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Effective techniques for spatial data analysis are required to address the growing need to develop and improve management plans at regional or continental scales. The main purpose of this research is to evaluate the impact of change of spatial support on digital soil mapping. The study is based on the availability of more than 3,300 soil samples collected from the uppermost horizons of farmlands in Campania, an administrative region of southern Italy of about 13,700 km2. Each soil sample was subjected to laboratory tests to determine the following point-referenced primary soil properties: sand and clay contents, oven-dry soil bulk density, soil organic matter, pH, calcium carbonate, and rock fragments. These seven soil properties were mapped over the entire region by using as covariates the following terrain attributes, obtained from the digital terrain model (DTM; 75 m/pixel): elevation, slope, plan curvature, profile curvature, and flow accumulation. Two composite indicators of soil quality were then determined: 1) the soil organic carbon stock (SOCS) in Campania, and 2) the recharge transit time in the alluvial plain of the Sele River where information about the mean annual depth to groundwater is available.In this study, it was crucial to evaluate the epistemic uncertainty associated with the change of support when fusing the point-referenced soil measurements with the block-based terrain attributes. A key issue of our analysis is the modification of the anamorphosis model based on a block rather than a point. Accordingly, our results show how the estimates change when a properly-corrected block Gaussian anamorphosis model is employed instead of the point Gaussian one.By way of conclusion, when the main interest of an investigation is to obtain a map of average soil attributes, the change of support might have little influence on the final estimates, especially when working with nearly symmetrical distributions of soil properties. On the other hand, if one should infer the uncertainty of a variable, as in soil vulnerability mapping, then the change of support matters and is an issue to be adequately accounted for in the spatial analysis of environmental data.

The temporal origin of root water uptake and drainage provides insights into the impact of natural and anthropogenic disturbances on plant resilience and aquifer vulnerability. In-situ and virtual tracer experiments provide information on rainfall partitioning and transit times, which help to enhance the understanding of the hydrological response of a soil-plant-atmosphere continuum (SPAC) to climate variability and contaminant transport. A virtual tracer experiment was carried out in a 150-cm-thick soil lysimeter planted with winter rye in Austria. Water flow and tracer transport dynamics were simulated in the SPAC using HYDRUS-1D, previously calibrated with hydrochemical measurements. The root water uptake (τR) and drainage (τD) transit times (τ) were assessed by identifying arrival times when a prescribed percentage of the tracer mass breakthrough curves was reached. The estimates of τR and τD were compared to those derived from a particle tracking algorithm that simulates the particle trajectories subject to convective transport. The tracer-based arrival times were in close agreement with those determined using particle tracking when 50% of the tracer output flux was reached. On average, it took 19 or 234 days for water originating from rainfall in the growing season to be taken up by roots (21%) or exit as drainage (36%), respectively. In contrast, in the dormant season, 10% of rainfall water was taken up by roots after 245 days, while 79% became drainage after 241 days. The results from each daily event can be aggregated at any desired temporal resolution to investigate the effects of climate seasonality on water balance and timing. The temporal origin of water can be explored in other plots using the standard guidelines proposed in this study.

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

The soil water content at the condition of field capacity (θFC) is a key parameter in irrigation scheduling and has been suggested to be determined by running a synthetic drainage experiment until the flux rate (q) at the bottom of the soil profile achieves a predefined negligible value (qFC). We question the impact of qFC on the assessment of field capacity. Moreover, calculating θFC as the integral mean of the water content profile when q is equal to qFC is strictly valid only for uniform soil profiles. By contrast, this practice is ambiguous and biased for stratified soil profiles due to the soil water content discontinuity at the layer interfaces. In this study, the concept of field capacity was revisited and adapted to practical agronomic heuristics. By resorting to the assessment of root-zone water storage capacity (W), we envision field capacity as a functional hydraulic parameter derived from synthetic irrigation scheduling scenarios to minimize drought stress, drainage, and nitrate leachate below the root zone. A functional analysis was carried out on a 135-cm-thick layered soil profile beneath maize in eastern Nebraska. On-farm irrigation scheduling applications and agricultural practices were recorded for 20 years (2001–2020) at a daily time step. Hydrus-1D was calibrated and validated with direct measurements of the soil water retention curve and soil water content data, respectively, in each soil layer. A set of functional field capacity values was derived from 24 irrigation scheduling scenarios, and the optimal water storage capacity at field capacity (WFC) was approximately 50 cm (corresponding to about 80% saturation in the soil profile). An average irrigation amount of 217.5 mm distributed over 21 events was obtained by using optimal irrigation scheduling, which was initiated when the matric pressure head took on a value of -700 cm and the irrigation rate was set at 1.0 cm d-1. This irrigation practice ensured water storage at approximately the same level (ideally at WFC) by sustaining only evapotranspiration fluxes in the uppermost portion of the root zone and by limiting excessive drainage. This protocol can be transferred to other agricultural fields.

[EN] The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m-1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results., The authors would like to thank the European Commission and Netherlands Organisation for Scientific Research (NWO) for funding, in the frame of the collaborative international consortium (iAqueduct) financed under the 2018 Joint call of the Water Works 2017 ERA-NET Cofund. This ERA-NET is an integral part of the activities developed by the Water JPI (Project number: ENWWW.2018.5); the EC and the Swedish Research Council for Sustainable Development (FORMAS, under grant 2018-02787); Contributions of B. Szabo was supported by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences (grant no. BO/00088/18/4).

