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

Abstract

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

A profile shape correction to reduce the vertical sensitivity of cosmic‐ray neutron sensing of soil moisture

“…Dimitrova‐Petrova et al. (2020) could not find a difference using either of the vertical weighting options on the estimated near‐surface water storage used in rainfall‐runoff modeling. This was also confirmed for the vertical weighting in the study of Sigouin, Dyck, Si, and Hu (2016).…”

“…For the calibration and weighting of a sensor network to compare water content with CRNS measurements, the Köhli nonlinear vertical and horizontal weighting approach outper-forms the Franz approach in the studies of Schrön et al (2017) and Cai et al (2018), whereas Nguyen et al (2017) found that a linear weighting leads to better results. Dimitrova-Petrova et al (2020) could not find a difference using either of the vertical weighting options on the estimated near-surface water storage used in rainfall-runoff modeling. This was also confirmed for the vertical weighting in the study of Sigouin, Dyck, Si, and Hu (2016).…”

“…For data assimilation purposes, the COSMIC operator was developed (Shuttleworth, Rosolem, Zreda, & Franz, 2013), which deals with the inherent weighting by directly using the neutron counts and not the CRNS‐derived soil moisture. Different studies tried to tackle the problem by extending the measurement depth of CRNS to the root zone with the use of an exponential filter (Dimitrova‐Petrova et al., 2020; Franz et al., 2020; Peterson, Helgason, & Ireson, 2016), or merging CRNS information with data from point sensors of deeper layers (Nguyen, Jeong, & Choi, 2019). However, they assumed CRNS to be representative for the upper most soil layer (down to 15‐to‐30‐cm depth) and did not specifically consider the decreasing sensitivity with depth.…”

Assessing the feasibility of a directional CRNS-sensor for estimating soil moisture

“…Its main application is the measurement of soil water content (Zreda et al, 2008). Such measurement could serve a variety of purposes in both, research and application, for example to close the water balance in atmospheric or hydrological models (Schreiner-McGraw et al, 2016;Dimitrova-Petrova et al, 2020), or to support irrigation management (Li et al, 2019;Franz et al, 2021) or snow cover analysis (Schattan et al, 2017). Furthermore, the use of CRNS to independently estimate biomass or even interception by vegetation has showed potential (Baroni and Oswald, 2015;Baatz et al, 2015).…”

Review of Novel and Emerging Proximal Soil Moisture Sensors for Use in Agriculture

“…Cosmic ray sensors have a very large measurement footprint of around 260-600 m radius [113] which maybe suited to broadscale cropping on uniform soils but are inoperable with the growing trend toward precision agriculture and variable rate irrigation [115,116]. Additional limitations with cosmic ray sensors include their high cost, very large and imprecise measured soil volume, long measurement durations which can be in excess of 4 h, variable depth of measurement which ranges from around 15 cm in wet soils, to approximately 70 cm in dry soils, and difficulty deriving precise calibrations [3,114,117,118].…”