WSL-Institut für Schnee-und Lawinenforschung SLF

Research interests

The central theme of my research is to understand how water flows within natural landscapes, tracing it from the time it enters the ecosystem through rainfall or snowfall to the time it finally emerges into the nearby stream. I have taken a data-centric approach to problem solving, working with a wide range of dataset ranging from tracers like stable water isotopes, eddy-covariance carbon and water flux measurements, satellite remote sensing, tree rings and forest inventory, etc.

SANTA (Spatial snow cover projections for Switzerland)

I am working with Dr. Christoph Marty (SLF) and Dr. Sven Kotlarski (Meteoswiss) to develop high-resolution (at 1km) spatial snow projections across Switzerland under the Klima CH2025 framework. For the first time, detailed snow water equivalent (SWE) and snow depth maps will be produced for entire Switzerland for different global warming levels. These snow projections will use the Klima CH2025 bias-adjusted GCM-RCM model runs as atmospheric forcing. SANTA will also provide key snow indicators tailored to different end-users, such as snow cover duration, snow fraction or snow disappearance date. Additionally, SANTA will upgrade the current degree-day snowmelt models used by the Operational Snow Hydrological Service (a joint initiative of SLF and MeteoSwiss) to a more advanced energy-balance approach.

CryoSCOPE (2025-2029)

Monitoring inland ice is essential for understanding environmental processes in cold regions and predicting climate-driven shifts and natural hazards. The EU-funded CryoSCOPE project unites a diverse consortium of experts, research organisations, and industry partners to address challenges related to in-situ and remote sensing observations, focusing on the interactions between land, ice, snow and permafrost in globally significant cold spots. By integrating novel measurements and advanced numerical models, CryoSCOPE aims to improve the representation of atmospheric, cryospheric, and hydrological processes in Earth System Models, providing high-resolution data in these landscapes. These insights will support environmental research and strengthen climate resilience in these cold spots.

https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/how-to-participate/org-details/999645820/project/101184736/program/43108390/details

Published articles

  1. Sprenger, M., R. Carroll, D. Marchetti, C. Bern, H. Beria, et al. (2024). Stream water sourcing from high-elevation snowpack inferred from stable isotopes of water: a novel application of d-excess values, Hydrology and Earth System Sciences, 28, 1711–1723, doi: 10.5194/hess-28-1711-2024. [Link]

  2. Michelon, A., N. Ceperley, H. Beria, J. Larsen, T. Vennemann, B. Schaefli (2023). Hydrodynamics of a high Alpine catchment characterized by four natural tracers, Hydrology and Earth System Sciences, 27(7): 1403-1430, doi: 10.5194/hess-27-1403-2023. [Link]

  3. Perga, M.-E., C. Minaudo, T. Doda, F. Arthaud, H. Beria, H.E. Chmiel, et al. (2023). Near-bed stratification controls bottom hypoxia in ice-covered alpine lakes, Limnology and Oceanography, 68(6), 1232–1246, doi: 10.1002/lno.12341. [Link]

  4. Michelon, A., L. Benoit, H. Beria, N.C. Ceperley, and B. Schaefli (2021). Benefits from high-density rain gauge observations for hydrological response analysis in a small alpine catchment, Hydrology and Earth System Sciences, 25(4): 2301–2325, doi: 10.5194/hess-25-2301-2021. [Link]

  5. Ceperley, N.C., G. Zuecco, H. Beria, L. Carturan, A. Michelon, D. Penna, J.R. Larsen, B. Schaefli (2020). Seasonal snow cover decreases young water fractions in high Alpine catchments, Hydrological Processes. 34: 4794– 4813, doi: 10.1002/hyp.13937 [Link]

  6. Beria, H., J.R. Larsen, A. Michelon, N.C. Ceperley, and B. Schaefli (2020). HydroMix v1.0: a new Bayesian mixing framework for attributing uncertain hydrological sources, Geoscientific Model Development, doi: 10.5194/gmd-13-2433-2020. [Link]

  7. Beria, H., J.R. Larsen, N.C. Ceperley, A. Michelon, T. Vennemann, and B. Schaefli (2018).Understanding snow hydrological processes through the lens of stable water isotopes, Wiley Interdisciplinary Review Water, 5(6): e1311, doi: 10.1002/wat2.1311 [Link]

  8. Beria, H., T. Nanda, D. S. Bisht, and C. Chatterjee (2017). Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale, Hydrology and Earth System Sciences, 21(12): 6117–6134, doi: 10.5194/hess-21-6117-2017. [Link]

  9. Nanda, T., B. Sahoo, H. Beria, and C. Chatterjee (2016), A wavelet-based non-linear autoregressive with exogenous inputs (WNARX) dynamic neural network model for real-time flood forecasting using satellite-based rainfall products, Journal of Hydrology, 539, 57–73, doi: 10.1016/j.jhydrol.2016.05.014. [Link]