Snow Telemetry Research Articles

Improving Snow Water Equivalent Maps With Machine Learning of Snow Survey and Lidar Measurements

AbstractIn the semiarid interior western USA, where a majority of surface water supply comes from mountain forests, high‐resolution aerial lidar‐based surveys are commonly used to study snow. These surveys provide rich information about snow depth, but they are usually not accompanied with spatially explicit measurements of snow density, which leads to uncertainty in the estimation of snow water equivalent (SWE). In this study, we use a novel approach to distribute ~300 field measurements of snow density with artificial neural networks. We combine the resulting density maps with aerial lidar snow depth measurements, bias corrected with a very large and precisely geolocated array of field‐measured snow depths (~4,000 observations), to create and validate maps of snow depth, snow density, and SWE over two sites along Arizona's Mogollon Rim in February and March 2017. These maps show differences between midwinter and late‐winter snow conditions. In particular, compared to that of snow depth, the spatial variability of snow density is smaller for the later snow survey than the earlier snow survey. These gridded data also show that the representativeness of Snow Telemetry and other point measurements is different for the midwinter and late‐winter snow surveys. Overall, the lidar artificial neural network SWE estimates can be as much as 30% different than if Snow Telemetry density were used with lidar snow depths to estimate SWE.

The Influence of Atmospheric Circulation on Abnormal Snowpack Melt‐Out Events and Drought in Wyoming

AbstractDeclines in high‐elevation snowpack and its effect on water availability in the western United States is of great interest given the projected changes in temperatures that may alter snowpack melt‐out timing. Because water from snowmelt is crucial to the West's water supply, this study examines the influence of continental‐scale atmospheric variables on snowpack melt out from 1980 to 2016 in Wyoming. Using snow telemetry station and North American Regional Reanalysis data, this research finds abnormally early snowpack melt out in 1987, 1992, and 2012 was associated with persistent above‐normal high pressure during spring, leading to sustained warmer‐than‐normal and drier‐than‐normal conditions. Conversely, abnormally late snowpack melt out in 2011, 1995, 1983, 1982, 2008, and 1999 was associated with lower‐than‐normal 500 mb geopotential heights, corresponding to very cold temperatures for April, May, and June, which set the stage for late snowpack melt out. Snowpack melt‐out departure values are also correlated with late‐season agricultural drought as indicated by 10 Drought Severity indices. Using snowpack melt‐out timing, in association with atmospheric variables in a predictive capacity may assist regulatory agencies, such as the Bureau of Reclamation, to make better informed decisions about when, or when not, to store or release water to mitigate late‐season agricultural drought impacts.

Moisture transport associated with large precipitation events in the Upper Colorado River Basin

Interannual streamflow variability poses numerous water supply forecasting and management challenges for mountain snowpack‐sourced basins across the western United States. Previous studies have shown that substantial proportions of annual precipitation occur during relatively few large precipitation events (LPEs) and consequently, such events tend to be significant predictors of streamflow for many basins, including the Upper Colorado River Basin (UCRB). While some primary pathways of atmospheric moisture transport, particularly atmospheric rivers, have been linked with extreme precipitation events across the western United States, this study seeks to expand on this knowledge by quantifying the relationships between moisture transport regimes, LPEs, and streamflow observed in the UCRB. A climatological analysis of integrated vapour transport, via a self‐organizing maps synoptic classification procedure, is used to identify geographic regimes of moisture transport that lead to LPEs, as observed at snow telemetry sites across the UCRB headwaters. Trajectory analyses are also calculated to further characterize LPE‐inducing atmospheric river pathways by subbasin in the UCRB. Results indicate that LPEs throughout the basin most commonly coincide with amplified trough patterns, which advect Pacific moisture into the UCRB from the southwest, through Southern California and the Four Corners region; patterns that are positively correlated to streamflow. LPEs occurring in northern portions of the UCRB exhibit a secondary corridor, featuring west to east‐oriented moisture transport, in association with zonal to meridional flow regimes. Highly amplified ridges over the region rarely lead to LPEs, as such patterns deflect or suppress moisture from the UCRB. These results suggest that climate projections of future enhanced meridional flow aloft will lead to continued streamflow variability in the UCRB.

