Displays

HydroGNSS is a mission concept selected by ESA as a Scout candidate, and consists of a 40 kg satellite that addresses land hydrological parameters using the technique of GNSS Reflectometry, a form of bistatic L-Band radar using satnav signals as the radar source. The four targeted essential climate variables (ECVs) are of established importance to our understanding of the climate evolution and human interaction, and comprise of soil moisture, inundation / wetlands, freeze /thaw (notably over permafrost) and above ground biomass.

The technique of GNSS Reflectometry shows potential over all geophysical surfaces for low cost measurement of ocean winds, ocean roughness, soil moisture, flood & ice mapping, and other climate and operational parameters. SSTL developed and flew the SGR-ReSI GNSS remote sensing instrument on the 160 kg UK TechDemoSat-1 (TDS-1) in July 2014 and, with sponsorship from ESA, collected data until TDS-1’s drag-sail was deployed in May 2019. TDS-1 was a precursor for NASA’s CYGNSS mission which uses the SGR-ReSI on its 8-microsatellite constellation for sensing hurricanes. The datasets from TDS-1 have been released via the MERRByS website, and include ocean wind speed measurements and ice extent maps from National Oceanography Centre’s C-BRE inversion. At the same time, researchers recognised the benefits of GNSS reflectometry over land, including the unique capability to sense rivers under forest canopies to a high resolution.

HydroGNSS has been proposed for the ESA Scout mission opportunity by a SSTL and a team of partners with a broad range of experience in GNSS technology, GNSS-Reflectometry modelling and applications, and Earth Observation from GNSS-R measurements. The instrument takes significant steps forward from previous GNSS-R experiments by including capability in dual polarisation, dual frequency and coherent reflected signal reception, that are expected to help separate out ECVs and improve measurement resolution. The satellite platform is the 40 kg SSTL-Micro, which has improved attitude determination and a high data link to support the collection of copious quantities scientific data with a short time delay. HydroGNSS builds upon the growing GNSS-R knowledge gained from UK-DMC, TDS-1, and ORORO / DoT-1, and is anticipated to generate a new research data set in GNSS Earth Observation, specifically targeting land and hydrological applications.

State of the art satellites that target soil moisture such as ESA SMOS and NASA SMAP are highly valued by scientists and operational weather forecasters, but will be expensive to replace. As evidenced by TDS-1 and CYGNSS, HydroGNSS will be able to take GNSS-R measurements using GNSS signals as a radar source, reducing the size of the satellite platform required. The forward scatter L-band nature of the measurement means that they are complementary to other techniques, and HydroGNSS brings further new measurement types compared to TDS-1 and CYGNSS. The small size and low recurring cost of the HydroGNSS satellite design opens the door to a larger constellation that can further improve spatial and temporal global hydrological measurements to an unprecedented resolution, invaluable to the better understanding of our climate.

How to cite: Unwin, M., Pierdicca, N., Rautiainen, K., Cardellach, E., Foti, G., Blunt, P., Tossaint, M., and Worsley, E.: Scene Setting for the ESA HydroGNSS GNSS-Reflectometry Scout Mission, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10728, https://doi.org/10.5194/egusphere-egu2020-10728, 2020.

The study of intermittent and ephemeral streams is gaining more and more popularity, as the scientific community has acknowledged the fundamental impact of these streams on basic hydrological processes and important ecosystem services. Nevertheless, the understanding of the physical processes that drive this intermittency has been long hampered by the limited availability of empirical data. In fact, monitoring the event-based expansion and contraction of temporary streams through visual inspection is very demanding and time-consuming. To circumvent this limitation, several low-cost sensor designs for monitoring flow presence have been suggested in recent years. These sensor exploit either water temperature or electrical conductivity. However, these sensors are typically characterized by pointwise probes that water flows can easily dodge, particularly in streams with complex and unstable morphologies. Moreover, very few studies have been conducted that use networks of probes to monitor stream intermittency at the catchment-scale.

