Understanding soil moisture dynamics through cosmic rays: a global analysis

Despite its importance in many hydrological and environmental applications, direct estimates of soil moisture at the field-scale is still challenging. The spatial gap between point scale sensors and satellite derived products is becoming increasingly important to consider in the push for hyper-resolution (sub)kilometre-hydrometeorological models. Cosmic-Ray Neutron Sensors (CRNS) can help to bridge this spatial gap. CRNS provide estimates of field-scale (sub-kilometre) root-zone integrated soil moisture typically at hourly intervals. They achieve this by counting fast neutrons which are produced in the atmosphere from incoming cosmic rays. Fast neutrons are mitigated primarily by hydrogen atoms, and it is this relationship that allows us to estimate field averaged soil moisture. National networks of CRNS are available in the USA, Australia, the UK, and Germany, along with individual sites across the globe. As these networks have expanded, so has our knowledge on best practices for calibration and correction of the sensor measurements. However, there continues to be a divergence and lack of harmonization in some processing data methods leading to an additional uncertainty when comparing sensors in different networks. This can undermine efforts to employ large-sample hydrological analysis of CRNS across a wide range of climate and biomes. To provide an easily accessible platform for multi-site comparison worldwide, we developed the Cosmic Ray Sensor Python tool (crspy). Crspy is an open-source Python package which is designed to process CRNS data from global networks in a uniform and harmonized way (https://www.github.com/danpower101/crspy). Additionally, crspy has been developed for multi-site ‘big-data’ analysis in hydrology. Our crspy tool produces detailed information in the form of metadata for each site, using both site specific data as well as global data products to give information on soil properties (SoilGridsv2), land cover/aboveground biomass (ESA CCI) and climate data (ERA5-land). Our preliminary analysis and tool development was carried out using data from more than 100 sites globally from the public domain. We will present an analysis of this large sample of data, utilising the harmonized soil moisture readings along with detailed metadata for each site. We aim to increase our understanding of the dominant mechanisms controlling soil moisture dynamics which will undoubtedly be useful in multiple areas of research such as catchment classification, agriculture and irrigation, and hydrological model development.