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The majority of rainfall measurements in the world is at the daily scale, i.e. related to a specific calendar day and measured over fixed 24 h. On these data, daily annual maximum rainfall depths (F-maxima) series are easily obtained. On the other hand, 24 h annual maximum rainfall depths (S-maxima), which refer to a period starting at any instant, are more useful indicators. S-maxima values cannot be less than the F-maxima, and are generally higher. The ratio between these extremes, called Hershfield factor (H), has been studied to move from F-maxima to S-maxima, allowing to take advantage of the relevant amount of information included in historical records of daily extremes. For instance, before 1980, in the Italian Hydrological Yearbooks only a subset (< 50 %) of the rain gauges was equipped with a recording device, from which annual maxima over 1, 3, 6, 12 and 24 consecutive hours can be derived. In this study we investigate the possibility of using F-maxima to complement the S-maxima records related to the Po river basin and the Liguria region (North of Italy). As a first step we retrieved from official databases all the daily rainfall measurements, available over this area, from early 1900 until today and we quality-controlled the measurements. We then computed the annual H for all the stations and all the years where both the F- and S-maxima were available, to obtain data that can be analyzed in their temporal and spatial variability. The spatial distribution of the Hershfield factor shows values similar to the ones suggested in the literature and is related to the geographic position of the stations, allowing the possibility to identify some distinct areas with positive or negative anomalies. The obtained map of the H factor, with interpolated local anomalies, allows to reconstruct the missing S-maxima in stations with only F-maxima, and improve the knowledge of the spatial variability of sub-daily rainfall extremes.

Here we present FOCA (Italian FlOod and Catchment Atlas), the first systematic collection of data on Italian river catchments for which historical discharge time series are available. Hydrometric information, including the annual maximum peak discharge and average daily annual maximum discharge, is complemented by several geomorphological, climatological, extreme rainfall, land-cover and soil-related catchment attributes. All hydrological information derives from the most recently released datasets of discharge and rainfall measurements. To enhance the reproducibility and transferability of the analysis, this paper provides a description of all the raw data and the algorithms used to build the basin attribute dataset. We also describe the approaches adopted to solve problems encountered during the digital elevation model elaboration in areas characterized by a complex morphology. Details about the data quality-control procedure developed to detect and correct errors are also reported. One of the main novelties of FOCA with respect to other national-scale datasets is the inclusion of a rich set of geomorphological attributes and extreme rainfall features for a large set of basins covering a wide range of elevations and areas. Using this first nationwide data collection (available at https://doi.org/10.5281/zenodo.10446258, Claps et al., 2023), a wide range of environmental applications, with a particular focus on flood studies, can be undertaken within the Italian territory.

The dependence of rainfall on elevation has frequently been documented in the scientific literature and may be relevant in Italy, due to the high degree of geographical and morphological heterogeneity of the country. However, a detailed analysis of the spatial variability of short-duration annual maximum rainfall depths and their connection to the landforms does not exist. Using a new, comprehensive and position-corrected rainfall extreme dataset (I2-RED, the Improved Italian-Rainfall Extreme Dataset), we present a systematic study of the relationship between geomorphological forms and the average annual maxima (index rainfall) across the whole of Italy. We first investigated the dependence of sub-daily rainfall depths on elevation and other landscape indices through univariate and multivariate linear regressions. The results of the national-scale regression analysis did not confirm the assumption of elevation being the sole driver of the variability of the index rainfall. The inclusion of longitude, latitude, distance from the coastline, morphological obstructions and mean annual rainfall contributes to the explanation of a larger percentage of the variance, even though this was in different ways for different durations (1 to 24 h). After analyzing the spatial variability of the regression residuals, we repeated the analysis on geomorphological subdivisions of Italy. Comparing the results of the best multivariate regression models with univariate regressions applied to small areas, deriving from morphological subdivisions, we found that “local” rainfall–topography relationships outperformed the country-wide multiple regressions, offered a uniform error spatial distribution and allowed the effect of morphology on rainfall extremes to be better reproduced.