Comprehensive accuracy assessment of MODIS daily snow cover products and gap filling methods

The accuracy of the standard Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products (Collection 5) and several of the most common and computationally frugal gap-filling methods were validated using 12 years of daily observations from over 800 Snow Telemetry (SNOTEL) stations. While several factors affect snow cover accuracy to some extent, the largest controls are associated with land cover type; accuracy fell from the maximum in croplands (94.9%) to the minimum for observations with a land cover type of water (79.5%). The second largest impact on accuracy is attributed to the changes in solar zenith angle, where accuracy decreased from a maximum of ∼96% at 42° to a minimum of ∼84% at 58°. Based on the results of this work, the highest accuracy binary daily snow cover dataset can be achieved by reclassifying the fractional snow band using a no-snow/snow threshold of 10 from the MODIS Terra sensor. Gap-filling is best accomplished by a temporal filter on the same dataset, and the window length depends on the desired completeness of the final dataset and the amount of missing values in the area of interest. Decreases in accuracy caused by this gap-filling method taper off after 7 days. Therefore, if a larger temporal window is needed to adequately fill in a time series it is best to extend the window to the largest gap present.

Quantifying the early snowmelt event of 2015 in the Cascade Mountains, USA by developing and validating MODIS-based snowmelt timing maps

Spring snowmelt serves as the major hydrological contribution to many watersheds of the US West. Since the 1970s the conterminous western USA has seen an earlier arrival of spring snowmelt. The extremely low snowpack and early melt of 2015 in the Cascade Mountains may be a harbinger of winters to come, underscoring the interest in advancements in spring snowmelt monitoring. Target-of-opportunity and point measurements of snowmelt using meteorological stations or stream gauges are common sources of these data, however, there have been few attempts to identify snowmelt timing using remote sensing. In this study, we describe the creation of snowmelt timing maps (STMs) which identify the day of year that each pixel of a remotely sensed image transitions from “snow-covered” to “no snow” during the spring melt season, controlling for cloud coverage and ephemeral spring snow storms. Derived from the 500 m MODerate-resolution Imaging Spectroradiometer (MODIS) standard snow map, MOD10A2, this new dataset provides annual maps of snowmelt timing, with corresponding maps of cloud interference and interannual variability in snow coverage from 2001–2015. We first show that the STMs agree strongly with in-situ snow telemetry (SNOTEL) meteorological station measurements in terms of snowmelt timing. We then use the STMs to investigate the early snowmelt event of 2015 in the Cascade Mountains, USA, highlighting the protected areas of Mt. Rainier, Crater Lake, and Lassen Volcanic National Parks. In 2015 the Cascade Mountains experienced snowmelt 41 days earlier than the 2001–2015 average, with 25% of its land area melting>65 days earlier than average. The upper elevations of the Cascade Mountains experienced the greatest snowmelt anomaly. Our results are relevant to land managers and biologists as they plan adaptation strategies for mitigating the effects of climate change throughout temperate mountains.

Alaska Snowpack Response to Climate Change: Statewide Snowfall Equivalent and Snowpack Water Scenarios

