scholarly journals Modeling bulk density and snow water equivalent using daily snow depth observations

2014 ◽  
Vol 8 (2) ◽  
pp. 521-536 ◽  
Author(s):  
J. L. McCreight ◽  
E. E. Small
Keyword(s):  
Time Series ◽  
Bulk Density ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Predictor Variable ◽  
Time Step ◽  
New Model ◽  
Snow Water ◽  
Daily Time Step ◽  
Daily Time

Abstract. Bulk density is a fundamental property of snow relating its depth and mass. Previously, two simple models of bulk density (depending on snow depth, date, and location) have been developed to convert snow depth observations to snow water equivalent (SWE) estimates. However, these models were not intended for application at the daily time step. We develop a new model of bulk density for the daily time step and demonstrate its improved skill over the existing models. Snow depth and density are negatively correlated at short (10 days) timescales while positively correlated at longer (90 days) timescales. We separate these scales of variability by modeling smoothed, daily snow depth (long timescales) and the observed positive and negative anomalies from the smoothed time series (short timescales) as separate terms. A climatology of fit is also included as a predictor variable. Over half a million daily observations of depth and SWE at 345 snowpack telemetry (SNOTEL) sites are used to fit models and evaluate their performance. For each location, we train the three models to the neighboring stations within 70 km, transfer the parameters to the location to be modeled, and evaluate modeled time series against the observations at that site. Our model exhibits improved statistics and qualitatively more-realistic behavior at the daily time step when sufficient local training data are available. We reduce density root mean square error (RMSE) by 9.9 and 4.5% compared to previous models while increasing R2 from 0.46 to 0.52 to 0.56 across models. Focusing on the 21-day window around peak SWE in each water year, our model reduces density RMSE by 24 and 17.4% relative to the previous models, with R2 increasing from 0.55 to 0.58 to 0.71 across models. Removing the challenge of parameter transfer over the full observational record increases R2 scores for both the existing and new models, but the gain is greatest for the new model (R2 = 0.75). Our model shows general improvement over existing models when data are more frequent than once every 5 days and at least 3 stations are available for training.

scholarly journals Modeling bulk density and snow water equivalent using daily snow depth observations.

2013 ◽  
Vol 7 (5) ◽  
pp. 5007-5049 ◽  
Author(s):  
J. L. McCreight ◽  
E. E. Small
Keyword(s):  
Bulk Density ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Predictor Variable ◽  
Fundamental Property ◽  
Time Step ◽  
New Model ◽  
Snow Water ◽  
Daily Time Step ◽  
Daily Time

Abstract. Bulk density is a fundamental property of snow relating its depth and mass. Previously, two simple models of bulk density (depending on snow depth, date, and location) have been developed to convert snow depth observations to snow water equivalent (SWE) estimates. However, these models were not intended for application at the daily time step. We develop a new model of bulk density for the daily timestep and demonstrate its improved skill over the existing models. Snow depth and density are negatively correlated at short (10 days) timescales while positively correlated at longer (90 days) timescales. We separate these scales of variability by modeling smoothed, daily snow depth (long time scales) and the observed positive and negative anomalies from the smoothed timeseries (short timescales) as separate terms. A climatology of fit is also included as a predictor variable. Over a half-million, daily observations of depth and SWE at 345 SNOTEL sites are used to fit models and evaluate their performance. For each location, we train the three models to the neighboring stations within 70 km, transfer the parameters to the location to be modeled, and evaluate modeled timeseries against the observations at that site. Our model exhibits improved statistics and qualitatively more-realistic behavior at the daily time step when sufficient local training data are available. We reduce density RMSE by 9.6% and 4.2% compared to previous models. Similarly, R2 increases from 0.46 to 0.52 to 0.56 across models. Removing the challenge of parameter transfer increases R2 scores for both the existing and new models, but the gain is greatest for the new model (R2 = 0.75). Our model shows general improvement over the existing models when data are more frequent than once every 5 days and at least 3 stations are available for training.

scholarly journals Changes in Snow Storage in the Upper Hron River Basin (Slovakia)

2014 ◽  
Vol 10 (2) ◽  
pp. 145-160
Author(s):  
Katarína Kotríková ◽  
Kamila Hlavčová ◽  
Róbert Fencík
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Satellite Images ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Kendall Test ◽  
Measured Data ◽  
Time Step ◽  
Snow Storage ◽  
Snow Water

Abstract An evaluation of changes in the snow cover in mountainous basins in Slovakia and a validation of MODIS satellite images are provided in this paper. An analysis of the changes in snow cover was given by evaluating changes in the snow depth, the duration of the snow cover, and the simulated snow water equivalent in a daily time step using a conceptual hydrological rainfall-runoff model with lumped parameters. These values were compared with the available measured data at climate stations. The changes in the snow cover and the simulated snow water equivalent were estimated by trend analysis; its significance was tested using the Mann-Kendall test. Also, the satellite images were compared with the available measured data. From the results, it is possible to see a decrease in the snow depth and the snow water equivalent from 1961-2010 in all the months of the winter season, and significant decreasing trends were indicated in the months of December, January and February

scholarly journals Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes

2010 ◽  
Vol 11 (6) ◽  
pp. 1380-1394 ◽  
Author(s):  
Matthew Sturm ◽  
Brian Taras ◽  
Glen E. Liston ◽  
Chris Derksen ◽  
Tobias Jonas ◽  
...  
Keyword(s):  
United States ◽  
Bulk Density ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Estimation Procedure ◽  
The United States ◽  
Practical Applications ◽  
Continental Scale ◽  
Depth Data ◽  
Snow Water