Soil moisture (SM) is an essential element in the hydrological cycle influencing land-atmosphere interactions and rainfall-runoff processes. Quantification of the spatial and temporal behaviour of SM at field scale is vital for understanding water availability in agriculture, ecosystems research, river basin hydrology and water resources management. Uncrewed Arial Systems (UAS) offer an extraordinary opportunity to bridge the existing gap between point-scale field observations and satellite remote sensing providing high spatial details at relatively low costs. Moreover, UAS data can help the construction of downscaling models which can link the land surface features and SM to identify the importance level of each predictor. To optimize the usage of data from UAS surveys for generating high-resolution SM at field scale, a comparative study of various SM retrieval or downscaling methods can be beneficial.In this study, four methods, which include the apparent thermal inertia method, Kubelka–Munk method (KM), simplified temperature-vegetation triangle method, and random forest model (RF), were compared by theory background, data requirements, operation procedures and SM estimation results. The above-mentioned models have been tested using UAS data and point measurements collected on the Monteforte Cilento site (MFC2) in the Alento river basin (Campania, Italy) which is an 8 hectares cropland area (covered by walnuts, cherry, and olive trees). A number of long-term studies on the vadose zone have been conducted across a range of spatial scales. The thermal inertia model is built upon the dependence of the thermal diffusion on SM, which were inferred from diachronic thermal infrared data. The Kubelka–Munk Model is a spectral model to retrieve surface SM using optical data. The simplified temperature–vegetation triangle model, was used to map surface SM based on simultaneous information of the vegetation coverage and surface temperature. In addition, we also introduce an SM downscaling method using the RF model and SENTINEL-1 CSAR 1km SM product.The study is concluded with the inter-comparison of methods. The results from KM have the highest resolution which is the same as the input multispectral data. The results of RF and KM provides information only for bare soil pixels according to the principle of the model. Results show good performances for all methods, but the simplified triangle and thermal inertia model provides better performances in terms of correlation coefficient and RMSE measured with respect to in-situ measurements. In addition, it is worthy to say that the RF downscaling method reveals the features controlling the spatial distributions of SM at a different scale.This research is a part of EU COST-Action “HARMONIOUS” and waterJPI project “iAqueduct”.

Hydrological processes along mountain hillslopes involve complex interaction between soil storage and surface and subsurface flow, drainage and evapotranspiration. To capture this complexity, time-lapse extensive and intensive measurements are needed, potentially capable of providing spatially dense information in 3D and time frequent data. To this end, hydro-geophysical methods (ground penetration radar, GPR, electromagnetic induction, EMI and electrical resistivity tomography, ERT) based on electrical and electromagnetic laws are widely used as they naturally link to the electrical properties of soil moisture. ERT produces, especially in time lapse mode and using permanent installations, very detailed images of the water dynamics along hillslopes. While ERT requires galvanic contact with the ground, and thus relatively slow operations, EMI can be applied over large areas in a very short time. This method has been used for decades, mainly to produce apparent electrical conductivity (ECa) maps. Only recently, inversion of EMI data as a function of depth has become a viable practice.In this work, we present two cases of hillslope monitoring using non-invasive methods, both performed as part of the WATZON project, funded by the Italian Ministry of University and Research (MIUR). The first case is the Mediterranean catchment of the Alento River, in southern Italy. The monitoring was carried out using 7 different EMI surveys, acquired in multifrequency mode (FDEM) between August 2020 and December 2021. The purpose of this survey was to characterize the structure of the basin’s subsoil within the first few meters, as well as to record the variation of electrical conductivity (EC) associated with seasonal variations. The second case is related to the Apennines catchment of the Re Della Pietra, located at the border between Tuscany and Emilia-Romagna in central Italy. The monitoring was carried out through 6 different EMI surveys, acquired in multifrequency mode (FDEM) between August 2020 and May 2021. The purpose of this survey was to characterize the structure of the basin’s subsoil within the first few meters, as well as to record the variation of electrical conductivity (EC) associated with seasonal variations. Furthermore, ERT measurements were carried out along a fixed line on the ground, according to the direction of the maximum slope. The combination of EMI and ERT proved particularly effective in delineating the hydrologic dynamics of the hillslope.

Water resources observation and modelling are essential to better understand hydrological processes and improve water resource management. However, the reliability of hydrological simulation is strongly controlled by the quality and type of field observations used for the calibration and validation processes. Therefore, it is critical to develop proper strategies for model calibration and validation in order to reduce prediction uncertainties. Standard hydrological calibration relies mainly on the time series of total streamflow at the catchment outlet; nevertheless, this leads to a limited insight into the spatial behaviour of a river basin. In this work, we use simulations from the physically-based distributed DREAM model to discuss the importance of multi-criteria calibration to obtain consistent parameter sets. The calibration methodology exploits a physical based filter to decompose the streamflow times series in two time series referring to the surface component and the baseflow. Therefore, we adopted a multi-criteria calibration procedures which optimizes: (a) the total streamflow measured at the basin outlet (used as a reference study case); b) both the surface runoff and baseflow measured at the basin outlet; and (c) the combination the time series of the two components along with the annual water balance components. In addition, we also explored the use of a lumped parametrization against a spatial parametrization derived from the soil type characteristics of the river basin. In all cases, parameter optimization was carried out using an automatic calibration performed by a genetic algorithm (GA) tool. The study was carried out for two experimental catchments located in Basilicata and Campania regions (Southern Italy). The performed experiments showed that the inclusion of physical information during the calibration process results in a general improvement of model reliability.This research is a part of iAqueduct project funded under the Water JPI 2018 Joint Call, Closing the Water Cycle Gap – on Sustainable Management of Water Resources - Water Works 2017.

One of the main environmental threats to sustainability and crop productivity in the agricultural sector is soil erosion. For the mitigation of this problem in agricultural fields, no-till management is considered a key approach. The measurement of soil erosion is particularly challenging, especially when surficial morphological changes are relatively small. Conventional experiments are commonly time-consuming and labour-intensive in terms of both field surveys and laboratory methods. However, the Structure from Motion (SfM) photogrammetry technique has enhanced the experimental activities by enabling the temporal evolution of soil erosion to be assessed through detailed micro-topography. This work presents a multitemporal quantification of soil erosion, using SfM through Uncrewed Aerial Vehicles (UAV) survey for understanding the evolution of no-till (NT) and conventional tillage (CT) in experimental plots. Considering that plot-scale soil surface (mm grid size) by several orders of magnitude, it was necessary to minimise SfM errors (e.g., co-registration and interpolation) in volumetric estimates to reduce noise as much as possible. Therefore, a methodological workflow was developed to analyse and identify the effectiveness of multi-temporal SfM-derived products, e.g. the conventional Difference of Digital Terrain Models (DoDs) and the less used Differences of Meshes (DoMs), for soil volume computations. To recognise the most suitable estimation method, the research validated the erosion volumetric changes calculated from the SfM outputs with the amount of soil directly collected through conventional runoff and sediment measurements in the field. This study presents a novel approach for using DoMs instead of DoDs to accurately describe the micro-topography changes and sediment dynamics. Another key and innovative aspect of this research, often overlooked in soil erosion studies, was to identify the contributing sediment surface, by delineating the channels potentially routing runoff directly to water collectors. The sediment paths and connected areas inside the plots were identified using a multi-temporal analysis of the sediment connectivity index for achieving the volumetric estimates. The DoM volume estimates showed better results with respect to DoDs and a mild overestimation compared to in-situ measurements. This difference was attributable to other factors (e.g., the soil compaction processes) or variables rather than to photogrammetric or geometric ones. The developed workflow enabled a very detailed quantification of soil erosion dynamics for assessing the mitigation effects of no-till management that can also be extended in the future to different scales with low-costs, based on SfM and UAV technologies.

