High-resolution, temporally consistent climate datasets are essential for hydrological modeling, water resource management, and climate impact assessments. The Po River District is the largest in Italy, spanning from the Alps to the plains, and exhibits substantial spatial heterogeneity in precipitation and temperature. However, existing datasets lack the spatial resolution necessary to capture the basin's diverse microclimates and complex orographic patterns, limiting their utility for process-based hydrological modeling and local-scale climate impact studies.In this study, we generated a high-resolution (1 km x 1 km) daily gridded precipitation and temperature dataset over the Po River District. Following WMO standards, this 30-year (1991–2020) dataset provides a robust baseline for a region identified as one of Europe's most vulnerable climate change hotspots. The datasets were generated using the Kriging module available within the GEOframe-NewAGE modeling system, applied to quality-controlled ground station data. To address the vast area and topographic complexity, we implemented a spatial regionalization framework using Gaussian Mixture Models (GMM) to identify homogeneous climate zones. Zone-specific variogram models were derived and applied within the optimized Kriging framework. The model performance was rigorously evaluated using Leave-One-Out Cross-Validation (LOOCV) method. The validation results show exceptional accuracy for both variables. For temperature, the Kling-Gupta Efficiency (KGE) exceeded 0.75 at 99.7% of the stations, with strong correlations (>0.95). Notably for precipitation, over 80% of stations achieved KGE and correlation values above 0.75. The KGE decomposition revealed that errors primarily stemmed from variability estimation rather than bias, with 93% of stations showing optimal variance ratios (α = 0.75–1.25) and 99% maintaining near-unity bias (β ≈ 1).This high-resolution dataset represents a significant advancement in regional climate data for the Po River District. The GMM-based regionalization successfully captured the basin's complex climatic regimes, enabling accurate spatial interpolation across diverse topographies. Beyond providing a WMO-compliant climatological baseline, these datasets are specifically designed to serve as high-resolution meteorological forcing input for distributed hydrological models, enabling process-based watershed simulations at unprecedented spatial detail. Future work will focus on coupling these datasets with the GEOframe-NewAGE hydrological modeling framework to assess the added value of 1-km climate forcing in capturing sub-basin scale hydrological responses, extreme event dynamics, and water balance components across the heterogeneous Po River landscape.AcknowledgementHS, JMW and RR would like to thank and acknowledge the funding support from Project “SPACE IT UP! ASI Contract n.2024-5-E.0 CUP Master n. I53D24000060005” SAP fund n: 000040104905.

The deep critical zone (CZ) has long been recognized for its importance in influencing shallow landslides but was not considered feasible to include in slope stability models at the watershed scale. Here, we demonstrate that simple approximations of the CZ in a fully coupled hydrologic and soil slope stability model can effectively capture the location, timing, and likely size of shallow landslides. To achieve this, we use coupled, process-based models that incorporate the effects of 1) deep CZ structures, 2) three-dimensional transient hydrology, and 3) multidimensional slope stability, calibrated with data from an intensively monitored field site. Our results show that the hydrologically active deep CZ guides groundwater flow, influencing where it drains from or exfiltrates to the soil mantle and producing distinct patterns of soil saturation and seepage forces at the soil-bedrock boundary. A deep conductive, weathered bedrock drains the soil mantle, reducing the likelihood of destabilizing pore pressures, while the downslope thinning of the CZ forces groundwater to the surface. This pattern creates localized instability and a tendency for similar-sized landslides across the landscape. In contrast, the absence of conductive weathered bedrock results in more widespread destabilizing pore pressures, leading to larger landslides and the likelihood of landslides earlier in a storm than in landscapes underlain by a deep CZ. Our findings suggest that first-order variations of deep CZs can provide physical explanations for variations observed in the susceptibility, magnitude, and timing of shallow landslides, and that CZ structure may be inferred from patterns and timing of landsliding.

