Accurate water balance estimation is crucial for sustainable water management, particularly under increasing climate variability. This study presents HYGRID-M, a monthly grid-based hydrological model developed in Python for the River Basin District (RBD) scale. Its main advantages lie in (i) explicit representation of spatial heterogeneity in soil properties derived from dynamic land use, (ii) the application of a temperature-based Hargreaves evapotranspiration formulation tailored to Mediterranean climatic conditions. The model was applied to the Southern Apennines District in Italy (2000−2023), where soils and land use are highly heterogeneous and temperature-driven evapotranspiration plays a dominant role. When validating the modeled AET against estimates from GLASS, ETMonitor, and MOD16, the HYGRID-M model exhibited significant agreement with MOD16 followed by ETMonitor and GLASS. Including heterogeneous soil depths derived from dynamic land use data and a regionalized Hargreaves coefficient for southern Italy significantly improved AET accuracy. Moreover, Q estimates closely aligned with precipitation forcing and were comparable to BIGBANG outputs. The model further revealed its sensitivity to the spatial heterogeneity in soil properties in estimating Q at the RBD scale. At the basin scale, the calibrated runoff coefficient (β) improved the model performance with KGE and NSE reaching 0.87 and 0.89, indicating the transferability of the model from the RBD to basin scales. Overall, these results demonstrated HYGRID-M's potential as a reliable tool for water management in Mediterranean climate-sensitive regions.

Bridge piers are critical structural elements whose interaction with river flow may significantly influence hydraulic behavior and structural integrity. This is due to the fact that piers geometry and their alignment can induce flow deviation, turbulence, and general or local scour.This paper presents the first results of statistical typological study conducted on bridge piers across Matera province road network (Basilicata, Southern Italy), collected on inspections of a total of 247 bridges. In particular, in this paper piers belonging to a sample of 95 bridges having a Hydraulic Hazard Class varying from High (H) to Medium-Low (ML) are considered, among which 48 of these are affected by scour. The relationship between pier typology and river morphological–hydraulic characteristics is investigated, considering pier shape, foundation type, materials, conservation state, and flow alignment, together with riverbed morphology and hydraulic behavior.The preliminary results show that rectangular piers with rounded corners account for 43.8% of scoured bridge cases, with higher scour susceptibility associated with moderate contraction ratios (10–30%), non-zero flow attack angles, and medium-sized basins. The main findings of this work may be a support for improving the multi-level approach applied within the Italian bridge Guidelines for risk assessment of existing bridges.

In this study, the performance of the modified soil moisture analytical relationship (MSMAR) in concurrent estimation of deep soil moisture (DSM), evapotranspiration (ET), and deep percolation (DP) derived from surface soil moisture (SSM) variations on a sprinkler-irrigated Triticale farm in northeast Iran was investigated. Soil moisture content across the growing season was monitored using time-domain reflectometry (TDR) sensors at seven depths down to 3 m, deployed at five locations on the farm. For model evaluation, HYDRUS-1D served as the benchmark, utilizing initial soil hydraulic parameters derived from RETC and ROSETTA software based on soil texture measurements. As a resource-efficient model, MSMAR underwent two calibration schemes employing MATLAB's genetic algorithm. The first scheme aimed to minimize the MSMAR's DSM errors with the TDR measurements, resulting in MSMAR's consistent DP and ET estimates comparable to those of HYDRUS-1D. Notably, the performance of the MSMAR's DSM estimates is equal or superior than those of HYDRUS-1D depending on the soil simulation depth. The second calibration scheme aimed to minimize the errors between the MSMAR's outputs with those of HYDRUS-1D (i.e., SM, ET and DP) demonstrating MSMAR adaptability relying on minimal information about soil texture, climate, and surface soil moisture variations. Detailed analysis via percentage root mean square error and R2 values across depths highlighted MSMAR's superior performance within the 50-100 cm soil depth. HYDRUS-1D's consideration of root water uptake led to sharp declines in DSM and DP at the Triticale root depth (100 cm), contrasting MSMAR's gradual decline continuing to 200 cm. As a promising tool, MSMAR can be implemented in diverse environmental applications, notably in resource-scarce regions.