Like other Mediterranean areas, Italy is prone to the development of events with significant rainfall intensity, lasting for several hours. The main triggering mechanisms of these events are quite well known, but the aim of developing rainstorm hazard maps compatible with their actual probability of occurrence is still far from being reached. A systematic frequency analysis of these occasional highly intense events would require a complete countrywide dataset of sub-daily rainfall records, but this kind of information was still lacking for the Italian territory. In this work several sources of data are gathered, for assembling the first comprehensive and updated dataset of extreme rainfall of short duration in Italy. The resulting dataset, referred to as the Italian Rainfall Extreme Dataset (I-RED), includes the annual maximum rainfalls recorded in 1 to 24 consecutive hours from more than 4500 stations across the country, spanning the period between 1916 and 2014. A detailed description of the spatial and temporal coverage of the I-RED is presented, together with an exploratory statistical analysis aimed at providing preliminary information on the climatology of extreme rainfall at the national scale. Due to some legal restrictions, the database can be provided only under certain conditions. Taking into account the potentialities emerging from the analysis, a description of the ongoing and planned future work activities on the database is provided.

Planning and verification of hydraulic infrastructures require a design estimate of hydrologic variables, usually provided by frequency analysis, and neglecting hydrologic uncertainty. However, when hydrologic uncertainty is accounted for, the design flood value for a specific return period is no longer a unique value, but is represented by a distribution of values. As a consequence, the design flood is no longer univocally defined, making the design process undetermined. The Uncertainty Compliant Design Flood Estimation (UNCODE) procedure is a novel approach that, starting from a range of possible design flood estimates obtained in uncertain conditions, converges to a single design value. This is obtained through a cost–benefit criterion with additional constraints that is numerically solved in a simulation framework. This paper contributes to promoting a practical use of the UNCODE procedure without resorting to numerical computation. A modified procedure is proposed by using a correction coefficient that modifies the standard (i.e., uncertainty-free) design value on the basis of sample length and return period only. The procedure is robust and parsimonious, as it does not require additional parameters with respect to the traditional uncertainty-free analysis. Simple equations to compute the correction term are provided for a number of probability distributions commonly used to represent the flood frequency curve. The UNCODE procedure, when coupled with this simple correction factor, provides a robust way to manage the hydrologic uncertainty and to go beyond the use of traditional safety factors. With all the other parameters being equal, an increase in the sample length reduces the correction factor, and thus the construction costs, while still keeping the same safety level.

BACKGROUND AND OBJECTIVE: The role of cytoreductive nephrectomy (CN) in the treatment of metastatic renal cell carcinoma (mRCC) has been called into question on the basis of clinical trial data from the tyrosine kinase inhibitor (TKI) era. Comparative analyses of CN for patients treated with immuno-oncology (IO) versus TKI agents are sparse. Our objective was to compare CN timing and outcomes among patients who received TKI versus IO therapy. METHODS: This was a multicenter retrospective analysis of patients who underwent CN using data from the REMARCC (Registry of Metastatic RCC) database. The cohort was divided into TKI versus IO first-line therapy groups. The primary outcome was all-cause mortality (ACM). Secondary outcomes included cancer-specific mortality (CSM). Multivariable analysis was used to identify factors predictive for ACM and CSM. The Kaplan-Meier method was used to analyze 5-yr overall survival (OS) and cancer-specific survival (CSS) with stratification by primary systemic therapy and timing in relation to CN. KEY FINDINGS AND LIMITATIONS: We analyzed data for 189 patients (148 TKI + CN, 41 IO +CN; median follow-up 23.2 mo). Multivariable analysis revealed that a greater number of metastases (hazard ratio [HR] 1.06; p = 0.015), greater primary tumor size (HR 1.10; p = 0.043), TKI receipt (HR 2.36; p = 0.015), and initiation of systemic therapy after CN (HR 1.49; p = 0.039) were associated with worse ACM. A greater number of metastases at diagnosis (HR 1.07; p = 0.011), greater primary tumor size (HR 1.12; p = 0.018), TKI receipt (HR 5.43; p = 0.004), and initiation of systemic therapy after CN (HR 2.04; p

