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The work proposes a procedure to build an early warning predictive tool to assess the occurrence of rainfall-induced landslides in silty volcanic covers. The procedure combines both an empirically and a physically based tool used sequentially: the former is designed to be calibrated using older, highly sized and coarser rainfall data, and the latter to interpret recent and finer weather data. Both approaches need to be informed by a common experimental reference summarising the rainfall history, the rainfall point, defined as the couple made of antecedent 4-month rainfall cumulative value (C4m) and last-persistent event (CPLE). The empirical approach aims to identify if, in the (C4m–CPLE) plane, the rainfall point falls in a ‘safe’ or ‘potentially unsafe’ zone where the two distinct regions are built by interpreting rainfall data associated or not with landslide events. In the physically based approach, evaporation and runoff are estimated to refine the assessment of ‘effective’ rainfall points. The resulting transformed rainfall point (C′4 m, C′PLE) is turned into a prediction of the suction level at the mid-depth assumed as a ‘reference’ for the entire cover. Such value is compared with a suction threshold empirically defined. Suction levels prediction is developed by computing in the C′4 m–C′PLE plane the iso-suction lines generated by several rainfall scenarios. The accuracy of the developed procedure is comparable with state-of-the-art literature or operational approaches, properly identifying landslide case events and minimising the number of false alarms. Furthermore, it can inform the preparedness stages more effectively, explicitly accounting for the antecedent slope wetness stage and how it could be far from the incipient slope failure conditions. The developed procedure takes into account the effects of evaporation and antecedent rainfalls that, in dry periods, lead to very dry conditions in the subsoil, making even significant rainfall events inconsequential. Conversely, other procedures already operating in LEWS or highly considered literature background overestimate the effects of rainfalls during dry periods. The developed procedure delivers a simple but robust way to derive landslide thresholds based on the interpretation of past rainfall histories. At the same time, literature methods often require sophisticated approaches to retrieve thresholds.

The European Extreme Events Climate Index (E3CI) is an innovative climate service (operational since January 2021) to assess the frequency and severity of weather-induced hazards over Europe. This service has been funded by Foundation Big Data and Artificial Intelligence for Human Development (IFAB) and developed through the collaboration between Fondazione CMCC Euro-Mediterranean Center on Climate Change (CMCC) and Leithà Unipol Group. Specifically, E3CI proposes to define a synthetic index aimed at providing information about the areas affected by different types of weather-induced hazards and the significance of such events. At this stage, E3CI permits equipping Europe, SADC (Southern African Development Community) and North America with an index similar to Actuaries Climate Index, (NA-ACI; actuariesclimateindex.org) developed over North America and already fully operational. From a methodological point of view, using ERA5 reanalysis data (Hersbach et al., 2020), E3CI permits evaluating seven main dynamics: cold and heat stresses, droughts, extreme precipitation, winds, the predisposing conditions leading to hail events and forest fire. Furthermore, five E3CI components are tested using a subset of regional climate projections from the EURO-CORDEX (Jacob et al., 2014, 2020; Giorgi and Gutowski, 2015) program at the highest available resolution (about 11 km) considering three different future periods (2011-2040; 2041-2070; 2017-2100) and three different concentration scenarios. The data are aggregated at different Administrative unit levels and combined into a single index providing an overall view for the domain and period of interest. A key aspect of E3CI is the standardization of the different components over a reference thirty-year period (1981-2010). It is a key aspect of the process because it makes comparable the outputs returned according to different approaches and for different issues; it can limit, in a way, also the influence of potential weaknesses affecting the original datasets used for computation. E3CI applications range in a wide area comprising both scientific and technical use cases. Such information can be useful to adjust rate adequacy levels, improve budgeting capabilities and risk management, designing index-linked financial instruments and new insurance and reinsurance products. In addition, E3CI can be adopted to pursue education and sustainability targets, such as to increase awareness about climate change impacts, to support public decision-making processes for sustainable development, to provide a scientifically-sounding measure to monitor climate trends. Finally, this service can change the way financial and insurance markets operate by providing indices that allow to more accurately measure risks related to extreme weather conditions.

Rainfall is considered the most important triggering factor for landslide initiation. It is expected that changes in the precipitation regimes, as a direct consequence of climate change, will influence slope stability at different temporal and geographical scales altering the frequency and the distribution of rainfall-induced landslides. Therefore, there is a need to develop and implement efficient landslide risk management to deal with the increasing landslide risks. In this context, territorial landslide early warning systems (Te-LEWS) can be valuable tools to warn authorities, civil protection personnel and the population about the occurrence of rainfall-induced landslides over wide areas, typically through the prediction and measurement of meteorological variables with a limited consideration of soil behaviour. Currently, widespread deployments of Te-LEWS integrating monitoring data collected at local scale have been inhibited by the high cost of sensors, the requirement of frequent maintenance and the inflexibility of cable-based systems.The use of advanced monitoring and communication technologies could provide the means to solve these challenges.This study proposes a four-phase approach to set up an IoT-based early warning system at municipal scale. The territory of a municipality has been chosen as the reference spatial unit of assessment because it has an extension that is intermediate between slope units and regional warning zones. The framework is based on the following four phases: monitoring, modelling, forecasting, and warning. The study focuses on the first phase of the proposed approach, i.e., combination of widespread meteorological data and local real-time measurements coming from monitoring networks installed at specific locations of great geomorphological interest within the study area. The measurements—specifically soil water content, pore water pressure and suction— are used to provide additional information to be used for enhancing the performance of the warning model. It is important to highlight that, within the proposed framework, an important role for the warning system will be played by community members and other people working or living in the municipality, herein called human sentinels, which will be involved, for instance, in the proper maintenance of sensors and for documenting the impacts of extreme climate events (e.g., photos and reports uploaded in local data platforms).The monitored sites are located within the municipality of Amalfi, southern Italy, and the implementation will be addressed within the activities of the Horizon Europe project “The HuT: The Human-Tech Nexus - Building a Safe Haven to cope with Climate Extremes”. The territory of Amalfi consists of a steep mountain front that rises abruptly from the Tyrrhenian Sea. Steep topographic gradients forced human settlements to develop along the coast at the mouth of the main streams. The town is a densely populated area with high touristic impact. The town is located in a morphologically complex zone of southern Italy frequently affected by dangerous and calamitous landslides.This study aims at highlighting importance of considering both climate forcing factors and in-situ geotechnical parameters within a warning model operational at municipal scale.

