Snow Measurements Research Articles

A simple snow temperature index model exposes discrepancies between reanalysis snow water equivalent products

Abstract. Current global reanalyses show marked discrepancies in snow mass and snow cover extent for the Northern Hemisphere. Here, benchmark snow datasets are produced by driving a simple offline snow model, the Brown Temperature Index Model (B-TIM), with temperature and precipitation from three reanalyses. The B-TIM offline snow performs comparably to or better than online (coupled land–atmosphere) reanalysis snow when evaluated against in situ snow measurements. Sources of discrepancy in snow climatologies, which are difficult to isolate when comparing online reanalysis snow products amongst themselves, are partially elucidated by separately bias-adjusting temperature and precipitation in the B-TIM. Interannual variability in snow mass and snow spatial patterns is far more self-consistent amongst offline B-TIM snow products than amongst online reanalysis snow products, and the self-consistent products are more similar to in situ observations, as evaluated in a validation study. Specific artifacts related to temporal inhomogeneity in snow data assimilation are revealed in the analysis. The B-TIM, released here as an open-source, self-contained Python package, provides a simple benchmarking tool for future updates to more sophisticated online and offline snow datasets.

Развитие модели динамики снежного покрова Гидрометцентра России

Conceptual models of runoff formation used in the operational practice of hydrological forecasting by the Hydrometeorological Center of Russia include simplified parameterization of snow cover dynamics based on the use of snow cover melting coefficient, water-retaining capacity of snow and secondary freezing of meltwater in case of return of negative air temperatures. This schematization of the process is well established for the reliable calculation of the snow water equivalent for subsequent use in the calculation of meltwater inflow to the catchment surface and the calculation of streamflow characteristics. At the same time, the lack of calculation of snow density and snow height in the used schematization restricts the use of schemes for calculating of soil freezing depth in hydrological models, which seems to be extremely important for modelling runoff on mid-latitudes rivers, i.e. for the most Russian rivers. To overcome this disadvantage, the snow density and snow height parameterization was added to the calculation scheme of the model, which was verified using Roshydromet’s snow measurement routes data and shown a good and satisfactory quality of modeling of model characteristics.

Inter-comparison of field snow measurements using different instruments in Türkiye

Inter-comparison of field snow measurements using different instruments in Türkiye

Operational and experimental snow observation systems in the upper Rofental: data from 2017 to 2023

Abstract. This publication presents a comprehensive hydrometeorological data set for three research sites in the upper Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria) and is a companion publication to a data collection published in 2018. The time series presented here comprise data from 2017 to 2023 and originate from three meteorological and snow hydrological stations at 2737, 2805, and 2919 m a.s.l. The fully equipped automatic weather stations include a specific set of sensors to continuously record snow cover properties. These are automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water contents, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow transport. These devices are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. We present data for temperature, precipitation, humidity, wind speed, and radiation fluxes and explore the continuous snow measurements by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations is demonstrated with examples of measured wind speeds, snow drift rates, and redistributed snow amounts during several blowing snow events. The data complement the scientific monitoring infrastructure in the research catchment and represent a unique time series of high-altitude mountain weather and snow observations. They enable comprehensive insights into the dynamics of high-altitude meteorological and snow processes and are collected to support the scientific community, local stakeholders, and the interested public, as well as operational warning and forecasting services. The data are publicly available from the GFZ Data Services repository: https://doi.org/10.5880/fidgeo.2023.037 (Department of Geography, University of Innsbruck, 2024).

Spatio‐temporal analysis of snow depth and snow water equivalent in a mountainous catchment: Insights from in‐situ observations and statistical modelling