The hydrological response of small basins remains complex and challenging to quantify in an accurate way, particularly during extreme events such as floods as well as in the context of sustainable water resources management (Sellami et al., 2016). The application of hydrological models at basin scale offers a promising solution to this challenge by providing valuable tools for water resources management, enabling the analysis of past and current basin conditions as well as the evaluation of the implications of management decisions and imposed changes. In this study, the distributed hydrological model DREAM (Manfreda et al., 2005; Perrini et al., 2024), which incorporates Dunnian and Hortonian mechanisms, was applied to simulate flood events in the Fiumarella di Corleto basin (32.5 km²) and its sub-basin (0.65 km²) in the Italian region of Basilicata. Simulations were conducted for flood events occurring over a 20-year period (2002–2022). These simulations were based on a detailed hydrological and geomorphological characterization of the study area, integrating a hydro-meteorological dataset and initial soil moisture conditions derived from monitoring instruments and a geographical database (Dal Sasso et al., 2023) . Significant flood events were selected for model parameter optimization, due to their representativeness, allowing for the verification of the model’s performance, ensuring its ability to accurately reproduce hydrological behavior as well as for belonging to a dataset characterized by complete hydrological information. The results show that the hydrological model, with the Hortonian runoff mechanism, outperforms in capturing the basin’s immediate response to rainfall events. Preliminary results revealed a satisfactory match between simulated and observed data, as evidenced by the Nash-Sutcliffe efficiency coefficient ranging from 0.52 to 0.73 and the Kling-Gupta efficiency coefficient between 0.56 and 0.75. While errors in simulated and observed peak outflows varied, ranging from acceptable (2–3%) to more significant (up to 20%), the overall performance metrics indicate reliable alignment. These findings underscore the model’s capability to accurately reproduce flood processes, confirming its reliability for simulating extreme hydrological events and supporting its application in watershed management and flood risk mitigation.DISCLAIMERSThe present research has been carried out within the RETURN Extended Partnership and received funding from the European Union Next-Generation EU (National Recovery and Resilience Plan - NRRP, Mission 4, Component 2, Investment 1.3 - D.D. 1243 2/8/2022, PE0000005).This abstract is part of the project NODES which has received fundining from the MUR-M4C2 1.5 of PNRR funded by the European Union - NextGenerationEU (Grant agreement no. ECS00000036).

An increasing amount of evidence is now available for demonstrating how flood series often incorporate data coming from different populations, thus emphasizing the need to understand the physical nature of floods before carrying out their probabilistic analysis. Theoretically derived distributions of floods were introduced by Eagleson (1972) as an alternative, probabilistic and physically based modelling of processes responsible for flood generation. Based on this framework, Iacobellis and Fiorentino (2000) proposed the IF probability model in which the direct contribution to peak flow is obtained as the product of partial contributing area and the discharge per unit of area, both considered as random mutually dependent variables. Moving from the consideration that floods can be triggered by different runoff productions mechanisms, Gioia et al. (2008) introduced the TCIF probability model. IF and TCIF distributions were successfully applied on a wide area of Southern Italy, which includes Puglia, Basilicata and Calabria regions, providing advances in the understanding of physical phenomenology of flood generation in these areas. In our research we revisited the parametric structure of these theoretically derived distributions applied in the entire Southern Italy, exploiting, among other, the availability of updated rainfall data and previous knowledge developed within the framework of VAPI project. Results showed the good performances of both distributions in fitting annual maxima of flood data, highlighting how IF and TCIF distributions possess a solid background for interpreting the actual underlying flood generation processes. Findings of the study can represent a reliable source of information for supporting model selection activities at both local and regional scales.

