to new technologies as needed. Sámi reindeer herding is integrating new technologies, and there is a general need for new energy systems in all arctic areas, especially in the indigenous areas. Senter for sami studies seeks to cooperate with the reindeer herding societies to support the development of sustainable technical and renewable energy plans for the future.

    Reintech and CASES- Partnership are two independent research approaches, where Reintech focuses on the effects of the technical development in Scandinavian reindeer herding.  The CASES- partnership focuses on renewable energy systems in the arctic region. Together these approaches initiates a discussion about what kinds of technologies, energy services and socio-technical arrangements are required to enable value creation in indigenous communities in the arctic? ARC- session Bård Kårtveit, Greg Poelzer og Niklas Labba

  • 09:30 Online at the world's end: how the advancing telecom technologies impact Svalbard communities
    Authors: Andrian Vlakhov ( Higher School of Economics )

    Svalbard, the Arctic archipelago of Norway, is a territory unique in many ways. Its political and legal status, defined by Svalbard treaty of 1920, is one of a kind in the Arctic; history of Svalbard exploration and industrial development, as well nature and environment of the archipelago, are also rather unique. So are its residents: Svalbard hosts several communities populated by dozens of nations, most important being Norwegian towns of Longyearbyen and Ny-Ålesund, as well as two Russian ones, namely Barentsburg and Pyramiden.

    One basic need of modern communities is the need to communicate (with each other or beyond), and the telecommunication in Svalbard is the focus of my study. The issue of telecommunication is a very topical one for the Arctic, as the advance of technology makes it vital for modern society, however that often comes in contradiction with the harsh Arctic natural conditions. My study aims to describe how the development of communication impacts remote Arctic communities, taking Svalbard as a case study.

    Such impact is evident, but its outcomes are still unclear: advance of new technologies like high-speed fiber optic or 4G mobile connection can provide a community with an impetus to develop post-industrial economies, giving it a chance for second life (which is the case for all Svalbard communities), or it call destroy their established lifestyle and deprive communities of sustainable futures. Such scenarios have been seen in the Arctic before, making the vulnerability of Arctic communities quite evident.

    Based on the longitudinal field research (2013–2018), I’m exploring the impact the high-tech advancement has had on Svalbard communities, with primary focus being on two Russian towns. Analyzing data obtained through participant observation and in-depth interviews, I’m tracing the societal changes observable in these towns and comparing them with their Norwegian neighbors which had experienced similar processes some time ago. I’m also trying to predict how these changes would impact the perspectives of post-industrial development of Svalbard, especially the areas of tourism, education and research.

  • 09:45 Machine Learning Remote Sensing for Ecosystem Monitoring in the Arctic
    Authors: Katalin Blix ( UIT THE ARCTIC UNIVERSITY OF NORWAY ); Torbjørn Eltoft ( UIT THE ARCTIC UNIVERSITY OF NORWAY )

    The Arctic has experienced dramatic changes in the past decades. Arctic sea ice extent and spring snow cover have rapidly decreased causing amplification in the warming of the Arctic. This warming has led to thinner sea ice, which again allows the penetration of light to waters beneath the ice. This affects the occurrence and pattern of the phytoplankton communities and has further impact on the ecosystems of the Arctic waters.  Phytoplankton are marine primary producers, they are at the beginning of the aquatic wood web, hence they play a key role in the aquatic ecosystems. Being able to monitor their occurrence, amount and distribution in Arctic waters has great ecological and economical importance.

     

    Traditional monitoring techniques are based on field measurements, which are inefficient, challenging and costly. Advances in remote sensing technologies in the past decades allow for efficient, environmentally friendly and cost-effective monitoring of global waters, included Arctic waters.

     

    In recent days, there are numerous satellites carrying various sensors designed for monitoring aquatic primary producers. These sensors differ in their spectral, spatial and temporal resolutions. There is often a trade-off between spatial and spectral resolution. Sensors with high spatial resolution have usually low spectral resolution and vice versa. The Ocean and Land Color Instrument (OLCI) onboard lately launched Sentinel 3 (S3) A and B satellites provides the unique possibility to monitor Arctic waters on a 300 m spatial resolution on 9 spectral bands in the visible part of the electromagnetic spectrum.

