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

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)

Advances in Cosmic-Ray Neutron Sensing by Monte Carlo simulations and neutron detector development

Epithermal cosmic-ray neutrons are widely used as a proxy for environmental hydrogen content for an area of up to 20 hectares, reaching maximum soil penetration depths of 80 cm. The present work deploys the multi-particle Monte Carlo code MCNP6 to simulate neutron production via cosmic-ray particles and their transport processes at the land-atmosphere interface. The simulation setup was validated against measured neutron flux attenuation in water, air and soil and shows an accurate reproduction of the data sets. This led to simulation-fitted analytical functions that are designed for soil moisture sensing below the soil surface and snow water equivalent monitoring via cosmic-ray neutron detectors. Additionally, the influence of a homogeneous snow cover on the intensity and transport dynamics of the airborne epithermal neutron flux was examined and approximated by analytical functions. The limitations of standard cosmic-ray neutron detectors and the shortage of 3He have led to a novel gaseous 10B-lined neutron detector design specifically tailored to the needs of Cosmic-Ray Neutron Sensing. The system features high count rates as well as an adapted energy response, dedicated readout electronics and low pressure neutron counters, which results in low statistical and systematic errors of the epithermal neutron flux measurement. Two systems, stationary and mobile, proved to be able to capture soil moisture dynamics on the hectare and square kilometre scale

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

Zur gesellschaftlichen Verantwortung der Wissenschaft : die Sicht der studentischen "Initiative Nachhaltige Universität Innsbruck"

Von der Wissenschaft wird erwartet, dass sie gesellschaftliches und individuelles Handeln orientiert. Doch was heißt es, Verantwortung für diese Orientierung zu tragen? Die Beiträger*innen des Bandes gehen auf die allgemeine sowie die fachspezifische Dimension geographischer Verantwortung ein und formulieren Merkmale einer verantwortungsvollen und guten Gründen folgenden Wissenschaftspraxis. Diese guten Gründe sind auf Reflexion verwiesen, die auf die Wissenschaftspraxis zurückwirken. Die hier präsentierten Reflexionsfolien können als Studier- und Diskussionsgrundlage dienen und einen Prozess der Selbstreflexion anstoßen, wie Geographie als Wissenschaft sein sollte – und wie nicht

Can Drip Irrigation be Scheduled with Cosmic-Ray Neutron Sensing?

Irrigation is essential for maintaining food production in water-scarce regions. The irrigation need depends on the water content of the soil, which we measured with the novel technique of cosmic-ray neutron sensing (CRNS). The potential of the CRNS technique for drip irrigation scheduling was explored in this study for the Picassent site near Valencia, Spain. To support the experimental evidence, the neutron transport simulation URANOS was used to simulate the effect of drip irrigation on the neutron counts. The overall soil water content (SWC) in the CRNS footprint was characterized with a root mean square error <0.03 cm3/cm3, but the experimental dataset indicated methodological limitations to detect drip water input. Both experimental data and simulation results suggest that the large-area neutron response to drip irrigation is insignificant in our specific case using a standard CRNS probe. Because of the small area of irrigated patches and short irrigation time, the limited SWC changes due to drip irrigation were not visible from the measured neutron intensity changes. Our study shows that CRNS modeling can be used to assess the suitability of the CRNS technique for certain applications. While the standard CRNS probe was not able to detect small-scale drip irrigation patterns, the method might be applicable for larger irrigated areas, in drier regions, and for longer and more intense irrigation periods. Since statistical noise is the main limitation of the CRNS measurement, the capability of the instrument could be improved in future studies by larger and more efficient neutron detectors

Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity

In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling