Using CloudSat‐Derived Snow Accumulation Estimates to Constrain Gridded Snow Water Equivalent Products

CloudSat is often the only measurement of snowfall rate available at high latitudes, making it a valuable tool for understanding snow climatology. The capability of CloudSat to provide information on seasonal and subseasonal time scales, however, has yet to be explored. In this study, we use subsampled reanalysis estimates to predict the uncertainties of CloudSat snow water equivalent (SWE) accumulation measurements at various space and time resolutions. An idealized/simulated subsampling model predicts that CloudSat may provide seasonal SWE estimates with median percent errors below 50% at spatial scales as small as 2° × 2°. By converting these predictions to percent differences, we can evaluate CloudSat snowfall accumulations against a blend of gridded SWE measurements during frozen time periods. Our predictions are in good agreement with results. The 25th, 50th, and 75th percentiles of the percent differences between the two measurements all match predicted values within eight percentage points. We interpret these results to suggest that CloudSat snowfall estimates are in sufficient agreement with other, thoroughly vetted, gridded SWE products. This implies that CloudSat may provide useful estimates of snow accumulation over remote regions within seasonal time scales.

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Estimating snow water equivalent over long mountain transects using snowmobile-mounted ground-penetrating radar

Geophysics ◽

10.1190/geo2015-0121.1 ◽

2016 ◽

Vol 81 (1) ◽

pp. WA183-WA193 ◽

Cited By ~ 9

Author(s):

W. Steven Holbrook ◽

Scott N. Miller ◽

Matthew A. Provart

Keyword(s):

Ground Penetrating Radar ◽

Snow Water Equivalent ◽

Ground Surface ◽

Snow Accumulation ◽

Accurate Estimation ◽

Snow Density ◽

Snow Stratigraphy ◽

Flood Estimation ◽

Snow Water ◽

Ground Penetrating

The water balance in alpine watersheds is dominated by snowmelt, which provides infiltration, recharges aquifers, controls peak runoff, and is responsible for most of the annual water flow downstream. Accurate estimation of snow water equivalent (SWE) is necessary for runoff and flood estimation, but acquiring enough measurements is challenging due to the variability of snow accumulation, ablation, and redistribution at a range of scales in mountainous terrain. We have developed a method for imaging snow stratigraphy and estimating SWE over large distances from a ground-penetrating radar (GPR) system mounted on a snowmobile. We mounted commercial GPR systems (500 and 800 MHz) to the front of the snowmobile to provide maximum mobility and ensure that measurements were taken on pristine snow. Images showed detailed snow stratigraphy down to the ground surface over snow depths up to at least 8 m, enabling the elucidation of snow accumulation and redistribution processes. We estimated snow density (and thus SWE, assuming no liquid water) by measuring radar velocity of the snowpack through migration focusing analysis. Results from the Medicine Bow Mountains of southeast Wyoming showed that estimates of snow density from GPR ([Formula: see text]) were in good agreement with those from coincident snow cores ([Formula: see text]). Using this method, snow thickness, snow density, and SWE can be measured over large areas solely from rapidly acquired common-offset GPR profiles, without the need for common-midpoint acquisition or snow cores.

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Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

Applied Sciences ◽

10.3390/app11188365 ◽

2021 ◽

Vol 11 (18) ◽

pp. 8365

Author(s):

Liming Gao ◽

Lele Zhang ◽

Yongping Shen ◽

Yaonan Zhang ◽

Minghao Ai ◽

...

Keyword(s):

Snow Cover ◽

River Basin ◽

Snow Depth ◽

Snow Water Equivalent ◽

Snow Accumulation ◽

Implicit Scheme ◽

Snow Water ◽

Irtysh River ◽

Simulation Results ◽

The Irtysh River

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

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How Do We Define Climate Change? Considering the Temporal Resolution of Niveo-Meteorological Data

Hydrology ◽

10.3390/hydrology7030038 ◽

2020 ◽

Vol 7 (3) ◽

pp. 38 ◽

Cited By ~ 2

Author(s):

Steven R. Fassnacht ◽

Glenn G. Patterson ◽

Niah B.H. Venable ◽

Mikaela L. Cherry ◽

Anna K.D. Pfohl ◽

...

