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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 = aKb + 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.

Deforestation can lead to an increase in the availability of nutrients in the soil and, in turn, have an impact on the quality of water in receiving water bodies. This study assesses the impact of deforestation by evaluating the in-stream concentrations of dissolved organic carbon (DOC) and nitrate, their internal relationship, and those with stream discharge in the Wüstebach headwater catchment (Germany). This catchment has monitored stream water and associated environmental parameters for over a decade as part of the TERENO initiative. Additionally, there is a paired undisturbed forested catchment that serves as a reference stream. Our approach included a more advanced correlation analysis, namely wavelet analysis, that assists in determining changes in the correlation and lag time between the variables of interest over different time scales. This study found that after deforestation, there was an immediate increase in in-stream DOC concentrations, followed by an increase in nitrate ~1 year later. Overall, the mean DOC concentration increased, and mean nitrate concentration decreased across the catchment post-deforestation. Elevated stream water nutrient levels peaked around 2 to 3 years after the clear-cutting, and returned to pre-deforestation levels after ~5 years. The deforestation had no influence on the anti-correlation between DOC and nitrate. However, the correlation between both compounds and discharge was likely altered due to the increased soil nutrients availability as a result of deforestation. Wavelet coherence analysis revealed the “underlying” changing strengths and directions of the main correlations between DOC, nitrate and discharge on different time scales resulting from severe forest management interventions (here deforestation). This information provides new valuable impact insights for decision making into such forest management interventions.

Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters (θNovSAR) or the combination of 43 SAR and 10 terrain parameters (θNovSAR+Terrain). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model (θNovSAR+Terrain with 0.025 and 0.020 m3 m−3, and 89% compared to θNovSAR with 0.028 and 0.022 m3 m−3, and 86% in terms of RMSE, MAE, and R2). The higher explanatory power for θNovSAR+Terrain is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon.

Water scarcity in semi-arid regions is expected to increase under climate change, which will significantly affect forest ecosystems by increasing fire risk, diminishing productivity and water provisioning. Eco-hydrological forest management is conceived here as an adequate strategy to buffer climate change effects and increase forest resilience. Under this context, soil moisture is a key variable to quantify the impacts of eco-hydrological forest management on forest-water relations. Cosmic-ray neutron and capacitance probes are two different techniques for measuring soil moisture, which differ greatly in the spatial scale of the measurement support (i.e., few centimeters vs. several hectares). This study compares the capability of both methodologies in assessing soil water dynamics as a key variable that reflects the effects of forest management in a semi-arid environment. To this end, two experimental plots were established in Sierra Calderona in the province of Valencia in Spain in a post-fire regeneration Aleppo pine forest with high tree density. One plot was thinned (T) and the other remained as control (C). Nine capacitance probes and one Cosmic Ray Neutron Probe (CRNP) were installed in each plot. First, the CRNP was calibrated and validated, and subsequently, the performance of both techniques was analyzed by comparing soil moisture and its relationship with environmental variables and stand transpiration. The validation results confirmed the general reliability of CRNP to obtain soil moisture under semi-arid conditions, with a Kling-Gupta efficiency coefficient (KGE) between 0.75 and 0.84, although this performance decreased significantly when dealing with extreme soil moisture (KGE: −0.06–0.02). A significant effect of forest biomass and litter layer was also observed on CRNP-derived soil moisture, which produced an overestimation of soil moisture. The performance of both methodologies was analyzed by partial correlations between soil moisture and environmental variables and transpiration, as well as by applying Boosted Regression Trees to reproduce tree transpiration with each soil moisture measurement technique together with the environmental variables. Both methodologies were capable to reproduce tree transpiration affected by soil moisture, environmental variables and thinning, although CRNP always appeared as the most affected by atmospheric driving forces.