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 Julich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021)., Earth System Science Data, 14 (3), ISSN:1866-3516, ISSN:1866-3508

Oven-dry soil bulk density (BD) is a key soil parameter in biophysical models. Yet, its direct determination for large-scale modeling applications is limited by excessive efforts required for labor-demanding, time-consuming, expensive field campaigns and laboratory-based measurements. To circumvent these shortcomings, BD can be estimated using pedotransfer functions (PTFs) that, however, offer their optimal prediction capability if calibrated and validated within the area of interest. In this study, we exploited the availability of a dataset comprising 3,316 soil samples collected in the farmlands of Campania (a region of southern Italy) to develop regional PTFs for predicting BD using the Random Forest (RF) algorithm. RF was executed considering different combinations of seven soil and three terrain attributes with a 10-fold cross-validation approach to avoid performance overestimation. In light of the RF-based results, we further developed two new PTFs based on multiple linear regression equations. The first regression-based PTF was multiparametric and employed eight features (i.e., six soil properties and two terrain features as environmental covariates), whereas the second PTF was parsimonious and based on three easily available soil predictors. Both regression-based PTFs consistently outperformed 62 existing published PTFs. We also enhanced PTF prediction capabilities by employing regionalization through a clustering approach by grouping soil samples in ten land system classes. Finally, transferability of our models was tested using an external large independent dataset of 12,019 soil samples extracted from the European EU-HYDI database. The parsimonious PTF proved satisfactory prediction performance by corroborating results found in the Campania dataset.

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).

We propose practical guidelines to predict biome-specific potential evapotranspiration (ETp) from the knowledge of grass-reference evapotranspiration (ET0) and a crop coefficient (Kc) in Mongolia. A paucity of land-based weather data hampers use of the Penman–Monteith equation (FAO-56 PM) based on the Food and Agriculture Organization (FAO) guidelines to predict daily ET0. We found that the application of the Hargreaves equation provides ET0 estimates very similar to those from the FAO-56 PM approach. The Kc value is tabulated only for crops in the FAO-56 guidelines but is unavailable for steppe grasslands. Therefore, we proposed a new crop coefficient, Kc adj defined by (a) net solar radiation in the Gobi Desert (Kc adjD) or (b) leaf area index in the steppe region (Kc adjS) in Mongolia. The mean annual ETp obtained using our approach was compared to that obtained by FAO-56 guidelines for forages (not steppe) based on tabulated Kc values in 41 locations in Mongolia. We found the differences are acceptable (RMSE of 0.40 mm d−1) in northern Mongolia under high vegetation cover but rather high (RMSE of 1.69 and 2.65 mm d−1) in central and southern Mongolia. The FAO aridity index (AI) is empirically related to the ETp/ET0 ratio. Approximately 80% and 54% reduction of ET0 was reported in the Gobi Desert and in the steppe locations, respectively. Our proposed Kc adj can be further improved by considering local weather data and plant phenological characteristics.

Choosing a suitable process-oriented eco-hydrological model is essential for obtaining reliable simulations of hydrological processes. Determining soil hydraulic and solute transport parameters is another fundamental prerequisite. Research discussing the impact of considering evaporation fractionation on parameter estimation and practical applications of isotope transport models is limited. In this study, we analyzed parameter estimation results for two datasets for humid and arid conditions using the isotope transport model in HYDRUS-1D, in which we either did or did not consider fractionation. The global sensitivity analysis using the Morris and Sobol ’ methods and the parameter estimation using the Particle Swarm Optimization algorithm highlight the significant impact of considering evaporation fractionation on inverse modeling. The Kling-Gupta efficiency (KGE) index for isotope data can increase by 0.09 and 1.49 for the humid and arid datasets, respectively, when selecting suitable fractionation scenarios. Differences in estimated parameters propagate into the results of two practical appli- cations of stable isotope tracing: i) the assessment of root water uptake (RWU) and drainage travel times (i.e., the time elapsed between water entering the soil profile as precipitation and leaving it as transpiration or drainage) in the lysimeter (humid conditions) and ii) evaporation estimation in a controlled experimental soil column (arid conditions). The peak displacement method with optimized longitudinal dispersivity provides much lower travel times than those obtained using the particle tracking algorithm in HYDRUS-1D. Considering evaporation frac- tionation using the Craig-Gordon (CG) and Gonfiantini models is likely to result in estimates of older water ages for RWU than the no fractionation scenario. The isotope mass balance method that uses the isotopic composition profile simulated by HYDRUS-1D while considering fractionation using the CG and Gonfiantini models, or the measured evaporation isotope flux, provides comparable results in evaporation estimation as the HYDRUS-1D water mass balance method and direct laboratory measurements. In contrast, the no fractionation scenario reasonably estimates evaporation only when using the HYDRUS-1D water mass balance method. The direct use of simulated isotopic compositions in the no fractionation scenario may result in large biases in practical ap- plications in the arid zone where evaporation fractionation is more extensive than in humid areas.

Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties.

