Annual Damage Research Articles

Increasing economic losses from natural disasters as a last decade trend

Two main methods were used for this article: individual regression analyses on historical data and a three-stage methodology to estimate direct losses, sector losses, and GDP losses that are attributable to natural disasters. The paper analyses using data from the insurance company Swiss Re on economic losses from natural disasters in recent years. In addition, statistics for 20 years (2002-2022) collected by CRED and the United Nations Office for Disaster Risk Reduction (UNDRR) are analysed. The global losses from natural disasters in the last ten years are also assessed. An upward trend in the annual increase of global economic damage from natural disasters is identified. The largest natural disasters in the world in terms of economic damage 2013-2022 are considered in detail. The data of damage from the listed natural disasters are compared with the economic damage from the earthquake in Turkey in 2023. The article considers the indicator of economic damage caused by natural disasters by region and concludes that it is heterogeneous. The correlation of economic losses of countries with the highest economic losses from natural disasters in the last 20 years with their level of GDP is examined. The World Bank’s estimate of annual damage from natural disasters is given. The conclusion gives a forecast for the period from 2022 to 2050 by Ortec Finance to estimate the direct losses, sector losses, and GDP losses that are attributable to natural disasters. Also in the conclusion, confirms the trend of recent decades is the increase in the magnitude of economic losses from natural disasters of global nature, which supports the hypothesis of the study. The conclusion gives a forecast of increase in economic losses from natural disasters in the near future.

An analysis of economic losses from cyberattacks: based on input–output model and production function

There has recently been a global increase in economic losses due to cyberattacks. However, research on the economic damage caused by cyberattacks has mainly focused on attacked companies, and spillover damage to other sectors has not been sufficiently investigated. This study analyzed the economic losses from cyberattacks in Japan using the production function and input–output model to improve the accuracy of damage prediction and various national measures. First, we provide an estimation method for the annual direct damage by industry using a production function. The mainstream input dataset is lost working hours owing to cyber incidents. Second, we devised a model to estimate the amount of spillover damage to the entire country using the input–output model. Third, although the cyber damage data were limited to only interview data by the JNSA and IPA, we showed the process of estimating direct and spillover damage in all sectors in Japan. As a result, we consider that our estimation method is feasible and effective at the national level. This study contributes to future research on cyber resilience by analyzing the damage caused by cyberattacks from a macroeconomic perspective using a production function and input–output model.

Flood risk analysis integrating of Bayesian-based time-varying model and expected annual damage considering non-stationarity and uncertainty in the coastal city

Flood disaster is more serious in coastal cities due to the combined impact of rainfall and tides. Accurate assessment of coastal flood risk is essential for planning effective and targeted adaptation under changing environment. The objective of the study is to propose an integrated framework for future flood risk assessment by Bayesian-based time-varying model and expected annual damage (EAD) in the coastal city. To decrease the uncertainty of non-stationary frequency, Bayesian Model Averaging (BMA) and time-varying parameter distribution (TVPD) models were employed to establish the non-stationary distributions of rainfall and tides, and copula function was adopted to determine the joint and co-occurrence probability. Subsequently, to reflect flooding probability and inundation damage simultaneously, the EAD was applied to quantify flood risk by copula function and hydrodynamic model. The variation and uncertainty of flood risk were also investigated under changing environment. Taking Haidian Island in Hainan Province, China as a case study, the results show that the non-stationary distributions of rainfall and tides can be appropriately derived based on BMA and TVPD models. The joint and co-occurrence probabilities increase significantly under non-stationary scenario with the average rates of 33.22 % and 64.82 %, respectively. Moreover, the EAD will be underestimated by 20.56 %–69.84 % in 2030–2060 year without considering non-stationarity, and the uncertainty of EAD rises with the increase of the design year. The approach and result of our study can help decision makers evaluate the future flood risk in the coastal city, and provide support for sustainable flood management to adapt the climate change.

