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ESCYMO – Enhancing Snow CompetencY of Models and Operators

Climate change has a significant impact on the prevalence and duration of seasonal snow cover. A goal in the ESCYMO project is to develop new models and improve education and competance to meet challenges with changes in snowhydrology and effects on water resources and power production.

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About the project

The expected climate change will influence extent and duration of the seasonal snow cover. For snow dominated regions, especially in Norway, this will have considerable socio-economic consequences, for instance for infrastructure, energy supply and recreation.

Snow hydrological models are important planning tools both in terms of short-term forecasts to optimize hydropower production or flood warning, as well as for assessments of long-term evolution of water resources in a changing climate.

Objectives

The primary goal of ESCYMO is to develop competence in the area of snow hydrology to optimize the use of water resources in a changing climate.

ESCYMO addresses these needs through i) research (developing the snow competence of models) and ii) education (developing the competence of operators).

The project consortium has identified a twofold competence need within operational snow hydrology:

i) Improved description of build-up and decay of the seasonal snow reservoir on various timescales. While the dynamics of the melting process and its relationship with meteorological conditions are reasonably well understood, there is a gap concerning the spatial distribution of snow within a catchment. Development of adequate methodology to model the snow distribution in terms of terrain and weather conditions is hence important. Recent technology (GPS, geo-radar, UAS) provides new possibilities to collect data of snow and related quantities.

ii) In times of changing methodology, the industry employing hydrologists has a need for adequately skilled candidates. ESCYMO will implement its findings and developments into university education to convey adequate process understanding as well as specific training in up-to-date methodology.

Outcomes

The project results will be helpful in identifying significant physical processes, deriving efficient parameterizations and analyzing uncertainties in hydrological modeling. The project will develop new methodology to include new types of data in hydrological models and to analyze the value of different input/criteria/structure to reduce uncertainty.

Furthermore, ESCYMO will develop learning modules and refine existing hydrology (and related topics) curriculum at the studies in geosciences at University of Oslo to enhance capability of future hydrologists to evaluate uncertainty in snow dominated catchments.

Background

ESCYMO is a knowledge-building project seeking to contribute to industry-oriented researcher training and long-term competence development in the Norwegian research community within topics that are crucial to the development of business and industry in Norway.

Financing

Full name of the research project ESCYMO is Enhancing Snow CompetencY of Models and Operators.

The project is jointly supported by the KLIMAFORSK program of the Research Council of Norway/NFR. The project number is 244024.

The project also get financing from the Norwegian hydropower industry, i.e. Agder Energi ASE-CO Energi ASGlommens og Laagens BrukseierforeningHydro Energi AS and Statkraft AS.

Cooperation

Besides the cooperation with partners from the hydropower industry, ESCYMO collaborates with Globesar AS and the SnowHow-Project/SINTEF.

Tools

ESCYMO conducts field measurements at the Finse Alpine Research Center, where considerable sensor infrastructure Finse Eco-Hydrological Observatory (Finse EcHO) of the interdisciplinary research initiative LATICE is available. More infromation on links:

Model development in this project is done by use of SHYFT.

Publications

  • John Burkhart; Felix Nikolaus Matt; Sigbjørn Helset; Yisak Sultan Abdella; Ola Skavhaug & Olga Silantyeva (2021). Shyft v4.8: A Framework for Uncertainty Assessment and Distributed Hydrologic Modelling for Operational Hydrology. Geoscientific Model Development.  ISSN 1991-959X.
  • Aynom Tesfay Teweldebrhan; John Burkhart; Thomas Schuler & Chong-Yu Xu (2019). Improving the Informational Value of MODIS Fractional Snow Cover Area Using Fuzzy Logic Based Ensemble Smoother Data Assimilation Frameworks. Remote Sensing.  ISSN 2072-4292.  11
  • Simon Filhol & Matthew Sturm (2019). The smoothing of landscapes during snowfall with no wind. Journal of Glaciology.  ISSN 0022-1430.  65, s 173- 187
  • Simon Filhol; Alexis Perret; Luc Maurice Ramuntcho Girod; Guillaume Sutter; Thomas Schuler & John Burkhart (2019). Time-lapse photogrammetry of distributed snow depth during snowmelt. Water Resources Research.  ISSN 0043-1397.  55, s 7916- 7926
  • Aynom Tesfay Teweldebrhan; John Burkhart & Thomas Schuler (2018). Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches. Hydrology and Earth System Sciences.  ISSN 1027-5606.  22, s 5021- 5039
  • Kjetil Schanke Aas; Kjersti Gisnås; Sebastian Westermann & Terje Koren Berntsen (2017). A Tiling Approach to Represent Subgrid Snow Variability in Coupled Land Surface–Atmosphere Models. Journal of Hydrometeorology.  ISSN 1525-755X.  18, s 49- 63
  • Kjersti Gisnås; Sebastian Westermann; Thomas Schuler; Kjetil Melvold & Bernd Etzelmüller (2016). Small-scale variation of snow in a regional permafrost model. The Cryosphere.  ISSN 1994-0416.  10, s 1201- 1215