Successful monitoring of soil moisture dynamics at high spatio-temporal resolutions globally is hampered by the heterogeneity of soil hydraulic properties in space and complex interactions between water and the environmental variables that control it. Current soil moisture monitoring schemes via in situ station networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical surface soil moisture (SSM) model was established via fusion of in situ continental and regional scale soil moisture networks, remote sensing data (SMAP and Sentinel-1) and high-resolution land surface parameters (e.g., soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100 m and performed well under cultivated, herbaceous, forest, and shrub soils (overall R2 = 0.524, RMSE = 0.07 m3 m−3). It has a relatively good transferability at the regional scale among different soil moisture networks (mean RMSE = 0.08–0.10 m3 m−3). The global model was applied to map SSM dynamics at 30–100 m across a field-scale soil moisture network (TERENO-Wüstebach) and an 80-ha cultivated cropland in Wisconsin, USA. Without the use of local training data, the model was able to delineate the variations in SSM at the field scale but contained large bias. With the addition of 10% local training datasets (“spiking”), the bias of the model was significantly reduced. The QRF model was relatively insensitive to the resolution of Sentinel-1 data but was affected by the resolution and accuracy of soil maps. It was concluded that the empirical model has the potential to be applied elsewhere across the globe to map SSM at the regional to field scales for research and applications. Future research is required to improve the performance of the model by incorporating more field-scale soil moisture sensor networks and assimilation with process-based models.

In recent decades, while great emphasis has been given to the monitoring of point-scale soil moisture patterns and field-scale integrated soil moisture, the measurement of matric potential has attracted little attention. Information on the soil matric potential is available in point-scale measurements but is still missing at field-scale. This state variable is necessary to understand hydrological fluxes and to determine the soil water retention function (WRF) for field-scale applications. In this study, we combine data from cosmic-ray neutron probes (CRNP, non-invasive proximal soil moisture sensors) and SoilNet wireless sensor networks (invasive ground-based soil moisture and matric potential sensors) installed in two sub-catchments with contrasting land-use (agroforestry vs. near-natural forest) to derive a field-scale WRF. We investigate the hypothesis that both sensor types provide effective measurements that are representative for the entire sub-catchment, as well as the drawbacks of integrating the different measurement scales of the sensor types (i.e., spatial-mean of distributed point-scale data vs. an integrated field-scale measurement). We found discrepancies in the data of the two sensor types related to the effects of the time-varying vertical measurement footprint of the CRNP, which induces a scale mismatch between CRNP-based soil moisture (referring mostly to near-surface depths) and the spatially averaged soil matric potential data measured at soil depths of 0.15 and 0.30 m. To remove the offsets, we opted to use the soil moisture index (SMI) based on the estimation of field capacity and wilting point, retrieved from the knowledge of the field-scale WRF. We found that the bimodality of SMI calculated with SoilNet-based soil moisture induced by Mediterranean rainfall seasonal behavior is not well-captured by CRNP-based soil moisture, except in a particularly dry year like 2017. The contrasts in SMI values between the two test sites were associated with differences in the spatial variability of soil moisture patterns explained by soil texture or terrain characteristics. We argue that field-scale WRFs are useful for the analysis of hydrological processes at the sub-catchment (field) scale and the application of distributed models.

Common snow monitoring instruments based on hydrostatic pressure such as snow pillows are often influenced by various disturbing effects, which result in a reduced quality of the snow cover and snow water equivalent estimates. Such disturbing effects include energy transport into the snowpack, wind fields, and variations of snow properties within the snowpack (e.g., ice layers). Recently, it has been shown that Cosmic-Ray Neutron Probes (CRNP) are a promising technique to monitor snow pack development. CRNP can provide larger support and need lower maintenance compared to conventional sensors. These instruments are sensitive to the intensity of epithermal neutrons that are produced in the soil by cosmic radiation and are widely used to determine soil moisture in the upper decimeters of the ground. The application of CRNP for snow monitoring is based on the principle that snow water moderates the epithermal neutron intensity, which can be directly related to the snow water equivalent (SWE) of the snow pack. In this study, long-term CRNP measurements in the Pinios Hydrologic Observatory (PHO), Greece, were used to test different methods for converting neutron count rates to snow pack characteristics: (i) linear regression, (ii) standard N0-calibration function, (iii) a physically-based calibration approach, and (iv) thermal to epithermal neutron ratio. For this, a sonic sensor located near the CRNP was used to compare CRNP-derived snow pack dynamics with snow depth measurements. We found that the above-ground CRNP is well-suited for measurement of field scale SWE, which is in agreement with findings of other studies. The analysis of the accuracy of the four conversion methods showed that all methods were able to determine the mass of the snow pack during the snow events reasonably well. The N0-calibration function and the physically-based calibration function performed best and the thermal to epithermal neutron ratio performed worst. Furthermore, we found that SWE determination with above-ground CRNP can be affected by other influences (e.g., heavy rainfall). Nevertheless, CRNP-based SWE determination is a potential alternative to established method like snow depth-based SWE methods, as it provides SWE estimate for a much larger scales (12–18 ha).

