Over the last decades, several new observation systems have been developed, tested, and implemented in dedicated experimental sites by research groups in many countries all around the World. The data collected at these high-level test sites have boosted research and collaborations providing the basis for new hypothesis testing, better process understanding and model improvements. In contrast, national and more operational ground monitoring networks are still largely based on traditional instruments. Moreover, the different networks and the data are not always well integrated, even at the regional level. For these reasons, the capability to monitor the main components of the hydrological cycle over large areas is still limited and the observation systems to support the management of the water resources and for environmental protections can be improved. By using soil moisture and snow water equivalent monitoring as examples, in this contribution we present and discuss challenges, results and opportunities in upgrading national weather stations and improving the service provided by the public environmental agencies. Specifically, the difficulties of implementing ground monitoring networks are first discussed. The opportunities provided by the development of new non invasive sensors based on cosmic-ray neutrons detection are then presented. The activities and the results conducted during the last years to move this technology further from research to operation are shown. The current uptake from a number of Italian environmental agencies is reported. The key components and current challenges for a successful implementation are finally discussed.

How to cite: Baroni, G., Gianessi, S., Mazzoleni, R., Alessandrini, C., Gandolfi, C., Cazzuli, O., Ragazzi, F., Ferraris, S., Canone, D., Ronchi, C., Cremonini, R., and Barbero, S.: Upgrading standard weather station with cosmic ray neutron sensors in Italy: challenges, results, and opportunities., A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-36, https://doi.org/10.5194/egusphere-gc8-hydro-36, 2023.

Overland and subsurface flow are major runoff processes but remain poorly understood for pre-Alpine catchments with low permeability soils. The connectivity of these near-surface flow pathways leads to quick changes in streamflow during events. However, their occurrence is highly variable in both space and time, and depends on the geomorphological setting and event characteristics. Therefore, the aim of the TopFlow project is to investigate the generation and connectivity of overland flow and shallow subsurface flow in a Swiss pre-Alpine catchment.

We installed runoff plots at 14 locations inside a 20-ha catchment to measure overland flow and shallow (up to ~40 cm) subsurface flow during two summers: 2021 and 2022. In addition, we used homemade electrical resistance sensors to detect near-surface flow and saturation and we measured groundwater levels and soil moisture. The plots are located at different topographic locations and under different vegetation. In this presentation, we will present our first results on the occurrence of the overland flow and shallow subsurface flow and relate them to the topographic position and rainfall- and antecedent wetness conditions.

How to cite: Gauthier, V., Leuteritz, A., and van Meerveld, I.: When does near-surface flow occur in a pre-Alpine headwater catchment?, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-47, https://doi.org/10.5194/egusphere-gc8-hydro-47, 2023.

Upscaling of soil water content (SWC) information towards the large-scale (>10 km) is highly desired to address the increasing demand on SWC products at various sectors. Random forest (RF) regression has been suggested as suitable method to generate large SWC maps from a limited amount of observations. RF deals with multiple prediction variables (predictors) to derive the missing values of a desired variable (e.g. SWC) based on their internal relationship. Cosmic ray neutron sensing (CRNS) is an alternative method for passive SWC mapping and monitoring, either by stationary CRNS sensors or by mobile CRNS roving. CRNS has a certain advantage over most classical hydrogeophysical approaches because of its footprint at the hectares-scale and beyond, particularly true for roving data, which qualifies CRNS data as suitable input for RF regressions. However, commonly CRNS roving data contain a high amount of noise and outlier values, related to the statistical distribution of neutron counting, which hinders the signal interpretation and could lower the quality of the RF regression performance. There are so far two ways to overcome the noise problem and to achieve a higher data stability; i) increasing of the aggregation time, which decreases the signal uncertainty but also reduces the spatial resolution and ii) applying smoothing algorithms, e.g. interpolation or moving averages, which results in more stable values, but it does not solve the outlier problem.