Pluvial flooding is a major concern in urban environments with limited or temporarily reduced efficiency of surface drainage. Climate change, land use change, and increased exposure in hazard prone areas play a role in increasing pluvial flood risks. We describe a methodology for rapid probabilistic pluvial flood hazard mapping and risk assessments in urban environments developed for the Copernicus Climate Change Service “Pluvial Flood Risk Assessment in Urban Areas” demonstrator project for large computational domains. The methodology is designed to be flexible and robust enough to be adapted to other cities in Europe and elsewhere, and is composed of three main steps: (i) computation of the intensity–frequency relationship to express the depth of extreme hourly rainfall events at the urban scale; (ii) characterization of the pluvial flood hazard by means of raster-based flood modeling, and; (iii) estimation of the direct tangible damages at the building level for residential, commercial, and industrial buildings. Methods are tested for 20 cities across Europe and considering two infiltration rate scenarios. Spatial-explicit results at the urban-scale highlight the neighborhoods and sub-urban areas more adversely affected in terms of direct tangible damage to buildings and assets. Probabilistic risk assessment results indicate that Brussels, Antwerp, and London are the cities with higher expected annual damages per built-up area among the 20 cities considered in this study. These results can be useful for supporting the implementation of the Sendai Framework for DRR 2015–2030, for developing multi-hazard risk assessments, and for supporting the development of national climate change adaptation plans.

Recently, ECMWF has released a new generation of reanalysis, acknowledged as ERA5, able to deliver a comprehensive, free, and operative picture of the past weather, exploiting the data assimilation of historical observations from different sources (satellite, in situ, multiple variables) for both atmospheric and soil variables. Experiences concerning flooding issues suggest that ERA5 could support also landslide investigations. In this sense, a number of questions may be raised: (i) can ERA5 data be reliable in reproducing rainfall histories leading to a landslide event? (ii) can ERA5 soil moisture estimations be reliable proxies for antecedent slope wetness condition? (iii) can ERA5 be implemented in a landslide early warning system to improve the performances? This study tries addressing these questions referring to some historical Campanian events (Southern Italy) with a special focus on the fully investigated event occurred in Nocera Inferiore on March 4, 2005. The study shows the following: (i) ERA5 precipitation can appropriately reproduce actual rainfall histories leading to the events occurred in the last years; (ii) ERA5 soil moisture may act as proxy of slope wetness conditions; (iii) ERA5 data could be easily implemented in LEWSs in areas poorly covered by field monitoring networks.

The study is aimed at verifying the reliability of the ERA5 reanalysis in reproducing histories of soil water fluxes exchanges (in terms of precipitation and evapotranspiration) leading to landslide events that actually occurred in Campania Region (Southern Italy). In the specific, the investigation deals with landslide events affecting pyroclastic covers result of repeated eruptions of Vesuvius and Campi Flegrei over the course of millennia. Indeed, for many events occurred in the last years, it is hard to retrieve continuous and reliable atmospheric data provided by weather stations in the vicinity of the affected slopes. Under such constraints, it could be difficult to identify the weather patterns triggering the events and then how they could vary in a climate change perspective. To deal with these issues, the fifth generation of atmospheric reanalysis made available by European Centre for Medium-Range Weather Forecasts (ECMWF) can represent a valuable support. ERA5 and its downscaling ERA5land return hourly data with an horizontal resolution of respectively 31km and 9 km over the entire globe. The data are available since Fifties and they are continuously updated with a delay of only 5 days for ERA5 and few months for ERA5land. Well-documented test cases over Campania Region for which long datasets of atmospheric data and details about the landslide events are available, are exploited to assess the capabilities of ERA5 reanalysis in reproducing antecedent and triggering soil water fluxes exchanges histories. Then, strengths and potential gaps are identified and thoroughly explained to permit a reliable adoption of the datasets.

Rainfall-induced shallow landslides and debris flows often cause casualties and significant damage to property. Territorial landslide early warning systems are recognized as an important countermeasure to avoid or reduce fatalities during rainfall events. A reliable warning model is a key component of these systems. Warning models operating over large areas usually relate the occurrence of landslides to rainfall monitoring data adopting appropriate thresholds (e.g., intensity-duration, cumulated rainfall-duration, hourly/daily rainfall indicators). The increasing availability of large sets of atmospheric and land monitoring data represents an opportunity to upgrade and improve existing landslide warning models. At the same time, appropriately treating such data may pose a significant challenge to analysts that are used to deal with much smaller amounts of data.The objective of this preliminary study is to demonstrate that machine learning techniques can be effectively used to process monitoring data over large areas at regional scale, with the aim of defining and selecting the variables that best correlate with the initiation of shallow landslides and debris flows. The machine learning models have been tested in one of the warning zones defined by the regional civil protection agency for hydrogeological risk management in Campania (Italy). Two categories of data are used for the analyses: distributed monitoring data, and a landslide inventory. The monitoring variables are derived from the fifth generation of ECMWF atmospheric reanalysis (ERA5), available with a spatial resolution of about 31 km and a temporal resolution of 1 h (http://dx.doi.org/10.24381/cds.adbb2d47). Data on landslide events come from “FraneItalia”, a geo-referenced openly available catalogue of Italian landslides created consulting online news from 2010 onwards (http://dx.doi.org/10.17632/zygb8jygrw.2). Different machine learning models have been defined, trained, and tested to relate the occurrence of landslides in the case study area to multiple variables arising from different combinations of the adopted monitoring data, mainly rainfall and soil water content. The performance of these models is evaluated by means of standard contingencies and skill scores. The best performing variables are used to define an optimal multivariate threshold to be adopted in the landslide warning model. The results of the optimal model are also compared with the outcomes of an application of a more classical exceedance probability statistical methodology based on cumulated rainfall-duration thresholds.

