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• Hydrological digital twins (HDT) are essential tools to understand the hydrological cycle especially in a changing climate. • We present, test, and validate an open-source framework to implement HDT in the large anthropized alpine Adige catchment. • The HDT is calibrated/validated on a variety of hydrological processes and data sources providing satisfactory performances. Understanding and simulating the hydrological cycle, especially in a context of climate change, is crucial for quantitative water risk assessment and basin management. The hydrological cycle is complex as it is a combination of non-linear natural processes and anthropogenic influences that alter landforms and water flows. Human-induced changes of relevance, including changes in land uses, construction of dams and artificial reservoirs, and diversion of the river course, lead to changes in water flows throughout the basin. These should be explicitly accounted for a realistic representation of the anthropogenically altered hydrological cycle. Such a realistic representation of the hydrological cycle is a necessary input for the water risk assessment in a particular region. In this paper, we present a hydrological digital twin (HDT) model of a large anthropized alpine basin: the Adige basin located in the northeast of Italy. Most catchments model often overlook land-uses changes over time and forget to model reservoir operation and their influence over time on water flow. Yet, for example, the Adige basin has>30 reservoirs affecting the water flow. We therefore use the GEOframe modeling framework to demonstrate the ability to create a hydrological twin model accounting for these anthropogenic changes. Specifically, we model each component of the water cycle over 39 years (1980–2018) at daily timescale through calibration of the Adige HDT with a multi-site approach using discharge data of 33 stations, based on a high-resolution (1 km) temperature and precipitation dataset and a calculated crop potential evapotranspiration (PETc) dataset, which accounts for human-induced change of the land cover over time. The modeling system also includes the simulation of artificial reservoirs and dams by the dynamically zoned target release (DZTR) reservoir model. The Adige HDT is assessed/validated/compared through a variety of hydrological processes (i.e., river and reservoir discharges, PETc and actual evapotranspiration, snow, and soil moisture) and data sources (i.e., observations and remote sensing data). Overall, the HDT reproduces well the measured discharge in space and time with a Kling Gupta Efficiency (KGE) above 0.7 (0.8) for 30 (23) of the 33 gauge-stations. For 7 artificial reservoirs with available data, the reservoir turbinated discharges are successfully reproduced with an average KGE of 0.92. A comparison between modeled and MODIS remote sensing snow data showed an average error of < 10% across the entire basin; the model also presented a good spatio-temporal agreement both with GLEAMS potential (and actual evapotranspiration) with an average KGE of 0.63 (0.60) and a high-level of correlation (0.5 on average) with the ASCAT satellite retrieved soil moisture. The findings of this paper demonstrate the potential of the open-source, component-based, GEOframe system to build a HDT, to provide a reliable and long term (39 years) estimation of all the water cycle components in a complex anthropized river basin at high spatial resolution. Spatially detailed HDT models results of this type can be used to inform basin-wise adaptation policy decisions and better water management practices in a time of changing climate. [ABSTRACT FROM AUTHOR]

• We propose a new method to quantify Climate Change Effects at hydrological scales. • The method allows to assimilate climatic projections in CDFs at rain gauge scale. • The initial (i.e., before any change scenario begins) skewness plays a crucial role. • A strong increase of outliers frequency is expected for series EV1 alike skewed. Quantifying the potential effects of Climate Change (CC) on hydrological scales is a topic with a more and more increasing interest for the scientific community, given the CC impact on agriculture, industry, economy, human health, ecosystems, among the others. In this context, the paper focuses on the sub-daily Annual Maxima (AM) of rainfall height time series, and specifically on the crucial role played by initial skewness, i.e. the skewness of the observed rainfall series used to evaluate potential parametric trends. Here a quick and user-friendly methodology is proposed, that is aimed at quantifying plausible future changes in terms of probability distributions assumed at rain gauge scale, from projections of any climatic model. In detail, the Generalized Extreme Value (GEV) and the Two-Component Extreme Value (TCEV) distributions are adopted as probability functions suitable for modelling observed rainfall AM series, which could increase their frequency and magnitude into future horizons under CC. EURO-CORDEX projections for Europe are considered, under the hypothesis that the values of the change factor, i.e. the ratio between the values of a specific quantile at two specific time horizons, are invariant when moving from an areally-averaged scale (typical for any climate model) to a point rain gauge scale, which induces that future changes are provided (in terms of frequency and magnitude of extreme events) without the need of any spatial downscaling from the assumed projections. The proposed methodology can contribute to hazard quantification associated to potential climate changes, and thus it can play a crucial role in the assessment of hydraulic structures resilience; the obtained results, specific for the study area of Italy, but easily extendable on a global scale, showed that larger increases in frequency of future heavy events are expected for time series with "EV1 alike" values of initial skewness. [ABSTRACT FROM AUTHOR]

