Information regarding bacterial diseases in Cuvier’s beaked whale (CBW, Ziphius cavirostris) is scattered and mostly incomplete. This report describes a case of septicemia by Morganella morganii in a juvenile male CBW with concurrent renal crassicaudiasis. The animal stranded along the Ligurian coastline (Italy) and underwent a systematic post-mortem examination to determine the cause of death. Histopathology showed lesions consistent with a septicemic infection, severe meningoencephalitis, and renal crassicaudiasis. An M. morganii alpha-hemolytic strain was isolated in pure culture from liver, lung, prescapular lymph node, spleen, hepatic and renal abscesses, and central nervous system (CNS). The antimicrobial susceptibility profile of the strain was evaluated with the minimum inhibitory concentrations (MICs) method and reduced susceptibility to Trimethoprim-Sulfamethoxazole is reported. Crassicauda sp. nematodes were retrieved from both kidneys. No other pathogens were detected by immunohistochemistry, serology, or biomolecular analyses. Toxicological investigations detected high concentrations of immunosuppressant pollutants in the blubber. The chronic parasitic infestation and the toxic effects of xenobiotics likely compromised the animal's health, predisposing it to an opportunistic bacterial infection. To our knowledge, this is the first description of M. morganii septicemia with CNS involvement in a wild cetacean.
Because of the ongoing changing climate, extreme rainfall events’ frequency at the global scale is expected to increase, thus resulting in high social and economic impacts. A Meteo/Hydro/Hydraulic forecasting chain combining heterogeneous observational data sources is a crucial component for an Early Warning System and is a fundamental asset for Civil Protection Authorities to correctly predict these events, their effects, and put in place anticipatory actions. During the last week of October 2021 an intense Mediterranean hurricane (Apollo) affected many Mediterranean countries (Tunisia, Algeria, Malta, and Italy) with a death toll of seven people. The CIMA Meteo/Hydro/Hydraulic forecasting chain, including the WRF model, the hydrological model Continuum, the automatic system for water detection (AUTOWADE), and the hydraulic model TELEMAC-2D, was operated in real-time to predict the Apollo weather evolution as well as its hydrological and hydraulic impacts, in support of the early warning activities of the Italian Civil Protection Department. The WRF model assimilating radar data and in situ weather stations showed very good predictive capability for rainfall timing and location over eastern Sicily, thus supporting accurate river flow peak forecasting with the hydrological model Continuum. Based on WRF predictions, the daily automatic system for water detection (AUTOWADE) run using Sentinel 1 data was anticipated with respect to the scheduled timing to quickly produce a flood monitoring map. Ad hoc tasking of the COSMO-SkyMed satellite constellation was also performed to overcome the S1 data latency in eastern Sicily. The resulting automated operational mapping of floods and inland waters was integrated with the subsequent execution of the hydraulic model TELEMAC-2D to have a complete representation of the flooded area with water depth and water velocity.
Susceptibility mapping represents a modern tool to support forest protection plans and to address fuel management. With the present work, we continue with a research framework developed in a pioneristic study at the local scale for Liguria (Italy) and recently adapted to the national scale. In these previous works, a random-forest-based modeling workflow was developed to assess susceptibility to wildfires under the influence of a number of environmental predictors. The main novelties and contributions of the present study are: (i) we compared models based on random forest, multi-layer perceptron, and support vector machine, to estimate their prediction capabilities; (ii) we used a more accurate vegetation map as predictor, allowing us to evaluate the impacts of different types of local and neighboring vegetation on wildfires’ occurrence; (iii) we improved the selection of the testing dataset, in order to take into account the temporal variability of the burning seasons. Wildfire susceptibility maps were finally created based on the output probabilistic predicted values from the three machine-learning algorithms. As revealed with random forest, vegetation is so far the most important predictor variable; the marginal effect of each type of vegetation was then evaluated and discussed.