The paper presents a new hourly high-resolution (i.e., at 0.02°, ≃2.2 km) precipitation dataset, labelled as ERA5@2km, obtained by dynamically downscaling ERA5 reanalysis at convection permitting scale (CPS) over 20 European cities for the recent past thirty years (1989–2018). The downscaling activity is performed within the framework of the Contract implemented by Fondazione CMCC to support Sectoral Information System about “Disaster Risk Reduction” (see https://climate.copernicus.eu/pluvial-flood-risk-assessment-urban-areas) of Copernicus Climate Change Service (C3S). Specifically, such an additional precipitation dataset is developed to provide precipitation data for estimating expected precipitations at fixed return periods used as input for pluvial flooding risk analysis where hazard (inundated areas, water levels) and risk (estimated damages) are assessed. The ambition is to support the Disaster Risk Reduction (DRR) community involved in pluvial flood risk assessment by providing a basis for impact analysis at city scale, in terms of extreme hourly precipitation, that matches with the expected spatial and temporal requirements. In this work, ERA5@2km precipitation dataset is introduced for the first time and its reliability and coherence are evaluated as for spatial patterns and trends as for extreme values against a set of available high-resolution observational datasets (comparable in terms of spatial and temporal resolution). Such an evaluation provides a clearer understanding about the added value of very high-resolution (VHR) dynamical downscaling reanalysis in terms of localization and magnitude of precipitation events at urban scale, confirming a general and relevant added value of this new configuration for the assessment of extreme atmospheric events (such as heavy precipitations).

Un tratto di rilevato dell’Autostrada A24 Roma-L’Aquila-Teramo, realizzato lungo una porzione della dorsale appenninica laziale posta tra i comuni di Roviano (RM) ed Arsoli (RM), è stato interessato a partire dalla fine degli anni ‘80 da continui fenomeni di avvallamento. I dati inclinometrici a disposizione hanno evidenziato che tali dissesti sono il risultato di un meccanismo di rottura che ha interessato non solo il rilevato ma anche il versante marnoso-arenaceo su cui esso è fondato. Avendo il monitoraggio messo in luce una variabilità stagionale delle velocità di spostamento, è stato sviluppato un modello semplificato finalizzato a ricostruire la cinematica meteo-indotta del fenomeno portando in conto le forzanti atmosferiche che regolano il processo. La nota descrive i principali risultati di tali analisi.

The evaluation of the resilience of flood protection systems requires the assessment of the impact of climate change scenarios on future flood regimes. Due to the high computational effort and to the scarcity of hourly climate modelling chains, expected changes in future floods are often simulated by hydrological models on a daily basis, even for basins with short response times, where hourly simulations would be needed.In this work, the expected occurrence and magnitude of future flood events is modelled through the coupling of bias-corrected local climate scenarios at hourly time scale and continuous rainfall-runoff modelling, in reference to the Panaro river (one of the OpenAir Laboratories in the OPERANDUM H2020 project), a tributary of the Po River in the Apennines.The investigation exploits hourly precipitation and daily max/min temperature (used for interpolation at hourly scale) timeseries for a subset of climate modelling chains included in the EURO-CORDEX initiative through the dynamical downscaling of Global Climate Models under the RCP 8.5 concentration scenario. The comparison with observed spatial fields obtained from weather stations and from gridded E-OBS products allows to assess the biases affecting the climate raw data.The Scaled Distribution Mapping (SDM) bias correction procedure (Switanek et al. 2017), that preserves raw climate model projected changes in the bias-corrected series, is then applied to adjust the raw model output towards observations.A semi-distributed, continuously simulating rainfall-runoff model is parameterised on the basis of the observed meteorological and streamflow time-series, especially focusing on the reproduction of past flood events. The model is then run to reproduce the continuous hourly streamflow time-series in the Panaro river over past and future decades, providing in input i) observed meteorological forcing based on ground stations, ii) raw and bias-corrected climate scenarios over the control period, iii) bias-corrected climate scenarios for the future decades. Finally, the flood events are extracted from the continuous streamflow simulations and the changes in the flood signals expected over the future decades are analysed, in terms of both peaks and volumes. References Switanek, M. B., Troch, P. A., Castro, C. L., Leuprecht, A., Chang, H.-I., Mukherjee, R., and Demaria, E. M. C.: Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes, Hydrol. Earth Syst. Sci., 21, 2649–2666, https://doi.org/10.5194/hess-21-2649-2017, 2017.