Cosmic ray neutron (CRN) sensing allows for non-invasive soil moisture measurements at the field scale and relies on the inverse correlation between aboveground measured epithermal neutron intensity (1 eV−100 keV) and environmental water content. The measurement uncertainty follows Poisson statistics and thus increases with decreasing neutron intensity, which corresponds to increasing soil moisture. In order to reduce measurement uncertainty, the neutron count rate is usually aggregated over 12 or 24 h time windows for stationary CRN probes. To obtain accurate soil moisture estimates with mobile CRN rover applications, the aggregation of neutron measurements is also necessary and should consider soil wetness and driving speed. To date, the optimization of spatial aggregation of mobile CRN observations in order to balance measurement accuracy and spatial resolution of soil moisture patterns has not been investigated in detail. In this work, we present and apply an easy-to-use method based on Gaussian error propagation theory for uncertainty quantification of soil moisture measurements obtained with CRN sensing. We used a 3rd order Taylor expansion for estimating the soil moisture uncertainty from uncertainty in neutron counts and compared the results to a Monte Carlo approach with excellent agreement. Furthermore, we applied our method with selected aggregation times to investigate how CRN rover survey design affects soil moisture estimation uncertainty. We anticipate that the new approach can be used to improve the strategic planning and evaluation of CRN rover surveys based on uncertainty requirements.

The Pinios Hydrologic Observatory (PHO) is located in the River Pinios basin, which is one of the most productive basins in Greece. The PHO was established to develop deep knowledge of water balance at the river basin scale and to improve understanding of the major hydrodynamic mechanisms to improve hydrological modeling and ultimately sustainable water resource management. The PHO comprises three meteorological stations, 12 groundwater monitoring sites, and one soil moisture monitoring site, which includes frequency domain reflectometry sensors (SoilNet) and a cosmic-ray neutron sensor (CRNS) probe. Although the PHO was recently established (at the end of 2015), the preliminary findings from data analysis are promising. Calculated reference evapotranspiration (ETo) gradients demonstrate differences regarding their annual cycle, total amount, and altitude level. Moreover, climate analysis indicates nocturnal mountain-valley winds. Groundwater level spatial distribution indicates the dominant recharge mechanisms to the alluvial aquifer system. These findings are also supported by the hydrochemical data analysis (electrical conductivity and, secondarily, NO distribution). Locally elevated NO concentrations are attributed to agricultural activities and call for review of the adopted farming practices. Results from the soil moisture monitoring site indicate a very good match between the CRNS probe and the average SoilNet data. Future perspectives of the PHO include geophysical surveys to accurately delineate the geometry of the groundwater system, the expansion of groundwater and soil moisture observation networks, and the application of the mGROWA hydrologic model to accurately simulate the hydrological processes in the PHO and upscale in the entire River Pinios basin. Finally, in support of the local farmers, we plan to develop and implement a distributed irrigation programming protocol in the entire area of the PHO.

The European heat wave of 2018 was characterized by extraordinarily dry and hot spring and summer conditions in many central and northern European countries. The average temperatures from June to August 2018 were the second highest since 1881. Accordingly, many plants, especially trees, were pushed to their physiological limits. However, while the drought and heat response of field crops and younger trees have been well investigated in laboratory experiments, little is known regarding the drought and heat response of mature forest trees. In this study, we compared the response of a coniferous and a deciduous tree species, located in western and central–western Germany, to the extreme environmental conditions during the European heat wave of 2018. Combining classic dendroecological techniques (tree–ring analysis) with measurements of the intra–annual stem expansion (dendrometers) and tree water uptake (sap flow sensors), we found contrasting responses of spruce and oak trees. While spruce trees developed a narrow tree ring in 2018 combined with decreasing correlations of daily sap flow and dendrometer parameters to the climatic parameters, oak trees developed a ring with above–average tree–ring width combined with increasing correlations between the daily climatic parameters and the parameters derived from sap flow and the dendrometer sensors. In conclusion, spruce trees reacted to the 2018 heat wave with the early completion of their growth activities, whereas oaks appeared to intensify their activities based on the water content in their tree stems.