• A new meteorological index (SDDI) based only on ET0 and P is proposed. • SDDI allows evaluating the presence of deficit peaks in the period considered. • Sunflower yield is highly correlated with the indices at the 2-month scale of July. • SPI performance is only slightly lower than the indices that also use evapotranspiration. • The new index SDDI shows good potential in the agricultural drought assessment. In the paper, we tested and compared the potential of some standardized meteorological indices to identify agricultural drought impacts in central Italy. The indices considered are the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Reconnaissance Drought Index (RDI), and the Standardized Deficit Distance Index (SDDI), a new index that is defined and evaluated in this paper. SDDI is a function of the Euclidean distance between the actual (P-ET0) and a reference deficit series (P-ET0 = max). SDDI, unlike other indices, also considers how the deficit is distributed in a certain span and assigns greater severities when the distribution is uneven (e.g., presence of peaks). The comparative analysis refers to 24 provinces of Central Italy and sunflower, a typical non-irrigated crop in the area considered. The sunflower yield time series (1980 – 2019) were de-trended and standardized for each province, finally obtaining Standardized Yield Residuals (SYRs). The climatic data required for calculating the drought indices (precipitation and temperatures) for each province derive from the E-OBS gridded dataset with 0.25° resolution in the period 1980 – 2019. From the minimum and maximum daily temperatures, the ET0 was estimated by the FAO Penman-Monteith equation. The drought indices were calculated for different time scales (from 1 to 5 months) and the months corresponding to the sunflower growing season (April–August). The performance of the various indices in the prediction of SYRs was assessed using the Pearson correlation coefficient. For all the indices, the best correlations are found for the 2-month time scale and for July. SPI's performance is only slightly lower than that of the indices that integrate both precipitation and evapotranspiration (SPEI, RDI, SDDI). Among these, SDDI and SPEI provide somewhat better results considering both the percentage of significant correlation (63% and 67%, respectively) and the corresponding mean correlation (0.49 and 0.48, respectively). SDDI demonstrates good potential in assessing agricultural drought impacts while maintaining the advantage of limited data input. [ABSTRACT FROM AUTHOR]

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

[Display omitted] • A new procedure for Groundwater Flooding Hazard (GFH) assessment has been developed. • A newly developed MATLAB™ code for hazard mapping has been provided. • The procedure is based on a probability model depicting long-term water table fluctuation. • A sixty-two-years piezometric time series and basement depths data were used. • The procedure has been tested and validated in a semi-urban aquifer. Many areas of the world are experiencing groundwater flooding (GF) events with serious impacts on the environment and human health. In Europe, GF has received specific attention with the Directive 2007/60/EC, which requires Member States to map GF hazard and risk and propose mitigation measures. In Italy, despite the commitment assigned by the Legislative Decree 49/2010 to River Basin District Authorities, no models have been developed for groundwater flooding hazard (GFH) evaluation also for the lack of standard methods based on commonly available data. In this paper, a new method has been developed through a specific procedure that accounts for i) probability of GF events estimation based on available time series of groundwater levels and ii) probability mapping through a newly developed MATLAB™ code, high-resolution topographic data and groundwater table model. The methodology has been tested and validated in the Metropolitan City of Naples (Italy), affected by GF since 2010, for which i) sixty-two-year long time series of groundwater levels, ii) high-resolution Digital Elevation and Digital Terrain Models and iii) basement/building geometry, are available. The results show that about 59% of the buildings present in the study area have potentially floodable underground structures. In the 2010–2021 period, only 2% of these buildings were flooded and most of them were built in the period 1980–2000, when the piezometric levels were deeper due to over-pumping of the groundwater. The obtained GFH map shows that only 2% of the built area is involved by 1- to 2- year events, while 6% is affected by 1- to 10- year events. A good spatial correspondence between the return period and locations of GF events occurred during 2010–2021 period was observed, thus indicating the significant performance of the model. The obtained GFH map output can support decisions of authorities and policy-makers to reduce the GF risk. [ABSTRACT FROM AUTHOR]

