Evaluation of SNODAS Snow Water Equivalent in Western Canada and Assimilation Into a Cold Region Hydrological Model

2019 ◽  
Vol 55 (12) ◽  
pp. 11166-11187 ◽  
Author(s):  
Zhibang Lv ◽  
John W. Pomeroy ◽  
Xing Fang
Keyword(s):  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Western Canada ◽  
Cold Region ◽  
Snow Water

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.

Stubble management effects on canola performance across different climatic regions of western Canada

2015 ◽  
Vol 95 (1) ◽  
pp. 149-159 ◽  
Author(s):  
Michael J. Cardillo ◽  
Paul Bullock ◽  
Rob Gulden ◽  
Aaron Glenn ◽  
Herb Cutforth
Keyword(s):  
Snow Water Equivalent ◽  
Growing Season ◽  
Moisture Stress ◽  
Climatic Conditions ◽  
Western Canada ◽  
Spring Precipitation ◽  
Canadian Prairies ◽  
Climatic Regions ◽  
Snow Water ◽  
Management Effects

Cardillo, M. J., Bullock, P., Gulden, R., Glenn, A. and Cutforth, H. 2015. Stubble management effects on canola performance across different climatic regions of western Canada. Can. J. Plant Sci. 95: 149–159. Previous research in the most arid region of the Canadian prairies has shown that wheat stubble cut tall the previous year can improve performance of the following canola crop. This study aimed to determine if tall stubble could benefit canola across the climatic conditions typically experienced in western Canada. Tall stubble impacts on canola were monitored over 11 site-years located throughout the prairies. At each site, tall stubble (50 cm) was compared with short stubble (20 cm). At some sites the stubble lodged allowing an unintended comparison between stubble that remained intact and stubble that was flattened. The comparison of snow water equivalent showed tall stubble caught more snow than short stubble but the benefit of additional spring soil moisture was masked by heavy spring precipitation in both 2011 and 2012. Canola biomass and yield were significantly lower in damaged versus intact stubble, either short or tall. In both years, wet spring conditions were followed by hotter and drier weather in the mid to late growing season. Soil under the damaged stubble (short or tall) likely warmed and dried more slowly in the spring, limiting early-season growth, biomass and yield. At sites where both tall and short stubble remained intact, there was a significant yield advantage with tall stubble. The intact tall stubble may have slowed evaporation and soil drying compared with intact short stubble, which reduced moisture stress later in the growing season, imparting a yield advantage.

regionalization of the potential to increase rainfall-runoff model performance by multi-objective calibration using modis data over Austria

2020 ◽  
Author(s):  
Martin Kubáň ◽  
Patrik Sleziak ◽  
Adam Brziak ◽  
Kamila Hlavčová ◽  
Ján Szolgay
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Model Performance ◽  
State Variables ◽  
Rainfall Runoff ◽  
Multi Objective ◽  
Snow Water ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>A multi-objective calibration of the parameters of conceptual hydrologic models has the potential to improve the consistency of the simulated model states, their representativeness with respect to catchment states and thereby to reduce the uncertainty in the estimation of hydrological model outputs. Observed in-situ or remotely sensed state variables, such as the snow cover distribution, snow depth, snow water equivalent and soil moisture were often considered as additional information in such calibration strategies and subsequently utilized in data assimilation for operational streamflow forecasting. The objective of this paper is to assess the effects of the inclusion of MODIS products characterizing soil moisture and the snow water equivalent in a multi-objective calibration strategy of an HBV type conceptual hydrological model under the highly variable physiographic conditions over the whole territory of Austria.</p><p>The methodology was tested using the Technical University of Vienna semi-distributed rainfall-runoff model (the TUW model), which was calibrated and validated in 213 Austrian catchments. For calibration we use measured data from the period 2005 to 2014. Subsequently, we simulated discharges, soil moisture and snow water equivalents based on parameters from the multi-objective calibration and compared these with the respective MODIS values. In general, the multi-objective calibration improved model performance when compared to results of model parametrisation calibrated only on discharge time series. Sensitivity analyses indicate that the magnitude of the model efficiency is regionally sensitive to the choice of the additional calibration variables. In the analysis of the results we indicate ranges how and where the runoff, soil moisture and snow water equivalent simulation efficiencies were sensitive to different setups of the multi-objective calibration strategy over the whole territory of Austria. It was attempted to regionalize the potential to increase of the overall model performance and the improvement in the consistency of the simulation of the two-state variables. Such regionalization may serve model users in the selection which remotely sensed variable or their combination is to be preferred in local modelling studies.</p>

scholarly journals Modelling the spatial distribution of snow water equivalent at the catchment scale taking into account changes in snow covered area

2011 ◽  
Vol 8 (6) ◽  
pp. 11485-11518 ◽  
Author(s):  
T. Skaugen ◽  
F. Randen
Keyword(s):  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Spatial Variance ◽  
Spatial Moments ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water ◽  
Snow Distribution ◽  
Hbv Model ◽  
Melting Season

