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Central Italy is characterized by complex orography. The territorial response to heavy precipitation may activate different processes in terms of hydrogeological hazards. Floods, flash floods, and wet mass movements are the main ground effects triggered by heavy or persistent rainfall. The main aim of this work is to present a unique tool that is based on a distributed hydrological model, able to predict different rainfall-induced phenomena, and essential for the civil protection early warning activity. The Cetemps Hydrological Model is applied to the detection of hydrologically stressed areas over a spatial domain covering the central part of Italy during a weather event that occurred in 2014. The validation of three hydrological stress indices is proposed over a geographical area of approximately 64,500 km2 that includes catchments of varying size and physiography. The indices were used to identify areas subject to floods, flash floods, or landslides. Main results showed very high accuracies (~90%) for all proposed indices, with flood false alarms growing downstream to larger basins, but very close to zero in most cases. The three indices can give complementary information about the predominant phenomenon and are able to distinguish fluvial floods from pluvial floods. Nevertheless, the results were influenced by the presence of artificial reservoirs that regulated flood wave propagation, therefore, indices timing slightly worsen downstream in larger basins. [ABSTRACT FROM AUTHOR]

The term nowcast in hydrometeorology reflects the need for timely and accurate predictions of risky environmental situations, which are related to the development of severe meteorological events at short time scales. The objective of this paper is to apply a fully neural-network approach to the rainfall field nowcasting from infrared (IR) and microwave (MW) passive-sensor imagery aboard, respectively, geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellites. The multi-satellite space-time prediction procedure, which is named Neural Combined Algorithm for Storm Tracking (NeuCAST), consists of two consecutive steps. First, the IR radiance field measured from a geostationary satellite radiometer (e.g., Meteosat) is projected ahead in time (e.g., 30 min); second, the projected radiance field is used in estimating the rainfall field by means of an MW-IR combined rain retrieval algorithm exploiting GEO-LEO observations. The NeuCAST methodology is extensively illustrated and discussed in this paper. Its accuracy is quantified by means of quantitative error indexes, which are evaluated on selected case studies of rainfall events in Southern Europe in 2003 and 2005. Index Terms--Neural network (NN), nowcasting methods, passive sensors, precipitation retrieval, satellite remote sensing.

Highly populated coastal environments receive large quantities of treated and untreated wastewater from human and industrial sources. Bivalve molluscs accumulate and retain contaminants, and their analysis provides evidence of past contamination. Rivers and precipitation are major routes of bacteriological pollution from surface or sub-surface runoff flowing into coastal areas. However, relationships between runoff, precipitation, and bacterial contamination are site-specific and dependent on the physiographical characteristics of each catchment. In this work, we evaluated the influence of precipitation and river discharge on molluscs' Escherichia coli concentrations at three sites in Central Italy, aiming at quantifying how hydrometeorological conditions affect bacteriological contamination of selected bivalve production areas. Rank-order correlation analysis indicated a stronger association between E. coli concentrations and the modelled Pescara River discharge maxima (r= 0.69) than between E. coli concentration and rainfall maxima (r= 0.35). Discharge peaks from the Pescara River caused an increase in E. coli concentration in bivalves in 87% of cases, provided that the runoff peak occurred 1-6 days prior to the sampling date. Precipitation in coastal area was linked to almost 60% of cases of E. coli high concentrations and may enhance bacterial transportation offshore, when associated with a larger-scale weather system, which causes overflow occurrence. [ABSTRACT FROM AUTHOR]

Floods and flash floods are complex events, depending on weather dynamics, basin physiographical characteristics, land use cover and water management. For this reason, the prediction of such events usually deals with very accurate model tuning and validation, which is usually site-specific and based on climatological data, such as discharge time series or flood databases. In this work, we developed and tested two hydrological-stress indices for flood detection in the Italian Central Apennine District: a heterogeneous geographical area, characterized by complex topography and medium-to-small catchment extension. The proposed indices are threshold-based and developed considering operational requirements of National Civil Protection Department end-users. They are calibrated and tested through the application of signal theory, in order to overcome data scarcity over ungauged areas, as well as incomplete discharge time series. The validation has been carried out on a case study basis, using flood reports from various sources of information, as well as hydrometric-level time series, which represent the actual hydrological quantity monitored by Civil Protection operators. Obtained results show that the overall accuracy of flood prediction is greater than 0.8, with false alarm rates below 0.5 and the probability of detection ranging from 0.51 to 0.80. Moreover, the different nature of the proposed indices suggests their application in a complementary way, as the index based on drained precipitation appears to be more sensitive to rapid flood propagation in small tributaries, while the discharge-based index is particularly responsive to main-channel dynamics. [ABSTRACT FROM AUTHOR]