We used SWC data from CRNS roving along the Selketal catchment at the Harz mountain, Germany, to test the performance of a score criteria for an adaptive removal of potential outliers. The score criteria are internal test parameters, providing an indication about the probability of values that might be an outlier or not. Therefore, each observation was subject to a group of queries, asking its conformity to the surrounding values by selected statistical parameters. Based on the total score of the queries, the potentially unreliable observations were removed using various thresholds and used as input for the RF regression. RF regression was performed using static (e.g. topographical indices, soil properties) and dynamic (precipitation) predictors generating SWC maps from an area of ~2700 km². SWC input data were split into training (~2/3) and validation sets (~1/3).

Preliminary results showed that the application of the score criteria resulted in more stable spatial pattern and improved the R² from 0.099 to 0.196, 0.266 and 0.308 for score 6, 4 and 3, respectably. Achieved root mean squared error also decreased with stronger filtering, ranging from 0.14 for the original datasets to 0.078 for score 3. However, by using the score 3 threshold, 22.4% of the data were omitted. Hence, an optimization between the amount of excluded data and the resulting improvement of prediction needs to be developed and tested. The implementation of the spatial relationship in-between the observations and a weighting of the score values according to their importance should further increase the performance. Due to its easy application and its adjustable criteria selection, the proposed filtering approach has the potential to become more popular in CRNS roving studies.

How to cite: Altdorff, D., Dega, S., Paasche, H., and Schrön, M.: Improvement of soil moisture regionalization based on random forest regression by applying score criteria for mobile cosmic-ray neutron sensing data, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-67, https://doi.org/10.5194/egusphere-gc8-hydro-67, 2023.

Here we present the UK vision for new world-leading hydrological observation networks and sensor innovation test beds that will provide the long-term datasets needed to enable the mitigation of the impacts of hydrological extremes. Plans are underway for a Floods and Droughts Research Infrastructure (FDRI). This represents a major capital investment expected to be funded by the UK Research and Innovation Infrastructure Fund and delivered through the Natural Environment Research Council (NERC) at an estimated cost of £38m. FDRI is urgently needed to make the UK more adaptable and resilient to floods and droughts. It will include major new hydrological catchment instrumentation, with innovative technology to provide observations of key components of the terrestrial water cycle, and in-field facilities for trialling and developing new sensing technologies. Extensive community consultation and reviews have identified key science questions that are being used to inform infrastructure design. Successful impact will be enabled through strong investment in digital infrastructure to achieve a hydrological data commons. Integrated near real-time datasets will be publicly accessible, consolidated and inter-operable, ready for application specific analysis and modelling. As FDRI planning develops, there are opportunities to design-in the latest thinking on catchment monitoring strategies with innovative sensing, and to ensure that long-term hydrological datasets will be able to answer a wide variety of future research questions.

How to cite: Evans, J., Bloomfield, J., Coxon, G., Teagle, S., Buytaert, W., Fry, M., Ball, L., Rudd, A., Sorensen, J., Chappell, N., Wagener, T., and Old, G.: A Vision for Transformative Hydrological Monitoring – Planning for the UK Floods and Droughts Research Infrastructure (FDRI), A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-85, https://doi.org/10.5194/egusphere-gc8-hydro-85, 2023.