Long-term monitoring datasets are fundamental to understand physical and ecological responses to environmental changes, supporting management and conservation. The data should be reliable, with the sources of bias identified and quantified. CETUS Project is a cetacean monitoring programme in the Eastern North Atlantic, based on visual methods of data collection. This study aims to assess data quality and bias in the CETUS dataset, by 1) applying validation methods, through photographic confirmation of species identification; 2) creating data quality criteria to evaluate the observer’s experience; and 3) assessing bias to the number of sightings collected and to the success in species identification. Through photographic validation, the species identification of 10 sightings was corrected and a new species was added to the CETUS dataset. The number of sightings collected was biased by external factors, mostly by sampling effort but also by weather conditions. Ultimately, results highlight the importance of identifying and quantifying data bias, while also yielding guidelines for data collection and processing, relevant for species monitoring programmes based on visual methods.
Reliable urban flood modeling is highly demanded in emergency response, risk management, and urban planning related to urban flooding. In this paper, the Storm Water Management Model (SWMM) is adapted to simulate urban rainfall‐runoff and pipe drainage processes within the Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE) model which accounts for natural river basin runoff generation and routing processes. The integrated DRIVE‐SWMM model (referred to as DRIVE‐Urban) allows to explicitly delineate the mass‐energy interactions between urban drainage system (e.g., pipes and dikes) and river networks. This presents a further step model development for accurate urban flooding prediction which is lacking in existing urban flood models and traditional hydrological models. The validity of the DRIVE‐Urban model is evaluated for three case studies in Haikou City, China, with camera observations of street inundation during typhoon landfalls and heavy rainfall events. The results show that the DRIVE‐Urban model successfully captures 62%, 69%, and 77% of the total observed inundated road‐sections for the three cases respectively. The third case study with severe flooding situation shows that the DRIVE‐Urban performance is further improved when given reliable river and tidal level information, indicating the importance of integrating river‐basin with urban hydrological and hydraulic modeling.
This article presents the Lightning Performance (LP) assessment of a realistic portion of the Italian distribution network with the use of probability distributions for lightning parameters inferred from local data recorded by a Lightning Location System (LLS). The procedure considers downward negative first, negative subsequent, and positive return strokes, taking into account the number of strokes per flash (flash multiplicity) and the distance among return stroke impact points within the same flash (stroke terminations distance). The analysis of the effect produced using LLS data on the estimated number of flashovers per year on the test line is achieved by replacing one by one the standard distributions and parameters typically adopted in the literature, with those inferred from the LLS, when they can be considered reliable. Results show that adopting LLS-inferred parameters and distributions produces significant (positive or negative) variations in the estimated number of flashovers with respect to values computed with the standard approach.
The vulnerability of flood-prone areas is determined by the susceptibility of the exposed assets to the hazard. It is a crucial component in risk assessment studies, both for climate change adaptation and disaster risk reduction. In this study, we analyse patterns of vulnerability for the residential sector in a frequently hit urban area of Milan, Italy. The conceptual foundation for a quantitative assessment of the structural dimensions of vulnerability is based on the modified source–pathway–receptor–consequence model. This conceptual model is used to improve the parameterization of the flood risk analysis, describing (i) hazard scenario definitions performed by hydraulic modelling based on past event data (source estimation) and morphological features and land-use evaluation (pathway estimation) and (ii) the exposure and vulnerability assessment which consists of recognizing elements potentially at risk (receptor estimation) and event losses (consequence estimation). We characterized flood hazard intensity on the basis of variability in water depth during a recent event and spatial exposure also as a function of a building's surroundings and buildings' intrinsic characteristics as a determinant vulnerability indicator of the elements at risk. In this sense the use of a geographic scale sufficient to depict spatial differences in vulnerability allowed us to identify structural vulnerability patterns to inform depth–damage curves and calculate potential losses from mesoscale (land-use level) to microscale (building level). Results produces accurate estimates of the flood characteristics, with mean error in flood depth estimation in the range 0.2–0.3 m and provide a basis to obtain site-specific damage curves and damage mapping. Findings show that the nature of flood pathways varies spatially, is influenced by landscape characteristics and alters vulnerability spatial distribution and hazard propagation. At the mesoscale, the “continuous urban fabric” Urban Atlas 2018 land-use class with the occurrence of at least 80 % of soil sealing shows higher absolute damage values. At microscale, evidence demonstrated that even events with moderate magnitude in terms of flood depth in a complex urbanized area may cause more damage than one would expect.
