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In recent years, wireless sensor network (WSN) technology has emerged as an important technique for wireless sensing of soil moisture from the field to the catchment scale. This review paper presents the current status of wireless sensor network (WSN) technology for distributed, near real-time sensing of soil moisture to investigate seasonal and event dynamics of soil moisture patterns. It is also discussed how WSN measurements of soil measurements contribute to the validation and downscaling of satellite data and non-invasive geophysical instruments as well as the validation of distributed hydrological models. Finally, future perspectives for WSN measurements of soil moisture are highlighted, which includes the improved integration of real-time WSN measurements with other information sources using the latest wireless communication techniques and cyberinfrastructures. [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 (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 scale difference between point in situ soil moisture measurements and low resolution satellite products limits the quality of any validation efforts in heterogeneous regions. Cosmic Ray Neutron Probes (CRNP) could be an option to fill the scale gap between both systems, as they provide area-average soil moisture within a 150-250 m radius footprint. In this study, we evaluate differences and similarities between CRNP observations, and surface soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the METOP-A/B Advanced Scatterometer (ASCAT), the Soil Moisture Active and Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), as well as simulations from the Global Land Data Assimilation System Version 2 (GLDAS2). Six CRNPs located on five continents have been selected as test sites: the Rur catchment in Germany, the COSMOS sites in Arizona and California (USA), and Kenya, one CosmOz site in New SouthWales (Australia), and a site in Karnataka (India). Standard validation scores as well as the Triple Collocation (TC) method identified SMAP to provide a high accuracy soil moisture product with low noise or uncertainties as compared to CRNPs. The potential of CRNPs for satellite soil moisture validation has been proven; however, biomass correction methods should be implemented to improve its application in regions with large vegetation dynamics. [ABSTRACT FROM AUTHOR]

Soil water content is a key variable for understanding and modelling ecohydrological processes. Low-cost electromagnetic sensors are increasingly being used to characterize the spatio-temporal dynamics of soil water content, despite the reduced accuracy of such sensors as compared to reference electromagnetic soil water content sensing methods such as time domain reflectometry. Here, we present an effective calibration method to improve the measurement accuracy of low-cost soil water content sensors taking the recently developed SMT100 sensor (Truebner GmbH, Neustadt, Germany) as an example. We calibrated the sensor output of more than 700 SMT100 sensors to permittivity using a standard procedure based on five reference media with a known apparent dielectric permittivity (1 < Ka < 34.8). Our results showed that a sensor-specific calibration improved the accuracy of the calibration compared to single "universal" calibration. The associated additional effort in calibrating each sensor individually is relaxed by a dedicated calibration setup that enables the calibration of large numbers of sensors in limited time while minimizing errors in the calibration process. [ABSTRACT FROM AUTHOR]

Headwaters are a source of fresh water and material transported to the downstream sections of river channels. In most regions of Europe, the natural environment of headwaters has been significantly transformed by human activities, which is reflected in the quality of surface waters and the rate of solute fluxes. The aim of the study was to determine the anthropogenic impact on solute fluxes in a headwater catchment in Germany. The study area (Wüstebach experimental catchment) is located in the Eifel Mountains and is part of the TERENO (Terrestrial Environmental Observatories) network. The catchment is currently subject to the following anthropogenic pressures: the use of de-icing salts on the motorway running through the catchment and the logging of 22% of the forest area in 2013 by the National Park to promote the regeneration of near-natural beech forest. We used data from almost 13 years (June 2009-April 2022) of environmental monitoring in the Wüstebach catchment to calculate monthly and annual rates of solute fluxes. The concentrations of ions in stream water and precipitation were determined: Ca2+, Mg2+, Na+, K+, Al3+, Fetot, Mn2+, NH4+, NO3−, SO4−, and Cl−. Data from the Wüstebach catchment were compared with data from the control (untreated) catchment. The average annual rate of total dissolved solids in the Wüstebach catchment was 83 t/km2/a. Only the loads of four ions performed a significant difference after deforestation (SO4−, NH4+, Al3+, Fetot). However, total dissolved solids and loads of most of the ions did not show any significant differences after deforestation, which indicates a slight impact of logging on the chemical denudation. This resulted from the limited soil erosion that followed deforestation as the soil was well protected during logging works by covering harvester lanes with branches. However, there is a significant impact of road salting on the solute fluxes in the headwater catchment. Assuming that the control catchment is not affected by using de-icing salts, the road salting inputs in the Wüstebach catchment calculated based on the precipitation share for Na+, Ca2+, Mg2+, K+, and Cl− ions were, on average, 88%, 53%, 18%, 53%, and 90%, respectively, of the original ion loads. The input with road salting was so large that elevated concentrations of Na+ and Cl− were noted throughout the study period with slight seasonal fluctuations. This proves the subsurface build-up of salt due to the long-term application of de-icing salt. Without the input of de-icing salts, the average annual rate of chemical denudation in the catchment would be about 19 t/km2/a. Thus, on average, this ion storage effect in the soil has increased the total dissolved solids rate by 62%. In summary, it can be concluded that long-term intensive road salting in winter has a greater impact on the solute fluxes than the clear-cut of 22% of the catchment area. [ABSTRACT FROM AUTHOR]