Accurate hydrological modelling is crucial for understanding natural processes and managing water resources. However, simulation accuracy depends on the availability of field observations for calibration and validation. It is therefore critical to develop effective calibration strategies to reduce prediction uncertainties. This study applies the DREAM model to the experimental basin of Fiumarella of Corleto in Southern Italy to assess the benefits of single and multicriteria calibration approaches. The former uses total runoff; the latter optimizes total runoff, baseflow, and annual water balance. The study also compares uniform or spatially-based parameterization, including correction factors and recession constants. Parameters were optimized through automatic calibration using a genetic algorithm (GA) and the Kling-Gupta efficiency (KGE) as the objective function. Results show that spatially distributed information improves model reliability compared to a uniform parameterization set-up. The multi-objective calibration constrained on baseflow and balance allowed us to optimize the model, reducing variability compared to mono-objective calibration.

Floods and landslides are two distinct natural phenomena influenced by different conditioning factors, though some environmental triggers may overlap. This study applied eXtreme Gradient Boosting (XGBoost) to develop susceptibility maps for both phenomena, using a unified approach based on the same geospatial predictors. The approach integrated topographical, geological, and remote sensing datasets. Flood event data were collected from institutional sources using multi-source and high-resolution remotely sensed data. The landslide inventory was compiled based on historical records and geomorphological analysis. Key conditioning factors such as elevation, slope, lithology, and land cover were analyzed to identify areas prone to floods and landslides. The methodology was applied to the Basento River basin in Southern Italy, a region frequently impacted by both hazards, to assess its vulnerability and inform risk management strategies. While flood susceptibility is primarily associated with low-lying areas near river networks, landslides are more influenced by steep slopes and geological instability. The XGBoost model achieved a classification accuracy close to 1 for flood-prone areas and 0.92 for landslide-prone areas. Results showed that flood susceptibility was primarily associated with low Elevation and Relative Elevation, and high Drainage Density, whereas landslide susceptibility was more influenced by a broader and balanced set of factors, including Elevation, Drainage Density, Relative Elevation, Distance and Lithology. The resulting susceptibility maps offered critical approaches for land use planning, emergency management, and risk mitigation. Overall, the results demonstrated the effectiveness of XGBoost in multi-hazard assessments, offering a scalable and transferable approach for similar at-risk regions worldwide.

Hydrological modeling is an essential tool for understanding and describing hydrological processes, serving as a cornerstone in the quantification and management of water resources. The major challenge of hydrological modeling lies in model calibration, which becomes particularly demanding in large-scale applications and in data-scarce regions.Data scarcity is a significant constraint in modeling, complicating the calibration process and reducing model accuracy. Generally, the availability of high-quality streamflow measurements is considered vital for the calibration and evaluation of hydrological models. However, in many scenarios data may be of low quality, incomplete, or entirely unavailable, as it happens in many areas of the National Territory, including regions in Southern Italy where the streamflow observations are limited, fragmented and discontinuous. Most hydrometric stations record only water levels, often without updated flow rating curves, making reliable hydrological model calibration a challenging task. In order to overcome such limitations, we compared three different setups to get the best parametrization during the model calibration. At first, we used the biggest hydrological basin (Volturno river catchment) of the entire district, as representative of the regional study area. The calibration of the model was done for the representative catchment, and the parameters were applied at the regional scale. Then, we used reconstructed streamflow measurements derived from water balance of nine artificial reservoirs as a reference for a multiobjective calibration. At last, we used remote sensing data, such as soil moisture maps, as a reference for calibrating the model. Multi-objective functions, focusing on high-flows and low-flows aspects of the time series, were used in automatic optimization based on genetic algorithms to perform space-time operational testing of the large-scale model. The reference hydrological model used is the DREAM model (Distributed model for Runoff, Evapotranspiration, and Antecedent Soil Moisture simulation), applied to the vast area within the jurisdiction of the Southern Apennine District Basin Authority.These calibration procedures have been compared exploiting available data. The study provides guidance in the use of limited data in order to identify the most suitable approach to build a reliable model calibration of the entire district and assess the impact of climate change on water resources in future climate scenarios. The encouraging performances of the regional model motivate the extension of the present approach to other data-scarce regions.

