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[Display omitted] • The role of the soil mantle on groundwater recharge of karst aquifers was investigated. • A characterization of soil and land cover was performed by field and laboratory analysis. • Soil Water Balance code was applied to evaluate the variability of the recharge. • Groundwater recharge rates depend on land cover types and soil thickness. Karst aquifers represent the fundamental groundwater resources in Italy and in many other countries because supplying urban areas of drinking water and sustaining industrial and agricultural activities. Accordingly, advancing the assessment of groundwater recharge of these aquifers is a major scientific challenge, specifically if considering the hydrological role exerted by soil covering and the related land cover types. In such a framework, the soil water balance model is an effective tool for providing accurate estimates of groundwater recharge depending on precise field and laboratory characterizations of the soil covering. Considering the features of karst aquifers of southern Italy and the ubiquitous occurrence of a soil mantle, mainly of ash-fall pyroclastic origin, a methodology based on field and laboratory characterizations, as well as modeling approaches, is proposed in this work to advance the knowledge on groundwater recharge processes. The experimental approach was applied to the Soprano-Vesole-Chianello Mts. karst aquifer (Campania region, southern Italy) which is considered representative of karst aquifers of southern Italy and Mediterranean areas as well. A comprehensive characterization of the soil mantle was developed, considering different land cover classes, measurements and stochastic spatial modeling of soil thickness. The unsaturated hydraulic properties of undisturbed soil samples were analyzed by laboratory tests resulting in the determination of Soil Water Retention Curves (SWRCs). Moreover, a field monitoring of soil water content was carried out by multi-profile soil moisture sensors, allowing the assessment of the hydrological response of the soil covering and of the regime of groundwater recharge process as well. Among the principal outcomes is the recognition, for each land cover class, of different values of the depth to which the evapotranspiration effect is extended. Based on a consistent spatial modeling of soil thickness and soil hydrologic properties, the Soil Water Balance code (SWB 1.2) was applied to evaluate the spatial and temporal variability of the groundwater recharge of the karst aquifer. As a principal outcome, groundwater recharge rates were found to be dependent on land cover class and soil thickness as well, showing lower values for wooded areas and higher for denudated ones, respectively due to the higher and lower evapotranspiration rates. [ABSTRACT FROM AUTHOR]

• Probabilistic threshold integrates antecedent soil moisture with rainfall threshold. • Bayes' theorem is applied to estimate the probability of landslide occurrences. • Soil moisture is estimated by the hydrological simulations. Various methods have been proposed to define the rainfall thresholds for the landslide prediction. Once the threshold is determined, it remains the same regardless of the antecedent soil moisture conditions. However, given the important role of the antecedent soil moisture in the initiation of landslides, it is considered if the rainfall threshold level varies according to the antecedent soil moisture conditions, the prediction performance will be improved. Therefore, in this study we propose a probabilistic threshold to integrate antecedent soil moisture conditions with rainfall thresholds. In order to take into account the conditions with landslides and without landslides, the Bayesian analysis is applied to estimate the landslide occurrence probability given the various combinations of two factors: the antecedent soil moisture and the severity of the recent rainfall event. These combinations are then divided into conditions that are likely to trigger landslides and those unlikely to trigger landslides by comparing their probabilities with a critical value. In this way, the probabilistic threshold is determined. Here the soil moisture is estimated using the distributed hydrological model, and the severity of the rainfall event is characterized by the cumulated event rainfall-rainfall duration (ED) thresholds with different exceedance probabilities. The proposed approach was applied to a sub-region of the Emilia-Romagna region in northern Italy. The results show that the probabilistic threshold has a better prediction performance than the ED rainfall threshold, especially in terms of reducing false alarms. This study provides an effective approach to improve the prediction capability of the ED rainfall threshold, benefiting its application in the landslide prediction. [ABSTRACT FROM AUTHOR]

