RiTHyMs: Foundational Dataset for Developing Large-Sample Stream Temperature Models in the Conterminous United States, Version 1.1
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RiTHyMs v1.1 is a reproducible large-sample deep-learning framework for estimating daily stream temperature at unregulated river reaches across the conterminous United States (CONUS). It builds on RiTHyMs v1, which established a transferable LSTM approach using nationally available meteorological, hydrologic, and geospatial inputs to extend daily stream-temperature prediction to ungauged reaches. RiTHyMs v1.1 uses 1980 through […]