Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Robust quantification of predictive uncertainty is critical for understanding factors that drive weather and climate outcomes. Ensembles provide predictive uncertainty estimates and can be decomposed physically, but both physics and machine learning ensembles are computationally expensive. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but do not account for epistemic uncertainty.. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainty with one model. This study compares the uncertainty derived from evidential neural networks to those obtained from ensembles. Through applications of classification of winter precipitation type and regression of surface layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods, while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. In order to encourage broader adoption of evidential deep learning in Earth System Science, we have developed a new Python package, MILES-GUESS (https://github.com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning

In situ observations of the Swiss periglacial environment using GNSS instruments

Monitoring of the periglacial environment is relevant for many disciplines including glaciology, natural hazard management, geomorphology, and geodesy. Since October 2022, Rock Glacier Velocity (RGV) is a new Essential Climate Variable (ECV) product within the Global Climate Observing System (GCOS). However, geodetic surveys at high elevation remain very challenging due to environmental and logistical reasons. During the past decades, the introduction of low-cost global navigation satellite system (GNSS) technologies has allowed us to increase the accuracy and frequency of the observations. Today, permanent GNSS instruments enable continuous surface displacement observations at millimetre accuracy with a sub-daily resolution. In this paper, we describe decennial time series of GNSS observables as well as accompanying meteorological data. The observations comprise 54 positions located on different periglacial landforms (rock glaciers, landslides, and steep rock walls) at altitudes ranging from 2304 to 4003 ma.s.l. and spread across the Swiss Alps. The primary data products consist of raw GNSS observables in RINEX format, inclinometers, and weather station data. Additionally, cleaned and aggregated time series of the primary data products are provided, including daily GNSS positions derived through two independent processing tool chains. The observations documented here extend beyond the dataset presented in the paper and are currently continued with the intention of long-term monitoring. An annual update of the dataset, available at https://doi.org/10.1594/PANGAEA.948334 (Beutel et al., 2022),​​​​​​​ is planned. With its future continuation, the dataset holds potential for advancing fundamental process understanding and for the development of applied methods in support of e.g. natural hazard management

Temporal variability of diverse mountain permafrost slope movements derived from multi-year daily GPS data, Mattertal, Switzerland

In this study, high resolution surface measurements of diverse slope movements are compared to environmental factors such as ground surface temperature (GST) and snow cover, in order to reveal and compare velocity fluctuations caused by changing environmental conditions. The data cover 2 years (2011–2013) of Global Positioning System (GPS) and GST measurements at 18 locations on various slope movement types within an alpine study site in permafrost (Mattertal, Switzerland). Velocities have been estimated based on accurate daily GPS solutions. The mean annual velocities (MAV) observed at all GPS stations varied between 0.006 and 6.3 ma−1. MAV were higher in the period 2013 compared to 2012 at all stations. The acceleration in 2013 was accompanied by a longer duration of the snow cover and zero curtain and slightly lower GST. The amplitude (0–600 %) and the timing of the intra-annual variability were generally similar in both periods. At most stations, an annual cycle in the movement signal was observed, with a phase lag of 1–4 months to GST. Maximum velocity typically occurred in late summer and autumn, and minimum velocity in late winter and beginning of spring. The onset of acceleration always started in spring during the snowmelt period. At two stations located on steep rock glacier tongues, overprinted on the annual cycle, short-term peaks of velocity increase, occurred during the snowmelt period, indicating a strong influence of meltwater

Temporal characteristics of different cryosphere-related slope movements in high mountains

Knowledge of processes and factors affecting slope instability is essential for detecting and monitoring potentially hazardous slopes. The overall aim of this study is to detect and characterize different slope movements in alpine periglacial environments, with the ultimate goal to understand the broad range of phenomena and processes encountered. In this article, a potential strategy for analyzing the spatio-temporal (seasonal and intra-annual) velocity fluctuations of various slope movements is explained and initial results are presented. GPS (Global Positioning System) devices have been developed and deployed to continuously measure the velocity of slope movements within an Alpine study site. The measurement devices have the potential to operate for several years. Since December 2010, first devices are successfully measuring. Based on these measurements, high-accuracy daily differential GPS-positions and the corresponding velocities are calculated. A steep rockglacier tongue showed a steady decrease in velocity in winter and a strong acceleration in May during the snowmelt period. These first results demonstrate the importance of continuous (here daily) measurements over longer periods and their potential to enable the inference of factors and processes controlling slope movement