• A Bayesian approach has been used for merging multiple satellite rainfall products. • We created a superior product that can be efficiently run in near-real time. • Soil moisture can provide useful information for improving satellite rainfall. This work investigates the potential of using the Bayesian-based Model Conditional Processor (MCP) for complementing satellite precipitation products with a rainfall dataset derived from satellite soil moisture observations. MCP – which is a Bayesian Inversion approach – was originally developed for predictive uncertainty estimates of water level and discharge to support real-time flood forecasting. It is applied here for the first time to precipitation to provide its probability distribution conditional on multiple satellite precipitation estimates derived from TRMM Multi-Satellite Precipitation Analysis real-time product v.7.0 (3B42RT) and the soil moisture-based rainfall product SM2RAIN-CCI. In MCP, 3B42RT and SM2RAIN-CCI represent a priori information (predictors) about the "true" precipitation (predictand) and are used to provide its real-time a posteriori probabilistic estimate by means of the Bayes theorem. MCP is tested across Italy during a 6-year period (2010–2015) at daily/0.25 deg temporal/spatial scale. Results demonstrate that the proposed methodology provides rainfall estimates that are superior to both 3B42RT (as well as its successor IMERG-early run) and SM2RAIN-CCI in terms of both median bias, random errors and categorical scores. The study confirms that satellite soil moisture-derived rainfall can provide valuable information for improving state-of-the-art satellite precipitation products, thus making them more attractive for water resource management and large scale flood forecasting applications. [ABSTRACT FROM AUTHOR]

• Impact-based flash flood probabilistic forecasting system is designed and described. • Sensitivity to high resolution weather prediction and soil moisture is assessed. • Benefit of using a multi-meteomodel approach is investigated. • Forecasts are expressed in terms of streamflow, damage and affected people. • A warning decision making process based on forecasted impacts is proposed. In recent years, continuous improvements have been made in weather forecasting and flood prediction with great benefit from Early Warning Systems (EWSs). Despite the continuous quest for innovation from the scientific and user communities, EWSs remain based mostly on hazard forecast, and the information on possible consequences and potential impacts is generally missing. In this work, a methodology for quantitative real-time impact assessment for flash floods is presented. The methodology uses a multi-model ensemble approach and considers soil moisture uncertainty. Moreover, the flood forecasting chain, which normally provides only the discharge probability of exceeding a given threshold, is extended to include a fully 2D hydraulic model and a damage estimation model to quantitatively assess impacts in terms of economic losses and the people involved. The procedure was tested on recent flood events occurring in Genoa in northwestern Italy. This paper discusses the potential challenges and opportunities offered by this approach in the decision-making workflow in an operational context. [ABSTRACT FROM AUTHOR]

• Bimodal Arya&Paris hydraulic properties were developed based on soil structure. • The bimodal PTF provides better prediction of un/saturated hydraulic conductivity. • The bimodal PTF predicts well the spatial variability of the hydraulic properties. The main purpose of this paper is to develop a bimodal pedotransfer function to obtain soil water retention (WRC) and hydraulic conductivity (HCC) curves. The proposed pedo-transfer function (PTF) extends the Arya and Paris (AP) approach, which is based on particle size distribution (PSD), by incorporating aggregate-size distribution (ASD) into the PTF to obtain the bimodal WRC. A bimodal porosity approach was developed to quantify the fraction of each of the porous systems (matrix and macropores) in overall soil porosity. Saturated hydraulic conductivity, K 0 , was obtained from WRC using the Kozeny-Carman equation, whose parameters were inferred from the behaviour of the bimodal WRC close to saturation. Finally, the Mualem model was applied to obtain the HCC. In order to calibrate the PTF, measured soil physical and hydraulic properties data were used, coming from field infiltration experiments from an irrigation sector of 140 ha area in the "Sinistra Ofanto" irrigation system in Apulia, southern Italy. The infiltration data were fitted by using both bimodal and unimodal hydraulic properties by an inverse solution of the Richards equation. The bimodal "measured" hydraulic properties were then used to calibrate the scaling parameter (α AP) of the proposed bimodal AP (bimAP) PTF. Similarly, for the sake of comparison with the bimodal results, the unimodal hydraulic properties were used to calibrate the α AP of the classical unimodal AP (unimAP) PTF. Compared to the unimAP PTF, the proposed bimAP significantly improves the predictions of the mean WRC parameters and K 0 , as well as the prediction of the shape of the whole HCC. Moreover, compared to the unimodal approach, it also allows to reproduce statistics of the hydraulic parameters (for example, variance) similar to those observed in the calibration dataset. Multiple linear regression (MLR) was also applied to analyse the sensitivity of the bimodal α AP parameter to textural and structural features, confirming significant predictive effects of soil structure. [ABSTRACT FROM AUTHOR]

