Combining static and portable Cosmic Ray Neutron Sensor data to assess catchment scale heterogeneity in soil water storage and their integrated role in catchment runoff response

Katya Dimitrova Petrova, Rafael Rosolem, Chris Soulsby, Mark E. Wilkinson, Allan Lilly, Josie Geris* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
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Soil water content (SWC) is a key variable in many land surface processes, such as runoff generation, thus knowledge about its spatiotemporal dynamics at the catchment scale can be useful for constraining and evaluating hydrological models. Cosmic ray neutron sensor (CRNS) technology provides hectare scale SWC data, and with recent advances in mobile CRNS such information value can be extended to the catchment scale, although challenges in calibration remain, especially in wet environments. This study presents a new methodology suited
for humid environments to explore spatio-temporal variability in near-surface soil water storage (SNS) dynamics at the catchment scale and its value in semi-distributed rainfall-runoff modelling calibration. For a humid mixedagricultural catchment (~10 km2
) in Scotland, we combined ~ 4-years of SWC data from a static CRNS at a landscape-representative location with “snapshots” at four key soil-land use (SLU) units to produce SWC timeseries for each one of those units. The SLU units involved a mixture of freely draining mineral and poorly draining organic-rich soils, supporting crop and livestock farming and moorland, respectively. We also explored the suitability of the standard CRNS calibration approach in the SLU units and found that the organic-rich soils
required an adapted parameter calibration for SWC. The moorland SLU unit had the greatest difference in SWC dynamics from the other agricultural SLU units. To explore the additional information generated by the combined CRNS approach, we calibrated a semi-distributed rainfall-runoff model (HBV-light) by using SNS dynamics in individual SLU units in addition to streamflow. Compared to a lumped approach, the semi-distributed SWC information and model structure helped produce better constrained stream flows and further improved the representation of catchment internal storage dynamics. Ultimately, the value of the SWC time series for different SLU units in rainfall-runoff modelling will depend on model structure and the degree to which SNS dynamics vary
within the landscape. This study showed the potential of expanding the information value of permanently installed CRNS sensors using portable CRNS surveys while addressing the various challenges related to organicrich soils and wetter environments, although testing in different environments would be required to evaluate the wider applicability
Original languageEnglish
Article number126659
Number of pages19
JournalJournal of Hydrology
Early online date15 Jul 2021
Publication statusPublished - 31 Oct 2021

Bibliographical note

We thank the Macaulay Development Trust and School of Geosciences, University of Aberdeen for KDP’s scholarship. JG would like to acknowledge funding from the Royal Society (RG140402), the Carnegie Trust for the Universities of Scotland (project 70112) and the UK Natural Environment Research Council (project NE/N007611/1 and CC13_080). RR received funding from the Natural Environment Research Council (projects NE/M003086/1, NE/ R004897/1 and NE/T005645/1) and from the International Atomic Energy Agency of the United Nations (IAEA/UN) (project CRP D12014). MW and AL were funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. We thank Lucile Verrot for helping with the depth-distance weighting of soil moisture. We thank to Hydroinnova for making available the solar intensity data from Jungfraujoch to us. Special thanks to Jessica Fennell, Lucile Verrot and Mark Grundy for assistance with fieldwork as well as to David Finlay and his team for enabling land access in Elsick.


  • Cosmic ray neutron sensor
  • soil moisture
  • spatial variability
  • portable CRNS
  • organic-rich soils
  • managed landscapes
  • semi-distributed rainfall-runoff modelling


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