Marine litter pollution, particularly plastics pollution, is an increasing global concern. While various studies have contributed useful information on this topic, there has been a scarcity of data on floating marine macro-litter (FMML) in poorly monitored areas such as the South China Sea (SCS). This paper describes a large-scale FMML assessment research in the northern SCS. Our data indicated the ubiquitous presence, abundant quantity, spatiotemporal variability, complex composition, and potential sources of FMML in the investigated region during boreal spring-summer periods over multiple years. According to observer-based records, the average FMML density was estimated to be 131.0 ± 91.8 items/km2 (mean ± SD), with anthropogenic FMML density of 118.7 ± 86.2 items/km2. Anthropogenic and natural items accounted for 90.6% and 5.5% of the total, respectively. Plastics (72.0%) and styrofoam (9.3%) dominated the recorded items. The great majority of items (92.1%) were characterized by small size of ≤20 cm. Labels of plastic bottle/packaging litter indicated that identifiable sources included surrounding countries of the SCS. Fishing activities were recognized as key sources of FMML, with 15.3% of FMML items likely being fishing-related. Globally, known estimates of FMML densities could vary from 0.002 to 578 items/km2, with plastics accounting for 34.8-99.0%. Therefore, marine pollution from anthropogenic FMML in our investigated area ranked at a medium-to-high level around the globe. To conclude, this study demonstrated that the SCS is one of the world's hotspot areas with FMML pollution and sheds light on marine litter pollution, especially plastics pollution, in this intensively human-exploited but poorly monitored region. In future research, FMML pollution in other sections of SCS and possible negative impacts of FMML on marine ecosystems and megafauna should be further examined.
COVID-19 challenged all national emergency management systems worldwide overlapping with other natural hazards. We framed a ‘parallel phases’ Disaster Risk Management (DRM) model to overcome the limitations of the existing models when dealing with complex multi-hazard risk conditions. We supported the limitations analysing Italian Red Cross data on past and ongoing emergencies including COVID-19 and we outlined three guidelines for advancing multi-hazard DRM: (i) exploiting the low emergency intensity of slow-onset hazards for preparedness actions; (ii) increasing the internal resources and making them available for international support; (iii) implementing multi-hazard seasonal impact-based forecasts to foster the planning of anticipatory actions.
Climate change produces new challenges to the development strategies of the most vulnerable countries, where there is urgent need of effective adaptation policies. However, the uncertainty about the expected costs of climate change and about the benefits of adaptation hampers the design and the implementation of adequate measures. Alternative decision-support tools and decision strategies can be adopted by policy makers and the private investors. In this paper, we assess the effectiveness of Portfolio Analysis (PA) as a decision-support tool for adaptation strategies under different climate change scenarios. PA effectiveness is explored in the context of tea plantation investments in Rwanda. Tea is a key commodity for the Rwandan economy, and the agriculture sector is one of the most negatively affected by climatic changes, especially in developing countries. It is a perennial crop, for which investments have high sunk costs and economic returns are highly sensitive to changes in the average temperature and in rainfall distributions. Thus, returns on investments in new tea plants are affected by high climate-induced uncertainty. PA aggregates different investment options into portfolios, instead of considering a single option, and allows identification of an efficiency frontier of best portfolios in terms of trade-off between economic efficiency (expected net present value) and risk (variance of the economic performance). In spite of its advantages, PA remains rarely used in climate change adaptation analysis. In this paper we show how PA can be performed in practice when evaluating adaptation investments against different climate change scenarios. The results show that, using PA, new possible allocations of resources emerge, identifying alternative investment solutions with a better trade-off between economic return and risk. PA here emerges as an effective tool in designing long-term investments in the agriculture sector, robust to the complex and uncertain effects of climate change. A Sensitivity analysis of the results to different social discount rates and different climate change scenarios suggests how these two factors can be relevant for the choice of investment portfolios.