The Italian Alpine region faces significant challenges in water resource management due to competing demands from agriculture, hydropower production, and flood risk mitigation. Rising temperatures and ongoing glacier retreat pose unprecedented pressures, highlighting the need for an improved understanding of hydrological cycle components. This region hosts numerous Alpine lakes that play a key role in water storage, including the four largest – Lake Maggiore, Lake Como, Lake Iseo, and Lake Garda – which together provide up to 1.2 billion m³ of storage capacity over a catchment area exceeding 12000 km².The aim of this work is to give an overview of the modelling framework and the calibration procedures that were performed on this area using the GEOFrame-NewAge hydrological modeling system, an open-source, modular platform based on Java components, designed to represent the complex physical processes of the hydrological cycle.The upstream lake catchments were discretized into sub-basins and modelled using a semi-distributed approach, with processes evaluated at sub-basin centroids based on spatially averaged properties. A major focus was placed on harmonizing regional meteorological datasets, as preliminary analyses revealed a systematic underestimation of precipitation at high-elevation gauges. This required a re-evaluation of input data using historical sources and atlases, particularly for the Swiss catchment of Lake Maggiore. Evapotranspiration estimates were improved by introducing a semi-distributed net radiation scheme computed on a 1500 m grid, which enhanced model performance compared to centroid-based calculations.Model calibration was challenging due to the dense network of water infrastructures that alter natural flow regimes and bypass many gauging stations. Calibration therefore relied on selected hydrometric stations and time periods minimally affected by anthropogenic influences, enabling a consistent basin-wide calibration. Using the Kling-Gupta Efficiency as the objective function, the model achieved excellent performance, with calibration and validation values often exceeding 0.8. Post-processing analyses also showed good agreement with long-term averages of key hydrological components.By enabling the estimation of impacts on long-term water availability, this successfully calibrated model provides a powerful tool to quantify water scarcity, optimize reservoir management, and minimize conflicts among stakeholders, especially under a changing climate.

Abstract. High-resolution daily climate data remain scarce for complex Alpine terrain, where coarse gridded products often fail to resolve orographic precipitation gradients and elevation-dependent temperature variability. Here we present a 1-km gridded daily precipitation and temperature dataset for the Po River District (Northern Italy) covering the 1991–2020 climatological period. The dataset is based on a harmonized multi-source observational network comprising 1,583 precipitation and 1,555 temperature stations after quality control. Spatial interpolation was performed using Ordinary Kriging for precipitation and Detrended Kriging for temperature, with elevation as the trend variable, and daily-adaptive semivariogram models selected from five candidate functions via Particle Swarm Optimisation. Leave-one-out cross-validation indicates strong overall performance. For precipitation, the mean Kling–Gupta Efficiency (KGE) across all station-wise leave-one-out cross-validations exceeds 0.84 under All-Days conditions and 0.82 under Wet-Days conditions, with mean absolute errors of 1.28 mm and 3.05 mm, respectively. Temperature interpolation achieves a mean KGE of 0.88 and a mean absolute error of 1.14 °C, with negligible bias. Spatial diagnostics reveal higher precipitation errors in high-relief Alpine sectors and along basin boundaries, while temperature performance remains comparatively uniform. Interpolation skill decreases with altitude, particularly for precipitation during wet events, due to decreasing station density. The resulting dataset provides spatially continuous daily climate fields suitable for hydrological modelling, climate variability assessment, and environmental analysis in one of Europe’s most topographically diverse river basins.

