scholarly journals Snow Water Equivalents exclusively from Snow Heights and their temporal Changes: The Δ<sub>SNOW.MODEL</sub>

2020 ◽  
Author(s):  
Michael Winkler ◽  
Harald Schellander ◽  
Stefanie Gruber
Keyword(s):  
Measurement Errors ◽  
Snow Water Equivalent ◽  
Mean Squared Error ◽  
Local Scale ◽  
Data Series ◽  
Model Parameters ◽  
Major Advance ◽  
Newtonian Viscosity ◽  
Validation Data ◽  
Snow Water

Abstract. Snow heights have been manually observed for many years, sometimes decades, at various places. These records are often of good quality. In addition, more and more data from automatic stations and remote sensing are available. On the other hand, records of snow water equivalent SWE – synonymous for snow load or mass – are sparse, although it might be the most important snowpack feature in fields like hydrology, climatology, agriculture, natural hazards research, etc. SWE very often has to be modeled, and those models either depend on meteorological forcing or are not intended to simulate individual SWE values, like the substantial seasonal peak SWE. The ΔSNOW.MODEL is presented as a new method to simulate local-scale SWE. It solely needs snow heights as input, though a gapless record thereof. Temporal resolution of the data series is no restriction per se. The ΔSNOW.MODEL is a semi-empirical multi-layer model and freely available as R-package. Snow compaction is modeled following the rules of Newtonian viscosity. The model considers measurement errors, treats overburden loads due to fresh snow as additional unsteady compaction, and melted mass is stepwise distributed top-down in the snowpack. Seven model parameters are subject to calibration, which was performed using 71 winters from 14 stations, well-distributed over different altitudes and climatic regions of the Alps. Another 73 rather independent winters act as validation data. Results are very promising: Median bias and root mean squared error for SWE are only −4.0 kg m−2 and 23.9 kg m−2, and +2.3 kg m−2 and 23.1 kg m−2 for peak SWE, respectively. This is a major advance compared to snow models relying on empirical regressions, but also much more sophisticated thermodynamic snow models not necessarily perform better. Not least, this study outlines the need for comprehensive comparison studies on SWE measurement and modeling at the point and local scale.

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

2021 ◽  
Vol 25 (3) ◽  
pp. 1165-1187
Author(s):  
Michael Winkler ◽  
Harald Schellander ◽  
Stefanie Gruber
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Optimal Parameter ◽  
R Package ◽  
Model Parameters ◽  
Major Advance ◽  
Newtonian Viscosity ◽  
Climatic Regions ◽  
Depth Data ◽  
Snow Water

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.

scholarly journals Converting snow depth to snow water equivalent using climatological variables

2019 ◽  
Vol 13 (7) ◽  
pp. 1767-1784 ◽  
Author(s):  
David F. Hill ◽  
Elizabeth A. Burakowski ◽  
Ryan L. Crumley ◽  
Julia Keon ◽  
J. Michelle Hu ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Mean Squared Error ◽  
Weather Data ◽  
Validation Data ◽  
Simple Method ◽  
Avalanche Forecasting ◽  
Mean Temperature ◽  
Snow Water ◽  
The Difference

Abstract. We present a simple method that allows snow depth measurements to be converted to snow water equivalent (SWE) estimates. These estimates are useful to individuals interested in water resources, ecological function, and avalanche forecasting. They can also be assimilated into models to help improve predictions of total water volumes over large regions. The conversion of depth to SWE is particularly valuable since snow depth measurements are far more numerous than costlier and more complex SWE measurements. Our model regresses SWE against snow depth (h), day of water year (DOY) and climatological (30-year normal) values for winter (December, January, February) precipitation (PPTWT), and the difference (TD) between mean temperature of the warmest month and mean temperature of the coldest month, producing a power-law relationship. Relying on climatological normals rather than weather data for a given year allows our model to be applied at measurement sites lacking a weather station. Separate equations are obtained for the accumulation and the ablation phases of the snowpack. The model is validated against a large database of snow pillow measurements and yields a bias in SWE of less than 2 mm and a root-mean-squared error (RMSE) in SWE of less than 60 mm. The model is additionally validated against two completely independent sets of data: one from western North America and one from the northeastern United States. Finally, the results are compared with three other models for bulk density that have varying degrees of complexity and that were built in multiple geographic regions. The results show that the model described in this paper has the best performance for the validation data sets.

