Assessing impact of snow sublimation for hydropower production in Norway (SnowSub)

• Dar, Shaakir; Silantyeva, Olga; Ala-aho, Pertti; Walter, Benjamin; Hulth, John & Hyöky, Valtteri [Show all 8 contributors for this article] (2026). Snow Structure Modulates the Isotopic Imprint of Sublimation. EGUsphere. doi: 10.5194/egusphere-2026-2589. Full text in Research Archive Show summary

  Abstract. Sublimation modifies the stable isotopic composition of snow. The physical controls governing this imprint, particularly the interplay between isotopic fractionation, snow structure, and airflow, remain poorly constrained. Current surface-exchange models commonly treat sublimation fractionation as a function of atmospheric forcing and boundary-layer exchange, with limited explicit representation of snow structural state. Here, we present controlled wind-tunnel experiments examining sublimation-driven isotopic changes (δ¹⁸O, δ²H, and d-excess) in three contrasting snow types (laboratory snow, fresh natural snow, and old natural snow) under varied airflow regimes, ambient humidity, and basal-to-air temperature contrasts. We show that airflow is the dominant experimental control on sublimation mass loss, but bulk isotopic change does not scale directly with mass loss, revealing a partial decoupling between sublimation magnitude and isotopic modification. High airflow produced the smallest isotopic change per unit mass loss, consistent with strong turbulent exchange reducing the isotopic imprint of sublimation. However, absolute isotopic change varied systematically with snow structural state, scaling strongly with bulk porosity in natural snow. This structural dependence weakened after mass normalization for δ¹⁸O and δ²H, but persisted for d-excess, suggesting that properties associated with porosity influence effective kinetic fractionation in addition to cumulative isotopic change. Laboratory snow deviated from this behavior: despite overlapping the porosity range of natural snow, it showed no porosity-isotopic change relationship. This suggests that bulk porosity alone is insufficient to capture the structural controls on isotopic response to sublimation. Within a relative snow-type comparison, the Craig–Gordon model systematically overpredicted δ¹⁸O and δ²H enrichment and underpredicted the range of d-excess change. The systematic nature of these discrepancies suggests that parameter uncertainty alone is unlikely to explain the mismatch and points to missing representation of internal vapor transport through the snow pore network. Our study shows that sublimation rate alone is insufficient to predict isotopic modification; snow structural state must be considered alongside atmospheric forcing in models of sublimation-driven isotopic evolution. Snow structural state can therefore impose a systematic overprint on isotopic signals in snow archives and meltwater that structure-independent models are likely to misrepresent.

• Huang, Shaochun; Silantyeva, Olga; Gelati, Emiliano; Yilmaz, Yeliz A.; Engeland, Kolbjørn & Tallaksen, Lena M. (2026). Can we better constrain evapotranspiration estimates in data scarce cold regions? Application of a discharge-constrained modelling framework in Norwegian catchments. Journal of Hydrology Regional Studies. 67, p. 103850–103850. doi: 10.1016/j.ejrh.2026.103850. Full text in Research Archive Show summary

  Study region: 66 Norwegian catchments representing five hydrological regimes in Norway. Study focus: This study focuses on estimation of evapotranspiration (ET) in Norwegian catchments where ET estimates are highly uncertain due to sparse ET measurements and high uncertainty in measured precipitation in complex terrain. We propose a large-sample multi-modelling framework to benchmark the ET estimate, using three hydrological models calibrated against daily discharge using three objective functions. The ET simulations were indirectly evaluated using discharge-based criteria in the snow free period, due to lack of direct ET measurements. The ET simulations from model runs fulfilling a set of performance criteria were considered as benchmarks to evaluate and constrain five fine-resolution global ET products. New hydrological insights for the region: The results show that the three hydrological models differ in the sensitivity to objective functions and in performance for each hydrological regime. Most global products tend to overestimate ET for the Mountain regime and vary in performance for the other hydrological regimes. By excluding the poorly performing hydrological models and global ET products, the mean uncertainty in annual ET estimates of all catchments reduces from 265 mm to 73 mm. The constrained annual ET is around 332 ± 47 mm in the Atlantic regime, 320 ±71 mm in the Baltic and Transition regimes, 287 ±62 mm in the Inland regime and 223 ±54 mm in the Mountain regime.