Alpine regions are highly sensitive to climate change, and snow melt dynamics are crucial in their hydrological processes. A key variable is the snow water equivalent (SWE), but direct measurements are rare and limited, making spatial estimates difficult. Remote sensing (RS) provides well-established, high-spatial resolution snow cover area (SCA) observations, yet lacks accurate water mass balance information essential for hydrological modeling. To overcome these limitations and improve high-resolution SWE estimates, we developed a redistribution approach combining semi-distributed hydrological modeling and remote sensing information. SWE simulated by a semi-distributed model (GEOframe) was redistributed at high spatial resolution using a random forest regression trained on topographic features and a dynamic snow cover duration (SCD) derived from optical remote sensing. The methodology was tested in an alpine catchment (Dischma, Switzerland) using available hydrological and high-resolution snow datasets for validation. The semi-distributed hydrological model reproduces discharge dynamics, achieving Kling – Gupta efficiency (KGE) values of 0.90 and 0.73 during calibration and validation, but provides coarse SWE spatial patterns. The proposed redistribution approach effectively improves the spatial representation of SWE, achieving a mean bias error (MBE) of -27.48 mm and a Pearson correlation coefficient (CORR) of 0.74 at SWE peaks. In addition, redistributed SWE maps show high agreement with the operational snow-hydrological model system (OSHD), with a CORR of 0.89. Overall, the catchment-average SWE is bound to the water mass balance estimated by the hydrological model, ensuring consistency with hydrological processes and improving the spatial and temporal representation of snow-melt dynamics.

In recent decades, climate change and rapid urbanization have increasingly affected the functioning of Urban Drainage Systems (UDS). Nature-Based Solutions (NBS), such as green roofs and permeable pavements, can help mitigate these impacts. This study assessed the effectiveness of NBS in reducing the hydrological impacts of climate change and urbanization in Gurugram, India. Climate and urban development trends for the near future were projected using parsimonious approaches based on publicly available remote sensing datasets, addressing data limitations in data-scarce regions. Scenarios representing different climate and urban conditions for 2025, 2030, and 2035 were developed using the 2020 baseline UDS model. Three NBS configurations were considered: green roofs, permeable pavements, and their combination. PySWMM was used to simulate the scenarios and automate model runs.Results indicated that urbanization had a greater hydrological impact than climate change. Compared with the baseline, the worst-case 2035 scenario increased flooding volume, runoff volume, and peak discharge at the drainage outfall by approximately 160%, 83%, and 37%, respectively. NBS reduced peak flowrate, flooding volume, and surface runoff by approximately 25%, 65%, and 40%, respectively, relative to the no-NBS condition. Peak flows across sub-catchments and scenarios ranged from −48% to 243% compared with the baseline. The combination of green roofs and permeable pavements reduced peak flows below baseline levels where urbanization is up to medium (<15% impervious area). Further analysis can evaluate the economic feasibility of NBS by comparing implementation costs with derived benefits. Overall, the methodology demonstrates potential for application in other data-scarce urban contexts.

Landslides represent a major threat to human safety and infrastructure, particularly in mountainous regions. Accurately predicting landslide susceptibility in a physically based deterministic manner requires an integrated, multidisciplinary approach that combines geology, geomorphology, and hydrology. In this work, a hydromechanical modeling framework is developed to forecast the initiation of large-scale shallow landslides by computing the local factor of safety (LFS) as a measure of slope instability. The framework couples (1) a finite element method (FEM) solver for hydromechanically coupled landslide processes implemented within a Java-based, object-oriented modeling environment, with (2) an external hydrologic model, allowing for detailed three dimensional simulations of slope response to transient rainfall events across extensive hillslope domains. The proposed framework is first validated using a benchmark test on a homogeneous hillslope with constant inclination and is subsequently applied to a real-world large-scale case study in the Braies Alpine Catchment, Alto Adige, Northern Italy. In the benchmark scenario, the model successfully reproduces shallow landslide triggering under prolonged rainfall, while in the real-case application it reliably captures the initiation of multiple landslides during an intense summer storm. These results highlight the framework’s robustness and accuracy in predicting landslide initiation in complex terrain, demonstrating its potential as a cost-effective tool for landslide hazard and risk assessment.