The flow duration curve (FDC) is a fundamental signature of the hydrological cycle to support water management strategies. Despite many studies on this topic, its estimation in ungauged basins is still a relevant issue as the FDC is controlled by different types of processes at different time-space scales, thus resulting quite sensitive to the specific case study. In this work, a regional spatially-smooth procedure to evaluate the annual FDC in ungauged basins is proposed, based on the estimation of the L-moments (mean, L-CV and L-skewness) through regression models valid for the whole case study area. In this approach, homogeneous regions are no longer required and the L-moments are allowed to continuously vary along the river network, thus providing a final FDC smoothly evolving for different locations on the river. Regressions are based on a set of topographic, climatic, land use and vegetation descriptors at the basin scale. Moreover, the model ensures that the mean annual runoff is preserved at the river confluences, i.e. the sum of annual flows of the upstream reaches is equal to the predicted annual downstream flow. The proposed model is adapted to incorporate different "sub-models" to account for local information within the regional framework, where man-induced alterations are known, as common in non-pristine catchments. In particular, we propose a module to consider the impact of existing/designed water withdrawals on the L-moments of the FDC. The procedure has been applied to a dataset of daily observation of about 120 gauged basins on the upper Po river basin in North-Western Italy.

The current work addresses one of the key building blocks towards an improved understanding of flood processes and associated changes in flood characteristics and regimes in Europe: the development of a comprehensive, extensive European flood database. The presented work results from ongoing cross-border research collaborations initiated with data collection and joint interpretation in mind. A detailed account of the current state, characteristics and spatial and temporal coverage of the European Flood Database, is presented. At this stage, the hydrological data collection is still growing and consists at this time of annual maximum and daily mean discharge series, from over 7000 hydrometric stations of various data series lengths. Moreover, the database currently comprises data from over 50 different data sources. The time series have been obtained from different national and regional data sources in a collaborative effort of a joint European flood research agreement based on the exchange of data, models and expertise, and from existing international data collections and open source websites. These ongoing efforts are contributing to advancing the understanding of regional flood processes beyond individual country boundaries and to a more coherent flood research in Europe.

Even in regions with mature hydropower development, requirements for stable renewable power sources suggest revision of plans of exploitation of water resources, while taking care of the environmental regulations. Mean Annual Flow (MAF) is a key parameter when trying to represent water availability for hydropower purposes. MAF is usually determined in ungauged basins by means of regional statistical analysis. For this study a regional estimation method consistent along-the-river network has been developed for MAF estimation; the method uses a multi-regressive approach based on geomorphoclimatic descriptors, and it is applied on 100 gauged basins located in NW Italy. The method has been designed to keep the estimates of mean annual flow congruent at the confluences, by considering only raster-summable explanatory variables. Also, the influence of human alterations in the regional analysis of MAF has been studied: impact due to the presence of existing hydropower plants has been taken into account, restoring the "natural" value of runoff through analytical corrections. To exemplify the representation of the assessment of residual hydropower potential, the model has been applied extensively to two specific mountain watersheds by mapping the estimated mean flow for the basins draining into each pixel of a the DEM-derived river network. Spatial algorithms were developed using the OpenSource Software GRASS GIS and PostgreSQL/PostGIS. Spatial representation of the hydropower potential was obtained using different mean flow vs hydraulic-head relations for each pixel. Final potential indices have been represented and mapped through the Google Earth platform, providing a complete and interactive picture of the available potential, useful for planning and regulation purposes.

There is growing concern that flooding is becoming more frequent and severe in Europe. A better understanding of flood regime changes and their drivers is therefore needed. The paper reviews the current knowledge on flood regime changes in European rivers that has traditionally been obtained through two alternative research approaches. The first approach is the data-based detection of changes in observed flood events. Current methods are reviewed together with their challenges and opportunities. For example, observation biases, the merging of different data sources and accounting for nonlinear drivers and responses. The second approach consists of modelled scenarios of future floods. Challenges and opportunities associated with flood change scenarios are discussed such as fully accounting for uncertainties in the modelling cascade and feedbacks. To make progress in flood change research, we suggest that a synthesis of these two approaches is needed. This can be achieved by focusing on long duration records and flood-rich and flood-poor periods rather than on short duration flood trends only, by formally attributing causes of observed flood changes, by validating scenarios against observed flood regime dynamics, and by developing low-dimensional models of flood changes and feedbacks. The paper finishes with a call for a joint European flood change research network.

