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Hazardous hydrological events cause soil erosion and it is essential to anticipate the potential environmental impacts of prevailing erosion processes that occur at different time-scales. Here, we present the modelling of net soil erosion rates for the Bradano River Basin (southern Italy), based on rainfall erosivity, surface overland flow and transport sub-models. A semi-empirical framework was developed, upscaling point rainfall values based on the Foster-Thornes approach in order to give an insight into monthly and annual soil losses over the period 1950–1958 and 1961 (calibration) and over a longer time-frame (1950–2020: reconstruction). In the 2765-km2 study area, ~ 68% of the sediment mobilized within the basin reached the basin outlet (mean value for 1950–2020: ~ 366 Mg km−2 yr−1). A moderate declining trend in net erosion rates was observed after the 1980s, concurrent with the contraction of cropland in favour of natural vegetation and river channelization. Our results suggest that the parsimonious principle used here seems sufficiently robust to be suitable for applications in other Mediterranean landscapes. [ABSTRACT FROM AUTHOR]

Hydrological disasters, such as floods, can have dire consequences for human societies. Historical information plays a key role in detecting whether particular types of hydrological disasters have increased in frequency and/or magnitude and, if so, they are more likely attributable to natural or human-induced climatic and other environmental changes. The identification of regions with similar flood conditions is essential for the analysis of regional flooding regimes. To this end, we here present the longest existing flood reconstruction for the Eastern Liguria Area (ELA) in northwestern Italy, covering 1582 to 2022 CE, which offers a case study representative of the central Mediterranean region. An Annual Flood Intensification Index was developed to transform the historical data into a continuous annual hydrological time-series contained by a homogeneous data structure for the study-area. We found two change-points (trend breaks) in the reconstructed time-series, in 1787 and 1967, with only occasional heavy floods comparable to present-day disasters occurring before the first change-point, and an increasing intensification of floods after the second change-point up to the present day. The recent intensification of flooding in the ELA, associated with changes in land use and land cover, also appears to coincide with phases in which hydrological hazards have become more changeable and extreme in disaster-affected areas. This is evidenced by river basin responses to human-induced disturbances. [ABSTRACT FROM AUTHOR]

Rainfall erosivity drives damaging hydrological events with significant environmental and socio-economic impacts. This study presents the world's hitherto longest time-series of annual rainfall erosivity (725–2019 CE), one from the Tiber River Basin (TRB), a fluvial valley in central Italy in which the city of Rome is located. A historical perspective of erosive floods in the TRB is provided employing a rainfall erosivity model based on documentary data, calibrated against a sample (1923–1964) of actual measurement data. Estimates show a notable rainfall erosivity, and increasing variability, during the Little Ice Age (here, ~ 1250–1849), especially after c. 1495. During the sixteenth century, erosive forcing peaked at > 3500 MJ mm hm–2 h–1 yr–1 in 1590, with values > 2500 MJ mm hm–2 h–1 yr–1 in 1519 and 1566. Rainfall erosivity continued into the Current Warm Period (since ~ 1850), reaching a maximum of ~ 3000 MJ mm hm–2 h–1 yr–1 in the 1940s. More recently, erosive forcing has attenuated, though remains critically high (e.g., 2087 and 2008 MJ mm hm–2 h–1 yr–1 in 1992 and 2005, respectively). Comparison of the results with sediment production (1934–1973) confirms the model's ability to predict geomorphological effects in the TRB, and reflects the role of North Atlantic circulation dynamics in central Italian river basins. [ABSTRACT FROM AUTHOR]

Rainfall erosivity and its derivative, erosivity density (ED, i.e., the erosivity per unit of rain), is a main driver of considerable environmental damages and economic losses worldwide. This study is the first to investigate the interannual variability, and return periods, of both rainfall erosivity and ED over the Mediterranean for the period 1680–2019. By capturing the relationship between seasonal rainfall, its variability, and recorded hydrological extremes in documentary data consistent with a sample (1981–2015) of detailed Revised Universal Soil Loss Erosion-based data, we show a noticeable decreasing trend of rainfall erosivity since about 1838. However, the 30-year return period of ED values indicates a positive long-term trend, in tandem with the resurgence of very wet days (> 95th percentile) and the erosive activity of rains during the past two decades. A possible fingerprint of recent warming is the occurrence of prolonged wet spells in apparently more erratic and unexpected ways. [ABSTRACT FROM AUTHOR]

