Interactive comment on „A model for the spatial distribution of snow water equivalent parameterised from the spatial variability of precipitation“ by Thomas Skaugen and Ingunn H. Weltzien

Abstract. Snow is an important and complicated element in hydrological modelling. The traditional catchment hydrological model with its many free calibration parameters, also in snow sub-models, is not a well-suited tool for predicting conditions for which it has not been calibrated. Such conditions include prediction in ungauged basins and assessing hydrological effects of climate change. In this study, a new model for the spatial distribution of snow water equivalent (SWE), parameterized solely from observed spatial variability of precipitation (SD_G), is compared with the current snow distribution model used in the operational flood forecasting models in Norway. The latter model (SD_LN) has a fixed, calibrated coefficient of variation, which parameterizes a log-normal model for snow distribution. The two models are implemented in the already parameter parsimonious rainfall runoff model Distance Distribution Dynamics (DDD) and their capability for predicting runoff, SWE and snow covered area (SCA) are tested and compared for 71 Norwegian catchments. Results show that SD_G better simulates SCA when compared with MODIS satellite derived snow cover. In addition, SWE is simulated more realistically in that seasonal snow is melted out and the building up of "snow towers" and giving spurious positive trends in SWE, typical for SD_LN, is prevented. The precision of runoff simulations using SD_G is slightly inferior, with a reduction in Nash-Sutcliffe and Kling Gupta Criterion of 0.01, but it is shown that high precision in runoff prediction using SD_LN is accompanied with erroneous simulations of SWE.

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Modelling the spatial variability of snow water equivalent at the catchment scale

Hydrology and Earth System Sciences ◽

10.5194/hess-11-1543-2007 ◽

2007 ◽

Vol 11 (5) ◽

pp. 1543-1550 ◽

Cited By ~ 32

Author(s):

T. Skaugen

Keyword(s):

Spatial Distribution ◽

Spatial Variability ◽

Gamma Distribution ◽

Snow Water Equivalent ◽

Temporal Correlation ◽

Catchment Scale ◽

Snow Season ◽

Dynamical Parameters ◽

Snow Water ◽

Alpine Catchments

Abstract. The spatial distribution of snow water equivalent (SWE) is modelled as a two parameter gamma distribution. The parameters of the distribution are dynamical in that they are functions of the number of accumulation and melting events and the temporal correlation of accumulation and melting events. The estimated spatial variability is compared to snow course observations from the alpine catchments Norefjell and Aursunden in Southern Norway. A fixed snow course at Norefjell was measured 26 times during the snow season and showed that the spatial coefficient of variation change during the snow season with a decreasing trend from the start of the accumulation period and a sharp increase in the melting period. The gamma distribution with dynamical parameters reproduced the observed spatial statistical features of SWE well both at Norefjell and Aursunden. Also the shape of simulated spatial distribution of SWE agreed well with the observed at Norefjell. The temporal correlation tends to be positive for both accumulation and melting events. However, at the start of melting, a better fit between modelled and observed spatial standard deviation of SWE is obtained by using negative correlation between SWE and melt.

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The spatial variability of snow water equivalent

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-4-1465-2007 ◽

2007 ◽

Vol 4 (3) ◽

pp. 1465-1489 ◽

Cited By ~ 3

Author(s):

T. Skaugen

Keyword(s):

Spatial Distribution ◽

Spatial Variability ◽

Gamma Distribution ◽

Snow Water Equivalent ◽

Temporal Correlation ◽

Snow Season ◽

Dynamical Parameters ◽

Snow Water ◽

Alpine Catchments ◽

Southern Norway

Abstract. The spatial distribution of snow water equivalent (SWE) is modelled as a two parameter gamma distribution. The parameters of the distribution are dynamical in that they are functions of the number of accumulation and ablation events and the temporal correlation of accumulation and ablation events. The estimated spatial variability is compared to snow course observations from the alpine catchments Norefjell and Aursunden in Southern Norway. A fixed snow course at Norefjell was measured 26 times during the snow season, which showed that the spatial coefficient of variation change during the snow season with a decreasing trend from the start of the accumulation period and a sharp increase in the ablation period. The gamma distribution with dynamical parameters reproduced the observed spatial statistical features of SWE well both at Norefjell and Aursunden. Also the shape of simulated spatial distribution of SWE agreed well with the observed at Norefjell. The temporal correlation tends to be positive for both accumulation and ablation events. However, at the start of ablation, a better fit between modelled and observed spatial standard deviation of SWE is obtained by using negative correlation between SWE and melt.

