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| Format: | Recurso digital |
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Zenodo
2025
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| Accès en ligne: | https://doi.org/10.5281/zenodo.17980723 |
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- <p>This repository contains code and data for Choi et al. (under review).</p> <p>Author: Seohye Choi<br>Last modified: 12/18/2025</p> <p># Code</p> <p>Transformer-RR.ipynb: Jupyter notebook for finetuning, training, validation, and interpretative analysis of a simplified Transformer model, using datasets described below. </p> <p>LSTM-RR.ipynb: Jupyter notebook for finetuning, training, and validation of a baseline LSTM model, using datasets described below. </p> <p># Data</p> <p>karst.mat: Inputs and target variables for training and validation, compiled for nine snow-dominated karst watersheds in the Western U.S. The first dimension represents each watershed in the following order:</p> <p>1. Little Bighorn River<br>2. Salina Creek<br>3. Steptoe Creek<br>4. Lamoille Creek<br>5. Mission Cr. ab Reservoir<br>6. Cache Creek<br>7. Logan River<br>8. Red Butte Creek<br>9. Blacksmith Fork</p> <p>non_karst.mat: Inputs and target variables for training and validation, compiled for nine snow-dominated non-karst watersheds in the Western U.S. The first dimension represents each watershed in the following order:</p> <p>1. Halfmoon Creek<br>2. Black Gore Creek<br>3. Andrews Creek<br>4. Minam River<br>5. Rock Creek<br>6. Bobtail Creek<br>7. Lake Fork<br>8. Stillwater Fork<br>9. Headwater Weber River</p> <p>For both datasets, the third dimension represents <br>1. Streamflow (m/d),<br>2. Liquid water input (i.e., snowmelt plus rain (m)),<br>3. PET (mm).</p> <p># Reference</p> <p>Choi, S., Tennant, H., Hill, D., Neilson, B., Newell, D., Ashmead, N., McNamara, J., & Xu, T. Transformers Improve Streamflow Prediction in Karst Watersheds by Capturing Long-Term Memory Effect of Storage. Under Review.</p>