The stable isotope composition of water (δ18O and δ2H) represents a useful tool to distinguish among different water pools along the soil-plant-atmosphere continuum. Using δ2H and δ18O as tracers helps gain a better understanding of plant root water uptake and dominant ecohydrological processes. To determine which pools of water are used for plant physiologic functions and returned to the atmosphere by transpiration, a common approach is to analyze the isotopic composition of water in both soil and plant. Cryogenic water extraction (CWE; Orlowski et al., 2016) is the most widely used laboratory-based technique to extract water from soil samples for isotopic analysis. However, recent studies have shown that the extraction conditions (time, temperature, and vacuum) and soil physical and chemical properties may affect the extracted soil-water isotope composition even significantly.We have developed an efficient and cost-effective cryogenic vacuum equipment to extract water from soil or vegetation and this presentation aims at discussing some preliminary results. The equipment has been specifically designed to meet the following requirements: i) enable to quantify the accuracy of a CWE continuous flow extraction line, and ii) identify a specific extraction standard protocol for soil and vegetation samples. Two experiments have been carried out to evaluate the isotope fractionation induced by the system and how different operational parameters (i.e. times and temperature of extraction) can affect the results. Firstly, a known water isotopic ratio was processed by the vacuum system to determine the measurement accuracy and reproducibility by comparing pre- and post-processed water isotopic signatures. The likely causes of observed biases induced by sample processing are assessed and a relevant correction procedure is suggested. Subsequently, measurements were carried out on replicated samples taken from two differently-textured soils that, after being dried, were saturated in the laboratory up to different water content values with water of known isotopic composition. Also, plant samples were collected from plants grown in a greenhouse and irrigated with water of known isotopic composition.Water from all samples was extracted by our CWE system and then analyzed using an isotope ratio mass spectrometer in Gas Bench mode for analyses and in temperature conversion elemental analysis (TC/EA) mode for. Preliminary results have quantified the isotope fractions on average of -1.6 ‰ for δ18O and 14.2 ‰ for δ2H. Normalization of stable isotopes from unknown samples according to observed fractionation has enabled the observed bias to become virtually zero, leading to a replicate reproducibility of δ18O and δ2H for soil water of 0.6 ‰ and 3 ‰, respectively. The analyses carried out up to now did not find statistical evidence that the soil types and soil-water contents may affect the extraction method and the accuracy of our protocol.

Soil moisture (SM) is an essential element in the hydrological cycle influencing land-atmosphere interactions and rainfall-runoff processes. High-resolution mapping of SM at field scale is vital for understanding spatial and temporal behavior of water availability in agriculture. Unmanned Arial Systems (UAS) offer an extraordinary opportunity to bridge the existing gap between point-scale field observations and satellite remote sensing providing high spatial details at relatively low costs. Moreover, this data can help the construction of downscaling models to generate high-resolution SM maps. For instance, random Forest (RF) regression model can link the land surface features and SM to identify the importance level of each predictor.The RF regression model has been tested using a combination of satellite imageries, UAS data and point measurements collected on the experimental area Monteforte Cilento site (MFC2) in the Alento river basin (Campania, Italy) which is an 8 hectares cropland area (covered by walnuts, cherry, and olive trees). This area has been selected given the number of long-term studies on the vadose zone that have been conducted across a range of spatial scales.The coarse resolution data cover from Jan 2015 to Dec 2019 and include SENTINEL-1 CSAR 1km SM product, 1km Land surface temperature and NDVI products from MODIS and 30m thermal band (brightness temperature), red and green band data (atmospherically corrected surface reflectance) from LANDSAT-8, and SRTM DEM from NASA. High-resolution land-surface features data from UAS-mounted optical, thermal, multispectral, and hyperspectral sensors were used to generate high-resolution SM and related soil attributes.It is to note that the available satellite-based soil moisture data has a coarse resolution of 1km while the UAS-based land surface features of the extremely high resolution of 16cm. We deployed a two-step downscaling approach to address the smooth effect of spatial averaging of soil moisture, which depends on different elements at small and large scale. Specifically, different combinations of predictors were adopted for different scales of gridded soil moisture data. For example, in the downscaling procedure from 1km resolution to 30m resolution, precipitation, land-surface temperature (LST), vegetation indices (VIs), and elevation were used while LST, VIs, slope, and topographic index were selected for the downscaling from 30m to 16cm resolution. Indeed, features controlling the spatial distributions of soil moisture at different scale reflect the characteristics of the physical process: i) the surface elevation and rainfall patterns control the first downscaling model; ii) the topographic convergence and local slope become more relevant to reach a more detailed resolution. In conclusion, the study highlighted that RF regression model is able to interpret fairly well the spatial patterns of soil moisture at the scale of 30m starting from a resolution of 1km, while it is highlighted that the second downscaling step (up to few centimeters) is much more complex and requires further studies.This research is a part of EU COST-Action “HARMONIOUS: Harmonization of UAS techniques for agricultural and natural ecosystems monitoring”.Keywords: soil moisture, downscaling, Unmanned Aerial Systems, random forest, HARMONIOUS

The precise estimation and mapping of the near-surface soil moisture (~5cm, SM5cm) is key to supporting sustainable water management plans in Mediterranean agroforestry environments. In the past few years, time series of Synthetic Aperture Radar (SAR) data retrieved from Sentinel-1 (S1) enable the estimation of SM5cm at relatively high spatial and temporal resolutions. The present study focuses on developing a reliable and flexible framework to map SM5cm in a small-scale agroforestry experimental site (~30 ha) in southern Italy over the period from November 2018 to March 2019. Initially, different SAR-based polarimetric parameters from S1 (in total 62 parameters) and hydrologically meaningful topographic attributes from a 5-m Digital Elevation Model (DEM) were derived. These SAR and DEM-based parameters, and two supporting point-scale estimates of SM5cm were used to parametrize a Random Forest (RF) model. The inverse modeling module of the Hydrus-1D model enabled to simulate two supporting estimates of SM5cm by using i) sparse soil moisture data at the soil depths of 15 cm and 30 cm acquired over 20 locations comprised in a SoilNet wireless sensor network (SoilNet-based approach), and ii) field-scale soil moisture monitored by a Cosmic-Ray Neutron Probe (CRNP-based approach). In the CRNP-based approach, the field-scale SM5cm was further downscaled to obtain point-scale supporting SM5cm data over the same 20 positions by using the physical-empirical Equilibrium Moisture from Topography (EMT) model. Our results show that the CRNP-based approach can provide reasonable SM5cm retrievals with RMSE values ranging from 0.034 to 0.050 cm³ cm-3 similar to the ones based on the SoilNet approach ranging from 0.029 to 0.054 cm³ cm-3. This study highlights the effectiveness of integrating S1 SAR-based measurements, topographic attributes, and CRNP data for mapping SM5cm at the small agroforestry scale with the advantage of being non-invasive and easy to maintain.

