50 citations as recorded by crossref.

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19. Topographic and Climatic Controls on Depth–Duration–Frequency Curve Parameters in the Gargano Promontory (Southern Italy) G. Iemmolo et al. https://doi.org/10.3390/hydrology13070188
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23. Comparing high-resolution snow mapping approaches in palsa mires: UAS lidar vs. modelling A. Störmer et al. https://doi.org/10.5194/tc-19-3949-2025
24. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4 O. Conrad et al. https://doi.org/10.5194/gmd-8-1991-2015
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26. Topoclimatic factors create favourable conditions for carbon-dense forests in the Central Himalayas S. Khanal et al. https://doi.org/10.1038/s41598-025-19127-y
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30. Determinants of COVID-19 Face Mask Litter in Coastal Urban Parking Lots: Implications for Source Modelling of Potential Microplastic Pollution R. France & B. Heung https://doi.org/10.4236/jep.2024.158050
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34. Comparison of modelled- and remote sensing- derived daily snow line altitudes at Ulugh Muztagh, northern Tibetan Plateau M. Spiess et al. https://doi.org/10.1007/s11629-015-3818-x
35. Topographic conditions dominate tree species recovery over 15 years post-fire in a temperate Pinus sylvestris forest J. Holík et al. https://doi.org/10.1186/s42408-025-00374-3
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42. Using Machine Learning to Map Western Australian Landscapes for Mineral Exploration T. Albrecht et al. https://doi.org/10.3390/ijgi10070459
43. Traumatic Resin Ducts in Alaska Mountain Hemlock Trees Provide a New Proxy for Winter Storminess B. Gaglioti et al. https://doi.org/10.1029/2018JG004849
44. Statistical modelling of snow cover dynamics in the Central Himalaya Region, Nepal J. Weidinger et al. https://doi.org/10.3354/cr01518
45. Climate–Glacier Dynamics and Topographic Forcing in the Karakoram Himalaya: Concepts, Issues and Research Directions I. Dobreva et al. https://doi.org/10.3390/w9060405
46. Evaluation and Correction of Climate Simulations for the Tibetan Plateau Using the CMIP6 Models J. Gao et al. https://doi.org/10.3390/atmos13121947
47. Hybrid height growth and survival model for juvenile Eucalyptus globoidea (Blakely) and E. bosistoana (F. Muell) in New Zealand S. Salekin et al. https://doi.org/10.1016/j.foreco.2021.119074
48. Spatially explicit prediction of Nepal’s forest biomass stocks, a data-driven bioregionalisation and machine learning approach S. Khanal et al. https://doi.org/10.1186/s13021-025-00367-4
49. Hidden Markov Model for quantitative prediction of snowfall and analysis of hazardous snowfall events over Indian Himalaya J. JOSHI et al. https://doi.org/10.1007/s12040-017-0810-6
50. An evaluation of terrain‐based downscaling of fractional snow covered area data sets based on LiDAR‐derived snow data and orthoimagery N. Cristea et al. https://doi.org/10.1002/2017WR020799