Snowpack Relative Permittivity and Density Derived from Near-Coincident Lidar and Ground-Penetrating Radar

Depth-based and radar-based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in-situ measurements or density models, to calculate SWE. However, in-situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near-coincident, lidar-measured snow depths with ground-penetrating radar (GPR) two-way travel times (twt) of snowpack thickness to derive \u3e20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in-situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to \u3e 30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged \u3e 400 mm, however, model uncertainty increased to \u3e 20% when SWE averaged \u3c 300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods

Horizontal Branch Stars: The Interplay between Observations and Theory, and Insights into the Formation of the Galaxy

We review HB stars in a broad astrophysical context, including both variable and non-variable stars. A reassessment of the Oosterhoff dichotomy is presented, which provides unprecedented detail regarding its origin and systematics. We show that the Oosterhoff dichotomy and the distribution of globular clusters (GCs) in the HB morphology-metallicity plane both exclude, with high statistical significance, the possibility that the Galactic halo may have formed from the accretion of dwarf galaxies resembling present-day Milky Way satellites such as Fornax, Sagittarius, and the LMC. A rediscussion of the second-parameter problem is presented. A technique is proposed to estimate the HB types of extragalactic GCs on the basis of integrated far-UV photometry. The relationship between the absolute V magnitude of the HB at the RR Lyrae level and metallicity, as obtained on the basis of trigonometric parallax measurements for the star RR Lyrae, is also revisited, giving a distance modulus to the LMC of (m-M)_0 = 18.44+/-0.11. RR Lyrae period change rates are studied. Finally, the conductive opacities used in evolutionary calculations of low-mass stars are investigated. [ABRIDGED]Comment: 56 pages, 22 figures. Invited review, to appear in Astrophysics and Space Scienc

Spatiotemporal variations in liquid water content in a seasonal snowpack: implications for radar remote sensing

2020 Summer.Includes bibliographical references.Mountain snowpacks act as seasonal reservoirs, providing a critical water resource to ~1.2 billion people globally. Regions with persistent snowpacks (e.g., mountain and polar environments) are responding quickly to climate change and are warming at faster rates than low-elevation temperate and equatorial regions. Since 1915, snow water equivalent (SWE) in the western U.S. snowpack has declined by 21% and snow covered area is contracting in the Rocky Mountains. Despite the clear importance of this resource and the identification of changes affecting it, no current remote sensing approach can accurately measure SWE at high spactiotemporal resolution. L-band (1-2 GHz) Interferometric Synthetic Aperture Radar (InSAR) is a promising approach for detecting changes in SWE at high spatiotemporal resolution in complex topography, but there are uncertainties regarding its performance, particularly when liquid water content (LWC) is present in the snowpack. LWC exhibits high spatial variability, causing spatially varying radar velocity that introduces significant uncertainty in SWE-retrievals. The objectives of this thesis include: (1) examine the importance of slope, aspect, canopy cover, and air temperature in the development of LWC in a continental seasonal snowpack using 1 GHz ground-penetrating radar (GPR), a proxy for L-band InSAR, and (2) quantify the uncertainty in L-band radar SWE-retrievals in wet-snow. This research was performed at Cameron Pass, a high elevation pass (3120 m) located in north-central Colorado, over the course of multiple survey dates during the melt season of 2019. Transects were chosen which represent a range in slope, aspect and canopy cover. Slope and aspect were simplified using the northness index (NI). Canopy cover was quantified using the leaf area index (LAI). Positive degree days (PDD) was used to represent available melt-energy from air temperature. The spatiotemporal development of LWC was studied along the transects using GPR, probed depths, and snowpit measured density. A subset of this project substituted Terrestrial LiDAR Scans (TLS) for probed depths. Surveys (17 in total, up to 3 surveys per date) were performed on seven dates which began on5 April 2019, where LWC values were ~0 vol. %, and ended on 19 June 2019 where LWC values exceeded 10 vol. %. Point measurements of LWC were observed to change (ΔLWC) by +9 vol. % or -8 vol. % over the course of a single day, but median ΔLWC were ~0 vol. % or slightly negative. LAI was negatively correlated with LWC for 13 out of the 17 surveys. NI was negatively correlated with LWC for 10 out of the 17 surveys. Multi-variable linear regressions to estimate ΔLWC identified several statistically significant variables (p-value < 0.10): LAI, NI, ΔPDD, and NI x ΔPDD. Snow-on Terrestrial LiDAR Scans (TLS) were conducted twice during the melt season, and a snow-off scan was conducted in late summer. Snow-on scans were differenced from the snow-off scan to produce distributed snow depth maps. TLS-derived snow depths compared poorly with probe-derived depths, which is attributed to poor LiDAR penetration through the thick vegetation present during the snow-off scan. Finally, radar measurements of SWE (SWE-retrievals), if coupled with velocities derived from dry-snow densities, overestimated the mean SWE along transects by as much as 40% during the melt season, highlighting a potential issue for water managers during the melt season. Future work to support the testing of L-band radar SWE-retrievals in wet-snow should test radar signal-power attenuation methods and the capabilities of snow models for estimating LWC