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  • Aynom Tesfay Teweldebrhan; John Burkhart & Thomas Schuler (2019). Balancing between type I and type II errors in testing hydrological models as hypotheses of catchment behaviour.
  • Aynom Tesfay Teweldebrhan; John Burkhart; Thomas Schuler & Chong-Yu Xu (2019). Fuzzy-logic based ensemble smoother data assimilation frameworks for improving the informational value of the assimilated data.
  • Simon Vincent P Filhol (2019). A Wireless Sensor Network: Status, development, and future.
  • Aynom Tesfay Teweldebrhan; John Burkhart; Thomas Schuler & Chong-Yu Xu (2019). Snow data assimilation into a hydrological model using fuzzy logic based ensemble smoothers.
  • Aynom Tesfay Teweldebrhan; John Burkhart; Thomas Schuler & Chong-Yu Xu (2019). Assimilation of MODIS fractional snow cover area into a hydrological model using fuzzy-logic based ensemble smoother data assimilation frameworks.
  • Aynom Tesfay Teweldebrhan; John Burkhart; Thomas Schuler & Morten Hjorth-Jensen (2019). Application of machine learning emulators in parameter identification for a distributed hydrological model.
  • Aynom Tesfay Teweldebrhan (2019). Ensemble-based uncertainty quantification and reduction in hydrological modelling and predictions. Series of dissertations submitted to the Faculty of Mathematics and Natural Sciences, University of Oslo.. 2191.
  • Aynom Tesfay Teweldebrhan; John Burkhart & Thomas Schuler (2018). Parameter uncertainty analysis for a distributed hydrological model.
  • Simon Filhol (2018). Snow distribution at Finse.
  • Simon Filhol (2018). Snow Science Activities and Instrumentation Development at Finse.
  • Simon Filhol; Schuler Thomas & John Burkhart (2017). The Morphological evolution of a wind-shaped snow surface during a storm event at Finse, NO.
  • Thomas Schuler; Aynom Tesfay Tweldebrahn; Simon Filhol & John Burkhart (2017). ESCYMO activities and linkage to SnowHow.
  • Simon Filhol; Norbert Pirk; Thomas Schuler & John Burkhart (2017). The morphological evolution of a wind-shaped snow surface during a storm event at Finse, Norway.
  • Simon Filhol; Thomas Schuler; John Burkhart; John Hulth & Sven Decker (2017). A network of instrumentation to keep track of snow distribution at Finse, Norway.
  • Aynom Tesfay Tweldebrahn; John Burkhart & Thomas Schuler (2017). Snow Distribution Modelling and Uncertainty Analysis using a Conceptual Hydrological Model.
  • Aynom Tesfay Tweldebrahn; John Burkhart & Thomas Schuler (2017). Parameterizing snow redistribution effect of topographic parameters in a conceptual hydrological model.
  • John Burkhart; Sven Decker; Simon Filhol; John Hulth; Atle Nesje; Thomas Schuler; Stefan Pieter Sobolowski & Lena M. Tallaksen (2017). Development of the Finse Alpine Research Station towards a platform for multi-disciplinary research on Land-Atmosphere Interaction in Cold Environments (LATICE).
  • Aynom Tesfay Teweldebrhan; John Burkhart & Thomas Schuler (2017). Parameterizing snow redistribution effect of topographic parameters in a conceptual hydrological model.
  • Aynom Tesfay Teweldebrhan; John Burkhart & Thomas Schuler (2017). Parameter identification for a Distributed hydrological model using the GLUE method.
  • Simon Filhol; Norbert Pirk; Thomas Schuler & John Burkhart (2017). The Evolution of a Snow Dune Field.
  • Thomas Schuler; Kristoffer Aalstad; Kjetil Schanke Aas; John Burkhart; Thorben Dunse; Simon Filhol; John Hulth; Torbjørn Ims Østby & Sebastian Westermann (2016). Towards real-time snow products for Svalbard.
  • Simon Filhol; John Burkhart; Thomas Schuler & John Hulth (2016). A distributed and real-time wireless network of weather stations for wind-blown snow at Finse, Norway.
  • Simon Filhol; John Burkhart; Thomas Schuler & John Hulth (2016). Weather stations for wind-blown snow at Finse, Norway: A distributed and real-time wireless network of.
  • Simon Filhol; John Burkhart; Thomas Schuler & John Hulth (2016). Capturing snow depth distribution with a low cost and wireless weather station network.
  • John Burkhart; Thomas Schuler; Lena M. Tallaksen; Simon Filhol; John Hulth & Sven Decker (2016). Snow model validation in Norway at the Land Atmosphere Interaction in Cold Environments (LATICE) Finse site.

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Published Sep. 30, 2016 4:32 PM - Last modified June 3, 2021 9:18 PM