Evaluation of LSTM Model for Stochastic Discharge Simulation

This study evaluates the capacity of a Long Short-Term Memory (LSTM) model trained on a diverse river discharge dataset from over 4,000 USGS gauges across the United States with the aim to generate extremely long stochastic discharge simulations. The LSTM model (Kratzert et al., 2022) was trained using 30 years of NLDAS v2 forcings, which were split into 10-year periods for training, validation, and testing respectively. Sixty percent of the gauges had a Nash Sutcliffe Efficiency (NSE) larger than 0.4 in the validation period, and ten percent had an NSE larger than 0.8, which was considered sufficient to proceed with applying the model using stochastic precipitation.  The stochastic simulations are evaluated in terms of the model’s ability to capture peak discharges. The stochastic return period (RP) curves were evaluated against those from the historical time period and the observed discharge. For most of the gauges, the stochastic RP curves are in line with the historical RP curves, and for all of the gauges, the stochastic RP curves discharge of the extreme return period extend far beyond the discharge of the historical time period, showing the capacity of the model to extrapolate beyond the training dataset. This capacity, which is usually lacking in single-basin trained models, most likely results from training on a large dataset with a wide range of climatic conditions and variability as suggested by Kratzert et al. (2024). These findings underscore the robustness and versatility of the LSTM model in long-term stochastic discharge simulations, highlighting its potential for broader hydrological applications. Kratzert, F., Gauch, M., Nearing, G., & Klotz, D. (2022). NeuralHydrology — A Python library for Deep Learning research in hydrology. Journal of Open Source Software, 7(71), 4050. https://doi.org/10.21105/joss.04050Kratzert, F., Gauch, M., Klotz, D., and Nearing, G. (2024). HESS Opinions: Never train an LSTM on a single basin. Hydrology and Earth System Sciences (HESS), Volume 28, Issue 17, published on September 12, 2024.  https://doi.org/10.5194/hess-28-4187-2024.