• Silantyeva, Olga; Huang, Shaochun & Xu, Chong-Yu (2025). Evaluating Flexible Configurations of the Shyft Hydrologic Model Framework Across Mainland Norway. Hydrology and Earth System Sciences Discussions (HESSD). ISSN 1812-2116. doi: 10.5194/egusphere-2025-4071. Full text in Research Archive Show summary

  The development and application of numerous hydrological models have played an indispensable role in advancing our understanding of hydrological processes, improving forecasting capabilities, supporting the design and operation of water conservancy projects, and facilitating water resource assessments. However, due to the spatial heterogeneity and temporal variability of climate and basin characteristics, the inherent complexity of hydrological processes, and data limitations, hydrological modeling faces two major bottlenecks: first, no single model is universally applicable to all river basins; second, further improvement in simulation accuracy of existing fixed-structure models remain challenging. As a result, the emergence of hydrological modeling frameworks with flexible structures and configurable components represents the next generation in the model development. Shyft is one of such flexible modeling frameworks fulfilling the above-mentioned purpose. It is cross platform and open source, jointly developed by academic and industrial partners. The framework allows uncertainty analysis, streamflow simulations, and forecasting. Most evaluation efforts of the framework to date have focused on smaller basins, but there is also a need to benchmark model performance more comprehensively. Here, we present a public benchmark for discharge simulation for 109 catchments across mainland Norway. Five model configurations are evaluated containing two different evapotranspiration routines (Priestley-Taylor and Penman-Monteith), two runoff methods (Kirchner and HBV) and two snow modules (temperature-index and semi-physical). The models are calibrated with 10 variants of target goal functions: KGE-based family, consisting of KGE, LKGE, bcKGE, KGE_LKGE, KGE_bcKGE, and NSE-based family, with NSE, LNSE, bcNSE, NSE_LNSE, NSE_bcNSE. The simulations are divided into two major groups: without precipitation correction and with precipitation correction. The evaluation is performed from 1981 to 2020 (approx. 40 years) at a daily time step. Using KGE, NSE and percent bias (PBIAS) as main evaluation metrics, the model configurations are compared against each other and against climatological benchmarks. The results show that all selected models were able to beat both mean and median flow benchmarks for the majority of catchments in all the target goal function set ups. 89 % of catchments gain higher performance scores with precipitation correction, but the picture is mixed for different metrics and models. The KGE and NSE performance metrics reveal that models, which combine temperature-index snow-tiles model and Kirchner runoff (-STK), perform best, but require precipitation correction to improve PBIAS. The models, which have semi-physical gamma-snow routine (-GSK), show relatively low performance with KGE and NSE scores, especially in Mountain and Inland hydrological regimes, but have the lowest |PBIAS|if no precipitation correction is applied. Precipitation correction shows limited effect on the -GSK models, even deteriorating some of the scores. The model, which combines temperature index snow-tiles and HBV runoff instead of Kirchner (-STHBV), is the most sensitive to precipitation correction: it has the worst PBIAS score across all models without precipitation correction, but jumps to third place in all three metrics, if the correction is applied. The study highlights that KGE-based goal functions reduce PBIAS more than any of the NSE-based goal functions. The study confirms that logarithmic transformation on streamflows, both if LKGE and LNSE are used as target goal functions, generate parameter sets with majority of outliers (KGE scores lower than -0.41). This new benchmark has potential to help with diagnosing problems, improving algorithms and further development within hydrological part of Shyft. Modeling results are made publicly available for further investigation.