Here we present a field-application of an advanced version of the low-cost water presence sensor developed by Chapin et al., 2016. In particular, we tested a new probe design to continuously measure the electrical conductivity across a channel cross-section and, thus, infer the presence of water therein. More than 50 probes were installed to monitor the dynamics of several intermittent tributaries of a small headwater catchment in northern Italy during the summer and fall of 2019. This catchment encompasses a wide variety of stream types: mild and steep slopes, incised and flat geometries, rocky and vegetated riverbeds. The field application shows that the proposed probes are able to provide useful information about the temporary activation of ephemeral streams under a variety of environments and conditions. The reconstructed temporal dynamics of the stream network comply with the persistency maps previously derived based on visual inspection. This new sensor design enables the continuous-time monitoring of the activity of intermittent streams, providing easily interpretable data under diverse conditions. We conclude that low-cost water presence sensors provide a unique opportunity to expand the coverage of the available datasets about the dynamics of intermittent streams.

How to cite: Zanetti, F., Durighetto, N., Vingiani, F., and Botter, G.: Monitoring intermittent streams with low-cost water-presence sensors, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8285, https://doi.org/10.5194/egusphere-egu2020-8285, 2020.

Irrigation of crops and grazed pastures can lead to harmful losses of nutrients via overland flow across the edge of the field. While good irrigation design can assist with avoiding overland flow, soil surface conditions can change rapidly and lead to surface flow even under well-designed irrigation systems. Therefore, real-time methods to detect emerging flow conditions, early enough to prevent substantial flow from the field during irrigation, is a potential mitigation option. But these methods require a prediction of the initiation of overland flow conditions in order to make the connection with real-time observations.

On a naturally-rough agricultural soil, triggering of overland flow is primarily related to the process of gradual filling of small (~50 mm across) depressions. As depressions fill, hydraulic connections are established with their neighbours and this eventually leads to sufficient connectivity that overland flow is initiated. The initiation of overland flow generally occurs at a critical value of connectivity (C_OF); the proportion of the soil surface that is connected via a water-filled pathway to an exit point of the field. As water ponding in, and flowing through, local depressions increases, the C_OF of the field increases and this leads to flow across the field boundaries. Quantifying the development of C_OF during an irrigation event, therefore, is key to predicting the initiation of overland flow.

We propose a method to continuously monitor the development of C_OF during an irrigation event that requires two elements. The first is a new proximal sensing technique, which exploits acoustic technology to continuously monitor A_sw, the proportion of the soil surface covered in water. The acoustic method comprises directional acoustic transmitter and receiver arrays. The directionality of the arrays provides a well-defined footprint area on the ground beneath the instrument. The A_sw can be reliably estimated from changes in the amplitude of reflected sound waves. The second element is a ponding and redistribution model which simulates the flow of water over a rough soil surface and assists by converting A_sw into C_OF.

Our preliminary results show that this real-time method of monitoring C_OF has a considerable scope in a variety of environments where prediction of overland flow initiation is desirable.

How to cite: Ghimire, C. P., Snow, V., Bradley, S., and Grundy, L.: Proximal remote sensing to quantify plot-scale overland flow connectivity, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11751, https://doi.org/10.5194/egusphere-egu2020-11751, 2020.

Cosmic-Ray neutron (CRN) sensors are widely used to determine soil moisture on the hectar 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 CRNS instruments have relied on helium-3 as an efficient neutron converter. Its ongoing scarcity demands for technological solutions using alternative converters, which are lithium-6 and boron-10. In order to scale up the method and to reduce costs we recently have developed large-scale neutron detectors including readout electronics and data acquisition systems based on Arduino microcontrollers. These boron-lined detectors shall offer an alternative platform to current Helium-3 based systems and allow for modular instrument designs. Individual shieldings of different segments within the detector introduces the capability of gaining spectral information. This opens the possibility for active signal correction during mobile measurements, where the influence of the 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 successful implementation of our design allowed also to build the largest up to now existing CRNS detector. 

How to cite: Köhli, M., Weimar, J., and Schmidt, U.: Large-scale alternative detection systems for CRNS, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16516, https://doi.org/10.5194/egusphere-egu2020-16516, 2020.

In the last decade the measurement of secondary cosmic ray neutrons has been established as a unique approach for intermediate scale observation of land surface hydrogen pools. Originally developed for soil moisture measurements, it has shown also promising applications for snow, biomass and canopy interception. The approach relies on the correlation between natural neutron background as created by cosmic-ray fluxes and local hydrogen pools. Due to the specific capabilities of the neutrons to move in air, the signal detected by the sensor installed above-ground is sensitive to an area of hundreds of meters providing a new perspective for proximal land-surface observations. The measurements are generally performed based on moderated proportional counters filled with Helium-3 or Boron and the moderation is created by adding shielding material (mostly polyethylene) around the counter.