Recent studies on fluxes of virtual water are showing how the global food and goods trade interconnects the water resources of different and distant countries, conditioning the local water balances. This paper presents and discusses the assessment of virtual water fluxes between a single country and its network of trading partners, delineating a country's virtual water budget in space and time (years 1986–2010). The fluxes between the country under study and its importing/exporting partners are visualized with a geographical representation shaping the trade network as a virtual river/delta. Time variations of exchanged fluxes are quantified to show possible trends in the virtual water balance, while characterizing the time evolution of the trade network and its composition in terms of product categories (plant-based, animal-based, luxury food, and non-edible). The average distance traveled by virtual water to arrive to the place of consumption is also introduced as a new measure for the analysis of globalization of the virtual water trade. Using Italy as an example, we find that food trade has a steadily growing importance compared to domestic production, with a major component represented by plant-based products, and luxury products taking an increasingly larger share (26% in 2010). In 2010 Italy had an average net import of 55 km3 of virtual water (38 km3 in 1986), a value which poses the country among the top net importers in the world. On average each cubic meter of virtual water travels nearly 4000 km before entering Italy, while export goes to relatively closer countries (average distance: 2600 km), with increasing trends in time which are almost unique among the world countries. Analyses proposed for Italy are replicated for 10 other world countries, triggering similar investigations on different socio-economic actualities.

This paper deals with the seasonality of hydroclimatic extremes and with the problem of accounting for their non-homogeneous character in determining the design value. To this aim we devise a simple stochastic experiment in which extremes are produced by a non-homogeneous extreme value generation process. The design values are estimated in closed analytical form both in a peak over threshold framework and by using the standard annual maxima approach. In this completely controlled world of generated hydrological extremes, a statistical measure of the error associated to the adoption of a homogeneous model is introduced. The sensitivity of this measure, named return period ratio, to the typology and strength of seasonality is investigated. We find that neglecting seasonality induces a downward bias in design value estimators. The magnitude of the bias may be large when the peak over threshold approach is adopted, while the return period distortion is limited when the annual maxima are considered. An application to rainfall data from a 30 000 km2 region located in North-Western Italy is presented to better clarify the effects of disregarding seasonality in a real case.

We develop a water balance model, parsimonious both in terms of parameterization and of required input data, to characterize the average runoff regime of high-elevation and scarcely monitored basins. The model uses a temperature threshold to partition precipitation into rainfall and snowfall, and to estimate evapotranspiration volumes. The role of snow in the transformation of precipitation into runoff is investigated at the monthly time scale through a specific snowmelt module that estimates melted quantities by a non-linear function of temperature. A probabilistic representation of temperature is also introduced, in order to mimic its sub-monthly variability. To account for the commonly reported rainfall underestimation at high elevations, a two-step precipitation adjustment procedure is implemented to guarantee the closure of the water balance. The model is applied to a group of catchments in the North-Western Italian Alps, and its performances are assessed by comparing measured and simulated runoff regimes both in terms of total bias and anomalies, by means of a new metric, specifically conceived to compare the shape of the two curves. The obtained results indicates that the model is able to predict the observed runoff seasonality satisfactorily, notwithstanding its parsimony (the model has only two parameters to be estimated). In particular, when the parameter calibration is performed separately for each basin, the model proves to be able to reproduce the runoff seasonality. At the regional scale (i.e., with uniform parameters for the whole region), the performance is less positive, but the model is still able to discern among different mechanisms of runoff formation that depend on the role of the snow storage. Because of its parsimony and the robustness in the approach, the model is suitable for application in ungauged basins and for large scale investigations of the role of climatic variables on water availability and runoff timing in mountainous regions.

