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UID:pretalx-foss4g-europe-2026-SJEX8J@talks.osgeo.org
DTSTART;TZID=EET:20260630T153000
DTEND;TZID=EET:20260630T160000
DESCRIPTION:Snowpack plays a fundamental role in alpine and periglacial env
 ironments\, acting as a key regulator of surface and subsurface processes.
  Beyond its well-known hydrological importance as a seasonal water reservo
 ir\, snow exerts a strong control on ground thermal regimes by functioning
  as an insulating layer that decouples near-surface ground temperatures fr
 om atmospheric forcing. This insulation effect influences permafrost occur
 rence\, stability\, and degradation\, particularly in marginal periglacial
  environments such as those found in the Southern Carpathians. At the same
  time\, snow cover modulates biological activity by controlling soil tempe
 rature\, moisture availability\, and the duration of the growing season\, 
 thereby shaping alpine ecosystem dynamics. Accurately characterizing snowp
 ack properties\, such as depth\, density\, and persistence is therefore es
 sential for understanding coupled cryospheric\, hydrological\, and ecologi
 cal processes in mountain regions.\nHowever\, capturing snow variability i
 n complex terrain remains challenging due to strong spatial heterogeneity 
 driven by topography\, wind redistribution\, and micro-scale surface condi
 tions. These challenges are further exacerbated in regions such as the Rom
 anian Carpathians\, where the availability of in situ meteorological obser
 vations is limited\, particularly at high elevations and in remote alpine 
 environments. The lack of dense and continuous meteorological measurements
  constrains the direct characterization of snow–climate interactions and
  limits the applicability of traditional observation-based approaches. Whi
 le climate reanalysis products provide continuous large-scale atmospheric 
 forcing\, their coarse spatial resolution limits their direct use in mount
 ainous environments. Conversely\, field observations and high-resolution s
 urveys\, such as UAV-based measurements\, provide detailed local informati
 on but are spatially limited and episodic. Bridging these scales requires 
 reproducible workflows that integrate climate data\, physically based mode
 ling\, and high-resolution observations within a coherent geospatial frame
 work.\nThis contribution presents an open geospatial workflow for climate-
 driven snow modeling in alpine terrain\, linking climate downscaling\, phy
 sically based snowpack simulation\, and UAV-based observations. The workfl
 ow integrates freely available hourly climate reanalysis data from the Cop
 ernicus Climate Data Store (ERA5)\, including both single-level and pressu
 re-level variables\, with topography-aware downscaling using the open-sour
 ce TopoPyScale tool. Implemented in a reproducible environment using Pytho
 n and Ubuntu via Windows Subsystem for Linux (WSL)\, the workflow transfor
 ms coarse-resolution atmospheric forcing (~31 km) into terrain-informed lo
 cal-scale inputs by incorporating high-resolution digital elevation models
  (DEMs) and its derived morphometric parameters such as elevation\, slope\
 , aspect\, and sky-view factor\, as well as horizon-based radiation correc
 tions.\nThe downscaled climate forcing is subsequently used to drive snowp
 ack simulations using the SURFEX–Crocus model developed by Météo-Franc
 e. While the model is distributed under an open-source license with contro
 lled access\, it can be readily obtained for research purposes. Within thi
 s workflow\, SURFEX–Crocus is employed to simulate detailed snowpack evo
 lution at both point-based locations and clustered terrain representations
 . The model provides a comprehensive set of snowpack variables\, including
  snow depth\, snow water equivalent (SWE)\, snow temperature profiles\, de
 nsity\, stratigraphy\, hardness\, and snow microstructural properties such
  as grain size and shape. These outputs enable a process-based representat
 ion of snow accumulation\, metamorphism\, and melt\, offering insights int
 o both seasonal dynamics and interannual variability.\nTo demonstrate the 
 integration of model outputs with observational data\, UAV-derived snow de
 pth is used as a high-resolution reference dataset. Repeated UAV surveys c
 onducted over an alpine site in the Retezat Mountains (Southern Carpathian
 s) across four winter seasons are processed using open-source photogrammet
 ric tools\, such as OpenDroneMap\, to generate digital surface models (DSM
 s) under snow-covered and snow-free conditions. Snow depth is then derived
  through a DEM of Difference (DoD) approach. The resulting high-resolution
  snow depth maps are spatially aggregated to match the resolution of the m
 odel outputs\, enabling direct comparison between simulated and observed s
 now conditions for selected time periods.\nThis study emphasizes the desig
 n of a transferable and reproducible workflow that enables the comparison 
 of climate-driven snow simulations with user-collected high-resolution obs
 ervations. The integration highlights how physically based models capture 
 broad-scale snow dynamics\, while UAV data reveal fine-scale variability a
 ssociated with terrain-driven redistribution processes that remain unresol
 ved at the model scale.\nThe presented workflow relies primarily on open d
 ata and open geospatial tools\, including ERA5 reanalysis\, TopoPyScale\, 
 Python-based processing libraries (GDAL\, rasterio\, xarray\, pandas\, num
 py\, netcdf4)\, and open photogrammetric solutions. By combining these com
 ponents within a coherent processing chain\, the approach demonstrates how
  complex cryospheric analyses can be conducted in a reproducible and adapt
 able manner. The proposed framework provides a practical pathway for integ
 rating climate reanalysis\, terrain-aware downscaling\, snow modeling\, an
 d UAV observations in alpine environments. It can be readily adapted to ot
 her mountain regions and applications\, supporting improved understanding 
 of snowpack dynamics and their implications for hydrology\, permafrost\, a
 nd ecosystem processes under changing climatic conditions.
DTSTAMP:20260604T233805Z
LOCATION:A13
SUMMARY:Linking Climate Downscaling and UAV Observations: An Open Workflow 
 for Snow Modeling in Alpine Terrain - Andrei Ioniță
URL:https://talks.osgeo.org/foss4g-europe-2026/talk/SJEX8J/
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