Snow Depth Measurements Research Articles

Snow depth estimation based on GNSS-IR cluster analysis

As the most influential factor of the cryosphere, snow is the weathervane of climate change. Real-time and accurate snow monitoring data play an important role in climate-change indication, water resource management, disaster prevention, and mitigation. As traditional snow monitoring cannot meet the current requirements, since 2007, the signal-to-noise ratio data has been used for snow-depth inversion. The accuracy of the long time-series global navigation satellite system interferometric reflectometry (GNSS-IR) is not high, although it is significantly better compared to the previous methods. When the ground is covered by shallow snow or there is no snow, the snow-depth inversion is affected by the vegetation and snow layer, lowering the reliability of the Lomb–Scargle spectrum (LSP) analysis, and reducing the snow-depth inversion accuracy. To address the instability of the LSP results, in this study, the dynamic clustering algorithm is used for screening the PSD of the LSP results, and the influence of the signal penetration is eliminated. The average peak of the frequency based on multi-satellite LSP is obtained, and finally, the Grubbs criterion is utilized for improving the reliability of the results. The data of the Altay GNSS snow monitoring station at Altay in Xinjiang and the plate boundary observation SG27 and P351 sites are used as the research data, and a representative time period for the snow depth is selected. The inversion snow depth of the traditional GNSS-IR method are compared with those of the improved GNSS-IR. The experimental results demonstrate that improved GNSS-IR results had better match with the measured snow depth.

Learning about precipitation lapse rates from snow course data improves water balance modeling

Abstract. Precipitation orographic enhancement is the result of both synoptic circulation and topography. Since high-elevation headwaters are often sparsely instrumented, the magnitude and distribution of this enhancement, as well as how they affect precipitation lapse rates, remain poorly understood. Filling this knowledge gap would allow a significant step ahead for hydrologic forecasting procedures and water management in general. Here, we hypothesized that spatially distributed, manual measurements of snow depth (courses) could provide new insights into this process. We leveraged over 11 000 snow course data upstream of two reservoirs in the western European Alps (Aosta Valley, Italy) to estimate precipitation orographic enhancement in the form of lapse rates and, consequently, improve predictions of a snow hydrologic modeling chain (Flood-PROOFS). We found that snow water equivalent (SWE) above 3000 m a.s.l. (above sea level) was between 2 and 8.5 times higher than recorded cumulative seasonal precipitation below 1000 m a.s.l., with gradients up to 1000 mm w.e. km−1. Enhancement factors, estimated by blending precipitation gauge and snow course data, were consistent between the two hydropower headwaters (median values above 3000 m a.s.l. between 4.1 and 4.8). Including blended gauge course lapse rates in an iterative precipitation spatialization procedure allowed Flood-PROOFS to remedy underestimations both of SWE above 3000 m a.s.l. (up to 50 %) and – importantly – of precipitation vs. observed streamflow. Annual runoff coefficients based on blended lapse rates were also more consistent from year to year than those based on precipitation gauges alone (standard deviation of 0.06 and 0.19, respectively). Thus, snow courses bear a characteristic signature of orographic precipitation, which opens a window of opportunity for leveraging these data sets to improve our understanding of the mountain water budget. This is all the more important due to the essential role of high-elevation headwaters in supporting water security and ecosystem services worldwide.

Snow Depth and Air Temperature Seasonality on Sea Ice Derived From Snow Buoy Measurements

