Snow model comparison to simulate snow depth evolution and sublimation at point scale in the semi-arid Andes of Chile

Abstract. Physically based snow models provide valuable information on snow cover evolution and are therefore key to provide water availability projections. Yet, uncertainties related to snow modelling remain large as a result of differences in the representation of snow physics and meteorological forcing. While many studies focus on evaluating these uncertainties, no snow model comparison has been done in environments where sublimation is the main ablation process. This study evaluates a case study in the semi-arid Andes of Chile and aims to compare two snow models with different complexities, SNOWPACK and SnowModel, at a local point over one snow season and to evaluate their sensitivity relative to parameterisation and forcing. For that purpose, the two models are forced with (i) the most ideal set of input parameters, (ii) an ensemble of different physical parameterisations, and (iii) an ensemble of biased forcing. Results indicate large uncertainties depending on forcing, the snow roughness length z0, albedo parameterisation, and fresh snow density parameterisation. The uncertainty caused by the forcing is directly related to the bias chosen. Even though the models show significant differences in their physical complexity, the snow model choice is of least importance, as the sensitivity of both models to the forcing data was on the same order of magnitude and highly influenced by the precipitation uncertainties. The sublimation ratio ranges are in agreement for the two models: 36.4 % to 80.7 % for SnowModel and 36.3 % to 86.0 % for SNOWPACK, and are related to the albedo parameterisation and snow roughness length choice for the two models.

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Snow model comparison to simulate snow depth evolution and sublimation at point scale in the semi-arid Andes of Chile

10.5194/tc-2021-9 ◽

2021 ◽

Author(s):

Annelies Voordendag ◽

Marion Réveillet ◽

Shelley MacDonell ◽

Stef Lhermitte

Keyword(s):

Model Calibration ◽

Model Comparison ◽

Meteorological Forcing ◽

Local Point ◽

Physically Based ◽

Order Of Magnitude ◽

Snow Model ◽

Snow Physics ◽

Semi Arid

Abstract. Physically-based snow models provide valuable information on snow cover evolution and are therefore key to provide water availability projections. Yet, uncertainties related to snow modelling remain large as a result of differences in the representation of snow physics and meteorological forcing. While many studies focus on evaluating these uncertainties, issues still arise, especially in environments where sublimation is the main ablation process. This study evaluates a case study in the semi-arid Andes of Chile and aims to compare two snow models with different complexities, SNOWPACK and SnowModel, at a local point, over one snow season. Their sensitivity relative i) to physical calibration for albedo and fresh snow density and ii) to forcing perturbation is evaluated based on ensemble approaches. Results indicate larger uncertainty depending on the model calibration than between the two models (even though the significant differences in their physical complexity). We also confirm the importance of albedo parameterization, even though ablation is driven by sublimation. SnowModel is particularly sensitive to this choice as it strongly affects both the sublimation and the melt rates. However, the day of snow-free snow surface is not sensitive to the parameterization as it only varies every eight days. The albedo parameterization of SNOWPACK has stronger consequences on melt at the end of the season leading to a date difference of the end of the season of 41 days. However, despite these differences, the sublimation ratio ranges are in agreement for the two models: 42.7–63.5 % for SnowModel and 51.3 and 64.6 % for SNOWPACK, and are related to the albedo calibration choice for the two models. Finally, the sensitivity of both models to the forcing data was in the same order of magnitude and highly influenced by the precipitation uncertainties.

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StreamFlow 1.0: an extension to the spatially distributed snow model Alpine3D for hydrological modelling and deterministic stream temperature prediction

Geoscientific Model Development ◽

10.5194/gmd-9-4491-2016 ◽

2016 ◽

Vol 9 (12) ◽

pp. 4491-4519 ◽

Cited By ~ 14

Author(s):

Aurélien Gallice ◽

Mathias Bavay ◽

Tristan Brauchli ◽

Francesco Comola ◽

Michael Lehning ◽

...

Keyword(s):

Source Code ◽

Stream Temperature ◽

Object Oriented Programming ◽

Temperature Prediction ◽

Uncertainty Estimates ◽

Physically Based ◽

Snow Model ◽

Alpine Catchments ◽

Alpine Rivers

Abstract. Climate change is expected to strongly impact the hydrological and thermal regimes of Alpine rivers within the coming decades. In this context, the development of hydrological models accounting for the specific dynamics of Alpine catchments appears as one of the promising approaches to reduce our uncertainty of future mountain hydrology. This paper describes the improvements brought to StreamFlow, an existing model for hydrological and stream temperature prediction built as an external extension to the physically based snow model Alpine3D. StreamFlow's source code has been entirely written anew, taking advantage of object-oriented programming to significantly improve its structure and ease the implementation of future developments. The source code is now publicly available online, along with a complete documentation. A special emphasis has been put on modularity during the re-implementation of StreamFlow, so that many model aspects can be represented using different alternatives. For example, several options are now available to model the advection of water within the stream. This allows for an easy and fast comparison between different approaches and helps in defining more reliable uncertainty estimates of the model forecasts. In particular, a case study in a Swiss Alpine catchment reveals that the stream temperature predictions are particularly sensitive to the approach used to model the temperature of subsurface flow, a fact which has been poorly reported in the literature to date. Based on the case study, StreamFlow is shown to reproduce hourly mean discharge with a Nash–Sutcliffe efficiency (NSE) of 0.82 and hourly mean temperature with a NSE of 0.78.

