Snow Depth Variability in the Northern Hemisphere Mountains Observed from Space

Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change

Airborne Snowsar Data at X and Ku Bands Over Boreal Forest, Alpine and Tundra Snow Cover

The European Space Agency SnowSAR instrument is a side-looking, dual-polarised (VV/VH), X/Ku band synthetic aperture radar (SAR), operable from various sizes of aircraft. Between 2010 and 2013, the instrument was deployed at several sites in Northern Finland, Austrian Alps and northern Canada. The purpose of the airborne campaigns was to measure the backscattering properties of snow-covered terrain to support the development of snow water equivalent retrieval techniques using SAR. SnowSAR was deployed in Sodankylä, Northern Finland, for a single flight mission in March 2011 and 12 missions at two sites (tundra and boreal forest) in the winter of 2011–2012. Over the Austrian Alps, three flight missions were performed between November 2012 and February 2013 over three sites located in different elevation zones representing a montane valley, Alpine tundra and a glacier environment. In Canada, a total of two missions were flown in March and April 2013 over sites in the Trail Valley Creek watershed, Northwest Territories, representative of the tundra snow regime. This paper introduces the airborne SAR data and coincident in situ information on land cover, vegetation and snow properties. To facilitate easy access to the data record, the datasets described here are deposited in a permanent data repository (https://doi.org/10.1594/PANGAEA.933255, Lemmetyinen et al., 2021)

Review Article: Global Monitoring of Snow Water Equivalent Using High-Frequency Radar Remote Sensing

Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth\u27s surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth\u27s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world\u27s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth\u27s cold regions\u27 ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements

Recent Precipitation Decrease Across the Western Greenland Ice Sheet Percolation Zone

The mass balance of the Greenland Ice Sheet (GrIS) in a warming climate is of critical interest in the context of future sea level rise. Increased melting in the GrIS percolation zone due to atmospheric warming over the past several decades has led to increased mass loss at lower elevations. Previous studies have hypothesized that this warming is accompanied by a precipitation increase, as would be expected from the Clausius–Clapeyron relationship, compensating for some of the melt-induced mass loss throughout the western GrIS. This study tests that hypothesis by calculating snow accumulation rates and trends across the western GrIS percolation zone, providing new accumulation rate estimates in regions with sparse in situ data or data that do not span the recent accelerating surface melt. We present accumulation records from sixteen 22–32m long firn cores and 4436 km of ground-penetrating radar, covering the past 20–60 years of accumulation, collected across the western GrIS percolation zone as part of the Greenland Traverse for Accumulation and Climate Studies (GreenTrACS) project. Trends from both radar and firn cores, as well as commonly used regional climate models, show decreasing accumulation rates of 2:4±1:5%a-1 over the 1996–2016 period, which we attribute to shifting storm tracks related to stronger atmospheric summer blocking over Greenland. Changes in atmospheric circulation over the past 20 years, specifically anomalously strong summertime blocking, have reduced GrIS surface mass balance through both an increase in surface melting and a decrease in accumulation rates

Accurate Inversion of High-Resolution Snow Penetrometer Signals for Microstructural and Micromechanical Properties

Measurements of snow using a high-resolution micropenetrometer can be used to discriminate between different snow types; in lower-density snow the signal is sensitive to microstructure, and micromechanical properties can be estimated. Although a physics-based snow penetration theory was first developed almost a decade ago, since that time the majority of studies using snow micropenetrometers have focused on using direct hardness measurements in statistical relationships. We use Monte-Carlo simulations to rigorously test the existing physics-based snow micropenetration theories over a wide range of parameters. These tests revealed four major sources of error in the inversion, which are corrected in this analysis. It is shown that this improved inversion algorithm can recover micromechanical parameters in synthetic data with much greater accuracy over the entire range of micromechanical properties observed in natural snow. Detailed examples of the inversion results are shown for eight different snow types, collected in both Alaskan and alpine snowpacks. The resulting micromechanical properties are distinctly different, indicating that a snow characterization from snow micropenetrometer estimates of micromechanical properties is likely possible. Estimates of the microscale elastic modulus, microscale strength, and structural element length make sense physically when compared to the qualitative descriptions of the different snow types. Microscale strength estimates are used to estimate macroscale strength values, and results from 33 different snow samples, covering a wide range of densities and snow types, are consistent with previously reported values from macroscale tests

Direct Insertion of NASA Airborne Snow Observatory-Derived Snow Depth Time Series Into the \u3cem\u3eiSnobal\u3c/em\u3e Energy Balance Snow Model

Accurately simulating the spatiotemporal distribution of mountain snow water equivalent improves estimates of available meltwater and benefits the water resource management community. In this paper we present the first integration of lidar-derived distributed snow depth data into a physics-based snow model using direct insertion. Over four winter seasons (2013–2016) the National Aeronautics and Space Administration/Jet Propulsion Laboratory (NASA/JPL) Airborne Snow Observatory (ASO) performed near-weekly lidar surveys throughout the snowmelt season to measure snow depth at high resolution over the Tuolumne River Basin above Hetch Hetchy Reservoir in the Sierra Nevada Mountains of California. The modeling component of the ASO program implements the iSnobal model to estimate snow density for converting measured depths to snow water equivalent and to provide temporally complete snow cover mass and thermal states between flights. Over the four years considered in this study, snow depths from 36 individual lidar flights were directly inserted into the model to provide updates of snow depth and distribution. Considering all updates to the model, the correlation between ASO depths and modeled depths with and without previous updates was on average r2 = 0.899 (root-mean-square error = 12.5 cm) and r2 = 0.162 (root-mean-square error = 41.5 cm), respectively. The precise definition of the snow depth distribution integrated with the iSnobal model demonstrates how the ASO program represents a new paradigm for the measurement and modeling of mountain snowpacks and reveals the potential benefits for managing water in the region