The advent of instrumental and digital weather observatories has not eliminated the substantial gaps within surface meteorological time series. In particular, the lack of long-term homogeneous snowfall records may be a major impediment for hydrologic studies. We tested the hypothesis that reliable estimates of snowy days per year (SDY) can be produced for peninsular Italy, based on weather and site measurements from a limited set of stations centrally located in the Central Mediterranean Area. The core weather data are those from three observatories: Naples (40° 51′ N, 14° 15′ E, 150 m a.s.l.), Rome (41° 53′ N, 12° 28′ E, 100 m a.s.l.) and Montevergine (40° 56′ N, 14° 43′ E, 1280 m a.s.l.). A linear multivariate regression model (LMRM) was developed with candidate predictors (elevation, snowfall days and winter temperatures) for SDY reconstructions in the Benevento Valley (calibration site), whose homogenised SDY time series covers the period 1869–2018 (the longest in Southern Italy, extending back to 1832 thanks to documentary proxies but with missing values from 1911 to 1949). Three validation sites across peninsular Italy were considered: Vesuvius (40° 49′ N, 14° 24′ E, 605 m a.s.l.), Perugia (43° 05′ N, 12° 30′ E, 205 m a.s.l.) and Padua (45° 23′ N, 11° 51′ E, 15 m a.s.l.). The percent relative mean absolute error (%RMAE) for Benevento Valley was 20.2%, and though higher model errors were encountered at validation sites, they were in an acceptable range (32.6, 39.3 and 39.5% at Vesuvius, Perugia and Padua, respectively). Our SDY reconstruction in the Benevento Valley parallels, the pattern of water discharge occurred in the region during the same period, whose fluctuations result in changes of the pattern of snowfall days (i.e. decreasing snowfall days in recent times was accompanied by decreasing groundwater levels). This corroborates that the approach used to reconstruct SDY data takes dominant climate controlling factors of hydrological changes. We conclude that the LMRM, a statistically developed model, is physically meaningful and may be reasonably used for estimating SDY in peninsular Italy roughly down from the sub-Alpine range. [ABSTRACT FROM AUTHOR]

Landscape and climate change interactions are considerably interrelated in mountainous area, where unsuitable or discontinuous surface meteorological variables constitute an impediment to the generation of homogeneous ecological and hydrological data, and may hinder long-term environmental studies. We developed a non-linear multivariate regression model (NLMRM) estimating snow days per year (SDY) in a focus area, the northern Swiss pre-alpine region (SPAR). The model was calibrated and assessed by using measured SDY data and other climatic variables in the period 1931–2006, and then used to estimate SDY for a longer period earlier than 1931. The extended series (1836–2017) showed a significant decrease of SDY passing from about 36 days year−1 in 1836–1943 to 29.9 days year−1 in 1944–2017, on average. This indicates that while warming is the major factor driving the SDY decrease recently observed in the study area, other processes related to local precipitation and large-scale climatic patterns emerge from our century-long perspective as important drivers of SDY variability in the Swiss pre-alpine region. [ABSTRACT FROM AUTHOR]

Groundwater extraction is used to alleviate drought in many habitats. However, widespread drought decreases spring discharge and there is a need to integrate climate change research into resource management and action. Accurate estimates of groundwater discharge may be valuable in improving decision support systems of hydrogeological resource exploitation. The present study performs a forecast for groundwater discharge in Aquifer's Cervialto Mountains (southern Italy). A time series starting in 1883 was the basis for long-term predictions. An Ensemble Discharge Prediction (EDisP) was applied, and the progress of the discharge ensemble forecast was inferred with the aid of an Exponential Smoothing (ES) model initialized at different annual times. EDisP-ES hindcast model experiments were tested, and discharge plume-patterns forecast was assessed with horizon placed in the year 2044. A 46-year cycle pattern was identified by comparing simulations and observations, which is essential for the forecasting purpose. ED is P-ES performed an ensemble mean path for the coming decades that indicates a discharge regime within ± 1 standard deviation around the mean value of 4.1 m s. These fluctuations are comparable with those observed in the period 1961-1980 and further back, with change-points detectable around the years 2025 and 2035. Temporary drought conditions are expected after the year 2030. [ABSTRACT FROM AUTHOR]