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• Silantyeva, Olga & Helset, Sigbjørn (2026). Shyft FSM2 Implementation. doi: https:/gitlab.com/shyft-os/shyft. Full text in Research Archive Show summary

  Shyft is a cross-platform framework built around high-performance time-series storage and analysis, providing the computational foundation for hydrological forecasting and energy-market modelling. The project originated at Statkraft in collaboration with the University of Oslo – Department of Geosciences, and is today used in operational energy-sector environments, including 24×7 systems within Statkraft’s Energy Management division. Shyft combines: - a high-performance C++ core - a Python orchestration layer - scalable compute and cache microservices - enterprise-grade time-series and model storage - model-driven system architecture The framework was created through close interdisciplinary collaboration and continues to enable cooperation between: - domain experts - researchers - software engineers - operational IT teams while maintaining the performance and robustness required for operational energy systems. The SnowSub project contributed to new snow module: https://gitlab.com/shyft-os/shyft/-/tree/master/cpp/shyft/hydrology/methods/snow/fsm2?ref_type=heads and related full hydrological models: PTFSM2K and RPMFSM2K.

• Silantyeva, Olga; Huang, Shaochun & Xu, Chong-Yu (2026). Evaluating flexible process-based hydrological configurations against data-driven benchmarks: a large-sample study across 109 Norwegian catchments. doi: https:/acrobat.adobe.com/id/urn:aaid:sc:EU:15b5965a-cc8b-44c2-ba59-2523cca43137. Full text in Research Archive Show summary

  Large-sample hydrology provides a rigorous basis for assessing model generality across diverse hydro-climatic conditions. We evaluate five configurations of the Shyft flexible modelling framework, varying evapotranspiration (Priestley–Taylor vs. Penman–Monteith), snow (temperature-index snow tiles vs. semi-physical gamma snow), and runoff response (Kirchner vs. HBV routines) across 109 Norwegian catchments spanning mountain, inland, atlantic, baltic, and transient hydrological regimes. Model skill is assessed using KGE and its components, percent bias (PBIAS), KGE on reciprocal flow, and interannual NSE, under ten calibration goal functions. Results are contextualised against the hydroBM benchmark ensemble (Knoben et al., 2024) and an LSTM baseline from the neuralhydrology package (Kratzert et al., 2022). One configuration, containing Penman–Monteith evapotranspiration, temperature-index snow, and Kirchner runoff, performs on par with the LSTM across all metrics. We attribute part of this competitiveness to the Kavetski–Kuczera (2007) smoothing algorithm, which mitigates calibration landscape discontinuities in a manner functionally analogous to gradient-based optimisation in neural network training. This highlights that fair comparison between process-based and data-driven models must account for the full complexity of their respective optimisation problems. The role of precipitation correction in modulating cross-regime performance is also discussed. This study establishes a reproducible baseline for process-based and hybrid model evaluation in the Norwegian context, and lays groundwork for integrating physics-informed constraints into data-driven approaches.

• Silantyeva, Olga; Huang, Shaochun & Xu, Chong-Yu (2026). Benchmarking flexible modelling framework Shyft across mainland Norway. doi: 10.5194/egusphere-egu26-22432. Full text in Research Archive Show summary

  Developing hydrological models, which are process-aware and reliably transferable across diverse environments remains a challenge. We benchmark Shyft – an open-source, fully FAIR (findable, accessible, interoperable and reusable) flexible modeling framework, across 109 catchments in mainland Norway to evaluate how model structure, forcing uncertainty and calibration objective jointly shape streamflow simulation performance. We adopt large sample hydrology perspective to probe five models “stacks”, providing alternative process choices, such as evapotranspiration (Penman-Monteith vs Priestley-Taylor), snowmelt (temperature-index vs semiphysical) and runoff response (Kirchner vs HBV tank and soil) with multiple goal functions drawn from KlingGupta Efficiency (KGE) and Nash-Sutcliffe Efficiency (NSE), with and without catchment specific precipitation correction. We use a suite of evaluation metrics targeting bias, hydrograph dynamics, low flows and interannual variability. We move beyond crude mean-flow benchmarks toward simple climatological benchmarks, providing an objective context for model skill evaluation, given the seasonal nature of Norwegian catchments.The evaluation revealed that configurations containing temperature-index snow simulation and Kirchner runoff offer the greatest robustness and generality across all hydrological regimes. In terms of objective functions, KGEbased targets outperform NSE-based targets, with metric combining KGE and box-cox transformed KGE (KGE_bcKGE) identified as a promising generalist objective, which performs well across diverse metrics, including low-flow targeted (KGE(1/Q)) and interannual NSE. Furthermore, precipitation correction was found to be essential for improving performance in Mountain and Inland regimes, suggesting snow undercatch as a primary source of precipitation uncertainty. Among simple benchmarks, daily mean was found to be best predictor setting model expectations for future model intercomparisons in the region. Our results demonstrate the need for balance of structural adequacy, forcing uncertainty and equifinality.This project is supported by Norwegian Research Council NFR project 336621.