Catchment-scale hydrological models encountered dichotomies with the numerical hydrodynamic models when describing surface routing process. We propose a new modeling framework, the so-called "Runoff-On-Grid" approach, for embedding distributed process-based hydrological modeling into shallow water models, as an alternative to the traditional Fully Hydrodynamic Approach (also known as Rain-On-Grid). Antecedent Soil Moisture, subsurface dynamics, and other topsoil hydrological processes are implicitly integrated in the governing hydrodynamic equations via the proposed methodology. The resulting hydrological-hydrodynamic coupling, based on the DREAM distributed hydrological model and the Iber+ shallow water model, enhances the capabilities of both reference models. Through introducing non-negligible runoff generation sources, the Runoff-On-Grid approach extends the surface hydrodynamic modeling to medium-sized vegetated and/or (semi)humid catchments, bypassing the limitations of the widespread hydrological losses' empirical formulations. Employed in an event-based analysis within a High-Performance Computing framework, the DREAM-Iber model provides an efficient and reliable reconstruction of the November 2020 flood that occurred in Crotone (Italy), envisaging consequences of similar future scenarios. We show that the proposed modeling technique, nested within emerging environmental technologies and robust on-site data, details the flood hazard inducing processes merging physical hydrology with advanced hydrodynamics. In this scientific contribution, the potential of combining two different operational tools, namely distributed rainfall-runoff and flood models, is investigated. An hindcast procedure has been used as reference to assess both the hydrological processes and the inundations at the catchment-scale. In this context, were exploited cutting edge computational and environmental technologies, which significantly quickened the simulations and enabled a high-fidelity reconstruction of the extreme meteorological event. According to our findings, there is merit of the proposed approach for bridging the dichotomies between the hydrological and hydrodynamic simulators. This can favor of a more comprehensive method to reduce the limitation of the standalone models. The Runoff-On-Grid approach integrates subsurface hydrological processes, antecedent soil moisture and soil physics in shallow water models The Runoff-On-Grid approach expands the capabilities of the Rain-On-Grid approach introducing non-negligible runoff generation sources The DREAM-Iber model supported by enabling technologies provides a high-fidelty reconstruction of the 2020 Esaro flood

Hydrological observations provided by in situ monitoring networks are essential to better understand hydrological processes and to improve water resource management. This is even more precious for small basins where large spatial coverage or remotely sensed data are not enough to represent hydrological behavior in space and time. In addition, the availability of several years of hydrological data is particularly useful for the application of hydrological models that usually requires long calibration data series in order to provide reliable results. Starting from 2002 and continuing for the subsequent two decades, the "Fiumarella of Corleto" basin, which spans an area of 32.5 km2 and is situated in the Basilicata region of Southern Italy, has been under observation (Manfreda et al., 2011). The basin is located on two slopes with differing land use patterns: the left slope is mostly comprised of agricultural land, while the right slope is predominantly covered by forests. The hydrometeorological network consists of three automated weather stations equipped with various sensors to monitor rainfall, snow depth, temperature, wind speed and direction, air temperature, relative humidity, solar radiation, atmospheric pressure, and hydrometric data. From 2006, a TDR100 system connected to 22 probes located at 11 different sampling sites was used to monitor soil moisture in the sub-basin. The system was set up along a transect measuring approximately 60 meters in length, with probes located at two different depths of 30 and 60 cm. In addition to this, a high-resolution (1x1 m) DSM of the basin was derived using LiDAR to provide a detailed characterization of the morphology of the two slopes. The catchment pedology was investigated through field campaigns and laboratory measurements to identify the primary soil types and units in the basin (Romano et al., 2002; Santini et al., 1999). Monitoring activities were conducted with reference to two different spatial scales: the entire basin (32.5 km2) and the sub-basin (0.65 km2). Hydrological signatures were used to characterize the hydrological behavior of the two drainage areas. Peak flow analyses were performed to define lag-time, soil moisture conditions before flood events evidencing the different hydrological responses of both basin and sub-basin. Some flow indicators (e.g., base flow and recession constant) were used to constrain a semi-distributed hydrological model in order to optimize performances in calibration and validation. In this contribution, an overview of the main results of hydrological data analyses and modeling obtained at different spatial scales is presented.