Assessing the impact of climate change scenarios on flood regimes is a crucial issue when evaluating the future resilience of flood protection systems. Mainly due to the high computational effort and to the scarcity of hourly climate projections, expected changes in future floods are often simulated by hydrological models on a daily basis, even for basins with short response times, where hourly simulations would be needed.In this work, the expected occurrence and magnitude of future flood events is modelled through the coupling of bias-corrected local climate scenarios at hourly time scale and continuous rainfall-runoff modelling. The case study for testing the procedure refers to the Panaro river (one of the OpenAir Laboratories in the OPERANDUM H2020 project).The analysis entailed first the collection, validation and spatialization of historical meteorological ground data over the catchment, to be used both to calibrate the hydrological model and in the bias-correction procedure.Secondly, the precipitation and temperature timeseries available at hourly time-scale for a set of climate modelling chains based on the same RCM nested in different GCMs, under RCP 8.5, are identified in the EURO-CORDEX ensemble and processed. The comparison with observed spatial fields obtained from weather stations and from gridded E-OBS products allows assessing the biases affecting “raw” data. The Scaled Distribution Mapping (SDM) bias correction procedure is then applied to “adjust” the raw model output towards hourly observations in a post processing step. The strength of such a procedure relies on preserving raw climate model projected changes in the bias-corrected series and on avoiding assumptions about stationarity.A semi-distributed, continuously simulating HBV-type rainfall-runoff model is parameterised, especially focusing on the reproduction of past flood events, and then run to reproduce the streamflow in the Panaro river, providing in input i) historical meteorological forcing based on ground stations, ii) raw and bias-corrected climate scenarios over the control period, iii) bias-corrected climate scenarios for the future decades. Finally, the flood events are extracted from the continuous streamflow simulations and the changes in the flood signals expected over the future decades are analysed, in terms of both peaks and volumes.

Several impacts of climate change are becoming evident and will be more and more appar ent in the future. The threats and challenges posed by weather patterns variations can change according to the variables of interest, geography, and socio-economic contexts. At the moment, we are compelled to assess their impacts, so as to identify priorities and devise efective countermeasures. A major concern is that of geo-hydrological risks, including the potential efects of landslide evolution in unstable areas. Based on available data on the relationship between soil–atmosphere interaction and mobility of a slow active earthfow in Southern Italy, the paper tries to correlate the expected future precipitation potential evapo transpiration balance and the water level fuctuations that govern landslide mobility. The investigation is carried out in the framework of the bottom-up approach known as deci sion scaling, which lets us evaluate landslide dynamics sensitivity to the variations in soil water budget by means of a simple abacus. Such a tool will make it possible to obtain fast updates on assessing landslide trends when new generations of climate projections become available

Understanding and modelling pluvial flood patterns is pivotal for the estimation of flood impacts in urban areas, especially in a climate change perspective. However, urban flood modelling under climate change conditions poses several challenges. On one hand, the identification and collection of climate change data suitable for flood-related evaluations requires consistent computational and scientific effort. On the other hand, large difficulties can arise in the reproduction of the rainfall-runoff transformation process in cases when only little information about the subsurface processes is known. In this perspective, a simplified approach is proposed to address the challenges regarding the quantitative estimation of climate change effects on urban flooding for real case applications. The approach is defined as “bottom-up” because climate change information is not included in flood modelling, but it is only invoked for the interpretation of results. In other words, the challenge faced in this work is the development of a modelling strategy that is expeditious, because it does not require flood simulations for future rainfall scenarios, but only under current climate conditions, thus reducing the overall computational effort; and it is flexible, because results can be easily updated once new climate change data, scenarios or methods become available, without the need of additional flood simulations. To simulate real case applications, the approach is tested for a scenario analysis, where different return periods and hyetograph shapes are used as input for urban inundation modelling in Naples, Italy. The approach can support public and private stakeholders, such as land administrators and water systems managers; moreover, it represents a valuable and effective basis for climate change risk communication strategies.

This work examines the hydrological behaviour of a silty volcanic layer exposed to the atmosphere for 3 years under vegetated conditions. The layer was extensively monitored to measure energy fluxes, water fluxes, and internal variables (suction, water content, water storage, and temperature). Measurements were used to build representations of the layer’s behaviour patterns depending on its surface covering, comparing the behaviour in vegetated conditions with behaviour observed under bare conditions over the previous 4 years. Results show that during cold–dry periods, differences in terms of fluxes and hydrological variables between the bare and vegetated conditions reduce to negligible levels, but increase significantly during hot–dry and transition periods. As the soil forming the layer was selected to have the same intrinsic and state properties as the layer in a specific rainfall-induced landslide case history that occurred in Nocera Inferiore (South Italy) in 2005, the experimental results are used to re-interpret such a landslide, considering the effects of vegetation and referring to a coupled thermohydraulic model. The experimental results are used to calibrate the model, and this is then used to interpret around 10 years of meteorological variables recorded at the landslide site, including the landslide time. Comparison with interpretations made previously as a bare soil hypothesis shows how neglecting the effects of vegetation might imply a loss in prediction accuracy of soil state variables (suction and water storage) related to the slope stability.

Soil erosion is one of the main threats facing the agriculture and natural resources sector all over the world, and the same is true for Syria. Several empirical and physically based tools have been proposed to assess erosion induced soil losses and runoff driving the processes, from plot to regional spatial scales. The main goal of this research is to evaluate the performance of the Water Erosion Prediction Project (WEPP) model in predicting runoff in comparison with field experiments in the Al-Sabahia region of Western Syria in three ecosystems: agricultural lands (AG), burned forest (BF) and forest (FO). To achieve this, field experimental plots (2∗1.65∗0.5 m) were prepared to obtain runoff observation data between September 2012 and December 2013. In addition, the input data (atmospheric forcing, soil, slope, land management) were prepared to run the WEPP model to estimate the runoff. The results indicate that the average observed runoffs in the AG, BF and FO were 12.54 ± 1.17, 4.81 ± 0.97 and 1.72 ± 0.16 mm/event, respectively, while the simulated runoffs in the AG, BF and FO were 15.15 ± 0.89, 9.23 ± 1.48 and 2.61 ± 0.47mm/event, respectively. The statistical evaluation of the model's performance showed an unsatisfactory performance of the WEPP model for predicting the run-offs in the study area. This may be caused by the structural flaws in the model, and/or the insufficient site-specific input parameters. So, to achieve good performance and reliable results of the WEPP model, more observation data is required from different ecosystems in Syria. These findings can provide guidance to planners and environmental engineers for proposing environmental protection and water resources management plans in the Coastal Region in Syria., Soil Erosion; WEPP model; Runoff prediction; Ecosystem; Statistical analysis