Climatically driven changes in snow characteristics (snowfall, snowpack, and snowmelt) will affect hydrologic and ecological systems in Alaska over the coming century, yet there exist no projections of downscaled future snow pack metrics for the state of Alaska. We updated historical and projected snow day fraction (PSF, the fraction of days with precipitation falling as snow) from McAfee et al. We developed modeled snowfall equivalent (SFE) derived from the product of snow-day fraction (PSF) and existing gridded precipitation for Alaska from Scenarios Network for Alaska and Arctic Planning (SNAP). We validated the assumption that modeled SFE approximates historical decadally averaged snow water equivalent (SWE) observations from snowcourse and Snow Telemetry (SNOTEL) sites. We present analyses of future downscaled PSF and two new products, October–March SFE and ratio of snow fall equivalent to precipitation (SFE:P) based on bias-corrected statistically downscaled projections of Coupled Model Intercomparison Project 5 (CMIP5) Global Climate Model (GCM) temperature and precipitation for the state of Alaska. We analyzed mid-century (2040–2069) and late-century (2070–2099) changes in PSF, SFE, and SFE:P relative to historical (1970–1999) mean temperature and present results for Alaska climate divisions and 12-digit Hydrologic Unit Code (HUC12) watersheds. Overall, estimated historical the SFE is reasonably well related to the observed SWE, with correlations over 0.75 in all decades, and correlations exceeding 0.9 in the 1960s and 1970s. In absolute terms, SFE is generally biased low compared to the observed SWE. PSF and SFE:P decrease universally across Alaska under both Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 emissions scenarios, with the smallest changes for RCP 4.5 in 2040–2069 and the largest for RCP 8.5 in 2070–2099. The timing and magnitude of maximum decreases in PSF vary considerably with regional average temperature, with the largest changes in months at the beginning and end of the snow season. Mean SFE changes vary widely among climate divisions, ranging from decreases between −17 and −58% for late twenty-first century in southeast, southcentral, west coast and southwest Alaska to increases up to 21% on the North Slope. SFE increases most at highest elevations and latitudes and decreases most in coastal southern Alaska. SFE:P ratios indicate a broad switch from snow-dominated to transitional annual hydrology across most of southern Alaska by mid-century, and from transitional to rain-dominated watersheds in low elevation parts of southeast Alaska by the late twenty-first century.

Sub-Seasonal Snowpack Trends in the Rocky Mountain National Park Area, Colorado, USA

We present a detailed study of the snowpack trends in the Rocky Mountain National Park (RMNP) using snow telemetry and snow course data at a monthly resolution. We examine the past 35 years (1981 to 2016) to explore monthly patterns over 36 locations and used some additional data to help interpret the changes. The analysis is at a finer spatial and temporal scale than previous studies that focused more on aggregate- or regional-scale changes. The trends in the first of the month’s snow water equivalent (SWE) varied more than the change in the monthly SWE, monthly precipitation or mean temperature. There was greater variability in SWE trends on the west side of the study area, and on average the declines in the west were greater. At higher elevations, there was more of a decline in the SWE. Changes in the climate were much less in winter than in summer. Per decade, the average decline in the winter precipitation was 4 mm and temperatures warmed by 0.29 °C, while the summer precipitation declined by 9 mm and temperatures rose by 0.66 °C. In general, November and March became warmer and drier, yielding a decline of the SWE on December 1st and April 1st, while December through February and May became wetter. February and May became cooler.

Dependence of C-Band Backscatter on Ground Temperature, Air Temperature and Snow Depth in Arctic Permafrost Regions

Microwave remote sensing has found numerous applications in areas affected by permafrost and seasonally frozen ground. In this study, we focused on data obtained by the Advanced Scatterometer (ASCAT, C-band) during winter periods when the ground is assumed to be frozen. This paper discusses the relationships of ASCAT backscatter with snow depth, air and ground temperature through correlations and the analysis of covariance (ANCOVA) to quantify influences on backscatter values during situations of frozen ground. We studied sites in Alaska, Northern Canada, Scandinavia and Siberia. Air temperature and snow depth data were obtained from 19 World Meteorological Organization (WMO) and 4 Snow Telemetry (SNOTEL) stations. Ground temperature data were obtained from 36 boreholes through the Global Terrestrial Network for Permafrost Database (GTN-P) and additional records from central Yamal. Results suggest distinct differences between sites with and without underlying continuous permafrost. Sites characterized by high freezing indices (>4000 degree-days) have consistently stronger median correlations of ASCAT backscatter with ground temperature for all measurement depths. We show that the dynamics in winter-time backscatter cannot be solely explained through snow processes, but are also highly correlated with ground temperature up to a considerable depth (60 cm). These findings have important implications for both freeze/thaw and snow water equivalent retrieval algorithms based on C-band radar measurements.