In the Upper Alento River Catchment (UARC; southern Italy), two test sites, having different physiographic and soil features, were instrumented with SoilNet wireless sensor networks controlling GS3 (METER Group, Inc. USA) capacitance sensors deployed at soil depths of 15 and 30 cm at twenty locations to monitor soil permittivity, soil temperature, and apparent electrical conductivity. The conversion of soil permittivity into soil water content is challenging given the high clay content of the soil. Therefore, the both factory and Topp’s calibration functions are compromising soil water content dynamics with underestimated dynamics during dry periods and overestimations in the wet season. In addition, the soil moisture measurements is influenced by the very strong soil temperature variations. In this study we combined a laboratory calibration with in-situ intensive validation campaigns. The laboratory calibration of GS3 electromagnetic sensors was carried out on repacked soil samples by simultaneously measuring soil permittivity and soil water content at prescribed temperature steps within the range observed in the field (from 4.5 °C and 31.9 °C). A new empirical regression-based calibration law was developed for the two study areas to relax the impact of soil temperature on the measurement of soil permittivity. The temperature-corrected approach was validated in the field by taking thermo-gravimetric measurements of soil water content using a stainless steel soil core sampler over five field campaigns at soil depths of 15 and 30 cm. Our results demonstrated that the proposed site-specific calibrations properly reflect the seasonal dynamics of observed soil water content and outperform both factory and Topp’s calibration functions.

How to cite: Nasta, P., Bogena, H., Huisman, A., Sica, B., Allocca, C., Lazzaro, U., Mazzitelli, C., Vereecken, H., and Romano, N.: Temperature-corrected calibration of GS3 dielectric sensors, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-89, https://doi.org/10.5194/egusphere-gc8-hydro-89, 2023.

Dielectric sensors have been used for decades to monitor soil water content. While such measurements are straightforward for insulations, they may be challenging for electrically conductive materials. As a result, the performance of soil moisture sensors based on capacitance technology rapidly decreases with soil salinity. In alternative, dielectric permittivity and electrical conductivity may be estimated at once and very accurately by measuring the complex impedance of the sample. We present a new method for measuring the complex impedance, which can be implemented with very inexpensive circuitry. The resulting dielectric measurements are shown to be accurate for conductivity up to 20 dS/m. We will present preliminary results in soilless material and discuss the benefits of accurate dielectric measurements in greenhouse applications. 

How to cite: Castiglione, P. and Campbell, G.: Complex dielectric measurements permit accurate estimate of soil water content in saline environments., A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-99, https://doi.org/10.5194/egusphere-gc8-hydro-99, 2023.

How to cite: Gazzola, E., Stevanato, L., Morselli, L., and Arnaud, P.: Improving the prediction of extreme events with new-generation CRNS probes, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-105, https://doi.org/10.5194/egusphere-gc8-hydro-105, 2023.

The proper analysis and prediction of the hydrodynamic interaction between water flow and vegetation covering natural and manmade vegetated rivers are among the main objectives of Ecohydraulics. Riverine and riparian plants have a paramount impact on flow resistance and water quality associated with vegetated water bodies. Also, the presence of vegetation considerably affects the mean and turbulent flow fields with important implications on oxygen production and nutrient transport within vegetated open channels. In this perspective, the use of advanced field and remote sensing techniques to measure the most relevant features of plants constitutes a stimulating open research window. The interplay between riparian vegetation and water flow in vegetated water bodies has a key role in the dynamic evolution of aquatic and terrestrial ecosystems in wetlands and lowlands. The present study analyzes the effects of the spatial distribution of reed (Phragmites australis (Cav.) Trin. ex Steud.) beds, an invasive riparian species extremely widespread in wetland and lowlands worldwide, on the main hydraulic and hydrodynamic properties of an abandoned vegetated reclamation channel located in southern Italy. A field campaign was carried out to obtain Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI) of reed beds through both ground-based and Unmanned Aerial Vehicle (UAV) methodologies, and to correlate them to the channel’s flow dynamic and water quality main features. Hydrodynamic simulations of the vegetated reclamation channel were performed and validated based on the experimental measurements of the hydraulic and vegetational parameters acquired in the field to build up a robust model to be employed also in future Ecohydraulic research. The evidence of this study constitutes useful insights into the quantitative analysis of the correlation between the spatial distribution of riparian vegetation stands in natural and manmade vegetated water bodies and their hydrodynamic and water quality main features. The outcomes of the present work can be seen as stimulating new viewpoints to be taken into account for the proper management of biomass belonging to riparian and riverine vegetation developing within vegetated water streams.