In the last years, Italy observed more frequent and intense drought events, with a particularly severe drought in 2022, leading to significant environmental, social and economic damages.Also at a global scale extreme events, floods and droughts, have been reported to be more likely due to climate change and environmental modification.For this reason, already in 2021, the Po River District Authority (AdbPo) started the implementation of the GEOframe modelling system on the whole territory of the district to update the existing numerical modelling for water resource management, and then to improve the planning activity of the Authority itself, producing a better quantification and forecast of the spatial and temporal water availability.The GEOframe system was developed by a scientific international community, led by the University of Trento, and is a semi-distributed conceptual model, with high modularity and flexibility, completely open-source.After a starting phase of data collection, validation, spatial interpolation (for the reference period 1991-2020), and geomorphological analysis, all the components of the hydrological balance (evapotranspiration, snow accumulation, water storage and discharge) have been simulated.Consequently, the “zonal calibration” phase was carried out on a 4 years period basis with the KGE method, consisting of the research of the values of the characteristic model parameters which fit the discharge evolution recorded in the hydrometers of the region in the best possible way, comparing the modelled discharge trend with the measured one.With the completion of the calibration process in the Piemonte region, one of the biggest regions of Italy, which contains more than 100 hydrometers, an analysis of the water balance components was undertaken, focusing especially on hydrological and agricultural drought events.In particular, water availability has been modelled in the whole regional territory, evaluating its impact on agriculture, namely studying how and when a hydrological drought affects agricultural drought according to the data collected in the last 30 years.Attention has been taken also to the snow precipitation contribution, which has a major impact in alpine regions, dominating local and regional hydrology, strongly influencing vegetation growth and the utilization of water resources (Wu et al., 2015), like the one of the Po River basin, characterized by the presence of the Alps along all of its route.In conclusion, it was possible to carry on a historical analysis of water availability in Piemonte, assessing the capacity of GEOframe to simulate all the components of the water cycle (evapotranspiration, water storage, snow accumulation and water discharge). Furthermore, implementing GEOframe in a mountainous area underlines the importance and the influence that snow and glaciers, especially in a higher temperature scenario due to climate change, can have on water availability and, therefore, a better modelling component of these elements will be implemented in the future developments of GEOframe.

Irrigation is an essential component of food systems. Worldwide, 40% of global food production comes from irrigated croplands despite the latter accounting for 20% of total cropland. With limited available locations to grow crops, an increasing population and a changing climate, irrigation is a crucial component to help meet a rising demand on food production systems. It is also a process with increasing consideration in current hydrological model developments.Building on a previously flexible and open-source hydrological digital twin for the Adige River basin (~11000 km2), located in the north-east of Italy, at high temporal (daily) and spatial resolution (5km2), a novel irrigation modelling component is implemented for the study area. Irrigation water is crucial to the economy of the region, for fruit productions (vineyards and apple) and necessary to be included into water budget quantification to accurately represent hydrological processes.The implementation includes water demand assessment through soil moisture and evapotranspiration, while accounting for the different type of crops and specific water needs. Irrigation is activated when volumetric soil water content (dependent on saturation and wilting points) falls below a fixed threshold. The flexibility of the digital twin framework allows us to quantify the effect of various threshold levels on irrigation estimates but also in terms of water processes. Water availability is considered through 2 scenarios (limited where water is taken from another component of the model or unlimited). The model accounts for daily limits in irrigation as well as efficiency.Results show the different range with regards to irrigation quantities and hydrological processes dependent on the different thresholds and limitation formulae retained, outlining the importance of diverse possibilities in the implementation of irrigation.Furthermore, integrating irrigation into the digital twin has been shown to improve the river discharge simulations under the limited irrigation scenario when compared with measured data and actual evapotranspiration. This enhancement is particularly evident in areas where irrigation represents an important input of the hydrological cycle.This study can be useful to regional water managers, policy makers, and stakeholders, especially in regions where conflicts are strife between the different usages (domestic, agricultural, industrial/ hydropower) and particularly in a changing climate. The work is supported by the project Fondo per il Programma Nazionale di Ricerca e Progetti di Rilevante Interesse Nazionale (PRIN) Control-based Optimization of the AnthropogeniC Hydrological cycle for a sustainable WATer management (COACH-WAT, CODE 2022FXJ3NN CUP E53D23004390001).Selected references:Morlot, M., Rigon, R., & Formetta, G. (2024). Hydrological digital twin model of a large anthropized italian alpine catchment: The Adige river basin. Journal of Hydrology, 629, 130587