Keyword(s):

Snow Water Equivalent ◽

Meteorological Data ◽

Snow Accumulation ◽

Temperature Trends ◽

Northern Colorado ◽

Snow Water ◽

Snow Telemetry ◽

Precipitation And Temperature ◽

Precipitation Trends ◽

Cumulative Precipitation

Historically, snowpack trends have been assessed using one fixed date to represent peak snow accumulation prior to the onset of melt. Subsequent trend analyses have considered the peak snow water equivalent (SWE), but the date of peak SWE can vary by several months due to inter-annual variability in snow accumulation and melt patterns. A 2018 assessment evaluated monthly SWE trends. However, since the month is a societal construct, this current work examines daily trends in SWE, cumulative precipitation, and temperature. The method was applied to 13 snow telemetry stations in Northern Colorado, USA for the period from 1981 to 2018. Temperature trends were consistent among all the stations; warming trends occurred 63% of the time from 1 October through 24 May, with the trends oscillating from warming to cooling over about a 10-day period. From 25 May to 30 September, a similar oscillation was observed, but warming trends occurred 86% of the time. SWE and precipitation trends illustrate temporal patterns that are scaled based on location. Specifically, lower elevations stations are tending to record more snowfall while higher elevation stations are recording less. The largest SWE, cumulative precipitation, and temperature trends were +30 to −70 mm/decade, +30 to −30 mm/decade, and +4 to −2.8 °C/decade, respectively. Trends were statistically significance an average of 25.8, 4.5, and 29.4% of the days for SWE, cumulative precipitation, and temperature, respectively. The trend in precipitation as snow ranged from +/−2%/decade, but was not significant at any station.

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Large scale snow water equivalent status monitoring: comparison of different snow water products in the upper Colorado Basin

Hydrology and Earth System Sciences ◽

10.5194/hess-17-5127-2013 ◽

2013 ◽

Vol 17 (12) ◽

pp. 5127-5139 ◽

Cited By ~ 6

Author(s):

G. A. Artan ◽

J. P. Verdin ◽

R. Lietzow

Keyword(s):

Energy Balance ◽

Water Content ◽

Snow Water Equivalent ◽

Weather Forecast ◽

Snow Accumulation ◽

Passive Microwave ◽

Data Sets ◽

Snow Water ◽

Trmm Precipitation ◽

Colorado Basin

Abstract. We illustrate the ability to monitor the status of snow water content over large areas by using a spatially distributed snow accumulation and ablation model that uses data from a weather forecast model in the upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) data-sets; remaining meteorological model input data were from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a region of the western US that covers parts of the upper Colorado Basin. We also compared the SWE product estimated from the special sensor microwave imager (SSM/I) and scanning multichannel microwave radiometer (SMMR) to the SNODAS and SNOTEL SWE data-sets. Agreement between the spatial distributions of the simulated SWE with MPE data was high with both SNODAS and SNOTEL. Model-simulated SWE with TRMM precipitation and SWE estimated from the passive microwave imagery were not significantly correlated spatially with either SNODAS or the SNOTEL SWE. Average basin-wide SWE simulated with the MPE and the TRMM data were highly correlated with both SNODAS (r = 0.94 and r = 0.64; d.f. = 14 – d.f. = degrees of freedom) and SNOTEL (r = 0.93 and r = 0.68; d.f. = 14). The SWE estimated from the passive microwave imagery was significantly correlated with the SNODAS SWE (r = 0.55, d.f. = 9, p = 0.05) but was not significantly correlated with the SNOTEL-reported SWE values (r = 0.45, d.f. = 9, p = 0.05).The results indicate the applicability of the snow energy balance model for monitoring snow water content at regional scales when coupled with meteorological data of acceptable quality. The two snow water contents from the microwave imagery (SMMR and SSM/I) and the Utah Energy Balance forced with the TRMM precipitation data were found to be unreliable sources for mapping SWE in the study area; both data sets lacked discernible variability of snow water content between sites as seen in the SNOTEL and SNODAS SWE data. This study will contribute to better understanding the adequacy of data from weather forecast models, TRMM, and microwave imagery for monitoring status of the snow water content.