Flood and flash floods are complex events, depending on weather dynamics, basin physiographical characteristics, land use cover and water management. For this reason, prediction of such events usually deals with very accurate model tuning and validation, which is usually site-specific and based on climatological data, such as discharge time series or flood databases. In this work, we developed and tested two hydrological stress indices for flood detection in the Central Italy Apennine District: a heterogeneous geographical area, characterized by complex topography and medium-to-small catchment extension. Proposed indices are threshold-based and developed taking into account operational requirements of civil protection end-users. They are calibrated and tested through the application of signal theory, in order to overcome data scarcity over ungauged areas, as well as incomplete discharge time series. The validation has been carried out on a case study basis, through the use of flood reports from various source of information, as well as hydrometric level time series, which represent the actual hydrological quantity monitored by civil protection operators. Obtained results shows as the overall accuracy of flood prediction is greater than 0.8, with false alarm rates below 0.5 and probability of detection ranging from 0.51 to 0.80. A slight anticipation of peak occurrence was found, however, the time shift of indices signal peak it is strongly dependent on the presence of dams that regulates the flood propagation. Moreover, the different nature of the proposed indices suggests their application in a complementary way, as the index based on drained precipitation appears to be more sensible to rapid flood propagation in small tributaries, while the discharge-based index is particularly responsive to main channel dynamics. [ABSTRACT FROM AUTHOR]

The weather forecasts for precipitation have considerably improved in recent years thanks to the increase of computational power. This allows for the use of both a higher spatial resolution and the parameterization schemes specifically developed for representing sub-grid scale physical processes at high resolution. However, precipitation estimation is still affected by errors that can impact the response of hydrological models. To the aim of improving the hydrological forecast and the characterization of related uncertainties, a regional-scale meteorological–hydrological ensemble is presented. The uncertainties in the precipitation forecast and how they propagate in the hydrological model are also investigated. A meteorological–hydrological offline coupled ensemble is built to forecast events in a complex-orography terrain where catchments of different sizes are present. The Best Discharge-based Drainage (BDD; both deterministic and probabilistic) index, is defined with the aim of forecasting hydrological-stress conditions and related uncertainty. In this context, the meteorological–hydrological ensemble forecast is implemented and tested for a severe hydrological event which occurred over Central Italy on 15 November 2017, when a flood hit the Abruzzo region with precipitation reaching 200 mm (24 h) -1 and producing damages with a high impact on social and economic activities. The newly developed meteorological–hydrological ensemble is compared with a high-resolution deterministic forecast and with the observations (rain gauges and radar data) over the same area. The receiver operating characteristic (ROC) statistical indicator shows how skilful the ensemble precipitation forecast is with respect to both rain-gauge- and radar-retrieved precipitation. Moreover, both the deterministic and probabilistic configurations of the BDD index are compared with the alert map issued by Civil Protection Department for the event showing a very good agreement. Finally, the meteorological–hydrological ensemble allows for an estimation of both the predictability of the event a few days in advance and the uncertainty of the flood. Although the modelling framework is implemented on the basins of the Abruzzo region, it is portable and applicable to other areas. [ABSTRACT FROM AUTHOR]

The weather forecasts for precipitation have considerably improved in recent years thanks to the increase of computational power. This allows to use both a higher spatial resolutions and the newly developed parameterization schemes for representing sub-grid scale physical processes. However, precipitation estimation is still affected by errors that can impact on the response of hydrological models. To the aim of considering the uncertainties in the precipitation forecast and how they propagate in the hydrological model, an ensemble approach is investigated. A meteo-hydro ensemble system is built to forecast events in a complex orography terrain where catchments of different size are present. In this context, the meteo-hydrological forecast system is implemented and tested for a severe hydrological event occurred over Central Italy on November 15, 2017. During this period, a flash flood hit the Abruzzo region causing precipitation up to 200 mm/24 hours and producing damages with a high impact on social and economic activities. The newly developed meteo-hydro ensemble system is compared with a high resolution deterministic forecast and with the observations over the same area, showing a very good response. In addition, the ensemble allows for an estimation of the predictability of the event a few days in advance and of the uncertainty of this flood. Although the modelling framework is implemented on the basins of Abruzzo region, it is portable and applicable to other areas. [ABSTRACT FROM AUTHOR]

Total peroxy nitrate ( ∑ PN) concentrations have been measured using a thermal dissociation laser-induced fluorescence (TD-LIF) instrument during the BORTAS campaign, which focused on the impact of boreal biomass burning (BB) emissions on air quality in the Northern Hemisphere. The strong correlation observed between the  ∑ PN concentrations and those of carbon monoxide (CO), a well-known pyrogenic tracer, suggests the possible use of the  ∑ PN concentrations as marker of the BB plumes. Two methods for the identification of BB plumes have been applied: (1)  ∑ PN concentrations higher than 6 times the standard deviation above the background and (2)  ∑ PN concentrations higher than the 99th percentile of the  ∑ PNs measured during a background flight (B625); then we compared the percentage of BB plume selected using these methods with the percentage evaluated, applying the approaches usually used in literature. Moreover, adding the pressure threshold ( ∼  750 hPa) as ancillary parameter to  ∑ PNs, hydrogen cyanide (HCN) and CO, the BB plume identification is improved. A recurrent artificial neural network (ANN) model was adapted to simulate the concentrations of  ∑ PNs and HCN, including nitrogen oxide (NO), acetonitrile (CH3CN), CO, ozone (O3) and atmospheric pressure as input parameters, to verify the specific role of these input data to better identify BB plumes. [ABSTRACT FROM AUTHOR]