The streamwater transit time distribution (TTD) of a catchment is used to derive insights into the movement of precipitation water via various flow paths to the catchment’s stream. Typically, TTDs are estimated by using the convolution integral to model a weekly tracer signal measured in streamflow. Another approach for evaluating the transit time of water to the catchment stream is the fraction of young water ( F yw ) in streamflow that is younger than a certain threshold age, which also relies on tracer data. However, few studies used tracer data with a higher sampling frequency than weekly. To investigate the influence of the sampling frequency of tracer data on estimates of TTD and F yw , we estimated both indicators for a humid, mesoscale catchment in Germany using tracer data of weekly and higher sampling frequency. We made use of a 1.5 year long time series of daily to sub-daily precipitation and streamwater isotope measurements, which were aggregated to create the weekly resolution data set. We found that a higher sampling frequency improved the stream isotope simulation compared to a weekly one (0.35 vs. 0.24 Nash-Sutcliffe Efficiency) and showed more pronounced short-term dynamics in the simulation result. The TTD based on the high temporal resolution data was considerably different from the weekly one with a shift towards faster transit times, while its corresponding mean transit time of water particles was approximately reduced by half (from 9.5 to 5 years). Similar to this, F yw almost doubled when applying high resolution data compared to weekly one. Thus, the different approaches yield similar results and strongly support each other. This indicates that weekly isotope tracer data lack information about faster water transport mechanisms in the catchment. Thus, we conclude that a higher than weekly sampling frequency should be preferred when investigating a catchment’s water transport characteristics. When comparing TTDs or F yw of different catchments, the temporal resolution of the used datasets needs to be considered. [ABSTRACT FROM AUTHOR]

Summary In distributed hydrological modelling one often faces the problem that input data need to be aggregated to match the model resolution. However, aggregated data may be too coarse for the parametrization of the processes represented. This dilemma can be circumvented by the adjustment of certain model parameters. For instance, the reduction of local hydraulic gradients due to spatial aggregation can be partially compensated by increasing soil hydraulic conductivity. In this study, we employed the information entropy concept for the scale dependent parameterization of soil hydraulic conductivity. The loss of information content of terrain curvature as consequence of spatial aggregation was used to determine an amplification factor for soil hydraulic conductivity to compensate the resulting retardation of water flow. To test the usefulness of this approach, continuous 3D hydrological simulations were conducted with different spatial resolutions in the highly instrumented Wüstebach catchment, Germany. Our results indicated that the introduction of an amplification factor can effectively improve model performances both in terms of soil moisture and runoff simulation. However, comparing simulated soil moisture pattern with observation indicated that uniform application of an amplification factor can lead to local overcorrection of soil hydraulic conductivity. This problem could be circumvented by applying the amplification factor only to model grid cells that suffer from high information loss. To this end, we tested two schemes to define appropriate location-specific correction factors. Both schemes led to improved model performance both in terms of soil water content and runoff simulation. Thus, we anticipate that our proposed scaling approach is useful for the application of next-generation hyper-resolution global land surface models. [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]

Summary Soil moisture plays a key role in the water and energy balance in soil, vegetation and atmosphere systems. According to Wood et al. (2011) there is a grand need to increase global-scale hyper-resolution water–energy–biogeochemistry land surface modelling capabilities. These modelling capabilities should also recognize epistemic uncertainties, as well as the nonlinearity and hysteresis in its dynamics. Unfortunately, it is not clear how to parameterize hydrological processes as a function of scale, and how to test deterministic models with regard to epistemic uncertainties. In this study, high resolution long-term simulations were conducted in the highly instrumented TERENO hydrological observatory of the Wüstebach catchment. Soil hydraulic parameters were derived using inverse modelling with the Hydrus-1D model using the global optimization scheme SCE-UA and soil moisture data from a wireless soil moisture sensor network. The estimated parameters were then used for 3D simulations of water transport using the integrated parallel simulation platform ParFlow-CLM. The simulated soil moisture dynamics, as well as evapotranspiration (ET) and runoff, were compared with long-term field observations to illustrate how well the model was able to reproduce the water budget dynamics. We investigated different anisotropies of hydraulic conductivity to analyze how fast lateral flow processes above the underlying bedrock affect the simulation results. For a detail investigation of the model results we applied the empirical orthogonal function (EOF) and wavelet coherence methods. The EOF analysis of temporal–spatial patterns of simulated and observed soil moisture revealed that introduction of heterogeneity in the soil porosity effectively improves estimates of soil moisture patterns. Our wavelet coherence analysis indicates that wet and dry seasons have significant effect on temporal correlation between observed and simulated soil moisture and ET. Our study demonstrates the usefulness of the EOF and wavelet coherence methods for a more in-depth validation of spatially highly resolved hydrological 3D models. [ABSTRACT FROM AUTHOR]

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite was launched in November 2009 and delivers now brightness temperature and soil moisture products over terrestrial areas on a regular three-day basis. In 2010, several airborne campaigns were conducted to validate the SMOS products with microwave emission radiometers at L-band (1.4 GHz). In this paper, we present results from measurements performed in the Rur and Erft catchments in May and June 2010. The measurement sites were situated in the very west of Germany close to the borders to Belgium and The Netherlands. We developed an approach to validate spatial and temporal SMOS brightness temperature products. An area-wide brightness temperature reference was generated by using an area-wide modeling of top soil moisture and soil temperature with the WaSiM-ETH model and radiative transfer calculation based on the L-band Microwave Emission of the Biosphere model. Measurements of the airborne L-band sensors EMIRAD and HUT-2D on-board a Skyvan aircraft as well as ground-based mobile measurements performed with the truck mounted JÜLBARA L-band radiometer were analyzed for calibration of the simulated brightness temperature reference. Radiative transfer parameters were estimated by a data assimilation approach. By this versatile reference data set, it is possible to validate the spaceborne brightness temperature and soil moisture data obtained from SMOS. However, comparisons with SMOS observations for the campaign period indicate severe differences between simulated and observed SMOS data. [ABSTRACT FROM PUBLISHER]

