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Accurate soil moisture (SM) monitoring is key in irrigation as it can greatly improve water use efficiency. Recently, Cosmic-Ray Neutron Sensors (CRNS) have been recognized as a promising tool in SM monitoring due to their large footprint of several hectares. CRNS have great potential for irrigation applications, but few studies have investigated whether irrigation monitoring with CRNS is feasible, especially for irrigated fields with a size smaller than the CRNS footprint. Therefore, the aim of this work is to use Monte Carlo simulations to investigate the feasibility of monitoring irrigation with CRNS. This was achieved by simulating irrigation scenarios with different field dimensions (from 0.5 ha to 8 ha) and SM variations between 0.05 and 0.50 cm³ cm-3. Moreover, the energy dependent response functions of eight moderators with different high-density polyethylene (HDPE) thickness or additional gadolinium thermal shielding were investigated. It was found that a considerable part of the neutrons that contribute to the CRNS footprint can originate outside an irrigated field, which is a challenge for irrigation monitoring with CRNS. The use of thin HDPE moderators (e.g., 5 mm) generally resulted in a smaller footprint and thus stronger contributions from the irrigated area. However, a thicker 25 mm HDPE moderator with gadolinium shielding improved SM monitoring in irrigated fields due to a higher sensitivity of neutron counts with changing SM. Such moderator and shielding provided high chances of detecting irrigation events, especially when the initial SM was relatively low. However, it was found that variations in SM outside a small, irrigated field (i.e., 0.5 and 1 ha) can affect the count rate more than SM variations due to irrigation. This suggests the importance of retrieving SM data from the surrounding of a target field to obtain more meaningful information for supporting irrigation management, especially for small irrigated fields. [ABSTRACT FROM AUTHOR]

Accurate soil moisture (SM) monitoring is key in irrigation as it can greatly improve water use efficiency. Recently, cosmic-ray neutron sensors (CRNSs) have been recognized as a promising tool in SM monitoring due to their large footprint of several hectares. CRNSs also have great potential for irrigation applications, but few studies have investigated whether irrigation monitoring with CRNSs is feasible, especially for irrigated fields with a size smaller than the CRNS footprint. Therefore, the aim of this study is to use Monte Carlo simulations to investigate the feasibility of monitoring irrigation with CRNSs. This was achieved by simulating irrigation scenarios with different field dimensions (from 0.5 to 8 ha) and SM variations between 0.05 and 0.50 cm 3 cm -3. Moreover, the energy-dependent response functions of eight moderators with different high-density polyethylene (HDPE) thickness or additional gadolinium thermal shielding were investigated. It was found that a considerable part of the neutrons that contribute to the CRNS footprint can originate outside an irrigated field, which is a challenge for irrigation monitoring with CRNSs. The use of thin HDPE moderators (e.g. 5 mm) generally resulted in a smaller footprint and thus stronger contributions from the irrigated area. However, a thicker 25 mm HDPE moderator with gadolinium shielding improved SM monitoring in irrigated fields due to a higher sensitivity of neutron counts with changing SM. This moderator and shielding set-up provided the highest chance of detecting irrigation events, especially when the initial SM was relatively low. However, variations in SM outside a 0.5 or 1 ha irrigated field (e.g. due to irrigation of neighbouring fields) can affect the count rate more than SM variations due to irrigation. This suggests the importance of retrieving SM data from the surrounding of a target field to obtain more meaningful information for supporting irrigation management, especially for small irrigated fields. [ABSTRACT FROM AUTHOR]

The integrated distributed hydrological model ParFlow-CLM was used to predict water and energy transport between subsurface, land surface, and atmosphere for the Stettbach headwater catchment, Germany. Based on this model, global sensitivity analysis was performed using the Latin-Hypercube (LH) sampling strategy followed by the One-factor-Ata-Time (OAT) method to identify the most influential and interactive parameters affecting the main hydrologic processes. total 12 parameters were evaluated including soil hydraulic properties, storage, Manning coefficient, leaf area index, stem area index, and aerodynamic resistance that characterize water and energy fluxes in soil and vegetation. In addition, the sensitivity analysis was also carried out for different slopes and meteorological conditions to test the transferability of the results regions with other topographies and climates. Our results show that the simulated energy fluxes, i.e. latent heat flux and sensible heat flux are sensitive to the parameters such as wilting point, leaf area index, and stem area index, especially for steep slope and subarctic climate conditions. The simulated soil evaporation, plant transpiration, infiltration, and runoff, are most sensitive to soil porosity, the van Genuchten parameter n representing the soil pore size distribution, soil wilting point, and leaf area index. The subsurface soil water storage and groundwater storage are most sensitive to soil porosity, while the surface water storage was most sensitive to the soil roughness parameter. For the different slope and climate conditions, the rank order of input parameter sensitivity is consistent, but the magnitude of parameter sensitivity is very different. The strongest deviation in parameter sensitivity occurred for sensible heat flux under the different slope conditions as well as for transpiration under different climate conditions. Overall, this study provides an insight into the most important input parameters that control hydrological fluxes and how the simulated variables vary with the change in parameter values, which can improve our understanding of the key processes in the model and help us to reduce the computational demands of completing multiple simulations of expensive domains. [ABSTRACT FROM AUTHOR]

