Cosmic-ray Neutron Research Articles

Analysis of the Snow Water Equivalent at the AEMet-Formigal Field Laboratory (Spanish Pyrenees) During the 2019/2020 Winter Season Using a Stepped-Frequency Continuous Wave Radar (SFCW)

Snow makes a great contribution to the hydrological cycle in cold regions. The parameter to characterize available the water from the snow cover is the well-known snow water equivalent (SWE). This paper presents a near-surface-based radar for determining the SWE from the measured complex spectral reflectance of the snowpack. The method is based in a stepped-frequency continuous wave radar (SFCW), implemented in a coherent software defined radio (SDR), in the range from 150 MHz to 6 GHz. An electromagnetic model to solve the electromagnetic reflectance of a snowpack, including the frequency and wetness dependence of the complex relative dielectric permittivity of snow layers, is shown. Using the previous model, an approximated method to calculate the SWE is proposed. The results are presented and compared with those provided by a cosmic-ray neutron SWE gauge over the 2019–2020 winter in the experimental AEMet Formigal-Sarrios test site. This experimental field is located in the Spanish Pyrenees at an elevation of 1800 m a.s.l. The results suggest the viability of the approximate method. Finally, the feasibility of an auxiliary snow height measurement sensor based on a 120 GHz frequency modulated continuous wave (FMCW) radar sensor, is shown.

The Indian COSMOS Network (ICON): Validating L-Band Remote Sensing and Modelled Soil Moisture Data Products

Availability of global satellite based Soil Moisture (SM) data has promoted the emergence of many applications in climate studies, agricultural water resource management and hydrology. In this context, validation of the global data set is of substance. Remote sensing measurements which are representative of an area covering 100 m2 to tens of km2 rarely match with in situ SM measurements at point scale due to scale difference. In this paper we present the new Indian Cosmic Ray Network (ICON) and compare it’s data with remotely sensed SM at different depths. ICON is the first network in India of the kind. It is operational since 2016 and consist of seven sites equipped with the COSMOS instrument. This instrument is based on the Cosmic Ray Neutron Probe (CRNP) technique which uses non-invasive neutron counts as a measure of soil moisture. It provides in situ measurements over an area with a radius of 150–250 m. This intermediate scale soil moisture is of interest for the validation of satellite SM. We compare the COSMOS derived soil moisture to surface soil moisture (SSM) and root zone soil moisture (RZSM) derived from SMOS, SMAP and GLDAS_Noah. The comparison with surface soil moisture products yield that the SMAP_L4_SSM showed best performance over all the sites with correlation (R) values ranging from 0.76 to 0.90. RZSM on the other hand from all products showed lesser performances. RZSM for GLDAS and SMAP_L4 products show that the results are better for the top layer R = 0.75 to 0.89 and 0.75 to 0.90 respectively than the deeper layers R = 0.26 to 0.92 and 0.6 to 0.8 respectively in all sites in India. The ICON network will be a useful tool for the calibration and validation activities for future SM missions like the NASA-ISRO Synthetic Aperture Radar (NISAR).

Soil Moisture and Air Humidity Dependence of the Above-Ground Cosmic-Ray Neutron Intensity

Investigations of neutron transport through air and soil by Monte Carlo simulations led to major advancements toward a precise interpretation of measurements; they particularly improved the understanding of the cosmic-ray neutron footprint. Up to now, the conversion of soil moisture to a detectable neutron count rate has relied mainly on the equation presented by Desilets and Zreda in 2010. While in general a hyperbolic expression can be derived from theoretical considerations, their empiric parameterization needs to be revised for two reasons. Firstly, a rigorous mathematical treatment reveals that the values of the four parameters are ambiguous because their values are not independent. We found a three-parameter equation with unambiguous values of the parameters that is equivalent in any other respect to the four-parameter equation. Secondly, high-resolution Monte-Carlo simulations revealed a systematic deviation of the count rate to soil moisture relation especially for extremely dry conditions as well as very humid conditions. That is a hint that a smaller contribution to the intensity was forgotten or not adequately treated by the conventional approach. Investigating the above-ground neutron flux through a broadly based Monte-Carlo simulation campaign revealed a more detailed understanding of different contributions to this signal, especially targeting air humidity corrections. The packages MCNP and URANOS were used to derive a function able to describe the respective dependencies, including the effect of different hydrogen pools and the detector-specific response function. The new relationship has been tested at two exemplary measurement sites, and its remarkable performance allows for a promising prospect of more comprehensive data quality in the future.