Cosmic ray neutron sensors (CRNS) are increasingly used to determine field-scale soil moisture (SM). Uncertainty of the CRNS-derived soil moisture strongly depends on the CRNS count rate subject to Poisson distribution. State-of-the-art CRNS signal processing averages neutron counts over many hours, thereby accounting for soil moisture temporal dynamics at the daily but not sub-daily time scale. This study demonstrates CRNS signal processing methods to improve the temporal accuracy of the signal in order to observe sub-daily changes in soil moisture and improve the signal-to-noise ratio overall. In particular, this study investigates the effectiveness of the Moving Average (MA), Median filter (MF), Savitzky–Golay (SG) filter, and Kalman filter (KF) to reduce neutron count error while ensuring that the temporal SM dynamics are as good as possible. The study uses synthetic data from four stations for measuring forest ecosystem–atmosphere relations in Africa (Gorigo) and Europe (SMEAR II (Station for Measuring Forest Ecosystem–Atmosphere Relations), Rollesbroich, and Conde) with different soil properties, land cover and climate. The results showed that smaller window sizes (12 h) for MA, MF and SG captured sharp changes closely. Longer window sizes were more beneficial in the case of moderate soil moisture variations during long time periods. For MA, MF and SG, optimal window sizes were identified and varied by count rate and climate, i.e., estimated temporal soil moisture dynamics by providing a compromise between monitoring sharp changes and reducing the effects of outliers. The optimal window for these filters and the Kalman filter always outperformed the standard procedure of simple 24-h averaging. The Kalman filter showed its highest robustness in uncertainty reduction at three different locations, and it maintained relevant sharp changes in the neutron counts without the need to identify the optimal window size. Importantly, standard corrections of CRNS before filtering improved soil moisture accuracy for all filters. We anticipate the improved signal-to-noise ratio to benefit CRNS applications such as detection of rain events at sub-daily resolution, provision of SM at the exact time of a satellite overpass, and irrigation applications. [ABSTRACT FROM AUTHOR]

Soil moisture is an essential climate variable of the Earth system. Understanding its spatiotemporal dynamics is essential for predicting weather patterns and climate variability, monitoring and mitigating the effects and occurrence of droughts and floods, improving irrigation in agricultural areas, and sustainably managing water resources. Here we review in depth how soils can remember information on soil moisture anomalies over time, as embedded in the concept of soil moisture memory (SMM). We explain the mechanisms underlying SMM and explore its external and internal drivers; we also discuss the impacts of SMM on different land surface processes, focusing on soil‐plant‐atmosphere coupling. We explore the spatiotemporal variability, seasonality, locality, and depth‐dependence of SMM and provide insights into both improving its characterization in land surface models and using satellite observations to quantify it. Finally, we offer guidance for further research on SMM. Plain Language Summary: Our review paper takes an in‐depth look at soil moisture memory, which is how soil records its moisture history over time and space. Analogous to human psychology, which seeks to understand how a person's/society's memory influences his/her present and future behavior, understanding soil moisture memory encourages consideration of how such memory determines present state and might determine future behavior of soils exposed to environmental disturbances. Soil moisture memory can be affected by a variety of factors, both external (e.g., weather extremes) and internal (soil's unique properties). It affects everything from the air to the way our landscapes respond to disasters like droughts, wildfires, and floods. We also studied how this phenomenon affects the balance of water and energy in our environment, the health of our plants, and even how it communicates with the atmosphere. We show how it can change depending on where you are on the planet, the time of year, and how deep you dig into the soil. We offer scientists insights into how weather and land surface models can become more accurate by accounting for soil moisture memory. Its understanding not only helps us predict and manage our environment, but also provides opportunities for exciting scientific discoveries. Key Points: Atmospheric forcings, land use and management, and soil processes and mechanisms explain how and why soil moisture memory emerges in ecosystemsNonlocality of moisture memory, its spread across different regions, and its interaction with large‐scale climate phenomena are underexploredFurther advances in land surface models and closer integration of model simulations and observations are needed to better characterize moisture memory [ABSTRACT FROM AUTHOR]

• Novel method to identify similar runoff responses at the catchment scale. • Similar runoff responses often accompanied by similar catchment wetness conditions. • Precipitation patterns with mostly weak correlations for similar runoff responses. • Long-term observation data allow for robust quantification of similar patterns. Runoff responses to precipitation at the catchment scale exhibit a high variability in space and time due to a complex interaction of numerous factors, e.g., topography, land use, soil properties, geology, and climatic conditions. To find similar patterns in the runoff response and examine the effects of these factors on runoff variability, previous studies have either compared and classified catchments in space or focused on grouping extreme events. Here, we analyzed runoff processes in three highly instrumented catchments in Germany and Austria individually and compared them to themselves in time. To this end, we used long-term time series of 10 to 13 years and classified runoff events as similar by performing a cluster analysis based on calculated goodness-of-fit criteria between each possible pair of runoff events. For each cluster, we examined the degree to which precipitation and catchment wetness conditions were similar to themselves at the respective times when similar runoff events occurred by calculating Spearman rank correlation coefficients (ρ) as well as their descriptive statistics. The similarities assessed varied among the three catchments, with the two catchments in western Germany with maritime climates showing a stronger correlation for soil moisture conditions (ρ = 0.76 and ρ = 0.74) for classified similar runoff events rather than precipitation (ρ = 0.26 and ρ = 0.36). The Austrian catchment with a predominantly continental climate showed an overall higher correlation for precipitation (ρ = 0.57) and a lower one for soil moisture (ρ = 0.53) for similar runoff events compared to the other two catchments. The proposed method assesses similarity of precipitation and wetness conditions under similar runoff responses, and gives an indication of possible influencing factors controlling runoff generation in the three catchments in relation to their respective wetness and precipitation patterns. The similarities investigated help identify similar catchment functioning and can be used, for instance, to develop enhanced catchment similarity indices. [ABSTRACT FROM AUTHOR]