Hourly concentrations of ozone (O 3 ) and nitrogen dioxide (NO 2 ) have been measured for 16 years, from 1998 to 2013, in a seaside town in central Italy. The seasonal trends of O 3 and NO 2 recorded in this period have been studied. Furthermore, we used the data collected during one year (2005), to define the characteristics of a multiple linear regression model and a neural network model. Both models are used to model the hourly O 3 concentration, using, two scenarios: 1) in the first as inputs, only meteorological parameters and 2) in the second adding photochemical parameters at those of the first scenario. In order to evaluate the performance of the model four statistical criteria are used: correlation coefficient, fractional bias, normalized mean squared error and a factor of two. All the criteria show that the neural network gives better results, compared to the regression model, in all the model scenarios. Predictions of O 3 have been carried out by many authors using a feed forward neural architecture. In this paper we show that a recurrent architecture significantly improves the performances of neural predictors. Using only the meteorological parameters as input, the recurrent architecture shows performance better than the multiple linear regression model that uses meteorological and photochemical data as input, making the neural network model with recurrent architecture a more useful tool in areas where only weather measurements are available. Finally, we used the neural network model to forecast the O 3 hourly concentrations 1, 3, 6, 12, 24 and 48 h ahead. The performances of the model in predicting O 3 levels are discussed. Emphasis is given to the possibility of using the neural network model in operational ways in areas where only meteorological data are available, in order to predict O 3 also in sites where it has not been measured yet. [ABSTRACT FROM AUTHOR]

The Po River is a crucial resource for the Italian economy, since 40% of the gross domestic product comes from this area. It is thus crucial to quantify the impact of climate change on this water resource in order to plan for future water use. In this paper a mini ensemble of 8 hydrological simulations is completed from 1960 to 2050 under the A1B emission scenario, by using the output of two regional climate models as input (REMO and RegCM) at two different resolutions (25 km-10 km and 25 km-3 km). The river discharge at the outlet point of the basin shows a change in the spring peak of the annual cycle, with a one month shift from May to April. This shift is entirely due to the change in snowmelt timing which drives most of the discharge during this period. Two other important changes are an increase of discharge in the wintertime and a decrease in the fall from September to November. The uncertainty associated with the winter change is larger compared to that in the fall. The spring shift and the fall decrease of discharge imply an extension of the hydrological dry season and thus an increase in water stress over the basin. The spatial distributions of the discharge changes are in agreement with what is observed at the outlet point and the uncertainty associated with these changes is proportional to the amplitude of the signal. The analysis of the changes in the anomaly distribution of discharge shows that both the increases and decreases in seasonal discharge are tied to the changes in the tails of the distribution, i.e. to the increase or decrease of extreme events. [ABSTRACT FROM AUTHOR]

The response of Mediterranean small catchments hydrology to climate change is still relatively unexplored. Regional Climate Models (RCMs) are an established tool for evaluating the expected climate change impact on hydrology. Due to the relatively low resolution and systematic errors, RCM outputs are routinely and statistically post-processed before being used in impact studies. Nevertheless, these techniques can impact the original simulated trends and then impact model results. In this work, we characterize future changes of a small Apennines (Central Italy) catchment hydrology, according to two radiative forcing scenarios (Representative Concentration Pathways, RCPs, 4.5 and 8.5). We also investigate the impact of a widely used bias correction technique, the empirical Quantile Mapping (QM) on the original Climate Change Signal (CCS), and the subsequent alteration of the original Hydrological Change Signal (HCS). Original and bias-corrected simulations of five RCMs from Euro-CORDEX are used to drive the CETEMPS hydrological model CHyM. HCS is assessed by using monthly mean discharge and a hydrological-stress index. HCS shows a large spatial and seasonal variability where the summer results are affected by the largest decrease of mean discharge (down to −50%). QM produces a small alteration of the original CCS, which generates a generally wetter HCS, especially during the spring season. [ABSTRACT FROM AUTHOR]

Abstract: In this study, we investigate the potential impact on local air quality of a biomass power plant, which is planned for installation near L''Aquila, a city of 70,000 people located in a mountain valley in Central Italy. The assessment is carried out by applying a one year simulation with the CALPUFF model, following the recommendations of the U. S. Environmental Protection Agency. Meteorological input is produced with CALMET model, fed with both MM5 meteorological fields at 3 km resolution and wind observations from a surface weather station. We estimate small (<0.5 μg m− 3) annual average increments to SO2, NO2 and PM10 ambient levels over the domain of interest, but significant (up to 50% for NO2) enhancements and several violations (up to 141 for NO2) of hourly limits for human protection within 1.5 km from the source. These results anticipate a larger negative effect on local air quality than those published by the building firm of the plant. We also suggest that a minimum distance of 5 km from the nearest residential area would represent a significant decrease of population exposure. [Copyright &y& Elsevier]