Validation of probabilistic models based on goodness-of-fit tests is an essential step for the frequency analysis of extreme events. The outcome of standard testing techniques, however, is mainly determined by the behavior of the hypothetical model, FX(x), in the central part of the distribution, while the behavior in the tails of the distribution, which is indeed very relevant in hydrological applications, is relatively unimportant for the results of the tests. The maximum-value test, originally proposed as a technique for outlier detection, is a suitable, but seldom applied, technique that addresses this problem. The test is specifically targeted to verify if the maximum (or minimum) values in the sample are consistent with the hypothesis that the distribution FX(x) is the real parent distribution. The application of this test is hindered by the fact that the critical values for the test should be numerically obtained when the parameters of FX(x) are estimated on the same sample used for verification, which is the standard situation in hydrological applications. We propose here a simple, analytically explicit, technique to suitably account for this effect, based on the application of censored L-moments estimators of the parameters. We demonstrate, with an application that uses artificially generated samples, the superiority of this modified maximum-value test with respect to the standard version of the test. We also show that the test has comparable or larger power with respect to other goodness-of-fit tests (e.g., chi-squared test, Anderson-Darling test, Fung and Paul test), in particular when dealing with small samples (sample size lower than 20–25) and when the parent distribution is similar to the distribution being tested.

Winter snowfall and its temporal variability are important factors in the development of water management strategies for snow-dominated regions. For example, mountain regions of Europe rely on snow for recreation, and on snowmelt for water supply and hydropower. It is still unclear whether in these regions the snow regime is undergoing any major significant change. Moreover, snow interannual variability depends on different climatic variables, such as precipitation and temperature, and their interplay with atmospheric and pressure conditions. This paper uses the EASE Grid weekly snow cover and Ice Extent database from the National Snow and Ice Data Center to assess the possible existence of trends in snow cover across Europe. This database provides a representation of snow cover fields in Europe for the period 1972–2006 and is used here to construct snow cover indices, both in time and space. These indices allow us to investigate the historical spatial and temporal variability of European snow cover fields, and to relate them to the modes of climate variability that are known to affect the European climate. We find that both the spatial and temporal variability of snow cover are strongly related to the Arctic Oscillation during wintertime. In the other seasons, weaker correlation appears between snow cover and the other patterns of climate variability, such as the East Atlantic, the East Atlantic West Russia, the North Atlantic Oscillation, the Polar Pattern and the Scandinavian Pattern.

The operational use of weather radars has become a widespread and useful tool for estimating rainfall fields. The radar-gauge adjustment is a commonly adopted technique which allows one to reduce bias and dispersion between radar rainfall estimates and the corresponding ground measurements provided by rain gauges. This paper investigates a new methodology for estimating radar-based rainfall fields by recalibrating at each time step the reflectivity-rainfall rate (Z-R) relationship on the basis of ground measurements provided by a rain gauge network. The power-law equation for converting reflectivity measurements into rainfall rates is readjusted at each time step, by calibrating its parameters using hourly Z-R pairs collected in the proximity of the considered time step. Calibration windows with duration between 1 and 24 h are used for estimating the parameters of the Z-R relationship. A case study pertaining to 19 rainfall events occurred in the north-western Italy is considered, in an area located within 25 km from the radar site, with available measurements of rainfall rate at the ground and radar reflectivity aloft. Results obtained with the proposed method are compared to those of three other literature methods. Applications are described for a posteriori evaluation of rainfall fields and for real-time estimation. Results suggest that the use of a calibration window of 2–5 h yields the best performances, with improvements that reach the 28% of the standard error obtained by using the most accurate fixed (climatological) Z-R relationship.

The European Alps rely on winter precipitation for various needs in terms of hydropower and other water uses. Major European rivers originate from the Alps and depend on winter precipitation and the consequent spring snow melt for their summer base flows. Understanding the fluctuations in winter rainfall in this region is crucially important to the study of changes in hydrologic regime in river basins, as well as to the management of their water resources. Despite the recognized relevance of winter precipitation to the water resources of the Alps and surrounding regions, the magnitude and mechanistic explanation of interannual precipitation variability in the Alpine region remains unclear and poorly investigated. Here we use gridded precipitation data from the CRU TS 1.2 to study the interannual variability of winter alpine precipitation. We found that the Alps are the region with the highest interannual variability in winter precipitation in Europe. This variability cannot be explained by large scale climate patterns such as the Arctic Oscillation (AO), North Atlantic Oscillation (NAO) or the East Atlantic/West Russia (EA/WR), even though regions below and above the Alps demonstrate connections with these patterns. Significant trends were detected only in small regions located in the Eastern part of the Alps.