The signal is affected by the temporal variability of the incoming neutron fluxes. At first, the variability of neutron fluxes is due to solar activities. The neutrons are further attenuated by the mass of the air and air humidity.

Specific corrections have been proposed to account for these effects. Air pressure and humidity corrections rely on local measurements that could be easily collected. Incoming correction due to solar cosmic-ray fluctuation is based on a worldwide network monitoring station (NMDB). This network provides online access to their data in real-time. However, this approach showed some limitations in region where incoming fluxes could be not representative of local conditions introducing errors that could be relevant for the estimation of the targeted variable. In addition, it requires the need of post-processing of the data resulting in some difficulties to provide, e.g., soil moisture observations in real-time.

In the present contribution, we show the results of tests conducted on an alternative commercial sensor based on scintillators. The new probe has the capability to identify different neutron energies ranges and gamma-rays providing new opportunities for hydrological observations at different spatial scales. In addition, the probe is sensitive to high energy particles that can be used for correcting the neutron signal by the variations of primary cosmic-ray flux. We present results from the comparison of the new probe with standard proportional counters and neutron monitor database in a long-term outdoor case study. We show how the use of local high energy particles is a practical alternative to account atmospheric corrections and overcome the limitation of using data from NMDB.

How to cite: Stevanato, L., Baroni, G., Fontana, C., Lunardon, M., Moretto, S., and Schattan, P.: Local high-energy particles measurements for detecting primary cosmic-ray variations: application for soil moisture estimation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11180, https://doi.org/10.5194/egusphere-egu2020-11180, 2020.

The novel method of Cosmic-ray neutron sensing (CRNS) allows non-invasive soil moisture measurements at a hectometer scaled footprint. Using this technique one can relate the flux density of albedo neutrons, generated in cosmic-ray induced air showers, to the amount of water within a radius of several hundred meters. In the recent years the understanding of neutron transport by Monte Carlo simulations led to major advancements in precision, which have successfully targeted a manifold of use cases. For example the improvements in the signal interpretation have meanwhile also been applied to the determination of snow water in Alpine regions. Up to now, the conversion of soil moisture to a detectable neutron count rate relies mainly on the equation presented by Desilets and Zreda. While in general a hyperbolic expression can be derived from theoretical considerations, their empiric parameterisation needs to be revised as many groups have found site-specific calibrations, which are simply based on different empirical data sets.

Investigating the above-ground neutron intensity by 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 sensor response function. The resulting formula significantly improves the soil-moisture-to-intensity conversion and allows for a more comprehensive instrument data quality, which especially closes the gap between observations of very dry and wet conditions.

How to cite: Weimar, J., Köhli, M., Schrön, M., and Schmidt, U.: Moisture and humidity dependence of the above-ground cosmic-ray neutron intensity, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17856, https://doi.org/10.5194/egusphere-egu2020-17856, 2020.

A Critical Zone Observatory (CZO) was recently established in the Alento River Catchment (ARC; southern Italy) within the TERENO (TERrestrial ENvironmental Observatories) long-term ecosystem infrastructure network. In 2016 SoilNet wireless sensor networks and cosmic ray neutron probes (CRNP) were installed in the upper part of this catchment and specifically in two experimental sub-catchments (MFC2 and GOR1) characterized by different topographic, pedological, land-use, and weather conditions. The Soilnet end-devices are monitoring soil moisture and matric potential at two different soil depths (15 cm and 30 cm) in 20 locations around the cosmic ray neutron probe. We evaluated the the relationship between Soil Moisture Index (SMI) and rainfall deficits (considered as rainfall minus potential evapotranspiration) at monthly time scale. The cropland site on the south-facing hillslope of ARC is characterized by more extreme dry and wet conditions. Another goal is to identify the dominant controls that most govern the spatial soil moisture patterns in these two different sites. The relationship between the CRNP-based soil moisture and spatial variability of SoilNet-based soil moisture is nearly linear in the case of the cropland site (MFC2) but follows a fairly concave curve in the case of the forestland site (GOR1). The majority of the spatial variance in MFC2 is explained by terrain attributes, i.e. slope-induced during wet conditions and aspect-induced during dry conditions. In GOR1 the spatial variance of soil moisture data is mostly explained by topographic factors under wet conditions during the rainy season. In both sites the soil texture is able to explain only less than 10% of spatial variability of soil moisture data.

