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Summary: Soil moisture refers to the water present in the uppermost part of a field soil and is a state variable controlling a wide array of ecological, hydrological, geotechnical, and meteorological processes. The literature on soil moisture is very extensive and is developing so rapidly that it might be considered ambitious to seek to present the state of the art concerning research into this key variable. Even when covering investigations about only one aspect of the problem, there is a risk of some inevitable omission. A specific feature of the present essay, which may make this overview if not comprehensive at least of particular interest, is that the reader is guided through the various traditional and more up-to-date methods by the central thread of techniques developed to measure soil moisture interwoven with applications of modeling tools that exploit the observed datasets. This paper restricts its analysis to the evolution of soil moisture at the local (spatial) scale. Though a somewhat loosely defined term, it is linked here to a characteristic length of the soil volume investigated by the soil moisture sensing probe. After presenting the most common concepts and definitions about the amount of water stored in a certain volume of soil close to the land surface, this paper proceeds to review ground-based methods for monitoring soil moisture and evaluates modeling tools for the analysis of the gathered information in various applications. Concluding remarks address questions of monitoring and modeling of soil moisture at scales larger than the local scale with the related issue of data aggregation. An extensive, but not exhaustive, list of references is provided, enabling the reader to gain further insights into this subject. [ABSTRACT FROM AUTHOR]

[Display omitted] • New regression-based equations were developed for predicting soil bulk density in Campania. • Random forest was used to assess benchmark prediction performance. • PTF performance was improved by adopting a hierarchical mapping criterion. 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. [ABSTRACT FROM AUTHOR]

Mapping soil hydraulic parameters with traditional scaling methods that use laboratory-determined hydraulic characteristics (the LAB method) is not always feasible as it involves expensive, time-consuming and sophisticated measurements on soil samples collected in several locations of the study area. An alternative scaling method (the AP method) has been recently proposed to indirectly retrieve the soil hydraulic properties following the Arya-Paris physico-empirical pedotransfer function, which makes use of particle-size distribution and bulk density values. In this synthetic study we verify the performance of the AP method from a functional perspective, by evaluating the differences in the simulated soil water budget through a Monte Carlo approach. Notwithstanding that the AP method can provide soil hydraulic property patterns with faster experimental procedures and minor costs, we observe significant bias in the predicted spatially-averaged soil water budget due to a poor parametric calibration of the AP method and an imprecise identification of the spatial correlation structure of the AP-estimated scaling factors. Citation Nasta, P., Romano, N., and Chirico, G.B., 2013. Functional evaluation of a simplified scaling method for assessing the spatial variability of soil hydraulic properties at the hillslope scale. Hydrological Sciences Journal, 58 (5), 1059–1071. [ABSTRACT FROM AUTHOR]