Global warming is associated with rising precipitation intensities, challenging urban drainage systems, and policymakers worldwide. Densely populated, highly sealed cities face high pluvial flooding risks. Nature-based Solutions have been identified as a promising and multifunctional approach to mitigating pluvial flooding impact. This study investigates the flood mitigation potential of various Nature-based Solutions scenarios and a green-grey infrastructure hybrid solution in a neighbourhood in Bochum, Germany. Using an integrated 1D-2D drainage model in PCSWMM, different sub-hourly storm events were simulated for current and future periods. The green-grey hybrid solution was the most effective in reducing flood area and depth. Among Nature-based Solutions, permeable pavement had the greatest impact, followed by rain gardens and tree pits. All Nature-based Solutions were able to prevent pluvial flooding in design storms with return intervals of 10 years. Runoff reduction rates exhibited relatively stable behavior throughout different precipitation intensities, suggesting that Nature-based Solutions’ potential to reduce runoff exceeds the standard design applications. The results suggest Nature-based Solutions are effective against pluvial floods in Bochum. Extensive, holistic Nature-based Solutions implementation is crucial for adapting sewer systems and enhancing city-wide resilience. While individual interventions can protect vulnerable infrastructures, city-level resilience must be prioritized to effectively address urban pluvial flood challenges.

High-resolution climate data significantly enhance the accuracy and understanding of water budgets, particularly in mountainous regions. Evapotranspiration (ET), the largest terrestrial water flux, is a critical parameter for surface water modelling and monitoring climate change impacts on water resources and agriculture. Its influence is especially pronounced in Europe and the Mediterranean, recognized as climate change hotspots.This study presents a high-resolution (1 km daily) Potential Evapotranspiration (PET) dataset, derived from a combination of ground-based and remote sensing data and adjusted with daily crop growth coefficients. Covering Europe and the Mediterranean region from 2004 to 2022, the dataset is validated through regional, basin, and local scales. Validation is performed using triple collocation metrics combining the PET-1km product, GLEAM (28 km) and hPET (11 km), as well as with daily measurements from 38 eddy covariance (EC) flux tower stations within the study domain. At the basin scale, the Adige River basin in the Italian Alps is modeled using the Adige Hydrological Digital Twin, incorporating PET-1 km as an input component.Regional-scale validation highlights the superior performance of the PET-1 km dataset, achieving better results in 86.7% of the study area compared to GLEAM and hPET. Site-scale validation against EC measurements indicates a high correlation coefficient (0.82) and a low RMSE (1.08 mm/day). Basin-scale validation in the Adige basin reveals improved modelling of water cycle components compared to previous findings on the same study area.This high-resolution PET dataset offers valuable insights for climate and hydrological studies, advancing water resource management and climate adaptation strategies in this crucial region.

