Towards Forecasting Future Snow Cover Dynamics in the European Alps—The Potential of Long Optical Remote-Sensing Time Series

Abstract

Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we, therefore, explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985–2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5–8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash–Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolute Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a combined forecast based on the best-performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022–2029. In the majority of the catchments, the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast, a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.

Drought in Northern Italy: Long Earth Observation Time Series Reveal Snow Line Elevation to Be Several Hundred Meters Above Long-Term Average in 2022

“…An unusually high SLE in the early season, such as in 2022, acquired in a timely manner can then serve as an indicator for an upcoming drought in a near-real-time drought early warning system and complement in situ or passive microwavebased SWE approaches that offer a higher temporal resolution [12]. Furthermore, long time series facilitate the detection of long-term trends and the modeling of future snow conditions [17], which are important factors for estimating the frequency of future drought events. Finally, these time series can contribute to the implementation of catchment-based runoff or discharge models by integrating further explanatory variables, such as SWE or precipitation.…”

“…To analyze recent and long-term snow-cover dynamics, we generated SLE time series from multispectral Landsat data ranging from 1985 to August 2022 for each catchment, as described in detail in Koehler et al [17]. The SLE retrieval approach is based on an algorithm developed by Hu et al [18][19][20].…”

“…The snow line elevation (SLE), i.e., the elevation of the border between snowfree and snow-covered elevation ranges in mountainous areas [16], enables the estimation of snow-covered areas even under partially cloudy conditions. The SLE can be retrieved on a catchment basis from snow-classifications in combination with a Digital Elevation Model (DEM) [17,18] and, thus, might complement the challenging SWE retrieval in the context of snow-drought monitoring.…”

An Empirical Model for Correction of Topographic Effect in Microwave Radiation of Mountainous Area

“…OUNTAINS cover about 24% of the Earth's land area and are essential in regulating the regional and global climate environment [1][2][3]. However, the topography is one of the critical factors affecting the microwave radiation of pixel-scale in mountainous regions.…”

Development of Global Snow Cover—Trends from 23 Years of Global SnowPack

“…Active microwave (Synthetic Aperture Radar; SAR) systems offer much better resolutions, but they are only capable of detecting melting (wet) snow [12]. High-resolution optical sensors such as the Landsat satellite family and Sentinel-2 offer a very good spatial resolution, which also allows analyses in the mountains [13], but they do not provide the desired temporal (daily) coverage. In order to meet the requirements specified by the GCOS (daily recording, at least 1000 m resolution), we therefore have to use medium-resolution optical remote sensing sensors such as the MODerate resolution Imaging Spectroradiometer (MODIS).…”