A gridded 0.25° reconstruction of rainfall kinetic energy (RKE) over the UK, on the basis of pluviometric observations and reanalysis back to 1765, shows that autumn RKE doubled in 1991-2013 (∼2 MJ m) compared to 1948-1990 (∼1 MJ m). A shift eastward is underway, which includes southern and northern portions of the country. Analyzing the long-running England and Wales precipitation series, we conclude that it is likely that increased precipitation amounts associated with more frequent convective storms created conditions for higher energy events. [ABSTRACT FROM AUTHOR]

This study presents a new proxy for the reconstruction of the historical activity of large earth flows. A simple relationship between rainfall, temperature and groundwater levels was established using available monthly time series and subsequently utilized to develop the Landslide Hydrological Climatological (LHC) indicator to simulate the effects of hydroclimatic influence on slope stability for the Montaguto earth flow in Southern Italy. In order to identify phases of earth-flow activity, an empirical threshold was assigned. Our result indicates a different response of the earth flow to hydroclimatic stress with both ordinary and extraordinary reactivations over the historic period. Additional information suggests that earth-flow reactivations are clustered in the spring and an extraordinary earth-flow activity follows periods with a LHC below the average. A modeling result shows that the LHC is able to realistically reconstruct the long-term activity of a complex earth flow with only a few false-positives in a very long period of application. Thus, it can be considered as a tool for long-term earth-flow activity reconstruction and assessment. [ABSTRACT FROM AUTHOR]

This work presents the reconstruction of a time series of annual winter air temperatures across Central and Southern Italy for the period 1500-2010 that largely overlaps the Little Ice Age (LIA) period (1300-1850). A detailed analysis was undertaken on winter mean temperature data using both observations (1871-2010) and proxy-based reconstructions (1500-1870). Based on this homogeneized reconstructed series, a time-dependency in low-frequency time-pattern of temperatures (70- and 130-year cycles) was suggested although the temporal oscillation was not merely periodic. The LIA was characterized by marked climatic variability over this part of Southern Europe, with particular emphasis during the so-called 'Maunder Minimum' (MM), between 1645 and 1715. The interannual variability of low temperatures, in particular, makes the MM an outstanding climatic period. There is some consistency that patterns of warming conditions observed in recent times also occurred in the past. Quasiperiodic cycles appear as a consequence of stochastic resonance emerging in long time scales but the variability inherent to the series of winter temperatures, although likely generated by processes internal to the climate system, is difficult to forecast because the system is chaotic and affected by unpredictable noise. [ABSTRACT FROM AUTHOR]

This study proposes a forecast skill scheme for winter temperatures in the central Mediterranean Sub-regional Area. An original series of mean winter (December to February) temperatures spanning the period 1698-2010 is the basis for developing long-term climate prediction. A procedure was identified where a predictable structure was first provided by reducing noise via the Empirical Mode Decomposition method and then an Ensemble Climate Prediction (ECP) was applied. The predictability limit of the decomposed series was assessed by applying a collection of methods from linear and nonlinear time series analysis (Hurst coefficients, Lyapunov exponents and wavelet patterns). The analysis was based on a set of tools that are suitable to discover the manifestation of a possible trajectory of projected temperature change. The progress of the winter temperature forecast was inferred by an Exponential Smoothing with a Damped (ESD) multiplicative trend model. ECP-ESD hindcast experiments were tested and ensemble forecasts run until the year 2040. ECP-ESD yields an ensemble mean path that consists: (1) of a sharp decline in winter temperature in the current decade; (2) more widespread predictions for 2020-2030, with fluctuations near the present multi-decadal average; and (3) a sharp warming for the 2030-2040 decade. Our results are compatible with projections for European winters and major climatic indices such as the Pacific Decadal Oscillation and the Atlantic Multi-decadal Oscillation. [ABSTRACT FROM AUTHOR]