Characterization of soil hydraulic behavior at large scales using traditional methods is time-consuming and very costly. Efficient and cheap means of providing hydrological models with such information are procedures based on pedotransfer functions (PTFs) that estimate soil hydraulic parameters from easily measurable or already available soil physical data. Major objectives of this study are to compare the prediction performance of some published PTFs and to improve their predictive capability by accounting for certain landscape variables, such as slope, aspect, and wetness index, for example. This additional information can be easily extracted from a digital elevation model of the area under study. While topographic attributes have shown potential for mapping soil properties over a region with higher precision and simplifying estimation of some model parameters, the challenge is also to examine whether, and to what extent, ancillary data of this kind can specifically contribute to improve the predictions of soil hydraulic characteristics. Since the most recent distributed hydrological models rely even more on an accurate representation of landscape features, improving PTFs with the inclusion of topographic variables is in line with this tendency. Statistical indices of goodness-of-fit are calculated to evaluate the effectiveness of the proposed methodology. It is shown, for example, that systematic biases in water retention predictions from an original PTF can be conveniently corrected by adding some primary or compound terrain attributes. The results confirm the role of terrain variables in assessing the spatial patterns of soil hydraulic characteristics. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Two pedotransfer functions (PTFs) are available in the literature enabling the soil water retention function (WRF) to be estimated from knowledge of the soil particle-size distribution (PSD), oven-dry soil bulk density (rb), and saturated soil water content (qs): i) the Arya and Heitman model (PTF-AH), and ii) the Mohammadi and Vanclooster model (PTF-MV). These physicoempirical PTFs rely on the hypothesis of shape similarity between PSD and WRF, and do not require the calibration of the input parameters. In the first stage, twenty-seven PSD models were evaluated using 4,128 soil samples collected in Campania (southern Italy). These models were ranked according to the root mean square residuals (RMSR), corrected Akaike information criterion (AICc), and adjusted coefficient of determination (R2adj). In the second stage, three subsets of PSD and WRF data (DS-1, DS-2, and DS-3), comprising 282 soil samples, were used to evaluate the two PTFs using the best three PSD models selected in the first stage. The hypothesis of shape similarity was assumed as acceptable only when the RMSR value was lower than the field standard deviation of the WRFs (s*), which is viewed as a tolerance threshold and computed from the physically based scaling approach proposed by Kosugi and Hopmans (1998). In the first study area (DS- 1), characterized by a fairly uniform, loamy textured volcanic soil, the PTF-AH outperformed the PTF-MV and both PTFs provided reasonable performance within the acceptance threshold (i.e., RMSR < s*). In the other two heterogeneous field sites (DS-2 and DS-3, characterized by soil textural classes that span from clay and clay-loam to loam and even sandy-loam soils), the PTF-MV (with 3% to 6% RMSR surpassing s*) outperformed the PTF-AH (with 8% to 30% RMSR surpassing s*) and the majority of RMSR values were larger than those obtained in the original studies. The mean relative error (MRE) revealed that the PTF-MV systematically underestimates the measured WRFs, whereas the PTF-AH provided negative MRE values indicating an overall overestimation. The outcomes of our study provide a critical evaluation when using calibration-free PTFs to predict WRFs over large areas [ABSTRACT FROM AUTHOR]

Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO2) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used hyperspectral remote sensing to model the SOC stock in the Sele River plain located in the Campania region in southern Italy. To this end, a soil spectral library (SSL) for the Campania region was combined with an aerial hyperspectral image acquired with the AVIRIS–NG sensor mounted on a Twin Otter aircraft at an altitude of 1433 m. The products of this study were four raster layers with a high spatial resolution (1 m), representing the SOC stocks and three other related soil attributes: SOC content, clay content, and bulk density (BD). We found that the clay minerals' spectral absorption at 2200 nm has a significant impact on predicting the examined soil attributes. The predictions were performed by using AVIRIS–NG sensor data over a selected plot and generating a quantitative map which was validated with in situ observations showing high accuracies in the ground-truth stage (OC stocks [RPIQ = 2.19, R2 = 0.72, RMSE = 0.07]; OC content [RPIQ = 2.27, R2 = 0.80, RMSE = 1.78]; clay content [RPIQ = 1.6 R2 = 0.89, RMSE = 25.42]; bulk density [RPIQ = 1.97, R2 = 0.84, RMSE = 0.08]). The results demonstrated the potential of combining SSLs with remote sensing data of high spectral/spatial resolution to estimate soil attributes, including SOC stocks. [ABSTRACT FROM AUTHOR]