Cosmic-ray neutron sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of metres and a depth of decimetres. Recent studies proposed operating CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km 2 Wüstebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation) and features a topographically distinct catchment boundary. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published data set (available at 10.23728/b2share.756ca0485800474e9dc7f5949c63b872;) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land–atmosphere exchange as well as hydrological and hydrogeological processes at the hillslope and the catchment scale; and to support the retrieval of soil water content from airborne and spaceborne remote sensing platforms. [ABSTRACT FROM AUTHOR]

Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information. In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation. The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability. The dataset, entitled "Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors", is shared via Forschungszentrum Jülich: 10.34731/x9s3-kr48 (Bogena and Ney, 2021). [ABSTRACT FROM AUTHOR]

Cosmic Ray Neutron Sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of meters and a depth of decimeters. Recent studies proposed to operate CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale, and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km2 Wüstebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation), and features a topographically distinct watershed. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published dataset (available at https://doi.org/10.23728/b2share.afb20a34a6ac429ca6b759238d842765) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land-atmosphere exchange as well as hydrological and hydrogeological processes at the hill-slope and the catchment scale; and to support the retrieval soil water content from airborne and spaceborne remote sensing platforms. [ABSTRACT FROM AUTHOR]

Human-caused climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns and reduced snow occurrence. For example, Europe has suffered from drought events in the last decade like never since the beginning of weather recording. Here we present soil moisture data from 65 Cosmic-ray neutron sensors (CRNS) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 23 research institutions and processed using state-of-the-art methods. The harmonised processing included correction of the raw neutron counts, and a harmonised methodology for the conversion into soil moisture based on available in-situ information. In addition, information on the data uncertainty is provided with the dataset, information that is particularly useful for remote sensing and modelling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products as well as first results on how the recent drought events have been captured by the CRNS network. This harmonised European soil moisture dataset will help both hydrologists and climate scientists to study individual drought events, to understand their causes, to evaluate and improve their modelling, and to estimate the extremity of current events. The dataset, entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021). [ABSTRACT FROM AUTHOR]

The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy, and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site-specific field data focusing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields, as well as water, energy, and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines following Lu et al. (2017) in CLM5; (2) implementing plant-specific parameters for sugar beet, potatoes, and winter wheat, thereby adding the two crop functional types (CFTs) for sugar beet and potatoes to the list of actively managed crops in CLM5; and (3) introducing a cover-cropping subroutine that allows multiple crop types on the same column within 1 year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is an agricultural management technique with a long history that is regaining popularity as it reduces erosion and improves soil health and carbon storage and is commonly used in the regions evaluated in this study. We compared simulation results with field data and found that both the new crop-specific parameterization and the winter wheat subroutines led to a significant simulation improvement in terms of energy fluxes (root-mean-square error, RMSE, reduction for latent and sensible heat by up to 57 % and 59 %, respectively), leaf area index (LAI), net ecosystem exchange, and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover-cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI magnitudes, seasonal cycle of LAI, and latent heat flux (reduction of wintertime RMSE for latent heat flux by 42 %). Our modifications significantly improved model simulations and should therefore be applied in future studies with CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes. [ABSTRACT FROM AUTHOR]