Abstract. A successful modelling of the snow reservoir is necessary for water resources assessments and the mitigation of spring flood hazards. A good estimate of the spatial probability density function (PDF) of snow water equivalent (SWE) is important for obtaining estimates of the snow reservoir, but also for modelling the changes in snow covered area (SCA), which is crucial for the runoff dynamics in spring. In a previous paper the PDF of SWE was modelled as a sum of temporally correlated gamma distributed variables. This methodology was constrained to estimate the PDF of SWE for snow covered areas only. In order to model the PDF of SWE for a catchment, we need to take into account the change in snow coverage and provide the spatial moments of SWE for both snow covered areas and for the catchment as a whole. The spatial PDF of accumulated SWE is, also in this study, modelled as a sum of correlated gamma distributed variables. After accumulation and melting events the changes in the spatial moments are weighted by changes in SCA. The spatial variance of accumulated SWE is, after both accumulation- and melting events, evaluated by use of the covariance matrix. For accumulation events there are only positive elements in the covariance matrix, whereas for melting events, there are both positive and negative elements. The negative elements dictate that the correlation between melt and SWE is negative. The negative contributions become dominant only after some time into the melting season so at the onset of the melting season, the spatial variance thus continues to increase, for later to decrease. This behaviour is consistent with observations and called the "hysteretic" effect by some authors. The parameters for the snow distribution model can be estimated from observed historical precipitation data which reduces by one the number of parameters to be calibrated in a hydrological model. Results from the model are in good agreement with observed spatial moments of SWE and SCA and found to provide better estimates of the spatial variability than the current model for snow distribution used in the HBV model, the hydrological model used for flood forecasting in Norway. When implemented in the HBV model, simulations show that the precision in predicting runoff is maintained although there is one parameter less to calibrate.

scholarly journals Comparison of manual snow water equivalent measurements: questioning the reference for the true SWE value

2021 ◽  
Author(s):  
Maxime Beaudoin-Galaise ◽  
Sylvain Jutras
Keyword(s):  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Large Diameter ◽  
Mean Bias Error ◽  
Bias Error ◽  
Apparent Lack ◽  
Lack Of Information ◽  
Different Types ◽  
Snow Water ◽  
Lower Uncertainty

Abstract. Manual measurement of snow water equivalent (SWE) is still important today for several applications such as hydrological model validation. This measurement can be performed with different types of snow tube sampler or by a snow pit. Although these methods have been performed for several decades, there is an apparent lack of information required to have a consensus regarding the best reference for “true” SWE. We define and estimate the uncertainty and measurement error of different methods of snow pits and snow samplers. Analysis was based upon measurements taken over five consecutive winters (2016–2020) from the same flat and open area. This study compares two snow pit methods and three snow samplers. In addition to including the Standard Federal sampler (SFS), this study documents the first use of two new large diameter samplers, the Hydro-Québec sampler (HQS) and Université Laval sampler (ULS). Large diameter samplers had lowest uncertainty (2.6 to 4.0 %). Snow pit methods had higher uncertainty due to instruments (7.1 to 11.4 %), close to that of the SFS (mean = 10.4 %). Given its larger collected snow volume for estimating SWE and its lower uncertainty, we posit that ULS represents the most appropriate method of reference for “true” SWE. By considering ULS as the reference in calculating mean bias error (MBE), different snow pit methods overestimated SWE by 16.6 to 26.2 %, which was much higher than SFS (8.4 %). This study suggests that large diameter samplers are the best method for estimating “true” SWE.

scholarly journals Discrimination of a wet snow cover using passive microwave satellite data

1993 ◽  
Vol 17 ◽  
pp. 307-311 ◽  
Author(s):  
A.E. Walker ◽  
B.E. Goodison
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Western Canada ◽  
Polarization Data ◽  
Wet Snow ◽  
Snow Water ◽  
Remote Sensing Methods

Snow-cover monitoring using passive microwave remote sensing methods has been shown to be seriously limited under melt conditions when the snowpack becomes wet. A wet snow indicator has been developed using DMSP SSM/I 37 GHz dual-polarization data for the open prairie region of western Canada. The indicator is used to identify areas of wet snow and discriminate them from areas of snow-free land. Validation and testing efforts have illustrated that the addition of the indicator to the current SSM/I snow water equivalent algorithm provides a more accurate representation of spatial snow coverage throughout the winter season for the open prairie region. The improved spatial and temporal information resulting from the use of the indicator enhances both climatological and hydrological analyses of snow-cover conditions using passive microwave data. Although the wet snow indicator has only been validated for the open prairie region of western Canada, it may also be applicable to other regions of similar terrain and vegetative characteristics. However, in areas of dense vegetation, such as the boreal forest, the performance of the indicator is poor due to the generally low 37 GHz polarization differences of the vegetation cover.

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