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

Agricultural and forest ecosystems in Europe, especially those distributed across the Mediterranean Belt, are experiencing rapid changes in climate seasonality and land use that might compromise the provision of key ecosystem services related to the hydrologic cycle. According to the last report released by IPCC in 2018 more frequent droughts might severely stress water supply in most catchments. Rural abandonment has induced a significant increase in forest cover, which is scarcely maintained and prone to wildfire risks under prolonged dry conditions. Moreover, while on the one hand annual-average rainfall seems to keep steady through time, on the other hand rainfall events at sub-daily temporal resolution are getting more intense by exacerbating floods, landslides, and soil erosion. It is therefore necessary to use advanced techniques and hydrological models to serve reliable scenarios-based projections to select cost-effective solutions to contrast negative effects to assure the provision of key ecosystem services. Reliable scenarios will certainly support the best adaptation options with respect to drought and wildfire occurrence as well as to prevent the risk of water stress and soil erosion.This study evaluates the effects exerted by changes in rainfall seasonality on the hydrological ecosystem services such as water yield and soil erosion. These attributes are evaluated at different spatial scales: at the large scale of the entire Campania Region and at catchment scale of the Upper Alento River in southern Italy. The first part of this investigation refers to the analysis of daily rainfall time series, from 1950 to 2018 recorded in 249 weather stations distributed across the region. Rainfall seasonality was characterized using the SI (Seasonality Index), whereas 3- and 6-month SPI (Standardized Precipitation Index) values are used to evaluate drought severity. Rainfall erosivity based on hourly measurements show a typical seasonality, with maximum values occurring mainly on fall season, and was also estimated by using simplified relationships based on daily rainfall values. The second part of this study focuses on the impact exerted by dry and wet seasons, but also by wildfires, on surface runoff and sediment yield in a pilot catchment of the Campania Region (the Upper Alento River Catchment). This catchment serves as a hydrological observatory where variables and fluxes are monitored with advanced devices (e.g. cosmic-ray probes, wireless sensor networks), whereas water and sediment budgets are simulated using the Soil Water Assessment Tool (SWAT) model. [ABSTRACT FROM AUTHOR]

An isotope-enabled module of Hydrus-1D was applied to a potted olive tree to trace water parcels originating from 26 irrigation events in a glasshouse experiment. The soil hydraulic parameters were optimized via inverse modelling by minimizing the discrepancies between observed and simulated soil water content and soil water isotope (18O) values at three soil depths. The model's performance was validated with observed sap flow z-scores and xylem water 18O. We quantified the source and transit time of irrigation water by analysing the mass breakthrough curves derived from a virtual tracer injection experiment. On average, 26% of irrigation water was removed by plant transpiration with a mean transit time of 94 hours. Our proof of concept work suggests that transit time may represent a functional indicator for the uptake of irrigation water in agricultural ecosystems. [ABSTRACT FROM AUTHOR]

Changes in the spatial and temporal patterns of extreme rainfall may have important effects on the magnitude and timing of rainfall erosivity, which in turn lead to even severe soil degradation phenomena. The Mediterranean belt is characterized by strong climatic variability and specific seasonal features, where dry periods are often interrupted by pulsing storms. Identifying the thresholds associated with extreme rainfall events is among the most important challenges for this region. To discern the spatial patterns of rainfall erosivity hazard in the Rhone region (eastern France), this study establishes thresholds in the power of rainstorms. An indicator Kriging approach was employed for computing probability maps of the annual rainfall erosivity exceeding the threshold of 1800 MJ mm ha−1 h−1, the latter being twice greater than the standard deviation. The interdecadal spatial patterns of hazard were assessed for recent decades (1991–2010) and the precedents ones (1961–1990). Climate fluctuations of rainfall erosivity revealed possible signals of increased storminess hazard across the region in recent times. We also discussed changes in the rainfall erosivity hazard forcing as related to climatic changes in daily rain rate, especially in autumn when the erosivity is likely affected by more intense storminess occurring across the southern part of the Rhone region. [ABSTRACT FROM AUTHOR]

Soil erosion is one of the main environmental threats to sus-tainability and crop productivity in the agricultural sector. In agricultural fields, no-till management is considered a key approach for mitigating soil erosion. The measurement of soil erosion is par-ticularly challenging, especially when surficial morphological changes are relatively small. Conventional experiments are com-monly time-consuming and labour-intensive in terms of both field surveys and laboratory methods. On the other hand, 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 under-standing the evolution of no-till (NT) and conventional tillage (CT) in experimental plots. Considering that morphological changes at the plot scale had millimetre 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. We 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. In this way, we recognised the most suitable estimation method. This study presents a novel approach for using DoMs instead of DoDs to describe the micro-topography changes and sediment dynamics accurately. Another key and innovative aspect of this work often overlooked in soil erosion studies, was identifying 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, using DoMs (e.g., 2213 cm3 for no-till management system - NT and 38155 cm3 for conventional tillage regime - CT during September 2018-June 2020) that showed mild overestimation respect to field measurements results (e.g., 2359 cm3 for NT and 4525 cm3 for CT in the same period). 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 low cos 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, based on SfM and UAV technologies. [ABSTRACT FROM AUTHOR]