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Critical Analysis of the Snow Survey Network According to the Spatial Variability of Snow Water Equivalent (SWE) on Eastern Mainland Canada

Hydrology ◽

10.3390/hydrology6020055 ◽

2019 ◽

Vol 6 (2) ◽

pp. 55

Author(s):

Sena ◽

Chokmani ◽

Gloaguen ◽

Bernier

Keyword(s):

Spatial Distribution ◽

Spatial Variability ◽

Critical Analysis ◽

Snow Water Equivalent ◽

Multiple Scales ◽

Regional Scale ◽

Spatial Structures ◽

Eastern Canada ◽

Snow Water ◽

Index Curve

In Eastern Canada, the snow survey network is highly optimized at the operational scale. However, it is commonly accepted that the network is limited when it comes to studying the spatial variability of the snow water equivalent (SWE), which forms different spatial structures that are active at multiple scales—from local to regional. The main objective of this study was to conduct a critical analysis of the existing snow survey network, based on the spatial variability of the existing SWE structures. To do so, we must (1) assess the snow survey network’s capacity to model spatial variability structures of SWE, and (2) study the spatial distribution based on the spatial variability structures of SWE. Initially, the snow survey network’s capacity to model the spatial variability structures of the SWE was evaluated by a variogram analysis. Second, the spatial distribution of the snow survey network’s data was analyzed through the Lorenz index curve and by measuring the spatial distribution using the Gini index. The results showed that, at a regional scale, the snow survey stations were evenly distributed within the spatial structures. However, at the local scale, the snow survey network was inadequate to model the spatial variability of SWE due to the reduced and uneven number of snow survey stations.

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A model for the spatial distribution of snow water equivalent parameterized from the spatial variability of precipitation

The Cryosphere ◽

10.5194/tc-10-1947-2016 ◽

2016 ◽

Vol 10 (5) ◽

pp. 1947-1963 ◽

Cited By ~ 13

Author(s):

Thomas Skaugen ◽

Ingunn H. Weltzien

Keyword(s):

Spatial Distribution ◽

Spatial Variability ◽

Snow Water Equivalent ◽

Distribution Model ◽

Efficiency Criterion ◽

Covered Area ◽

Snow Water ◽

Snow Distribution ◽

Rainfall Runoff Model ◽

Log Normal

Abstract. Snow is an important and complicated element in hydrological modelling. The traditional catchment hydrological model with its many free calibration parameters, also in snow sub-models, is not a well-suited tool for predicting conditions for which it has not been calibrated. Such conditions include prediction in ungauged basins and assessing hydrological effects of climate change. In this study, a new model for the spatial distribution of snow water equivalent (SWE), parameterized solely from observed spatial variability of precipitation, is compared with the current snow distribution model used in the operational flood forecasting models in Norway. The former model uses a dynamic gamma distribution and is called Snow Distribution_Gamma, (SD_G), whereas the latter model has a fixed, calibrated coefficient of variation, which parameterizes a log-normal model for snow distribution and is called Snow Distribution_Log-Normal (SD_LN). The two models are implemented in the parameter parsimonious rainfall–runoff model Distance Distribution Dynamics (DDD), and their capability for predicting runoff, SWE and snow-covered area (SCA) is tested and compared for 71 Norwegian catchments. The calibration period is 1985–2000 and validation period is 2000–2014. Results show that SD_G better simulates SCA when compared with MODIS satellite-derived snow cover. In addition, SWE is simulated more realistically in that seasonal snow is melted out and the building up of "snow towers" and giving spurious positive trends in SWE, typical for SD_LN, is prevented. The precision of runoff simulations using SD_G is slightly inferior, with a reduction in Nash–Sutcliffe and Kling–Gupta efficiency criterion of 0.01, but it is shown that the high precision in runoff prediction using SD_LN is accompanied with erroneous simulations of SWE.

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Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

The Cryosphere ◽

10.5194/tc-13-3045-2019 ◽

2019 ◽

Vol 13 (11) ◽

pp. 3045-3059 ◽

Cited By ~ 3

Author(s):

Nick Rutter ◽

Melody J. Sandells ◽

Chris Derksen ◽

Joshua King ◽

Peter Toose ◽

...