• Ahlbäck, Malin; Pirk, Norbert; Tallaksen, Lena M. & Silantyeva, Olga (2025). Temporal Dynamics and Hydrological Impacts of Snow-Atmosphere Vapor Exchanges in a High-Altitude Catchment. doi: https:/www.hydrologiraadet.no/wp-content/uploads/2025/08/AbstractsLillehammer2025_210825.pdf. Full text in Research Archive Show summary

  The evolution of the snowpack is strongly influenced by turbulent fluxes, where latent heat transfer alters the snowpack water equivalent through processes such as snow sublimation and deposition, in addition to changing the cold content and structure of the snow. In alpine regions, the contribution of sublimation to the water balance varies widely, with results indicating only a negligible part of snowfall is sublimated, to up to 90% locally at wind-exposed ridges and crests. To improve our understanding of these dynamics, this study integrates small-scale micrometeorological observations with catchment-scale modelling, enabling a multi-scale analysis of snow-atmosphere water vapour exchanges and their impacts on the snowpack water balance in a high-altitude catchment in southern Norway. We will outline preliminary insights into the temporal scales governing water vapour fluxes and its linkage to environmental drivers, using micrometeorological site data and wavelet analysis. Compared to the commonly used eddy covariance method, processing the high-frequency site data using wavelet analysis allowed for resolving fluxes over small (<1 min) time scales, and offer a detailed analysis of the eddy distribution over time, and as such, new insights into the dynamics governing sublimation. In addition, the wavelet analysis method handled non-stationary time periods where the eddy covariance method fails, indicating the potential for using wavelet analysis over eddy covariance for processing complex micrometeorological data where traditional methods are not possible. To evaluate the broader hydrological impacts of sublimation and deposition fluxes, we also plan to present results from a snow model framework over the whole catchment and contrast the annual variability between dry and wet winters. Our findings are expected to contribute to a deeper understanding of sublimation and deposition processes in Nordic regions, and how they may change in a future climate. By combining field measurements and modelling approaches, these insights can be used for advancing the parametrisation of winter vapour fluxes for applications in snow hydrology.

• Valseth, Kristen Joy; Valnes, Lars Magnus; Silantyeva, Olga; Lappegard, Gaute & Mardal, Kent-Andre (2025). CAMELS-Nordic, a large-scale hydrometeorological and catchment properties dataset for Norway and Sweden. doi: https:/www.hydrologiraadet.no/wp-content/uploads/2025/08/AbstractsLillehammer2025_210825.pdf. Full text in Research Archive Show summary

  Historically, hydrologic studies have focused on one or a small number of basins. In many cases, these studies were limited by data availability and computational resources. In the last 20 years, the availability of large hydrological datasets, such as gridded meteorological data sets and streamflow timeseries, and increased computing resources have empowered large-sample hydrology studies. Having accessible and high-quality large datasets available to the science community facilitates the evaluation of hydrologic processes and prediction questions. To support modeling and climate research efforts in the Nordics the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies)-Nordic was collected and processed into a coherent dataset for the entirety of Norway and Sweden. CAMELS-Nordic combines not only meteorological and hydrological, but also topography, climate, streamflow, land cover, and soil data with python package to update time series automatically where possible. The development of the data package takes advantage of high-quality and freely available data from various Norwegian, Swedish, and European agencies. It includes: (1) daily forcing data (e.g. observations, interpolations, and modeled data) for catchments located in Norway and Sweden; (2) daily streamflow data; (3) digital elevation model; (4) catchment properties (size, location, elevation, and catchment files); (5) landcover; and (6) soil type data. Dataset time series span 1980 to 2022.