Large precipitation event variability among headwater SNOTEL sites and impacts on streamflow in the Upper Colorado River Basin

ABSTRACTThis exploratory study defines and characterizes large precipitation events (LPEs) at a sample of snow telemetry (SNOTEL) sites located in headwater subbasins of the Upper Colorado River Basin (UCRB) and assesses the relationships of interannual LPE variability on basin streamflow. LPE thresholds are defined as the 80th percentile of daily precipitation increments observed at each SNOTEL site from 1981 to 2014. On average, LPEs accounted for more than half (55.5%) of annual precipitation. Total precipitation, total LPE precipitation, LPE count, and LPE magnitude are assessed annually at each SNOTEL site. Statistical analysis shows that these LPE metrics, observed at the headwater SNOTEL sites, are strongly correlated with total annual streamflow downstream, as measured at river gages on major tributaries in the UCRB and at Lake Powell. Further analysis of streamflow during the early twenty-first century drought suggests that drought conditions have coincided with lower precipitation observed at the SNOTEL sites, due in part to fewer and drier LPEs in recent years. Investigation of upper air patterns during LPEs reveals that such events most commonly coincide with zonal, onshore flow among the northern subbasins and amplified troughs towards the south of the UCRB, patterns which have been less common during the drought.

Multi-Scale Correlation between the Western U.S. Snow Water Equivalent and ENSO/PDO Using Wavelet Analyses

This study evaluated the correlation between the western U.S. snow water equivalent (SWE) and two major oceanic-atmospheric indices of the Pacific Ocean, namely, the El Nino Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), using continuous wavelet transformation and its derivatives. Snow Telemetry (SNOTEL) data for 1 April SWE from 323 sites (out of which 258 are in six hydrologic regions) were obtained for a study period of 56 years (1961–2016). The results showed that ENSO had a much higher influence than PDO throughout the western U.S. SWE across the study period. Both ENSO and PDO showed a higher correlation with SWE at multiple timescale bands across different time intervals, although significant intervals in the higher timescales were of longer duration. ENSO showed a higher correlation in the 10-to-16-years band across the entire study period as well as in the lower timescales. PDO showed a higher correlation below the 4-years band. The relative phase relationship suggested that ENSO led SWE, with certain lags, while both were moving in the same direction in many instances. The lag-response behavior of SWE and PDO was not found to be uniform. The regional analyses, based on the western U.S. hydrologic regions, suggested significant variation across the adjacent regions in terms of their correlation with ENSO/PDO. Association with ENSO was also observed to be higher compared to PDO among the regions. Regions close to the ocean and at lower elevation showed higher correlation compared to the inland regions with higher elevation.

GEFS Precipitation Forecasts and the Implications of Statistical Downscaling over the Western United States

Abstract Contemporary operational medium-range ensemble modeling systems produce quantitative precipitation forecasts (QPFs) that provide guidance for weather forecasters, yet lack sufficient resolution to adequately resolve orographic influences on precipitation. In this study, cool-season (October–March) Global Ensemble Forecast System (GEFS) QPFs are verified using daily (24 h) Snow Telemetry (SNOTEL) observations over the western United States, which tend to be located at upper elevations where the orographic enhancement of precipitation is pronounced. Results indicate widespread dry biases, which reflect the infrequent production of larger 24-h precipitation events (≳22.9 mm in Pacific ranges and ≳10.2 mm in the interior ranges) compared with observed. Performance metrics, such as equitable threat score (ETS), hit rate, and false alarm ratio, generally worsen from the coast toward the interior. Probabilistic QPFs exhibit low reliability, and the ensemble spread captures only ~30% of upper-quartile events at day 5. In an effort to improve QPFs without exacerbating computing demands, statistical downscaling is explored based on high-resolution climatological precipitation analyses from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM), an approach frequently used by operational forecasters. Such downscaling improves model biases, ETSs, and hit rates. However, 47% of downscaled QPFs for upper-quartile events are false alarms at day 1, and the ensemble spread captures only 56% of the upper-quartile events at day 5. These results should help forecasters and hydrologists understand the capabilities and limitations of GEFS forecasts and statistical downscaling over the western United States and other regions of complex terrain.