Current precipitation and past climate variability induce considerable intermonthly fluctuations in spring discharges. This study presents the DISHMET model (Discharge Hydro-Climatological Model) developed to perform historical spring reconstructions in the lack of physical assumptions. We analyzed discharge data of the Caraventa spring, located on the southern side of Mount La Montagna in Southern Italy, which has been monitored since the 1996s. The La Montagna aquifer is tectonically and litologically complex and deformed bedding controls the groundwater flow. Due to this aspect a parsimonious model should be more suitable than a complex model in spring discharge estimation. Thus, the DISHMET model incorporates monthly and annual precipitation only. The model is able to estimate sufficiently well the monthly fluctuations of groundwater discharge. DISHMET can be easily used to assess historical discharge, even when hydrological data is discontinuously available. The magnitude of this discharge is linked to the frequency and type of weather patterns transiting over the central Mediterranean area during the autumn and winter seasons. It is mainly related to the local precipitation that recharges the Mt. La Montagna aquifer. An analysis of antecedent rainfall and spring discharge reveal moderate to strong relationships. [ABSTRACT FROM AUTHOR]

A Correction to this paper has been published: https://doi.org/10.1038/s41612-021-00164-z [ABSTRACT FROM AUTHOR]

This paper has exploited, for Central and Southern Italy (Mediterranean Sub-regional Area), an unprecedented historical dataset as an attempt to model seasonal (winter and summer) air temperatures in pre-instrumental time (back to 1500). Combining information derived from proxy-documentary data and large-scale simulation, a statistical downscaling approach in the form of mixed regression model was developed to adapt larger-scale estimations (regional component) to the sub-regional temperature pattern (local component). It interprets local temperature anomalies by means of monthly based Temperature Anomaly Scaled Index in the range −5 (very cold conditions in June) to 2 (very warm conditions). The modelled response agrees well with the independent data from the validation sample (Nash-Sutcliffe efficiency coefficient, >0.60). The advantage of the approach is not merely increased accuracy in estimation. Rather, it relies on the ability to extract (and exploit) the right information to replicate coherent temperature series in historical times. [ABSTRACT FROM AUTHOR]

This study reveals the changes and evolution of rainstorm-driven intermediate floods occurring and driving multiple damaging hydrological events in the Rhone River Basin (RRB), since 1500 until 2010. A parsimonious approach was developed to simulate the major hydroclimatological flood-producing forcing, the Multiscale Rainstorm Climate Model ( STORMCLIMM). We collected the frequency of intermediate floods-a type of particularly hazardous floods commonly taking place between June and beginning of November-from the RCB to be compared to STORMCLIMM estimates. The latter, smoothed by a moving window of 21 years, results in a high-pass filter in the time domain, which magnifies the signal of forcing variations causing intermediate floods. The RRB showed large temporal variations in both extreme rainstorms and associated multidecadal intermediate-floods (MUDIF) frequency at different climatic periods and land-use systems through historical times. An important peak was observed in the Maunder Minimum (1645-1715 AD). The model allowed detecting MUDIF occurred in the historical times. The situation becomes interesting with respect to recent times, because the Rhone landscape looks more vulnerable in the last decades as a consequence of land-use changes and climate shift towards more erratic and intensive storms. This evidence suggests that the interactions of land-use and climatic changes may turn into considerable vulnerability to fluvial flooding and agro-ecosystem connected to them for upcoming years. The Rhone, for example, provides basis for use of hydrological indicators (such as the one represented by STORMCLIMM) for one site or region and which, through minor modifications, can be made relevant to specific needs. [ABSTRACT FROM AUTHOR]

