Sample results and model weights are provided in this repo: Template code for KD can be found at: https://github.com/sinajahangir/KD-DL-LSTM The random numbers folder contains CSV files indicating 8-fold cross-validation catchments. Results (NSE and KGE) for model compression and generalization are provided Ensemble model weights are provided. These weights can be used for fine-tuning or KD This study demonstrates how a technique known as knowledge distillation can enhance the predictive capabilities of deep learning models, even when a smaller model is employed or when the available input data is noisy. Deep learning models, such as long short-term memory networks, work well for hydrological prediction. However, they typically require a substantial amount of high-quality input data to perform optimally, which is not always available. Additionally, averaging over many deep learning models often improves performance, but this increases computational costs during operational use. Knowledge distillation helps by training a smaller or simpler "student" model to learn from a more accurate "teacher" model (or group of models) rather than learning only from the raw data. Overall, this approach makes deep learning models more efficient and transferable. The proposed method is helpful in applications where data is poor or incomplete, or when computational costs are limiting.