The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site specific field data focussing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields as well as water, energy and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines after Lu et al. (2017) in CLM5; (2) implementing plant specific parameters for sugar beet, potatoes and winter wheat, thereby adding these crop functional types (CFT) to the list of actively managed crops in CLM5; (3) introducing a cover cropping subroutine that allows multiple crop types on the same column within one year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is a common agricultural management technique in humid and sub-humid regions. We compared simulation results with field data and found that both the parameterization of the CFTs, as well as the winter wheat subroutines, led to a significant simulation improvement in terms of energy fluxes, leaf area index (LAI), net ecosystem exchange (RMSE reduction for latent and sensible heat by up to 57 % and 59 % respectively) and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI curve and latent heat flux (reduction of winter time RMSE for latent heat flux by 42 %). We anticipate that our model modifications offer opportunities to improve yield predictions, to study the effects of large-scale cover cropping on energy fluxes, soil carbon and nitrogen pools, and soil water storage in future studies with CLM5. [ABSTRACT FROM AUTHOR]

The Rollesbroich headwater catchment located in Western Germany is a densely instrumented hydrological observatory and part of the TERENO (Terrestrial Environmental Observatories) initiative. The measurements acquired in this observatory present a comprehensive dataset that contains key hydrological fluxes in addition to important hydrological states and properties. Meteorological data (i.e. precipitation, air temperature, air humidity, radiation components, and wind speed) are continuously recorded and actual evapotranspiration is measured using the eddy covariance technique. Runoff is measured at the catchment outlet with a gauging station. In addition, spatio-temporal variations in soil water content and temperature are measured at high resolution with a wireless sensor network (SoilNet). Soil physical properties were determined using standard laboratory procedures from samples taken at a large number of locations in the catchment. This comprehensive data set can be used to validate remote sensing retrievals and hydrological models; to improve the understanding of spatial temporal dynamics of soil water content; to optimize data assimilation and inverse techniques for hydrological models; and to develop upscaling and downscaling procedures of soil water content information. [ABSTRACT FROM AUTHOR]

In 2008 the TERENO project was initiated with the general aim to conduct integrated and long-term observation studies of climate change and global change impacts on the terrestrial system across Germany. Central elements of the TERENO network are "terrestrial observatories" at the catchment scale which were selected in climate sensitive regions of Germany. Within these observatories small scale research facilities and test areas were placed in order to accomplish energy, water, carbon and nutrient process studies across the different compartments of the terrestrial environment. Following a hierarchical scaling approach (point-plot-field) these detailed information and the knowledge gained is transferred to the regional scale using integrated modelling approaches. In addition, mobile ground-based and airborne measurement platforms are used to monitor spatial patterns of dynamic processes at the regional scale. By combining the observatories within Germany, large-scale atmospheric feedbacks and impacts can be investigated, and thus a more pronounced general link to the atmospheric research community can be established. A data policy statement was developed in a common approach by all TERENO partners. As a rule, all TERENO data are freely accessible for the scientific community and a data portal facilitates the online data provision. In the ten years since its foundation, TERENO has become a key infrastructure in the German and international research landscape and has made a significant contribution to the development of integrated approaches in long-term environmental research. In this contribution the concept of TERENO will be illustrated with example key findings of the ongoing interdisciplinary research work aiming to advance the understanding of complex hydrological and biogeochemical processes and interactions. [ABSTRACT FROM AUTHOR]

The time precipitation needs to travel through a catchment to its outlet is an important descriptor of a catchment's susceptibility to pollutant contamination, nutrient loss and hydrological functioning. The fast component of total water flow can be estimated by the fraction of young water (Fyw) which is the percentage of streamflow younger than three months. Fyw is calculated by comparing the amplitudes of sine waves fitted to seasonal precipitation and streamflow tracer signals. This is usually done for the complete tracer time series available neglecting annual differences in the amplitudes of longer time series. Considering inter-annual amplitude differences, we here employed a moving time window of one-year length in weekly time steps over a 4.5-years 18O tracer time series to calculate 189 Fyw results. The results were then tested against the following null hypotheses, defining 2% difference in Fyw as significant based on results of previous studies: (1) Fyw does not deviate more than ±2% from the mean of all Fyw results indicating long-term invariance. Larger deviations would indicate either flow path changes or a change in the relative contribution of different flow paths; (2) for any four-week window Fyw does not change more than ±2% indicating short-term invariance. Larger deviations would indicate a high sensitivity of Fyw to a 1-4 weeks shift in the start of a one-year sampling campaign; (3) for a given calendar month Fyw does not change more than ±2% indicating seasonal invariance of Fyw. In our study, all three null hypotheses were rejected. Thus, the Fyw results were time-variable, showed a high variability in the chosen sampling time and had no pronounced seasonality. Based on high short-term variability of Fyw when the mean adjusted R² was below 0.2 we recommend that a low R² should be regarded as indicating potentially highly uncertain Fyw results. Furthermore, while investigated individual meteorological factors could not sufficiently explain variations of Fyw, the runoff coefficient showed a moderate negative correlation of r = -0.54 with Fyw. This indicated that when annual runoff exceeded precipitation the catchment received the water deficit from storage which is old water causing a decrease in Fyw. The results of this study suggest that care must be taken when comparing Fyw of catchments that were based on different calculation time periods. [ABSTRACT FROM AUTHOR]