Different approaches were used to model soil losses in the Sele River basin (southern Italy) characterized by data scarcity. The suitability of models interpolating different sources of data was evaluated with the aim to suggest similar methodologies in other regions where data availability is not sufficient to use the more complex and detailed models. The first approach is based on the concept of the balance between driving and resisting forces. Rainfall is considered as both a driving and resisting factor: the rain erosivity not only increases with its amount and intensity but also enhances the protective effect of vegetation. The long-term erosion rate of the basin resulted mainly affected by local land-cover conditions that showed a more dramatic effect than the variability of rain erosivity. In the period during which soils were protected by natural woodlands, net erosion rates were extremely low, while the elimination of forest (AD 1780-1810) increased erosion that reached annual rates from 20 to 300 Mg km. The second approach is a revised and scale-adapted Foster-Meyer-Onstad model suitable for scarce input data (CliFEM = Climate Forcing and Erosion Modelling). This new idea was addressed to develop a monthly Net Erosion model (NER) and gross erosion was estimated from the sediment delivery ratio (SDR). From this approach it is clear that the erosion regime was clearly autumnal with a mean rate of 8 Mg ha per month. The long-term average soil erosion highlighted, since 1990, a more irregular temporal pattern, with the highest annual erosion (200 Mg ha) in 2002. The third approach combines the revised universal soil loss equation (RUSLE) with GIS-geospatial technology. Regression Ordinary Kriging (ROK)-based maps of erosive rainfall were made on annual and monthly basis. The months following soil tillage (from August to November) have become even more hazardous for soil erosion, with values higher than 80% of total yearly soil losses, because in this period the highest rainfall erosivity is coupled to the lowest soil cover due to soil tillage at the end of summer. In these conditions soil can be protected only by the agro-environmental measures aimed at reducing soil erodibility and at increasing soil cover, such as conservative soil tillage (i.e. sod seeding) and perennial cover crops in orchards and vineyards. [ABSTRACT FROM AUTHOR]

Rainfall and overland flow are fundamental processes for Earth's ecosystems but can also be land disturbing forces, particularly when triggered by extreme hydro-meteorological events. Examples of these extremes are rainstorms and related phenomena due to rainfall aggressiveness. They produce high-impact land processes such as soil erosion and nutrient losses. Economic and social consequences of these processes can be quite severe. However, hydrological extremes and their environmental implications are still poorly understood, particularly if analyzed in the context of climate change. Here, we analyze a 300 year long times series of historical rainfall patterns across the Mediterranean in the last three centuries and we investigate changes in the erosive forcing as related to climate changes. Our results show that the erosive forcing increased towards the end of the Little Ice Age (~1850) over western and central Mediterranean and that has been increasing in the recent warming period at low Mediterranean latitudes, due to a higher frequency of intensive storms. Such increased concentrated precipitation may lead to an intensification of land degradation processes triggered by soil erosion and transport across a range of scales from hillslopes to small catchments. [ABSTRACT FROM AUTHOR]

Evaluation of soil erodibility is an important task for Mediterranean lands, in which fertility and crop yield are significantly affected by soil erosion. The soil physicochemical parameters affecting soil erodibility are highly variable in space and, as for many other environmental variables, sample measurements are generally not enough for assessing its spatial variability with an acceptable level of uncertainty at the scales of practical interest. This study illustrates the procedure applied for estimating the pattern of soil erodibility across the Sele Basin (Southern Italy), where soil properties have been measured on a limited number of sparse samples. Sampled data were integrated with other sparse data estimated by local regression functions, which relate soil erodibility to auxiliary variables, such as terrain attributes and land system class memberships. Sampled and estimated data were merged in a composed data set to assess the spatial pattern of soil erodibility by ordinary kriging. The proposed approach offers effective spatial predictions, and it is exportable to regions where financial costs for soil sampling are not feasible. [ABSTRACT FROM AUTHOR]