A Method to Assess the Performance of SAR-Derived Surface Soil Moisture Products

Synthetic aperture radar (SAR) is a remote sensing technique for mapping of soil moisture with high spatial resolution. C -band SAR can resolve features at field scale, or better, but responds to moisture only within the top 1 to 2 cm of the soil. When validating SAR-derived soil moisture products against standard in situ measurements at 5 to 10 cm depth, the greater moisture variability at the soil surface may be inaccurately categorized as measurement error. An alternative method was developed where the C -band SAR product is validated against soil moisture simulated at 2 cm depth by the HYDRUS-1D model. This reproduces soil moisture depth profiles from daily meteorological observations, leaf area index, and soil hydraulic parameters. The model was fitted at 13 COSMOS-UK sites so that the model output at 10 cm depth closely reproduced the cosmic ray neutron sensor data. At ten of the sites studied, there was an improvement of up to 8% in root-mean-squared difference by validating the Copernicus surface soil moisture (SSM) product at 2 cm compared to 10 cm. This suggests that Copernicus SSM and other C -band SAR surface soil moisture algorithms may be more accurate than have hitherto been acknowledged.

Corrigendum: Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys

Cosmic ray neutron (CRN) sensing allows for non-invasive soil moisture measurements at the field scale and relies on the inverse correlation between aboveground measured epithermal neutron intensity (1eV – 100 keV) and environmental water content. The measurement uncertainty follows Poisson statistics and thus increases with decreasing neutron intensity, which corresponds to increasing soil moisture. In order to reduce measurement uncertainty, the neutron count rate is usually aggregated over 12 or 24 h time windows for stationary CRN probes. To obtain accurate soil moisture estimates with mobile CRN rover applications, the aggregation of neutron measurements is also necessary and should consider soil wetness and driving speed. To date, the optimization of spatial aggregation of mobile CRN observations in order to balance measurement accuracy and spatial resolution of soil moisture patterns has not been investigated in detail. In this work, we present and apply an easy-to-use method based on Gaussian error propagation theory for uncertainty quantification of soil moisture measurements obtained with CRN sensing. We used a 3rd order Taylor expansion for estimating the soil moisture uncertainty from uncertainty in neutron counts and compared the results to a Monte Carlo approach with excellent agreement. Furthermore, we applied our method with selected aggregation times to investigate how CRN rover survey design affects soil moisture estimation uncertainty. We anticipate that the new approach can be used to improve the strategic planning and evaluation of CRN rover surveys based on uncertainty requirements.

Cosmogenic activation of silicon

The production of $^{3}$H, $^{7}$Be, and $^{22}$Na by interactions of cosmic-ray particles with silicon can produce radioactive backgrounds in detectors used to search for rare events. Through controlled irradiation of silicon CCDs and wafers with a neutron beam that mimics the cosmic-ray neutron spectrum, followed by direct counting, we determined that the production rate from cosmic-ray neutrons at sea level is ($112 \pm 24$) atoms/(kg day) for $^{3}$H, ($8.1 \pm 1.9 $) atoms/(kg day) for $^{7}$Be, and ($43.0 \pm 7.1 $) atoms/(kg day) for $^{22}$Na. Complementing these results with the current best estimates of activation cross sections for cosmic-ray particles other than neutrons, we obtain a total sea-level cosmic-ray production rate of ($124 \pm 24$) atoms/(kg day) for $^{3}$H, ($9.4 \pm 2.0 $) atoms/(kg day) for $^{7}$Be, and ($49.6 \pm 7.3 $) atoms/(kg day) for $^{22}$Na. These measurements will help constrain background estimates and determine the maximum time that silicon-based detectors can remain unshielded during detector fabrication before cosmogenic backgrounds impact the sensitivity of next-generation rare-event searches.