Snow depth on sea ice is an essential state variable of the polar climate system and yet one of the least known and most difficult to characterize parameters of the Arctic and Antarctic sea ice systems. Here, we present a new type of autonomous platform to measure snow depth, air temperature, and barometric pressure on drifting Arctic and Antarctic sea ice. “Snow Buoys” are designed to withstand the harshest environmental conditions and to deliver high and consistent data quality with minimal impact on the surface. Our current dataset consists of 79 time series (47 Arctic, 32 Antarctic) since 2013, many of which cover entire seasonal cycles and with individual observation periods of up to 3 years. In addition to a detailed introduction of the platform itself, we describe the processing of the publicly available (near real time) data and discuss limitations. First scientific results reveal characteristic regional differences in the annual cycle of snow depth: in the Weddell Sea, annual net snow accumulation ranged from 0.2 to 0.9 m (mean 0.34 m) with some regions accumulating snow in all months. On Arctic sea ice, the seasonal cycle was more pronounced, showing accumulation from synoptic events mostly between August and April and maxima in autumn. Strongest ablation was observed in June and July, and consistently the entire snow cover melted during summer. Arctic air temperature measurements revealed several above-freezing temperature events in winter that likely impacted snow stratigraphy and thus preconditioned the subsequent spring snow cover. The ongoing Snow Buoy program will be the basis of many future studies and is expected to significantly advance our understanding of snow on sea ice, also providing invaluable in situ validation data for numerical simulations and remote sensing techniques.

Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice

Abstract. Owing to differing and complex snow geophysical properties, radar waves of different wavelengths undergo variable penetration through snow-covered sea ice. However, the mechanisms influencing radar altimeter backscatter from snow-covered sea ice, especially at Ka- and Ku-band frequencies, and the impact on the Ka- and Ku-band radar scattering horizon or the “track point” (i.e. the scattering layer depth detected by the radar re-tracker) are not well understood. In this study, we evaluate the Ka- and Ku-band radar scattering horizon with respect to radar penetration and ice floe buoyancy using a first-order scattering model and the Archimedes principle. The scattering model is forced with snow depth data from the European Space Agency (ESA) climate change initiative (CCI) round-robin data package, in which NASA's Operation IceBridge (OIB) data and climatology are included, and detailed snow geophysical property profiles from the Canadian Arctic. Our simulations demonstrate that the Ka- and Ku-band track point difference is a function of snow depth; however, the simulated track point difference is much smaller than what is reported in the literature from the Ku-band CryoSat-2 and Ka-band SARAL/AltiKa satellite radar altimeter observations. We argue that this discrepancy in the Ka- and Ku-band track point differences is sensitive to ice type and snow depth and its associated geophysical properties. Snow salinity is first increasing the Ka- and Ku-band track point difference when the snow is thin and then decreasing the difference when the snow is thick (>0.1 m). A relationship between the Ku-band radar scattering horizon and snow depth is found. This relationship has implications for (1) the use of snow climatology in the conversion of radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both (1) and (2), the impact of using a snow climatology versus the actual snow depth is relatively small on the radar freeboard, only raising the radar freeboard by 0.03 times the climatological snow depth plus 0.03 times the real snow depth. The radar freeboard is a function of both radar scattering and floe buoyancy. This study serves to enhance our understanding of microwave interactions towards improved accuracy of snow depth and sea ice thickness retrievals via the combination of the currently operational and ESA's forthcoming Ka- and Ku-band dual-frequency CRISTAL radar altimeter missions.

Crowd sourcing snow depth measurements – a case study during the easterly outbreak of February 2021

Crowd sourcing snow depth measurements – a case study during the easterly outbreak of <scp>February</scp> 2021

Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model

Abstract. Reliable historical manual measurements of snow depths are available for many years, sometimes decades, across the globe, and increasingly snow depth data are also available from automatic stations and remote sensing platforms. In contrast, records of snow water equivalent (SWE) are sparse, which is significant as SWE is commonly the most important snowpack feature for hydrology, climatology, agriculture, natural hazards, and other fields. Existing methods of modeling SWE either rely on detailed meteorological forcing being available or are not intended to simulate individual SWE values, such as seasonal “peak SWE”. Here we present a new semiempirical multilayer model, Δsnow, for simulating SWE and bulk snow density solely from a regular time series of snow depths. The model, which is freely available as an R package, treats snow compaction following the rules of Newtonian viscosity, considers errors in measured snow depth, and treats overburden loads due to new snow as additional unsteady compaction; if snow is melted, the water mass is stepwise distributed from top to bottom in the snowpack. Seven model parameters are subject to calibration. Snow observations of 67 winters from 14 stations, well-distributed over different altitudes and climatic regions of the Alps, are used to find an optimal parameter setting. Data from another 71 independent winters from 15 stations are used for validation. Results are very promising: median bias and root mean square error for SWE are only −3.0 and 30.8 kg m−2, and +0.3 and 36.3 kg m−2 for peak SWE, respectively. This is a major advance compared to snow models relying on empirical regressions, and even sophisticated thermodynamic snow models do not necessarily perform better. As such, the new model offers a means to derive robust SWE estimates from historical snow depth data and, with some modification, to generate distributed SWE from remotely sensed estimates of spatial snow depth distribution.