Rainfall and overland flow are fundamental processes for Earth's ecosystems but can also be land disturbing forces, particularly when triggered by extreme hydro-meteorological events. Examples of these extremes are rainstorms and related phenomena due to rainfall aggressiveness. They produce high-impact land processes such as soil erosion and nutrient losses. Economic and social consequences of these processes can be quite severe. However, hydrological extremes and their environmental implications are still poorly understood, particularly if analyzed in the context of climate change. Here, we analyze a 300 year long times series of historical rainfall patterns across the Mediterranean in the last three centuries and we investigate changes in the erosive forcing as related to climate changes. Our results show that the erosive forcing increased towards the end of the Little Ice Age (~1850) over western and central Mediterranean and that has been increasing in the recent warming period at low Mediterranean latitudes, due to a higher frequency of intensive storms. Such increased concentrated precipitation may lead to an intensification of land degradation processes triggered by soil erosion and transport across a range of scales from hillslopes to small catchments. [ABSTRACT FROM AUTHOR]

Thermal inertia has been applied to map soil water content exploiting remote sensing data in the short and long wave regions of the electromagnetic spectrum. Over the last years, optical and thermal cameras were sufficiently miniaturized to be loaded onboard of unmanned aerial systems (UASs), which provide unprecedented potentials to derive hyperspatial resolution thermal inertia for soil water content mapping. In this study, we apply a simplification of thermal inertia, the apparent thermal inertia (ATI), over pixels where underlying thermal inertia hypotheses are fulfilled (unshaded bare soil). Then, a kriging algorithm is used to spatialize the ATI to get a soil water content map. The proposed method was applied to an experimental area of the Alento River catchment, in southern Italy. Daytime radiometric optical multispectral and day and nighttime radiometric thermal images were acquired via a UAS, while in situ soil water content was measured through the thermo-gravimetric and time domain reflectometry (TDR) methods. The determination coefficient between ATI and soil water content measured over unshaded bare soil was 0.67 for the gravimetric method and 0.73 for the TDR. After interpolation, the correlation slightly decreased due to the introduction of measurements on vegetated or shadowed positions (r2 = 0.59 for gravimetric method; r2 = 0.65 for TDR). The proposed method shows promising results to map the soil water content even over vegetated or shadowed areas by exploiting hyperspatial resolution data and geostatistical analysis. [ABSTRACT FROM AUTHOR]