AbstractThis research, conducted in the mountainous catchment near Abant Lake in the Western Black Sea region of Türkiye, aimed to investigate the spatiotemporal variations of snow depth (SD) and snow water equivalent (SWE) throughout the snow season from December 2019 to March 2020, encompassing both accumulation and melting periods. In total, 14 snow surveys were conducted, covering 58 permanent snow measurement points (PSMP) marked with snow poles. The classification and regression tree (CART) method was employed to statistically analyse their relationships with eight variables: snow period, forest canopy, aspect, slope, elevation, slope position, plan and profile curvature. The root mean square error (RMSE) for SD and SWE was determined to be 0.15 m and 46 mm, respectively. The study findings revealed that mean SD and SWE values were higher in forest gaps compared with under‐forest and open areas. Although the snow cover disappeared earliest in under‐forest areas, the melting rate was observed to be 43% and 17% slower compared with forest gaps and open areas, respectively. Wind redistribution resulted in minimum snow accumulation on western aspects, upper slope positions and ridges, while maximum accumulation was observed on southern aspects, valleys and lower slope positions. Higher elevations (>1580 meters) experienced faster snow melting rates, leading to earlier disappearance of snow cover. PSMPs located on slopes with lower degrees (<15°) exhibited lesser accumulation and earlier snow disappearance. The CART model identified the snow period as the most significant factor in predicting SD and SWE, based on variations in snowfall and air temperature. Other significant variables included forest canopy, aspect and elevation. The study suggests that the CART method is well‐suited for modelling complex snow dynamics, providing valuable insights into spatiotemporal variations in SD and SWE in mountainous regions.

Sea ice mass balance during the MOSAiC drift experiment: Results from manual ice and snow thickness gauges

Precise measurements of Arctic sea ice mass balance are necessary to understand the rapidly changing sea ice cover and its representation in climate models. During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we made repeat point measurements of snow and ice thickness on primarily level first- and second-year ice (FYI, SYI) using ablation stakes and ice thickness gauges. This technique enabled us to distinguish surface and bottom (basal) melt and characterize the importance of oceanic versus atmospheric forcing. We also evaluated the time series of ice growth and melt in the context of other MOSAiC observations and historical mass balance observations from the Surface Heat Budget of the Arctic (SHEBA) campaign and the North Pole Environmental Observatory (NPEO). Despite similar freezing degree days, average ice growth at MOSAiC was greater on FYI (1.67 m) and SYI (1.23 m) than at SHEBA (1.45 m, 0.53 m), due in part to initially thinner ice and snow conditions on MOSAiC. Our estimates of effective snow thermal conductivity, which agree with SHEBA results and other MOSAiC observations, are unlikely to explain the difference. On MOSAiC, FYI grew more and faster than SYI, demonstrating a feedback loop that acts to increase ice production after multi-year ice loss. Surface melt on MOSAiC (mean of 0.50 m) was greater than at NPEO (0.18 m), with considerable spatial variability that correlated with surface albedo variability. Basal melt was relatively small (mean of 0.12 m), and higher than NPEO observations (0.07 m). Finally, we present observations showing that false bottoms reduced basal melt rates in some FYI cases, in agreement with other observations at MOSAiC. These detailed mass balance observations will allow further investigation into connections between the carefully observed surface energy budget, ocean heat fluxes, sea ice, and ecosystem at MOSAiC and during other campaigns.

How three-dimensional forest structure regulates the amount and timing of snowmelt across a climatic gradient of snow persistence

Across the western United States, forests are changing rapidly, with uncertain impacts on snowmelt water resources. Snow partitioning is controlled by forest effects on interception, radiation, and sublimation. Yet, models often lack snow measurements with sufficiently high spatial and temporal resolution across gradients of forest structure to accurately represent these fine-scale processes. Here, we utilize four Snowtography stations in Arizona, in the lower Colorado River Basin, with daily measurements over 3–5 years at ~110 positions distributed across gradients of forest structure resulting from wildfires and mechanical thinning. We combine Snowtography with lidar snapshots of forest and snow to train a high-resolution snow model and run it for 6 years to quantify how forest structure regulates snowpack and snowmelt. These study sites represent a climate gradient from lower/warmer ephemeral snowpack (~2,100 m asl) to higher/colder seasonal snowpack (~2,800 m asl). Forest cover reduced snowpack and snowmelt through canopy sublimation. Forest advanced snowmelt timing at lower/warmer sites but delayed it at higher/colder sites. Within canopy gaps, shaded cool edges had the greatest peak snow water equivalent (SWE). Surprisingly, sunny/warm gap edges produced more snowmelt than cool edges, because high radiation melted snow quickly, reducing exposure to sublimation. Therefore, peak SWE is not an ideal proxy for snowmelt volume from ephemeral snowpacks, which are becoming more prevalent due to warming. The results imply that forest management can influence the amount and timing of snowmelt, and that there may be decision trade-offs between enhancing forest resilience through delayed snowmelt and maximizing snowmelt volumes for downstream water resources.

