# Soil Moisture Simulation Ensemble Data

## Overview

This repository contains simulation outputs from [Lafferty et al. (2025) Combined climate and hydrologic uncertainties shape projections of future soil moisture in the central and eastern United States](https://doi.org/10.22541/essoar.173878030.00737104/v1). The data represents a large ensemble (2340 members) of daily 1m depth soil moisture simulations at 12.5km spatial resolution across the eastern half of the United States.

## Methodology

The ensemble was created by:

1. Calibrating a conceptual water balance model against multiple target datasets using various loss functions to characterize parameter uncertainty
2. Combining the resulting parameter ensemble with climate projections to generate soil moisture simulations

For more details, please read the paper.

## Directory Structure

The repository contains three main directories (total size approximately 6.2TB):

### forcing/

Contains climate forcing data used as input for the simulations. The original data source is the LOCA2 downscaled ensemble (https://loca.ucsd.edu/). Files are in NetCDF format:

```
{gcm}_{ssp}_{member}_{time-range}_LOCA2_{loca2-version}_regridded.nc
```

Example: `ACCESS-CM2_ssp245_r1i1p1f1_2016-2100_LOCA2_v20220519_regridded.nc`

Components:
- `gcm`: Climate model name (e.g., ACCESS-CM2)
- `ssp`: SSP scenario (e.g., ssp245)
- `member`: Climate model run identifier (e.g., r1i1p1f1)
- `time-range`: Time period of simulation (2016-2100)
- `loca2-version`: LOCA2 version identifier (e.g., v20220519)

The original LOCA2 dataset was regridded using conservative mapping to match the study's spatial resolution (NLDAS-2 grid).

### pywbm-parameters/

Contains the parameter sets derived from model calibration. For more information on pyWBM, please read the paper and look at the model [GitHub repository](https://github.com/david0811/pyWBM). pyWBM is based on the University of New Hampshire Water Balance Model ([Grogan et al. (2022)](https://doi.org/10.5194/gmd-15-7287-2022)).

### soil-moisture/

Contains the simulation output files in NetCDF format:

```
{gcm}_{ssp}_{member}_{calibration-target}_{loss-function}_{time-range}.nc
```

Example: `TaiESM1_ssp370_r1i1p1f1_SMAP_mse_2016-2100.nc`

Components:
- `gcm`: Climate model name (e.g., TaiESM1)
- `ssp`: SSP scenario (e.g., ssp370)
- `member`: Climate model run identifier (e.g., r1i1p1f1)
- `calibration-target`: Dataset used for calibration (e.g., SMAP)
- `loss-function`: Loss function used in calibration (e.g., mse)
- `time-range`: Time period of simulation (2016-2100)

## Usage

Please consult relevant domain experts before using this data for commercial applications or in any decision context.

## Citation

[Lafferty et al. (2025) Combined climate and hydrologic uncertainties shape projections of future soil moisture in the central and eastern United States](https://doi.org/10.22541/essoar.173878030.00737104/v1)

## License

MIT License

Copyright (c) 2025 David Lafferty

Permission is hereby granted, free of charge, to any person obtaining a copy
of this data and associated documentation files (the "Data"), to deal
in the Data without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Data, and to permit persons to whom the Data is
furnished to do so, subject to the following conditions:

The above copyright notice and this citation requirement shall be included in all
copies or substantial portions of the Data.

THE DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE DATA OR THE USE OR OTHER DEALINGS IN THE
DATA.

## Contact

David Lafferty - dcl257@cornell.edu

## Acknowledgments

This work was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, Earth and Environmental Systems Modeling, MultiSector Dynamics under Cooperative Agreement DE-SC0022141. Computations for this research were performed on the Pennsylvania State University's Institute for Computational and Data Sciences' Roar Collab supercomputer. We thanks Robert Nicholas, Jeffrey Nucciarone, and the PSU Data Commons team for help in making this data publicly accessible. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.