Downscaling and multi-scale modeling of stream temperature in five watersheds of the Delaware River Basin, 1979-2021

This model archive (Fan et al. 2025a) provides all data, code, and model outputs used in the corresponding manuscript (Fan et al. 2025b) to test machine learning (ML) methods for downscaling and multi-scale modeling of stream temperature to combine an ML model and/or input data at coarse spatial resolution with an ML model and/or input data at fine spatial resolution to predict stream temperatures at fine spatial resolution in a watershed.
The data are organized into these child items:

1. Geospatial Information - Stream reach and catchment shapefiles
2. Model Inputs - Meteorological data, river network matrices, and stream temperature observations
3. Model Code - Python files and README for reproducing model training and evaluation
4. Coarse Model - Trained coarse stream temperature model to be downscaled
5. Model Outputs - Model simulation outputs and evaluation metrics

 
The publication associated with this model archive is: Fan, Yingda, Runlong Yu, Janet R. Barclay, Alison P. Appling, Yiming Sun, Yiqun Xie, and Xiaowei Jia. 2025. "Multi-Scale Graph Learning for Anti-Sparse Downscaling." In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. Washington, DC, USA: AAAI Press.
This data compilation was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Environmental System Science Data Management Program, as part of the ExaSheds project, under Award Number 89243021SSC000068. Work was also supported by the U.S. Geological Survey, Water Availability and Use Science Program.