Preprints
https://doi.org/10.5194/gmd-2021-77
https://doi.org/10.5194/gmd-2021-77

Submitted as: model description paper 07 May 2021

Submitted as: model description paper | 07 May 2021

Review status: this preprint is currently under review for the journal GMD.

Cosmic-Ray neutron Sensor PYthon tool (crspy): An open-source tool for the processing of cosmic-ray neutron and soil moisture data

Daniel Power1, Miguel Angel Rico-Ramirez1, Sharon Desilets2, Darin Desilets2, and Rafael Rosolem1,3 Daniel Power et al.
  • 1Faculty of Engineering, University of Bristol, Bristol, UK
  • 2Hydroinnova, Albuquerque, New Mexico, USA
  • 3Cabot Institute for the Environment, University of Bristol, Bristol, UK

Abstract. Understanding soil moisture dynamics at the sub-kilometre scale is increasingly important especially with continuous development of hyper-resolution land-surface and hydrological models. Cosmic Ray Neutron Sensors (CRNS) are able to provide estimates of soil moisture at this elusive scale and networks of these sensors have been expanding across the world over the previous decade. However, each network currently implements its own protocol when processing raw data into soil moisture estimates. As a consequence, this lack of a harmonized global dataset can ultimately lead to limitations in the global assessment of the CRNS technology from multiple networks. Here we present crspy, an open-source python tool that is designed to facilitate the processing of raw CRNS data into soil moisture estimates in an easy and harmonized way. We outline the basic structure of this tool discussing the correction methods used as well as discussing the metadata that crspy can create about each site. Metadata can add value to global scale studies of field scale soil moisture estimates by providing additional routes to understanding catchment similarities and differences. We demonstrate that current differences in processing methodologies can lead to misinterpretations when comparing sites from different networks and having a tool to provide a harmonized dataset can help to mitigate these issues. By being open source, crspy can also serve as a development and testing tool for new understanding of the CRNS technology as well as being used as a teaching tool for the community.

Daniel Power et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-77', Anonymous Referee #1, 17 May 2021
  • RC2: 'Comment on gmd-2021-77', Anonymous Referee #2, 05 Jun 2021
  • RC3: 'Comment on gmd-2021-77', Anonymous Referee #3, 02 Jul 2021

Daniel Power et al.

Daniel Power et al.

Viewed

Total article views: 568 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
417 134 17 568 2 4
  • HTML: 417
  • PDF: 134
  • XML: 17
  • Total: 568
  • BibTeX: 2
  • EndNote: 4
Views and downloads (calculated since 07 May 2021)
Cumulative views and downloads (calculated since 07 May 2021)

Viewed (geographical distribution)

Total article views: 500 (including HTML, PDF, and XML) Thereof 500 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 30 Jul 2021
Download
Short summary
Cosmic Ray Neutron Sensors estimate root-zone soil moisture at sub-kilometre scales. There are national-scale networks of these sensors across the globe, however methods for converting neutron signals to soil moisture values are inconsistent. This paper describes our open-source python tool that processes raw sensor data into soil moisture estimates. The aim is to allow a user to ensure they have a harmonized dataset, along with informative metadata, to facilitate both research and teaching.