     

    Information about phytoplankton is retrieved by estimating Chlorophyll-a (Chl-a) concentration by using S3 OLCI algorithms. There are two state-of-art algorithms providing Chl-a content estimates for S3 OLCI. One of the models is the OC4 algorithm used for Chl-a estimation in oligotrophic waters, such as open oceans. The other algorithm is a machine learning Neural Network (NN), which is frequently used for coastal and inland waters.  Even though both models have advantageous properties, there are uncertainties about their performances, especially with regard to Arctic waters.

     

    In this work, we study how S3 OLCI can be used to monitor aquatic primary producers in the Arctic by evaluating the state-of-the-art models for estimating Chl-a content in Arctic waters. We also show how new, innovative machine learning approaches can be used to select the most suitable Chl-a content retrieval model for monitoring Arctic waters by using S3 OLCI.

  • 10:00 Wind resource assessment using Weather Research and Forecasting modelling in northern Norway
    Authors: Yngve Birkelund ( UiT The Arctic University of Norway ); Kine Solbakken ( UiT The Arctic University of Norway )

    NB: Please consider this submission for the session(s) organised by the Arctic Research Centre (ARC), UiT.

    Wind power is currently the second largest renewable electricity resource globally, and the wind power capacity increased by 10% in 2017. As an intermittent resource, wind power production strongly depended of the local wind conditions, and wind resource assessment is an important part in the development of new wind farms. During the last decade, numerical weather models has proven to be a natural step for mesoscale wind resource assessment when considering large areas for country based or global scale wind atlas.

    In this work, we will investigate the use of the mesoscale Weather Research and Forecasting model (WRF) for wind resource assessment for several different type of terrain in northern Norway. This region consists of a rugged landscape with several fjord and valleys that goes into the Scandinavian mountain range between Norway and Sweden, which represent an additional challenge for numerical weather models.

    The complex terrain adds an additional challenge to the numerical wind modelling, and we will show how higher resolutions models, updated reanalysis data and different physical schemes can change the WRF results. The modelled wind resources are compared to in-situ measurements, and the performance of each model set-up is evaluated in terms of wind speed distributions, wind direction and root-mean square error.

    Our initial results shows that the Weather Research and Forecasting model is able to reproduce the average wind speed and wind direction quite well, but the root mean square error in complex terrain are larger than in other comparable studies. This indicates that WRF can be used as a first rough estimate of the wind resources in the northern Norway and similar Arctic terrain.

  • 10:15 Innovative User Communities in the Arctic: Materials, Skillsets, and Identities
    Authors: Svetlana Usenyuk-Kravchuk ( Ural State University of Architecture and Art )

    In the Arctic, where everyday existence depends on proper equipment, more attention needs to be paid to the “materialities of everyday living”, and to how evolving technologies and practices support or undermine sustainable environment-society interactions. Here, the fruitful combination of historical and ethnographic investigations in the form of the "biographies of artifacts and practices / BoAP" approach (Hyysalo et al. 2018) can further our understanding of material triggers of change and help to evaluate/envision unfolding change in socio-technical engagement with the environment. Through field encounters in different areas of the Russian North and Siberia (2006-2018) various expressions of user inventiveness in the transport sector were discovered: the concept of "proximal design" (Usenyuk et al., 2016) encompassed not only users’ ability to adjust, repair and redesign their machines, but the very ability to create entirely new kinds of technology and, eventually, to come up with enduring design principles without the participation of design professionals. To illustrate this, I present two Russian-based case studies of collective making and tinkering. Finally, I propose specific identity features of innovative user communities with regard to available materials and acquired skills that become clearly visible and relevant in periphery/remote localities of the Arctic.

Science Science

Thursday 24th January 2019

09:00 - 10:30

Clarion Hotel The Edge - Kjøpmannskontoret

Add to Calendar 2019-01-24 09:00 2019-01-24 10:30 Europe/Oslo A Smart Arctic Future - Technology Part I Clarion Hotel The Edge - Kjøpmannskontoret

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