The stable isotope composition of water (δ18O and δ2H) represents a useful tool to distinguish among different water pools along the soil-plant-atmosphere continuum. Using δ2H and δ18O as tracers helps gain a better understanding of plant root water uptake and dominant ecohydrological processes. To determine which pools of water are used for plant physiologic functions and returned to the atmosphere by transpiration, a common approach is to analyze the isotopic composition of water in both soil and plant. Cryogenic water extraction (CWE; Orlowski et al., 2016) is the most widely used laboratory-based technique to extract water from soil samples for isotopic analysis. However, recent studies have shown that the extraction conditions (time, temperature, and vacuum) and soil physical and chemical properties may affect the extracted soil water isotope composition even significantly. We have developed an efficient and cost-effective cryogenic vacuum equipment to extract water from soil or vegetation and this presentation aims at discussing some preliminary results. The equipment has been specifically designed to meet the following requirements: i) enable to quantify the accuracy of a CWE continuous flow extraction line, and ii) identify a specific extraction standard protocol for soil and vegetation samples. Two experiments have been carried out to evaluate the isotope fractionation induced by the system and how different operational parameters (i.e. times and temperature of extraction) can affect the results. Firstly, a known water isotopic ratio was processed by the vacuum system to determine the measurement accuracy and reproducibility by comparing pre- and post-processed water isotopic signatures. The likely causes of observed biases induced by sample processing are assessed and a relevant correction procedure is suggested. Subsequently, measurements were carried out on replicated samples taken from two differently-textured soils that, after being dried, were saturated in the laboratory up to different water content values with water of known isotopic composition. Also, plant samples were collected from plants grown in a greenhouse and irrigated with water of known isotopic composition. Water from all samples was extracted by our CWE system and then analyzed using an isotope ratio mass spectrometer in Gas Bench mode for analyses and in temperature conversion elemental analysis (TC/EA) mode for. Preliminary results have quantified the isotope fractions on average of -1.6 ‰ for δ18O and 14.2 ‰ for δ2H. Normalization of stable isotopes from unknown samples according to observed fractionation has enabled the observed bias to become virtually zero, leading to a replicate reproducibility of δ18O and δ2H for soil water of 0.6 ‰ and 3 ‰, respectively. The analyses carried out up to now did not find statistical evidence that the soil types and soil water contents may affect the extraction method and the accuracy of our protocol. Powered by TCPDF (www.tcpdf.org).

Soil moisture varies, even significantly, over both space and time through natural and anthropic processes and is controlled by interweaved time-variant (e.g. climate, vegetation) and time-invariant (e.g. terrain attributes, soil characteristics) factors. In the last decades, many different research investigations have been carried out in the Alento River Catchment (ARC; Campania Region, southern Italy), particularly concerning the building of a system of water reservoirs, but recently these activities have become more systematic and comprehensive within a Critical Zone Observatory (CZO; Romano et al. (2018). This CZO is included in the TERENO (TERrestrial ENvironmental Observatories) long-term ecosystem infrastructure network. ). Recent progress in the development of dielectric-based sensors and data communication/transmission has led to the ever-increasing use of wireless sensor networks enabling soil moisture dynamics to be monitored in both space and time very efficiently.

Conservation agriculture, and in particular no-till systems, generally yield improvements in both soil characteristics (e.g. structure, and water holding capacity) and soil processes (such as runoff and hence erosion). Nevertheless, during the first years of no-till, the soil is prone to compaction due to the poor structure, missing ploughing activities, and passage of tractors and machinery, thus favouring surface runoff and soil erosion. Little information exists about the effect of no-till when applied during the transition period from conventional to conservation agriculture. This study aimed at analysing runoff and soil erosion in a non-tilled field in comparison with a tilled field during the transition period. The study was conducted at the Padova University Experimental Farm, in northeast Italy. Six sub-plots (2.5 m × 5 m) were established, three in a tilled field (CT plot) and three in a non-tilled field (NT plot). Each sub-plot was equipped with a runoff water collection system. Runoff was monitored during two sampling seasons: from May to October 2017 and May to September 2018. Runoff water volume was measured at each rainfall event, and the amount of sediment was quantified by drying the runoff samples. This technique is simple and inexpensive and suitable to be applied also in rural areas with inadequate infrastructures and economic resources. Two indices, runoff reduction benefits (RRB) and sediment reduction benefits (SRB), were computed. During the monitoring period, 24 runoff events occurred. NT practices coincided with reductions of over 50% in runoff volumes and 50% to 95% in sediment losses. Only the runoff event just after the CT soil harrowing produced a significantly lower runoff and sediment loss in CT than in NT field, due to the effect of soil tillage on surface roughness and rainfall infiltration. The average sediment concentration in NT was only 47% of CT. The RRB and SRB values confirmed a reduction in runoff and sediment loss in the NT compared with the CT plot, but SRB was greater than the RRB, indicating that the no-till regime showed a better control of sediment loss than it did the runoff amount. The reduced runoff and sediment yield in the NT plot could have important on-site benefits in terms of both sustainable soil management and surface water quality.

Accurate near-surface soil moisture (θ; ~ 5 cm) estimation is one of the most crucial challenges in agricultural management and hydrological studies. This study aims to map θ at high spatiotemporal resolution (17 m grid size, satellite overpass of 6 days) in a small-scale agroforestry experimental site (~ 30 ha) in southern Italy. The observation period is from November 2018 until March 2019. We employed an ensemble machine-learning method based on Random Forest (RF) to map θ. This RF method is based on three input data types: i) Sentinel-1 (S1) Synthetic Aperture Radar (SAR) measurements, ii) terrain features, and iii) supporting values of sparse point-scale θ simulated in HYDRUS-1D. We propose two different approaches to obtain supporting θ simulations via inverse modeling in HYDRUS-1D. The first approach is based on θ simulated in HYDRUS-1D, which was calibrated on soil moisture data monitored at two soil depths of 15 cm and 30 cm over 20 positions belonging to the SoilNet wireless sensor network installed in the experimental site. The second approach is based on the downscaling of field-scale θ simulated in HYDRUS-1D which was calibrated on Cosmic-Ray Neutron Probe (CRNP) data. The field-scale θ was downscaled in order to obtain sparse point-scale supporting θ over the same 20 positions by using the physical-empirical Equilibrium Moisture from Topography (EMT) model. The CRNP-based approach performed similarly to the one based on SoilNet data. Therefore, this study highlights the enormous potential for modeling reliable θ maps by integrating soft data such as S1 SAR-based measurements, topographic information, and CRNP data, having the advantage of being non-invasive and easy to maintain.