Cameron Pass, CO Spring 2019: Ground-penetrating radar surveys, snow depths, and snowpits

This dataset was collected to study the development of liquid water content along repeated transects using ground-penetrating radar during the spring of 2019. Data includes raw ground-penetrating radar files, snow depths, measurements from snowpits, and derived liquid water content. The data were collected over seven survey dates spanning 2019-04-05 to 2019-06-19 at Cameron Pass, Colorado.This dataset contains snow depths, measurements from snowpits, ground-penetrating radar raw files, and derived liquid water content values collected at Cameron Pass, CO during Spring 2019. Snow depths were measured using a manual probe. Snowpit data includes density and temperature measurements, stratigraphy notes, and weather notes. Ground-penetrating radar was collected using a 1 GHz Sensors & Software ProEx unit coupled to the snow surface. Liquid water content was calculated using the picked two-way travel time from processed ground-penetrating radar, snow densities, and snow depths.Funding for this dataset was provided by NASA Terrestrial Hydrology Program Award 80NSSC18K0877

Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing

Radar instruments have been widely used to measure snow water equivalent (SWE) and Interferometric Synthetic Aperture Radar is a promising approach for doing so from spaceborne platforms. Electromagnetic waves propagate through the snowpack at a velocity determined by its dielectric permittivity. Velocity estimates are a significant source of uncertainty in radar SWE retrievals, especially in wet snow. In dry snow, velocity can be calculated from relations between permittivity and snow density. However, wet snow velocity is a function of both snow density and liquid water content (LWC); the latter exhibits high spatiotemporal variability, there is no standard observation method, and it is not typically measured by automated stations. In this study, we used ground-penetrating radar (GPR), probed snow depths, and measured in situ vertically-averaged density to estimate SWE and bulk LWC for seven survey dates at Cameron Pass, Colorado (~3120 m) from April to June 2019. During this cooler than average season, median LWC for individual survey dates never exceeded 7 vol. %. However, in June, LWC values greater than 10 vol. % were observed in isolated areas where the ground and the base of the snowpack were saturated and therefore inhibited further meltwater output. LWC development was modulated by canopy cover and meltwater drainage was influenced by ground slope. We generated synthetic SWE retrievals that resemble the planned footprint of the NASA-ISRO L-band InSAR satellite (NISAR) from GPR using a dry snow density model. Synthetic SWE retrievals overestimated observed SWE by as much as 40% during the melt season due to the presence of LWC. Our findings emphasize the importance of considering LWC variability in order to fully realize the potential of future spaceborne radar missions for measuring SWE

Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing

Radar instruments have been widely used to measure snow water equivalent (SWE) and Interferometric Synthetic Aperture Radar is a promising approach for doing so from spaceborne platforms. Electromagnetic waves propagate through the snowpack at a velocity determined by its dielectric permittivity. Velocity estimates are a significant source of uncertainty in radar SWE retrievals, especially in wet snow. In dry snow, velocity can be calculated from relations between permittivity and snow density. However, wet snow velocity is a function of both snow density and liquid water content (LWC); the latter exhibits high spatiotemporal variability, there is no standard observation method, and it is not typically measured by automated stations. In this study, we used ground-penetrating radar (GPR), probed snow depths, and measured in situ vertically-averaged density to estimate SWE and bulk LWC for seven survey dates at Cameron Pass, Colorado (~3120 m) from April to June 2019. During this cooler than average season, median LWC for individual survey dates never exceeded 7 vol. %. However, in June, LWC values greater than 10 vol. % were observed in isolated areas where the ground and the base of the snowpack were saturated and therefore inhibited further meltwater output. LWC development was modulated by canopy cover and meltwater drainage was influenced by ground slope. We generated synthetic SWE retrievals that resemble the planned footprint of the NASA-ISRO L-band InSAR satellite (NISAR) from GPR using a dry snow density model. Synthetic SWE retrievals overestimated observed SWE by as much as 40% during the melt season due to the presence of LWC. Our findings emphasize the importance of considering LWC variability in order to fully realize the potential of future spaceborne radar missions for measuring SWE

In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications

Extensive efforts have been made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at global scales is challenging. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately constrained. The dielectric constant (k) determines the velocity of a radar wave through snow, which is a critical component of time-of-flight radar techniques such as ground penetrating radar and interferometric synthetic aperture radar (InSAR). However, equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work was to further understand the dielectric permittivity of seasonal snow under both dry and wet conditions. We utilized extensive direct field observations of k, along with corresponding snow density and liquid water content (LWC) measurements. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We present empirical relationships based on 146 snow pits for dry snow conditions and 92 independent LWC observations in naturally melting snowpacks. Regression results had r2 values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in the shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, suggesting that further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ measurements. Many previous equations assume a background (dry snow) k that we found to be inaccurate, as previously stated, and is the primary driver of resulting uncertainty. Our results suggest large errors in SWE (10–15%) or LWC (0.05–0.07 volumetric LWC) estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future InSAR opportunities such as NISAR and ROSE-L

In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications

Extensive efforts have been made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at global scales is challenging. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately constrained. The dielectric constant (k) determines the velocity of a radar wave through snow, which is a critical component of time-of-flight radar techniques such as ground penetrating radar and interferometric synthetic aperture radar (InSAR). However, equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work was to further understand the dielectric permittivity of seasonal snow under both dry and wet conditions. We utilized extensive direct field observations of k, along with corresponding snow density and liquid water content (LWC) measurements. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We present empirical relationships based on 146 snow pits for dry snow conditions and 92 independent LWC observations in naturally melting snowpacks. Regression results had r2 values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in the shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, suggesting that further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ measurements. Many previous equations assume a background (dry snow) k that we found to be inaccurate, as previously stated, and is the primary driver of resulting uncertainty. Our results suggest large errors in SWE (10–15%) or LWC (0.05–0.07 volumetric LWC) estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future InSAR opportunities such as NISAR and ROSE-L

Declines in Peak Snow Water Equivalent and Elevated Snowmelt Rates Following the 2020 Cameron Peak Wildfire in Northern Colorado

Abstract Wildfires are increasingly impacting high‐elevation forests in the western United States that accumulate seasonal snowpacks, presenting a major disturbance to a critical water reservoir for the region. In the first winter following the 2020 Cameron Peak wildfire in Colorado, the peak snow water equivalent in a high burn severity forest was 17%–25% less than nearby unburned sites. The loss of the forest canopy and a lower surface albedo led to an increasingly positive net shortwave radiation balance in the burned area, resulting in melt rates that were 82%–144% greater than unburned sites and snow disappearance occurred 11–13 days earlier. Late‐season snow storms temporarily buried soot, thus increasing the albedo and delaying melt‐out by an estimated 4 days per storm in our study area. While these storms temporarily reduce the higher melt rates imposed by wildfire impacts, SNOTEL measurements show that they occur non‐uniformly across the western U.S