• Silantyeva, Olga; Ahlbäck, Malin & Dar, Shaakir (2025). Assessing snow sublimation in Norway: insights from field and laboratory work of SnowSub project. doi: https:/www.hydrologiraadet.no/wp-content/uploads/2025/08/AbstractsLillehammer2025_210825.pdf. Full text in Research Archive Show summary

  Hydrologic modelling is an important part of decision making within hydropower companies in Norway, as the country produces most of its electricity from hydropower. In the SnowSub project we work towards improving hydrologic modelling within Shyft software, used by companies such as Statkraft AS and Skagerak AS in their operations. A major aspect of hydrology in Norwegian catchments is snow, which acts as a storage for streamflow. However, this storage can lose its reserves due to sublimation. Dry air, strong wind and solar radiation favor sublimation in certain areas of Norway. Sublimation flux can be measured in the field using eddy covariance flux station. However, filed observations can not be controlled, making it difficult to systematically test different hypotheses. Given this, our project combines field measurements in the Tuddal site with experimental studies of sublimation under the controlled conditions of the Cold Climate Container facility at the University of Oslo. These studies provide insights into sublimation and snow processes in Norway, which can further inform hydrological modeling within Shyft. In this talk, we present results from the two field seasons at Tuddal. Fluxes were analyzed using eddy covariance method and wavelet analysis, with the two methods showing good agreement. The analysis revealed the importance of smaller timescales for distinguishing sublimation fluxes. Laboratory studies were conducted during two periods, in November 2024 and February 2025. The wind tunnel in the laboratory showed good stability of temperatures and relative humidity, allowing us to investigate effects of wind and humidity across temperature ranges from -20 to 0 degrees Celsius and time scales from 3 to 24 hours. Experiments were performed in two setups – one with continuous isotopic content measurement in the air and snowpack, and another measuring mass changes using Petri dishes setup. The results showed that even air speeds below 3.2 m/s can cause noticeable snow mass loss, with relative humidity maintained between 58% to 80%, matching the lower humidity values observed at the Tuddal site. As a part of the project, we also investigate potential of FSM2 snow model in its ability to capture sublimation effects at our study site. The flexible hydrological modelling framework Shyft can incorporate the FSM2 approach into its architecture, providing a physics-based snow model as part of the operational components. We benchmarked hydrologic models within Shyft across a large sample of catchments in Norway. The results suggest that, although the model performs well in discharge simulations over historical periods, the snow modelling capabilities of the toolbox – despite offering a variety of model choices - still remain limited and could be substantially improved. This project is supported by Norwegian Research Council NFR 336621.

• Huang, Shaochun; Silantyeva, Olga; Gelati, Emiliano; Yilmaz, Yeliz A.; Engeland, Kolbjørn & Tallaksen, Lena M. (2025). Quantifying the uncertainty of evapotranspiration estimates for unregulated Norwegian catchments using multiple hydrological models and remote sensing products. doi: 10.5194/egusphere-egu25-14913. Full text in Research Archive Show summary