Abstract I am often reminded of the famous saying of Goethe: "Vedi Napoli e poi muori! - See Naples and die!". Sadly, Naples is now confronted with a number of serious, ongoing problems with a need to alleviate pressure on the worsening environment. One serious problem facing the environment is the presence of the potentially hazardous persistent organic pollutants (POPs), although few systematic studies at regional scale have been conducted. In this study, samples of soil, air, and bulk deposition were collected in Naples metropolitan area (NMA) to characterize the status of POPs, including organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs). The results obtained showed that most of these compounds are pervasive in all the studied environmental matrices, especially in some hotspot areas, such as the Bagnoli Brownfield Site and the infamous "Triangle of the Death", where unwanted ecological risk conditions for PAHs and Endosulfan were determined, respectively. The interactional complexity between urban and the surrounding rural areas was also confirmed, as is the role that urban areas play in the migration and transformation process of POPs. High urban-rural gradients for atmospheric PAHs and PCBs were observed in the NMA, and the urban areas were identified as the emission source of these contaminants. Similarly, the OCP residues, historically originated from the nearby agricultural regions, experience long-term soil re-emission and continuously influence the connected urban environment via atmospheric transport processes. Graphical abstract Unlabelled Image Highlights • Presents systemic data on POP concentrations in a multimedia environment. • Urban areas were identified as the emission source of PAHs and PCBs. • Validates long-term soil re-emission of the OCP residues. • Identifies ongoing effects of "hotspots" on surrounding areas via atmospheric transport. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate the capability of hybrid poplar (Populus deltoides × Populus nigra) to reduce cadmium (Cd) concentrations in an experimental site of Campania Region (southern Italy) subjected to illegal deposit of industrial and household waste. We propose to evaluate the efficiency of poplar for Cd phytoextraction by coupling the use of a process-based, distributed hydrological model (HydroGeoSphere, HGS) with photogrammetric images acquired by Unmanned Aerial Vehicle (UAV). This scenario-based approach exploits in-situ measurements so as to be able to reproduce reliable near-real-world processes. The original bare soil (BS; unplanted reference location) is used as benchmark and compared to the situation where poplar trees are planted (PP) for bioremediation purposes. The 'virtual' positions of poplars were chosen by considering the expected Cd accumulation areas that are correlated to topographic indices retrieved from the high-resolution (0.03 × 0.03 m) digital elevation model (DEM) generated by UAV photogrammetric photos. Transfer and accumulation of Cd in the poplars were described by a timevariant sink term featuring the HGS transport equation. The numerical simulations show that poplar trees are able to reduce Cd concentrations by 15%, 36%, and 64% in spring, summer, and autumn, respectively. Coupling an advanced 3D hydrological model with a high-resolution DEM generated by UAV-photogrammetry seems a promising and viable approach for assessing the efficiency of phytoremediation techniques. [ABSTRACT FROM AUTHOR]

The soil water content at the condition of field capacity (θ FC) 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 (q FC). We question the impact of q FC on the assessment of field capacity. Moreover, calculating θ FC as the integral mean of the water content profile when q is equal to q FC 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 (W FC) 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 W FC) 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. • The concept of field capacity in layered soil profiles is unclear for irrigation. • The functional field capacity helps minimize overwatering effects and crop stress. • The functional analysis corrected the vagueness in the synthetic drainage process. [ABSTRACT FROM AUTHOR]

In the last decades, a growing interest in fostering advanced interdisciplinary studies is leading to the establishment of observatories in pilot catchments for long-term monitoring of hydrological variables and fluxes. Nevertheless prior to sensor network installation, this investment necessitates preliminary surveys on key-variables such as near-surface soil moisture in order to prevent risks of erronously distributing sensors by missing sufficient spatial information for understanding hydrological processes within the landatmosphere interactions. The availability of maps describing areas with similar morphological, topographical, soil, and vegetation characteristics enable preliminary surveys to be organized for capturing spatio-temporal variability of soil moisture as best as possible. The soil-landscape classification can be considered as an interesting approach for grouping mapping units with similar hydrological behavior. Therefore, we assume the soil-landscape units as hydrotopes or hydrological similar units. Six transects were established along two hillsides of the Upper Alento River catchment (southern Italy) which is a proper candidate to become a Critical Zone Observatory. In this paper we use a soil-landscape map to infer spatial and temporal dynamics of soil moisture measured along these transects, whereas quantitative analyses were obtained by using multivariate techniques. The effectiveness of available information on soil-landscape mapping units is evaluated with respect to different observed patterns of soil moisture: wetter- and drier-than average observation points belong to agricultural and forested hillslopes, respectively. Soil texture and topographical controlling factors, especially clay content and slope gradient, are found to explain approximately 70% of the observed spatial variations in soil moisture along the forested hillslopes. The spatial structure explained by the environmental controlling factors decreases to 45% in the cases of the agricultural hillslopes mainly due to perturbations induced by grazing and tillage practices. [ABSTRACT FROM AUTHOR]