The continuous monitoring of soil water content is commonly carried out using low-frequency capacitance sensors that require a site-specific calibration to relate sensor readings to apparent dielectric bulk permittivity (Kb) and soil water content (θ). In fine-textured soils, the conversion of Kb to θ is still challenging due to temperature effects on the bound water fraction associated with clay mineral surfaces, which is disregarded in factory calibrations. Here, a multi-point calibration approach accounts for temperature effects on two soils with medium to high clay content. A calibration strategy was developed using repacked soil samples in which the Kb-θ relationship was determined for temperature (T) steps from 10 to 40 °C. This approach was tested using the GS3 and TEROS-12 sensors (METER Group, Inc. Pullman, WA, USA; formerly Decagon Devices). Kb is influenced by T in both soils with contrasting T-Kb relationships. The measured data were fitted using a linear function θ = a K b + b with temperature-dependent coefficients a and b. The slope, a(T), and intercept, b(T), of the loam soil were different from the ones of the clay soil. The consideration of a temperature correction resulted in low RMSE values, ranging from 0.007 to 0.033 cm3 cm−3, which were lower than the RMSE values obtained from factory calibration (0.046 to 0.11 cm3 cm−3). However, each experiment was replicated only twice using two different sensors. Sensor-to-sensor variability effects were thus ignored in this study and will be systematically investigated in a future study. Finally, the applicability of the proposed calibration method was tested at two experimental sites. The spatial-average θ from a network of GS3 sensors based on the new calibration fairly agreed with the independent area-wide θ from the Cosmic Ray Neutron Sensor (CRNS). This study provided a temperature-corrected calibration to increase the accuracy of commercial sensors, especially under dry conditions, at two experimental sites. [ABSTRACT FROM AUTHOR]

Soil water content (SWC) monitoring is often used to optimize agricultural irrigation. Commonly, capacitance sensors are used for this task. However, the factory calibrations have been often criticized for their limited accuracy. The aim of this paper is to test the degree of improvement of various sensor- and soil-specific calibration options compared to factory calibrations by taking the 10HS sensor as an example. To this end, a two-step sensor calibration was carried out. In the first step, the sensor response was related to dielectric permittivity using calibration in media with well-defined permittivity. The second step involved the establishment of a site-specific relationship between permittivity and soil water content using undisturbed soil samples and time domain reflectometry (TDR) measurements. Our results showed that a model, which considered the mean porosity and a fitted dielectric permittivity of the solid phase for each soil and depth, provided the best fit between bulk permittivity and SWC. Most importantly, it was found that the two-step calibration approach (RMSE: 1.03 vol.%) provided more accurate SWC estimates compared to the factory calibration (RMSE: 5.33 vol.%). Finally, we used these calibrations on data from drip-irrigated almond and apple orchards and compared the factory calibration with our two-step calibration approach. [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]

• Net ecosystem exchange measured above the spruce forest showed a strong carbon sink. • Clearcut area acts as a strong source for CO 2 in the first year after deforestation. • Clearcut annual net CO 2 release decreased continuously with recovering vegetation. • At the same time, soil respiration and CO 2 uptake increased steadily for the clearcut. • Albedo effect of clearcut overweighed warming effect of increased CO 2 release. A seven year CO 2 -flux dataset measured in a 70 year old spruce monoculture is presented, of which 22% was deforested three years after the start of the measurements to accelerate regeneration towards natural deciduous vegetation. An eddy covariance (EC) system, mounted on top of a tower within the spruce forest, continuously sampled fluxes of momentum, sensible heat, latent heat and CO 2. After clear-cutting, a second EC station with an identical set of instruments was installed inside the deforested area. In total, we examined an EC dataset including three years before (forest) and four years after partial deforestation (forest and deforested). Full time series and annual carbon budgets of the net ecosystem exchange (NEE) and its components gross primary production (GPP) and total ecosystem respiration (R eco) were calculated for both EC sites. Soil respiration was measured with manual chambers on average every month after the deforestation at 75 measurement points in the forest and deforested area. Annual sums of NEE measured above the forest indicated a strong carbon sink of -660 (-535) g C m-2 y-1 with small interannual variability ±78 (72) g C m-2 y-1 (values in brackets including correction for self-heating of the open-path gas analyzer). In the first year after partial deforestation, regrowth on the clearcut consisted mainly of grasses, with beginning of the second year shrubs and young trees became increasingly important. The regrowth of vegetation is reflected in the annual sums of NEE, which decreased from a carbon source of 521 (548) g C m-2 y-1 towards 82 (236) g C m-2 y-1 over the past four years, due to an increase in the magnitude of GPP from 385 (447) to 892 (1036) g C m-2 y-1. [ABSTRACT FROM AUTHOR]