Summary: We test the ability of three box models () to describe soil moisture dynamics in a regularly monitored experimental site in northwestern Italy. The models include increasingly complex representations of leakage and evapotranspiration processes. We force the models with the local rainfall, and we determine model parameters with a least-square minimization procedure. We study separately summer and annual dynamics. When we use the models to reproduce annual dynamics, we include a simple representation of the annual cycle of evapotranspiration. We test the models both in simulation mode, estimating their parameters from the same data used to verify them, and in forecast mode, determining the model parameters from one period, and using them to forecast soil water dynamics in a different period. The results indicate that simple box models can estimate soil water dynamics at midlatitudes, using rainfall as the only meteorological driver. The models displaying the best performances include the representation of leakage and evapotranspiration as nonlinear functions of soil water content. [ABSTRACT FROM AUTHOR]

Summary: Analyses of event runoff coefficients provide essential insight on catchment response, particularly if a range of catchments and a range of events are compared by a single indicator. In this study we examine the effect of climate, geology, land use, flood types and initial soil moisture conditions on the distribution functions of the event runoff coefficients for a set of 14 mountainous catchments located in the eastern Italian Alps, ranging in size from 7.3 to 608.4km2. Runoff coefficients were computed from hourly precipitation, runoff data and estimates of snowmelt. A total of 535 events were analysed over the period 1989–2004. We classified each basin using a “permeability index” which was inferred from a geologic map and ranged from “low” to “high permeability”. A continuous soil moisture accounting model was applied to each catchment to classify ‘wet’ and ‘dry’ initial soil moisture conditions. The results indicate that the spatial distribution of runoff coefficients is highly correlated with mean annual precipitation, with the mean runoff coefficient increasing with mean annual precipitation. Geology, through the ‘permeability index’, is another important control on runoff coefficients for catchments with mean annual precipitation less than 1200mm. Land use, as indexed by the SCS curve number, influences runoff coefficient distribution to a lesser degree. An analysis of the runoff coefficients by flood type indicates that runoff coefficients increase with event snowmelt. Results show that there exists an intermediate region of subsurface water storage capacity, as indexed by a flow–duration curve-based index, which maximises the impact of initial wetness conditions on the runoff coefficient. This means that the difference between runoff coefficients characterised by wet and dry initial conditions is negligible both for basins with very large storage capacity and for basins with small storage capacity. For basins with intermediate storage capacities, the impact of the initial wetness conditions may be relatively large. [Copyright &y& Elsevier]

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]

Summary: In this study we analyse space–time variability of soil moisture data collected at 0–6, 0–12 and 0–20cm depth over three hillslopes with contrasting steep relief and shallow soil depth in the Dolomites (central-eastern Italian Alps). The data have been collected during two summer seasons (2005 and 2006) with different precipitation distribution. Analysis of soil moisture data shows that different physical processes control the space–time distribution of soil moisture at the three soil depths, with a marked effect of dew on the 0–6cm soil depth layer. The range of skewness values decreases markedly from the surface to deeper layers. More symmetric distributions, characterised by relatively low skewness, are found for mid-range soil moisture contents, while highly skewed distributions (generally with more log–normal shape) are found at dry and wet conditions. Scatter plots drawn for the whole data set and the analysis of the correlation coefficients suggest a good persistence of soil moisture with depth: the highest degree of correlation was observed between data collected at 0–12 and 0–20cm. Examination of correlation between soil moisture fields and topographical attributes shows that, notwithstanding the steep relief and the humid conditions, terrain indices are relatively poor predictors of soil moisture spatial variability. The slope and the topographic wetness index, which are found here the best univariate spatial predictors of soil moisture, explains up to 42% of the time-averaged moisture spatial variation. A negative relationship between the soil moisture spatial mean and the corresponding spatial standard deviation is found for mean water contents exceeding 25–30%, while a transition to a positive relationship is observed with drier conditions. Overall, soil moisture variability shows the highest values at moderate moisture conditions (23–29%) and reduced values for wetter and drier conditions for all depths. A negative linear relationship between mean soil moisture content and the coefficient of variation was observed. A soil moisture dynamics model proved to successfully capture the soil moisture variability at the hillslope scale. The simulated time series of hillslope-averaged soil moisture are in good agreement with the observed ones. Moreover, the model reproduces consistently the observed relationships between soil moisture spatial mean and corresponding variability. [Copyright &y& Elsevier]