Indirect measurements of field-scale (hectometer grid-size) spatial-average near-surface soil moisture are becoming increasingly available by exploiting new-generation ground-based and satellite sensors. Nonetheless, modeling applications for water resources management require knowledge of plot-scale (1–5 m grid-size) soil moisture by using measurements through spatially-distributed sensor network systems. Since efforts to fulfill such requirements are not always possible due to time and budget constraints, alternative approaches are desirable. In this study, we explore the feasibility of determining spatial-average soil moisture and soil moisture patterns given the knowledge of long-term records of climate forcing data and topographic attributes. A downscaling approach is proposed that couples two different models: the Eco-Hydrological Bucket and Equilibrium Moisture from Topography. This approach helps identify the relative importance of two compound topographic indexes in explaining the spatial variation of soil moisture patterns, indicating valley- and hillslope-dependence controlled by lateral flow and radiative processes, respectively. The integrated model also detects temporal instability if the dominant type of topographic dependence changes with spatial-average soil moisture. Model application was carried out at three sites in different parts of Italy, each characterized by different environmental conditions. Prior calibration was performed by using sparse and sporadic soil moisture values measured by portable time domain reflectometry devices. Cross-site comparisons offer different interpretations in the explained spatial variation of soil moisture patterns, with time-invariant valley-dependence (site in northern Italy) and hillslope-dependence (site in southern Italy). The sources of soil moisture spatial variation at the site in central Italy are time-variant within the year and the seasonal change of topographic dependence can be conveniently correlated to a climate indicator such as the aridity index. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

An in-depth analysis of the interannual variability of storms is required to detect changes in soil erosive power of rainfall, which can also result in severe on-site and off-site damages. Evaluating long-term rainfall erosivity is a challenging task, mainly because of the paucity of high-resolution historical precipitation observations that are generally reported at coarser temporal resolutions (e.g., monthly to annual totals). In this paper we suggest overcoming this limitation through an analysis of long-term processes governing rainfall erosivity with an application to datasets available the central Ruhr region (Western Germany) for the period 1701–2011. Based on a parsimonious interpretation of seasonal rainfall-related processes (from spring to autumn), a model was derived using 5-min erosivity data from 10 stations covering the period 1937–2002, and then used to reconstruct a long series of annual rainfall erosivity values. Change-points in the evolution of rainfall erosivity are revealed over the 1760s and the 1920s that mark three sub-periods characterized by increasing mean values. The results indicate that the erosive hazard tends to increase as a consequence of an increased frequency of extreme precipitation events occurred during the last decades, characterized by short-rain events regrouped into prolonged wet spells. [ABSTRACT FROM AUTHOR]

The boreal forest of the northern hemisphere represents one of the world's largest ecozones and contains nearly one third of the world's intact forests and terrestrially stored carbon. Long-term variations in temperature and precipitation have been implied in altering carbon cycling in forest soils, including increased fluxes to receiving waters. In this study, we use a simple hydrologic model and a 40-year dataset (1971–2010) of dissolved organic carbon ( DOC ) from two pristine boreal lakes (ELA, Canada) to examine the interactions between precipitation and landscape-scale controls of DOC production and export from forest catchments to surface waters. Our results indicate that a simplified hydrologically-based conceptual model can enable the long-term temporal patterns of DOC fluxes to be captured within boreal landscapes. Reconstructed DOC exports from forested catchments in the period 1901–2012 follow largely a sinusoidal pattern, with a period of about 37 years and are tightly linked to multi-decadal patterns of precipitation. By combining our model with long-term precipitation estimates, we found no evidence of increasing DOC transport or in-lake concentrations through the 20th century. [ABSTRACT FROM AUTHOR]