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Humidity determines snowpack ablation under a warming climate

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1716789115 ◽

2018 ◽

Vol 115 (6) ◽

pp. 1215-1220 ◽

Cited By ~ 37

Author(s):

Adrian A. Harpold ◽

Paul D. Brooks

Keyword(s):

Climate Change ◽

Solar Radiation ◽

Snow Water Equivalent ◽

Critical Role ◽

Radiant Energy ◽

Snow Accumulation ◽

Atmospheric Conditions ◽

Atmospheric Humidity ◽

Dry Conditions ◽

Snow Water

Climate change is altering historical patterns of snow accumulation and melt, threatening societal frameworks for water supply. However, decreases in spring snow water equivalent (SWE) and changes in snowmelt are not ubiquitous despite widespread warming in the western United States, highlighting the importance of latent and radiant energy fluxes in snow ablation. Here we demonstrate how atmospheric humidity and solar radiation interact with warming temperature to control snowpack ablation at 462 sites spanning a gradient in mean winter temperature from −8.9 to +2.9 °C. The most widespread response to warming was an increase in episodic, midwinter ablation events. Under humid conditions these ablation events were dominated by melt, averaging 21% (202 mm/year) of SWE. Winter ablation under dry atmospheric conditions at similar temperatures was smaller, averaging 12% (58 mm/year) of SWE and likely dominated by sublimation fluxes. These contrasting patterns result from the critical role that atmospheric humidity plays in local energy balance, with latent and longwave radiant fluxes cooling the snowpack under dry conditions and warming it under humid conditions. Similarly, spring melt rates were faster under humid conditions, yet the second most common trend was a reduction in spring melt rates associated with earlier initiation when solar radiation inputs are smaller. Our analyses demonstrate that regional differences in atmospheric humidity are a major cause of the spatial variability in snowpack response to warming. Better constraints on humidity will be critical to predicting both the amount and timing of surface water supplies under climate change.

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Development of a Snow Load Alert System, “YukioroSignal” for Aiding Roof Snow Removal Decisions in Snowy Areas in Japan

Journal of Disaster Research ◽

10.20965/jdr.2020.p0688 ◽

2020 ◽

Vol 15 (6) ◽

pp. 688-697

Author(s):

Hiroyuki Hirashima ◽

Tsutomu Iyobe ◽

Katsuhisa Kawashima ◽

Hiroaki Sano ◽

◽

...

Keyword(s):

Snow Depth ◽

Load Distribution ◽

Snow Water Equivalent ◽

Snow Accumulation ◽

Risk Level ◽

Residential Areas ◽

Alert System ◽

Snow Removal ◽

Snow Load ◽

Snow Water

This study developed a snow load alert system, known as the “YukioroSignal”; this system aims to provide a widespread area for assessing snow load distribution and the information necessary for aiding house roof snow removal decisions in snowy areas of Japan. The system was released in January 2018 in Niigata Prefecture, Japan, and later, it was expanded to Yamagata and Toyama prefectures in January 2019. The YukioroSignal contains two elements: the “Quasi-Real-Time Snow Depth Monitoring System,” which collects snow depth data, and the numerical model known as SNOWPACK, which can calculate the snow water equivalent (SWE). The snow load per unit area is estimated to be equivalent to SWE. Based on the house damage risk level, snow load distribution was indicated by colors following the ISO 22324. The system can also calculate post-snow removal snow loads. The calculated snow load was validated by using the data collected through snow pillows. The simulated snow load had a root mean square error (RMSE) of 21.3%, which was relative to the observed snow load. With regard to residential areas during the snow accumulation period, the RMSE was 13.2%. YukioroSignal received more than 56,000 pageviews in the snowheavy 2018 period and 26,000 pageviews in the less snow-heavy 2019 period.

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Investigating hemispherical trends in snow accumulation using GlobSnow snow water equivalent data

2011 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2011.6050051 ◽

2011 ◽

Cited By ~ 5

Author(s):

Kari Luojus ◽

Jouni Pulliainen ◽

Matias Takala ◽

Juha Lemmetyinen ◽

Chris Derksen ◽

...