Mitigation of air pollution and corresponding impacts during a global energy transition towards 100% renewable energy system by 2050

Air pollution from the combustion of fossil fuels has adverse health impacts and is linked to cardiovascular disease, strokes, acute respiratory disease and cancer, predominantly in urban centres around the world. Increasing use of renewables for power generation has been seen to bring about the benefits of cleaner air in regions and countries that have high shares of renewable energy installations. This research aims to estimate current NOx, SOx, PM2.5, and PM10 emissions from the energy sector and identify the trends in development of these emissions during a global energy transition towards 100% renewable energy. In this energy transition, total emissions from the global energy sector are projected to drop by almost 92% in 2050 compared to 2015, with annual premature deaths from energy sector-related air pollution reducing by about 97% from 5.2 million deaths in 2015 to 150 thousand deaths by 2050. Annual damage costs are forecast to drop drastically by 88.5% from about 4600 b€ in 2015 to 529 b€ by 2050. This research shows that defossilisation and a shift to renewable forms of energy enables a massive reduction in air pollutant emissions that are directly harmful to human health, resulting in immense health and economic benefits in terms of avoided fatalities and saved health expenditures across the world.

Quercetin Alleviates Lipopolysaccharide-Induced Cell Damage and Inflammation via Regulation of the TLR4/NF-κB Pathway in Bovine Intestinal Epithelial Cells.

Acute diarrhoea and intestinal inflammation represent one of the most prevalent clinical disorders of milk production, resulting in enormous annual financial damage for the dairy sector. In the context of an unsatisfactory therapeutic effect of antibiotics, the natural products of plants have been the focus of research. Quercetin is an important flavonoid found in a variety of plants, including fruits and vegetables, and has strong anti-inflammatory effects, so it has received extensive attention as a potential anti-inflammatory antioxidant. However, the underlying basis of quercetin on inflammatory reactions and oxidative tension generated by lipopolysaccharide (LPS) in bovine intestinal epithelial cells (BIECs) is currently unexplained. This research aimed to determine the influence of quercetin on LPS-induced inflammatory reactions, oxidative tension, and the barrier role of BIECs. Our findings demonstrated that BIEC viability was significantly improved in LPS-treated BIEC with 80 μg/mL quercetin compared with the control group. Indicators of oxidative overload and genes involved in barrier role revealed that 80 μg/mL quercetin efficiently rescued BIECs from oxidative and barrier impairment triggered by 5 μg/mL LPS. In addition, the mRNA expression of pro-inflammatory cytokines TNF-α, IL-1β, and IL-6, as well as chemokines CXCL2, CXCL5, CCL5, and CXCL8, was diminished in LPS-treated BIECs with 80 μg/mL quercetin compared with LPS alone. Furthermore, the mRNA expression of toll-like receptor 4 (TLR4), CD14, myeloid differential protein-2 (MD2), and myeloid differentiation primary response protein (MyD88) genes associated with the TLR4 signal mechanism was markedly reduced by the addition of quercetin to LPS-modulated BIECs, indicating that quercetin can suppress the TLR4 signal mechanism. We performed Western blotting on the NF-κB signalling mechanism and compared it with immunofluorescence to further corroborate this conclusion. The LPS treatment enhanced the proportions of p-IκBα/GAPDH and p-p65/GAPDH. Compared with the LPS-treated group, quercetin administration decreased the proportions of p-IκBα/GAPDH and p-p65/GAPDH. In addition, immunofluorescence demonstrated that quercetin greatly reduced the LPS-induced nuclear translocation of NF-κB p65 in BIECs. The benefits of quercetin on inflammatory reactions in LPS-induced BIECs may be a result of its capacity to inhibit the TLR4-mediated NF-κB signalling mechanism. These findings suggest that quercetin can be used as an anti-inflammatory reagent to treat intestinal inflammation induced by LPS release.