[Display omitted] • Pseudo-total and bioavailable fractions of some elements were evaluated in topsoil. • Average values of percent bioavailability follows the order Ca > K≫Mg≃Tl≫Cu≫Al. • Multifractal IDW was used to maps elemental distribution and percent bioavailability. • A multiple regression unveiled dependences among chemical fractions. • Grain size and organic matter differenzially improve the predictive models. Bioavailability of some major and trace elements was evaluated in 1,993 topsoil samples collected across Campania region (Southern Italy). A main focus was made on Al, Ca, K, Mg, Cu, Tl since they are linked, for different reasons, to agriculture. Bioavailability was assessed by an extraction with ammonium nitrate and the data were compared with the pseudo-total concentration determined by Aqua Regia digestion. Geochemical maps of the pseudo-total and bioavailable concentrations were generated using a multifractal inverse distance weighted (MIDW) interpolation. In addition, the spatial distribution patterns of the percent bioavailability of elements, based on the ratio among bioavailable and the pseudo-total fractions, were also determined. The median value of the percent bioavailability showed the order Ca > K ≫ Mg ≃ Tl ≫ Cu ≫ Al and it represents a positive finding in terms of both agricultural productivity and environmental quality. Further, a multiple linear regression was finally applied to data to unveil any dependence of the bioavailable fraction on the pseudo-total content of elements. The grain size distribution and organic matter content of samples were later included to evaluate their possible role in promoting the environmental availability of elements. The pseudo-total concentrations of Al, Ca, K, and Mg alone resulted to be poorly able to predict the variability of the bioavailable fraction. The addition of the grain size distribution and organic matter content to the models expanded the predictive capability of Ca, K, and Mg whereas a marginal improvement was showed by Al, Cu, and Tl. This study represents a methodological contribution to a better understanding of the processes underlying the spatial variability of chemical elements in soil. Considering the positive outcomes obtained, further researches were planned to include more variables (e.g. soil pH, redox potential, content in Iron and Manganese oxides, etc.) in the predictive models. [ABSTRACT FROM AUTHOR]

The EU Nitrates Directive calls for urgent integration of process-oriented indicators of nitrate fate with map overlay 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: i) 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 overlay procedures for delineating nitrate vulnerable zones. • Comparing nitrate fate from two fertilizer applications in the Sele plain. • Assessing mean annual cumulative nitrate leaching and root nitrate uptake. • Probability density functions of transit time are generated. [ABSTRACT FROM AUTHOR]

Summary: The presented study explores the prediction of soil water retention and its variability from soil texture and bulk density measurements, using a physically-based scaling technique. Specifically, the Arya–Paris (AP) physico-empirical model is applied to two soil datasets that are collected from two catchments located in different areas of Southern Italy. Laboratory-measured soil water retention functions are scaled to characterize soil variability. The laboratory-measured and AP-predicted reference water retention functions are compared by evaluating the lognormal distribution of derived scaling factors, relative to the mean reference retention function. Since the scaling theory assumes geometric similitude for the investigated soils, successful application of using particle-size distribution to estimate soil water retention requires separation of soils with different textures, using variance analysis. We conclude that variability in soil water retention can be determined from limited soil water retention data using the scaling approach when combined with particle-size distribution measurements. This method can potentially be used as an effective tool for identifying soil hydrologic response at catchment scales. [Copyright &y& Elsevier]

Schematic illustration of the experimental plots (on the left); runoff volumes and sediment loss sampled at each runoff event from non-tilled and tilled plots (on the right). • No-till was beneficial in runoff and soil erosion reduction in transition period. • In non-tilled soil, runoff volumes were reduced by 58% compared to the tilled plot. • No-till practices coincided with a 50–95% reduction in sediment losses. • No-till decreased sediment concentration by 58% compared to the tilled plot. 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. [ABSTRACT FROM AUTHOR]