Contemporary movement of rock glaciers in the La Sal and Uinta Mountains, Utah, USA

Rock glaciers are common landforms in mountainous areas of the western US. The motion of active rock glaciers is a key indicator of ice content, offering connections to climate and hydrologic systems. Here, we quantified the movement of six rock glaciers in the La Sal and Uinta Mountains of Utah through repeat differential GPS surveying. Networks of 10–41 points on each rock glacier were surveyed in September 2021; July 2022; September 2022; and July 2023. We found that all features are moving with average annual rates of motion from 1.5 ± 0.8 to 18.5 ± 7.5 cm/yr. Rock glaciers move up to 3× faster in the summer than in the winter, and rates of motion were greater in 2023 after a winter with above-average snowfall, emphasizing the role of liquid water availability. Velocities of individual points in the winter of 2021–22 are positively correlated with velocities during the winter of 2022–23, suggesting that spatial variability of motion is not stochastic, but rather reflects internal properties of each rock glacier. Bottom temperature of snow measurements during winter, and the temperature of springs discharging water in summer, suggest that these rock glaciers contain modern permafrost. Radiocarbon data document advance of one rock glacier during the Little Ice Age. Our GPS dataset reveals complicated patterns of rock glacier movement, and the network of survey points we established will be a valuable baseline for detecting future cryosphere change in these mountains.

A 20-Year Ecotone Study of Pacific Northwest Mountain Forest Vulnerability to Changing Snow Conditions

(1) Background: Global climate change is expected to significantly alter growing conditions along mountain gradients. Landscape ecological patterns are likely to shift significantly as species attempt to adapt to these changes. We evaluated the extent to which spatial (elevation and canopy cover) and temporal (decadal trend and El Niño–Southern Oscillation/Pacific Decadal Oscillation) factors impact seasonal snowmelt and forest community dynamics in the Western Hemlock–True Fir ecotone region of the Oregon Western Cascades, USA. (2) Methods: Tsuga heterophylla and Abies amabilis seedling locations were mapped three times over 20 years (2002–2022) on five sample transects strategically placed to cross the ecotone. Additionally, daily ground temperature readings were collected over 10 years for the five transects using 123 data loggers to estimate below-canopy snow metrics. (3) Results: Based on validation using time-lapse cameras, the data loggers proved highly reliable for estimating snow cover. The method reported fewer days of snow cover as compared to meteorological station-based snow products for the region, emphasizing the importance of direct under-canopy field observations of snow. Snow season variability was most significantly impacted temporally by cyclical ENSO/PDO climate patterns and spatially by differences in canopy cover within the ecotone. The associated seedling analysis identified clear sorting of species by elevation within the ecotone but reflected a lack of a long-term trend, as species dominance in the seedling strata did not significantly shift along the elevation gradient over the 20-year study. (4) Conclusions: The data logger-based approach provided estimates of snow cover at ecologically significant locations and fine enough spatial resolutions to allow for the study of forest regeneration dynamics. The results highlight the importance of long-term, understory snow measurements and the influence of climatic oscillations in understanding the vulnerability of mountain areas to the changing climate.

Аналіз тенденцій довготривалих змін кліматичних навантажень

The purpose of the work is to identify changes in the characteristic values of loads from snow and wind over the past decades, as well as to justify the need to take these changes into account when developing load standards for buildings and structures. Characteristic values of wind pressure are determined by the probabilistic model of a random process. The necessary parameters of the Weibull distribution for 13 weather stations of Ukraine are determined by the histograms of the distribution of the results of wind speed measurements obtained in three different periods of meteorological observations during the years 1961...2020. The characteristic values of the snow load are determined by the probabilistic model of the sequence of annual maxima with the Gumbel distribution. For this purpose, the average values and standards of the annual maximum snow cover weight at 54 weather stations, obtained from the results of snow measurement surveys in three different observation periods during 1950...2020, were used. According to the data of each weather station, the rates of characteristic values of loads change over time are determined. Characteristic values of wind pressure at 11 weather stations out of 13 that were considered decrease by an average of 4.5 Pa/year. For two weather stations, the intensity of the decrease is much greater and equals to 13.4 Pa/year and 25.6 Pa/year. At 41 weather stations out of 54 studied, a decrease in the characteristic values of snow cover weight is observed, and at 13 weather stations, the snow load increases over time. Annual increases in the characteristic values of the weight of the snow cover vary within the range of -22...+25 Pa/year with an average decrease of 4.5 Pa/year. As a result of the study, it was found that the significant spread of the rates of change of the characteristic values of loads over time at different weather stations does not allow to generalize this indicator on the territory of Ukraine. In the presence of several groups of data, it is recommended to determine the characteristic values of wind and snow load through the statistical characteristics of wind speed and weight of snow cover, averaged over the data of all available periods of meteorological observations, without taking into account the detected changes in time.