How to cite: Nasta, P., Bogena, H., Sica, B., Vereecken, H., and Romano, N.: Understanding the spatio-temporal variability of soil moisture by integrating cosmic-ray neutron probes with SoilNet wireless sensor netwoks under a seasonal Mediterranean-climate regime, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5295, https://doi.org/10.5194/egusphere-egu2020-5295, 2020.

The H2020 ATLAS project (www.atlas-h2020.eu/) aims to develop an open, flexible and distributed platform that will provide services for the agricultural sector based on the seamless interconnection of sensors and machines. Two interconnected services that will be included in the platform are the soil moisture monitoring and the irrigation management services. The soil moisture monitoring service will integrate both invasive (wireless sensor network (SoilNet)) and non-invasive soil moisture monitoring methods (cosmic-ray neutron sensors (CRNS)). Ultimately, a model will be developed that combines SoilNet and CRNS measurements to predict soil moisture time series. Soil water potential sensors will be incorporated as well.

Data provided by the above described service will be incorporated in an irrigation management service which will be based on hydrological modelling. The fully distributed, deterministic Community Land Model (CLM, version 5) will be applied which incorporates physically-based simulation of soil water balance and crop growth. Two different levels of application will be considered, namely the farm and watershed scale, which will be combined to weather forecast in order to provide irrigation scheduling advice. The farm scale application will take advantage of soil moisture monitoring data and provide farm specific irrigation scheduling, while the watershed scale application will provide a more generic irrigation advice based on the average cultivation practices. Furthermore, the CLM model will be coupled to a groundwater flow model in order to connect irrigation to groundwater availability. By doing so, it will be possible to support the efficient and sustainable groundwater management as well as competent water uses in an area that suffers from water scarcity.

These services will be implemented in the area of Pinios Hydrologic Observatory, located in central Greece. Three pilot orchards will be established introducing different soil moisture monitoring setups, while the boundaries of the Observatory will be used for the pilot implementation of irrigation management service on the watershed scale. Furthermore, two pilot vineyards located in northern Greece will be established in order to further test the services functionality on the farm scale.

How to cite: Pisinaras, V., Brogi, C., Bogena, H., Hendricks-Franssen, H.-J., Dombrowski, O., and Panagopoulos, A.: Development of irrigation management services based on integration of innovative soil moisture monitoring and hydrological modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4093, https://doi.org/10.5194/egusphere-egu2020-4093, 2020.

Since it has become clear that climate change is having a major impact on water availability for agriculture and crop productivity, an accurate estimation of field-scale root-zone soil moisture (RZSM) is essential for improved agricultural water management. The Cosmic Ray Neutron Sensor (CRNS) has recently been used for field-scale soil moisture (SM) monitoring in large areas and is a credible and robust technique. Like other remote or proximal sensing techniques, the CRNS provides only SM data in the near surface. One of the challenges and needs is to extend the vertical footprint of the CRNS to the root zone of major crops. This can be achieved by coupling the CRNS measurements with conventional methods for soil moisture measurements, which provide information on soil moisture for whole rooting depth.

The objective of this poster presentation is to estimate field-scale RZSM by correlating the CRNS information with that from soil moisture sensors that provide soil moisture data for the whole root depth. In this study, the Drill and Drop probes which provide continuous profile soil moisture were selected. The RZSM estimate was calculated using an exponential filter approach.

Winter Wheat cropped fields in Rutzendorf, Marchfeld region (Austria) were instrumented with a CRNS and Drill & Drop probes. An exponential filter approach was applied on the CRNS and Drill and drop sensor data to characterize the RZSM. The preliminary results indicate the ability of the merging framework procedure to improve field-scale RZSM in real-time. This study demonstrated how to combine the advantages of CRNS nuclear technique (especially the large footprint and good representativeness of obtained data) with the advantages of conventional methods (providing data for whole soil profile) and overcome the shortcoming of both methods (the lack of information in the deeper part of soil profile being the major disadvantage of CRNS and the spatial limitation and low representativeness of point data being the major disadvantage of conventional capacitance sensors). This approach can be very helpful for improving agricultural water management.

