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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-programme 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.

The project startet up in 2015, and will end in 2020. 

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 information on links:

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

Publications

  • Burkhart, John; Matt, Felix Nikolaus; Helset, Sigbjørn; Abdella, Yisak Sultan; Skavhaug, Ola & Silantyeva, Olga (2021). Shyft v4.8: A Framework for Uncertainty Assessment and Distributed Hydrologic Modelling for Operational Hydrology . Geoscientific Model Development. ISSN 1991-959X. 14(2), p. 821–842. doi: 10.5194/gmd-14-821-2021. Full text in Research Archive
  • Teweldebrhan, Aynom Tesfay; Burkhart, John; Schuler, Thomas & Xu, Chong-Yu (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(1). doi: 10.3390/rs11010028. Full text in Research Archive
  • Filhol, Simon & Sturm, Matthew (2019). The smoothing of landscapes during snowfall with no wind. Journal of Glaciology. ISSN 0022-1430. 65(250), p. 173–187. doi: 10.1017/jog.2018.104. Full text in Research Archive
  • Filhol, Simon; Perret, Alexis; Girod, Luc Maurice Ramuntcho; Sutter, Guillaume; Schuler, Thomas & Burkhart, John (2019). Time-lapse photogrammetry of distributed snow depth during snowmelt. Water Resources Research. ISSN 0043-1397. 55(9), p. 7916–7926. doi: 10.1029/2018WR024530. Full text in Research Archive
  • Teweldebrhan, Aynom Tesfay; Burkhart, John & Schuler, Thomas (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(9), p. 5021–5039. doi: 10.5194/hess-22-5021-2018. Full text in Research Archive
  • Aas, Kjetil Schanke; Gisnås, Kjersti; Westermann, Sebastian & Berntsen, Terje Koren (2017). A Tiling Approach to Represent Subgrid Snow Variability in Coupled Land Surface–Atmosphere Models. Journal of Hydrometeorology. ISSN 1525-755X. 18(1), p. 49–63. doi: 10.1175/JHM-D-16-0026.1. Full text in Research Archive
  • Gisnås, Kjersti; Westermann, Sebastian; Schuler, Thomas; Melvold, Kjetil & Etzelmüller, Bernd (2016). Small-scale variation of snow in a regional permafrost model. The Cryosphere. ISSN 1994-0416. 10(3), p. 1201–1215. doi: 10.5194/tc-10-1201-2016.

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  • Teweldebrhan, Aynom Tesfay; Burkhart, John; Schuler, Thomas & Xu, Chong-Yu (2019). Assimilation of MODIS fractional snow cover area into a hydrological model using fuzzy-logic based ensemble smoother data assimilation frameworks.
  • Teweldebrhan, Aynom Tesfay; Burkhart, John; Schuler, Thomas & Xu, Chong-Yu (2019). Snow data assimilation into a hydrological model using fuzzy logic based ensemble smoothers.
  • Teweldebrhan, Aynom Tesfay; Burkhart, John; Schuler, Thomas & Hjorth-Jensen, Morten (2019). Application of machine learning emulators in parameter identification for a distributed hydrological model.
  • Teweldebrhan, Aynom Tesfay; Burkhart, John; Schuler, Thomas & Xu, Chong-Yu (2019). Fuzzy-logic based ensemble smoother data assimilation frameworks for improving the informational value of the assimilated data.
  • Teweldebrhan, Aynom Tesfay; Burkhart, John & Schuler, Thomas (2019). Balancing between type I and type II errors in testing hydrological models as hypotheses of catchment behaviour .
  • Filhol, Simon Vincent P (2019). A Wireless Sensor Network: Status, development, and future.
  • Filhol, Simon (2018). Snow distribution at Finse.
  • Filhol, Simon (2018). Snow Science Activities and Instrumentation Development at Finse.
  • Teweldebrhan, Aynom Tesfay; Burkhart, John & Schuler, Thomas (2018). Parameter uncertainty analysis for a distributed hydrological model.
  • Filhol, Simon; Schuler, Thomas; Burkhart, John; Hulth, John & Decker, Sven (2017). A network of instrumentation to keep track of snow distribution at Finse, Norway.
  • Tweldebrahn, Aynom Tesfay; Burkhart, John & Schuler, Thomas (2017). Parameterizing snow redistribution effect of topographic parameters in a conceptual hydrological model.
  • Schuler, Thomas; Tweldebrahn, Aynom Tesfay; Filhol, Simon & Burkhart, John (2017). ESCYMO activities and linkage to SnowHow.
  • Tweldebrahn, Aynom Tesfay; Burkhart, John & Schuler, Thomas (2017). Snow Distribution Modelling and Uncertainty Analysis using a Conceptual Hydrological Model.
  • Filhol, Simon; Pirk, Norbert; Schuler, Thomas & Burkhart, John (2017). The morphological evolution of a wind-shaped snow surface during a storm event at Finse, Norway.
  • Filhol, Simon; Thomas, Schuler & Burkhart, John (2017). The Morphological evolution of a wind-shaped snow surface during a storm event at Finse, NO.
  • Teweldebrhan, Aynom Tesfay; Burkhart, John & Schuler, Thomas (2017). Parameter identification for a Distributed hydrological model using the GLUE method.
  • Filhol, Simon; Pirk, Norbert; Schuler, Thomas & Burkhart, John (2017). The Evolution of a Snow Dune Field.
  • Teweldebrhan, Aynom Tesfay; Burkhart, John & Schuler, Thomas (2017). Parameterizing snow redistribution effect of topographic parameters in a conceptual hydrological model.
  • Burkhart, John; Decker, Sven; Filhol, Simon; Hulth, John; Nesje, Atle & Schuler, Thomas [Show all 8 contributors for this article] (2017). Development of the Finse Alpine Research Station towards a platform for multi-disciplinary research on Land-Atmosphere Interaction in Cold Environments (LATICE).
  • Filhol, Simon; Burkhart, John; Schuler, Thomas & Hulth, John (2016). Weather stations for wind-blown snow at Finse, Norway: A distributed and real-time wireless network of.
  • Schuler, Thomas; Aalstad, Kristoffer; Aas, Kjetil Schanke; Burkhart, John; Dunse, Thorben & Filhol, Simon [Show all 9 contributors for this article] (2016). Towards real-time snow products for Svalbard.
  • Filhol, Simon; Burkhart, John; Schuler, Thomas & Hulth, John (2016). A distributed and real-time wireless network of weather stations for wind-blown snow at Finse, Norway.
  • Filhol, Simon; Burkhart, John; Schuler, Thomas & Hulth, John (2016). Capturing snow depth distribution with a low cost and wireless weather station network.
  • Burkhart, John; Schuler, Thomas; Tallaksen, Lena M.; Filhol, Simon; Hulth, John & Decker, Sven (2016). Snow model validation in Norway at the Land Atmosphere Interaction in Cold Environments (LATICE) Finse site.
  • Teweldebrhan, Aynom Tesfay; Burkhart, John & Schuler, Thomas (2019). Ensemble-based uncertainty quantification and reduction in hydrological modelling and predictions. University of Oslo. ISSN 1501-7710.

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Published Sep. 30, 2016 4:32 PM - Last modified Feb. 21, 2022 11:29 AM