Assessing Future Flood Risk and Developing Integrated Flood Risk Management Strategies: A Case Study from the UK Climate Change Risk Assessment

As Earth’s climate changes, individual nations must develop adaptation plans to respond to increasing or new climate risks. This study focuses on changing flood risk in England, UK, and examines the policy framework and actions that underpin England’s adaptation from a flood risk management (FRM) perspective. Specifically, the flood risk projections that fed into the UK’s Climate Change Risk Assessment were analysed alongside newly developed FRM adaptation portfolios that modified the flood risk projections to identify the potential of different measures to reduce Expected Annual Damages (EAD). The key findings indicate that: the range of EAD for all flood sources combined is projected to increase by 18–160% by the 2080s depending on the climate change, population growth and adaptation assumptions applied; adopting an enhanced adaptation approach presents an opportunity to manage much of the climate driven change in flood risk, particularly from river flooding; EAD from coastal flood risk shows the greatest increase relative to present day; and surface water flooding will become an increasingly more significant source of flood risk. Interpretation of the results in the context of the policy framework shows how greater coordination and integration of risk managers and interventions is required to improve adaptation planning.

Economic Analysis of Flood Risk Applied to the Rehabilitation of Drainage Networks

Over time, cities have grown, developing various activities and accumulating important economic assets. Floods are a problem that worry city administrators who seek to make cities more resilient and safer. This increase in flood events is due to different causes: poor planning, population increase, aging of networks, etc. However, the two main causes for the increase in urban flooding are the increment in frequency of extreme rainfall, generated mainly by climate change, and the increase in urbanized areas in cities, which reduce green areas, decreasing the percentage of water that seeps naturally into the soil. As a contribution to solve these problems, the work presented shows a method to rehabilitate drainage networks that contemplates implementing different actions in the network: renovation of pipes, construction of storm tanks and installation of hydraulic controls. This work focuses on evaluating the flood risk in economic terms. To achieve this, the expected annual damage from floods and the annual investments in infrastructure to control floods are estimated. These two terms are used to form an objective function to be minimized. To evaluate this objective function, an optimization model is presented that incorporates a genetic algorithm to find the best solutions to the problem; the hydraulic analysis of the network is performed with the SWMM model. This work also presents a strategy to reduce computation times by reducing the search space focused mainly on large networks. This is intended to show a complete and robust methodology that can be used by managers and administrators of drainage networks in cities.

Ordered iterative colour channel selection enhances segmentation of brain slice image neurons of the sham fetal sheep model

• We propose a novel segmentation model to extract the densely populated overlapping cortical neurons from the background image, which is essential to rapidly qualify and quantify the brain cortical neurons from brain slice images of in utero fetal sheep model . • The ordered iterative colour channel selection (OICCS) segmentation model enhances the performance of the marker controlled watershed (MCW) K-means segmentation technique. • The OICCS model involves the iterative application of the MCW K-means segmentation method in the three colour channels (red, green, and blue) of an RGB image in a selective order. • The segmentation model is significant as it is the essential first step to accelerate the histological analysis process over the currently employed manual assessment. Hypoxic Ischemia (HI) accounts for 23% of annual worldwide neonatal death, brain damage and long-term disability. Histopathological studies of infants with HI are rare, so the assessment of neuronal survival in HI animal models compared to the neuronal survival of their sham control, such as the term in utero fetal sheep model, is pivotal to increasing our understanding of HI at the cellular level and for the development of novel therapeutics and treatments. Histopathological studies of the in utero fetal sheep model rely heavily on the manual identification of neurons in histological brain slice images. However, manual identification is prone to being highly subjective, costly and time-consuming. Thus, there is an urgent need to automate the segmentation process, which is complicated by the existence of densely populated overlapping cells with no prior information of shape or size, resulting in images of high complexity. In this article, we propose an ‘Ordered Iterative Colour Channel Selection (OICCS) method. Such that, when applied to the standard ‘Marker Controlled Watershed k-means segmentation’ (MCW k-means) method, serves to effectively segment cortical neurons from the background in brain slice images of the term equivalent sham control in utero fetal sheep model. Thus, tackling the over-segmentation issue that was encountered by MCW k-means alone. In the OICCS model, we determine that by iteratively applying MCW k-means segmentation: firstly, to the blue channel image; then again on the green channel image for cortical neurons that remained unsegmented from the blue channel segmentation; and finally again on the red channel image for cortical neurons that remained unsegmented from the green channel segmentation that optimal performance results could be achieved of 97.98% ± 0.97% over 90.65 ± 3.32% achieved by the standard MCW k-means applied to the R, G, B channel alone or other permutations of the colour channels. The new OICCS enhanced MCW k-means segmentation method presented here is a significant step in automating the segmentation process of cortical brain cells from brain slice images of the sham control fetal sheep model. This will serve to accelerate the histological segmentation process over the manually intensive identification process that is currently employed.