How to cite: Said, H., Weltin, G., Heng, L. K., Franz, T., Fulajtar, E., and Dercon, G.: Field scale root zone soil moisture estimation by coupling cosmic-ray neutron sensor with soil moisture sensors, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4618, https://doi.org/10.5194/egusphere-egu2020-4618, 2020.

This study presents the results of soil moisture investigation carried by the Joint FAO/IAEA Division using Cosmic-Ray Neutron Sensor (CRNS). The measurements have been collected at several studied sites in Austria. The Petzenkirchen study which is within the Austrian Institute for Land and Water Management Research employing stationary CRNS has been established in Dec. 2013 and it provides major dataset for this study. It represents small watershed in hilly area of northern footslopes of Alps. Apart of that the short-term measurement campaigns were carried out using back-pack CRNS in alluvial plain east of Neusiedler See and in mountainous areas of Rauris Municipality in central part of Austrian Alps.

This study describes the results and interpretation of about 7 years of soil moisture data set (2013-2020). The analysis focused on improving the calibration approaches, CRNS footprint, heterogeneity soil moisture mapping, impacts of biomass and altitude on neutron counts. Further, the use of CRNS data for calibrating soil moisture calculated by soil water balance model was tested. The overall application is aimed at supporting agricultural water management and in developing methodology for soil moisture monitoring for water management in agriculture (under rainfed agriculture as well as for irrigation scheduling). This unique data-set can also provide additional information for hydrological modelling and remote sensing applications (at regional and global scales), as well as for extreme weather events (drought and flood) management and forecasting.

How to cite: Fulajtar, E., Said Ahmed, H., Wahbi, A., Baroni, G., Rosolem, R., Power, D., Franz, T., and Kheng Heng, L.: Long-term soil moisture observations using cosmic-ray neutron sensing in Austria, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7668, https://doi.org/10.5194/egusphere-egu2020-7668, 2020.

The cosmic ray neutron (CRN) probe is a non-invasive device to measure soil moisture at the field scale. This instrument relies on the inverse correlation between aboveground epithermal neutron intensity (1eV – 100 keV) and environmental water content. The measurement uncertainty of the neutron detector follows Poisson statistics and thus decreases with decreasing neutron intensity, which corresponds to increasing soil moisture. In order to reduce measurement uncertainty (e.g. < 0.03 m³/m³), the neutron count rate is often aggregated over large time windows (e.g. 12h or 24h). To enable shorter aggregation intervals, the measurement uncertainty can be reduced either by using more efficient detectors or by using arrays of detectors, as in the case of CRN rover applications. Depending on soil moisture and driving speed, aggregation of neutron counts may also be necessary to obtain sufficiently accurate soil moisture estimates in rover applications. To date, signal aggregation has not been investigated sufficiently with respect to the optimisation of temporal (stationary probes) and spatial (roving applications) resolution. In this work, we present an easy-to-use method for uncertainty quantification of soil moisture observations from CRN sensors based on Gaussian error propagation theory. We have estimated the uncertainty using a third order Taylor expansion and compared the result with a more computationally intensive Monte Carlo approach and found excellent agreement. Furthermore, we used our method to quantify the dependence of soil moisture uncertainty on CRN rover survey design and on selected aggregation time. We anticipate that the new approach helps to quantify cosmic ray neutron measurement uncertainty. In particular, it is anticipated that the strategic planning and evaluation of CRN rover surveys based on uncertainty requirements can be improved considerably.

How to cite: Jakobi, J., Huisman, J. A., Schrön, M., Fiedler, J., Brogi, C., Vereecken, H., and Bogena, H.: Error estimation for soil moisture measurements with cosmic-ray neutron sensing and implications for rover surveys, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8488, https://doi.org/10.5194/egusphere-egu2020-8488, 2020.

Snow monitoring instruments like snow pillows are influenced by disturbances such as energy transport into the snowpack, influences from wind fields or varying snow properties within the snowpack (e.g. ice layers). The intensity of epithermal neutrons that are produced in the soil by cosmic radiation and measured above the ground surface is sensitive to soil moisture in the upper decimetres of the ground within a radius of hectometres. Recently, it has been shown that aboveground cosmic ray neutron sensors (CRNS) are also a promising technique to monitor snow pack development thanks to the larger support that they provide and to the lower need for maintenance compared to conventional sensor systems. The basic principle is that snow water moderates neutron intensity in the footprint of the CRNS probe. The epithermal neutrons originating from the soil become increasingly attenuated with increasing depth of the snow cover, so that the neutron intensity measured by the CRN probe above the snow cover is directly related to the snow water equivalent.