The paper considers a methodology to assess rainfall erosivity in Italy for the recent decades (1981–2010), building on datasets and materials freely available, such as those included within the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S). Twenty-one referenced empirical models to assess rainfall erosivity (R-factor) based on coarse rainfall data are tested and compared; then, a custom model is calibrated, with the support of seasonal rainfall pattern clustering by means of the Self-Organizing Map. Moreover, a large database of sub-hourly rainfall observations, covering the period 2002–2011, is collected and used for validation. Model performances are analysed at the point-scale of the rain gauges and at the spatial scale of different relevant gridded rainfall products: the fifth generation of ECMWF ReAnalysis (ERA5, ERA5-Land), the gridded European observational dataset (E-OBS), all included in the CDS, and SCIA-ISPRA (the Italian standard rainfall gridded dataset). New Hydrological Insights from the Region The proposed methodology provides four alternatives of spatially distributed rainfall erosivity datasets covering Italy with diverse levels of reliability. Analysis of results shows that the best performance is achieved by the combined use of the custom model and the SCIA-ISPRA dataset, followed by ERA5-Land, with the main source of error lying in the use of an empirical model instead of the rigorous model for R-factor estimation, and secondly in the use of gridded rainfall data instead of point-scale rainfall observations.

Flooding is one of the most challenging weather-induced risks in urban areas, due both to the typically high exposures in terms of people, buildings, and infrastructures, and to the uncertainties lying in the modelling of the involved physical processes. The modelling of urban flooding is usually performed by means of different strategies in accordance with the specific purpose of the analysis, ranging from detailed simulations, requiring large modelling and computational efforts, and typically adopted for design purposes, to simplified evaluations, particularly feasible for scenario analyses, when a large number of simulations is required perturbing one or more input parameters.According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, intensity of precipitation events could be greatly impacted by the expected climate change primarily due to the increase in temperature, entailing an increase in the atmospheric moisture retention capability. However, the effect of climate change on the rainfall regime of local areas is not straightforward, but deeply depends on local features such as latitude, topography, distance from the coast. Over Europe, an ensemble of climate simulations coming from the application of different Regional Climate Models (RCMs) (able to perform a dynamical downscaling of General Circulation Models, GCMs, available at the global scale) is freely available within the EURO-CORDEX initiative, which is the current standard for climate change analysis over EU countries. The spatial resolution of EURO-CORDEX simulations (about 12km) is too coarse to be directly used in local impact analyses; in this case, bias corrections are usually performed using local rainfall observations, to adjust climate simulation results to the local rainfall regime. The availability of multiple climate projections coming from different Climate Simulation Chains (in other words, different RCM/GCM couplings) allows to quantify the uncertainty in climate modelling, that should be accounted for in impact analyses.In the present work, an approach is proposed that aims to quantify the uncertainty caused by the use of an ensemble of climate projections on urban flood modelling, taking a limited area within the City of Naples (Italy) as test case. The specific purpose is that of understanding the resilience of the area with respect to any variation in rainfall intensity such as those possibly caused by climate change, building on 19 climate projections available within the EURO-CORDEX initiative and bias-corrected to make them suitable to be used for impact analyses at the local scale. The concept of resilience is expressed by a selection of indicators considered useful both in the framework of classical hazard analysis and for transport network, considered a strategic service for the test case. Urban flood modelling is undertaken by using two different numerical codes characterized by two different levels of complexity. In this way, it will be possible to draw conclusions about the computational costs that are actually needed, in terms of input data and resources, when integrating uncertainties due to climate projections in urban flood modelling for multi-purpose analyses.

Erosion processes are caused by a combination of predisposing factors (slope, intrinsic soil properties), accelerating factors (removal of vegetation cover, altered soil properties due e.g. to fires, overgrazing, tillage) and triggering factors (water – from rain and rivers – and wind). While the first components are rather unchanging (or changing slowly) at the human time scale, the last two must deal with the consequences of global changes. Indeed, modifications in land use, land management and climate have strong feedbacks so that, from one side, lands are more and more overexploited, degraded and exposed to erosion and, on the other side, over these lands, the frequency, magnitude, duration and timing of triggering events could deviate from their “normal” conditions.According to the well-known RUSLE soil loss estimation model, the triggering effect of rainfall for sheet and rill erosion is accounted for by means of the so-called rainfall erosivity or “R-factor”. R-factor consists of the annual summation of the erosive power of relevant storm events, averaged over a significant period of observation. For each storm event, computation of R-factors requires high-resolution rainfall information for the evaluation of the maximum rainfall intensity occurring over a time window of 30 min during the rainfall event. Due to the generally limited access to sub-hourly precipitation observations, a number of empirical models relating R-factor to easily accessible climate, physical and geographical covariates, such as rainfall data at coarse aggregation levels, have been developed for different areas of the world.As concerns Italy, a novel empirical model is proposed relating rainfall erosivity to cumulative precipitation, elevation and latitude. Such model, calibrated for a significant selection of relevant rain gauges with available sub-hourly data, showed a good accordance with observations and a large amount of explained variance at the annual scale, with promising results also at the monthly level. The model was effectively extended to cover the whole Italian Country for the period 1981-2010 by means of gridded rainfall datasets retrievable in the Copernicus Climate Change Service (C3S) Climate Data Store (CDS), with limited performance loss, exploring the feasibility of Copernicus products for erosion-related assessments. Although affected by limitations, the proposed model is particularly suitable for applications involving future rainfall projections since it explicitly accounts for monthly cumulative precipitation as the only climate covariate, differently for other proposed methodologies also including rainfall-related variables with higher temporal resolution, whose future trends cannot be robustly evaluated with current climate modelling tools. In the present research an ensemble of twelve future rainfall projections included in the Euro-Cordex initiative, bias-adjusted by means of the ERA5-Land reanalysis dataset, is considered to account for the uncertainties coming from the use of multiple projections. The proposed approach provides a unique example of rainfall erosivity dataset accounting for a wide ensemble of bias-adjusted rainfall projections resulting from different General Circulation Models/Regional Climate Models coupling, for multiple Representative Concentration Pathway scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) and different future horizons (near, 2021-2050, and far, 2051-2080, future).