The Influence of Snow and Ice Albedo towards Improved Lake Ice Simulations

Lake ice models are a vital tool for studying the response of ice-covered lakes to changing climates throughout the world. The Canadian Lake Ice Model (CLIMo) is a one-dimensional freshwater ice cover model that simulates Arctic and sub-Arctic lake ice cover well. Modelling ice cover in temperate regions has presented challenges due to the differences in ice composition between northern and temperate region lake ice. This study presents a comparison of measured and modelled ice regimes, with a focus on refining CLIMo for temperate regions. The study sites include two temperate region lakes (MacDonald Lake and Clear Lake, Central Ontario) and two High Arctic lakes (Resolute Lake and Small Lake, Nunavut) where climate and ice cover information have been recorded over three seasons. The ice cover simulations were validated with a combination of time lapse imagery, field measurements of snow depth, snow density, ice thickness and albedo data, and historical ice records from the Canadian Ice Database (for Resolute Lake). Simulations of High Arctic lake ice cover show good agreement with previous studies for ice-on and ice-off dates (MAE 6 to 8 days). Unadjusted simulations for the temperate region lakes show good ice-on timing, but an under-representation of ice thickness, and earlier complete ice-off timing (~3 to 5 weeks). Field measurements were used to adjust the albedo values used in CLIMo, which resulted in improvements to both simulated ice thickness (~3 cm MAE compared to manual measurements), and ice-off timing, within 0 to 7 days (2 days MAE) of observations. These findings suggest regionally specific measurements of albedo can improve the accuracy of lake ice simulations, which further our knowledge of the response of temperate and High Arctic lake ice regimes to climate conditions.

A low-cost method for monitoring snow characteristics at remote field sites

Abstract. The lack of spatially distributed snow depth measurements in natural environments is a challenge worldwide. These data gaps are of particular relevance in northern regions such as coastal Labrador where changes to snow conditions directly impact Indigenous livelihoods, local vegetation, permafrost distribution and wildlife habitat. This problem is exacerbated by the lack of cost-efficient and reliable snow observation methods available to researchers studying cryosphere–vegetation interactions in remote regions. We propose a new method termed snow characterization with light and temperature (SCLT) for estimating snow depth using vertically arranged multivariate (light and temperature) data loggers. To test this new approach, six snow stakes outfitted with SCLT loggers were installed in forested and tundra ecotypes in Arctic and subarctic Labrador. The results from 1 year of field measurement indicate that daily maximum light intensity (lux) at snow-covered sensors is diminished by more than an order of magnitude compared to uncovered sensors. This contrast enables differentiation between snow coverage at different sensor heights and allows for robust determination of daily snow heights throughout the year. Further validation of SCLT and the inclusion of temperature determinants is needed to resolve ambiguities with thresholds for snow detection and to elucidate the impacts of snow density on retrieved light and temperature profiles. However, the results presented in this study suggest that the proposed technique represents a significant improvement over prior methods for snow depth characterization at remote field sites in terms of practicality, simplicity and versatility.