Lifetime multi-hazard fragility analysis of transmission towers under earthquake and wind considering wind-induced fatigue effect

Wind-induced fatigue damage is a predominant factor eventuating in structural performance degradation of transmission towers exposed to long-term environmental wind. Moreover, transmission towers may suffer from multiple extreme natural hazards (such as earthquake and strong wind) during their service lives. However, these phenomena have not been well accounted for in current design specifications and previous studies for transmission towers. To this end, this paper proposes a comprehensive probabilistic analysis framework for achieving the lifetime multi-hazard fragility evaluation by considering the wind-induced fatigue, which is applied in a 1000 kV steel tubular transmission tower. Firstly, based on the field wind data measured at the meteorological station of Qingdao city, China, from Jan. 1, 1971 to Dec. 31, 2020, a fatigue analysis is performed on an actual transmission tower constructed in this region to compute the annual mean fatigue damage. Secondly, a series of time-dependent uncertain numerical models of the tower are established by integrating the accumulated fatigue damage and material deterioration model, and nonlinear time history analyses are imposed on these models to examine the effect of fatigue damage on lifetime responses under combined earthquake and wind loads. Analogously, the time-dependent probabilistic multi-hazard demand models are derived via the dynamic analyses and statistical regression, and these demand models are employed to further develop the lifetime multi-hazard fragility surfaces of the tower. Finally, a fast lifetime safety assessment method, i.e. the critical safety surface, is proposed to efficiently judge the collapse state of the transmission tower at various wind speeds, PGAs and service times. Numerical results indicate that the structural capacity of the transmission tower will experience significant deterioration due to wind-induced fatigue damage; the multi-hazard induced structural responses and fragilities will considerably increase with the service life although the tower is initially well-designed. The proposed analysis framework will be beneficial for the design and maintenance of transmission towers subjected to both earthquake and wind hazards for designers and decision-makers.

Optimal PV system capacity ratio and power limit value selection based on a novel fast calculation method of IGBT junction temperature and IGBT annual damage analysis

In order to solve the problem of long calculation time of insulated gate bipolar transistor (IGBT) junction temperature, the XGBoost machine learning algorithm is used to calculate IGBT junction temperature in the annual damage assessment process. The XGBoost machine learning algorithm can greatly reduce the calculation time of IGBT junction temperature while ensuring the accuracy, which provides conditions for finding the optimal PV system capacity ratio and power limit value by heuristic algorithm such as differential evolution algorithm later. For PV system capacity ratio and power limit, it is necessary to consider the annual damage of the PV inverter, the increase of power generation due to capacity ratio and the power generation loss due to power limit. This paper proposes an optimization goal that considers the above factors, and uses the differential evolution algorithm to obtain the optimal PV system capacity ratio and power limit value.