Landslides pose a significant hazard to citizens, infrastructures, especially in mountainous landscapes. The deterministic prediction of landslide susceptibility requires an incorporated approach between multiple disciplines such as geology, geomorphology, and hydrology. This study uses a hydromechanical framework aimed at predicting large scale shallow landslide initiation by calculating the local field factor of safety (LFS) as an indicator of slope instability. The framework integrates (1) a finite element method (FEM) solver for a coupled hydromechanical landslide model within a Java object-oriented modeling system, and (2) a third-party hydrologic model, enabling detailed multidimensional simulations of slope stability under transient rainfall conditions in large-scale hillslopes. The hydromechanical framework is validated through a benchmark test on a homogeneous, constant-slope hillslope and then are further applied to a large-scale real-world scenario in the Braies Alpine Catchment, Alto Adige, Northern Italy. The first application successfully predicted the triggering of a shallow landslide under sustained rainfall, while the second application accurately identified the initiation of multiple landslides during an intense summer storm. The results demonstrate the framework's capability to predict landslide initiation with high precision, even in complex slope environments, providing a cost-effective tool for landslide risk assessment.

This paper presents a long-term snow water equivalent dataset in the Po River District, Italy, spanning from 1991 to 2021 at daily time step and 500 m spatial resolution partially covering the mountain ranges of Alps and Apennines. The data has been generated using a hybrid modelling approach integrating the hydrological modelling conducted with the physically-based GEOtop model, preprocessing of the meteorological data, and assimilation of in-situ snow measurements and Earth Observation snow products to enhance the quality of the model estimates. A rigorous quality assessment of the dataset has been performed at different control points selected based on reliability, quality, and territorial distribution. The point validation between simulated and observed snow depth across control points shows the accuracy of the dataset in simulating the normal and relatively high snow conditions, respectively. Additionally, satellite snow cover maps have been compared with simulated snow depth maps, as a function of elevation and aspect. 2D Validation shows accurate values over time and space, expressed in terms of snowline along the cardinal directions.

Study region: The EU member states plus UK, Norway, and Switzerland. Study Focus: In the recent past, Europe has faced several extreme drought events that have generated high economic losses across socioeconomic sectors. Climate change is expected to alter the frequency and intensity of these events. In order to increase drought resilience through adaptation it is essential to discern regional and sectoral drought risk patterns that account for the specific characteristics of local territories. In this study, we integrate drought hazard modelling with regional exposure mapping and vulnerability assessment to quantify drought impacts in drought-sensitive sectors across 1366 administrative territorial units of Europe, considering present climate conditions and a range of global warming levels. New hydrological insights for the region: Our results show a strong regional disparity in future drought impacts, with an overall increase in drought losses in southern, south-eastern, and western regions of Europe and a decrease in northern regions. Regional differences are amplified with increasing global warming and depend on the intensity of the drought, modulated by the exposure and vulnerability of drought-sensitive socioeconomic sectors. In some of the most impacted regions, economic losses could become substantial, with expected annual losses corresponding to 1-2 % of regional gross domestic product, while the agriculture sector could lose 15 % of its gross value added with high levels of global warming.

Hydrological extremes (drought and floods) have undeniable financial implications and are predicted to grow in the next years. Yet to understand their future local impacts it is necessary to understand the evolution of the governing hydrological processes. Such is the case for the Adige basin, an important basin in Italy, where understanding changing patterns of hydrological processes is crucial to optimally plan competing water uses, such as hydroelectric production and agricultural water allocation. Euro-CORDEX models provide future climate projections throughout the region, for different emissions scenarios (RCP 2.6, 4.5, 8.5) and climate models (13). Upon the application of a downscaling and bias correction methodology against observed climate variables (i.e. air temperature and precipitation), a process-based semi-distributed digital twin of the Adige River basin is implemented. Hydrological process variables (snow, actual evapotranspiration, soil moisture and discharge) are obtained for the entire basin and the timespans of the different Euro-CORDEX models (1980-2005 for the historical baselines, 2005-2100 for the projections) at daily temporal scale and 5 km2 spatial resolution. The temporal and spatial patterns for discharges are evaluated through the average monthly values for 6 sub-catchments. Other process variables such as snow, actual-evapotranspiration (AET) and soil moisture (SM) are assessed against remote sensing datasets. The resulting climate and hydrological end of the century projections (2075-2100) are compared to historical baselines (1980-2005), to assess projected changes. The digital-twin model is found to reproduce discharge patterns accurately, with an average KGE of 0.8, and provides a good fit for snow and AET, with average correlations of 0.95 and 0.96 respectively. A reasonable fit is found for SM, with an average correlation of 0.5. Careful assessment of the digital twin model through these variables ensures that it reproduces accurately historical local hydrological processes and increases confidence in the quantification of these variables under future projections. The results of our study give regional policymakers insights into possible future scenarios and how these affect water resources and their potential impacts and adaptations on several economic sectors.