Summary: Soil moisture is widely recognized as a key parameter in environmental processes mainly for the role of rainfall partitioning into runoff and infiltration. Therefore, for storm rainfall–runoff modeling the estimation of the antecedent wetness conditions (AWC) is one of the most important aspect. In this context, this study investigates the potential of scatterometer on board of the ERS satellites for the assessment of wetness conditions in three Tiber sub-catchments (Central Italy), of which one includes an experimental area for soil moisture monitoring. The satellite soil moisture data are taken from the ERS/METOP soil moisture archive. First, the scatterometer-derived soil wetness index (SWI) data are compared with two on-site soil moisture data sets acquired by different methodologies on areas of different extension ranging from 0.01km2 to ∼60km2. Moreover, the reliability of SWI to estimate the AWC at a catchment scale is investigated considering the relationship between SWI and the soil potential maximum retention parameter, S, of the Soil Conservation Service-Curve Number (SCS-CN) method for abstraction. Several flood events occurred from 1992 to 2005 are selected for this purpose. Specifically, the performance of the SWI for S estimation is compared with two antecedent precipitation indices (API) and one base flow index (BFI). The S values obtained through the observed direct runoff volume and rainfall depth are used as benchmark. Results show the great reliability of the SWI for the estimation of wetness conditions both at the plot and catchment scale despite the complex orography of the investigated areas. As far as the comparison with on site soil moisture data set is concerned, the SWI is found quite reliable in representing the soil moisture at layer depth of 15cm, with a mean correlation coefficient equal to 0.81. The characteristic time length parameter variations, as expected, is depended on soil type, with values in accordance with previous studies. In terms of AWC assessment at catchment scale, based on selected flood events, the SWI is found highly correlated with the observed maximum potential retention of the SCS-CN method with a correlation coefficient R equal to −0.90. Besides, SWI in representing the AWC of the three investigated catchments, outperformed both API indices, poorly representative of AWC, and BFI. Finally, the classical SCS-CN method applied for direct runoff depth estimation, where S is assessed by SWI, provided good performance with a percentage error not exceeding ∼25% for 80% of investigated rainfall–runoff events. [Copyright &y& Elsevier]

Summary: The main objective of this paper is to evaluate a threshold-based flash flood warning method, by considering a wide range of climatic and physiographic conditions, and by focusing on ungauged basins. The method is derived from the flash flood guidance (FFG, hereafter) approach. The FFG is the depth of rain of a given duration, taken as uniform in space and time on a certain basin, necessary to cause minor flooding at the outlet of the considered basin. This rainfall depth, which is computed based on a hydrological model, is compared to either real-time-observed or forecasted rainfall of the same duration and on the same basin. If the nowcasted or forecasted rainfall depth is greater than the FFG, then flooding in the basin is considered likely. The study provides an assessment of this technique based on operational quality data from 11 mountainous basins (six nested included in five larger parent basins) located in north-eastern Italy and central France. The model used in this study is a semi-distributed conceptual rainfall–runoff model, following the structure of the PDM (probability distributed moisture) model. Two general questions are addressed: (1) How does the efficiency of the method evolve when the simulation parameters can not be calibrated but must be transposed from parent gauged basins to ungauged basins? (2) How sensitive are the results to the method used to estimate the initial soil moisture status? System performances are evaluated by means of categorical statistics, such as the critical success index (CSI). Results show that overall CSI is equal to 0.43 for the parent basins, where the hydrological model has been calibrated. CSI reduces to 0.28 for the interior basins, when model parameters are transposed from parent basins, and to 0.21, when both model parameters and soil moisture status is transposed from parent basins. Performance differences between FFG and use of time-constant soil moisture status are very high for the parent basins and decrease with decreasing the system accuracy. The percent difference amounts to 53% for the parent basins, to 25% for interior basins with parameter transposition, and to 19% for interior basins with parameter and soil moisture status transposition. These results improve our understanding of the applicability and reliability of this method at various scales and under various scenarios of data availability. [Copyright &y& Elsevier]