Can 7000 Years of flood history inform actual flood risk management? A case study on Lake Mondsee, Austria

Flood risk models typically rely on discharge records of time series no longer than a few hundred years, which leads to high uncertainty in the estimates for major flood events. In this paper, we investigate the capability of geological flood records retrieved from lake sediment cores to prolong available data sets, lower the uncertainty of flood risk analysis, and detect changes in flood patterns by releasing the common assumption of stationarity. Paleoflood records of Lake Mondsee, Austria, covering 7000 years are linked to flood damage data of the considered catchment area to calculate (1) the average annual damage, i.e. the fair premium and (2) the damage at 99.5% Value at Risk, i.e. the Solvency II capital requirement. To illustrate our results, the performance of a hypothetical catastrophe fund for these 7000 years is studied at current vulnerability levels. Our findings show that high resolution paleoflood records with robust chronologies and flood information have significant impacts on flood model outcomes for disaster risk management and that damage potentials derived merely from recent damage data may overestimate actual risks.

Damage mechanics based analysis of hail impact on metal roofs

Hail is one of the leading causes of annual property damage, that in turn results in economic loss and material waste. The associated risk affects several industries and parties including owners, insurance carriers and building material manufacturers. Due to the lack of detailed understanding of hail impact consequences, damage to building materials due to hail impact is primarily assessed by means of physical (visual) inspection supported by experimental data. Previously presented analytical and numerical models proposed in the literature have not offered a sufficiently robust representation of the damage on building materials due to hail impact. In this study, we present a novel model for hail deformation and impact damage in the hailstorm impact speed range.The proposed hailstone model is based on a strain-rate dependent and pressure-dependent visco-plastic model which allows for the representation of the different deformation regimes that hailstones experience during impact. The model is calibrated based on split-Hopkinson bar experimental results available in the literature. The model is then validated against widely accepted results of hailstone impact on metal sheets. The numerical experiments show that the model successfully captures hail strength, ductility and energy dissipated during impact. Consequently, the validation study shows the capability of the model to represent experimentally observed contact force changes during impact, which is achieved at an accuracy higher than the currently available models. In addition, the modeled response of hailstone deformation and damage is compared qualitatively to high speed camera images available in the literature. Additional parametric studies are performed to investigate the deformation and fracture of metal roofing subject to hail impact. The parametric studies investigate how different impact conditions (e.g. hail size, shape, strength and density, and roof detailing) affect the response and behavior of the metal roofing, associated deformation and potential fracture of the steel material. The proposed numerical modeling framework allows for a more reliable investigation of boundless design variations, while mitigating the labor-intensive costs and material waste associated with physical testing which is widely adopted in the industry right now.

Estimation of the effect of future changes in precipitation in Japan on pluvial flood damage and the damage reduction effect of mitigation/adaptation measures

This study estimated the effect of changes in the amount of precipitation associated with climate change on pluvial flood damage and the effectiveness of mitigation and adaptation measures throughout Japan. First, the cost of damage caused by pluvial flooding was calculated based on extreme rainfall, assuming a situation in which river levels are high, and rainwater does not drain into the rivers. Additionally, extreme rainfall in future climates was estimated from the output values of five general circulation models. Then, using these figures for extreme rainfall, the cost of pluvial flood damage in future climates was estimated. Improving the maintenance level of inland water drainage facilities and converting buildings to a piloti design were selected as adaptation measures. The results showed that in the Representative Concentration Pathway (RCP) 8.5 scenario, the expected annual damage cost (EADC) in the late 21st-century climate (2081–2100) scenario increases to approximately 2.3 times that of the baseline climate (1981–2000). If climate change is mitigated to RCP 2.6, the EADC in the late 21st-century climate scenario is estimated to be reduced by 28% compared to the EADC in the RCP 8.5 scenario. It is also estimated that the EADC in future climates could be kept lower than in the baseline climate by taking multiple rather than single measures. However, in the RCP 8.5 scenario for the late 21st-century climate, even if multiple adaptation measures are taken, the EADC was estimated to increase by 9% compared to the EADC in the baseline climate.