Keyword(s):

Snow Water Equivalent ◽

Snow Accumulation ◽

Snow Water

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Experimental measurements for improved understanding and simulation of snowmelt events in the Western Tatra Mountains

Journal of Hydrology and Hydromechanics ◽

10.1515/johh-2016-0038 ◽

2016 ◽

Vol 64 (4) ◽

pp. 316-328 ◽

Cited By ~ 5

Author(s):

Pavel Krajčí ◽

Michal Danko ◽

Jozef Hlavčo ◽

Zdeněk Kostka ◽

Ladislav Holko

Keyword(s):

Spatial Variability ◽

Snow Cover ◽

Air Temperature ◽

Snow Water Equivalent ◽

Flood Forecasting ◽

Snow Accumulation ◽

Small Scale ◽

Climatic Data ◽

Tatra Mountains ◽

Snow Water

AbstractSnow accumulation and melt are highly variable. Therefore, correct modeling of spatial variability of the snowmelt, timing and magnitude of catchment runoff still represents a challenge in mountain catchments for flood forecasting. The article presents the setup and results of detailed field measurements of snow related characteristics in a mountain microcatchment (area 59 000 m2, mean altitude 1509 m a. s. l.) in the Western Tatra Mountains, Slovakia obtained in winter 2015. Snow water equivalent (SWE) measurements at 27 points documented a very large spatial variability through the entire winter. For instance, range of the SWE values exceeded 500 mm at the end of the accumulation period (March 2015). Simple snow lysimeters indicated that variability of snowmelt and discharge measured at the catchment outlet corresponded well with the rise of air temperature above 0°C. Temperature measurements at soil surface were used to identify the snow cover duration at particular points. Snow melt duration was related to spatial distribution of snow cover and spatial patterns of snow radiation. Obtained data together with standard climatic data (precipitation and air temperature) were used to calibrate and validate the spatially distributed hydrological model MIKE-SHE. The spatial redistribution of input precipitation seems to be important for modeling even on such a small scale. Acceptable simulation of snow water equivalents and snow duration does not guarantee correct simulation of peakflow at short-time (hourly) scale required for example in flood forecasting. Temporal variability of the stream discharge during the snowmelt period was simulated correctly, but the simulated discharge was overestimated.

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Probabilistic Forecasts of Snow Water Equivalent and Runoff in Mountainous Areas*

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0193.1 ◽

2015 ◽

Vol 16 (5) ◽

pp. 2169-2186 ◽

Cited By ~ 28

Author(s):

Stefanie Jörg-Hess ◽

Nena Griessinger ◽

Massimiliano Zappa

Keyword(s):

Snow Water Equivalent ◽

Similarity Score ◽

Snow Accumulation ◽

Ensemble Prediction ◽

Regional Patterns ◽

Ensemble Prediction System ◽

Mountainous Regions ◽

Runoff Volume ◽

Probabilistic Forecasts ◽

Snow Water

Abstract Good initial states can improve the skill of hydrological ensemble predictions. In mountainous regions such as Switzerland, snow is an important component of the hydrological system. Including estimates of snow cover in hydrological models is of great significance for the prediction of both flood and streamflow drought events. In this study, gridded snow water equivalent (SWE) maps, derived from daily snow depth measurements, are used within the gridded version of the conceptual hydrological model Precipitation Runoff Evapotranspiration Hydrotope (PREVAH) to replace the model SWE at initialization. The ECMWF Ensemble Prediction System (ENS) reforecast is used as meteorological input for 32-day forecasts of streamflow and SWE. Experiments were performed in several parts of the Alpine Rhine and the Thur River. Predictions where modeled SWE estimates were replaced with SWE maps could successfully enhance the predictability of SWE up to a lead time of 25 days, especially at the beginning and the end of the snow season. Additionally, the prediction of the runoff volume was improved, particularly in catchments where the snow accumulation, and thus the runoff volume, had been greatly overestimated. These improvements in predictions have been made without affecting the ability of the forecast system to discriminate between the different runoff volumes observed. A spatial similarity score was first used in the context of SWE forecast verification. This confirmed the findings of the time series analysis and yielded additional insight on regional patterns of extended range SWE predictability.

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