The study presents a geospatial knowledge transfer framework by accommodating precipitation maps for the Eastern Nepalese Highland (ENH) across an area of about 100,000 km2. For this remote area, precipitation–elevation relationships are not homogeneously distributed, but present a chaotic gradient of correlations at altitude ranges. This is mainly due to impervious orography, extreme climate, and data scarcity (most of the rain gauges in Himalaya are located at valley bottoms). Applying geostatistical models (e.g. multivariate geospatial approaches) is difficult in these zones. This makes the ENH an interesting test area where we obtained monthly precipitation spatial patterns for a 30-year period (1961–1990). The aim was to both capture orographic meso- α spatial regimen (~30 km) and local pattern variability (~10 km). Data from 58 FAO raingauges were used plus data from an atmospheric weather station (AWS Pyramid) operating at 5,050 m a.s.l., used to compensate the gap of precipitation pattern presents in the area surrounding the Mount Everest. In these complex orographically remote areas of the Himalayas, monsoon precipitation systems exhibit important topographical interactions and spatial correlations, depending on the scale at which the primary variable (e.g., precipitation) and co-variables (e.g., elevation) are recorded and analysed. Precipitations were assessed for months—May, July and September—representative of the monsoon season. For the rainiest month (July), cokriging indicated a range of precipitation values from ~100 mm over the Tibetan Plateau to ~500 mm in the southern part of Nepal, up to ~900 mm towards the pre-Himalayan range. For July, cokriging precipitation map also showed correspondence with the map of vegetation pattern, and therein lies the clue to using multivariate geostatistical models as flexible approaches for estimating precipitation spatial patterns in remote areas. [ABSTRACT FROM AUTHOR]

A simplified regression model is here calibrated on the basis of rainfall data records of Sicily (southern Italy), in order to show the model reliability in assessing the R-factor of the Universal Soil Loss Equation and its revised version (RUSLE) and to provide an estimate of long-term rainfall erosivity at medium-regional scale. The proposed model is a rearrangement of a former simplified model, formulated for the Italian environment, grouping three easily available rainfall variables on various time scales, which has been shown to be more successful than others in reproducing the rainfall erosive power over different locations of Italy. A geostatistical interpolation procedure is then applied for generating the regional long-term erosivity map with associated standard error. Areas with severe erosive rainfalls (from 2,000 up to more than 6,000 MJ mm ha−1 h−1) are pointed out which will correspond to areas suffering from severe soil erosion. Solving the problem of calculating the R-factor value in the RUSLE equation by means of such a simplified model here formulated will allow to predict the related soil loss. Moreover, given the availability of long time-series of concerned rainfall data, it will be possible to analyse the variability of rainfall erosivity within the last 50 years, and to investigate the application of RUSLE or similar soil erosion models with forecasting purposes of soil erosion risk. [ABSTRACT FROM AUTHOR]

In the present work, a new simplified model approach for sediment delivery ratio (SDR) assessment is proposed. Modelling and assessing SDR is still an open question. Difficulties rise from the lack of sufficient data availability, on one side, and, on the other, from the inherent uncertainties including spatial variability and temporal discontinuity of the land cover, climatic, hydrological and geomorphological variables involved. The proposed SDRSIM model tries to skip over the limitations observed in other models generally adopted. A comparison with two different selected models amongst the most widespread simplified models, i.e. the area-model and the slope-model, is showed in application to a wide range of catchments extensions across different landscapes of Italy. The SDRSIM estimates were also evaluated against observed SDR over a validation dataset of 11 basins sparse over different regions of the world. The results showed the effectiveness of the proposed model approach based on easily available catchment parameters. [ABSTRACT FROM AUTHOR]

This work presents a modelling study where monthly-based climate data are used to estimate the Normalized Difference Vegetation Index (NDVI). The latter is a measure of vegetation greenness, usually derived from satellite-driven information. A model was developed to link NDVI data to rainfall and temperature measures. The test area was a 3 × 3 km grid centred to the top of Monte Pino hill (Southern Italy), for which multi-year (from 1996 to 2004) climate and satellite-derived NDVI data were available. The simulated NDVI data compared well with the remote-sensed measurements (e.g. modelling efficiency ∼0.80), thus showing a strong linking between vegetation greenness and climate patterns in spite of the many disturbances exerted from farming. The model was used to reconstruct an extended series of monthly NDVI values for a period antecedent 1996 (1972–1995). The analysis of long-term anomalies indicated a positive trend of NDVI over time, consistent with the air temperature increase registered in the same period. [ABSTRACT FROM AUTHOR]