Estimating the economic damage caused by jellyfish to fisheries in Morocco

Although there is great fluctuation in annual recordings of jellyfish along the Moroccan Mediterranean coast and an absence of long-term datasets, there is a common perception that jellyfish numbers are rising. To better understand the possible economic losses sustained by Moroccan fisheries caused by the effects of jellyfish, we undertook a survey among fishers at the Atlantic port of Tangier and the Mediterranean port of M’diq. We focused on fishers’ perceptions about jellyfish blooms in Moroccan waters and whether and how blooms affect their fishing activities. Our findings confirm that blooms have negatively affected fishing activities over the past decade in the Moroccan Mediterranean, but that the degree of impact varies considerably by fishery and location. Of the total respondents at M’diq, 86% reported that jellyfish reduced their seasonal revenue. The species mainly responsible for these losses were Pelagia noctiluca, followed by Rhizostoma pulmo and Chrysaora hysoscella. The annual direct damage caused by jellyfish has been estimated to be as high as US$3.26 million per year for the fleet at the port of M’diq. When jellyfish outbreaks occur, fishers must spend time repairing nets damaged by jellyfish, which adds more than 1 783 person-hours of work per year.

Probabilistic Assessment of Pluvial Flood Risk Across 20 European Cities: A Demonstrator of the Copernicus Disaster Risk Reduction Service for Pluvial Flood Risk in Urban Areas

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.

Cyclonic damage assessment of rural houses for the east coastal region of India

Tropical cyclones are one of the most frequently occurring natural hazards in the coastal regions of India that cause extensive damage to life as well as property. Undertaking damage assessment of houses in a cyclone-prone region is important for cyclone disaster mitigation. In this work, a systematic procedure has been outlined for conducting damage assessment of rural houses caused due to cyclonic wind using a component approach. By combining the damage states and using the damage ratio of the component, a damage probability matrix (DPM) is determined. By Integrating the probability of cyclonic wind speed with the conditional probability of failure of rural houses and conditional mean damage, the overall damage estimate for a specified region is obtained. Three types of analysis are carried out showing three types of fragility curves, which include, annual damage vs. wind velocity, probability of complete failure of houses vs. wind velocity, and probability of failure of some important components vs. wind velocity. The effects of several types of wind speed distributions, component distribution parameters, and wind speed are investigated.

Flood Risk Modeling under Uncertainties: The Case Study of Croatia

This study presents an approach for assessing the flood risk using the fully probabilistic description of the annual damage using the derived higher-order statistical moments of the annual damage random variable. The annual damage distribution is used to analyze the impact on the macro-scale agglomeration described with the unified damage function in the case study of the river Kupa, Croatia. The agglomeration damage function is derived through the unit damage function and estimated distribution of building thresholds within the studied agglomeration. The unit damage is described with the shape function, which can easily accommodate different forms of damage assessment depending on the available information. The estimated distribution of annual maximum flood levels is propagated through the agglomeration damage function into the annual damage distribution derived using higher-order statistical moments and presented with the exceedance probability, indicating the likelihood that a certain annual maximum damage can be exceeded. Besides the aleatory uncertainty of annual maximum flood levels, we analyzed the impact of the uncertainty in estimating the unit damage function upon the annual damage distribution. This uncertainty shows a significant impact on the shape of the annual damage distribution, particularly in the region of extreme flood events. Analyzing the annual damage distribution range, resulting from the uncertainty in damage functions, provides more information to policymakers in assessing the potential consequences on the future spatial planning programs, particularly from the strategic environmental assessment point of view.