The concurrence of flash floods and debris flows is of particular concern, because it may amplify the hazard corresponding to the individual generative processes. This paper presents a coupled modelling framework for the predictions of flash flood response and of the occurrence of debris flows initiated by channel bed mobilization. The framework combines a spatially distributed flash flood response model and a debris flow initiation model to define a threshold value for the peak flow which permits identification of channelized debris flow initiation. The threshold is defined over the channel network as a function of the upslope area and of the local channel bed slope, and it is based on assumptions concerning the properties of the channel bed material and of the morphology of the channel network. The model is validated using data from an extreme rainstorm that impacted the 140 km 2 Vizze basin in the Eastern Italian Alps on August 4–5, 2012. The results show that the proposed methodology has improved skill in identifying the catchments where debris-flows are triggered, compared to the use of simpler thresholds based on rainfall properties. [ABSTRACT FROM AUTHOR]

This paper analyzes the historical precipitation and temperature trends and the future climate projections with reference to the northern part of Tuscany (Italy). The trends are identified and quantified at monthly and annual scale at gauging stations with data collected for long periods (60–90 years). An ensemble of 13 Regional Climate Models (RCMs), based on two Representative Concentration Pathways (RCP4.5 and RCP8.5), was then used to assess local scale future precipitation and temperature projections and to represent the uncertainty in the results. The historical data highlight a general decrease of the annual rainfall at a mean rate of 22 mm per decade but, in many cases, the tendencies are not statistically significant. Conversely, the annual mean temperature exhibits an upward trend, statistically significant in the majority of cases, with a warming rate of about 0.1 °C per decade. With reference to the model projections and the annual precipitation, the results are not concordant; the deviations between models in the same period are higher than the future changes at medium- (2031–2040) and long-term (2051–2060) and highlight that the model uncertainty and variability is high. According to the climate model projections, the warming of the study area is unequivocal; a mean positive increment of 0.8 °C at medium-term and 1.1 °C at long-term is expected with respect to the reference period (2003–2012) and the scenario RCP4.5; the increments grow to 0.9 °C and 1.9 °C for the RCP8.5. Finally, in order to check the observed climate change signals, the climate model projections were compared with the trends based on the historical data. A satisfactory agreement is obtained with reference to the precipitation; a systematic underestimation of the trend values with respect to the models, at medium- and long-term, is observed for the temperature data. [ABSTRACT FROM AUTHOR]

• Accurate mapping of dam failure-induced floods is crucial to increase population preparedness. • A procedure based on high-resolution shallow water modelling is described. • The adopted GPU shallow water model copes with the challenges of the simulated phenomenon. • The procedure is applied to two dams in northern Italy. This paper presents a procedure based on high-resolution shallow water modelling for assessing dam-failure hypothetical scenarios that can be useful for designing emergency action plans. The capability of accurately predicting the areas flooded by these catastrophic events, both in terms of timing and magnitude, represents indeed a key task for emergency planning. The proposed procedure relies on the adoption of a parallel two-dimensional shallow water numerical model capable of both preventing large computational times and enabling the use of high-resolution meshes without introducing approximations in the set of the governing equations. Criteria for selecting the numerical model, the input data such as bathymetry, and the initial conditions are analysed in detail. The procedure is adopted to model the flooding scenarios induced by the failure of two dams in northern Italy built for irrigation and flood mitigation purposes, respectively. [ABSTRACT FROM AUTHOR]