This work analyses the climatic information of 607 weather anomalies belonging to a large documentary sources heritage of the continental southern Italy during the period 1675-1868. The collected information, mainly originating in Samnium River Region (SRR), were codified to obtain quantitative indices representative of a preliminary reconstruction of the precipitation anomalies. Historical written records of weather conditions that affect agriculture and living conditions have been taken as a proxy for instrumental observations of the relative wetness and dryness. As a consequence a numerical index was established to characterize the rainfall regime and its evolution. So, for the first time a series of the precipitation anomalies in SRR-continental southern Italy during the second half of the Little Ice Age was generated, and subsequently jointed to the instrumental series (1869-2002). Afterwards, in order to identify possible climatic change situations from 1675 today Normalized Cumulative Anomalies (NCA)-serie's and Climograms were produced. This historical period offered a sufficient range of natural variability in climate and circulation together with their relationships. Wettest period were detected in the 19th century, while that driest in the 18th century. However, the Mediterranean climate appearing from our study is far more complex than can be captured by a simple classification. In this way, the final picture is one switching between significantly different climate modes becoming apparent on several space-time-scales during the Late Little Ice Age. [ABSTRACT FROM AUTHOR]

Seasonal rainerosivity is important in the structure and dynamics of Mediterranean ecosystems. The present paper contributes to the quantitative assessment of RUSLE's monthly erosion index in a data-scarce Mediterranean region. Therefore, a regionalized relationship for estimating monthly erosion index ( EI 30-month) from only three rainfall parameters has been obtained. Knowledge of the seasonal and annual distribution of erosivity index, permit soil and water conservationists to make improved designs for erosion control, water harvesting or small hydraulic structures. Although a few long data sets were used in the analysis, validation with established monthly erosivity index values from other Italian locations, suggest that the model presented ( r 2 = 0.973) is robust. It is recommended to monthly erosivity estimates when experimental data-scarce rainfall become available. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach for mapping leaching risk at large sub-regional scale under limited information is presented, with acronym environinformatics in ecological risk assessment. The problem consists into quantifying the exchange frequency of the plant available soil water (Narula et al. in J Geogr Inform Decision Anal 7(1)32–46, 2002), this frequency can be adopted as a measure indicating the nutrient and contaminant leaching risk for a site. Our approach is based on integrating soil water balance with spatial analysis tools. However, any decision involved in scientific risk evaluation requires the accurate quantification of the degree of uncertainty arising from sampling, modelling and interpolation errors. The non-parametric geostatistical procedure of Indicator Kriging enables to circumvent this problem by estimating the probability that the true value exceed a set of threshold values. The transformation of leaching data to a binary response variable, known as “indicator”, can lead to a soft description of leaching. Such soft description can mitigate the uncertainty in exchange frequency estimates of the plant available soil water. The approach was applied to a test site in Beneventan agroecosystem (South Italy) by using a long-term hydrological water balance acquired in a 40-years period. In this way, about 400 km2 (25%) of the total 2,000 km2 of the Benevento province were classified as areas sensitive to nutrient and contaminant leaching. [ABSTRACT FROM AUTHOR]

The Chernobyl plume contaminated vast lands of Europe with radiocaesium (137Cs) in 1986 because of the deposition of radionuclides on the ground by wet and dry deposition processes. Nevertheless, in a nuclear emergency, contamination data may be very sparse and there is need to make rapid and scientifically supported decisions. Here we analyze the rainfall field, an important precursor of the wet deposition, during the passage of the plume. Thus, estimating rainfall spatial variability can help to identify possible contaminated areas and associated risks when rainfall exceeded a given threshold. In this paper, we show that the conditional probabilities of exceeding threshold rainfall values could be spatially assessed using the mutual benefits of linking geostatistical and geographical information system (GIS) to quantify the evaluation of the risk involved in decision making. In particular, the non-parametric geostatistic technique, termed Indicator Kriging (IK), enables one to efficiently estimate the probability that the true value exceeds the threshold values by means of the indicator coding transform. Afterward, GIS has been used to find the areas probably affected by wash-out (probability >0.5 that rainfall is above a certain threshold). The experimental study has been focused on a test site in Beneventan agroecosystem (Southern Italy) to model the spatial uncertainty over a continuous area from sparse rainfall data. This enabled to generate probability maps delineating area potentially affected by to contamination to be monitored after wet deposition of Chernobyl releases. [ABSTRACT FROM AUTHOR]