Soils are the dominant source of atmospheric nitrous oxide (N2O), especially agricultural soils that experience both waterlogging and intensive nitrogen fertilization. However, soil heterogeneity and the irregular occurrence of hydrological events hamper the prediction of the temporal and spatial dynamics of N2O production and transport in soils. Because soil moisture influences soil redox potential, and as soil N cycling processes are redox-sensitive, redox potential measurements could help us to better understand and predict soil N cycling and N2O emissions. Despite its importance, only a few studies have investigated the control of redox potential on N2Oemission from soils in detail. This study aimed to partition the different microbial processes involved in N2O production (nitrification and denitrification) by using redox measurements combined with isotope analysis at natural abundance and 15N-enriched. To this end, we performed long-term laboratory lysimeter experiments to mimic common agricultural irrigation and fertilization procedures. In addition, we used isotope analysis to characterize the distribution and partitioning of N2O sources and explored the 15N-N2O site preference to further constrain N2O microbial processes. We found that irrigation, saturation, and drainage induced changes in soil redox potential, which were closely related to changes in N2O emission from the soil as well as to changes in the vertical concentration profiles of dissolved N2O, nitrate (NO3−) and ammonium (NH4+). The results showed that the redox potential could be used as an indicator for NH4+, NO3−, and N2O production and consumption processes along the soil profile. For example, after a longer saturation period of unfertilized soil, the NO3− concentration was linearly correlated with the average redox values at the different depths (R2 = 0.81). During the transition from saturation to drainage, but before fertilization, the soil showed an increase in N2O emissions, which originated mainly from nitrification as indicated by the isotopic signatures of N2O (δ15N bulk, δ18O and 15N-N2O site preference). After fertilization, N2O still mainly originated from nitrification at the beginning, also indicated by high redox potential and the increase of dissolved NO3−. Denitrification mainly occurred during the last saturation period, deduced from the simultaneous 15N isotope analysis of NO3− and N2O. Our findings suggest that redox potential measurements provide suitable information for improving the prediction of soil N2O emissions and the distribution of mineral N species along the soil profile under different hydrological and fertilization regimes.

Soil greenhouse gas (GHG) emissions contribute to global warming. To support mitigation measures against global warming, it is important to understand the controlling processes of GHG emissions. Previous studies focusing mainly on paddy rice fields or wetlands showed a strong relationship between soil redox potential and GHG emission (e.g., NO). However, the interpretation of redox potentials for the understanding of the controlling factors of GHG emission is limited due to the low number of continuous redox measurements in most ecosystems. Recent sensor developments open the possibility for the long-term monitoring of field-scale soil redox potential changes. We performed laboratory lysimeter experiments to investigate how changes in the redox potential, induced by changes in the water level, affect GHG emissions from agricultural soil. Under our experimental conditions, we found that NO emissions followed closely the changes in redox potential. The dynamics of redox potential were induced by changing the water-table depth in a laboratory lysimeter. Before fertilization during saturated conditions, we found a clear negative correlation between redox potentials and NO emission rates. After switching from saturated to unsaturated conditions, NO emission quickly decreased, indicating denitrification as the main source of NO. In contrast, the emissions of CO increased with increasing soil redox potentials. After fertilization, NO emission peaked at high redox potential, suggesting nitrification as the main production pathway, which was confirmed by isotope analysis of NO. We propose that redox potential measurements are a viable method for better understanding of the controlling factors of GHG emissions, for the differentiation between different source processes, and for the improvement of process-based GHG models.

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.

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 South Wales (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.

Parameterization of physically based and distributed hydrological models for mesoscale catchments remains challenging because the commonly available data base is insufficient for calibration. In this paper, we parameterize a mesoscale catchment for the distributed model HydroGeoSphere by transferring evapotranspiration parameters calibrated at a highly-equipped headwater catchment in addition to literature data. Based on this parameterization, the sensitivity of the mesoscale catchment to spatial variability in land use, potential evapotranspiration and precipitation and of the headwater catchment to mesoscale soil and land use data was conducted. Simulations of the mesoscale catchment with transferred parameters reproduced daily discharge dynamics and monthly evapotranspiration of grassland, deciduous and coniferous vegetation in a satisfactory manner. Precipitation was the most sensitive input data with respect to total runoff and peak flow rates, while simulated evapotranspiration components and patterns were most sensitive to spatially distributed land use parameterization. At the headwater catchment, coarse soil data resulted in a change in runoff generating processes based on the interplay between higher wetness prior to a rainfall event, enhanced groundwater level rise and accordingly, lower transpiration rates. Our results indicate that the direct transfer of parameters is a promising method to benefit highly equipped simulations of the headwater catchments.

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