Synchronous Retreat of Southeast Greenland\u27s Peripheral Glaciers

Recently, scientific attention has focused on estimating Greenland\u27s dynamic mass loss through changes to flow speeds, thickness, and length on its marine outlet glaciers. For the ice sheet outlet glaciers, dynamic mass loss has been found to be highly sensitive to changes in climate and individual glacier geometry. For the ice-sheet-independent marine glaciers around Greenland\u27s periphery, dynamic mass loss is presently overlooked. Here, we apply an open-source, automated method of measuring glacier length changes using satellite imagery, to produce highly detailed records of length changes for 135 peripheral marine glaciers in southeast Greenland. We find evidence for anomalous retreat across 56 glaciers coincident with elevated surface melt in 2016, with melt 22% above the 2013–2019 average. Our detailed observations resolve the widespread, rapid, and synchronous response of these independent marine glaciers to increased meltwater input in 2016, indicating that their dynamics may be more sensitive to atmospheric warming than currently thought

Snow Water Equivalent Retrieval Over Idaho – Part 1: Using Sentinel-1 Repeat-Pass Interferometry

Snow water equivalent (SWE) is identified as the key element of the snowpack that impacts rivers\u27 streamflow and water cycle. Both active and passive microwave remote sensing methods have been used to retrieve SWE, but there does not currently exist a SWE product that provides useful estimates in mountainous terrain. Active sensors provide higher-resolution observations, but the suitable radar frequencies and temporal repeat intervals have not been available until recently. Interferometric synthetic aperture radar (InSAR) has been shown to have the potential to estimate SWE change. In this study, we apply this technique to a long time series of 6 d temporal repeat Sentinel-1 C-band data from the 2020–2021 winter. The retrievals show statistically significant correlations both temporally and spatially with independent in situ measurements of SWE. The SWE change measurements vary between −5.3 and 9.4 cm over the entire time series and all the in situ stations. The Pearson correlation and RMSE between retrieved SWE change observations and in situ stations measurements are 0.8 and 0.93 cm, respectively. The total retrieved SWE in the entire 2020–2021 time series shows an SWE error of less than 2 cm for the nine in situ stations in the scene. Additionally, the retrieved SWE using Sentinel-1 data is well correlated with lidar snow depth data, with correlation of more than 0.47. Low temporal coherence is identified as the main reason for degrading the performance of SWE retrieval using InSAR data. We also show that the performance of the phase unwrapping algorithm degrades in regions with low temporal coherence. A higher frequency such as L-band improves the temporal coherence and SWE ambiguity. SWE retrieval using C-band Sentinel-1 data is shown to be successful, but faster revisit is required to avoid low temporal coherence. Global SWE retrieval using radar interferometry will have a great opportunity with the upcoming L-band 12 d repeat-pass NASA-ISRO Synthetic Aperture Radar (NISAR) data and the future 6 d repeat-pass Radar Observing System for Europe in L-band (ROSE-L) data

A Laser Ultrasound System to Non-Invasively Measure Compression Waves in Granular Ice Mixes

Accurate knowledge of snow mechanical properties, including Young\u27s modulus, shear modulus, Poisson\u27s ratio, and density, is critical to many areas of snow science and to snow-related engineering problems. To facilitate the assessment of these properties, an innovative non-contacting laser ultrasound system (LUS) has been developed. This system acquires ultrasound waveform data at frequencies ranging from tens to hundreds of kHz in a controlled cold-lab environment. Two different LUS devices were compared in this study to determine which recorded more robust ultrasound in granular ice mix samples. We validated the ultrasound observations with poro-elastic traveltime modeling based on physical and empirical constitutive relationships, comparison to and replication of previous studies, and the use of other accredited snow property measurement systems, i.e., the SnowMicroPen. For ice mixes, we determined that the PSV-400 Scanning Vibrometer (Polytec GmbH) produces higher quality ultrasonic wavefield observations (i.e. has a better signal-to-noise ratio) than the VibroFlex Fiber Vibrometer (Polytec GmbH) in the lab conditions tested here. Using the PSV-400, we then demonstrated the utility of this new LUS to study the relationship between snow compression-wave speed and density during snow compaction experiments

Calculating the Velocity of a Fast-Moving Snow Avalanche Using an Infrasound Array

On 19 January 2012, a large D3 avalanche (approximately 103 t) was recorded with an infrasound array ideally situated for observing the avalanche velocity. The avalanche crossed Highway 21 in Central Idaho during the largest avalanche cycle in the 15 years of recorded history and deposited approximately 8 m of snow on the roadway. Possible source locations along the avalanche path were estimated at 0.5 s intervals and were used to calculate the avalanche velocity during the 64 s event. Approximately 10 s prior to the main avalanche signal, a small infrasound signal originated from the direction of the start zone. We infer this to be the initial snow pack failure, a precursory signal to the impending avalanche. The avalanche accelerated to a maximum velocity of 35.9 ± 7.6 m s−1 within 30 s before impacting the highway. We present a new technique to obtain high spatial and temporal resolution velocity estimates not previously demonstrated with infrasound for avalanches and other mass wasting events