Erosive storms constitute a major natural hazard. They are frequently a source of erosional processes impacting the natural landscape with considerable economic consequences. Understanding the aggressiveness of storms (or rainfall erosivity) is essential for the awareness of environmental hazards as well as for knowledge of how to potentially control them. Reconstructing historical changes in rainfall erosivity is challenging as it requires continuous time-series of short-term rainfall events. Here, we present the first homogeneous environmental (1500–2019 CE) record, with the annual resolution, of storm aggressiveness for the Po River region, northern Italy, which is to date also the longest such time-series of erosivity in the world. To generate the annual erosivity time-series, we developed a model consistent with a sample (for 1981–2015 CE) of detailed Revised Universal Soil Loss Erosion-based data obtained for the study region. The modelled data show a noticeable descending trend in rainfall erosivity together with a limited inter-annual variability until ~1708, followed by a slowly increasing erosivity trend. This trend has continued until the present day, along with a larger inter-annual variability, likely associated with an increased occurrence of short-term, cyclone-related, extreme rainfall events. These findings call for the need of strengthening the environmental support capacity of the Po River landscape and beyond in the face of predicted future changing erosive storm patterns. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Damaging hydrological events are extreme phenomena with potentially severe impacts on human societies. Here, we present the hitherto longest reconstruction of damaging hydrological events for Italy, and for the whole Mediterranean region, revealing 674 such events over the period 800–2017. For any given year, we established a severity index based on information in historical documentary records, facilitating the transformation of the data into a continuous time-series. Episodes of hydrological extremes disrupted ecosystems during the more severe events by changing landforms. The frequency and severity of damaging hydrological events across Italy were likely influenced by the mode of the Atlantic Multidecadal Variability (AMV), with relatively few events during the warm Medieval Climate Anomaly dominated by a positive phase of the AMV. More frequent and heavier storms prevailed during the cold Little Ice Age, dominated by a more negative phase of the AMV. Since the mid-19th century, a decreasing occurrence of exceptional hydrological events is evident, especially during the most recent decades, but this decrease is not yet unprecedented in the context of the past twelve centuries. [ABSTRACT FROM AUTHOR]

On 22nd November 2011, after extreme rainfall (more than 350 mm fell in 10 h), thousands of shallow landslides evolving into debris flows occurred in several torrent catchments of Peloritani ridge, northeastern Sicily, carrying a large amount of wood. A rapid increase in torrent discharge occurred during the storm, inducing floods of the Longano and Mela torrents, also amplified by wood obstructions of bridges. A detailed reconstruction of the main geo-hydrological events has been done. Landslides have been mapped by satellite photos and checked by field surveys, and were found to be particularly concentrated in the Longano, Mela and Saponara catchments. The timing of landslide initiation has been estimated by technical report data, eyewitnesses, and by calibration of the Leaky Barrel Model [Wilson, Wiezoreck, Environ Eng Geosci 1(1):11-27, 1995]. The recorded amount of rainfall has been transformed into the torrent discharge by the HEC-HMS model, using a detailed digital elevation model (DEM) with resolution of 2 × 2 m, and the torrent hydrograph has been obtained corresponding to several river sections. In the urban area of Barcellona Pozzo di Gotto, the torrent discharge reached its peak before the arrival of the large trunks, indicating that the main cause of the inundation in the urban area was the bridge obstruction. The aim of this study is to provide a detailed case history of landslides inducing floods, and their relationship with the use of simple hydrological models, which could help in estimating the risk of inundation caused by a large amount of wood in unmonitored catchments. [ABSTRACT FROM AUTHOR]