• K S and SM r are the dominant spatial factors controlling preferential flow. • The temporal occurrence pattern of preferential flow can be constructed by CART. • Thresholds exist for temporal factors to initiate preferential flow. The complexity of the spatial distribution and temporal occurrence of preferential flow (PF) makes it challenging to understand the mechanisms of PF. This study aims to identify the spatial and temporal patterns of PF occurrence using machine learning (Classification and Regression Trees and Random Forests) in the Qilian Mountains, Northwest China. Our results show that detected PF events transport much more rainfall down to the subsoil than non-PF events. Different vegetation types exhibit variations in the main soil layers where PF occurs, which is closely related to the distribution of roots. The PF proportion varies significantly both vertically and horizontally. Based on the Random Forests, we found that the spatial distribution of the PF proportion is mainly controlled by the saturated hydraulic conductivity and residual soil moisture, which cannot be identified by conventional correlation analysis methods. With these soil properties, the spatial distribution of the PF proportion can be estimated with reasonable performance. Using the Classification and Regression Trees method, we identified the temporal occurrence pattern of the PF for different vegetation types and all observation stations. Results indicate that the dominant factors controlling the temporal occurrence of the PF varied for different vegetation types. The thresholds at which these factors initiate the PF also varied. Finally, we found that the PF occurs particularly under wet conditions (except for hydrophobic soils), under denser vegetation, and under conditions of high rainfall amount and intensity, regardless of vegetation type. Our study confirms that both site factors (e.g., soil properties and vegetation) and temporal factors (e.g., initial soil moisture and rainfall characteristics) control the occurrence of the PF in mountainous regions such as the Qilian Mountains and that the Classification and Regression Trees has great potential to study the temporal occurrence of the PF. [ABSTRACT FROM AUTHOR]

Soil moisture profile sensors (SMPSs) have a high potential for climate-smart agriculture due to their easy handling and ability to perform simultaneous measurements at different depths. To date, an accurate and easy-to-use method for the evaluation of long SMPSs is not available. In this study, we developed laboratory and field experiments to evaluate three different SMPSs (SoilVUE10, Drill&Drop, and SMT500) in terms of measurement accuracy, sensor-to-sensor variability, and temperature stability. The laboratory experiment features a temperature-controlled lysimeter to evaluate intra-sensor variability and temperature stability of SMPSs. The field experiment features a water level-controlled sandbox and reference TDR measurements to evaluate the soil water measurement accuracy of the SMPS. In both experiments, a well-characterized fine sand was used as measurement medium to ensure homogeneous dielectric properties in the measurement domain of the sensors. The laboratory experiments with the lysimeter showed that the Drill&Drop sensor has the highest temperature sensitivity with a decrease of 0.014 m3 m−3 per 10 °C, but at the same time showed the lowest intra- and inter-sensor variability. The field experiment with the sandbox showed that all three SMPSs have a similar performance (average RMSE ≈ 0.023 m3 m−3) with higher uncertainties at intermediate soil moisture contents. The presented combination of laboratory and field tests were found to be well suited to evaluate the performance of SMPSs and will be used to test additional SMPSs in the future. [ABSTRACT FROM AUTHOR]

Several studies currently strive to improve the spatial resolution of coarse scale high temporal resolution global soil moisture products of SMOS, SMAP, and ASCAT. Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. With the recent development of high resolution maps of basic soil properties such as soil texture and bulk density, relevant information to estimate soil moisture variability within a satellite product grid cell is available. We use this information for the prediction of the sub-grid soil moisture variability for each SMOS, SMAP, and ASCAT grid cell. The approach is based on a method that predicts the soil moisture standard deviation as a function of the mean soil moisture based on soil texture information. It is a closed-form expression using stochastic analysis of 1D unsaturated gravitational flow in an infinitely long vertical profile based on the Mualem-van Genuchten model and first-order Taylor expansions. We provide a look-up table that indicates the soil moisture standard deviation for any given soil moisture mean, available at https://doi.org/10.1594/PANGAEA.878889. The resulting data set helps identify adequate regions to validate coarse scale soil moisture products by providing a measure of representativeness of small-scale measurements for the coarse grid cell. Moreover, it contains important information for downscaling coarse soil moisture observations of the SMOS, SMAP, and ASCAT missions. In this study, we present a simple application of the estimated sub-grid soil moisture heterogeneity scaling down SMAP soil moisture to 1 km resolution. Validation results in the TERENO and REMEDHUS soil moisture monitoring networks in Germany and Spain, respectively, indicate a similar or slightly improved accuracy for downscaled and original SMAP soil moisture in the time domain for the year 2016, but with a much higher spatial resolution. [ABSTRACT FROM AUTHOR]

Due to their unique characteristics, cosmic-ray neutron sensors (CRNSs) have potential in monitoring and informing irrigation management, and thus optimising the use of water resources in agriculture. However, practical methods to monitor small, irrigated fields with CRNSs are currently not available and the challenges of targeting areas smaller than the CRNS sensing volume are mostly unaddressed. In this study, CRNSs are used to continuously monitor soil moisture (SM) dynamics in two irrigated apple orchards (Agia, Greece) of ~1.2 ha. The CRNS-derived SM was compared to a reference SM obtained by weighting a dense sensor network. In the 2021 irrigation period, CRNSs could only capture the timing of irrigation events, and an ad hoc calibration resulted in improvements only in the hours before irrigation (RMSE between 0.020 and 0.035). In 2022, a correction based on neutron transport simulations, and on SM measurements from a non-irrigated location, was tested. In the nearby irrigated field, the proposed correction improved the CRNS-derived SM (from 0.052 to 0.031 RMSE) and, most importantly, allowed for monitoring the magnitude of SM dynamics that are due to irrigation. The results are a step forward in using CRNSs as a decision support system in irrigation management. [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]

