Abstract

Snowpack properties like temperature or density are the result of a complex energy and mass balance process in the snowpack that varies temporally and spatially. The Snow Thermal Model (SNTHERM) is a 1-dimensional model, energy and mass balance-driven, that simulates these properties. This article analyzes the simulated snowpack properties using SNTHERM forced with two datasets, namely measured meteorological data at the Cooperative Remote Sensing Science and Technology-Snow Analysis and Field Experiment (CREST-SAFE) site and the National Land Data Assimilation System (NLDAS). The study area is located on the premises of Caribou Municipal Airport at Caribou (ME, USA). The model evaluation is based on properties such as snow depth, snow water equivalent, and snow density, in addition to a layer-by-layer comparison of snowpack properties. The simulations were assessed with precise in situ observations collected at the CREST-SAFE site. The outputs of the SNTHERM model showed very good agreement with observed data in properties like snow depth, snow water equivalent, and average temperature. Conversely, the model was not very efficient when simulating properties like temperature and grain size in different layers of the snowpack.

Highlights

  • Information on snowpack properties is of great interest to the hydrologists’ community

  • CRESTSIM was more accurate in simulating snow depth (Figure 5)

  • Using 2012 as a study case, the analysis shows the higher the concurrence between simulated snowpack temperature and observed data, the higher the agreement in the simulations

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Summary

Introduction

Information on snowpack properties is of great interest to the hydrologists’ community. Applications like water availability, reservoirs managing, flood forecasting, and assessing climate change impacts are just a few examples in which these data play a critical role [1,2,3]. For this reason, many efforts have been devoted to making this information available [4,5,6]. Snow Physical Models (SPMs) and satellite remote sensing have been used for estimation of the snowpack properties for several decades [9,10,11,12]. One question that is still unresolved is: What level of accuracy can be reached using snow physical models with several input sources?

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