Mapping near-surface soil moisture (θ) is of tremendous relevance for a broad range of environment-related disciplines and meteorological, ecological, hydrological and agricultural applications. Globally available products offer the opportunity to address θ in large-scale modelling with coarse spatial resolution such as at the landscape level. However, θ estimation at higher spatial resolution is of vital importance for many small-scale applications. Therefore, we focus our study on a small-scale catchment (MFC2) belonging to the “Alento” hydrological observatory, located in southern Italy (Campania Region). The goal of this study is to develop new machine-learning approaches to estimate high grid-resolution (about 17 m cell size) θ maps from mainly backscatter measurements retrieved from C-band Synthetic Aperture Radar (SAR) based on Sentinel-1 (S1) images and from gridded terrain attributes. Thus, a workflow comprising a total of 48 SAR-based θ patterns estimated for 24 satellite overpass dates (revisit time of 6 days) each with ascendant and descendent orbits will be presented. To enable for the mapping, SAR-based θ data was calibrated with in-situ measurements carried out with a portable device during eight measurement campaigns at time of satellite overpasses (four overpass days in total with each ascendant and descendent satellite overpasses per day in November 2018). After the calibration procedure, data validation was executed from November 10, 2018 till March 28, 2019 by using two stationary sensors monitoring θ at high-temporal (1-min recording time). The specific sensor locations reflected two contrasting field conditions, one bare soil plot (frequently kept clear, without disturbance of vegetation cover) and one non-bare soil plot (real-world condition). Point-scale ground observations of θ were compared to pixel-scale (17 m × 17 m), SAR-based θ estimated for those pixels corresponding to the specific positions of the stationary sensors. Mapping performance was estimated through the root mean squared error (RMSE). For a short-term time series of θ (Nov 2018) integrating 136 in situ, sensor-based θ (θinsitu) and 74 gravimetric-based θ (θgravimetric) measurements during a total of eight S1 overpasses, mapping performance already proved to be satisfactory with RMSE=0.039 m³m-³ and R²=0.92, respectively with RMSE=0.041 m³m-³ and R²=0.91. First results further reveal that estimated satellite-based θ patterns respond to the evolution of rainfall. With our workflow developed and results, we intend to contribute to improved environmental risk assessment by assimilating the results into hydrological models (e.g., HydroGeoSphere), and to support future studies on combined ground-based and SAR-based θ retrieval for forested land (future missions operating at larger wavelengths e.g. NISARL-band, Biomass P-band sensors).

Water balance components depend on annual rainfall amount and seasonality in Mediterranean catchments. A high percentage of the annual rainfall occurs between late fall and early spring and feeds natural and artificial water reservoirs. This amount of water stored in the mild-rainy season is used to offset rainfall shortages in the hot-dry season (between late spring and early fall). Observed seasonal anomalies in historical records are quite episodic, but an increase of their frequency might exacerbate water stress or water excess if the rainy season shortens or extends its duration, e.g. due to climate change. Hydrological models are useful tools to assess the impact of seasonal anomalies on the water balance components and this study evaluates the sensitivity of water yield, evapotranspiration and groundwater recharge on changes in rainfall seasonality by using the Soil Water Assessment Tool (SWAT) model. The study area is the Upper Alento River Catchment (UARC) in southern Italy where a long time-series of daily rainfall is available from 1920 to 2018. To assess seasonality anomalies, we compare two approaches: a “static” approach based on the Standardized Precipitation Index (SPI), and a “dynamic” approach that identifies the rainy season by considering rainfall magnitude, timing, and duration. The former approach rigidly selects three seasonal features, namely rainy, dry, and transition seasons, the latter being occasionally characterized by similar properties to the rainy or dry periods. The “dynamic” approach, instead, is based on a time-variant duration of the rainy season and enables to corroborate the aforementioned results within a probabilistic framework. A dry seasonal anomaly is characterized by a decrease of 241 mm in annual average rainfall inducing a concurrent decrease of 116 mm in annual average water yield, 60 mm in actual evapotranspiration and 66 mm in groundwater recharge. We show that the Budyko curve is sensitive to the seasonality regime in UARC by questioning the implicit assumption of temporal steady-state between annual average dryness and evaporative index. Although the duration of the rainy season does not exert a major control on water balance, we have been able to identify seasonal-dependent regression equations linking water yield to dryness index over the rainy season.

Quantification of the spatial and temporal behavior of soil moisture is vital for understanding water availability in agriculture, ecosystems research, river basin hydrology and water resources management. Unmanned Aerial Systems (UAS) offer a great potential in monitoring this parameter at sub-meter level and at relatively low cost. The standardization of operational procedures for soil moisture monitoring with UAS can be beneficial to understanding and quantify the quality of retrieved soil moisture (e.g., from different platforms and sensors).In this study, soil moisture retrieved from UAS using different retrieval algorithms was compared to collocated ground measurements. The thermal inertia model builds upon the dependence of the thermal diffusion on soil moisture. The soil thermal inertia is quantified by processing visible and near-infrared (VIS-NIR) and thermal infrared (TIR) images, acquired at two different times of a day. The temperature–vegetation trapezoidal model is also used to map soil moisture over vegetated pixels. This trapezoidal model depicts the soil moisture dependence of the surface energy balance. The comparison of the two algorithms helps define a preliminary standard procedure for retrieving soil moisture with UAS.As a case study, a typical cropland area with olive orchard, cherry and walnut trees in the region of Monteforte Cilento (Italy, Salerno) is used, where optical and thermal images and in situ data were simultaneously acquired. In the Alento observatory, long-term studies on vadose zone hydrology have been conducting across a range of spatial scales. Our findings provide an important contribution towards improving our knowledge on evaluating the ability of UAS to map soil moisture, in support of sustainable natural resources management and climate change studies.This research is a part of EU COST-Action “HARMONIOUS: Harmonization of UAS techniques for agricultural and natural ecosystems monitoring”.Keywords: soil moisture, Unmanned Aerial Systems, thermal inertia, HARMONIOUS