Developing an intelligent expert system for streamflow prediction, integrated in a dynamic decision support system for managing multiple reservoirs: A case study

Extracting and using of time-dependent indices improved prediction accuracy.Pre-processing of data improved prediction accuracy.Intelligent selection of predictors via sensitivity analysis and data mining.Successful integration of a novel forecast expert system in an operation system. Since fresh water is limited while agricultural and human water demands are continuously increasing, optimal prediction and management of streamflows as a source of fresh water is crucially important. This study investigates and demonstrates how data preprocessing and data mining techniques would improve the accuracy of streamflow predictive models. Based on easily accessible Snow Telemetry data (SNOTEL), four streamflow prediction models autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs), a hybrid-model of ANN and ARIMA (ANN-ARIMA), and an adaptive neuro fuzzy inference system (ANFIS) were developed and utilized in a streamflow prediction process on Elephant Butte Reservoir. Utilizing the statistical correlation analysis and the extracting importance degrees of predictors led to efficiently select the most effective predictors for daily and monthly streamflow to Elephant Butte Reservoir. For the daily prediction time step, by preprocessing the historical data and extracting and utilizing the extracted climate variability indices through data mining techniques, the ANFIS model achieved a superior streamflow prediction performance for Elephant Butte Reservoir compared to the other three evaluated prediction models. Additionally, for predicting monthly streamflow to the Elephant Butte Reservoir, ANFIS showed significantly higher accuracy than the ANNs. As an optimal application of the developed predictive expert systems, successful integrating the prediction models in integrated reservoir operations balanced the need for a reliable supply of irrigation water against losses through evaporation. The optimal operation plan significantly minimizes the total evaporation loss from both reservoirs by providing the optimal storage levels in both reservoirs. This study provides the conceptual procedures of non-seasonal (ARIMA) model, and since the model is univariate, it demonstrates a strongly-reliable inflow prediction when existing information is limited to streamflow data as a predictor.

Using ground penetrating radar to assess the variability of snow water equivalent and melt in a mixed canopy forest, Northern Colorado

Snow is an important environmental variable in headwater systems that controls hydrological processes such as streamflow, groundwater recharge, and evapotranspiration. These processes will be affected by both the amount of snow available for melt and the rate at which it melts. Snow water equivalent (SWE) and snowmelt are known to vary within complex subalpine terrain due to terrain and canopy influences. This study assesses this variability during the melt season using ground penetrating radar to survey multiple plots in northwestern Colorado near a snow telemetry (SNOTEL) station. The plots include south aspect and flat aspect slopes with open, coniferous (subalpine fir, Abies lasiocarpa and engelman spruce, Picea engelmanii), and deciduous (aspen, populous tremuooides) canopy cover. Results show the high variability for both SWE and loss of SWE during spring snowmelt in 2014. The coefficient of variation for SWE tended to increase with time during snowmelt whereas loss of SWE remained similar. Correlation lengths for SWE were between two and five meters with melt having correlation lengths between two and four meters. The SNOTEL station regularly measured higher SWE values relative to the survey plots but was able to reasonably capture the overall mean loss of SWE during melt. Ground Penetrating Radar methods can improve future investigations with the advantage of non-destructive sampling and the ability to estimate depth, density, and SWE.