Heatwaves and droughts are among the natural hazards with frequencies and severities expected to increase due to climate change. Furthermore, they are responsible for a large range of social and economic impacts, such as agricultural losses, energy shortages, heat related mortality, etc. Previous works have shown that co-occurring drought and heatwave events lead to higher significant socio-economic damages compared to independent events. However, limited knowledge is available on quantifying spatial patterns of co-occurring droughts and heatwaves events, their severity, and frequency of occurrence, especially at high spatial and temporal resolution. The aim of this study is to quantify spatio-temporal changes of compound drought and heat wave events in a large anthropized alpine Italian basin, the Adige basin, located in the North of Italy, with area greater than 10,000km2 and containing a wide range of elevation from 160m to 3905m. We quantify changes in single and multiple drought and heat wave hazards during the period 1980-2018, based on hydrological simulations performed using a recently produced hydrological digital twin model at high spatial (5 km2) and temporal (daily) resolution. The model also includes artificial reservoirs and the combination of high resolution hydrological modeling and compound hazard estimation framework has a key advantage that: i) it captures single hazard evolution at daily time scale and ii) explicitly estimate the dependence between co-occurred events directly mapping critical susceptible regions. Preliminary results show increasing trends in number and severity of compound heat waves and drought events. Ongoing work aim to quantify the spatial distribution of the analysed compound events and the exposure in terms of population impacted and main land cover types. The proposed modeling framework may help improve the prediction and assessment of occurrences of compound heat waves and droughts events and the possible implementation of mitigation actions. The authors are supported by the WATERSTEM MUR PRIN 2020 (Prot. Number 20202WF53Z) and the COACH-WAT PRIN 2022 (Prot. Number 2022FXJ3NN).

Hydrological models are influenced by multiple choices, which can significantly affect all phases of their application. These choices impact the calibration process by influencing the estimation of optimized parameters, the validation phase, and the model's overall performance in forecasting applications. Among these sources, input data, such as meteorological variables, play a pivotal role. While accurate collection and validation of such data are essential, they are often insufficient. For example, in the case of semi-distributed hydrological models applied to a basin divided into multiple hydrological response units (HRUs), most of them typically lack adequate instrumentation. Consequently, it becomes necessary to estimate or simulate meteorological inputs, such as precipitation and air temperature through appropriate geostatistical modeling. Beyond the choice of estimation method, a critical consideration is what constitutes a representative value for an HRU: whether it originates from a single point (e.g. the HRU centroid) or is an appropriate statistic from a grid of points. This decision has substantial implications for the model's computational time, performance, and reliability and can introduce uncertainties in the final modeled product.This study investigates how different configurations of input data may affect model performance in the upper part of the Noce River, located in the Trento province of Italy. The analysis was conducted using the GEOframe framework, its kriging method and semi-distributed model. Four configurations were analyzed moving from the most simplified and computationally convenient (one representative point over the subbasin) towards the most complex (average of gridded values over the subbasin). The effects of the different scenarios are evaluated over several hydrological processes (river discharges, soil moisture, snow evolution), quantifying the trade-offs between computational efficiency and the accuracy of input data representation. The work offers insights into how different configurations can influence the reliability of hydrological forecasts and the uncertainties in the final results.