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

Soil is an essential component in the environment and is vital for food security. It provides ecosystem services, filters water, supplies nutrients to plants, provides us with food, stores carbon, regulates greenhouse gases emissions and it affects our climate. Traditional soil survey methodologies are complicated, expensive, and time-consuming. Visible and infrared spectroscopy can effectively characterize soil properties. Spectral measurements are rapid, precise and inexpensive. The spectra contain information about soil properties, which comprises minerals, organic compounds, and water. Today, several Soil Spectral Libraries (SSLs) are being created worldwide because these datasets have a notable potential to be used as training datasets for machine learning methods that will benefit precision agriculture activity for better management of food production. Nonetheless, as SSL's are created under laboratory conditions it is not clear if it can be used to infer field conditions in situ and/or from the sky. Thus, study the relationship between RS, field spectroscopy and the laboratory measurements of soil is very important. Accordingly, this study postulates that traditional SSLs don't simulate the real spectral signatures in the field that both, satellite and airborne sensors measure as well, because they are affected by factors that are not an integral part of the soil, such as: moisture, litter, human and animal activity, plow, grass, dung, waste, etc… However, under laboratory conditions, these factors are usually removed for the preparation of SSLs. Thus, given the several SSLs available, it is necessary to evaluate the protocols that were used in these SSLs. The objective of this study is to evaluate the gap between field and laboratory spectral measurements through the analysis of the performance of spectral based models. This procedure combined two soil spectral libraries that contain 114 samples that were measured in the laboratory as well as in the field. The nature of the dataset is varied, because these samples were collected from six different fields in three countries of the Mediterranean basin: Israel, Greece and Italy. Moreover, 63 samples are mainly sandy and 51 are mainly clayey. In order to obtain optimal spectral measurements in the field, we used a new optical apparatus that simulates the sun's radiation. Next, we generated PLSR models to estimate one of the most important hydrological parameters namely “infiltration rate” that control the runoff stage, soil erosion and water storage in the soil profile. This property is strongly affected by the surface characteristics. Finally, the field based spectral model was adapted to an UAV hyperspectral sensor in order to estimate the infiltration rate from the sky. The results were successfully validated in field, and we concluded that for the estimation of the infiltration rate, SSLs must be created using surface reflectance in field because laboratory protocols can be detrimental for the performance of the dataset in question.

Groundwater recharge rates emerge from complex interactions within the soil-vegetation-atmosphere system. While it is widely recognized that numerical models are able to properly simulate soil water dynamics depending on boundary forcing data, in-depth information on episodic recharge generation needs to be gained. In this study, the water balance of a heterogeneous 300 cm-deep soil profile beneath a rainfed maize agro-ecosystem in eastern Nebraska was numerically simulated in HYDRUS-1D for 12 years (2001–2012) on daily time steps. We estimated potential groundwater recharge as the downward flux simulated below maximum maize root depth (150 cm). This model was calibrated in a previous study by using direct field measurements. Simulated results indicated that annual rainfall scarcely controls the amount of annual recharge. The impact of individual storm event on recharge episodicity was assessed through iterative scenario modeling. We investigated the importance of rainfall events classified according to convective-stratiform scoring, spanning from 0 and 1, designating extremely stratiform and convective, respectively. The inequality of frequency distribution of rainfall and recharge was assessed by using the Gini coefficient. We detected relatively few significant recharge responses clustering near the 1:1 rainfall/recharge line in late winter (38% of events) and spring (only 13% of events). Generally, the recharge events are triggered by stratiform (28% of total sum) and convective (44% of total sum) precipitation events. Nonetheless, the most efficient recharge events are generated by stratiform rainfall when soil saturation increases without runoff generation. The intense convective storm events often cause saturation excess with runoff generation and mostly occur when evapotranspiration demand is high. For these two main reasons, convective storms lead to inefficient recharge rates characterized by extreme episodicity.

The EU Nitrates Directive calls for urgent integration of process-oriented indicators of nitrate fate with map overlaying approaches for assessing nitrate vulnerable zones (NVZs). In the region of Campania (southern Italy) groundwater contamination represents a serious concern because of the presence of intensive agricultural practices and livestock farming. A protocol was proposed to assess the probability distribution of the following three indicators of nitrate transport across the vadose zone (obtained by using a physically-based model): i) an annual cumulative nitrate flux entering the shallow aquifer, ii) annual cumulative root nitrate uptake, and iii) nitrate transit time across the vadose zone. This method involves numerical simulations of soil water flow and solute transport using Hydrus-1D for a representative 10-m-thick soil profile beneath an irrigated maize plot located in a study area within the Sele plain, Campania. Two scenarios are built by running a set of one hundred (20-yr-long) simulations at a daily time resolution: i) 195 kg N ha-1 of nitrate fertilizer (urea) is applied annually to estimate the probability distributions of annual cumulative nitrate leaching and root nitrate uptake (Scenario 1), ii) 195 kg N ha-1 of nitrate fertilizer (urea) is applied once to estimate the nitrate transit time distribution across the soil profile (Scenario 2). In each Scenario, the simulations consider two agricultural practices with either one or three annual nitrate fertilizer applications subject to randomly generated climate forcing using a Monte Carlo approach. Variations in soil and rainfall properties are described by the Miller-Miller geometric similitude and the Poisson parameterization, respectively. In Scenario 1, roots absorb on average 80.3 kg N ha-1 every year (corresponding to 40% of applied nitrate), and the median annual cumulative nitrate flux across the soil profile bottom is 74.9 kg N ha-1 (representing about 38% of applied nitrate), when urea is applied in a single treatment. In contrast, when fertilizer is applied in three treatments, 112.4 kg N ha-1 (corresponding to almost 60% of applied nitrate) of nitrate is removed by root water uptake, and the median annual cumulative nitrate leaching is 52.4 kg N ha-1 (corresponding to about 27% of applied nitrate). In Scenario 2, mean transit time values are 2741 days, 2707 days, and 2650 days when urea is applied on April 1st, June 1st, and August 1st, respectively. Our model simulations provide useful indicators of nitrate transport and can be integrated with map overlaying procedures for delineating nitrate vulnerable zones.