• Cumulative rain depths overcome the lack of temporal memory of Feed Forward NN. • The Softmax function in the ML models enables the probabilistic nowcasts. • 95 models were independently trained for lead times up to 6 h. • The models provided better nowcasts when the extension of the event was homogeneous. • According to physical models, as the lead-time increases, predictive accuracy decreases. Rainfall nowcasting supports emergency decision-making in hydrological, agricultural, and economical sectors. However, short-term prediction is challenging because meteorological variables are strongly interconnected and rapidly change during an event. Since machine learning do not require any previous physical assumption, this study investigates their ability to provide reliable and quick forecasts. This paper proposes a machine learning model for probabilistic rainfall nowcasting at 10 min intervals for short lead times - from 30 min up to 6 h. The model employs cumulative rainfall fields from station data as inputs for a feed forward neural network to predict rainfall interval and the corresponding probability of occurrence. Cumulative rainfall depths from station data were used to overcome the lack of temporal memory of the feed forward neural networks. In this way, using only the current rain field as input, the model exploited pattern recognition techniques combining both temporal - cumulative rainfall depth - and spatial - cumulative rainfall field – information. Based on 359 rain events observed in an area of 1619 km2 in Southern Italy, 95 machine learning models were independently trained for 19 recording stations and for each target lead-time (30, 60, 120, 180 and 360 min). Comprehensive nowcasts verification was performed to analyse the reliability of probabilistic nowcasts using both continuous (RMSE and RAE) and categorical (POD, FAR and CSI) indicators. The performance of the models was also compared with the results of Eulerian Persistence. All the models produced consistent nowcasts and learnt the complex relationship describing space–time rainfall evolution. As expected, predictive accuracy gradually decreased as the lead-time increase, according to physically based models. Results showed that the use of both temporal and spatial information enables the model to predict short-term rainfall using only the current measurements as input, resulting in a rapid, easily replicable and convenient nowcasting approach. The procedure is an effective way to predict multi-step rainfall fields and is suitable for operational early warning system. [ABSTRACT FROM AUTHOR]

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

• New expressions for ARF estimation based on a storm-centered approach are proposed. • Precipitation systems representative of typical Mediterranean conditions are considered. • The proposed approach is compared with some of the most widespread literature ones. The areal reduction factor (ARF) is a concept widely used in many hydrological applications. In this paper, assuming a "storm-centered" approach, new empirical laws for ARF estimation have been derived for the Calabria region (southern Italy), which are distinguished according to rainfall generation mechanisms typical of the Mediterranean area. Specifically, using data from the regional rain gauge network for the period 2002–2015, extreme events were separated into Convective Cells and Frontal Systems through a heuristic criterion to identify type-dependent ARFs. Significant differences were observed: more intense convective precipitation events evidenced a more pronounced decrease in empirical ARFs than storms related to frontal systems, with reductions up to approximately 50% for the median values at about 2000 km2. Results were also compared to some of the most widely used "fixed-area" laws for ARF evaluation; overall, these lead to larger values compared to storm-centered ARFs derived for the investigated region. The proposed storm-centered methodology can find important applications in providing reference limits of ARF values to be properly used in the engineering design of hydraulic and hydrological infrastructures, and appears particularly suited for effective and reliable flood defense in early warning systems. [ABSTRACT FROM AUTHOR]

• Percolation efficiency in rice areas is higher for winter than for summer flooding. • Winter percolation rates are site-specific and higher or lower than summer by case. • Local groundwater under winter flooded areas reaches similar levels than in summer. • Groundwater decrease rate after end of flooding is faster in winter than in summer. • Larger winter flooded areas could maintain higher groundwater levels in summer. Recent research suggests that groundwater overdraft can be mitigated by application of water onto agricultural lands for direct groundwater recharge. This technique is often called Agricultural Managed Aquifer Recharge (Ag-MAR) and is carried out in wintertime. Winter flooding of rice paddies could be considered as a form of Ag-MAR. Currently, it is implemented in Europe as an environmentally friendly practice, which could also bring many agronomic and hydrological benefits; however, especially the latter aspects have been poorly investigated in the past. Results of a hydrological study focused on winter flooding in three rice areas in northern Italy and lasting 18 months (winter seasons 2017–2018 and 2018–2019, and summer season 2018) are presented in this paper. The objective of winter flooding was to maintain higher groundwater levels till the beginning of the agricultural season in order to increase rice irrigation efficiency and protect some groundwater dependent ecosystems. Main findings are: (1) the percolation efficiency of winter flooding is very high (92–97% of the water volume applied on the fields); (2) local groundwater levels underneath the recharge areas increase at a similar rate as during the summer flooding and reach peak values that are close; (3) however, the rate of level decrease after the end of the flooding period is much faster with the winter flooding, when pre-flooding groundwater levels are re-established in 1 month, in contrast with at least 2 months with the summer flooding: this is due to the fact that the areas adopting winter flooding are less extended and more fragmented than the ones flooded during the agricultural season; (4) consequently, to maintain higher groundwater levels until the beginning of the cropping season, winter flooding should last longer and involve larger areas. [ABSTRACT FROM AUTHOR]

