Lechner et al. (2026) Hydrological drivers of surface runoff during high intensity rainfall experiments in Alpine ski regions

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-03-01
• Authors: Veronika Lechner, Christian Scheidl, Matthias Schlögl, Andreas Huber, Elena Baldauf, Bernhard Kohl, Klaus Klebinder, Gertraud Meißl, Gerhard Markart
• DOI: 10.1016/j.jhydrol.2026.135302

Research Groups

• Austrian Research Center for Forests (BFW), Department for Natural Hazards, Innsbruck, Austria
• BOKU University, Institute of Mountain Risk Engineering, Vienna, Austria
• GeoSphere Austria, Department for Climate Impact Research, Vienna, Austria
• University of Innsbruck, Department of Geography, Innsbruck, Austria
• University of Innsbruck, Unit of Hydraulic Engineering, Institute for Infrastructure Engineering, Innsbruck, Austria

Short Summary

This study investigates surface runoff behavior in 12 Eastern Alpine ski regions using 74 rainfall simulation experiments, revealing significantly higher surface runoff coefficients on ski slopes (median 0.57) compared to reference areas (median 0.07). A random forest model identified geological factors as the strongest predictors on ski slopes, while soil and land use variables were more influential on reference areas.

Objective

• Are there differences in surface runoff dynamics between ski slopes and reference areas in Alpine ski regions?
• Which site-specific variables (e.g., summer management practices, soil and vegetation characteristics) affect the surface runoff response on ski slopes and reference areas?
• Which sites are particularly vulnerable or resilient in terms of alteration of surface runoff characteristics?

Study Configuration

• Spatial Scale: 12 ski regions in the Eastern Alps (Austria and South Tyrol, Italy). Experiments were conducted on plots ranging from 50 square meters to 80 square meters.
• Temporal Scale: Data were collected between 1997 and 2018. Each rainfall simulation experiment lasted for at least 1 hour.

Methodology and Data

• Models used: Random Forest Regression model (implemented using the ranger package within the mlr3 framework in R).
• Data sources:
  • 74 artificial rainfall simulation (ARS) experiments (35 on ski slopes, 39 on reference areas).
  • Time-series data for applied precipitation intensity and measured surface runoff.
  • Soil moisture content (SMC) monitored using Time Domain Reflectometry (TDR) probes at 0-20 centimeters depth.
  • Field observations: land use characteristics (pasture intensity, vegetation class, ground cover), topographic data (geomorphon, slope inclination), geological information (geological class, coarse fraction embedment, soil depth, humus type, soil class).
  • Laboratory analysis of soil samples: soil skeleton content, pore volume, bulk density, and soil texture (grain size distribution for sand, silt, and clay).

Main Results

• Surface runoff coefficients (C_const) were significantly higher on ski slopes (median 0.57, range [0, 1.14]) compared to reference areas (median 0.07, range [0, 0.85]) (Wilcoxon rank sum test, p = 0.027).
• The random forest models showed adequate predictive accuracy with R² values of 0.62 for ski slopes (RMSE = 0.256, MAE = 0.219) and 0.59 for reference areas (RMSE = 0.195, MAE = 0.163).
• On ski slopes, geological variables (geological class, soil skeleton, soil texture) were the most important predictors of surface runoff. Sites with "cover layer on carbonate rock" (CLC) and "potentially very loose glacial sediments" (GSpl) were linked to higher runoff.
• On reference areas, soil variables (coarse fraction embedment, saturation deficit difference) and land use variables (pasture intensity, vegetation class) were more influential.
• Increasing soil skeleton content generally led to decreased surface runoff for both ski slopes and reference areas.
• Cohesive coarse fraction embedment resulted in higher predicted surface runoff coefficients, while loose embedment was associated with lower values.
• Higher pasture intensities generally led to increased runoff regimes on both ski slopes and reference areas.
• On ski slopes, predicted surface runoff showed a more pronounced decrease with slope inclinations above approximately 15 degrees.
• Ski slopes on siliceous rock generally exhibited higher surface runoff coefficients than those on limestone.

Contributions

• Provides a comprehensive, systematic analysis of surface runoff drivers in Alpine ski regions using a large dataset of 74 rainfall simulation experiments spanning over 20 years, addressing a previous data gap.
• Quantifies the significant hydrological alteration caused by ski slope development, demonstrating substantially higher runoff coefficients on ski slopes compared to unmodified reference areas.
• Identifies distinct sets of key hydrological drivers for ski slopes (primarily geological factors) versus reference areas (soil and land use variables), offering targeted insights for management.
• Utilizes advanced machine learning (Random Forest) to model complex, non-linear relationships between site characteristics and surface runoff, enhancing predictive understanding.
• Underscores the critical need for sustainable soil management and restoration strategies to mitigate the hydrological impacts of ski slope construction and maintain stability in Alpine environments.

Funding

• Federal Ministry of Agriculture and Forestry, Climate and Environmental Protection, Regions and Water Management (BMLUK) - Department of Torrent and Avalanche Control, Protective Forest Policy.
• Sections and regional offices of the Austrian Torrent and Avalanche Control Service (WLV).
• Provincial Forestry Services and the Hydrographic Service of Tyrol.

Citation

@article{Lechner2026Hydrological,
  author = {Lechner, Veronika and Scheidl, Christian and Schlögl, Matthias and Huber, Andreas and Baldauf, Elena and Kohl, Bernhard and Klebinder, Klaus and Meißl, Gertraud and Markart, Gerhard},
  title = {Hydrological drivers of surface runoff during high intensity rainfall experiments in Alpine ski regions},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135302},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135302}
}