  Previous studies show high uncertainty of evapotranspiration (ET) estimates for Norway, ranging from about 200 to more than 500 mm/year. It is partly due to sparce ET measurements in Norway to constrain the ET process in the hydrological modelling, and partly due to the uncertainty of precipitation (P) observations, especially in high mountainous regions with complex topography. In this study, we aim to quantify the uncertainty of ET estimates for 66 Norwegian catchments based on three hydrological models and four global remote sensing products. All the selected catchments are small (< 1000 km2), non-regulated and non-glacierized, with long-term discharge (Q) observations and runoff coefficient less than one (i.e. P>Q). These catchments differ substantially in climatic and hydrological characteristics and span the five hydrological regimes commonly used in Norway (Atlantic, Mountain, Inland, Baltic, and Transition). The three hydrological models (HBV, LISFLOOD and Shyft) are calibrated against daily discharge between 1981 and 2000 using three objective functions (KGE, KGE+LKGE, and KGE+BoxcoxKGE) and validated between 2001 and 2020 using six criteria (KGE, LKGE, BoxcoxKGE, percent Bias (PBias), and KGE &#160;and PBias in the snow free period). The latter two criteria are specifically selected to evaluate the model performance in simulating ET given the absence of direct ET measurements. The snow free period is identified for each catchment and year based on a daily fractional snow-covered area data at 500 m spatial resolution produced from a combined MODIS dataset (MOD10A1 and MYD10A1). The four global remote sensing estimates of ET are obtained from BESSV2, ETMonitor, PML_V2 and SSEBop_V2 datasets and they are available at fine spatial resolution (&#8804;0.05&#176;) and monthly time steps. The calibration and validation results show that the three hydrological models perform best using different objective functions for each hydrological regime and target variable (discharge and ET). The uncertainty ranges of annual mean ET are up to 177 mm based on all hydrological model results and remote sensing products. Using the best performing hydrological model results as the benchmark, all remote sensing products overestimate ET for the mountain regime and PML_V2 gives the best estimate for the other four hydrological regimes.

• Valseth, Kristen Joy; Valnes, Lars Magnus; Lappegard, Gaute; Silantyeva, Olga & Mardal, Kent-Andre (2025). Development of CAMELS-Nordic, a large-scale hydrometeorological and catchment properties dataset for Norway and Sweden. doi: 10.5194/egusphere-egu25-10411. Full text in Research Archive Show summary

  Historically, hydrologic studies have focused on one or a small number of basins, in many cases these studies were limited by data availability and computational resources. The increased availability of large hydrological datasets in the last 20 years, such as gridded meteorological data sets and streamflow timeseries, and increased computing resources have empowered large-sample hydrology studies having accessible and high-quality large datasets available to the science community to facilitate the evaluation of hydrologic processes and prediction questions. To support modeling and climate research efforts in the Nordics the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies)-Nordic was collected and processed from multiple sources and databases into a coherent dataset for the entirety of Norway and Sweden. CAMELS-Nordic combines not only meteorological and hydrological, but also topography, climate, streamflow, land cover, soil, and geology data with python tools to update time series automatically. The development of the data package takes advantage of high-quality and freely available data from various Norwegian, Swedish, and European agencies. &#160;It includes: (1) daily forcing data (e.g. observations, interpolations, and modeled data) for catchments located in Norway and Sweden; (2) daily streamflow data; (3) digital elevation model; (4) catchment properties (size, location, elevation, and catchment files); (5) landcover; and (6) soil type data. Dataset time series span 1980 to 2022.

• Ahlbäck, Malin & Huang, Shaochun (2025). Assessing snow sublimation in Norway: Lessons learned from field and laboratory work of SnowSub project. doi: https:/www.hydrologiraadet.no/wp-content/uploads/2025/08/AbstractsLillehammer2025_210825.pdf. Full text in Research Archive Show summary

