16 citations as recorded by crossref.

1. Hyper-resolution large-scale hydrological modelling benefits from improved process representation in mountain regions J. Janzing et al.
2. An integrated snow–hydrology–operation framework for snow–fed hydropower: Validation and uncertainty analysis at Kurobe Dam, Japan Z. Zhang et al.
3. An algorithm to generate 2D bathymetry of an Alpine river for habitat suitability assessment. L. Stucchi et al.
4. GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers A. Umirbekov et al.
5. Monolayer or Multilayer Snow Model: Implications for the HYDROTEL Hydrological Model for Flow Modeling J. Augas et al.
6. Operational snow-hydrological modeling for Switzerland R. Mott et al.
7. High-resolution assessment of climate change impacts on the surface energy and water balance in the glaciated Naryn River basin, Central Asia S. Sadyrov et al.
8. Performance assessment of air-to-water heat pumps in alpine regions under present and future climate: Impacts of start-up and defrosting cycles F. Eze et al.
9. Turbulence in the Strongly Heterogeneous Near-Surface Boundary Layer over Patchy Snow M. Haugeneder et al.
10. High-resolution hydrometeorological and snow data for the Dischma catchment in Switzerland J. Magnusson et al.
11. Large eddy simulation of near-surface boundary layer dynamics over patchy snow M. Haugeneder et al.
12. Climate change impact assessment on a German lowland river using long short-term memory and conceptual hydrological models A. Ley et al.
13. Snow sensitivity to temperature and precipitation change during compound cold–hot and wet–dry seasons in the Pyrenees J. Bonsoms et al.
14. Evaluating precipitation corrections to enhance high-alpine hydrological modeling T. Pulka et al.
15. A novel approach for bridging the gap between climate change scenarios and avalanche hazard indication mapping G. Ortner et al.
16. SMamba-KAN: an advanced temporal-nonlinear model for precise water level prediction X. Yan et al.