Abstract
Abstract. Understanding soil moisture dynamics at the sub-kilometre scale is increasingly important, especially with the continuous development of hyper-resolution land surface and hydrological models. Cosmic-ray neutron sensors (CRNSs) 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 harmonised global data set 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 harmonised way. We outline the basic structure of this tool, discussing the correction methods used as well as 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 that having a tool to provide a harmonised data set 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.
Highlights
The recent push for hyper-resolution global modelling means that we require measurements at a finer spatial resolution, on the order of sub-kilometre scales (Wood et al, 2011)
In order to mitigate this ongoing issue of lack of harmonisation in the soil moisture estimates from the Cosmic-ray neutron sensors (CRNSs) technology, we present here an open-source Python tool to process raw CRNS data into soil moisture estimates, using the most current methods identified in the literature
In order to demonstrate some of the features that can be accessed with the help of metadata, we show an example using the compiled Cosmic-Ray Soil Moisture Observing System (COSMOS) network data for the continental USA (CONUS)
Summary
The recent push for hyper-resolution global modelling means that we require measurements at a finer spatial resolution, on the order of sub-kilometre scales (Wood et al, 2011). Where θvol is volumetric soil moisture (cm cm−3); a0, a1, and a2 are coefficients obtained from neutron particle physics modelling (Zreda et al, 2008; Desilets et al, 2010) and are assumed to be constants; LW is the lattice (chemically bounded mineral) water (g g−1); WSOM is the water equivalent of soil organic carbon (gram of water per gram of soil); ρbd is the bulk density of the dry soil (g cm−3); ρw is the density of water defined as 1 g cm−3; Nraw is the measured raw, uncorrected, neutron count identified over the given integration time, usually set to 1 h; fp, fi, fh, and fv represent correction factors for air pressure, incoming neutron intensity, atmospheric water vapour, and above-ground biomass respectively that are applied to Nraw to account for additional influences on the neutron signal other than soil moisture; and N0 is the theoretical neutron count found in absolutely dry conditions (i.e. the maximum number of neutrons that can be found at the site without the direct presence of hydrogen) This last term is unique to each site and is found through the calibration process, explained in detail in Sect. The interdependencies of a database mean that it does not lend itself to quick changes; a post-processing method could alleviate some of these issues
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