• Available rainfall data are characterized by different time resolution, "ta" • A database involving metadata from many geographic areas is presented. • The "ta" history of rainfall data in a variety of rain gauges is reconstructed. • The registration methods of the rainfall data changed over the years. • Currently about 50% of rain gauge stations provide data with any "ta" Collected rainfall records by gauges lead to key forcings in most hydrological studies. Depending on sensor type and recording systems, such data are characterized by different time-resolutions (or temporal aggregations), t a. We present an historical analysis of the time-evolution of t a based on a large database of rain gauge networks operative in many study areas. Globally, t a data were collected for 25,423 rain gauge stations across 32 geographic areas, with larger contributions from Australia, USA, Italy and Spain. For very old networks early recordings were manual with coarse time-resolution, typically daily or sometimes monthly. With a few exceptions, mechanical recordings on paper rolls began in the first half of the 20th century, typically with t a of 1 h or 30 min. Digital registrations started only during the last three decades of the 20th century. This short period limits investigations that require long time-series of sub-daily rainfall data, e.g, analyses of the effects of climate change on short-duration (sub-hourly) heavy rainfall. In addition, in the areas with rainfall data characterized for many years by coarse time-resolutions, annual maximum rainfall depths of short duration can be potentially underestimated and their use would produce errors in the results of successive applications. Currently, only 50% of the stations provide useful data at any time-resolution, that practically means t a = 1 min. However, a significant reduction of these issues can be obtained through the information content of the present database. Finally, we suggest an integration of the database by including additional rain gauge networks to enhance its usefulness particularly in a comparative analysis of the effects of climate change on extreme rainfalls of short duration available in different locations. [ABSTRACT FROM AUTHOR]

• We propose a new practice for flood hazard mapping in presence of multiple gauging stations. • The method uses an original interpolation/assignation coded algorithm for multiple probability models fusion. • The use of LiDAR data and multiple probability models increases the reliability of the hazard map. • The method has the potential to support flood hazard mapping over large areas. • Comparison with 2D hydraulic simulation confirm the high performance of the proposed approach. Hazard mapping is essential for risk assessment and mitigation measurement design in flood prone areas. In Europe, long-term fluvial stage data, acquired since the 18th century, represent a resource of fundamental importance in this perspective, especially where rivers monitoring is completed by multiple stations distributed along the course. In these conditions, a major challenge is represented by the possibility of incorporating multiple probability models, representative of river dynamics at different distance from the mouth, in flood hazard estimation over so large areas. In this paper, we propose a new procedure of hazard estimation based on LiDAR derived flood inundation model and multiple hydrometric time series that, using a specifically developed algorithm/code of interpolation/assignation of multiple probability models, has the potential to work at local to national scale providing reliable estimation also in presence of urban areas. We applied the developed procedure and associated algorithm/code to a selected study area in southern Italy, recently hit by a destructive flood event, and quantitatively evaluate model performance. Confidence interval computation provides an overview of uncertainty related to flood magnitude estimation by extreme value analysis, indicating a substantial uncertainty related to 500 years flood magnitude estimation. Sensitivity analysis indicates a high degree of robustness of the developed procedure. Result validation through comparison against the observed 2015 flood event indicates that the method has the potential to support flood hazard analysis at regional to national scale. Limits of method application are related to the basic assumption of stationarity of hydrologic time series that might be considered too "simplicistic" in a changing climate also related to the limited length of some time series that only in few cases have no discontinuities. The absence of propagation modelling as part of the estimation procedure might be considered as an additional limit since in complex topographic and hydrological conditions it might provide a better evaluation of flood hazard. However, comparison of the 500 years flood derived from our procedure and 500 years flood scenarios derived by 2D hydraulic simulations indicate the capabilities of our procedure in identifying area floodable by specific events with only local overestimation that generally increase safety in human life protection perspective. This confirms the potential of considering multiple probability models distributed along the river course in flood hazard estimation perspective and indicate that our procedure can be a valid alternative to simulation based flood hazard estimation procedures. [ABSTRACT FROM AUTHOR]