Abstract In many practical applications snow depth is known, but snow water equivalent (SWE) is needed as well. Measuring SWE takes ∼20 times as long as measuring depth, which in part is why depth measurements outnumber SWE measurements worldwide. Here a method of estimating snow bulk density is presented and then used to convert snow depth to SWE. The method is grounded in the fact that depth varies over a range that is many times greater than that of bulk density. Consequently, estimates derived from measured depths and modeled densities generally fall close to measured values of SWE. Knowledge of snow climate classes is used to improve the accuracy of the estimation procedure. A statistical model based on a Bayesian analysis of a set of 25 688 depth–density–SWE data collected in the United States, Canada, and Switzerland takes snow depth, day of the year, and the climate class of snow at a selected location from which it produces a local bulk density estimate. When converted to SWE and tested against two continental-scale datasets, 90% of the computed SWE values fell within ±8 cm of the measured values, with most estimates falling much closer.

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 Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

2017 ◽  
Vol 11 (4) ◽  
pp. 1647-1664 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F. P. Bierkens ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Snow Cover ◽  
Climate Sensitivity ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Model

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

Alternative climate data sources for distributed hydrological modelling on a daily time step

2010 ◽  
Vol 25 (10) ◽  
pp. 1542-1557 ◽  
Author(s):  
Ashraf El-Sadek ◽  
Max Bleiweiss ◽  
Manoj Shukla ◽  
Steve Guldan ◽  
Alexander Fernald
Keyword(s):  
Hydrological Modelling ◽  
Data Sources ◽  
Climate Data ◽  
Time Step ◽  
Daily Time Step ◽  
Distributed Hydrological Modelling ◽  
Daily Time

scholarly journals Snowpack variability and trends at long-term stations in northern Colorado, USA

2015 ◽  
Vol 371 ◽  
pp. 131-136 ◽  
Author(s):  
S. R. Fassnacht ◽  
M. Hultstrand
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Entire Period ◽  
Northern Colorado ◽  
Snow Water ◽  
Highly Correlated ◽  
The Individual

Abstract. The individual measurements from snowcourse stations were digitized for six stations across northern Colorado that had up to 79 years of record (1936 to 2014). These manual measurements are collected at the first of the month from February through May, with additional measurements in January and June. This dataset was used to evaluate the variability in snow depth and snow water equivalent (SWE) across a snowcourse, as well as trends in snowpack patterns across the entire period of record and over two halves of the record (up to 1975 and from 1976). Snowpack variability is correlated to depth and SWE. The snow depth variability is shown to be highly correlated with average April snow depth and day of year. Depth and SWE were found to be significantly decreasing over the entire period of record at two stations, while at another station the significant trends were an increase over the first half of the record and a decrease over the second half. Variability tended to decrease with time, when significant.

scholarly journals Improving the snow physics of WEB-DHM and its point evaluation at two SnowMIP alpine sites

2010 ◽  
Vol 7 (3) ◽  
pp. 3481-3519 ◽  
Author(s):  
M. Shrestha ◽  
L. Wang ◽  
T. Koike ◽  
Y. Xue ◽  
Y. Hirabayashi
Keyword(s):  
Surface Temperature ◽  
Liquid Water ◽  
Snow Depth ◽  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Bias Error ◽  
Snow Albedo ◽  
Basin Scale ◽  
Snow Water ◽  
Snow Physics

Abstract. The snow physics of a distributed biosphere hydrological model, referred to as the Water and Energy Budget based Distributed Hydrological Model (WEB-DHM) is improved by incorporating the three-layer physically based energy balance snowmelt model of Simplified Simple Biosphere 3 (SSiB3) and the Biosphere-Atmosphere Transfer Scheme (BATS) albedo scheme. WEB-DHM with improved snow physics (WEB-DHM-S) can simulate the variability of snow density, snow depth and snow water equivalent, liquid water and ice content in each layer, prognostic snow albedo, diurnal variation in snow surface temperature, thermal heat due to conduction and liquid water retention. The performance of WEB-DHM-S is evaluated at two alpine sites of the Snow Model Intercomparison Project with different climate characteristics: Col de Porte in France and Weissfluhjoch in Switzerland. The simulation results of the snow depth, snow water equivalent, surface temperature, snow albedo and snowmelt runoff reveal that WEB-DHM-S is capable of simulating the internal snow process better than the original WEB-DHM, with the root mean square error and bias error being remarkably reduced. Although WEB-DHM-S is only evaluated at a point scale for the simulation of snow processes, this study provides a benchmark for the application of WEB-DHM-S in cold regions in the assessment of the basin-scale snow water equivalent and seasonal discharge simulation for water resources management.

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