Spatiotemporal snow water storage uncertainty in the midlatitude American Cordillera

Abstract. This work quantifies the uncertainty of accumulation-season peak snow water storage in the portions of the midlatitude American Cordillera where snow is a dominant driver of hydrology. This is accomplished through intercomparison of commonly used global and regional products over the Western United States (WUS) and Andes domains, which have similar hydrometeorology but are disparate with respect to the amount of available in situ information. The recently developed WUS Snow Reanalysis (WUS-SR) and Andes Snow Reanalysis (Andes-SR) datasets, which have been extensively verified against in situ measurements, are used as baseline reference datasets in the intercomparison. Relative to WUS-SR climatological peak snow water equivalent (SWE) storage (269 km3), high- and moderate-resolution products (i.e., those with resolutions less than ∼10 km) are in much better agreement (284±14 km3; overestimated by 6 %) compared to low-resolution products (127±54 km3; underestimated by 53 %). In comparison to the Andes-SR peak snow storage (29 km3), all other products show large uncertainty and bias (19±16 km3; underestimated by 34 %). Examination of spatial patterns related to orographic effects showed that only the high- to moderate-resolution Snow Data Assimilation System (SNODAS) and University of Arizona (UA) products show comparable estimates of windward–leeward SWE patterns over a subdomain (Sierra Nevada) of the WUS. Coarser products distribute too much snow on the leeward side in both the Sierra Nevada and Andes, missing orographic and rain shadow patterns that have important hydrological implications. The uncertainty of peak seasonal snow storage is primarily explained by precipitation uncertainty in both the WUS (R2=0.55) and Andes (R2=0.84). Despite using similar forcing inputs, snow storage diverges significantly within the ECMWF Reanalysis v5 (ERA5) (i.e., ERA5 vs. ERA5-Land) products and the Global Land Data Assimilation System (GLDAS) (modeled with Noah, Variable Infiltration Capacity (VIC), and Catchment model) products due to resolution-induced elevation differences and/or differing model process representation related to rain–snow partitioning and accumulation-season snowmelt generation. The availability and use of in situ precipitation and snow measurements (i.e., in WUS) in some products adds value by reducing snow storage uncertainty; however, where such data are limited, i.e., in the Andes, significant biases and uncertainty exist.

Divergent community trajectories with climate change across a fine‐scale gradient in snow depth

Abstract Fine‐scale microclimate variation due to complex topography can shape both current vegetation distributional patterns and how vegetation responds to changing climate. Topographic heterogeneity in mountains is hypothesized to mediate responses to regional climate change at the scale of metres. For alpine vegetation especially, the interplay between changing temperatures and topographically mediated variation in snow accumulation will determine the overall impact of climate change on vegetation dynamics. We combined 30 years of co‐located measurements of temperature, snow and alpine plant community composition in Colorado, USA, to investigate vegetation community trajectories across a snow depth gradient. Our analysis of long‐term trends in plant community composition revealed notable directional change in the alpine vegetation with warming temperatures. Furthermore, community trajectories are divergent across the snow depth gradient, with exposed parts of the landscape that experience little snow accumulation shifting towards stress‐tolerant, cold‐ and drought‐adapted communities, while snowier areas shifted towards more warm‐adapted communities. Synthesis: Our findings demonstrate that fine‐scale topography can mediate both the magnitude and direction of vegetation responses to climate change. We documented notable shifts in plant community composition over a 30‐year period even though alpine vegetation is known for slow dynamics that often lag behind environmental change. These results suggest that the processes driving alpine plant population and community dynamics at this site are strong and highly heterogeneous across the complex topography that is characteristic of high‐elevation mountain systems.