ОПЫТ ПРИМЕНЕНИЯ ВЫСОКОЧАСТОТНОГО ГЕОРАДАРА ДЛЯ ЛАНДШАФТНОЙ СНЕГОМЕРНОЙ СЪЁМКИ В ОКРЕСТНОСТЯХ ГОРОДОВ КИРОВСК (ХИБИНЫ) И АПАТИТЫ

The results of processing of a profile snow-measuring survey of snow cover in the Khibiny Mountains are presented. The survey was performed during the period of maximum snow accumulation (March of 2020) on the main elements of the landscape: mixed forest on the plain, open woodlands at the bottom of valleys, plateaus, wooded slopes, and upper slopes without woody vegetation. The averaged values of snow storage for different types of the landscapes were obtained for the period of the maximum snow accumulation in the snowy winter of 2019/20. The maximum snow storage (> 700 mm w.e.) was determined for areas on the high plateaus and open woodlands at the bottom of valleys. Minimum snow storage (> 400 mm w.e.) was recorded in areas of mixed forest on the plain and on an ice cover of lakes. Measurements of snow depth were carried out by the standard method (a handspike) and the ground-based radio-echo sounding using georadar with the frequency of 1600 MHz. The accuracy of this method allows measuring of the snow depth with accuracy of 1 cm for a dense snow and 2 cm for a loose one. Thus, the accuracy of measuring the snow depth with the radar is comparable to the accuracy of a handspike. A large number of radar measurements of snow depth on the profiles makes possible to determine the spatial variability of this value and its statistical characteristics. As a result, a vertical gradient of snow accumulation was defined as 25 mm w.e. per 100 m. The smallest spatial variability of snow depth was observed on profiles in the forests on the plain, in woodlands, and on the upper slopes. On profiles with complex relief (plateau, lower slopes), the spatial variability of snow depth is significant – the standard deviation was within limits of 30%. Based on the results of processing the field data, a map of snow storage over the studying area during the period of maximum snow accumulation was constructed. When constructing the map, we took into account the averaged data of the measurements for each type of landscape, the boundaries of woody vegetation, the height, steepness of slopes, and the high-altitude gradient of snow accumulation. It was found that features of the spatial distribution of snow cover were primarily due to the location of natural landscape complexes. The role of changes in snow storages with altitude was found to be insignificant.

Performance Assessment of Optical Satellite-Based Operational Snow Cover Monitoring Algorithms in Forested Landscapes

Forest cover is a crucial factor that influences the performance of optical satellite-based snow cover monitoring algorithms. However, evaluation of such algorithms in forested landscapes is rare due to lack of reliable in situ data in such regions. In this investigation, we assessed the performance of the operational snow detection (SCA) and fractional snow cover estimation (FSC) algorithms employed by the Copernicus Land Monitoring Service for High-Resolution Snow & Ice Monitoring (HRSI) with a combination of Sentinel-2 and Landsat-7/8 satellite scenes, lidar-based, and in situ datasets. These algorithms were evaluated over test sites located in the forested mountainous landscape of the Pyrenees in Spain and the Sierra Nevada in the USA. Over the Pyrenees site, the effectiveness of snow cover detection was evaluated with respect to a time-series of in situ snow depth measurements logged over test plots with different aspects, canopy cover, and solar irradiance. Over the Sierra Nevada site, the impact of ground vegetation was assessed over the under canopy fractional snow cover retrievals using airborne lidar-derived fractional vegetation cover information. The analyses over the Pyrenees indicated a good accuracy of snow detection with the exception of plots with either dense canopy cover or insufficient solar exposure (shaded forested slope), or both. The operational HRSI algorithm yielded similar performances (25–30% RMSE) as the computationally intensive spectral unmixing approach while retrieving the subcanopy ground FSC over the Sierra Nevada site. It was observed that a more accurate lidar-derived tree cover density map did not improve the subcanopy FSC retrievals.