How to cite: Lama, G. F. C. and Crimaldi, M.: On the Key Eco-hydrodyanmic Features of Vegetated Rivers: a Case Study in southern Italy, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-108, https://doi.org/10.5194/egusphere-gc8-hydro-108, 2023.

The cosmic-ray neutron (CRN) probe has successfully been used to estimate soil moisture time series at various soil and land cover types across several climatic zones. Previous studies also demonstrate the value of mobile CRN campaigns (i.e. roving) for soil moisture mapping. However, the published study locations have mainly been homogeneous in terms of land cover and soil type. At such conditions, simple on-site calibration using small-scale soil samples can be sufficient. Nonetheless, soil sampling is typically time-consuming, is not always possible and/or is not necessarily representable of the true conditions. Furthermore, measured neutron intensities are dependent on all the hydrogen pools within the footprint, primarily soil moisture, but also litter layer and biomass. Thus, calibration using 100+ soil samples for each point along the survey route is necessary in highly heterogeneous agricultural landscapes. The requirement of such spatiotemporal calibration has hindered a full embracement of the CRN roving method. Neutron sensors with multiple energy sensitivities, here named dual-spectra CRN, can instead be combined to separate the influence of various hydrogen pools.

In this work, we used a dual-spectra CRN rover to collect data along a 34.5 Km NE-SW line in Denmark (approximately 176 line Km) at an average speed of 29 Km/h. The data was collected biweekly from December 2018 to May 2019 resulting in 12 campaigns in total. The dual-spectra averages of each campaign correlate to the SMAP soil moisture product, with a higher correlation for purer signals of thermal and epithermal neutron counts. Furthermore, the epithermal neutron intensity and thermal-to-epithermal ratio maps are dependent on the land cover type enabling the use of a simple and robust methodology to obtain spatiotemporal soil moisture maps using these multiple signals. The estimated spatiotemporal soil moisture values are within expected ranges and are not as the neutron signal dependent on land cover. Instead, the average soil moisture appears to be dependent on the estimated hydraulic conductivity map for the area.

How to cite: Looms, M., Kragh, S. J., Meyer, R., Jensen, K. H., and Andreasen, M.: Mapping spatiotemporal soil moisture in highly heterogeneous agricultural landscapes using mobile dual-spectra cosmic-ray neutron probes, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-120, https://doi.org/10.5194/egusphere-gc8-hydro-120, 2023.

Soil moisture is a critical hydrological variable affecting rainfall-runoff processes, regulating net ecosystem exchange, and an essential agricultural variable that constrains food production. Soil water deficiency results in water stress to plants, causing a reduction in biomass and yield production. Thus, assessing soil moisture conditions and estimating the effects of drought or water excess are relevant and important information associated with a decline in agricultural yield. This study analyses time series data of the Cosmic Ray Neutron Sensor (CRNS) to estimate soil moisture in three different climatic zones with distinct seasonal dynamics. The CRNS allows non-invasive and continuous monitoring of soil moisture by detecting the neutrons generated by cosmic rays that mainly interact with the hydrogen atoms in soil water. This study demonstrates an improved CRNS signal processing to enhance the temporal accuracy and overall signal-to-noise ratio by observing sub-daily soil moisture changes. In particular, this study investigates the effectiveness of the Moving Average (MA), Median filter (MF), Savitzky-Golay (SG) filter, and Kalman filter (KF) to minimize errors in soil moisture estimates at distinct points in time. We anticipate the improved signal-to-noise ratio to benefit CRNS applications such as the detection of rain events at the sub-daily resolution, provision of SM at the exact time of a satellite overpass, and irrigation applications.

How to cite: Baatz, R., Davies, P., Bogena, H., Quansah, E., and Amekudzi, L. K.: Improving Soil Moisture Monitoring from Cosmic Ray Neutron Sensors under Various Climate Conditions, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-130, https://doi.org/10.5194/egusphere-gc8-hydro-130, 2023.