The exchange of water between atmosphere, biosphere, and hydrosphere can be viewed as a result of complex interactions of dynamic feedback mechanisms, where soil moisture content acts as the key state variable. Approaches are required to handle land surface complexity and scale dependency of water fluxes. State-of-the-art observations of soil moisture content are ranging from continuous point-scale measurements via field-scale snapshots to remote sensing products on the basin scale and beyond. They have to deal with a space-time trade-off since the measurement frequency typically decreases with the covered spatial scale. Cosmic-ray neutron sensing is one of the growing techniques to move soil hydrological observations forward. We have established a research unit (DFG FOR 2694) named "Cosmic Sense" to jointly improve such measurements with or in combination with cosmic-ray neutron sensing, especially in annual joint field campaigns at small catchment scale. Key aims are to- bridge existing gaps between scales through additional techniques and sources of information about the soil water storage. Cosmic-ray neutron sensing measures the presence of water by sensing changes in neutron density above the ground. We want to develop a quantitative, adaptable, and transferable approach for observing representative soil moisture content values on the field scale while accounting for other dynamic water pools on the land surface, such as biomass, interception, and snow. Furthermore, by sensor clusters and mobile neutron detectors the mapping of soil moisture can be transferred to larger scales. - bridge gaps between disciplines. Cosmic-ray neutron sensing shall function as a unique combination of invasive and non-invasive observations in joint field campaigns. The measurement and interpretation of soil moisture by Cosmic-ray neutron sensing requires advanced knowledge of soil hydrology as well as particle physics including neutron transport modeling. Efforts will be made for detector development that aims to substantially improve the temporal resolution and spatial coverage. - foster an approach that is especially tailored to resolve the discrepancy in support volume and timing between hydrological models and field observations. Comprehensive observations by cosmic-ray neutron sensing, remote sensing and complementary methods will be used concertedly with hydrological and land-surface models at the regional scale to infer also groundwater recharge and atmospheric fluxes. Thereby, models and observations together will allow for identifying patterns and processes at the scale of small catchments with improved spatio-temporal resolution. [ABSTRACT FROM AUTHOR]

The inclusion of perennial, woody crops in land surface models (LSMs) is crucial for addressing their role in carbon (C) sequestration, food production, and water requirements under climate change. To help quantify the biogeochemical and biogeophysical processes associated with these agroecosystems, we developed and tested a new sub-model, CLM5-FruitTree, for deciduous fruit orchards within the framework of the Community Land Model version 5 (CLM5). The model development included (1) a new perennial crop phenology description, (2) an adapted C and nitrogen allocation scheme, considering both storage and photosynthetic growth of annual and perennial plant organs, (3) typical management practices associated with fruit orchards, and (4) the parameterization of an apple plant functional type. CLM5-FruitTree was tested using extensive field measurements from an apple orchard in South Tyrol, Italy. Growth and partitioning of biomass to the individual plant components were well represented by CLM5-FruitTree, and average yield was predicted within 2.3 % of the observed values despite low simulated inter-annual variability compared to observations. The simulated seasonal course of C, energy, and water fluxes was in good agreement with the eddy covariance (EC) measurements owing to the accurate representation of the prolonged growing season and typical leaf area development of the orchard. We found that gross primary production, net radiation, and latent heat flux were highly correlated (r>0.94) with EC measurements and showed little bias (<±5 %). Simulated respiration components, sensible heat, and soil heat flux were less consistent with observations. This was attributed to simplifications in the orchard structure and to the presence of additional management practices that are not yet represented in CLM5-FruitTree. Finally, the results suggested that the representation of microbial and autotrophic respiration and energy partitioning in complex, discontinuous canopies in CLM5 requires further attention. The new CLM5-FruitTree sub-model improved the representation of agricultural systems in CLM5 and can be used to study land surface processes in fruit orchards at the local, regional, or larger scale. [ABSTRACT FROM AUTHOR]