Accuracy assessment of late winter snow depth mapping for tundra environments using Structure-from-Motion photogrammetry

Arctic tundra environments are characterized by a spatially heterogeneous end-of-winter snow depth resulting from wind transport and deposition. Traditional methods for measuring snow depth do not accurately capture such heterogeneity at catchment scales. In this study we address the use of high-resolution, spatially distributed, snow depth data for Arctic environments through the application of unmanned aerial systems (UASs). We apply Structure-from-Motion photogrammetry to images collected using a fixed-wing UAS to produce a 1 m resolution snow depth product across seven areas of interest (AOIs) within the Trail Valley Creek Research Watershed, Northwest Territories, Canada. We evaluated these snow depth products with in situ measurements of both the snow surface elevation (n = 8434) and snow depth (n = 7191). When all AOIs were averaged, the RMSE of the snow surface elevation models was 0.16 m (&lt;0.01 m bias), similar to the snow depth product (UASSD) RMSE of 0.15 m (+0.04 m bias). The distribution of snow depth between in situ measurements and UASSD was similar along the transects where in situ snow depth was collected, although similarity varies by AOI. Finally, we provide a discussion of factors that may influence the accuracy of the snow depth products including vegetation, environmental conditions, and study design.

Snowmobile effects on height and live stem abundance of wetland shrubs in south-central Alaska

Snowmobiling is an important recreational activity at higher latitudes and in alpine areas where snow occurs. The Kenai National Wildlife Refuge in south-central Alaska allows snowmobiling access to >500,000 ha between December–April but not until snow depths are ≥50 cm to reduce snowmobile impacts to vegetation. We used a Before-After-Control-Impact experimental design to determine snowmobile effects on height and abundance of live stems of dwarf birch (Betula nana), shrubby cinquefoil (Dasiphora fruticosa) and sweet gale (Myrica gale) at snow depths ≥50 cm and <50 cm. We traversed 50x2-m transects with 10 snowmobile passes, taking snow depth measurements before and after treatments in January 2012 (≥50 cm) and April 2012 (<50 cm). Vegetation was measured in September 2011 and August 2012. Data were analyzed with general linear mixed effects models. Snowmobile traffic compacted snow by >40%. Dwarf birch was the most intolerant of snowmobiles with ≥7% decline in mean height (p ≤ 0.05) and ≥28% decline in mean live stem abundance (p ≤ 0.02). Shrubby cinquefoil was the most resilient as plant height was unaffected and mean live stem abundance declined by 20% only when snow was <50 cm (p = 0.01). Mean height of sweet gale was also unaffected at both snow depths but mean live stem abundance declined by ≥ 15% regardless of snow depth (p ≤ 0.04). When snowmobiles did affect height or live stems, the impacts were greater in all three species at snow depths <50 cm. In contrast, both vegetative metrics for all three species remained the same or increased in the control group. Snow depths ≥50 cm reduce snowmobile impacts to wetland shrubs but did not entirely eliminate them. Thinning snowpack in a rapidly warming climate suggests more damage to vegetation is likely where snowmobiling occurs.Management implications: Land managers may specify a minimum snow depth to protect vegetation from the physical damage of snowmobiles and their indirect effects through degradation of the subnivean environment. Snow depths ≥50 cm observed in our study reduced the negative effects of snowmobile traffic on three wetland shrub species but did not eliminate them. Snowmobiling as a winter recreational activity is likely to decrease in the foreseeable future in a warming climate. However, declining snowmobile opportunity does not necessarily translate to declining impacts in fact, the opposite may be true at least in the near term. Our data suggest snowmobiling over poor snowpack likely means more vegetation damage. We recommend partial openings when snow depth in the regulated area varies above and below 50 cm over large areas. The development of a maintained trail system, rather than open access, is an alternative way to reduce landscape-scale impacts.

The Added Value of Different Data Types for Calibrating and Testing a Hydrologic Model in a Small Catchment.