  Hydrologic modelling is an important part of decision making within hydropower companies in Norway, as the country produces most of its electricity from hydropower. In the SnowSub project we work towards improving hydrologic modelling within Shyft software, used by companies such as Statkraft AS and Skagerak AS in their operations. A major aspect of hydrology in Norwegian catchments is snow, which acts as a storage for streamflow. However, this storage can lose its reserves due to sublimation. Dry air, strong wind and solar radiation favor sublimation in certain areas of Norway. Sublimation flux can be measured in the field using eddy covariance flux station. However, filed observations can not be controlled, making it difficult to systematically test different hypotheses. Given this, our project combines field measurements in the Tuddal site with experimental studies of sublimation under the controlled conditions of the Cold Climate Container facility at the University of Oslo. These studies provide insights into sublimation and snow processes in Norway, which can further inform hydrological modeling within Shyft. In this talk, we present results from the two field seasons at Tuddal. Fluxes were analyzed using eddy covariance method and wavelet analysis, with the two methods showing good agreement. The analysis revealed the importance of smaller timescales for distinguishing sublimation fluxes. Laboratory studies were conducted during two periods, in November 2024 and February 2025. The wind tunnel in the laboratory showed good stability of temperatures and relative humidity, allowing us to investigate effects of wind and humidity across temperature ranges from -20 to 0 degrees Celsius and time scales from 3 to 24 hours. Experiments were performed in two setups – one with continuous isotopic content measurement in the air and snowpack, and another measuring mass changes using Petri dishes setup. The results showed that even air speeds below 3.2 m/s can cause noticeable snow mass loss, with relative humidity maintained between 58% to 80%, matching the lower humidity values observed at the Tuddal site. As a part of the project, we also investigate potential of FSM2 snow model in its ability to capture sublimation effects at our study site. The flexible hydrological modelling framework Shyft can incorporate the FSM2 approach into its architecture, providing a physics-based snow model as part of the operational components. We benchmarked hydrologic models within Shyft across a large sample of catchments in Norway. The results suggest that, although the model performs well in discharge simulations over historical periods, the snow modelling capabilities of the toolbox – despite offering a variety of model choices - still remain limited and could be substantially improved. This project is supported by Norwegian Research Council NFR 336621. Keywords: snow; sublimation; Norway; Tuddal; catchment hydrology; Cold Climate Container; laboratory experiments;

• Silantyeva, Olga; Ahlbäck, Malin; Yilmaz, Yeliz A.; Tallaksen, Lena M.; Lappegard, Gaute & Erlandsen, Helene B. [Show all 11 contributors for this article] (2024). SnowSub: Assessing snow sublimation for hydropower production in Norway. Project results at Snow Bridges workshop. Full text in Research Archive
• Ahlbäck, Malin (2024). Assessing the impact of sublimation for hydrologic modelling. Full text in Research Archive
• Silantyeva, Olga (2024). Shyft development/usage at UiO. SnowSub project dissemination for Statkraft. Full text in Research Archive
• Silantyeva, Olga (2024). Shyft development/usage at UiO for Skagerak. Full text in Research Archive
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Pirk, Norbert & Remmers, Janneke [Show all 9 contributors for this article] (2024). Leveraging Earth observations to model snow cover dynamics over Scandinavia. Full text in Research Archive
• Ahlbäck, Malin; Pirk, Norbert; Tallaksen, Lena M. & Silantyeva, Olga (2024). Assessing the impact of sublimation for hydrological forecasting. Full text in Research Archive
• Treichler, Désirée; Filhol, Simon; Mazzolini, Marco; Yilmaz, Yeliz A. & Silantyeva, Olga (2024). UiO’s snow field sites, measurements, remotely sensed datasets, and climate container. Full text in Research Archive
• Ahlbäck, Malin (2024). Assessing the impact of sublimation for hydrological modelling. Full text in Research Archive
• Silantyeva, Olga (2023). SnowSub: Assessing snow sublimation for hydropower production in Norway. Project presentation at NVE seminar series. Full text in Research Archive
• Silantyeva, Olga (2023). SnowSub: Assessing snow sublimation for hydropower production in Norway. Project kick-off presentation. Full text in Research Archive
• Silantyeva, Olga (2023). SnowSub: Assessing snow sublimation for hydropower production in Norway. Project presentation at Latice Annual Meeting. Full text in Research Archive
• Silantyeva, Olga (2023). SnowSub project and Cold Climate Container at UiO. Full text in Research Archive

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