Wildfires can significantly affect mountain hillslopes through the combustion of trees and shrubs and changes in soil properties. The type and magnitude of the associated post-fire effects depend on several factors, including fire severity and soil physical–mechanical-hydraulic features that, coupled with climate and topographic conditions, may cause increased runoff, erosion, and slope instability as consequence of intense rainfall. The post-fire response of slopes is highly site-specific. Therefore, in situ surveys and laboratory tests are needed to quantify changes in key soil parameters. The present study documents the post-fire physical and hydromechanical properties of pyroclastic topsoil collected from three test sites that suffered wildfires and rainfall-induced post-fire events in 2019 and 2020 in the Sarno Mountains (Campania Region, southern Italy). The tested pyroclastic soils in burned conditions show (i) no significant changes in grain size distribution, soil organic matter, and specific gravity; (ii) a deterioration in shear strength in terms of decreased soil cohesion caused by the fire-induced weakening of root systems; and (iii) a decrease in hydraulic conductivity. Accordingly, it can be argued that the documented post-fire erosion responses were mainly caused by the reduced cohesion and hydraulic conductivity of the burned topsoil layer, as well as by the loss of vegetation cover and the deposition of fire residues. Although deserving further deepening, this study can represent the necessary background for understanding the initiation mechanism of post-fire erosion processes in the analyzed area and on several natural slopes under similar conditions.
Environmental policies, including the European Marine Strategy Framework Directive (MSFD), generally rely on the measurement of indicators to assess the good environmental status (GES) and ensure the protection of marine ecosystems. However, depending on available scientific knowledge and monitoring programs in place, quantitative GES assessments are not always feasible. This is specifically the case for marine turtle species, which are listed under the Biodiversity Descriptor of the MSFD. Relying on an expert consultation, the goal of this study was to develop indicators and a common assessment approach to be employed by European Union Member States to evaluate the status of marine turtle populations in the frame of the MSFD. A dedicated international expert group was created to explore and test potential assessment approaches, in coherence with other environmental policies (i.e. Habitats Directive, OSPAR and Barcelona Conventions). Following a series of workshops, the group provided recommendations for the GES assessment of marine turtles. In particular, indicators and assessment methods were defined, setting a solid basis for future MSFD assessments. Although knowledge gaps remain, data requirements identified in this study will guide future data collection initiatives and inform monitoring programs implemented by EU Member States. Overall this study highlights the value of international collaboration for the conservation of vulnerable species, such as marine turtles.
The deep sea is the largest ecosystem on Earth, yet little is known about the processes driving patterns of genetic diversity in its inhabitants. Here, we investigated the macro- and microevolutionary processes shaping genomic population structure and diversity in two poorly understood, globally distributed, deep-sea predators: Cuvier’s beaked whale (Ziphius cavirostris) and Blainville’s beaked whale (Mesoplodon densirostris). We used double-digest restriction associated DNA (ddRAD) and whole mitochondrial genome (mitogenome) sequencing to characterise genetic patterns using phylogenetic trees, cluster analysis, isolation-by-distance, genetic diversity and differentiation statistics. Single nucleotide polymorphisms (SNPs; Blainville’s n=43 samples, SNPs=13988; Cuvier’s n=123, SNPs= 30479) and mitogenomes (Blainville’s n=27; Cuvier’s n=35) revealed substantial hierarchical structure at a global scale. Both species display significant genetic structure between the Atlantic, Indo-Pacific and in Cuvier’s, the Mediterranean Sea. Within major ocean basins, clear differentiation is found between genetic clusters on the east and west sides of the North Atlantic, and some distinct patterns of structure in the Indo-Pacific and Southern Hemisphere. We infer that macroevolutionary processes shaping patterns of genetic diversity include biogeographical barriers, highlighting the importance of such barriers even to highly mobile, deep-diving taxa. The barriers likely differ between the species due to their thermal tolerances and evolutionary histories. On a microevolutionary scale, it seems likely that the balance between resident populations displaying site fidelity, and transient individuals facilitating gene flow, shapes patterns of connectivity and genetic drift. Based on these results, we propose management units to facilitate improved conservation measures for these elusive species.