This study investigated the added value of different data for calibrating a runoff model for small basins. The analysis was performed in the 66 ha Hydrological Open Air Laboratory, in Austria. An Hydrologiska Byråns Vattenbalansavdelning (HBV) type, spatially lumped hydrologic model was parameterized following two approaches. First, the model was calibrated using only runoff data. Second, a step‐by‐step approach was followed, where the modules of the model (snow, soil moisture, and runoff generation) were calibrated using measurements of runoff and model state variables and output fluxes. These measurements comprised laser‐based measurements of precipitation, satellite and camera observations of snow, ultrasonic measurements of snow depth, eddy covariance measurements of evapotranspiration, time domain transmissometry‐based soil moisture measurements, time‐lapse photography of overland flow, and groundwater level measurements by piezometers. The two model parameterizations were evaluated on annual, seasonal, and daily time scales, in terms of how well they simulated snow, soil moisture, evapotranspiration, overland flow, storage change in the saturated zone, and runoff. Using the proposed step‐by‐step approach, the relative runoff volume errors in the calibration and validation periods were 0.00 and −0.01, the monthly Pearson correlation coefficients were 0.92 and 0.82, and the daily logarithmic Nash Sutcliffe efficiencies were 0.59 and 0.18, respectively. By using different sources of data besides runoff, the overall process consistency improved, compared to the case when only runoff was used for calibration. Soil moisture and evapotranspiration observations had the largest influence on simulated runoff, while the parameterization of the snow and runoff generation modules had a smaller influence.

Spatiotemporal Variation of Snow Depth in the Northern Hemisphere from 1992 to 2016

A comprehensive and hemispheric-scale snow cover and snow depth analysis is a prerequisite for all related processes and interactions investigation on regional and global surface energy and water balance, weather and climate, hydrological processes, and water resources. However, such studies were limited by the lack of data products and/or valid snow retrieval algorithms. The overall objective of this study is to investigate the variation characteristics of snow depth across the Northern Hemisphere from 1992 to 2016. We developed long-term Northern Hemisphere daily snow depth (NHSnow) datasets from passive microwave remote sensing data using the support vector regression (SVR) snow depth retrieval algorithm. NHSnow is evaluated, along with GlobSnow and ERA-Interim/Land, for its accuracy across the Northern Hemisphere against meteorological station snow depth measurements. The results show that NHSnow performs comparably well with a relatively high accuracy for snow depth with a bias of −0.6 cm, mean absolute error of 16 cm, and root mean square error of 20 cm when benchmarked against the station snow depth measurements. The analysis results show that annual average snow depth decreased by 0.06 cm per year from 1992 to 2016. In the three seasons (autumn, winter, and spring), the areas with a significant decreasing trend of seasonal maximum snow depth are larger than those with a significant increasing trend. Additionally, snow cover days decreased at the rate of 0.99 day per year during 1992–2016. This study presents that the variation trends of snow cover days are, in part, not consistent with the variation trends of the annual average snow depth, of which approximately 20% of the snow cover areas show the completely opposite variation trends for these two indexes over the study period. This study provides a new perspective in snow depth variation analysis, and shows that rapid changes in snow depth have been occurring since the beginning of the 21st century, accompanied by dramatic climate warming.

Monitoring of Snowpack Dynamics With Cosmic-Ray Neutron Probes: A Comparison of Four Conversion Methods