In this paper, we use long-term CRNS measurements in the Pinios Hydrologic Observatory, Greece, to test different methods for the conversion from neutron count rates to snow pack characteristics, namely: i) linear regression, ii) the standard N₀-calibration function, iii) a physically-based calibration approach and iv) the thermal to epithermal neutron ratio. The latter was also tested for its reliability in determining the start and end of snowpack development, respectively. The CRNS-derived snow pack dynamics are compared with snow depth measurements by a sonic sensor located near the CRNS probe. In the presentation, we will discuss the accuracy of the four conversion methods and provide recommendations for the application of CRNS-based snow pack measurements.

How to cite: Bogena, H. R., Herrmann, F., Jakobi, J., Pisinaras, V., Brogi, C., Huisman, J. A., and Panagopoulos, A.: Cosmic-ray neutron sensing based monitoring of snowpack dynamics: A comparison of four conversion methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8827, https://doi.org/10.5194/egusphere-egu2020-8827, 2020.

Cosmic-ray neutron albedo sensing (CRNS) is a modern technology that can be used to non-invasively measure the average water content in the environment (i.e., in soil, snow, or vegetation). The sensor footprint encompasses an area of 10-15 hectares and extends tens of decimeters deep into the soil. This method might have the potential to bridge the scale gap between conventional in-situ sensors and remote-sensing data in both, the horizontal and the vertical domain.

Currently, more than 200 sensors are operated in the growing networks of national and continental observatories. While single CRNS stations are continuously monitoring the local water dynamics at fixed field sites, mobile CRNS platforms are used for on-demand soil moisture mapping at the regional scale. The sensors are rapidly operational on any ground- or airborne vehicle. The data is particularly useful to study hydrological extreme events, heatwaves, and snow melt/accumulation, and it is being applied in hydrological models and agricultural irrigation management.

In the presentation we will explore the potential of the CRNS method to support and complement in-situ and remote-sensing data for hydrological event monitoring. We will discuss ongoing research activities that are aimed at improving the operationality, frequency, and spatial extend of CRNS measurements. New measurement strategies that are currently explored are, for example: dense clusters of 20 CRNS stations fully covering a 100 hectare catchment; heat wave monitoring with mobile car-based CRNS; regular soil/snow water mapping using mobile CRNS on cars and trains; and airborne surveys using CRNS on gyrocopters.

Future CRNS observations could provide a valuable contribution to the multi-sensor approach, e.g. to help tracking and characterizing surface water movement, to map regional-scale soil moisture patterns, or to calibrate and evaluate satellite data.

How to cite: Schrön, M., Oswald, S. E., Zacharias, S., Dietrich, P., and Attinger, S.: Monitoring and Mapping of Soil and Snow Water Across Scales with Cosmic-Ray Neutron Sensor Networks and Mobile Platforms, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22317, https://doi.org/10.5194/egusphere-egu2020-22317, 2020.

Cosmic-ray neutron sensing (CRNS) is a non-invasive method that provides an average soil moisture for a large support volume (radial footprint up to 240 m, depth up to 80 cm) with high temporal resolution. It covers the most dynamic part of the vadose zone at a scale that is already a more substantial part of the landscape then local point measurements. This integral soil moisture value overcomes the limitations regarding issues of small-scale heterogeneity. Therefore, the use of CRNS soil moisture could improve the estimation of potential groundwater (GW) recharge at the field.

Besides the stochastic integration of point-scale soil moisture profiles, CRNS soil moisture estimates could be used for the inverse estimation of effective soil hydraulic properties by applying unsaturated soil hydrological models and to determine environmental fluxes such as GW recharge.

Within this study CRNS soil moisture is used to estimate the effective soil hydraulic properties within the model HYDRUS 1D. Resulting GW recharge represents the field scale because of the integrated nature of the soil moisture product, even though the model is calculating percolation fluxes for 1D - profiles. These integrated GW recharge fluxes are compared to established point scale methods of GW estimation using soil moisture from a distributed sensor network to inversely estimate the effective soil hydraulic properties within HYDRUS 1D.