scholarly journals Converting Snow Depth to Snow Water Equivalent Using Climatological Variables

2019 ◽  
Author(s):  
David F. Hill ◽  
Elizabeth A. Burakowski ◽  
Ryan L. Crumley ◽  
Julia Keon ◽  
J. Michelle Hu ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Mean Squared Error ◽  
Independent Set ◽  
Weather Data ◽  
Mean Annual Precipitation ◽  
Validation Data ◽  
Data Set ◽  
Simple Method ◽  
Snow Water

Abstract. We present a simple method that allows snow depth measurements to be converted to snow water equivalent (SWE) estimates. These estimates are useful to individuals interested in water resources, ecological function, and avalanche forecasting. They can also be assimilated into models to help improve predictions of total water volumes over large regions. The conversion of depth to SWE is particularly valuable since snow depth measurements are far more numerous than costlier and more complex SWE measurements. Our model regresses SWE against snow depth and climatological (30-year normal) values for mean annual precipitation (MAP) and mean February temperature, producing a power-law relationship. Relying on climatological normals rather than weather data for a given year allows our model to be applied at measurement sites lacking a weather station. Separate equations are obtained for the accumulation and the ablation phases of the snowpack, which introduces day of water year (DOY) as an additional variable. The model is validated against a large database of snow pillow measurements and yields a bias in SWE of less than 0.5 mm and a root-mean-squared-error (RMSE) in SWE of approximately 65 mm. When the errors are investigated on a station-by-station basis, the average RMSE is about 5 % of the MAP at each station. The model is additionally validated against a completely independent set of data from the northeast United States. Finally, the results are compared with other models for bulk density that have varying degrees of complexity and that were built in multiple geographic regions. The results show that the model described in this paper has the best performance for the validation data set.

scholarly journals An integrated uncertainty and ensemble-based data assimilation approach for improved operational streamflow predictions

2012 ◽  
Vol 16 (3) ◽  
pp. 815-831 ◽  
Author(s):  
M. He ◽  
T. S. Hogue ◽  
S. A. Margulis ◽  
K. J. Franz
Keyword(s):  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Model Parameters ◽  
Streamflow Prediction ◽  
Forecasting Method ◽  
Uncertainty Structure ◽  
The Us ◽  
Ensemble Mean ◽  
Snow Water ◽  
Parameter Uncertainty Analysis

Abstract. The current study proposes an integrated uncertainty and ensemble-based data assimilation framework (ICEA) and evaluates its viability in providing operational streamflow predictions via assimilating snow water equivalent (SWE) data. This step-wise framework applies a parameter uncertainty analysis algorithm (ISURF) to identify the uncertainty structure of sensitive model parameters, which is subsequently formulated into an Ensemble Kalman Filter (EnKF) to generate updated snow states for streamflow prediction. The framework is coupled to the US National Weather Service (NWS) snow and rainfall-runoff models. Its applicability is demonstrated for an operational basin of a western River Forecast Center (RFC) of the NWS. Performance of the framework is evaluated against existing operational baseline (RFC predictions), the stand-alone ISURF and the stand-alone EnKF. Results indicate that the ensemble-mean prediction of ICEA considerably outperforms predictions from the other three scenarios investigated, particularly in the context of predicting high flows (top 5th percentile). The ICEA streamflow ensemble predictions capture the variability of the observed streamflow well, however the ensemble is not wide enough to consistently contain the range of streamflow observations in the study basin. Our findings indicate that the ICEA has the potential to supplement the current operational (deterministic) forecasting method in terms of providing improved single-valued (e.g., ensemble mean) streamflow predictions as well as meaningful ensemble predictions.

scholarly journals The Cache la Poudre river basin snow water equivalent modeling with NewAge-JGrass

2013 ◽  
Vol 6 (3) ◽  
pp. 4447-4474 ◽  
Author(s):  
G. Formetta ◽  
S. K. Kampf ◽  
O. David ◽  
R. Rigon
Keyword(s):  
Time Series ◽  
River Basin ◽  
Snow Water Equivalent ◽  
Model Parameters ◽  
Automatic Calibration ◽  
Snow Melting ◽  
Time Step ◽  
Equivalent Time ◽  
Cache La Poudre River ◽  
Snow Water

Abstract. The paper presents a snow water equivalent model as part of the hydrological modeling system NewAge-JGrass. The model take in account of the main physical processes influencing the snow melting (precipitation form separation, melting and freezing modeling) coupled with the snowpack mass conservation equation. The snow melting depends not only on the air temperature but also on the radiation received by the pixel. The model is perfectly integrated in the NewAge-JGrass modeling system and uses many of its components such as shortwave radiation balance, krigings and automatic calibration algorithms. As all the NewAge-JGrass components, the presented model can be executed both in raster and in vector mode and the simulation time step can be daily, hourly or sub-hourly as the user needs. The model is applied on the Cache la Poudre river basin (CO, USA). Three are the applications presented in the paper. Firstly, the simulation of snow water equivalent in three different measurement stations is performed. Model parameters are calibrated and model performances are quantitatively computed by comparing simulated and measured snow water equivalent time series. Indices of goodness of fit such as Kling–Gupta Efficiency, Index of Agreement and Percentage Bias are computed. Secondly, the representativeness of the model parameters in different locations is discussed. Finally a raster mode application is performed: snow water equilvalent maps on the whole Cache la Poudre river are computed. In all the applications the model performance are satisfactory in term of goodness of fitting measured snow water equivalent time series. The integration of the model in the NewAge-JGrass system allows the used to o enjoy all the component of the system: input data computation, output maps visualizetion in the GIS JGrass, model parameters automatic calibration.

scholarly journals Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

2014 ◽  
Vol 11 (12) ◽  
pp. 13745-13795 ◽  
Author(s):  
M. S. Raleigh ◽  
J. D. Lundquist ◽  
M. P. Clark
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Snow Water Equivalent ◽  
Model Parameters ◽  
Random Errors ◽  
Global Sensitivity ◽  
Error Characteristics ◽  
Error Distributions ◽  
Physically Based ◽  
Snow Water

Abstract. Physically based models provide insights into key hydrologic processes, but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snow-affected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology. Here we employ global sensitivity analysis to explore how different error types (i.e., bias, random errors), different error distributions, and different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use Sobol' global sensitivity analysis, which is typically used for model parameters, but adapted here for testing model sensitivity to co-existing errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 520 000 Monte Carlo simulations across four sites and four different scenarios. Model outputs were generally (1) more sensitive to forcing biases than random errors, (2) less sensitive to forcing error distributions, and (3) sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a significant impact depending on forcing error magnitudes. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.

scholarly journals Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS

2021 ◽  
Author(s):  
Dhiraj Raj Gyawali ◽  
András Bárdossy
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Mean Squared Error ◽  
Snow Melt ◽  
Data Intensive ◽  
Degree Day ◽  
Snow Water ◽  
Snow Distribution ◽  
Wide Scale ◽  
The Time Domain

Abstract. Given the importance of snow on different land and atmospheric processes, accurate representation of seasonal snow evolution including distribution and melt volume, is highly imperative to any water resources development trajectories. The limitation of reliable snow-melt estimation in these regions is however, further exacerbated with data scarcity. This study attempts to develop relatively simpler extended degree-day snow-models driven by freely available snow cover images in snow-dominated regions. This approach offers relative simplicity and plausible alternative to data intensive models as well as in-situ measurements and have a wide scale applicability, allowing immediate verification with point measurements. The methodology employs readily available MODIS composite images to calibrate the snow-melt models on snow-distribution in contrast to the traditional snow-water equivalent based calibration. The spatial distribution of snow cover is simulated using different extended degree-day models calibrated against MODIS snow-cover images for cloud-free days or a set of images representing a period within the snow season. The study was carried out in Baden-Württemberg in Germany, and in Switzerland. The simulated snow cover show very good agreement with MODIS snow cover distribution and the calibrated parameters exhibit relative stability across the time domain. The snow-melt from these calibrated models were further used as standalone inputs to a “truncated” HBV without the snow component in Reuss (Switzerland), and Horb and Neckar (Baden-Wuerttemberg) catchments, to assess the performance of the melt outputs in comparison to a calibrated standard HBV model. The results show slight increase in overall NSE performance and a better NSE performance during the winter. Furthermore, 3–15 % decrease in mean squared error was observed for the catchments in comparison to the results from standard HBV. The increased NSE performance, albeit less, can be attributed to the added reliability of snow-distribution coming from the MODIS calibrated outputs. This paper highlights that the calibration using readily available images used in this method allows a flexible regional calibration of snow cover distribution in mountainous areas across a wide geographical extent with reasonably accurate precipitation and temperature data. Likewise, the study concludes that simpler specific alterations to processes contributing to snow-melt can contribute to identifying the snow-distribution and to some extent the flows in snow-dominated regimes.

