Models and predictions for "How to deal w___ missing input data"

How to deal w___ missing input data This repository contains the models, configs, and results files for the paper Gauch et al., "How to deal w___ missing input data". The corresponding analysis code is available on GitHub: https://github.com/gauchm/missing-inputs. This zenodo archive provides two identical download options: a zip file and a tar.xz file. Both contain the same contents. Some people expressed problems with extracting the zip file, which is why we also provide a tar.xz file. Contents of this repository missing-inputs.ipynb -- Jupyter notebook to reproduce figures from the paper. results/ -- Folder with model weights, configs, and predictions used in missing-inputs.ipynb. 2026 update: included NetCDF files that contain the same data as the pickle files. This is to avoid problems with reading the files with future versions of pandas, xarray, etc., which sometimes break the pickle files. patches/ -- Contains patches for local modifications to reproduce experiments from the paper. Required setup Clone neuralhydrology: git clone https://github.com/neuralhydrology/neuralhydrology.git. Install an editable version of neuralhydrology: cd neuralhydrology && pip install -e .. Download the following data: the CAMELS US dataset (CAMELS time series meteorology, observed flow, meta data, version 1.2) from NCAR into some data directory (has to match data_dir in the config files). the extended Maurer and NLDAS forcings set available on HydroShare: Maurer, NLDAS. the models, results, and config files from this paper avaliable on this Zenodo repository. Note that to reproduce the experiments, local modifications to NeuralHydrology are necessary. To do so, apply the patches in the patches/ directory: git apply patches/experiment-N.patch.