Understanding the response of headwater catchments to increasing anthropogenic influences is one of the key issues in hydrological and geomorphological research. Two headwater catchments with contrasting human impacts on the environment were chosen to determine changes in surface water chemistry and solute fluxes. In one catchment, 22 % of the forest area was cleared in 2013 and in the other, 75 % of the arable lands was converted to grassland and forest in the last 25 years. Atmospheric input and solute mass balance were analysed using homogeneity Pettitt test. It seems that in both areas, a very important factor affecting solute fluxes was most likely changes, or lack thereof, in soil erosion. In the partially clear-cut catchment, a small clear-cut area and adequate protection of the soil cover had little effect on the stream water chemistry, and the total solute mass balance remained unchanged during the study period. In this catchment, the more pronounced effect on solute fluxes was using de-icing salts in winter, which contributed to a 62 % increase in total solute flux (mainly by supplying Na+ and Cl−, but also Ca2+, Mg2+ and K+). In a catchment with a decrease in arable lands, there was a 55 % decrease in total solute mass balance (including mass balance of Ca2+, Mg2+, Na+, K+, NH 4 +, SO 4 −, NO 3 −, Cl−) in the stream likely associated with a decrease in soil erosion and fertilization. In both catchments there was a decrease in total monthly atmospheric input (especially in input of NH 4 +, SO 4 − and NO 3 −) of 53 % and 21 %, respectively, over the study period. To summarize, the road salting has increased the amount of transported solutes in the stream and deteriorated surface water chemistry, while the reduction of arable land area has reduced the amount of transported solutes in the stream and improved surface water quality. • We examine effect of human activity on solute fluxes in two contrasting catchments. • 22 % catchment clear-cut did not affect the total solute mass balance in stream. • 42 % catchment LULC changes caused 55 % decrease in solute mass balance in stream. • The use of de-icing salts increased TDS in stream by 62 %. [ABSTRACT FROM AUTHOR]

Highlights: [•] 3D-simulation of hydrology and soil moisture at two spatial and temporal resolutions. [•] Topsoil soil moisture dynamics and patterns well captured by all model versions. [•] Influence of bedrock aquifer on hydrological processes and soil moisture dynamics. [•] Role of bedrock for water balance is larger than that of spatio-temporal resolution. [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]

• Sobol' method is used to evaluate the sensitivity of APSIM-Wheat yield predictions. • Yield is most sensitive to initial nitrogen amount, soil parameters, precipitation. • Estimated yield is influenced most by the limiting resource under given conditions. • Scenarios for high fertilization level or dry seasons have lower yield variability. The performance of cropping system models is often limited by uncertainties in model parameters and inputs. This study aims to explore how model yield prediction responds to these uncertainties under various environmental and management conditions. The Sobol' method was used to investigate the sensitivity of the Agricultural Production Systems SIMulator (APSIM)-Wheat yield prediction to the following factors: air temperature (maximum and minimum), precipitation, initial soil nitrogen content, nitrogen fertilization amount, and soil hydraulic parameters. Eighteen scenarios were defined to consider a combination of three weather conditions (wet, normal, and dry growing season, based on the historical climatology of the Wimmera district of Victoria, Australia), three soil types (sandy, loamy, and clayey soils), and two nitrogen fertilization applications (50 and 100 kg N/ha). Eight thousand APSIM simulations were used to calculate the first order and total sensitivity indices for each scenario. The effects of weather, soil water characteristics and nitrogen availability were estimated by measuring their impacts on sensitivity indices. Our results show that yield was more sensitive either to variables that control water availability (precipitation and soil type) or variables that control nitrogen availability, depending on which was more limiting to wheat growth under the scenario. For example, in case of low nitrogen fertilization scenario, the initial nitrogen content sensitivity ranked first. Variation of this factor contributed 64% to 79% to the variance in simulated yield for clayey soils, nearly four times higher than the second-ranked factor (nitrogen fertilization amount). Soil hydraulic parameters and precipitation were most important when the crop growth was more constrained by water availability than by nitrogen availability. In the case of sandy soils in dry years with high nitrogen fertilization level, soil parameters and precipitation accounted for 83% of the yield variability. Minimum temperature consistently ranked as the least important factor under all scenarios. This work will help researchers better understand the model inputs' impacts on the simulated yield variability under various soil, management, and weather conditions. The results also support agronomists and practitioners in the optimal use of the APSIM-Wheat model as a management tool. [Display omitted] [ABSTRACT FROM AUTHOR]

Drought and heat events, such as the 2018 European drought, interact with the exchange of energy between the land surface and the atmosphere, potentially affecting albedo, sensible and latent heat fluxes, as well as CO2 exchange. Each of these quantities may aggravate or mitigate the drought, heat, their side effects on productivity, water scarcity and global warming. We used measurements of 56 eddy covariance sites across Europe to examine the response of fluxes to extreme drought prevailing most of the year 2018 and how the response differed across various ecosystem types (forests, grasslands, croplands and peatlands). Each component of the surface radiation and energy balance observed in 2018 was compared to available data per site during a reference period 2004–2017. Based on anomalies in precipitation and reference evapotranspiration, we classified 46 sites as drought affected. These received on average 9% more solar radiation and released 32% more sensible heat to the atmosphere compared to the mean of the reference period. In general, drought decreased net CO2 uptake by 17.8%, but did not significantly change net evapotranspiration. The response of these fluxes differed characteristically between ecosystems; in particular, the general increase in the evaporative index was strongest in peatlands and weakest in croplands. This article is part of the theme issue 'Impacts of the 2018 severe drought and heatwave in Europe: from site to continental 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]

