Work

Snow water equivalent forecasting in sub-arctic and arctic regions: Efficient recurrent neural networks approach

Year: 2025

Type: article

Abstract: Snow water equivalent (SWE) expresses the amount of liquid water in the snow pack. Accurate SWE forecasts are essential for reliable hydrological modeling, as direct SWE measuring is labor-intensive. In this study, we systematically compared gated recurrent unit (GRU) and long short-term memory (LSTM) architectures, showing that GRU models achieve comparable accuracy with greater computational efficiency. By applying Bayesian optimization, data preprocessing, and a time-to-vector representation of temporal features, we introduce two novel GRU-based models: a lightweight model (321 parameters; average NSE = 0.91) and a more complex model (51973 parameters, average NSE = 0.95). Importantly, these models generalize effectively across geographically distant stations, demonstrating robust predictive performance under varied climatic conditions. The primary novelty of our study is identifying GRU as a computationally efficient, accurate alternative to LSTM in SWE forecasting, combined with demonstrating that compact models with smaller hidden states provide strong spatial generalization and excellent accuracy. • Recurrent neural networks show strong potential in snow water equivalent forecasting. • We compare long short term memory and gated recurrent units, detailing their design. • We propose two efficient gated recurrent unit models with state-of-the-art accuracy.

Source: Environmental Modelling & Software

DOI: 10.1016/j.envsoft.2025.106695

Authors: Miika Malin, Jarkko Okkonen, Jaakko Suutala

Institutions: University of Oulu, Geological Survey of Finland

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: hybrid

Estimated APC paid: $3,500