Spatially explicit soil moisture information in a high temporal resolution plays an essential role in environmental modeling for improving risk assessment, for quantifying the effects of rainfall seasonality and climatic variability, and for addressing ecosystem services. In this context, remote sensing data, particularly from the Copernicus mission is highly acknowledged to serve as a fast and available supplier for the derivation of area wide and high-resolution spatial-temporal information. However, to reliably estimate near-surface soil moisture patterns based on remote sensing, robust ground-truthing for calibration and validation procedures is required. Against this background, the present study will introduce into an in situ sampling framework that is optimized towards an eventual spatial-temporal SAR (Synthetic Aperture Radar)-based near-surface soil moisture mapping. The framework comprises a survey scheme, measurement sequence, and survey calendar in accordance to satellite overpasses. The spatial focus of the study lies on an experimental site (denoted by the acronym MFC2) close to Monteforte Cilento (Salerno province) in southern Italy. MFC2 is a pilot agricultural field belonging to the Alento terrestrial observatory within the TERENO (TERrestrial ENvironmental Observatories) network across the Mediterranean region. Ground-based sensors, namely a cosmic-ray probe and a wireless sensor network comprising 20 measurement nodes were installed in 2016 in MFC2 for real-time soil moisture monitoring. During three weeks in November 2018 covering in total eight satellite overpasses at four days of satellite revisits, near-surface soil moisture (0-5 cm) was sampled each time at 20 different plots in MFC2. In addition, soil physical and chemical properties (i.e., soil texture, bulk density, and soil organic carbon content) and vegetation parameters (e.g., LAI) were sampled at times of satellite overpasses, supplemented by few UAV flights for the photogrammetric analysis of the soil surface roughness. To capture the fluctuation of near-surface soil moisture during the satellite overpasses and its dynamics for a longer period, namely from the wet period to the wet-to-dry transition period (November 2018 – March 2019), two further stationary soil moisture sensors were installed for high-temporal monitoring (1 min recording time). By doing so, acquisition lags between field measurements and overpass times will be addressed, too. The sensor locations were chosen to reflect real field conditions (i.e., non-bare soil plot in MFC2) and to act as a reference plot for calibration for SAR-based soil moisture mapping under non-vegetated conditions. Latter is represented by a 20 x 20 m square in close proximity to MFC2. The grass cover is frequently cut for keeping the bare soil conditions during the entire monitoring period.Gathered ground-truth data and eventual validated SAR-based soil moisture mapping will support manifold further research activities such as the development of a local-specific transfer function to relate soil moisture patterns from remote sensing data to soil moisture ground-based sensors and vice versa. By assimilating those data into hydrological models, groundwater flow and surface flow under consideration of the climatic variability and in feedback with the vegetation can be simulated for different scenarios of water usage in a Mediterranean environment. [ABSTRACT FROM AUTHOR]

Optimal management of water resources at catchment scale depends on the prediction capability of process-based hydrological models. In particular, the Richards-equation-based distributed models require lowering the mismatch between the scale of the process dynamics and that of the variability of input parameters. Therefore, a detailed description of the hydrological processes highly depends on the proper assessment of spatial variability of the soil hydraulic properties (SHPs), namely the soil water retention (WRF) and unsaturated hydraulic conductivity (HCF) functions. Field surveys and sampling campaigns commonly provide sparse local scale measurements of SHPs. The performances of two parameterization approaches are compared to simulate water budget in a small catchment of the Alento Hydrological Observatory by using HydroGeoSphere (HGS) model. The first approach relies on a scaling technique by providing the so-called "aggregated" SHPs over the spatial domain of interest. The second approach, instead, is based on indirect measurement of soil saturation in one representative plot during an infiltration experiment by using electrical-resistivity tomography (ERT). The measured ERT dataset is exploited to estimate the "equivalent" SHPs through an inverse modeling approach that combines HGS and DREAMZS global optimization tool. Within the framework of a functional evaluation, we compare the outputs of HGS simulations in terms of streamflow (as a lumped flux) and near-surface soil moisture patterns (as distributed state variables). Better comparisons between observed and simulated streamflow values are obtained when HGS is run by using the ERT-based "equivalent" SHPs. Conversely, water budget simulations depending on the "aggregated" SHPs are quite close to the observed soil moisture patterns. The two measurement techniques differ because the ERT-based is affected by hydraulic nonequilibrium during transient conditions characterizing the infiltration experiment. This study highlights the pros and cons and potential flaws when choosing two different parameterization techniques in spatially distributed modeling applications. [ABSTRACT FROM AUTHOR]