Investigating ANN architectures and training to estimate snow water equivalent from snow depth
Open Access
- 4 June 2021
- journal article
- research article
- Published by Copernicus GmbH in Hydrology and Earth System Sciences
- Vol. 25 (6), 3017-3040
- https://doi.org/10.5194/hess-25-3017-2021
Abstract
Canada's water cycle is driven mainly by snowmelt. Snow water equivalent (SWE) is the snow-related variable that is most commonly used in hydrology, as it expresses the total quantity of water (solid and liquid) stored in the snowpack. Measurements of SWE are, however, expensive and not continuously accessible in real time. This motivates a search for alternative ways of estimating SWE from measurements that are more widely available and continuous over time. SWE can be calculated by multiplying snow depth by the bulk density of the snowpack. Regression models proposed in the literature first estimate snow density and then calculate SWE. More recently, a novel approach to this problem has been developed and is based on an ensemble of multilayer perceptrons (MLPs). Although this approach compared favorably with existing regression models, snow density values at the lower and higher ends of the range remained inaccurate. Here, we improve upon this recent method for determining SWE from snow depth. We show the general applicability of the method through the use of a large data set of 234 779 snow depth–density–SWE records from 2878 nonuniformly distributed sites across Canada. These data cover almost 4 decades of snowfall. First, it is shown that the direct estimation of SWE produces better results than the estimation of snow density, followed by the calculation of SWE. Second, testing several artificial neural network (ANN) structural characteristics improves estimates of SWE. Optimizing MLP parameters separately for each snow climate class gives a greater representation of the geophysical diversity of snow. Furthermore, the uncertainty of snow depth measurements is included for a more realistic estimation. A comparison with commonly used regression models reveals that the ensemble of MLPs proposed here leads to noticeably more accurate estimates of SWE. This study thus shows that delving deeper into ANN theory helps improve SWE estimation.Keywords
Funding Information
- Environment and Climate Change Canada (GCXE20M017)
This publication has 38 references indexed in Scilit:
- Improving Snow Water Equivalent Maps With Machine Learning of Snow Survey and Lidar MeasurementsWater Resources Research, 2019
- Update of Canadian Historical Snow Survey Data and Analysis of Snow Water Equivalent Trends, 1967–2016Atmosphere-Ocean, 2019
- Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershedHydrology and Earth System Sciences, 2018
- Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitationsThe Cryosphere, 2016
- The Canadian Land Data Assimilation System (CaLDAS): Description and Synthetic Evaluation StudyJournal of Hydrometeorology, 2015
- Spatial and temporal variability in seasonal snow densityJournal of Hydrology, 2013
- Near-Surface and Land Surface Forecast System of the Vancouver 2010 Winter Olympic and Paralympic GamesJournal of Hydrometeorology, 2011
- An experiment on the evolution of an ensemble of neural networks for streamflow forecastingHydrology and Earth System Sciences, 2010
- Potential impacts of a warming climate on water availability in snow-dominated regionsNature, 2005
- A Method for Producing and Evaluating Probabilistic Forecasts from Ensemble Model IntegrationsJournal of Climate, 1996