Common snow monitoring instruments based on hydrostatic pressure such as snow pillows are often influenced by various disturbing effects, which result in a reduced quality of the snow cover and snow water equivalent estimates. Such disturbing effects include energy transport into the snowpack, wind fields, and variations of snow properties within the snowpack (e.g. ice layers). Recently, it has been shown that Cosmic-Ray Neutron Probes (CRNP) are a promising technique to monitor snow pack development. CRNP can provide larger support and need lower maintenance compared to conventional sensors. These instruments are sensitive to the intensity of epithermal neutrons that are produced in the soil by cosmic radiation and are widely used to determine soil moisture in the upper decimetres of the ground. The application of CRNP for snow monitoring is based on the principle that snow water moderates the epithermal neutron intensity, which can be directly related to the snow water equivalent (SWE) of the snow pack. In this study, long-term CRNP measurements in the Pinios Hydrologic Observatory (PHO), Greece, were used to test different methods for converting neutron count rates to snow pack characteristics: i) linear regression, ii) standard N0-calibration function, iii) a physically-based calibration approach, and iv) thermal to epithermal neutron ratio. For this, a sonic sensor located near the CRNP was used to compare CRNP-derived snow pack dynamics with snow depth measurements. We found that the above-ground CRNP is well suited for measurement of field scale SWE, which is in agreement with findings of other studies. The analysis of the accuracy of the four conversion methods showed that all methods were able to determine the mass of the snow pack during the snow events reasonably well. The N0-calibration function and the physically-based calibration function performed best and the thermal to epithermal neutron ratio performed worst. Furthermore, we found that SWE determination with above-ground CRNP can be affected by other influences (e.g. heavy rainfall). Nevertheless, CRNP-based SWE determination is a potential alternative to established method like snow depth-based SWE methods, as it provides SWE estimate for a much larger scales (12-18 ha).

Drone-mounted UWB snow radar: technical improvements and field results

Drone borne radar systems have seen considerable advances over recent years, and the application of drone-mounted continuous wave (CW) radars for remote sensing of snow properties has great potential. Regardless, major challenges remain in antenna design for which both low weight and small size combined with high gain and bandwidth are important design parameters. Additional limiting factors for CW radars include range ambiguities and antenna isolation. To solve these problems, we have developed an ultra-wideband snow sounder (UWiBaSS), specifically designed for drone-mounted measurements of snow properties. In this paper, we present the next iteration of this prototype radar system, including a novel antenna configuration and useful processing techniques for drone borne radar. Finally, we present results from a field campaign on Svalbard aimed to measure snow depth distribution. This radar system is capable of measuring snow depth with a correlation coefficient of 0.97 compared to in situ depth probing.

Structure from Motion of Multi-Angle RPAS Imagery Complements Larger-Scale Airborne Lidar Data for Cost-Effective Snow Monitoring in Mountain Forests

Snowmelt from mountain forests is critically important for water resources and hydropower generation. More than 75% of surface water supply originates as snowmelt in mountainous regions, such as the western U.S. Remote sensing has the potential to measure snowpack in these areas accurately. In this research, we combine light detection and ranging (lidar) from crewed aircraft (currently, the most reliable way of measuring snow depth in mountain forests) and structure from motion (SfM) remotely piloted aircraft systems (RPAS) for cost-effective multi-temporal monitoring of snowpack in mountain forests. In sparsely forested areas, both technologies give similar snow depth maps, with a comparable agreement with ground-based snow depth observations (RMSE ~10 cm). In densely forested areas, airborne lidar is better able to represent snow depth than RPAS-SfM (RMSE ~10 cm vs ~10–20 cm). In addition, we find the relationship between RPAS-SfM and previous lidar snow depth data can be used to estimate snow depth conditions outside of relatively small RPAS-SfM monitoring plots, with RMSE’s between these observed and estimated snow depths on the order of 10–15 cm for the larger lidar coverages. This suggests that when a single airborne lidar snow survey exists, RPAS-SfM may provide useful multi-temporal snow monitoring that can estimate basin-scale snowpack, at a much lower cost than multiple airborne lidar surveys. Doing so requires a pre-existing mid-winter or peak-snowpack airborne lidar snow survey, and subsequent well-designed paired SfM and field snow surveys that accurately capture substantial snow depth variability.