Snow Cover Reconstruction in the Brunswick Peninsula, Patagonia, Derived from a Combination of the Spectral Fusion, Mixture Analysis, and Temporal Interpolation of MODIS Data

Several methods based on satellite data products are available to estimate snow cover properties, each one with its pros and cons. This work proposes and implements a novel methodology that integrates three main processes applied to MODIS satellite data for snow cover property reconstruction: (1) the increase in the spatial resolution of MODIS (MOD09) data to 250 m using a spectral fusion technique; (2) a new proposal of snow-cloud discrimination; (3) the daily spatio-temporal reconstruction of snow extent and its albedo signature using the endmembers extraction and spectral mixture analyses. The snow cover reconstruction method was applied to the Brunswick Peninsula, Chilean Patagonia, a low-elevation (<1500 m a.s.l.) mid-latitude area. The results show a 98% agreement between MODIS snow detection and ground-based snow measurements at the automatic weather station, Tres Morros (53.3174°S, 71.2790°W), with fractional snow cover values between 20% and 50%, showing a close relationship between snow and vegetation type. The number of snow days compiled from the MODIS data indicates a good performance (Pearson’s correlation of 0.9) compared with the number of skiing days at the Cerro Mirador ski center, Punta Arenas. Although the number of seasonal snow days showed a significant increasing trend of 0.54 days/year in the Brunswick Peninsula during the 2000–2020 period, a significant decrease of −4.64 days/year was detected in 2010–2020.

Which adjustment methods are suitable for the wind-induced errors of Geonor T-200BM3 precipitation weighing gauges in a periglacial site?

Single Alter shielded T-200BM3 weighing precipitation gauges are widely used in the measurement of all precipitation types (rainfall, snow and mixed precipitation) in unattended boreal or alpine regions, but their original datasets must be adjusted for undercatch errors caused by wind in snowy, windy and harsh environments. Therefore, previous researchers have developed many adjustment methods for all precipitation types on different time scales. However, which adjustment method is suitable for T-200BM3 weighing gauge wind-induced error adjustment in harsh alpine regions is unclear. Therefore, precipitation measurement intercomparison experiments were conducted in the Qilian Mountains from July 2018 to July 2021, and eight adjustment methods; were evaluated for wind-induced errors for daily, individual precipitation event, hourly, and half-hourly time scales. Z2004 outperformed the other adjustment methods in regard to the daily measurements of snow and mixed precipitation. Regarding individual snowfall events, M2007 reduced the absolute value of RMSE (bias) from 1.44 to 1.32 mm (0.77–0.24 mm) and could be recommended for snowfall event adjustment. K2017-1 attained a better performance than K2017-2 in regard to half-hourly snowfall and mixed sample adjustment and was more suitable for half-hourly snowfall sample adjustment. K2017-1 reduced the absolute value of bias from 0.07 to 0.00 mm for snowfall. Finally, Z2004, M2007, and K2017-1 yielded better adjustment results for the daily accumulation precipitation amount (>2 mm d−1), individual snowfall events (>2 mm per event), and half-hourly accumulation snowfall or mixed samples (>1 mm 30 min−1), respectively. However, further intercomparison in different climate regions is needed for trace precipitation samples.

Assessment of L-band InSAR snow estimation techniques over a shallow, heterogeneous prairie snowpack