The variability of the second order moments of flood peaks with respect to geomorphoclimatic basin characteristics was investigated. In particular, the behaviour of the coefficient of variation (Cv) of the series of annual maximum floods was analysed with respect to its dependence on physically consistent quantities. The results achieved were in fairly good agreement with real world observed characteristics and interesting insights on the relationship between Cv and basin size were found. It appears that Cv is controlled mainly by the climate and by some water loss features. Many observations reported in the literature show a decrease of Cv with basin area A, usually ascribed to the limited spatial extent of extreme events, which leads to a decrease with area of the Cv of areal rainfall intensity. An increase of Cv with the area is also sometimes observed for small basins. Such different behaviours were accounted for by the concurrent effect on two parameters that affect the Cv (A) relationship, representative of the way rainfall losses and effective rainfall intensity scale with the basin area. Keywords: floods, climate, coefficient of variation, scaling.

PurposeThe COVID-19 outbreak has substantially altered residents' training activities. While several new virtual learning programs have been recently implemented, the perspective of urology trainees regarding their usefulness still needs to be investigated.MethodsA cross-sectional, 30-item, web-based Survey was conducted through Twitter from April 4th, 2020 to April 18th, 2020, aiming to evaluate the urology residents' perspective on smart learning (SL) modalities (pre-recorded videos, webinars, podcasts, and social media [SoMe]), and contents (frontal lessons, clinical case discussions, updates on Guidelines and on clinical trials, surgical videos, Journal Clubs, and seminars on leadership and non-technical skills).ResultsOverall, 501 urology residents from 58 countries completed the survey. Of these, 78.4, 78.2, 56.9 and 51.9% of them considered pre-recorded videos, interactive webinars, podcasts and SoMe highly useful modalities of smart learning, respectively. The contents considered as highly useful by the greatest proportion of residents were updates on guidelines (84.8%) and surgical videos (81.0%). In addition, 58.9 and 56.5% of responders deemed seminars on leadership and on non-technical skills highly useful smart learning contents. The three preferred combinations of smart learning modality and content were: pre-recorded surgical videos, interactive webinars on clinical cases, and pre-recorded videos on guidelines.ConclusionOur study provides the first global «big picture» of the smart learning modalities and contents that should be prioritized to optimize virtual Urology education. While this survey was conducted during the COVID-19 outbreak, our findings might have even more impact in the future.

PurposeThe COVID-19 outbreak has substantially altered residents' training activities. While several new virtual learning programs have been recently implemented, the perspective of urology trainees regarding their usefulness still needs to be investigated.MethodsA cross-sectional, 30-item, web-based Survey was conducted through Twitter from April 4th, 2020 to April 18th, 2020, aiming to evaluate the urology residents' perspective on smart learning (SL) modalities (pre-recorded videos, webinars, podcasts, and social media [SoMe]), and contents (frontal lessons, clinical case discussions, updates on Guidelines and on clinical trials, surgical videos, Journal Clubs, and seminars on leadership and non-technical skills).ResultsOverall, 501 urology residents from 58 countries completed the survey. Of these, 78.4, 78.2, 56.9 and 51.9% of them considered pre-recorded videos, interactive webinars, podcasts and SoMe highly useful modalities of smart learning, respectively. The contents considered as highly useful by the greatest proportion of residents were updates on guidelines (84.8%) and surgical videos (81.0%). In addition, 58.9 and 56.5% of responders deemed seminars on leadership and on non-technical skills highly useful smart learning contents. The three preferred combinations of smart learning modality and content were: pre-recorded surgical videos, interactive webinars on clinical cases, and pre-recorded videos on guidelines.ConclusionOur study provides the first global «big picture» of the smart learning modalities and contents that should be prioritized to optimize virtual Urology education. While this survey was conducted during the COVID-19 outbreak, our findings might have even more impact in the future.