The present work pursues theoretical and empirical objectives. With regards to the former, it is demonstrated that the natural tendency to uniformity of both the probability distribution of a city to have a certain number of inhabitants and that of a person to reside in a town of a given number of citizens leads to a competition between their information entropies, which provides the power law distribution as the most probable one for city size. It is also shown that Zipf's law reflects the significant control of the existence of interconnections between cities on the self-organization of their size. With regards to the empirical objectives, based on population data of European countries and Italian municipalities, the theoretical approach proposed is validated. At the Italian scale, city distribution is shown to be a power law for cities above 10,000 inhabitants. In the 20 Italian regions, the breakpoint in the distribution is generally lower. Finally, the geographical control on city distribution is discussed based on the results achieved in some regions.

Soil moisture (SM) is a connective hydrological variable between the Earth's surface and atmosphere and affects various climatological processes. Surface soil moisture (SSM) is a key component for addressing energy and water exchanges and can be estimated using different techniques, such as in situ and remote sensing (RS) measurements. Discrete, costly and prolonged, in situ measurements are rarely capable in demonstration of moisture fluctuations. On the other hand, current high spatial resolution satellite sensors lack the spectral resolution required for many quantitative RS applications, which is critical for heterogeneous covers. RS-based unmanned aerial systems (UASs) represent an option to fill the gap between these techniques, providing low-cost approaches to meet the critical requirements of spatial, spectral and temporal resolutions. In the present study, SM was estimated through a UAS equipped with a thermal imaging sensor. To this aim, in October 2018, two airborne campaigns during day and night were carried out with the thermal sensor for the estimation of the apparent thermal inertia (ATI) over an agricultural field in Iran. Simultaneously, SM measurements were obtained in 40 sample points in the different parts of the study area. Results showed a good correlation (R-2=0.81) between the estimated and observed SM in the field. This study demonstrates the potential of UASs in providing high-resolution thermal imagery with the aim to monitor SM over bare and scarcely vegetated soils. A case study based in a wide agricultural field in Iran was considered, where SM monitoring is even more critical due to the arid and semi-arid climate, the lack of adequate SM measuring stations, and the poor quality of the available data.

Monitoring Surface Soil Moisture (SSM) and Root Zone Soil Moisture (RZSM) dynamics at the regional scale is of fundamental importance to many hydrological and ecological studies. This need becomes even more critical in arid and semi-arid regions, where there are a lack of in situ observations. In this regard, satellite-based Soil Moisture (SM) data is promising due to the temporal resolution of acquisitions and the spatial coverage of observations. Satellite-based SM products are only able to estimate moisture from the soil top layer; however, linking SSM with RZSM would provide valuable information on land surface-atmosphere interactions. In the present study, satellite-based SSM data from Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Soil Moisture Active Passive (SMAP) are first compared with the few available SM in situ observations, and are then coupled with the Soil Moisture Analytical Relationship (SMAR) model to estimate RZSM in Iran. The comparison between in situ SM observations and satellite data showed that the SMAP satellite products provide more accurate description of SSM with an average correlation coefficient (R) of 0.55, root-mean-square error (RMSE) of 0.078 m3 m−3 and a Bias of 0.033 m3 m−3. Thereafter, the SMAP satellite products were coupled with SMAR model, providing a description of the RZSM with performances that are strongly influenced by the misalignment between point and pixel processes measured in the preliminary comparison of SSM data.