• Thresholds to separate erosive storms are developed and tested from rainfall data. • Thresholds to separate rill storms are developed and tested from rainfall data. • Rainfall pattern characteristics are effective in separating erosive and rill events. • Thresholds changes with soil characteristics and plot steepness. • Thresholds changes with the level of accuracy in estimating rainfall erosivity. In this paper, 1017 rainfall events from 2008 to 2017 are used to identify the rainfall threshold that produces upland erosion at the Masse (central Italy) and Sparacia (southern Italy) experimental stations. The rainfall events are classified into three classes: non-erosive, interrill-only and rill. The threshold values for separating as correctly as possible the erosive rains (case I) and the rill rains (case II) are derived solely from the hyetograph. Each threshold value is obtained by imposing that the long-term erosivity of the events above the threshold is equal to the long-term erosivity of all erosive events (case I) or only rill events (case II). The performances of selective criteria based on 31 threshold variables are compared, and those most effective in separating erosive and rill events are identified. The identification of the best criterion depends on the aim of the analysis. It could be required to provide the greatest accuracy for separating erosive and rill events or the lowest error in the prediction of long-term erosivity. In general, the results clearly show that the best variables are those that quantify the characteristics of rainfall patterns, such as rain showers (periods of continuous rain) and the deviations in the rain records over a truncation level. These results are especially significant for the operational estimation of rainfall erosivity and for identifying the trigger of the erosion process and rill development by using only a hyetograph. This is obtained by relatively simple field measurements and is also widely available on a global scale. The most effective variables are potentially usable in water erosion prediction models as proxies of variables that are more rarely available and/or more difficult to measure. [ABSTRACT FROM AUTHOR]

• The kinetic power per unit volume of rainfall is related to the mean drop diameter. • The kinetic power is accurately estimated by the median volume diameter. • The theoretical kinetic power-median volume diameter equation is not site-specific. • The theoretical equation is independent of measurement technique. • Median volume diameter is estimated by mean diameter and its standard deviation. Empirical relationships linking kinetic energy to rainfall intensity are commonly used to estimate rainfall erosivity. In this paper, using the drop-size distribution (DSD) proposed by Marshall and Palmer, a relationship for calculating the rainfall kinetic power was firstly deduced. This equation requires the simultaneous measurement of both the median volume diameter and the rainfall intensity and its application also reveals the drawback of needing the measurement of the whole DSD by an expensive disdrometric technique. Then, using a fictitious precipitation, which is both constituted by droplets having the same diameter (mean volume diameter) and having the same volume of the actual rainfall, a new expression of the rainfall kinetic power was deduced. This last equation has the advantage of allowing the estimate of the rainfall kinetic power by using the measurements of both rainfall intensity and the total number of droplets, reaching a horizontal unit area in an unit time, which can be acquired by a cheap device using a piezoelectric force transducer. This new relationship for estimating the rainfall kinetic power was tested using disdrometric measurements carried out at two experimental sites in Italy (Palermo) and in Spain (El Teularet). These experimental tests demonstrated that the rainfall kinetic power estimated by the mean volume diameter has an accuracy similar to the literature relationships proposed by Wischmeier and Smith, Brown and Foster and McGregor et al. Finally this investigation also showed that this novel approach can be applied estimating the median volume diameter by the mean volume diameter and the standard deviation of the measured drop diameters, whose measurement can be obtained by a force transducer. [ABSTRACT FROM AUTHOR]