The inclusion of perennial, woody crops in land surface models is crucial to address their role in carbon (C) sequestration, food production, and water requirements under climate change. To help quantifying the biogeochemical and biogeophysical processes associated with these agro-ecosystems, we developed and tested a new sub-model, CLM-FruitTree, for deciduous fruit orchards within the framework of the Community Land Model version 5 (CLM5). The model development included (1) a new perennial crop phenology description, (2) an adapted C and nitrogen allocation scheme, considering both storage and photosynthetic growth of annual and perennial plant organs, (3) typical management practices associated with fruit orchards, and (4) the parameterization of an apple plant functional type. CLM-FruitTree was tested using extensive field measurements from an apple orchard in South Tyrol, Italy. Growth and partitioning of biomass to the individual plant components was well represented by CLM-FruitTree and average yield was predicted within 2.3 % of the observed values despite low simulated inter-annual variability compared to observations. The simulated seasonal course of C, energy, and water fluxes was in good agreement with the EC measurements owing to the accurate representation of the prolonged growing season and typical leaf area development of the orchard. We found that gross primary production, net radiation, and latent heat flux were highly correlated (r > 0.94) with EC measurements and showed little bias (< ±5 %). Simulated respiration components, sensible heat, and ground heat flux were less consistent with observations. This was attributed to simplifications in the orchard structure and to the presence of additional management practices that are not yet represented in CLM-FruitTree. Finally, the results suggested that the representation of microbial and autotrophic respiration, and energy partitioning in complex, heterogeneous canopies in CLM5 requires further attention. The new sub-model CLM-FruitTree improved the representation of agricultural systems in CLM5 and can be used to study land surface processes in fruit orchards at the local, regional or larger scale. [ABSTRACT FROM AUTHOR]

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 km 2 : 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: 10.23728/b2share.282675586fb94f44ab2fd09da0856883 and 10.23728/b2share.bd89f066c26a4507ad654e994153358b. [ABSTRACT FROM AUTHOR]

Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while well established sensors typically suffer from a small spatial support. With a sensor footprint up to several hectares, Cosmic-Ray Neutron Sensing (CRNS) is an emerging 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, that 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 to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from Unmanned Areal Systems (UAS), permanent and temporary wireless sensor networks, profile probes as well as 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 dataset 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 spatio-temporal 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 EUDAT, splitted into the two subsets https://doi.org/10.23728/b2share.85fe0f9dac0f48df9215c17e65d1f1e1 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.93ed99e486904d48a8a6a68083066198 (Fersch et al., 2020b). [ABSTRACT FROM AUTHOR]

The water storage capacity in the unsaturated root zone of soils is the principal source of nonlinearity in the response of terrestrial hydrological systems. This storage capacity between field capacity and the permanent wilting point represents the water volume required by and accessible to vegetation to ensure continuous access to water to bridge dry periods. With detailed data on root depth distributions and soil porosities, this volume can be estimated. However, while such data may be available for individual plants or for small forest stands at experimental sites, they remain problematic to acquire at an adequate spatial resolution for scales larger than that, let alone for entire catchments or river basins. Zooming out to the macroscale and following evidence that plants efficiently develop root systems that allow them to meet the canopy water demand while minimizing sub-surface resource allocation provides a new perspective on the understanding of the storage capacity in the unsaturated root zone (Sumax). A series of experiments based on data from more than 400 catchments across a wide gradient of climates and landscapes suggests that Sumax can be robustly estimated at the catchment scale based exclusively on long-term water balance data, independently of detailed information on root systems and soil porosities. It is essentially controlled by climate characteristics, such as precipitation seasonality and by different types of vegetation. Based on further data from catchments that underwent well documented land use change (i.e. deforestation), experiments suggest that these storage capacities are not only significantly and predictably affected by deforestation, but also that Sumax exhibits distinct post-deforestation signatures in different environments, which can be directly linked to contrasting changes in post-deforestation hydrological response dynamics in these systems. More specifically, while in humid climates with little seasonality vegetation only needs to develop rather small additional storage volumes to ensure continuous access to water, much larger root-accessible water volumes are needed in seasonal climates. Consequently, deforestation in the latter does affect the partitioning of water into drainage and evaporative fluxes, and thus the fundamental hydrological response dynamics, much stronger than in the first case. Due to a more significant reduction of the storage capacity, less water can be stored for eventual evaporation and plant transpiration, while more water is available for drainage. These changes in how catchments store and release water as a function of the storage capacity in the unsaturated root zone, does, as a consequence, not only affect stream flow, but also catchment-scale transport dynamics: it is here demonstrated that changes in the root zone storage capacity due to deforestation significantly alters transit time distributions. In particular during storm events much higher proportions of young water reach the stream. This does not only have implications for the nutrient budget of a system but also changes the susceptibility of a system to pollution. Pollutant inputs will be more directly and with less attenuation routed to the stream, resulting in higher pollutant peak concentrations. This in turn illustrates the importance of vegetation for moderating peak pollutant concentrations in streams. [ABSTRACT FROM AUTHOR]