Detecting Rain–Snow-Transition Elevations in Mountain Basins Using Wireless Sensor Networks

AbstractTo provide complementary information on the hydrologically important rain–snow-transition elevation in mountain basins, this study provides two estimation methods using ground measurements from basin-scale wireless sensor networks: one based on wet-bulb temperature Twet and the other based on snow-depth measurements of accumulation and ablation. With data from 17 spatially distributed clusters (178 nodes) from two networks, in the American and Feather River basins of California’s Sierra Nevada, we analyzed transition elevation during 76 storm events in 2014–18. A Twet threshold of 0.5°C best matched the transition elevation defined by snow depth. Transition elevations using Twet in upper elevations of the basins generally agreed with atmospheric snow level from radars located at lower elevations, while radar snow level was ~100 m higher due to snow-level lowering on windward mountainsides during orographic lifting. Diurnal patterns of the difference between transition elevation and radar snow level were observed in the American basin, related to diurnal ground-temperature variations. However, these patterns were not found in the Feather basin due to complex terrain and higher uncertainties in transition-elevation estimates. The American basin tends to exhibit 100-m-higher transition elevations than does the Feather basin, consistent with the Feather basin being about 1° latitude farther north. Transition elevation averaged 155 m higher in intense atmospheric river events than in other events; meanwhile, snow-level lowering was enhanced with a 90-m-larger difference between radar snow level and transition elevation. On-the-ground continuous observations from distributed sensor networks can complement radar data and provide important ground truth and spatially resolved information on transition elevations in mountain basins.

The Use of a Monte Carlo Markov Chain Method for Snow-Depth Retrievals: A Case Study Based on Airborne Microwave Observations and Emission Modeling Experiments of Tundra Snow

Snow-depth retrieval from passive microwave observations without a priori information is a highly undetermined problem. Achieving accurate snow-depth retrievals requires a priori information on the snowpack properties, such as grain size, density, physical temperature, and stratigraphy. On a practical level, however, retrieval algorithms must consider prior information, while minimizing the dependence on it, as accurate ancillary data are not globally available. In this study, we build on the previously published Bayesian Algorithm for Snow Water Equivalent Estimation (BASE) to retrieve snow depth using an airborne passive microwave data set over the tundra snow in the Eureka region. The method computes the optimal estimates of snow depth, density, grain size, and other variables, given the brightness temperature observations and prior information, using Markov chain Monte Carlo (MCMC). The airborne data set includes passive microwave brightness temperature (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> ) at 18.7 and 36.5 GHz. The in situ measurements of the snow depth provide validation data for 464 sensor footprints. The microwave radiative transfer (RT) model used is the Dense Media RT-Multilayered (DMRT-ML) model. We use a two-layer wind slab and depth hoar assumption based on the local snow cover knowledge from the previous research on the study area. To improve our understanding of the results using the airborne T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> s, the inversion was also applied using the synthetic observations, where T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> s were generated from the RT model. For the case with synthetic observations, the snow-depth RMSE was 0.07 cm. When the airborne T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> s are used, the snow-depth RMSE was 21.8 cm. This discrepancy is due to the large spatial variability in the MagnaProbe snow-depth measurements and the fact that not all physical processes affecting the airborne T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> s are represented in the RT model. Our work verifies the feasibility and applicability of the proposed methodology regionally for the airborne retrievals and reinforces the tractable applicability of a physics-based RT model in the SWE retrievals.

Using artificial neural networks to estimate snow water equivalent from snow depth

Snow water equivalent (SWE) is among the most important variables in the hydrological modelling of high latitude and mountainous areas. While manual snow surveys can directly provide SWE measurements, they are time consuming and costly, especially compared to automated snow depth measurements. Moreover, SWE is strongly correlated to snow depth. For this reason, several empirical equations relating snow depth to SWE have been proposed. The present study investigates the potential of artificial neural networks for estimating SWE from snow depth and commonly available data, and the proposed method is compared to existing, regression-based methods. An ensemble of multilayer perceptrons is constructed and trained using gridded meteorological variables and a data set of almost 40,000 SWE and depth measurements from the province of Quebec (eastern Canada). Overall, the proposed artificial neural network-based method reached a RMSE of 28 mm and outperforms by 17% a series of empirical equations for estimating the SWE of an independent set of measurement sites. Nevertheless, all the tested methods demonstrated limits to estimate lowest values of snow bulk density.