On the Information Content of Cosmic‐Ray Neutron Data in the Inverse Estimation of Soil Hydraulic Properties

Abstract

Core Ideas Cosmic‐ray neutron data are used to inversely estimate soil hydraulic properties. The forward neutron operator COSMIC is coupled with the vadose zone model HYDRUS‐1D. Bayesian analyses confirm the information content of cosmic‐ray neutron data. Observations of soil moisture content from remote sensing platforms can be used in conjunction with hydrological models to inversely estimate soil hydraulic properties (SHPs). In recent years, cosmic‐ray neutron sensing (CRNS) has proven to be a reliable method for the estimation of area‐average soil moisture at field scales. However, its use in the inverse estimation of the effective SHPs is largely unexplored. Thus, the main objective of this study was to assess the information content of aboveground fast‐neutron counts to estimate SHPs using both a synthetic modeling study and actual experimental data from the Rollesbroich catchment in Germany. For this, the forward neutron operator COSMIC was externally coupled with the hydrological model HYDRUS‐1D. The coupled model was combined with the Affine Invariant Ensemble Sampler to calculate the posterior distributions of effective soil hydraulic parameters as well as the model‐predictive uncertainty for different synthetic and experimental scenarios. Measured water contents at different depths were used to assess estimated SHPs. The analysis of both synthetic and actual CRNS data from homogenous and heterogeneous soil profiles, respectively, led to confident estimations of the shape parameters α and n, while higher uncertainty was observed for the saturated hydraulic conductivity. Furthermore, results demonstrated that neutron data are less influenced by local sources of uncertainty compared with near‐surface point measurements. The simultaneous use of CRNS and water content data further reduced the overall uncertainty, opening up new perspectives for the combination of CRNS with other remote sensing techniques for the inverse estimation of the effective SHPs.

Opportunities and challenges in using catchment-scale storage estimates from cosmic ray neutron sensors for rainfall-runoff modelling

“…Compared to traditional distributed point scale measurements (Brunetti et al, 2019), CRNS sensors require little maintenance and are likely to be less influenced by the typical sources of uncertainty in hydraulic properties induced by soil management (e.g. ploughing).…”

“…Indeed, CRNS data has already been used in some modelling applications, e.g. for inverse estimation of soil hydraulic properties (Brunetti et al, 2019;Foolad et al, 2017;Rivera Villarreyes et al, 2014), improving evapotranspiration estimates (Han et al, 2015) and calibration of land-surface models (Baatz et al, 2017;Iwema et al, 2017). Nevertheless, while CRNS applications are growing, they have mainly focused on physically-based modelling either at the plot or at large scale (i.e.…”

Modeling the Translocation and Transformation of Chemicals in the Soil‐Plant Continuum: A Dynamic Plant Uptake Module for the HYDRUS Model

“…Numerical models from vadose zone hydrology, which have been used extensively in the last decades (Jarvis & Larsbo, 2012;Šimůnek et al, 2016), offer great opportunities to head in this direction. In particular, Richards-based approaches have been used successfully in many applications and scientific fields (e.g., Brunetti et al, 2017Brunetti et al, , 2019Brunetti, Porti, & Piro, 2018;Brunetti, Šimůnek, & Bautista, 2018;Cheyns et al, 2010, Hanson et al, 2006, Jellali et al, 2016. Nolan et al (2005) compared the performance of the Richards-based and tipping bucket models in reproducing measured breakthrough curves of bromide and atrazine from two experimental facilities in Merced, California, and White River Basin, Indiana, respectively.…”

Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

“…An alternative approach to assimilation of CRNS soil moisture measurements into land surface models is taken in Brunetti et al (2019) and Han et al (2015). These studies both use neutron counts from CRNS instruments as observations, combined with the method presented in Shuttleworth et al (2013) to map modelled soil moisture estimates into equivalent neutron counts.…”