Irrigation is essential for maintaining food production in water-scarce regions. However, unnecessary water usage should be avoided. The irrigation need depends on the water content in the soil, which can be measured by the Cosmic-ray Neutron Sensing (CRNS) technique. It is promising for irrigation management as the measurement footprint is representative for the root-zone at the scale of tens of hectares. The potential of the CRNS technique for drip irrigation scheduling is explored in this work for the drip irrigated Picassent site near Valencia, Spain. To support the experimental evidence, the neutron transport simulation URANOS was used to mimic the Picassent site and to simulate the effect of drip irrigation on the epithermal neutron counts.The experimental dataset showed methodological limitations to detect drip water input with CRNS, although the overall soil water content in the CRNS footprint has been well characterized within a root mean square error less than 0.03 cm3/cm3. Furthermore, we found that the CRNS performance to sense soil water content in the overall footprint varied significantly along the study period. Both, the experimental data and the simulation results suggest that the large-area neutron response to drip irrigation is dominated by statistical noise in our specific case. The performance is mainly limited by the following conditions:1. The irrigated area is only 8 % of the total area. Since CRNS has an area-averaging footprint, irrigation of larger areas would be much better visible for CRNS. 2. Changes of water content due to irrigation are small (5 %) under rather wet conditions (35% soil moisture). Neutrons are much more sensitive to changes of water content at the lower end of the soil moisture spectrum. 3. The irrigation period is only a few hours. The employed CRNS detector can resolve small changes of water content only with integration period of 12 hours.We conclude that CRNS was not able to detect irrigation in our specific case. But the method might be capable of detecting drip irrigation in 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. [ABSTRACT FROM AUTHOR]

While "classical" stationary cosmic-ray neutron sensors (CRNS) measure the temporal variability of soil moisture at the field scale, the mobile application of CRNS (CRNS rover) enables to capture the spatial variability of soil moisture across larger areas or transects. The CRNS rover consists of two components - a vehicle and a set of large neutron counters. The detectors need to have higher count rates in order to achieve sufficient sensitivity for very short recording periods (e.g. 1 min). To date, only few experiments have been conducted with the CRNS rover over a wide range of different land-use types. Such experiments are needed as the neutron intensity is not only sensitive to soil moisture, but also to all other additional sources of hydrogen (e.g. biomass), which need to be corrected for. Interestingly, the ratio between thermal and fast neutron intensity (Nr) seems to be a very good indicator for vegetation and can be used for the correction of aboveground biomass effects on soil moisture measurements with cosmic-ray neutrons. This enables the possibility for "on-the-fly" correction of soil moisture measurements without the need for laborious local biomass sampling during CRNS roving campaigns. In addition, using the Nr-information from CRNS roving would allow for the simultaneous measurements of soil moisture and biomass pattern at the regional scale. In this contribution, we present results from a recent roving campaign at the TERENO test site Selhausen (about 1 km²). We used a new cosmic rover with 9 detectors and a total sensitivity of approx. 1350 counts per minute, which enables higher speed and larger coverage compared to other roving systems. In addition, the detectors can be modified to measure either thermal or fast neutron intensity measurements. During the two-week campaign a large set of different field sites with various vegetation covers has been repeatedly monitored. The preliminary results show that CRNS rover was able to capture the general soil moisture pattern and that the Nr-information could be used to improve the soil moisture estimation. In future we hope to enable the simultaneous measurements of soil moisture and biomass patterns at the regional scale with Nr-information from CRNS roving. [ABSTRACT FROM AUTHOR]