Keyword(s):

Spatial Variability ◽

Layer Thickness ◽

Snow Water Equivalent ◽

Estimation Accuracy ◽

Snow Layer ◽

Data Set ◽

Snow Water ◽

Retrieval Errors ◽

Snow Microstructure ◽

The Impact

Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.

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Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions

10.5194/egusphere-egu21-14972 ◽

2021 ◽

Author(s):

Ilaria Clemenzi ◽

David Gustafsson ◽

Jie Zhang ◽

Björn Norell ◽

Wolf Marchand ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Distribution ◽

Data Assimilation ◽

Snow Water Equivalent ◽

Distribution Model ◽

Accurate Estimation ◽

Snowmelt Runoff ◽

Snow Water ◽

Snow Distribution ◽

The Impact

<p>Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake &#214;veruman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km<sup>2</sup> grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.</p>

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Spatial variability of snow water equivalent in a mountainous area of the Japanese Central Alps

Journal of Geophysical Research Atmospheres ◽

10.1029/2006jf000711 ◽

2008 ◽

Vol 113 (F3) ◽

Cited By ~ 2

Author(s):

Motoki Tanaka ◽

Keisuke Suzuki

Keyword(s):

Spatial Variability ◽

Snow Water Equivalent ◽

Mountainous Area ◽

Central Alps ◽

Snow Water

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The effect of spatial variability on the sensitivity of passive microwave measurements to snow water equivalent

Remote Sensing of Environment ◽

10.1016/j.rse.2013.05.002 ◽

2013 ◽

Vol 136 ◽

pp. 163-179 ◽

Cited By ~ 37

Author(s):

Benjamin J. Vander Jagt ◽

Michael T. Durand ◽

Steven A. Margulis ◽

Edward J. Kim ◽

Noah P. Molotch

Keyword(s):

Spatial Variability ◽

Snow Water Equivalent ◽

Passive Microwave ◽

Microwave Measurements ◽

Snow Water

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Spatial Variability in Seasonal Snowpack Trends across the Rio Grande Headwaters (1984–2017)

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0077.1 ◽

2020 ◽

Vol 21 (11) ◽

pp. 2713-2733 ◽

Cited By ~ 1

Author(s):

Graham A. Sexstone ◽

Colin A. Penn ◽

Glen E. Liston ◽

Kelly E. Gleason ◽

C. David Moeser ◽

...

Keyword(s):

Spatial Variability ◽

Forest Canopy ◽

Snow Water Equivalent ◽

Rio Grande ◽

Winter Precipitation ◽

Time Step ◽

The North ◽

Spatial Coverage ◽

Snow Water ◽

Snowpack Properties

AbstractThis study evaluated the spatial variability of trends in simulated snowpack properties across the Rio Grande headwaters of Colorado using the SnowModel snow evolution modeling system. SnowModel simulations were performed using a grid resolution of 100 m and 3-hourly time step over a 34-yr period (1984–2017). Atmospheric forcing was provided by phase 2 of the North American Land Data Assimilation System, and the simulations accounted for temporal changes in forest canopy from bark beetle and wildfire disturbances. Annual summary values of simulated snowpack properties [snow metrics; e.g., peak snow water equivalent (SWE), snowmelt rate and timing, and snow sublimation] were used to compute trends across the domain. Trends in simulated snow metrics varied depending on elevation, aspect, and land cover. Statistically significant trends did not occur evenly within the basin, and some areas were more sensitive than others. In addition, there were distinct trend differences between the different snow metrics. Upward trends in mean winter air temperature were 0.3°C decade−1, and downward trends in winter precipitation were −52 mm decade−1. Middle elevation zones, coincident with the greatest volumetric snow water storage, exhibited the greatest sensitivity to changes in peak SWE and snowmelt rate. Across the Rio Grande headwaters, snowmelt rates decreased by 20% decade−1, peak SWE decreased by 14% decade−1, and total snowmelt quantity decreased by 13% decade−1. These snow trends are in general agreement with widespread snow declines that have been reported for this region. This study further quantifies these snow declines and provides trend information for additional snow variables across a greater spatial coverage at finer spatial resolution.

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