scholarly journals GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient

2021 ◽  
Author(s):  
Achille Capelli ◽  
Franziska Koch ◽  
Patrick Henkel ◽  
Markus Lamm ◽  
Florian Appel ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Low Cost ◽  
Elevation Gradient ◽  
Liquid Water Content ◽  
Climatic Conditions ◽  
Swiss Alps ◽  
Snow Density ◽  
Validation Data ◽  
Snow Water ◽  
Snow Conditions

Abstract. Snow water equivalent (SWE) can be measured using low-cost Global Navigation Satellite System (GNSS) sensors with one antenna placed below the snowpack and another one serving as a reference above the snow. The underlying GNSS signal-based algorithm for SWE determination for dry- and wet-snow conditions processes the carrier phases and signal strengths and derives additionally liquid water content (LWC) and snow depth (HS). So far, the algorithm was tested intensively for high-alpine conditions with distinct seasonal accumulation and ablation phases. In general, snow occurrence, snow amount, snow density and LWC can vary considerably with climatic conditions and elevation. Regarding alpine regions, lower elevations mean generally earlier and faster melting, more rain-on-snow events and shallower snowpack. Therefore, we assessed the applicability of the GNSS-based SWE measurement at four stations along a steep elevation gradient (820, 1185, 1510 and 2540 m a.s.l.) in the eastern Swiss Alps during two winter seasons (2018–2020). Reference data of SWE, LWC and HS were collected manually and with additional automated sensors at all locations. The GNSS-derived SWE estimates agreed very well with manual reference measurements along the elevation gradient and the accuracy (RMSE = 34 mm, RMSRE = 11 %) was similar under wet- and dry-snow conditions, although significant differences in snow density and meteorological conditions existed between the locations. The GNSS-derived SWE was more accurate than measured with other automated SWE sensors. However, with the current version of the GNSS algorithm, the determination of daily changes of SWE was found to be less suitable compared to manual measurements or pluviometer recordings and needs further refinement. The values of the GNSS-derived LWC were robust and within the precision of the manual and radar measurements. The additionally derived HS correlated well with the validation data. We conclude that SWE can reliably be determined using low-cost GNSS-sensors under a broad range of climatic conditions and LWC and HS are valuable add-ons.

scholarly journals In situ snow water equivalent observations in the US Arctic

2012 ◽  
Vol 44 (1) ◽  
pp. 21-34 ◽  
Author(s):  
Svetlana Stuefer ◽  
Douglas L. Kane ◽  
Glen E. Liston
Keyword(s):  
Snow Water Equivalent ◽  
Winter Precipitation ◽  
Local Scale ◽  
Arctic Alaska ◽  
The Us ◽  
Snow Water ◽  
In Situ Observations ◽  
Best Fit ◽  
Collection Methods

This paper summarizes 12 years of snow water equivalent (SWE) observations collected in the data-sparse region of Arctic Alaska, United States. The in situ observations are distributed across a 200 × 300 km domain that includes the Kuparuk River watershed from the Brooks Range to the Beaufort Sea coast. Data collection methods and analyses were classified to distinguish between snow observation sites representing regional- and local-scale variability. Average SWE for the entire domain ranges from 92 mm in 2008 to 148 mm in 2011. Regional end-of-winter SWE indicates that both the extreme high SWE in 2009 and 2011 and the extreme low SWE in 2008 occurred during recent and alternating years, suggesting the limitations of 12 years of data for detecting SWE trends. By assimilating the observational datasets into SnowModel, hourly 100 m gridded SWE distributions for the central Alaska Arctic were created to provide a best-fit to the observations where and when they occur. The model simulations highlight how observed SWE data can be used as a surrogate for the more problematic winter precipitation measurements. The resulting SWE distributions are readily available to support ecological and hydrologic studies in this region.

Sign in / Sign up

Export Citation Format

Share Document