CRNS is, however, sensitive to the vertical distribution of water content and this behavior should be explicitly considered. Two approaches are assessed further to account for that. On the one hand, a correction of CRNS, based on measured soil moisture profiles, is tested and CRNS soil moisture is directly used for recharge calculation in HYDRUS. On the other hand, the COSMIC-Operator, as implemented within HYDRUS, is used for calibrating the model by directly comparing neutron count rates from simulated soil moisture. Both approaches are assessed with respect to their ability to estimate natural groundwater recharge rates.

How to cite: Scheiffele, L. M., Munz, M., Baroni, G., Bauer, S., and Oswald, S. E.: Dynamic groundwater recharge rates at field scale: how to successfully use soil moisture from cosmic-ray neutron sensing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18563, https://doi.org/10.5194/egusphere-egu2020-18563, 2020.

Time-lapse ground-based gravimetry is increasingly applied in subsurface hydrology, providing mass balance constraints on water storage dynamics. For a given water content change as e.g. after a precipitation event, the simplest assumption is that of a homogeneous, infinite slab (Bouguer plate) of water column causing the measurable increase in gravitational attraction. For heterogeneous subsurface environments such as karst aquifers at field scale this assumption may not always hold. The gravity signal is depth-integrated and non-unique, hence indistinguishable from a heterogeneous distribution without further information.

Exploiting the different spatial sensitivities of gravity and vertical gravity gradient (VGG) data can shed light on the following questions:

• Is the subsurface water content within the gravimeter’s footprint likely to be homogeneous or showing small-scale heterogeneity?

• If not, at which distance are these mass heterogeneities and how large are they?

• Which monitoring set-ups (tripod heights, number of and distance between VGG measurement locations) are likely to detect mass heterogeneity of which spatial characteristics?

One year of monthly vertical gravity gradient surveys has been completed in the geodetic observatory in karstic environment on the Larzac plateau in southern France. We interpret the VGG observations obtained in this field study in the context of further available hydraulic and geophysical data and hydro-gravimetrical simulation. Finally, practical applications in view of detecting near-surface voids and reservoirs of different porosities as well as their storage capacity and seasonal dynamics are evaluated.

How to cite: Cooke, A.-K., Champollion, C., Vermeulen, P., Janvier, C., Desruelle, B., Le Moigne, N., and Merlet, S.: Detection of subsurface water storage dynamics with combined gravity - vertical gravity gradient monitoring and hydrological simulation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9020, https://doi.org/10.5194/egusphere-egu2020-9020, 2020.

Soil water content in the unsaturated zone is a key parameter of the environmental system. The understanding of soil moisture plays a major role with regard to questions of water and nutrient supply to plants, groundwater recharge, soil genesis and climatic interactions.

In our study we aim to test a new technology for the non-invasive measurement of soil moisture profiles, the so-called Surface-NMR (Nuclear Magnetic Resonance). The instrument applies magnetic fields to the ground and detects its changes caused by mobile and immobile hydrogen atoms in the soil column. Using four different frequencies, the data may provide insights into the water content of four distinct soil layers between the surface and 20 cm depth.

We carried out multiple NMR measurements at four different field sites in Germany and compared the data with conventional methods, such as gravimetric soil samples, Time Domain Reflectometry (TDR), and Cosmic-Ray Neutron Sensing (CRNS).

The dataset will be used to investigate the following research questions:

1. Is the Surface-NMR method suitable to provide depth-resolved information of soil moisture under field conditions?
2. Does Surface-NMR have the potential to replace or complement conventional methods of soil moisture measurement in the field?
3. What can we learn about the spatial variability and scale dependency of soil moisture by combining three measurement methods of different scale (TDR, NMR, CRNS)?

How to cite: Bauckholt, M., Pohle, M., Schrön, M., Zacharias, S., Landmark, S., Kathage, S., Kathage, A., Zengerle, C., Kasner, M., and Werban, U.: Evaluation of NMR and other soil water content measurement methods at the point and field scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9522, https://doi.org/10.5194/egusphere-egu2020-9522, 2020.