• Response amplitudes are different for meadow, HCG and MCG, while similar for others. • Rate of wetting event reduced significantly with depth for all land covers except HCG. • Temporal patterns of soil wetting event varied greatly along depths and land covers. • Preferential flow occurred mostly in soils covered by scrubland. • Soil hydraulic properties are key factors in regulating soil wetting events. Understanding the dynamic response of soil moisture to rainfall is critical for hydrological modelling in arid and semi-arid basins. However, little is known about rainfall-related soil moisture dynamics in arid high-altitude mountainous areas due to the absence of long-term high-resolution soil moisture observations. In this study, we investigated the dynamic response processes of profile soil moisture using data from a soil moisture monitoring network in the Qilian Mountains established in 2013 covering altitudes from 2,000–5,000 m a.s.l. To investigate the effects of different land covers on soil moisture response, we selected data from eight soil moisture stations with the same soil textural class and slope, but different land covers (scrubland, meadow, high coverage grassland (HCG), medium coverage grassland (MCG) and barren land). Several indices were evaluated to quantitatively describe soil moisture dynamics during the growing seasons of 2014–2016 based on soil wetting events. In addition, HYDRUS-1D simulations were used to further analyze the effect of land cover on soil moisture dynamics. Our results showed that soil moisture response amplitudes along profile are similar under MCG and barren land, but significantly different under scrubland, meadow and HCG. The rate of soil moisture increment decreased significantly with depth for all land covers, except for the HCG. The temporal pattern of soil moisture increase was highly variable along the soil profiles depending on land cover type. In particular, the difference of response time between the adjacent layers varied from negative values to 280 h with depth. Preferential flow occurred mostly in soils covered by scrubland. Water transferability was higher in deeply rooted soil. Furthermore, sensitivity analysis indicated that soil hydraulic properties are key factors in regulating profile soil wetting events. Our results show that the soil moisture response indices are useful to quantitatively characterize patterns in profile soil moisture dynamics, and provide new insights into the soil moisture profile wetting process (e.g. occurrence of preferential flow etc.), which helps for effective model parameterization and validation, in turn improving hydrological modelling in arid high-altitude mountainous areas. [ABSTRACT FROM AUTHOR]

To promote the advancement of novel observation techniques that may lead to new sources of information to help better understand the hydrological cycle, the International Association of Hydrological Sciences (IAHS) established the Measurements and Observations in the XXI century (MOXXI) Working Group in July 2013. The group comprises a growing community of tech-enthusiastic hydrologists that design and develop their own sensing systems, adopt a multi-disciplinary perspective in tackling complex observations, often use low-cost equipment intended for other applications to build innovative sensors, or perform opportunistic measurements. This paper states the objectives of the group and reviews major advances carried out by MOXXI members toward the advancement of hydrological sciences. Challenges and opportunities are outlined to provide strategic guidance for advancement of measurement, and thus discovery. [ABSTRACT FROM AUTHOR]

Forest canopy interception alters the isotopic tracer signal of precipitation leading to significant isotopic differences between open precipitation (δOP) and throughfall (δTF). This has important consequences for the tracer-based modeling of streamwater transit times. Some studies have suggested using a simple static correction to δOP by uniformly increasing it because δTF is rarely available for hydrological modeling. Here, we used data from a 38.5 ha spruce forested headwater catchment where three years of δOP and δTF were available to develop a data driven method that accounts for canopy effects on δOP. Changes in isotopic composition, defined as the difference δTF-δOP, varied seasonally with higher values during winter and lower values during summer. We used this pattern to derive a corrected δOP time series and analyzed the impact of using (1) δOP, (2) reference throughfall data (δTF ref ) and (3) the corrected δOP time series (δOP Sine ) in estimating the fraction of young water ( F yw ), i.e., the percentage of streamflow younger than two to three months. We found that F yw derived from δOP Sine came closer to δTF ref in comparison to δOP. Thus, a seasonally-varying correction for δOP can be successfully used to infer δTF where it is not available and is superior to the method of using a fixed correction factor. Seasonal isotopic enrichment patterns should be accounted for when estimating F yw and more generally in catchment hydrology studies using other tracer methods to reduce uncertainty. [ABSTRACT FROM AUTHOR]