Snow water equivalent (SWE) is a critical input for weather, climate, and water resource management models at local to global scales. Despite its importance, global SWE measurements that are accurate, consistent, and at sufficiently high spatiotemporal resolutions are not currently available. L-band interferometric synthetic aperture radar (InSAR) techniques have been used to measure SWE at local to regional scales, and two upcoming L-band SAR satellite missions have renewed interest in these techniques to provide regular SWE measurements at the global scale. However, previous research demonstrating the capabilities of L-band InSAR-SWE measurement has been limited to mountain or tundra snowpack regimes. Here we examine the feasibility of applying the same techniques over a prairie snowpack, which are typically characterized by shallow snow depths (mean snow depth of 0.22 m in this study), exposed agricultural vegetation, and high spatial variability over short distances. Our study area in central Montana, USA (47.060, -109.951) was a validation site for NASA SnowEx 2021, as part of the UAVSAR snow timeseries. Airborne L-band SAR imagery was acquired by the UAVSAR platform while concurrent snow measurements were collected using uncrewed aerial vehicle (UAV)-based LiDAR, UAV-based photogrammetry, and ground-based manual techniques. This validation dataset enables an investigation of the effects of sub-pixel snow cover heterogeneity and exposed agricultural vegetation stubble on SAR data and the resulting SWE estimations. Results based on repeated application of the Kolmogorov–Smirnov test show that UAVSAR VV phase change is sensitive to differences in snow cover but relatively unaffected by differences in agricultural stubble height. However, we did not find similarly definitive results when we used the same phase change data to estimate SWE. Although broad spatial patterns were similar in both LiDAR-derived and InSAR-derived SWE estimates, considerable differences in the two estimates were apparent in areas with large sub-pixel snow depth variability. Our results indicate that additional work is necessary to derive accurate SWE estimates in prairie environments. Regular measurements from L-band SAR satellites will provide an excellent opportunity to refine InSAR-based snow estimation techniques over shallow, heterogeneous snowpacks.

Significance and issues of automatic snow cover measurement – case study for the Šumava Mountains region

The study evaluates the contribution of automatic snow measuring stations for snow monitoring in the Šumava region. Using climatological maps, the results of spatial analysis of the maximum depth of the snow cover for six winter seasons were compared, namely first from the measured data of the basic CHMI stations and second from the data of all stations. The biggest differences occurred in the comparison at the highest locations on the windward side of the Šumava (>1 200 m a.s.l.), where there is no corresponding network of basic meteorological stations. In the case of the snow-poor winter season 2020/21, these differences were relatively higher than in the 2018/19 season, with more extensive snow cover in all positions. The evaluation of the entire period (2017/18–2022/23) produced similar results, as snow-poor winter seasons prevailed here. We can therefore assume that the differences in snow characteristics will continue to be rather significant in the Šumava region. Accordingly, the importance of automatic snow measurement will also increase, especially in higher altitudes of the windward border area. In addition to the expansion of the measuring network and the significant refinement of information about the snow cover, especially in the mountains, the main benefit of data on the depth of snow from automatic stations is their operability and immediate use for other applications.

Development of the snow- and ice-accounting routine (SIAR)

This paper evaluates the degrees of freedom and complexity warranted in temperature-index models to jointly simulate local snow measurements, remotely-sensed snow cover and runoff in mountainous areas. To address this issue, the snow- and ice-accounting routine (SIAR) on top of the GR4J model was developed on a dataset covering 17 mountainous catchments (45 to 3580 km2) in the French Alps and Pyrenees. Model calibration and control were based on streamflow series, fractional snow cover (FSC) computed from MODIS snow products and at least one chronicle of local measurements of snow water equivalent (SWE) acquired in each catchment for the period 2004–2016. SIAR was applied according to an adaptable number of elevation bands and different parametrizations ranging from 11 free parameters (precipitation orographic correction, temperature lapse rate, variation in the temperature lapse rate, snowfall adjustment, rainfall lapse rate, thermal inertia of the snow pack, constant and variable part of the degree-day snow melt factor, degree-day ice melt factor, 2-parameter hysteresis between SWE and FSC), to only fixed parameters. Results showed that the one-free-parameter SIAR is as efficient as more parametrized versions in simulating both local and distributed snow dynamics as well as runoff. Interestingly, using SIAR without any free parameters by fixing the snowfall adjustment to a median value of 60% only led to slight impairment of local SWE dynamics. Certain processes represented in SIAR (glacier-component, sublimation, simple energy balance, snowpack cold-content, variable melt factor) were then alternatively turned off to justify those retained in its final version. The modeling performances were also compared by applying SIAR with different distribution options ranging from full distribution according to 0.25 km2 cells to lumped mode. A number of equal-area elevation bands according to the catchment hypsometry proved to be a good compromise as it allowed snow and runoff simulations of similar accuracy to the full distribution mode, while limiting computational time. Finally, SIAR was compared with the Cemaneige snow routine, which showed its modeling performance was better. These findings suggest that it is possible, and even advisable, to limit the number of free parameters in temperature-index models in order to reduce problems of over-parameterization and equifinality.