The growth of air transport demand expected over the next decades, along with the increasing frequency and intensity of extreme weather events, such as heavy rainfalls and severe storms due to climate change, will pose a tough challenge for air traffic management systems, with implications for flight safety, delays and passengers. In this context, the Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project has a dual aim, first to investigate if very short-range high-resolution weather forecast, including data assimilation, can improve the predictive capability of these events, and then to understand if such forecasts can be suitable for air traffic management purposes. The intense squall line that affected Malpensa, the major airport by passenger traffic in northern Italy, on 11 May 2019 is selected as a benchmark. Several numerical experiments are performed with a Weather Research and Forecasting (WRF) model using two assimilation techniques, 3D-Var in WRF Data Assimilation (WRFDA) system and a nudging scheme for lightning, in order to improve the forecast accuracy and to evaluate the impact of assimilated different datasets. To evaluate the numerical simulations performance, three different verification approaches, object-based, fuzzy and qualitative, are used. The results suggest that the assimilation of lightning data plays a key role in triggering the convective cells, improving both location and timing. Moreover, the numerical weather prediction (NWP)-based nowcasting system is able to produce reliable forecasts at high spatial and temporal resolution. The timing was found to be suitable for helping Air Traffic Management (ATM) operators to compute alternative landing trajectories.
Flooding is the most costly natural hazard in Europe. Climatic and socioeconomic change are expected to further increase the amount of loss in the future. To counteract this development, policymaking, and adaptation planning need reliable large-scale risk assessments and an improved understanding of potential risk drivers. In this study, recent datasets for hazard and flood protection standards are combined with high resolution exposure projections and attributes of vulnerability derived from open data sources. The independent and combined influence of exposure change and climate scenarios rcp45 and rcp85 on fluvial flood risk are evaluated for three future periods centered around 2025, 2055 and 2085. Scenarios with improved and neglected private precaution are examined for their influence on flood risk using a probabilistic, multivariable flood loss model — BN-FLEMOps — to estimate fluvial flood losses for residential buildings in Europe. The results on NUTS-3 level reveal that urban centers and their surrounding regions are the hotspots of flood risk in Europe. Flood risk is projected to increase in the British Isles and Central Europe throughout the 21st century, while risk in many regions of Scandinavia and the Mediterranean will stagnate or decline. Under the combined effects of exposure change and climate scenarios rcp45, rcp85, fluvial flood risk in Europe is estimated to increase seven-fold and ten-fold respectively until the end of the century. Our results confirm the dominance of socioeconomic change over climate change on increasing risk. Improved private precautionary measures would reduce flood risk in Europe on an average by 15%. The quantification of future flood risk in Europe by integrating climate, socioeconomic and private precaution scenarios provides an overview of risk drivers, trends, and hotspots. This large-scale comprehensive assessment at a regional level resolution is valuable for multi-scale risk-based adaptation planning.
Subsurface storage changes (ΔS) represent a key modulator of drought propagation through the hydrological cycle, but their contribution to the annual water balance, and to drought propagation and recovery has rarely been explicitly assessed across catchments and climates. To expand on previous work on this matter, here we performed a large-sample analysis of precipitation, discharge, actual evapotranspiration (ET), and ΔS for 10 hydrological years and 102 catchments across various hydro-climatological regimes in Italy. We found that ΔS cannot be neglected in the annual water balance. Storage depletion leads to the attenuation of hydrological drought compared to meteorological drought, meaning that subsurface storage actively supports discharge during drought. We also found that storage generally recovers from precipitation deficits over time scales similar to the discharge recovery time, while recovery times for ET are longer. These findings show that subsurface storage drives drought propagation and recovery, regardless of climatic and catchment characteristics, and are thus relevant to properly inform water managers about surface- and ground-water availability in a changing climate.