Comparison of Soil Water Estimates From Cosmic-Ray Neutron and Capacity Sensors in a Semi-arid Pine Forest: Which Is Able to Better Assess the Role of Environmental Conditions and Thinning?

This study is a component of the research projects: EHIDROMED (CGL2014-58127-C3) and CEHYRFO-MED (CGL2017-86839-C3-2-R) funded by the Spanish Ministry of Science and Innovation and FEDER funds, and LIFE17 CCA/ES/000063 RESILIENTFORESTS. AM is beneficiary of an APOSTD fellowship (APOSTD/2019/111) funded by the Generalitat Valenciana.

Cosmic Ray Neutron Soil Moisture Estimation Using Physically Based Site‐Specific Conversion Functions

AbstractIn order to advance the use of the cosmic ray neutrons (CRNs) to map soil moisture in heterogeneous landscapes, we need to develop a methodology that reliably estimates soil moisture without having to collect 100+ soil samples for each point along the survey route. In this study, such an approach is developed using physically based modeling with the numerical MCNP neutron transport code. The objective is to determine site‐specific conversion functions to estimate soil moisture from CRNs for the dominant land covers. Here, we assess this methodology at three field sites with similar mineral soil composition, but different land covers. First, we ensure that the developed models capture the most important differences in neutron transport behavior across sites. For this, we use measured time series and height profiles of thermal and epithermal neutrons. Then, we compare the estimates obtained from the site‐specific conversion functions with the standard N0‐calibration function. Finally, we compare the CRN soil moisture estimates with independent soil moisture estimates. Overall, the site‐specific models are in agreement with the observed trends in neutron intensities. The site‐specific soil moisture is similar to the N0‐estimated soil moisture, which results in comparable statistical measures. We show that various land covers have a significant impact on the amount and soil moisture sensitivity of epithermal neutrons, while the thermal neutrons are affected to a less degree. Thereby, thermal‐to‐epithermal neutron ratios can be used to identify the land cover type and thereby the appropriate conversion function for soil moisture estimation for each point along the survey route.

A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-Alpine headwater catchment in Germany

Abstract. Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km2: the pre-Alpine Rott headwater catchment in Southern Germany, which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, this network was designed to study root-zone soil moisture dynamics at the catchment scale. The observations of the dense CRNS network were complemented by extensive measurements that allow users to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from unmanned areal systems (UASs), permanent and temporary wireless sensor networks, profile probes, and comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets: https://doi.org/10.23728/b2share.282675586fb94f44ab2fd09da0856883 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.bd89f066c26a4507ad654e994153358b (Fersch et al., 2020b).

Large-Scale Boron-Lined Neutron Detection Systems as a 3He Alternative for Cosmic Ray Neutron Sensing

Cosmic-Ray neutron sensors are widely used to determine soil moisture on the hectare scale. Precise measurements, especially in the case of mobile application, demand for neutron detectors with high counting rates and high signal-to-noise ratios. For a long time Cosmic Ray Neutron Sensing (CRNS) instruments have relied on $^3$He as an efficient neutron converter. Its ongoing scarcity demands for technological solutions using alternative converters, which are $^6$Li and $^{10}$B. Recent developments lead to a modular neutron detector consisting of several $^{10}$B-lined proportional counter tubes, which feature high counting rates via its large surface area. The modularity allows for individual shieldings of different segments within the detector featuring the capability of gaining spectral information about the detected neutrons. This opens the possibility for active signal correction, especially useful when applied to mobile measurements, where the influence of constantly changing near-field to the overall signal should be corrected. Furthermore, the signal-to-noise ratio could be increased by combining pulse height and pulse length spectra to discriminate between neutrons and other environmental radiation. This novel detector therefore combines high-selective counting electronics with large-scale instrumentation technology.

