A ConvLSTM surrogate model to predict high-resolution daily snow water equivalent in Norway

In regions where the hydrological cycle is strongly influenced by seasonal snow dynamics, accurate estimation and prediction of Snow Water Equivalent (SWE) are essential for water resource management, hydropower planning, and flood forecasting. While traditional methods like in-situ observations, numerical models, and remote sensing provide robust and reliable approaches for monitoring SWE, challenges remain with respect to ungauged regions and precdictions. These include the difficulty of installing measurement stations in certain regions and resulting observation scarcity, high computational costs, complex parametrization, and low spatial resolution or limited temporal data availability.

Data-driven methods enable the creation and transfer of surrogate models capable of learning complex spatiotemporal relationships between meteorological forcings and SWE dynamics directly from high-fidelity simulations. This study develops a surrogate model using a Convolutional Long Short-Term Memory (ConvLSTM) architecture to provide high-resolution daily SWE estimates and forecasts for Norway. Specifically, the ConvLSTM is trained to emulate the operational SeNorge snow model, creating a portable and computationally efficient tool that can generate accurate SWE fields from diverse meteorological inputs.

The proposed ConvLSTM framework integrates spatial and temporal dependencies by processing sequences of gridded meteorological forcings (precipitation and temperature), static topographic features, and cyclical temporal indicators. To enable robust multi-day forecasting, the model employs an autoregressive training scheme with scheduled sampling. This approach gradually shifts the model from using true SWE values to its own previous predictions as inputs during training, effectively reducing error accumulation within a 7-day prediction horizon.

To evaluate the potential for areal transfer of the surrogate model for pan-European applications, we additionally forced the trained architecture with bias-corrected meteorological data from the ERA5-Land reanalysis. The results demonstrate that the ConvLSTM surrogate model accurately captures the spatiotemporal evolution of SWE across Norway's complex terrain, which suggests that the model indeed learned general physical relationships between input feature and target. Therefore, when driven by SeNorge data, the model achieves good fidelity with a median KGE of 0.8, effectively replicating seasonal accumulation, peak SWE magnitudes, and melt dynamics. Notably, when forced with the global ERA5 reanalysis dataset, the model maintains robust performance (KGE ~ 0.60), indicating its ability to generate reliable SWE estimates and potential transferability to other regions worldwide. .

This work is funded by the European Union’s HORIZON Research and Innovation Actions Program under Grant Agreement No. 101059372 (STARS4Water project) and the BMBF BioökonomieREVIER funding scheme with its BioRevierPlus project (funding reference 031B1137D/031B1137DX).