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StreamFlow 1.0: An extension to the spatially distributed snow model Alpine3D for hydrological modeling and deterministic stream temperature prediction

10.5194/gmd-2016-167 ◽

2016 ◽

Cited By ~ 1

Author(s):

Aurélien Gallice ◽

Mathias Bavay ◽

Tristan Brauchli ◽

Francesco Comola ◽

Michael Lehning ◽

...

Keyword(s):

Hydrological Modeling ◽

Source Code ◽

Stream Temperature ◽

Object Oriented Programming ◽

Temperature Prediction ◽

Uncertainty Estimates ◽

Physically Based ◽

Snow Model ◽

Alpine Catchments

Abstract. Climate change is expected to strongly impact the hydrological and thermal regimes of Alpine rivers within the coming decades. In this context, the development of hydrological models accounting for the specific dynamics of Alpine catchments appears as a one of the promising approaches to reduce our uncertainty on future mountain hydrology. This paper describes the improvements brought to StreamFlow, an existing model for hydrological and stream temperature prediction built as an external extension to the physically-based snow model Alpine3D. StreamFlow's source code has been entirely written anew, taking advantage of object-oriented programming to significantly improve its structure and ease the implementation of future developments. The source code is now publicly available online, along with a complete documentation. A special emphasis has been put on modularity during the re-implementation of StreamFlow, so that many model aspects can be represented using different alternatives. For example, several options are now available to model the advection of water within the stream. This allows for an easy and fast comparison between different approaches and helps in defining more reliable uncertainty estimates of the model forecasts. In particular, a case study in a Swiss Alpine catchment reveals that the stream temperature predictions are particularly sensitive to the approach used to model the temperature of subsurface runoff, a fact which has been poorly reported in the literature to date. Based on the case study, StreamFlow is shown to reproduce hourly mean discharge with a Nash–Sutcliffe efficiency (NSE) of 0.82, and hourly mean temperature with a NSE of 0.78.

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Supplementary material to "Snow model comparison to simulate snow depth evolution and sublimation at point scale in the semi-arid Andes of Chile"

10.5194/tc-2021-9-supplement ◽

2021 ◽

Author(s):

Annelies Voordendag ◽

Marion Réveillet ◽

Shelley MacDonell ◽

Stef Lhermitte

Keyword(s):

Snow Depth ◽

Model Comparison ◽

Point Scale ◽

Supplementary Material ◽

Snow Model ◽

Semi Arid

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How Does Availability of Meteorological Forcing Data Impact Physically Based Snowpack Simulations?*

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0235.1 ◽

2015 ◽

Vol 17 (1) ◽

pp. 99-120 ◽

Cited By ~ 34

Author(s):

Mark S. Raleigh ◽

Ben Livneh ◽

Karl Lapo ◽

Jessica D. Lundquist

Keyword(s):

Snow Water Equivalent ◽

Longwave Radiation ◽

Data Availability ◽

Model Complexity ◽

Shortwave Radiation ◽

Meteorological Forcing ◽

Temperature And Precipitation ◽

Physically Based ◽

Snow Model ◽

The Impact

Abstract Physically based models facilitate understanding of seasonal snow processes but require meteorological forcing data beyond air temperature and precipitation (e.g., wind, humidity, shortwave radiation, and longwave radiation) that are typically unavailable at automatic weather stations (AWSs) and instead are often represented with empirical estimates. Research is needed to understand which forcings (after temperature and precipitation) would most benefit snow modeling through expanded observation or improved estimation techniques. Here, the impact of forcing data availability on snow model output is assessed with data-withholding experiments using 3-yr datasets at well-instrumented sites in four climates. The interplay between forcing availability and model complexity is examined among the Utah Energy Balance (UEB), the Distributed Hydrology Soil Vegetation Model (DHSVM) snow submodel, and the snow thermal model (SNTHERM). Sixty-four unique forcing scenarios were evaluated, with different assumptions regarding availability of hourly meteorological observations at each site. Modeled snow water equivalent (SWE) and snow surface temperature Tsurf diverged most often because of availability of longwave radiation, which is the least frequently measured forcing in cold regions in the western United States. Availability of longwave radiation (i.e., observed vs empirically estimated) caused maximum SWE differences up to 234 mm (57% of peak SWE), mean differences up to 6.2°C in Tsurf, and up to 32 days difference in snow disappearance timing. From a model data perspective, more common observations of longwave radiation at AWSs could benefit snow model development and applications, but other aspects (e.g., costs, site access, and maintenance) need consideration.

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