 In this work, we test the cross-correlation of ambient seismic noise method in monitoring underground water variations. Within this perspective we applied the abovementioned technique to study the water table changes occurring both in areas exploited for drinking water needs and inside landslides. Into detail, surveys were carried out in Crépieux-Charmy and Ventasso water catchment fields and in the Cà Lita landslide, respectively. Our aim is to optimize the outcome of the method by studying the effect of different processing steps involved in the computation of the cross-correlation technique. For this purpose, we analyzed the influence of filter types and different time windows length. Additionally, in order to address the problem of localization of the change in the medium the seismic velocity variations have been also derived from limited frequency bandwidths according to the characteristics observed in the signals spectrum. This work has shown the potential of this methodology as a valuable non-destructive toll to accurately describe hydrogeological dynamics. The monitoring system could thus be coupled with the traditional tools to improve the reconstruction of the underground water variations.

How to cite: Taruselli, M., Arosio, D., Longoni, L., Papini, M., and Zanzi, L.: Optimization of ambient seismic noise interferometry to monitor groundwater level variations., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1072, https://doi.org/10.5194/egusphere-egu2020-1072, 2020.

Estimation of aquifer hydraulic properties is necessary for predicting groundwater flow and hence managing groundwater resources. Analysis of tide-induced groundwater table fluctuations in unconfined aquifers is one of the methods to estimate aquifer properties. Changes in groundwater level affect surface gravity. Consequently, surface gravity in coastal regions is expected to fluctuate due to the groundwater table fluctuations and is potentially useful for estimating aquifer properties. Moreover, gravity measurements are sensitive to mass redistribution around the observation location and therefore are useful for estimating the storage coefficient of an aquifer. In this study, surface gravity and unconfined groundwater level were measured continuously near the coast of Japan to observe gravity fluctuations due to the tide-induced groundwater table fluctuations. Groundwater level measured in two wells at 60 and 90 m distances from the coastline fluctuated in response to ocean tides. Two superconducting gravimeters (SGs) were installed at 70 and 80 m distances from the coastline and at an elevation of 8 m. After taking the difference between gravity values recorded with the two SGs and then correcting the gravity difference for ocean loading effects, diurnal and semi-diurnal gravity fluctuations, which are possibly due to tide-induced groundwater table fluctuations, were recognized. These results suggest that gravity monitoring with two SGs at different distances from the coastline can be useful for observing gravity fluctuations due to tide-induced groundwater table fluctuations and possibly for estimating aquifer hydraulic properties.

How to cite: Goto, H., Sugihara, M., Nishi, Y., and Ikeda, H.: Observation of gravity fluctuations due to tide-induced groundwater table fluctuations with two superconducting gravimeters, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19014, https://doi.org/10.5194/egusphere-egu2020-19014, 2020.

The exceptional capabilities of proximal radiometric measurements to estimate Soil Water Content (SWC) have recently been proven effective for precision farming applications. The water contained in the growing vegetation (i.e. Biomass Water Content, BWC) attenuates the terrestrial gamma signal acquired by a permanent station in a crop field and it represents the most relevant source of systematic bias. In the perspective of employing proximal gamma-ray spectroscopy for automatic irrigation scheduling, the Biomass Water Content (BWC) correction is mandatory for assessing crop water demand and for a sustainable use of water.

In this study we model the time dependent gamma signal attenuation due to BWC and we demonstrate that the SWC estimated through the corrected spectrometric data during a crop life-cycle agrees on average within 4% with the measurements obtained by gravimetric sampling campaigns. A reliable Monte Carlo simulation of the gamma photon generation, propagation and detection phenomena permits to evaluate the shielding effect due to the linear increase of BWC associated to stems, leaves and fruits of the tomatoes during their crop life-cycle. Compared to a SWC gamma estimation in the case of bare soil, the percentage overestimation δ is linearly correlated with the thickness of a biomass equivalent water layer (Tk) as δ (%) = 9.7 · Tk (mm), with a coefficient of determination r² = 0.99.

Generalizing this approach, we can conclude that the plant growth curve is a fundamental input for correcting the SWC estimates in proximal gamma-ray spectroscopy via Monte Carlo simulation, in the perspective of filling the gap between punctual and satellite soil moisture measurements using this technique.

How to cite: Mantovani, F., Albéri, M., Bottardi, C., Chiarelli, E., Raptis, K. G. C., Serafini, A., and Strati, V.: Discriminating biomass and soil water content with proximal gamma-ray spectroscopy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22370, https://doi.org/10.5194/egusphere-egu2020-22370, 2020.