The neutron component of background of an HPGe detector operating in a surface laboratory

Investigation of neutron-induced background was carried out by studying interactions of cosmic-ray neutrons with an HPGe detector inside its shield placed on a ground floor of a 3-storey building. The study was conducted experimentally and by Monte Carlo simulations using GEANT4 simulation tool. Detailed analysis of measured background γ-ray spectra showed that many γ-lines visible in the spectra were induced by neutrons. The majority of detected γ-rays originated in germanium, copper, lead and tin. Iron and aluminium components were less important background sources. Inelastic scattering and neutron capture were the most often occurring processes of neutron interactions with the detector and its shielding. The contamination by natural radionuclides, specifically of 40K, 214Pb, 214Bi and 208Tl, was also present in the background spectra. Nevertheless, approximately 35% of 208Tl peak at the energy of 2614.51 keV was produced by inelastic scattering of neutrons on 208Pb nuclei. The experimental background was compared with GEANT4 simulations, which were carried out without and with the shielding layer of the building. The final integral counting rates for measured spectrum in the energy range from 50 keV to 2875 keV was 1.26 ± 0.07 s−1 and for simulated one 1.25 ± 0.13 s−1, indicating a good agreement with the experiment.

Integrating Invasive and Non-invasive Monitoring Sensors to Detect Field-Scale Soil Hydrological Behavior

The Critical Zone Observatory (CZO) in the Alento River Catchment (ARC; southern Italy) has been collecting data for some decades and recently became part of the TERENO (TERrestrial ENvironmental Observatories) long-term ecosystem infrastructure network. In 2016, cosmic-ray neutron probes (CRNP, non-invasive proximal sensors) together with SoilNet wireless sensor networks (invasive ground-based sensors) were installed in two sub-catchments (MFC2 and GOR1), characterized by different weather, topographic, pedological, and land-use conditions. The SoilNet sensors are measuring soil apparent permittivity (converted into soil moisture), electrical conductivity, and temperature at two soil depths of 0.15 m and 0.30 m in 20 locations around the CRNP. The SoilNet sensor network includes also soil matric pressure potential sensors at the same soil depths and locations. A major aim of this study is to compare the hydrological response of the two experimental sites to climate forcings (rainfall minus potential evapotranspiration, hereby defined as rainfall deficit) through the use of the monthly Soil Moisture Index (SMI) computed from daily values of soil moisture measured by the CRNPs. For both sites, strong correlations are detected between monthly rainfall deficit and SMI, even if the cropland site (MFC2) is characterized by more extreme dry and wet conditions. For these two different sites, our investigations also aim at identifying the dominant controls governing the seasonal dynamics of spatial soil moisture patterns. The majority of the spatial variance in the cropland site is explained by terrain attributes, under both wet and dry conditions, whereas the second-order statistical moment of soil moisture in the forested site is mostly explained by topographic factors under wet conditions during the rainy season. For both sites, our data show that soil texture exerts a minimal impact on the spatial variations of soil moisture.

Applicability of cosmic-ray neutron sensor for measuring soil moisture at the agricultural-pastoral ecotone in northwest China

Accurate monitoring of soil moisture is crucial in hydrological and ecological studies. Cosmic-ray neutron sensors (CRNS) measure area-average soil moisture at field scale, filling a spatial scale gap between in-situ observations and remote sensing measurements. However, its applicability has not been assessed in the agricultural-pastoral ecotone, a data scarce semi-arid and arid region in Northwest China (APENC). In this study, we calibrated and assessed the CRNS (the standard N0 method) estimates of soil moisture. Results show that Pearson correlation coefficient, RP, and the root mean square error (RMSE) between the CRNS soil moisture and the gravimetric soil moisture are 0.904 and less than 0.016 m3 m−3, respectively, indicating that the CRNS is able to estimate the area-average soil moisture well at our study site. Compared with the in-situ sensor network measurements (ECH2O sensors), the CRNS is more sensitive to the changes in moisture in its footprint, which overestimates and underestimates the soil moisture under precipitation and dry conditions, respectively. The three shape parameters a0, a1, a2 in the standard calibration equation (N0 method) are not well suited to the study area. The calibrated parameters improved the accuracy of the CRNS soil moisture estimates. Due to the lack of low gravimetric soil moisture data, performance of the calibrated N0 function is still poor in the extremely dry conditions. Moreover, aboveground biomass including vegetation biomass, canopy interception and widely developed biological soil crusts adds to the uncertainty of the CRNS soil moisture estimates. Such biomass impacts need to be taken into consideration to further improve the accuracy of soil moisture estimation by the CRNS in the data scarce areas such as agricultural-pastoral ecotone in Northwest China.

