Data for the paper "Temporal Inductive Biases in Hourly Flood Forecasting: A Comparative Analysis of Recurrent, Attention-Based, and State-Space Neural Networks"

# Data for the paper "Temporal Inductive Biases in Hourly Flood Forecasting: A Comparative Analysis of Recurrent, Attention-Based, and State-Space Neural Networks". We encourage and thank all contributors to open source work. Github:https://github.com/binbinlan/RF-Bench # version 1.0 - File attributes.rar contains catchments attributes data. By using the CAMELS attributes in your publication(s), you agree to cite: Addor, N., Newman, A. J., Mizukami, N. and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrology and Earth System Sciences, doi:10.5194/hess-2017-169, 2017. - File forcings_and_runoff.rar contains the meteorological forcings and runoff records . By using the meteorological forcings and runoff records in your publication(s), you agree to cite: Martin Gauch , Frederik Kratzert, Daniel Klotz, Grey Nearing, jimmy Lin, and Sepp Hochreiter. :Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network, Hydrology and Earth System Sciences,doi:10.5194/hess-25-2045-2021,2021. Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T. and Duan, Q.: Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional variability in hydrologic model performance, Hydrology and Earth System Sciences, 19, 209–223, doi:10.5194/hess-19-209-2015, 2015.