This research aims at understanding and analyzing the possible effect of climate change on the seasonal rainfall regime over Italy. First, rainfall patterns are identified by applying a clustering procedure based on the Self-Organizing Map, by adopting and comparing the results of different gridded datasets describing current climate (1981-2010). Second, for the identified clusters the impact of climate change in the near (2021-2050) and the far future (2051-2080) is assessed by employing an ensemble of bias-adjusted EUROCORDEX climate projections under the Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5. The results of this study show that, for the considered models’ ensemble, a significant decrease in cumulative rainfall values should be expected in the future, reflecting in a decrease in monthly values across all the seasons.

Proper soil water retention curves (SWRCs) are necessary for a fair analysis of groundwater flow in unsaturated slopes. The question is whether hydraulic parameters operating in situ can be reliably determined from laboratory tests or physical prototype models in order to interpret and predict soil water distributions in the field. In this paper, some results obtained by tests at different scales (testing on laboratory specimens and a physical prototype) are presented to explore the hydraulic behavior of pyroclastic soils. A theoretical interpretation of the observed behavior in the laboratory and using a physical prototype is proposed by adopting the hysteretic model of Lenhard and Parker. For each tested soil, the main hysteretic loop determined by interpreting experimental tests (at laboratory and prototype scales) overlaps with paths detected by coupling the field measurements of matric suction and water content collected at the site at the same depth. From these results, the physical prototype (medium scale) and the soil specimen (small scale) seem to be acceptable for determinations of SWRC, provided that the air entrapment value is well known.

Environmental monitoring often requires the observation of phenomena at different spatial and temporal scales. For example, to study the anthropic impact on natural ecosystems, it is necessary both to evaluate its effects on a large scale and to detect and recognize the environmental criticalities that, locally, determine these effects. These needs impose tight requirements on data temporal, spatial and spectral resolution that a single aerospace platform can hardly satisfy. Therefore, it is necessary to develop new collaborative paradigms between different platforms to improve their observation capabilities, exploiting interoperability between heterogeneous platforms and sensors. However, even multi-platform approaches, due to the limitations of the individual platforms currently available in terms of revisit time and sensor spatial resolution, cannot fully comply with the requirements imposed by some specific environmental issues at acceptable costs.In this paper, HAPS (High Altitude Pseudo-Satellite) use is proposed as a tool to overcome these limitations and to extend the applicability of the multi-platform paradigm. In environmental monitoring context, one of the main advantages offered by HAPSs consists in the possibility of providing data with higher spatial resolution than satellites, and at lower cost compared to aerial platforms. Moreover, HAPSs offer a larger field of view than UAVs and can provide data types such as fluorescence or hyperspectral ones that, because of sensor weight and cost, rarely could be acquired by UAVs. Finally, HAPS platforms, thanks to their station-keeping capability on a desired area, offer the possibility of having data with a high temporal resolution to monitor the temporal evolution of phenomena at a rate currently not possible with other platforms.Different HAPS configurations have been proposed, based on aerostatic or aerodynamic forces. CIRA is designing a HAPS that, thanks to its hybrid configuration, is able to generate aerodynamic and aerostatic forces. It could fly at an altitude of 18-20 km, from this altitude range, the field of view has a diameter length of about 600 km. Maintenance and updating of its equipment and payload is also possible because the platform can land and take-off again.CIRA is also designing the platform payload. The design goal is to define a new wide-area sensor based on visible, thermal, or hyperspectral cameras, with a better resolution than satellites. In this way, it will be possible to detect environmental anomalies in persistence in order to alert the other platforms. A very high focal second-reading sensor will also be used to avoid false-positive alerts. In this paper, we will present the main characteristics of HAPS platforms and how they, in synergy with other ones, would lead to considerable advantages in environmental monitoring. In particular, we will discuss the multi-platform paradigm, the current platform limits and their influence on the paradigm effectiveness in the context of environmental monitoring, characteristics of the HAPS platform that CIRA is currently conceptually designing in the context of the OT4clima Project and the main issues relative to its payload design.

Soil erosion by water greatly affects Italy impacted by 24% of total soil loss of Europe, 33% of agricultural lands exposed, and costs, e.g. for crop production, up to about 600Meuro. Furthermore, expected increases in severity and magnitude of extreme precipitation events could exacerbate such an issue.In this regard, rainfall information at very fine spatial and temporal resolution represents a key point; unfortunately, weather stations are not spread uniformly across regions and they uncommonly provide free data at sub-daily scale. Moreover, the reliable projections of how rainfall will change in the coming decades are hard to store and manage for non-experts.In trying to overcome such a gap, Copernicus Climate Change Service (C3S) provides several tools. The C3S is part of the Copernicus Earth Observation Programme and is implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. In particular, Climate Data Store (CDS) hosts rainfall time series for the historical period and most recent decades from observational (E-OBS) and reanalysis (ERA5, ERA5-Land, UERRA) datasets, at (sub) daily time step and with horizontal resolution ranging from 31 km to 5.5 km. For the future, the simulations’ ensemble within EURO-CORDEX (resolution ~12 km, daily time step) are available for robust evaluations, i.e. to consider the uncertainty due to alternative greenhouse gas concentration scenarios and model chain used.In this context, in the last months, C3S funded the Demo Case SOIL EROSION implemented by the CMCC Foundation and aimed at assessing ongoing and future soil loss by water erosion over Italy. The Demo Case is expected to develop further specific datasets and a web-application by exploiting products and tools also provided by Climate Data Store (CDS) infrastructure.To assess soil losses, the largely adopted Revised Universal Soil Loss Equation (RUSLE) is selected. Such an empirical equation combines rainfall erosivity (R-factor), evaluated in this case by exploiting datasets in CDS, to soil susceptibility to erosion due to soil intrinsic properties but also to land cover, land management, and topography. Gridded datasets related to R-factor and soil losses will be then made available within the CDS catalog. Moreover, the web application will permit visualizing and retrieving trends and results for specific areas (e.g. NUTS) in the way of maps and graphs. In addition to the "Basic" mode, the Application is expected to support "what-if" analysis ("Advanced" mode) permitting to understand how variations in land use (C-factor) or management practice (P-factor) can influence soil losses at large scale under current and future conditions.