Understanding the spatial and temporal dynamics of vegetation monitoring and water stress in crops is essential for water resources management in agricultural fields and inferring land–atmosphere interactions. The characterization of vegetation biophysical variables in heterogeneous fields is limited by high spatial resolution satellite sensors, which lack the spectral resolution required for vegetation monitoring; and, by the physical constraints of ground point measurements. Unmanned Aerial Systems (UASs) represent an option to fill the gap in precision agriculture between satellite imagery and ground point measurements, and managed to compete successfully with these traditional remote sensing acquisition platforms by providing fast and low-cost high spatial resolution products with high revisit frequency. The present research gives a description on generating high-resolution remote sensing products using a rotary wing UAS equipped with thermal and multispectral imaging sensors in a vineyard. It also focuses on generating different vegetation indices such as: Excess Green Index (ExG), Normalized Green-Red Difference Index (NGRDI), Normalized Difference Vegetation Index (NDVI), and Crop Water Stress Index (CWSI) derived from different cameras. The use of such indices is explored in order to evaluate the potential of low cost technologies for precision viticulture. We carried out three surveys with a DJI Phantom 4 Pro on 22 May, 23 June and 28 July 2017, over the vineyards of Maschito located in Basilicata region in the south of Italy. The UAS is equipped with an RGB camera and in addition, we have mounted a Thermal and a Multispectral camera on board. The thermal camera applied was a FLIR Tau2 operating in the wavelengths of 7.5 – 13.5 μm. We calculated Vegetation Indices (VI) such as CWSI based on canopy temperature in heterogeneous vineyards. The multispectral images were obtained using a TETRACAMADC SNAP camera in a wavelength range of 700 1000 nm, and later processed to extract some Vegetation Indices (VI) such as NDVI. The vegetation indices obtained with the different sensors have been compared with the aim to identify the ability of each one to capture and describe properly the spatial characteristics of vegetation. This study demonstrates the great potential of high-resolution UAS data and photogrammetric techniques applied in the agriculture framework suggesting that these instruments represent a fast, reliable, and cost-effective resource in crop assessment for precision farming applications.

High and low flows and associated floods and droughts are extreme hydrological phenomena mainly caused by meteorological anomalies and modified by catchment processes and human activities. They exert increasing on human, economic, and natural environmental systems around the world. In this context, global climate change along with local fluctuations may eventually trigger a disproportionate response in hydrological extremes. This special issue focuses on observed extreme events in the recent past, how these extremes are linked to a changing global/regional climate, and the manner in which they may shift in the coming years.

In the last few years, the scientific community has developed several hydrological models aimed at the simulation of hydrological processes acting at the basin scale. In this context, the portion of peak runoff contributing areas represents a critical variable for a correct estimate of surface runoff. Such areas are strongly influenced by the saturated portion of a river basin (influenced by antecedent conditions) but may also evolve during a specific rainfall event. In the recent years, we have developed 2 theoretically derived probability distributions that attempt to interpret these 2 processes adopting daily runoff and flood-peak time series. The probability density functions (PDFs) obtained by these 2 schematisations were compared for humid river basins in southern Italy. Results highlighted that the PDFs of the peak runoff contributing areas can be interpreted by a gamma distribution and that the PDF of the relative saturated area provides a good interpretation of such process that can be used for flood prediction.

The goal of this paper is to introduce the first clear-water scour model based on both the informational entropy concept and the principle of maximum entropy, showing that a variational approach is ideal for describing erosional processes under complex situations. The proposed bridge–pier scour entropic (BRISENT) model is capable of reproducing the main dynamics of scour depth evolution under steady hydraulic conditions, step-wise hydrographs, and flood waves. For the calibration process, 266 clear-water scour experiments from 20 precedent studies were considered, where the dimensionless parameters varied widely. Simple formulations are proposed to estimate BRISENT’s fitting coefficients, in which the ratio between pier-diameter and sediment-size was the most critical physical characteristic controlling scour model parametrization. A validation process considering highly unsteady and multi-peaked hydrographs was carried out, showing that the proposed BRISENT model reproduces scour evolution with high accuracy.