This study presents a high-resolution (1 km-daily) gridded dataset of potential evapotranspiration (PET) for 2004-2022 across Europe and the Mediterranean. PET estimates are derived from a combination of ground-based meteorological data and remote sensing products. Four PET models are tested, ranging from simple approaches, i.e. the temperature-based Hamon and Priestley-Taylor (PT) models, to more advanced formulations, including the calibration-free Priestley-Taylor (mPT) model and the Penman-Monteith FAO model, both of which explicitly account for aerodynamic and radiative influences. Evaluation against 38 FLUXNET sites and triple collocation analysis with satellite-based PET products demonstrates that FAO and mPT outperform other models across more than 80% of the study domain, with higher accuracy in grasslands, croplands, forests, shrublands, and wetlands. These models also exhibit consistent performance across climate zones, particularly excelling in arid steppe and temperate regions. At the river basin scale, daily crop coefficients are incorporated to refine PET models based on crop growth phases, and these PET estimates are integrated into a hydrological digital twin model of the Adige River basin, a heavily human-influenced watershed in northern Italy. Simulations using FAO and mPT better reproduced key components of the water cycle. Modelled actual evapotranspiration (AET) exhibits a high correlation (0.78) and low RMSE (1.15 mm. day-1) compared to ground-based observations from two FLUXNET sites within the basin. This high-resolution PET dataset represents a valuable resource for water resources management and regional-scale agricultural applications across Europe and the Mediterranean.

Rainfall infiltration plays a crucial role in the near-surface response of soils, influencing other hydrological processes (such as surface and subsurface runoff, groundwater dynamics), and thus determining hydro-geomorphological risk assessment and the water resources management policies. In this study, we investigate the infiltration processes in pyroclastic soils of the Campania region, Southern Italy, by combining measured in situ data, physical laboratory model observations and a 3D physically based hydrological model. First, we validate the numerical model against the soil pore water pressure and soil moisture measured at several points in a small-scale flume of a layered pyroclastic deposit during an infiltration test. The objective is to (i) understand and reproduce the physical processes involved in infiltration in layered volcanoclastic slope and (ii) evaluate the ability of the model to reproduce the measured data and the observed subsurface flow patterns and saturation mechanism. Second, we setup the model on the real site where soil samples were collected and simulate the 3D hydrological response of the hillslope. The aim is to understand and model the dynamics of hydrological processes captured by the field observations and explain the redistribution of water in different layers during 2 years of precipitation. For both applications, a Monte Carlo analysis has been performed to account for the hydrological parameter uncertainty. Results show the capability of the model to reproduce the observations in both applications, with mean KGE of 0.84 and 0.68 for pressure and soil moisture data in the laboratory, and 0.83 and 0.55 in the real site. Our results are significant not only because they provide insight into understanding and simulating infiltration processes in layered pyroclastic slopes but also because they may provide the basis for improving geohazard assessment systems, which are expected to increase, especially in the context of a warming climate. Combining physical model and in situ measurements of soil water content and soil water pressure together with a 3D hydrological models, we detailed and disentangled the infiltrations processes trough layered pyroclastic soils. The finding will be relevant for accurate geo-hydro risk management in a changing climate. image

Quantifying the tendency of flood events to demonstrate clustering in time and space is crucial for flood risk assessments. We analyse the temporal (TC) and spatial (SC) coherence of floods in 554 catchments over Great Britain. TC was assessed using the dispersion-index and Conway-Maxwell-Poisson regression, with both methods applied to aggregation windows of 1-5 years. SC was investigated using the flood susceptibility index. Results show that i) most of the UK peak floods are overdispersed and ii) a positive relationship exists between winter mean North Atlantic Oscillation anomalies and the annual number of peak floods across western Britain. The susceptibility to widespread floods is higher for the southeast parts of Britain and for the Clyde-Forth valleys, and it increases with catchment permeability and with the influence of lakes/reservoirs. These findings are relevant to enhance existing flood hazard estimation methods and, in turn, will lead to more realistic flood risk quantification.