The report presents the work of the Joint Research Centre’s scientific network on adaptation of structural design to climate change focusing on the thermal design of buildings and infrastructure considering the changing climate. It presents scientific and technical background intended to stimulate debate and serve as a basis for further work to study the implications of climate change on the thermal design of structures. The report first outlines recent EU policies in support of sustainability and climate resilience of infrastructure and buildings. It highlights how the construction sector is encouraged to adopt more sustainable and circular economy practices, extend the lifetime of buildings and strive for better performance of buildings and infrastructure throughout their life cycle. It further emphasises the ongoing action plan to adapt the European standards to a changing climate. Following, the report explains the concept of the definition of thermal actions for the design of buildings and infrastructure using the European standards for structural design, i.e. the Eurocodes. It is showed that the adaptation of structural design to the implications of climate change is strongly linked with the assessment of changing characteristics of climatic actions (including thermal ones) in terms of the Eurocodes concept for the variable climatic actions. Variations in temperature that would directly affect the design values for thermal actions in the European standards are studied in depth for the case study of Italy. It is concluded that an increase in the maximum and minimum temperature used for structural design is expected all over Italy. It is discussed that structures, as bridges for example, are expected to be influenced by stresses from extreme temperatures and thus, should be designed for temperature amplitudes justified from climate projections for the actual region. However, the current European maps for thermal design are based on climatic data which, with some exceptions, are mostly 10 to 15 years old and ignore the potential effects of climate change. Thus, new European maps for the thermal design of structures should be developed using data that project more realistically the future climate. To this end, the authors present a methodology for developing thermal maps for structural design taking into account the influence of the changing climate and present an implementation of the methodology using the example of Italy., JRC.E.4-Safety and Security of Buildings

The paper compares the findings provided by empirical and physically-based models in estimating future evolutions in landslide occurrence as a consequence of the ongoing Climate Change. As test case, the phenomena affecting pyroclastic covers on Lattari Mts. (Campania Region) are considered. Simulation chains developed for both the approaches, pros and cons are carefully displayed pointing out the uncertainties currently associated to projections and the main gaps to bridge. Whatever the approach, an increase is expected but the relative magnitude and the evolution is deeply influenced by impact tools beyond the selected climate projections and concentration scenarios.

At present, the urban population has to cope with the effects caused from Urban Heat Island (UHI), poor air quality and increased frequency and/or intensity of extreme weather and climate events. The expected increase of these extremes in areas of the planet and the way to adapt to them has emphasized the need to investigate in detail the climate of the cities. Local vulnerability and risk assessments, supported by using regional climate models at very high resolution, are key to support development and implementation of effective local adaptation measures to make well-adapted and climate-resilient cities, i.e. more sustainable ones. This study aims to provide some quantitative information on the effectiveness of main local adaptation measures to reduce the magnitude of UHI, in terms of temperature and energy fluxes. The investigation was conducted by adopting the TEB 1D model for the Toulouse city case-study. Different urban configurations and adaptation measures have been considered in the model set up. The results confirm that different adaptation measures may reduce the temperature on the town elements during the daylight hours; among the different measures, the green roof prevent the radiative cooling, increasing the roof night temperature and contributing to the night UHI.

Flooding is one of the most challenging weather-induced risks in urban areas. However, in a climate change perspective, significant gaps can still be observed in literature addressing the key role of rainfall input and related variability within urban flood impact models. The present research attempts to bridge this gap by investigating the effect of using a large ensemble of bias-corrected Euro-CORDEX climate projections on flood hazard estimations, with the goal of understanding the propagation of future climate variability in terms of flooding outputs for the urban environment. With this aim, a scenario analysis is performed for two return periods (10 and 200 years) building on nineteen climate projections for future horizon 2071–2100 under RCP 4.5 and 8.5 scenarios by means of CADDIES Caflood inundation model, using a test case in the City of Naples (Italy). Model outcomes are investigated and discussed in terms of relevant metrics and indicators available in literature targeting both general hazards, evaluated in terms of runoff volumes and flooded areas, and sectoral hazards, specified by a number of relevant literature hazard classifications. Modelling outcomes are analysed and normalised with respect to baseline values representing current climate conditions to emphasize potential impacts of climate change. Results show that flood features increase more slowly than rainfall, and the relationship between rainfall and flood increase is linear, with decreasing slope with increasing return period. Furthermore, the spread in rainfall input due to the use of different climate projections does not inflate through the impact modelling stages, albeit a dependence on the indicators used to model flood impacts can be observed. The outcomes of the research can be of aid to managers, designers and policy makers to understand the impacts of climate change in flood-prone urban areas and, in perspective, to adapt urban areas exploring the feasibility and effectiveness of solutions.