Monitoring of Snowpack Dynamics With Cosmic-Ray Neutron Probes: A Comparison of Four Conversion Methods

Common snow monitoring instruments based on hydrostatic pressure such as snow pillows are often influenced by various disturbing effects, which result in a reduced quality of the snow cover and snow water equivalent estimates. Such disturbing effects include energy transport into the snowpack, wind fields, and variations of snow properties within the snowpack (e.g. ice layers). Recently, it has been shown that Cosmic-Ray Neutron Probes (CRNP) are a promising technique to monitor snow pack development. CRNP can provide larger support and need lower maintenance compared to conventional sensors. These instruments are sensitive to the intensity of epithermal neutrons that are produced in the soil by cosmic radiation and are widely used to determine soil moisture in the upper decimetres of the ground. The application of CRNP for snow monitoring is based on the principle that snow water moderates the epithermal neutron intensity, which can be directly related to the snow water equivalent (SWE) of the snow pack. In this study, long-term CRNP measurements in the Pinios Hydrologic Observatory (PHO), Greece, were used to test different methods for converting neutron count rates to snow pack characteristics: i) linear regression, ii) standard N0-calibration function, iii) a physically-based calibration approach, and iv) thermal to epithermal neutron ratio. For this, a sonic sensor located near the CRNP was used to compare CRNP-derived snow pack dynamics with snow depth measurements. We found that the above-ground CRNP is well suited for measurement of field scale SWE, which is in agreement with findings of other studies. The analysis of the accuracy of the four conversion methods showed that all methods were able to determine the mass of the snow pack during the snow events reasonably well. The N0-calibration function and the physically-based calibration function performed best and the thermal to epithermal neutron ratio performed worst. Furthermore, we found that SWE determination with above-ground CRNP can be affected by other influences (e.g. heavy rainfall). Nevertheless, CRNP-based SWE determination is a potential alternative to established method like snow depth-based SWE methods, as it provides SWE estimate for a much larger scales (12-18 ha).

The Applicability of the Cosmic Ray Neutron Sensor to Simultaneously Monitor Soil Water Content and Biomass in an Acacia mearnsii Forest

Soil water content is an important hydrological parameter, which is difficult to measure at a field scale due to its spatial and temporal heterogeneity. The Cosmic Ray Neutron Sensor (CRNS) is a novel and innovative approach to estimate area-averaged soil water content at an intermediate scale, which has been implemented across the globe. The CRNS is moderated by all hydrogen sources within its measurement footprint. In order to isolate the soil water content signal from the neutron intensity, the other sources of hydrogen need to be accounted for. The CRNS’s applications are not only limited to soil water content estimation, as it can potentially be used to monitor biomass. The Two-Streams clear-felling provided the unique opportunity to monitor the cosmic ray neutron intensities before, during, and after the clear-felling. The cadmium-difference method was used to obtain the pure thermal and epithermal neutron intensities from the bare and moderated detectors. The study concluded that the presence of biomass within the site reduced the epithermal neutron intensity by 12.43% and the N0 value by 13.8%. The use of the neutron ratio to monitor biomass was evaluated and changes in the neutron ratio coincided with biomass changes and resulted in a high correlation (R2 of 0.868) with the normalized difference vegetation index (NDVI) and (R2 of 0.817) leaf area index (LAI). The use of the CRNS to simultaneously monitor soil water content and biomass will be beneficial in providing more reliable soil water content estimates, provide biomass estimates at a field scale, and aid in understanding the dynamics between soil water content and vegetation.

Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys

Cosmic ray neutron (CRN) sensing allows for non-invasive soil moisture measurements at the field scale and relies on the inverse correlation between aboveground measured epithermal neutron intensity (1 eV−100 keV) and environmental water content. The measurement uncertainty follows Poisson statistics and thus increases with decreasing neutron intensity, which corresponds to increasing soil moisture. In order to reduce measurement uncertainty, the neutron count rate is usually aggregated over 12 or 24 h time windows for stationary CRN probes. To obtain accurate soil moisture estimates with mobile CRN rover applications, the aggregation of neutron measurements is also necessary and should consider soil wetness and driving speed. To date, the optimization of spatial aggregation of mobile CRN observations in order to balance measurement accuracy and spatial resolution of soil moisture patterns has not been investigated in detail. In this work, we present and apply an easy-to-use method based on Gaussian error propagation theory for uncertainty quantification of soil moisture measurements obtained with CRN sensing. We used a 3rd order Taylor expansion for estimating the soil moisture uncertainty from uncertainty in neutron counts and compared the results to a Monte Carlo approach with excellent agreement. Furthermore, we applied our method with selected aggregation times to investigate how CRN rover survey design affects soil moisture estimation uncertainty. We anticipate that the new approach can be used to improve the strategic planning and evaluation of CRN rover surveys based on uncertainty requirements.

Practical Data Products From Cosmic-Ray Neutron Sensing for Hydrological Applications

The Cosmic-Ray Neutron Sensor (CRNS) technique for estimating landscape average soil water content (SWC) is now a decade old and includes now many practical methods for implementing measurements, such as identification of detection area and depth, installation, calibration, and validation. However, in order to maximize the societal relevance of CRNS SWC data, practical value-added products need to be developed that can estimate both water flux (i.e. rainfall, deep percolation, evapotranspiration) and root zone storage changes. In particular, simple methods that can be used to estimate daily values at landscape average scales are needed by decision makers and stakeholders interested in utilizing this technique. Moreover, landscape average values are necessary for better comparisons with remote sensing products. In this work we utilize three well established algorithms to enhance the usability of the CRNS data. The algorithms aim to: 1) temporally smooth the neutron intensity and SWC time series, 2) estimate a daily rainfall product using the Soil Moisture 2 Rain (SM2RAIN) algorithm, and 3) estimate daily root zone SWC using an exponential filter algorithm. The algorithms are tested on the CRNS site at the Hydrological Open Air Laboratory experiment in Petzenkirchen, Austria. Independent observations of rainfall and point SWC data are used to calibrate and validate the algorithms. With respect to rainfall, the SM2RAIN algorithm resulted in a Kling-Gupta-Efficiency (KGE) criteria of 0.665 for daily and 0.819 for 5 day totals. With respect to SWC, the exponential filter algorithm resulted in a KGE of 0.909 for the 0-30cm depth and 0.912 for the 0-60 cm depth. A methodological framework is presented that summarizes the different processes, required data, algorithms, and products.