Estimating velocity from noisy GPS data for investigating the temporal variability of slope movements

Detecting and monitoring of moving and potentially hazardous slopes requires reliable estimations of velocities. Separating any movement signal from measurement noise is crucial for understanding the temporal variability of slope movements and detecting changes in the movement regime, which may be important indicators of the process. Thus, methods capable of estimating velocity and its changes reliably are required. In this paper we develop and test a method for deriving velocities based on noisy GPS (Global Positioning System) data, suitable for various movement patterns and variable signal-to-noise-ratios (SNR). We tested this method on synthetic data, designed to mimic the characteristics of diverse processes, but where we have full knowledge of the underlying velocity patterns, before applying it to explore data collected

Temporal characteristics of different cryosphere-related slope movements in high mountains

Knowledge of processes and factors affecting slope instability is essential for detecting and monitoring potentially hazardous slopes.The overall aimof this study is to detect and characterize different slope movements in alpine periglacial environments, with the ultimate goal to understand the broad range of phenomena and processes encountered. In this article, our measuremen

Short-term velocity variations at three rock glaciers and their relationship with meteorological conditions

In recent years, strong variations in the speed of rock glaciers have been detected, raising questions about their stability under changing climatic conditions. In this study, we present continuous time series of surface velocities over 3 years of six GPS stations located on three rock glaciers in Switzerland. Intra-annual velocity variations are analysed in relation to local meteorological factors, such as precipitation, snow(melt), and air and ground surface temperatures. The main focus of this study lies on the abrupt velocity peaks, which have been detected at two steep and fast-moving rock glacier tongues ( ≥  5 m a−1), and relationships to external meteorological forcing are statistically tested. The continuous measurements with high temporal resolution allowed us to detect short-term velocity peaks, which occur outside cold winter conditions, at these two rock glacier tongues. Our measurements further revealed that all rock glaciers experience clear intra-annual variations in movement in which the timing and the amplitude is reasonably similar in individual years. The seasonal decrease in velocity was typically smooth, starting 1–3 months after the seasonal decrease in temperatures, and was stronger in years with colder temperatures in mid winter. Seasonal acceleration was mostly abrupt and rapid compared to the winter deceleration, always starting during the zero curtain period. We found a statistically significant relationship between the occurrence of short-term velocity peaks and water input from heavy precipitation or snowmelt, while no velocity peak could be attributed solely to high temperatures. The findings of this study further suggest that, in addition to the short-term velocity peaks, the seasonal acceleration is also influenced by water infiltration, causing thermal advection and an increase in pore water pressure. In contrast, the amount of deceleration in winter seems to be mainly controlled by winter temperatures

Short-term variations of three rock glaciers and their relationship with meteorological conditions

In recent years, strong variations in the speed of rock glaciers have been detected,raising questions about their stability in a changed climate. In this study, we present continuous time series over three years of surface velocities of six GPS stations located on three rock glaciers in Switzerland. Intra-annual velocity variations are analyzed in relation to local meteorological factors, such as precipitation, snow(melt), as well as air and ground surface temperatures. A main focus of this study lies on the abrupt velocity peaks, which have been detected at two steep and fast moving rock glacier tongues.The continuous measurements with high temporal resolution revealed that all rock glaciers experience clear intra annual variations in movement where the timing and the amplitude is rather similar between individual years. The seasonal decrease in velocity was typically smooth, starting one to three months after the seasonal decrease in temperatures, and was stronger in years with colder temperatures in mid winter. The seasonal acceleration always started during the zero curtain period, often was abrupt and rapid compared to the winter deceleration, and at two stations it was interrupted by short velocity peaks, occurring immediately after high water input from snow melt or heavy precipitation. The findings of this study suggest that both, the seasonal acceleration and the short velocity peaks are strongly influenced by water infiltration, causing thermal advection and increase in pore water pressure, and that likely no velocity peak was solely caused by high temperatures. In contrast, the amount of deceleration in winter seems to be mainly controlled by winter temperatures.ISSN:2196-633