Land use and land cover (LULC), as well as their geographical and temporal variations, affect landslide occurrence and the related risk, in ways that are difficult to determine. Here, we propose a method for the regional analysis of variations in landslide frequency and distribution in response to observed and projected LULC changes. The method is quantitative and reproducible. We test it in Calabria, southern Italy, where a catalogue of 7037 landslides occurred between 1921 and 2010 is available. First, we defined empirical relationships linking the observed LULC variations to landslide occurrence. We found that, in the period 1921-1965, the majority of the landslides occurred in forests and grassland areas, while, in the period 1966-2010, the landslide density became higher in grassland areas, lower in arable lands and remained about constant in the forests. We consider this an evidence of the positive effect of agricultural practices and land management in reducing landslide occurrence. We noticed that both the landslide occurrence and the distribution of the vulnerable elements changed in the observation period. Then, we calculated the projected variations (to 2050) in landslide occurrence related to 32 scenarios of LULC changes. Our projections reveal a modest increase in landslide occurrence in all scenarios, in the range from 0.9 to 3.2%, with an average increase of 2%. Considering all scenarios, we expect an increase in the number of landslides due to LULC variations in 291 municipalities in Calabria (71%), with 4 municipalities where the increase is expected to exceed 50%. We maintain that the observed and the projected variations in the occurrence of landslides in Calabria are related to changes in natural (i.e. the number and distribution of the triggering events) and environmental and societal (i.e. the number and the distribution of the exposed elements) components.

The paper analyses how rainfall-induced landslide risks may be mitigated by implementing early warning systems in sloping silty volcanic covers. Their answer to rainfall and other atmospheric variables seems to contain features and factors such to provide generality to the topic. The work firstly investigates the factors influencing the hydrological response of a silty volcanic cover referring to results provided by two physical models. Experimental results evidence the importance of accounting for evaporation (or evapotranspiration) and a realistic lowermost boundary condition when the evolution of the hydrological behaviour has to be predicted for early warning purposes. All findings are used to develop a physically-based model suitable for early warning prediction and provide an example of application. © Springer Nature Switzerland AG 2020.

Climate and land-use changes are posing increasing threats to freshwater-related ecosystem services, acting both on the supply and the demand side. A better understanding of the dynamics of these potential services, driven by the interactions between the factors mentioned above, could bring benefits to water resources management, the environment, and human well-being. In this work, we developed an integrated modeling approach to assess the conjoined impacts of land-use and climate changes on the potential ecosystem services (i.e. water yield and nutrients retention) until 2050. This approach was applied to the Taro River basin in Italy. Firstly, the results showed a 20% reduction in water yield was driven mainly by the increases in evapotranspiration demand and changes in rainfall patterns. Furthermore, a mean decrease of approximately 3% of the total nitrogen retention and a mean increase of 3% for the total phosphorus retention could be mainly attributed to land-use changes. Secondly, the rate of change would be different over time with the most pronounced differences between 2020 and 2030 and slower variations afterward. Finally, the obtained results could be a valuable support to identify and prioritize the best management practices for sustainable water use, balancing the tradeoffs among services.

A significant part of the recent geotechnical literature concerning pyroclastic soils is focused on the characterization of the hydrological effects of precipitations and their implications for the stability conditions of unsaturated sloping covers. Recent experience shows that suction-induced strength reduction is influenced by various factors including hydraulic hysteresis. A deeper insight into the hysteretic water retention behavior of these materials and its effects upon their response to dry/wetting conditions is a major goal of this paper, which exploits the data provided by the monitoring of a volcanic ash. Based on the parameters retrieved from data calibration, the hydrological response of a virtual slope subject to one-dimensional rainfall infiltration is investigated by numerical analyses and compared with the results obtained through the usually adopted non-hysteretic approaches. The analysis demonstrates that considering the hysteretic behavior may be crucial for a proper evaluation of the conditions leading to slope failure.

The aim of this work is to investigate the soil water budget across China by means of hydrological modeling under current and future climate conditions and to evaluate the sensitivity to soil parameters. For this purpose, observed precipitation and temperature data (1981–2010) and climate simulations (2021–2050, 2071–2100) at high resolution (about 14 km) on a large part of China are used as weather forcing. The simulated weather forcing has been bias corrected by means of the distribution derived quantile mapping method to eliminate the effects of systematic biases in current climate modeling on water cycle components. As hydrological models, two 1D models are tested: TERRA-ML and HELP. Concerning soil properties, two datasets, provided respectively by Food and Agriculture Organization and U.S. Department of Agriculture, are separately tested. The combination of two hydrological models, two soil parameter datasets and three weather forcing inputs (observations, raw and bias corrected climate simulations) results in five different simulation chains. The study highlights how the choice of some approaches or soil parameterizations can affect the results both in absolute and in relative terms and how these differences could be highly related to weather forcing in inputs or investigated soil. The analyses point out a decrease in average water content in the shallower part of the soil with different extents according to climate zone, concentration scenario and soil/cover features. Moreover, the projected increase in temperature and then in evapotranspirative demand do not ever result in higher actual evapotranspiration values, due to the concurrent variations in precipitation patterns.

Evaluation of the monthly soil water balance (SWB) provides a tool for understanding and predicting the effects of seasonal and long-term changes in soil water conditions within many geotechnical problems. In this paper, two applications of the SWB approach in the pyroclastic partially saturated soils are shown. Firstly, rainfall, evapotranspiration, water storage measured or estimated at the experimental site in Monteforte Irpino (in Southern Italy) are shown. Secondly, rainfall, infiltration, actual evapotranspiration and water storage measured by data provided by a physical model are shown. In the both cases, data are reported over two years (2010-2012). The physical model was constituted by a wooden tank filled with reconstituted silty pyroclastic soil taken from experimental site at Monteforte Irpino (AV) and it was exposed to the atmosphere at a site in Napoli. Comparison between soil hydraulic behaviours observed is discussed and the scale effects on the estimation of the SWB are analysed, treating with practical implications. From the results, it is clear that similar trends in SWB and the same value of suction over wet season (10 kPa) can be observed at both the scales in spite of differences in meteorological conditions and hydraulic properties of soils exposed to atmosphere.