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

Adequate characterization of catchment storage dynamics is crucial in hydrological models, yet scale-representative storage measurements are rare. Recent developments in Cosmic Ray Neutron Sensor (CRNS) technology and monitoring networks provide a powerful source of more scale-appropriate soil moisture data for many modelling applications. However, the potential in rainfall-runoff modelling is undeveloped. Here we present the first application of CRNS data in conceptual rainfall-runoff modelling and explore this potential in the context of a mixed-agricultural landscape in Scotland. We deployed and calibrated a CRNS in a heterogeneous soil-land use footprint over a ∼3-year period. In this generally wet environment, the CRNS shallow sensing depth and relatively high neutron count uncertainty were identified as major challenges. However, given the better spatial coverage (up to 14 ha) and ease for maintenance, CRNS was thought to represent the simplest approach for long-term monitoring of managed mixed-agricultural sites. We used CRNS-derived, as well as single point-scale estimates, of near-surface soil storage (SNS) to explore their characterisation of storage dynamics at the catchment-scale. Inter-comparison using linear regression showed that SNS related well to catchment-scale storage dynamics, however this relationship was stronger for CRNS (R2 = 0.91) compared to point-scale derived estimates (R2 = 0.76). Based on this, we evaluated the effect of using the CRNS and point scale derived SNS data to constrain storage estimates controlling runoff generation in a common rainfall-runoff model (HBV-light). Including CRNS or point-scale field SNS data alone in model calibration was especially useful for intermediate and wet periods. A combined model calibration using discharge and either SNS storage estimates provided a better representation of catchment internal dynamics, additionally reducing uncertainty during low flows. In the context of mixed-agricultural landscapes in humid environments, this study showed the potential of using CRNS over point scale data (in terms of representativeness for single point data and practicality for point sensor networks) to characterise the catchment storage-discharge relationship and inform hydrological modelling.

Assimilation of Cosmic‐Ray Neutron Counts for the Estimation of Soil Ice Content on the Eastern Tibetan Plateau

AbstractAccurate observations and simulations of soil moisture phasal forms are crucial in cold region hydrological studies. In the seasonally frozen ground of eastern Tibetan Plateau, water vapor, liquid, and ice coexist in the frost‐susceptible silty‐loam soil during winter. Quantification of soil ice content is thus vital in the investigation and understanding of the region's freezing‐thawing processes. This study focuses on the retrieval of soil ice content utilizing the in situ soil moisture (i.e., liquid phase) and cosmic ray neutron measurements (i.e., total water including liquid and ice), with Observing System Simulation Experiments. To derive the total soil water from neutron counts, different weighting methods (revised, conventional, and uniform) for calibrating the cosmic‐ray neutron probe (CRNP) were intercompared. The comparison showed that the conventional nonlinear method performed the best. Furthermore, to assimilate fast neutrons using the particle filter, the STEMMUS‐FT (Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil) model was used as the physically based process model, and the COSMIC model (Cosmic‐ray Soil Moisture Interaction Code) used as the observation operator (i.e., forward neutron simulator). Other than background inputs from disturbed initializations in the STEMMUS‐FT, model uncertainties were predefined to assimilate fast neutrons. We observed that with enough spread of uncertainties, the updated states could mimic the CRNP observation. In all setups, assimilating CRNP measurements could enhance total soil water analyses, which consequently led to the improved detection of soil ice content and therefore the freezing thawing‐process at the field scale.

First Observations of Cosmic Ray Neutrons by a Mini Neutron Monitor at Riyadh, Saudi Arabia

In April 2017, a mini neutron monitor (NM) was installed at King Abdulaziz City for Science and Technology (KACST) central Saudi Arabia (Riyadh; cut-off rigidity, Rc = 14.4 Gv) for continuous observation of the cosmic ray (CR) neutrons. The detector was built as a major aspect of the international scientific joint effort between the Centre of Space Research (North-West University, Potchefstroom, South Africa) and KACST. The recorded data correspond to low energy neutrons that primarily have energies lower than 20 GeV. In this paper, a brief description about the mini NM detector will be given. The influence of atmospheric pressure on the recorded CR neutrons was studied and the barometric coefficient was calculated and used to eliminate the pressure effects from the measured data. The obtained coefficient was consistent with those previously obtained by several investigators. The daily variation of the CR neutron was studied and characterized. Short-term CR periodicities, such as the 27-day period, and its two harmonics, were identified. The obtained periodicities are in agreement with those reported by different researchers. The obtained results from this detector have been compared to the existing 1 m2 scintillator detector showing comparable results. Long-term data from this detector will be of incredible significance to the research community to investigate several types of CR variations resulting from solar activity at such high cut off rigidity site.