smap-drydown

This repository contains code for analyzing soil moisture drydowns from the Soil Moisture Active Passive (SMAP) as detailed in the corresponding manuscript:

Araki, R., Morgan, B.E., McMillan, H.K., Caylor, K.K. Nonlinear Soil Moisture Loss Function Reveals Vegetation Responses to Water Availability. Submitted to Geophysical Research Letters (in review)

Getting started

1. Clone the repository

$ git clone [email protected]:RY4GIT/smap-drydown.git

2. Create a virtual environment (select an appropriate yml file according to your OS).

$ cd smap-drydown
$ conda env create -f environment_linux.yml
$ conda activate SMAP

3. Download the SMAP and ancillary data from appropriate sources using scripts in data_mng

4. In the analysis directory, create config.ini, based on config_example.ini

5. Run analysis\__main__.py

6. Visualize the results using scripts in notebooks. The results file is large (~130 MB) and is therefore available upon request.

Contents

analysis

Contains scripts to implement the drydown analysis and model fits.

The functions for loss calculations and drydown models are contained in DrydownModel.py. This code has been further refactored in https://github.com/ecohydro/drydowns; check it out if you are interested.

data_mng

Contains scripts to retrieve and curate input data.

All data are pre-curated in "datarods" format, which stores the data as a long time series at a single SMAP grid.

• SMAP soil moisture data
  • Download data using retrieve_NSIDC_Data_SPL3SMP.ipynb
  • Preprocess data using create_datarods_SPL3SMP.ipynb
• SMAP precipitation data
  • Download data using retrieve_NSIDC_Data_SPL4SMGP.ipynb
  • Preprocess data using create_datarods_SPL4SMGP.py
• dPET (Singer et al., 2020) data
  • Download daily data from the website
  • Preprocess data using create_datarods_PET.py
• SMAP ancillary data
  • Download data from the website
  • Preprocess data using read_ancillary_landcover_data.ipynb
  • After obtaining precipitation and PET data, run calc_aridityindex.py
• Rangeland data
  • Download data using retrieve_rangeland_data.sh
  • Preprocess data using read_rangeland_data.py
• Other utilities
  • retrieve_NSIDC_Data_datacheck.ipynb: check if all the data are downloaded from NSIDC
  • create_datarods_datacheck.ipynb: check if all the data are preprocessed
  • identify_unusable_grid.ipynb: identify grids located on open water

notebooks

Contains scripts used to test functions or visualize the models and results

• figs_stats_datapreprocess.py: Preprocess result files to reduce execution time
• figs_method.py & figs_method_tau.py: Visualize the loss functions and drydown models
• figs_stats.py and figs_stats_rev.py: Visualize the results
• figs_drydown.py: Plot observed and modeled drydown curves

Contact

Ryoko Araki, raraki8159 (at) sdsu.edu