Study region Nebraska National Forest in the High Plains Aquifer, Nebraska Sand Hills, U.S.A. Study focus This research aimed to investigate the effects of grassland conversions to forest on recharge rates in a century-old experimental forest. The DiffeRential Evolution Adaptive Metropolis (DREAMZS) global optimization algorithm was used to calibrate the effective soil hydraulic parameters from observed soil moisture contents for 220 cm deep uniform soil profiles. The historical recharge rates were then estimated by applying the numerical model HYDRUS 1-D for simulation of two plots representing grasslands and dense pine forest conditions. New hydrological insights The results indicate that conversion from grasslands to dense pine forests led to vegetation induced changes in soil hydraulic properties, increased rooting depth, and greater leaf area index, which together altered the water budget considerably. The impacts of land use change, expressed in percent of gross precipitation, include a 7% increase in interception associated with an increase in leaf area index, a nearly 10% increase in actual evapotranspiration, and an overall reduction of groundwater recharge by nearly 17%. Simulated average annual recharge rates decreased from 9.65 cm yr−1 in the grassland to 0.07 cm yr−1 in the pine plot. These outcomes highlight the significance of the grassland ecology for water resources, particularly groundwater recharge, in the Nebraska Sand Hills and the overall sustainability and vitality of the High Plains Aquifer.

Irrigation exhibits large variation across producer fields, even within same region and year. A knowledge gap exists relative to factors that explain this variation, in part due to lack of availability of high-quality irrigation data from multiple field-years. This study assessed sources of variation in irrigation using a large database collected during 9 years (2005–2013) from ca. 1400 maize and soybean producer fields in Nebraska, central USA (total of 12,750 field-year observations). The study area is representative of ca. 4.5 million ha of irrigated land sown with maize and soybean. Influence of biophysical (weather, soil, and crop type) and behavioral (producer skills, risk aversion) factors on irrigation was investigated. Field irrigation distributions showed a substantial number of fields received irrigation amounts that were well above average irrigation for same region-year. Variation in irrigation across fields, within the same region, was as large as year-to-year variation. Seasonal water deficit (defined as total reference evapotranspiration minus precipitation), soil available water holding capacity, and crop type explained about half of observed variation in field irrigation, indicating that producers adjusted irrigation depending upon site-year variation in these parameters. However, half of the variation in irrigation remained unexplained, indicating that producer behavior and skills play also an important role. There was evidence of a “neighbor” effect as fields that received large irrigation were surrounded by other fields with similarly large irrigation. Likewise, fields with above- or below-average irrigation in one year remained consistently above and below regional average irrigation, respectively, in other years despite similarity in weather and soil among fields. These findings indicate that irrigation decisions are influenced by both biophysical and behavioral factors, making predictions of field and regional irrigation extremely difficult. This study highlights the value of collecting on-farm irrigation data to understand producer decision-making and find opportunities to improve current water management in irrigated crop systems.

During the second half of the 20th century farming systems in hilly and mountainous areas of Mediterranean Europe were progressively abandoned and the forest cover subsequently re-expanded. This paper investigates the environmental impacts of land-use/land-cover (LULC) changes on hydrological ecosystem functions (HEFs) in the Upper Alento River Catchment (UARC) in southern Italy. We document the human-driven post-war landscape in 1955 with its maximum expansion of pasture and crops. By 1998 forest cover had doubled and cropland had roughly halved due to decades of land abandonment and reduction in human pressure. In 2015 secondary forests occupied about 70% of the catchment and orchards about 20%. The 1998 land-use scenario was implemented within the Automated Geospatial Watershed Assessment (AGWA) model which was calibrated and validated by direct water yield measurements recorded in the period 1995–2004 in the water reservoir delimited by an earth-dam located at the UARC outlet. Numerical simulations, assumed under “steady” climate conditions, offer “pseudo-realistic” scenarios that help interpret differences in water budget and sediment transport when the 1998 land-use scenario is compared to those of 1955 and 2015. With reference to funding opportunities offered by the European Union in the recent decades, this study provides some practical guidance on the impact of cropland reconversion on HEFs and on measures to mitigate soil erosion in this Mediterranean area. Viewed from the demand-side perspective, natural re-expansion of forest and afforestation reduce water yield and increase actual evapotranspiration. However, our modeling results are framed also within a supply-side approach examining the sensitivity of water yield to precipitation characteristics.

Biogeochemical cycling of elements largely occurs in dissolved state, but many elements may also be bound to natural nanoparticles (NNP, 1-100 nm) and fine colloids (100-450 nm). We examined the hypothesis that the size and composition of stream water NNP and colloids vary systematically across Europe. To test this hypothesis, 96 stream water samples were simultaneously collected in 26 forested headwater catchments along two transects across Europe. Three size fractions (~1-20 nm, >20-60 nm, and >60 nm) of NNP and fine colloids were identified with Field Flow Fractionation coupled to inductively coupled plasma mass spectrometry and an organic carbon detector. The results showed that NNP and fine colloids constituted between 2 ± 5% (Si) and 53 ± 21% (Fe; mean ± SD) of total element concentrations, indicating a substantial contribution of particles to element transport in these European streams, especially for P and Fe. The particulate contents of Fe, Al, and organic C were correlated to their total element concentrations, but those of particulate Si, Mn, P, and Ca were not. The fine colloidal fractions >60 nm were dominated by clay minerals across all sites. The resulting element patterns of NNP

This study addresses relationships between soil hydrophobicity and groundwater recharge through the unsaturated zone in semiarid sandy grasslands containing mature tree plantations. Field and laboratory investigations of soil properties and recharge rates were undertaken at 10 experimental plots within the Nebraska Sand Hills. Plots included a range of plantation species and planting densities in addition to grasslands. Hydrophobicity was characterized using a combination of methods, including water and ethanol drop penetration tests, nuclear magnetic resonance spectral analysis, and field infiltration tests. Water and ethanol drop tests indicate that surface soils beneath pine plots were moderately to strongly hydrophobic and that plantations are 3‐13 times more hydrophobic than the grasslands. The spectral analysis suggested that the surface soil organic carbon beneath pine plantations contains up to 3 times the ratio of hydrophobic components compared with that for the grasslands. Mini-infiltrometer tests demonstrate that changing grassland into tree plantations leads to declining soil infiltration capacity and lowering of sorptivity and hydraulic conductivity (at −2 cm pressure head) by an order of magnitude. Previously published chloride-based annual recharge estimates beneath these plantations represent reductions of up to 90% relative to the grassland and showed a strong relationship (R 2 = 0.94) with unsaturated hydraulic conductivity retrieved from the field infiltration experiments. HYDRUS-1D synthetic numerical modeling was also performed to corroborate decreasing recharge rates (25‐31.4%) under land-use change dynamics.