Spatio-temporal snowmelt variability across the headwaters of the Southern Rocky Mountains

Understanding the rate of snowmelt helps inform how water stored as snow will transform into streamflow. Data from 87 snow telemetry (SNOTEL) stations across the Southern Rocky Mountains were used to estimate spatio-temporal melt factors. Decreases in snow water equivalent were correlated to temperature at these monitoring stations for eight half-month periods from early March through late June. Time explained 70% of the variance in the computed snow melt factors. A residual linear correlation model was used to explain subsequent spatial variability. Longitude, slope, and land cover type explained further variance. For evergreen trees, canopy density was relevant to find enhanced melt rates; while for all other land cover types, denoted as non-evergreen, lower melt rates were found at high elevation, high latitude and north facing slopes, denoting that in cold environments melting is less effective than in milder sites. A change in the temperature sensor about mid-way through the time series (1990 to 2013) created a discontinuity in the temperature dataset. An adjustment to the time series yield larger computed melt factors.

Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow‐Dominated Watersheds?

AbstractChanging climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. We investigate whether also including SNOTEL soil moisture observations improve April‐July streamflow volume forecast accuracy at 0, 1, 2, and 3‐month lead times at 12 watersheds in Utah and California. We found statistically significant improvement in 0 and 3‐month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2‐month lead times. Surprisingly, these improvements were insensitive to soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0‐month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3‐month lead times, the inclusion of soil moisture explained 12.3‐26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.

Evaluation and Uncertainty Estimation of the Latest Radar and Satellite Snowfall Products Using SNOTEL Measurements over Mountainous Regions in Western United States

Snow contributes to regional and global water budgets, and is of critical importance to water resources management and our society. Along with advancement in remote sensing tools and techniques to retrieve snowfall, verification and refinement of these estimates need to be performed using ground-validation datasets. A comprehensive evaluation of the Multi-Radar/Multi-Sensor (MRMS) snowfall products and Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) precipitation products is conducted using the Snow Telemetry (SNOTEL) daily precipitation and Snow Water Equivalent (SWE) datasets. Severe underestimations are found in both radar and satellite products. Comparisons are conducted as functions of air temperature, snowfall intensity, and radar beam height, in hopes of resolving the discrepancies between measurements by remote sensing and gauge, and finally developing better snowfall retrieval algorithms in the future.

Estimating daily root-zone soil moisture in snow-dominated regions using an empirical soil moisture diagnostic equation

Soil moisture in snow-dominated regions has many important applications including evapotranspiration estimation, flood forecasting, water resource and ecosystem services management, weather prediction and climate modeling, and quantification of denudation processes. A simple and robust empirical approach to estimate root-zone soil moisture in snow-dominated regions using a soil moisture diagnostic equation that incorporates snowfall and snowmelt processes is suggested and tested. A five-water-year dataset (10/1/2010–9/30/2015) of daily precipitation, air temperature, snow water equivalent and soil moistures at three depths (i.e., 5cm, 20cm, and 50cm) at each of 12 Snow Telemetry (SNOTEL) sites across Utah (37.583°N–41.883°N, 110.183°W–112.9°W), is applied to test the proposed method. The first three water years are designated as the parameter-estimation period (PEP) and the last two water years are chosen as the model-testing period (MTP). Applying the estimated soil moisture loss function parameters and other empirical parameters in the soil moisture diagnostic equation in the PEP, soil moistures in three soil columns (0–5cm, 0–20cm, and 0–50cm) are estimated in the MTP. The relatively accurate soil moisture estimations compared to the observations at 12 SNOTEL sites (RMSE⩽6.23 (%V/V), average RMSE=4.28 (%V/V), correlation coefficient ⩾0.75, average correlation coefficient =0.89, the Nash-Sutcliffe efficient coefficient Ec⩾0.24, average Ec=0.72) indicate that the soil moisture diagnostic equation is capable of accurately estimating soil moisture in snow-dominated regions after the snowfall and snowmelt processes are included in the soil moisture diagnostic equation.

