- 1Federal Office of Meteorology and Climatology MeteoSwiss, Switzerland (annie.chang@meteoswiss.ch)
- 2ETH Zurich, Switzerland
- 3Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Switzerland
Like much of Europe, Switzerland is increasingly experiencing severe summer droughts and heatwaves, prompting the mandate for an advanced national drought monitoring and early warning system. A key component of this initiative is the generation of gridded soil moisture estimates that are spatially distributed, extending beyond measurement stations. Here, we present the concept of a novel physics-constrained land surface model emulator designed to produce high-resolution (e.g. finer than 250m), gridded soil moisture estimates up to 2m depth across Switzerland's diverse topography and climatic conditions.
This framework aims to integrate multi-source datasets, including in-situ measurements, and reanalysis products, to train a machine learning based (e.g. Convolutional LSTM, or XGBoost) hybrid emulator that ensures physically consistent outputs. Compared to conventional dynamical land surface models, an emulator has the advantage of being more computationally efficient and less constrained by the specific requirements of a given numerical model (in terms of input variables and technical dependencies). To fulfil the needs of a very diverse user community, ranging from numerical weather prediction to agricultural decision-making, the emulator should be optimized for multi-scale applications, from climatological analysis, to near-real-time monitoring, and to medium-term forecasting.
How to cite: Chang, A. Y.-Y., Leonarduzzi, E., Grams, C. M., and Humphrey, V. W.: A Physics-Constrained Emulator for High-Resolution Soil Moisture , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12068, https://doi.org/10.5194/egusphere-egu25-12068, 2025.