The advent of unoccupied aerial vehicles (UAVs) has enhanced our capacity to survey wildlife abundance, yet new protocols are still required for collecting, processing, and analysing image-type observations. This paper presents a methodological approach to produce informative priors on species misidentification probabilities based on independent experiments. We performed focal follows of known dolphin species and distributed our imagery amongst 13 trained observers. Then, we investigated the effects of reviewer-related variables and image attributes on the accuracy of species identification and level of certainty in observations. In addition, we assessed the number of reviewers required to produce reliable identification using an agreement-based framework compared with the majority rule approach. Among-reviewer variation was an important predictor of identification accuracy, regardless of previous experience. Image resolution and sea state exhibited the most pronounced effects on the proportion of correct identifications and the reviewers’ mean level of confidence. Agreement-based identification resulted in substantial data losses but retained a broader range of image resolutions and sea states than the majority rule approach and produced considerably higher accuracy. Our findings suggest a strong dependency on reviewer-related variables and image attributes, which, unless considered, may compromise identification accuracy and produce unreliable estimators of abundance.
The Weather Research and Forecast (WRF) model is used to simulate atmospheric circulation during the summer season in a coastal region of central Italy, including the city of Rome. The time series of surface air temperature, wind speed, and direction are compared with in situ observations in urban Rome and its rural surroundings. Moreover, the vertical wind profiles are compared to sodar urban measurements. To improve the WRF model’s ability to reproduce the local circulation, and the onset and propagation of the sea breeze, several simulations are carried out modifying the land use and the thermal and physical properties of the surfaces. Based on the results of the correlation coefficient and the RMSE, the heat capacity and albedo are the parameters mostly influencing the daily temperature cycle. Particularly, the temperature in the urban area is reproduced more realistically when the heat capacity is increased. Hence, the best simulations are used to initialize a large-eddy simulation at high spatial resolution to analyze the interaction between the sea breeze and the urban heat island and to investigate the interaction of the sea breeze front with orography and surface roughness. As confirmed by observations collected by in situ weather stations in the surroundings of Rome, the front, entering the city, splits into three branches: (i) a west component in the western flank of the city, closer to the sea; (ii) a north-west component in the northern, inland side, and (iii) a south-west component in the south area of the city.
Global drought risk assessments have been conducted with the objective of highlighting the regions or countries most at risk, and their outcomes are deemed useful to inform decisions on the implementation of risk reduction, transfer, financing, and adaptation strategies. However, by virtue of the scale of the assessment, some countries and regions experiencing negative impacts of droughts may not appear in “high” risk categories in global comparisons. This limits and potentially biases the ability of decision-makers, regional organisations, or funding mechanisms to recognise which countries under their purview should be targeted for assistance. This paper addresses this gap by comparing the outcomes of global and regional drought risk assessments for different clusters of countries of particular relevance to international climate and disaster risk policy. Results show that 50 countries changed the risk category to “high” or “very high” in their clusters compared to a lower risk category at the global level, due to the renormalisation of raw indicator values with different ranges for each cluster. The findings highlight the importance of analysing risk at multiple spatial scales to ensure no country is “left behind” in global risk and adaptation finance decisions.
Satellite-based Earth observations (EO) are an accurate and reliable data source for atmospheric and environmental science. Their increasing spatial and temporal resolutions, as well as the seamless availability over ungauged regions, make them appealing for hydrological modeling. This work shows recent advances in the use of high-resolution satellite-based EO data in hydrological modeling. In a set of six experiments, the distributed hydrological model Continuum is set up for the Po River basin (Italy) and forced, in turn, by satellite precipitation and evaporation, while satellite-derived soil moisture (SM) and snow depths are ingested into the model structure through a data-assimilation scheme. Further, satellite-based estimates of precipitation, evaporation, and river discharge are used for hydrological model calibration, and results are compared with those based on ground observations. Despite the high density of conventional ground measurements and the strong human influence in the focus region, all satellite products show strong potential for operational hydrological applications, with skillful estimates of river discharge throughout the model domain. Satellite-based evaporation and snow depths marginally improve (by 2 % and 4 %) the mean Kling–Gupta efficiency (KGE) at 27 river gauges, compared to a baseline simulation (KGEmean= 0.51) forced by high-quality conventional data. Precipitation has the largest impact on the model output, though the satellite data on average shows poorer skills compared to conventional data. Interestingly, a model calibration heavily relying on satellite data, as opposed to conventional data, provides a skillful reconstruction of river discharges, paving the way to fully satellite-driven hydrological applications.
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