A Database of Snow on Sea Ice in the Central Arctic Collected during the MOSAiC expedition

Snow plays an essential role in the Arctic as the interface between the sea ice and the atmosphere. Optical properties, thermal conductivity and mass distribution are critical to understanding the complex Arctic sea ice system’s energy balance and mass distribution. By conducting measurements from October 2019 to September 2020 on the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have produced a dataset capturing the year-long evolution of the physical properties of the snow and surface scattering layer, a highly porous surface layer on Arctic sea ice that evolves due to preferential melt at the ice grain boundaries. The dataset includes measurements of snow during MOSAiC. Measurements included profiles of depth, density, temperature, snow water equivalent, penetration resistance, stable water isotope, salinity and microcomputer tomography samples. Most snowpit sites were visited and measured weekly to capture the temporal evolution of the physical properties of snow. The compiled dataset includes 576 snowpits and describes snow conditions during the MOSAiC expedition.

Full-band, multi-angle, multi-scale, and temporal dynamic field spectral measurements in China

Field-measured spectra are critical for remote sensing physical modelling, retrieval of structural, biophysical, and biochemical parameters, and other practical applications. We present a library of field spectra, which includes (1) portable field spectroradiometer measurements of vegetation, soil, and snow in the full-wave band, (2) multi-angle spectra measurements of desert vegetation, chernozems, and snow with consideration of the anisotropic reflectance of land surface, (3) multi-scale spectra measurements of leaf and canopy of different vegetation cover surfaces, and (4) continuous reflectance spectra time-series data revealing vegetation growth dynamics of maize, rice, wheat, rape, grassland, and so on. To the best of our knowledge, this library is unique in simultaneously providing full-band, multi-angle, multi-scale spectral measurements of the main surface elements of China covering a large spatial extent over a 10-year period. Furthermore, the 101 by 101 satellite pixels of Landsat ETM/OLI and MODIS surface reflectance centered around the field site were extracted, providing a vital linkage between ground measurements and satellite observations. The code language used for this work is Matlab 2016a.

Meteorological Driving Datasets for the U.S. Midwest and Great Lakes Region Incorporating Precipitation Gauge Undercatch Corrections

Abstract Precipitation (P) gauge undercatch (PUC) is an important source of error when using observed meteorological datasets for hydrologic modeling studies in regions with cold and windy winters. Preliminary simulations using the Variable Infiltration Capacity (VIC) hydrological model forced with different meteorological datasets showed significant underprediction of simulated streamflow throughout the domain. A new hybrid gridded meteorological dataset at 1/16° resolution based on observed station data was assembled over the U.S. Midwest and Great Lakes region from 1915 to 2021 at a daily time step. Correction of primary station data using existing techniques is generally difficult or infeasible in the United States due to missing station metadata and lack of local wind speed (WS) measurements. We developed and tested several different postprocessing adjustment techniques using regridded WS obtained from the NCEP–NCAR reanalysis. The most effective approach corrected rain or mixed P using WS alone, and P as snow using a regressed snow-to-P ratio from a group of wind-shielded reference stations (to account for different and generally unknown snow measurement techniques). The PUC-corrected gridded products were validated against high-quality shielded stations and corrected Global Historical Climatology Network stations with in situ WS, showing good overall agreement. Observed monthly streamflow at 40 river basins was also compared to hydrologic model simulations forced by datasets with and without PUC corrections. The best PUC-corrected dataset produced improvements in streamflow simulations in at least 80% of the streamflow locations for three validation metrics (r2, Nash–Sutcliff efficiency, bias in the mean), demonstrating its value for hydrometeorological studies in the greater Midwest region. Significance Statement Many applications in hydrology require in situ precipitation (P) measurements, which are known to have a systematic low bias due to the effects of wind, also known as precipitation undercatch (PUC). Addressing PUC is problematic in the United States due to limited access to detailed station metadata (SMD) and local wind speed (WS) measurements. In this paper we develop a set of procedures to create gridded precipitation datasets for the U.S. Midwest region that incorporate corrections for PUC without needing either (i) detailed SMD or (ii) local WS measurements. Among other tests, results in 40 test basins throughout the Midwest show substantial improvements in simulated streamflow in 32 out of 40 basins when PUC corrections are included in meteorological driving datasets.