Testing the recent snow drought as an analog for climate warming sensitivity of Cascades snowpacks

Record low snowpack conditions were observed at Snow Telemetry stations in the Cascades Mountains, USA during the winters of 2014 and 2015. We tested the hypothesis that these winters are analogs for the temperature sensitivity of Cascades snowpacks. In the Oregon Cascades, the 2014 and 2015 winter air temperature anomalies were approximately +2 °C and +4 °C above the climatological mean. We used a spatially distributed snowpack energy balance model to simulate the sensitivity of multiple snowpack metrics to a +2 °C and +4 °C warming and compared our modeled sensitivities to observed values during 2014 and 2015. We found that for each +1 °C warming, modeled basin-mean peak snow water equivalent (SWE) declined by 22%–30%, the date of peak SWE (DPS) advanced by 13 days, the duration of snow cover (DSC) shortened by 31–34 days, and the snow disappearance date (SDD) advanced by 22–25 days. Our hypothesis was not borne out by the observations except in the case of peak SWE; other snow metrics did not resemble predicted values based on modeled sensitivities and thus are not effective analogs of future temperature sensitivities. Rather than just temperature, it appears that the magnitude and phasing of winter precipitation events, such as large, late spring snowfall, controlled the DPS, SDD, and DSC.

Linking snowfall and snow accumulation to generate spatial maps of SWE and snow depth

AbstractIt is critically important but challenging to estimate the amount of snow on the ground over large areas due to its strong spatial variability. Point snow data are used to generate or improve (i.e., blend with) gridded estimates of snow water equivalent (SWE) by using various forms of interpolation; however, the interpolation methodologies often overlook the physical mechanisms for the snow being there in the first place. Using data from the Snow Telemetry and Cooperative Observer networks in the western United States, we show that four methods for the spatial interpolation of peak of winter snow water equivalent (SWE) and snow depth based on distance and elevation can result in large errors. These errors are reduced substantially by our new method, i.e., the spatial interpolation of these quantities normalized by accumulated snowfall from the current or previous water years. Our method results in significant improvement in SWE estimates over interpolation techniques that do not consider snowfall, regardless of the number of stations used for the interpolation. Furthermore, it can be used along with gridded precipitation and temperature data to produce daily maps of SWE over the western United States that are comparable to existing estimates (which are based on the assimilation of much more data). Our results also show that not honoring the constraint between SWE and snowfall when blending in situ data with gridded data can lead to the development and propagation of unrealistic errors.

Freeze/Thaw Detection and Validation Using Aquarius’ L-Band Backscattering Data

The seasonal cycle of landscape freeze/thaw (FT) state across mid- to high latitudes influences critical processes such as the land surface energy balance, carbon cycle dynamics related to vegetation growth, and hydrological partitioning between surface runoff and infiltration. In this paper, we produce the first daily FT classification for the 2011–2014 period based on L-band radar measurements from Aquarius. The radar FT algorithm used in this paper is based on a seasonal threshold approach, which is also the baseline algorithm applied to higher-resolution (3 km) radar measurements from NASA’s Soil Moisture Active/Passive (SMAP) mission (Launched January 31, 2015). The lower frequency (L-band) radar backscatter measurements from Aquarius provide enhanced sensitivity to FT conditions in vegetation canopy, snow and surface soil layers, although the relative radar penetration depth and sensitivity of the FT signal to these landscape elements will vary according to surface moisture and vegetation biomass conditions, and underlying land cover and terrain heterogeneity [1] , [2] . Evaluation of the seasonal threshold FT algorithm using Aquarius was performed using surface air and soil temperatures from selected stations in the Snow Telemetry (SnoTel) network. Analysis identified good agreement during the fall freeze-up period with flag agreement exceeding the 80% SMAP accuracy target when summarized on a monthly basis. Disagreement was greater during the spring thaw transition due in part to uncertainty in characterizing thaw from in situ measurements. Unlike the fall season, stronger agreement in the spring was identified when the reference state was characterized with air temperature compared to soil temperature.