Summary: Several hydrological applications require the characterisation of the soil hydraulic properties at large spatial scales. Pedotransfer functions (PTFs) are being developed as simplified methods to estimate soil hydraulic properties as an alternative to direct measurements, which are unfeasible for most practical circumstances. The objective of this study is to quantify the uncertainty in PTFs spatial predictions at the hillslope scale as related to the sampling density, due to: (i) the error in estimated soil physico-chemical properties and (ii) PTF model error. The analysis is carried out on a 2-km-long experimental hillslope in South Italy. The method adopted is based on a stochastic generation of patterns of soil variables using sequential Gaussian simulation, conditioned to the observed sample data. The following PTFs are applied: Vereecken’s PTF [Vereecken, H., Diels, J., van Orshoven, J., Feyen, J., Bouma, J., 1992. Functional evaluation of pedotransfer functions for the estimation of soil hydraulic properties. Soil Sci. Soc. Am. J. 56, 1371–1378] and HYPRES PTF [Wösten, J.H.M., Lilly, A., Nemes, A., Le Bas, C., 1999. Development and use of a database of hydraulic properties of European soils. Geoderma 90, 169–185]. The two PTFs estimate reliably the soil water retention characteristic even for a relatively coarse sampling resolution, with prediction uncertainties comparable to the uncertainties in direct laboratory or field measurements. The uncertainty of soil water retention prediction due to the model error is as much as or more significant than the uncertainty associated with the estimated input, even for a relatively coarse sampling resolution. Prediction uncertainties are much more important when PTF are applied to estimate the saturated hydraulic conductivity. In this case model error dominates the overall prediction uncertainties, making negligible the effect of the input error. [Copyright &y& Elsevier]

Summary: The knowledge of soil moisture spatial variability is an important issue for hydrological and climatic studies. This paper investigates the soil moisture spatial pattern obtained from spot measurements carried out in different experimental plots located in small basins of Central Italy. The near-surface volumetric soil moisture content was measured using a portable time domain reflectometer (TDR) over regular grids whose areas ranging in extension from 9 to 8800m2. The sampling in each site was periodically repeated in order to investigate the moisture pattern as function of wetness conditions. The statistical properties of the measurements and the relation of the statistical parameters with the field-mean soil moisture content were analyzed. The results states that the probability distribution can be assumed normal for soil moisture in flat areas and for normalized soil moisture in gentle slope areas. A general decreasing trend of variance with increasing mean moisture content was observed. Moreover, considering the relationship between the field-mean water content and the coefficient of variation an optimal measurements number ranging from 15 to 35, in the worse case, was found for the study areas. The higher number was required for the site with more significant relief. These results were also confirmed through the geostatistical analysis and the correlation with the topographical attributes. Specifically, the correlation lengths were found increasing with the mean areal slope and the mean drainage direction and ranging between 10 and 15m. This is corroborated through geostatistical analysis showing that the natural logarithm of the specific upslope area was the best spatial predictor for near-surface soil moisture when the water volume in the soil is higher. On this basis, the surface and subsurface lateral redistribution of water can be surmised fundamental in spatial organization of soil moisture. [Copyright &y& Elsevier]