Summary Neutron intensity measured by the aboveground cosmic-ray neutron intensity probe (CRP) allows estimating soil moisture content at the field scale. In this work, synthetic neutron intensities were used to remove the bias of simulated soil moisture content or update soil hydraulic properties (together with soil moisture) in the Community Land Model (CLM) using the Local Ensemble Transform Kalman Filter. The cosmic-ray forward model COSMIC was used as the non-linear measurement operator which maps between neutron intensity and soil moisture. The novel aspect of this work is that synthetically measured neutron intensity was used for real time updating of soil states and soil properties (or soil moisture bias) and posterior use for the real time scheduling of irrigation (data assimilation based real-time control approach). Uncertainty of model forcing and soil properties (sand fraction, clay fraction and organic matter density) were considered in the ensemble predictions of the soil moisture profiles. Horizontal and vertical weighting of soil moisture was introduced in the data assimilation in order to handle the scale mismatch between the cosmic-ray footprint and the CLM grid cell. The approach was illustrated in a synthetic study with the real-time irrigation scheduling of fields of citrus trees. After adjusting soil moisture content by assimilating neutron intensity, the irrigation requirements were calculated based on the water deficit method. Model bias was introduced by using coarser soil texture in the data assimilation experiments than in reality. A series of experiments was done with different combinations of state, parameter and bias estimation in combination with irrigation scheduling. Assimilation of CRP neutron intensity improved soil moisture characterization. Irrigation requirement was overestimated if biased soil properties were used. The soil moisture bias was reduced by 35% after data assimilation. The scenario of joint state-parameter estimation resulted in the best soil moisture characterization (50% decrease in root mean square error compared to open loop simulations), and the best estimate of needed irrigation amount (86% decrease in Hausdorff distance compared to open loop). The coarse scale synthetic CRP observation was proven to be useful for the fine scale soil moisture and soil properties estimation for the objective of irrigation scheduling. [ABSTRACT FROM AUTHOR]

The objective of the NASA Soil Moisture Active Passive (SMAP) mission is to provide global measurements of soil moisture and freeze/thaw states. SMAP integrates L-band radar and radiometer instruments as a single observation system combining the respective strengths of active and passive remote sensing for enhanced soil moisture mapping. Airborne instruments are a key part of the SMAP validation program. Here, we present an airborne campaign in the Rur catchment, Germany, in which the passive L-band system Polarimetric L-band Multi-beam Radiometer and the active L-band system F-SAR of DLR were flown simultaneously on six dates in 2013. The flights covered the full heterogeneity of the area under investigation, i.e., the main land cover types and all experimental monitoring sites. Here, we used the obtained data sets as a test bed for the analysis of three active–passive fusion techniques: 1) estimation of soil moisture by passive sensor data and subsequent disaggregation by active sensor backscatter data; 2) disaggregation of passive microwave brightness temperature by active microwave backscatter and subsequent inversion to soil moisture; and 3) fusion of two single-source soil moisture products from radar and radiometer. Results indicate that the regression parameters $\beta$ are dependent on the radar vegetation index. The best performance was obtained by the fusion of radiometer brightness temperatures and radar backscatter, which was able to reach the same accuracy as single-source coarse-scale radiometer soil moisture retrieval but on a higher spatial resolution. [ABSTRACT FROM AUTHOR]

Summary The objective of this study is to inter-compare three spatially distributed hydrological models (HydroGeoSphere, MIKE SHE and ParFlow-CLM) by means of their ability to simulate soil moisture patterns. This study pools the catchment modeling efforts which have been undertaken at the Wüstebach catchment; one of TERENO’s hydrological observatories. The catchment is densely instrumented with a wireless sensor network (SoilNET) which allows continuous measurements of the spatio-temporal soil moisture dynamics. This unique dataset is ideal to benchmark hydrological models as it poses distinct challenges like seasonality and spatial heterogeneity. Two scenarios of soil parametrization assess the modeling implications of moving from homogeneous to heterogeneous porosity. The three given models perform well in terms of discharge and accumulated water balance components. However, their ability to predict soil moisture is found to be more diverging. Interpretations are ambiguous and depend on what performance metric and what level of spatial aggregation is chosen. In comparison to the other models, ParFlow-CLM performs more accurate at predicting the temporal dynamics and the heterogeneity aggregated to catchment scale. Nevertheless, at local scale HydroGeoSphere and MIKE SHE provide more detailed soil moisture predictions. Overall, a clear increase in performance can be attested to the scenario that includes heterogeneous porosity. Next to soil parametrization, topography is among the main drivers of soil moisture variability which was found to have an overemphasized feedback in ParFlow-CLM compared to the other models. This study stresses that further efforts toward spatially distributed input data need to emerge alongside a more suitable soil parametrization that can account for the observed heterogeneity and seasonality of soil moisture. [ABSTRACT FROM AUTHOR]

Abstract: For the soil moisture retrieval from passive microwave sensors, such as ESA’s Soil Moisture and Ocean Salinity (SMOS) and the NASA Soil Moisture Active and Passive (SMAP) mission, a good knowledge about the vegetation characteristics is indispensable. Vegetation cover is a principal factor in the attenuation, scattering and absorption of the microwave emissions from the soil; and has a direct impact on the brightness temperature by way of its canopy emissions. Here, brightness temperatures were measured at three altitudes across the TERENO (Terrestrial Environmental Observatories) Rur catchment site in Germany to achieve a range of spatial resolutions using the airborne Polarimetric L-band Multibeam Radiometer 2 (PLMR2). The L-band Microwave Emission of the Biosphere (L-MEB) model which simulates microwave emissions from the soil–vegetation layer at L-band was used to retrieve surface soil moisture for all resolutions. A Monte Carlo approach was developed to simultaneously estimate soil moisture and the vegetation parameter b’ describing the relationship between the optical thickness τ and the Leaf Area Index (LAI). LAI was retrieved from multispectral RapidEye imagery and the plant specific vegetation parameter b′ was estimated from the lowest flight altitude data for crop, grass, coniferous forest, and deciduous forest. Mean values of b’ were found to be 0.18, 0.07, 0.26 and 0.23, respectively. By assigning the estimated b′ to higher flight altitude data sets, a high accuracy soil moisture retrieval was achieved with a Root Mean Square Difference (RMSD) of 0.035m3 m−3 when compared to ground-based measurements. [Copyright &y& Elsevier]