Cosmic-Ray neutron Sensor PYthon tool (crspy 1.2.1): an open-source tool for the processing of cosmic-ray neutron and soil moisture data

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.

Snow water equivalent measurement in the Arctic based on cosmic ray neutron attenuation

Grounded in situ, or invasive, cosmic ray neutron sensors (CRNSs) may allow for continuous, unattended measurements of snow water equivalent (SWE) over complete winter seasons and allow for measurements that are representative of spatially variable Arctic snow covers, but few studies have tested these types of sensors or considered their applicability at remote sites in the Arctic. During the winters of 2016/2017 and 2017/2018 we tested a grounded in situ CRNS system at two locations in Canada: a cold, low- to high-SWE environment in the Canadian Arctic and at a warm, low-SWE landscape in southern Ontario that allowed easier access for validation purposes. Five CRNS units were applied in a transect to obtain continuous data for a single significant snow feature; CRNS-moderated neutron counts were compared to manual snow survey SWE values obtained during both winter seasons. The data indicate that grounded in situ CRNS instruments appear able to continuously measure SWE with sufficient accuracy utilizing both a linear regression and nonlinear formulation. These sensors can provide important SWE data for testing snow and hydrological models, water resource management applications, and the validation of remote sensing applications.

Technical note: A revised incoming neutron intensity correction factor for soil moisture monitoring using cosmic-ray neutron sensors

The cosmic-ray neutron sensor method of soil moisture measurement is now widely used and is fundamental to the COSMOS-UK soil moisture monitoring network. The method is based on a relationship between a measured flux of neutrons and soil moisture, and requires the neutron count to be adjusted for time variations of atmospheric pressure, humidity and the incoming flux of cosmic-ray neutrons. This note describes an empirical approach to the development of a revised correction factor for the last of these. Using the revised correction factor makes a significant difference to the derived soil moisture at wetter sites. This has implications for quantifying the soil moisture regime at these sites and management decisions that depend on a proper understanding of soil moisture dynamics, such as flood management and the release of greenhouse gases.

Cosmic-Ray neutron Sensor PYthon tool (crspy): An open-source tool for the processing of cosmic-ray neutron and soil moisture data

Understanding soil moisture dynamics at the sub-kilometre scale is increasingly important especially with continuous development of hyper-resolution land-surface and hydrological models. Cosmic Ray Neutron Sensors (CRNS) 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 harmonized global dataset 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 harmonized way. We outline the basic structure of this tool discussing the correction methods used as well as discussing 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 having a tool to provide a harmonized dataset 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.

Deep learning approach for calibrating Cosmic-Ray Neutron Sensors (CRNS) in area-wide soil moisture monitoring

<p>Traditional field calibration of cosmic-Ray neutron sensors (CRNS) for area-wide soil moisture monitoring is based on time-consuming and often expensive soil sample collection and conventional soil moisture measurement. This calibration requires two field campaigns, one under dry and one under wet soil conditions. However, depending on the agro-ecological context more field campaigns may be required for calibration, due to for instance crop biomass water interference. In addition, the current calibration method includes corrections considering several parameters influencing neutron counts, the proxy for soil moisture, such as soil lattice water, organic carbon, and biomass which need to be measured.</p><p>The main objective of this study is to investigate and develop an alternative calibration method to the currently available field calibration method. To this end, a Deep Learning model architecture under the TensorFlow machine learning framework is used to calibrate the Cosmic-Ray sensor.</p><p>The Deep Learning model is built with more than 8 years of CRNS data from Petzenkirchen (Austria) and consists of four hidden layers with activation function and a succession of batch normalization. Prior to build the Deep Learning model, data analysis consisting of pertinent variables selection was performed with multivariate statistical analysis of correlation. Among nine features, five were effectively pertinent and included in the machine learning artificial neural network architecture. The five input variables were the raw neutrons counts (N1 and N2), humidity (H), air pressure (P4) and temperature (T7).</p><p>The preliminary results show a linear regression with an R<sup>2 </sup>of 0.97 and the model predicted the soil moisture with less than 1% error.</p><p>These preliminary results are encouraging and proved that a machine learning based method could be a valuable alternative calibration method for CRNS against the current field calibration method.</p><p>More investigation will be performed to test the model under different agro-ecological conditions, such as Nebraska, USA. Further, additional input variables will be considered in the development of machine learning based models, to bring in agro-ecological information, such as crop cover, growth stage, precipitation related to the CRNS footprint. </p>

The sensitivity of CRNS and Sentinel-2 products to detect differences in soil water content along a toposequence with two contrasting parent materials

<p>Soil moisture controls hydrological processes in natural and agricultural systems. A clear understanding of their temporal dynamics and spatial variability is essential to control soil degradation processes, irrigation management and water use efficiency. In recent years, the measurement of soil water content (SWC) with ground-based neutron sensors and remote sensing products have become promising non-invasive methods for different spatial scales. In this study, we are investigating the sensitivity of using cosmic ray neutron sensor (CRNS) and Sentinel-2 SWC index for quantifying different dynamics of soil moisture along a toposequence with underlying contrasting parent materials. For this study, three sites were selected in the upper section (US) soils on limestones correspond to Muschelkalk facies, and another three in the lower section (LS) siliciclastic materials composed of low-permeability marls and claystone formation with primarily silty clay texture (Keuper facies). During two surveys, which correspond to wet (spring 2018/05/05) and dry conditions (summer 2018/08/05), a set of soil moisture data were obtained by using i) portable CRNS backpack, ii) satellite-based information and iii) HS200 sensor Delta-T Devices. The physical composition of the studied soils reflects the clear difference in parent material, with mean content of soil organic carbon of 6% in US against 1% in LW, while the mean clay content was lower in US (21%) than in LS (26%). The infiltration measurements also show different responses for water infiltration capacity, with a much higher mean value of hydraulic conductivity for the soils in the US (317 mm per day), reflecting the karst features, than in the LS (35 mm per day) corresponding to the siliciclastic materials. Our results show similar trends during the two surveys, obtaining significantly lower soil water content on limestones at the US where infiltration processes prevailed thus facilitating leaching and limiting runoff. In contrast, the higher soil water content was on siliciclastic soils at the LS where the low permeability of soils due to the clayed substrate promoted increased runoff. Focusing on the comparison of soil moisture data obtained during the wet and dry surveys, a soil characteristic dependency is observed, with a more different soil moisture state on siliciclastic soils (LS) between the two surveys than for the soils on limestones. Our preliminary results pinpoint that CRNS, Sentinel-2 index and field data captured soil moisture dynamics along the toposequence and demonstrated the sensitivity of neutron sensors and remote sensing products to investigate the effect of parent material on soil water content at sampling scale.</p>

Power Level Control of Nuclear Power Plant Based on Asymptotical State Observer under Neutron Sensor Fault

Power level control is one of the critical functions in the instrument and control system of nuclear power plants (NPPs). In most power level control systems of NPPs, the power level or average neutron flux in reactor cores provided by out-of-core neutron sensors are usually measured as feedback of power control systems, while, as critical measuring devices, there is a risk of damage to out-of-core neutron sensors. For improving the operation reliability of NPPs under the neutron sensors’ failure, a power control system based on power observer is developed in this work. The simulation based on NPP simulator shows the power control system based on the observer is effective when neutron sensors fail.