A Flood Risk Management Model to Identify Optimal Defence Policies in Coastal Areas Considering Uncertainties in Climate Projections

Coastal areas are particularly vulnerable to flooding from heavy rainfall, sea storm surge, or a combination of the two. Recent studies project higher intensity and frequency of heavy rains, and progressive sea level rise continuing over the next decades. Pre-emptive and optimal flood defense policies that adaptively address climate change are needed. However, future climate projections have significant uncertainty due to multiple factors: (a) future CO2 emission scenarios; (b) uncertainties in climate modelling; (c) discount factor changes due to market fluctuations; (d) uncertain migration and population growth dynamics. Here, a methodology is proposed to identify the optimal design and timing of flood defense structures in which uncertainties in 21st century climate projections are explicitly considered probabilistically. A multi-objective optimization model is developed to minimize both the cost of the flood defence infrastructure system and the flooding hydraulic risk expressed by Expected Annual Damage (EAD). The decision variables of the multi-objective optimization problem are the size of defence system and the timing of implementation. The model accounts for the joint probability density functions of extreme rainfall, storm surge and sea level rise, as well as the damages, which are determined dynamically by the defence system state considering the probability and consequences of system failure, using a water depth–damage curve related to the land use (Corine Land Cover); water depth due to flooding are calculated by hydraulic model. A new dominant sorting genetic algorithm (NSGAII) is used to solve the multi-objective problem optimization. A case study is presented for the Pontina Plain (Lazio Italy), a coastal region, originally a swamp reclaimed about a hundred years ago, that is rich in urban centers and farms. A set of optimal adaptation policies, quantifying size and timing of flood defence constructions for different climate scenarios and belonging to the Pareto curve obtained by the NSGAII are identified for such a case study to mitigate the risk of flooding and to aid decision makers.

Development of a risk-based optimization approach to improve the performance of urban drainage systems

ABSTRACT Considering the great deal of rainfall uncertainty in urban drainage system (UDS) design/rehabilitation, a copula-based design approach is developed within a Monte Carlo simulation (MCS) to improve system performance. Firstly, inundation zones are determined using HEC-RAS 2D and GIS tools. Then, inundation zones of flood scenarios are integrated with damage–depth functions in GIS to obtain the associated damage costs. Accordingly, a mapping is made between the flood volume (calculated by EPA-SWMM) and its related damage. This information is used in a coupled EPA-SWMM and differential evolution (DE) search algorithm to determine the best rehabilitation strategies. The approach is applied to a catchment in Tehran and its results are compared with the return-period method. Results show that the risk-based design approach could provide a 92% reduction in expected annual flood damage (EAD) and presents optimal designs with benefit-to-cost ratios up to 11, according to budget level. This approach yields more robust designs/strategies relative to the return-period method.

Climate change impact on fungi in the atmospheric microbiome

The atmospheric microbiome is one of the least studied microbiomes of our planet. One of the most abundant, diverse and impactful parts of this microbiome is arguably fungal spores. They can be very potent outdoor aeroallergens and pathogens, causing an enormous socio-economic burden on health services and annual damages to crops costing billions of Euros. We find through hypothesis testing that an expected warmer and drier climate has a dramatic impact on the atmospheric microbiome, conceivably through alteration of the hydrological cycle impacting agricultural systems, with significant differences in leaf wetness between years (p-value <0.05). The data were measured via high-throughput sequencing analysis using the DNA barcode marker, ITS2. This was complemented by remote sensing analysis of land cover and dry matter productivity based on the Sentinel satellites, on-site detection of atmospheric and vegetation variables, GIS analysis, harvesting analysis and footprint modelling on trajectory clusters using the atmospheric transport model HYSPLIT. We find the seasonal spore composition varies between rural and urban zones reflecting both human activities (e.g. harvest), type and status of the vegetation and the prevailing climate rather than mesoscale atmospheric transport. We find that crop harvesting governs the composition of the atmospheric microbiome through a clear distinction between harvest and post-harvest beta-diversity by PERMANOVA on Bray-Curtis dissimilarity (p-value <0.05). Land cover impacted significantly by two-way ANOVA (p-value <0.05), while there was minimal impact from air mass transport over the 3 years. The hypothesis suggests that the fungal spore composition will change dramatically due to climate change, an until now unforeseen effect affecting both food security, human health and the atmospheric hydrological cycle. Consequently the management of crop diseases and impact on human health through aeroallergen exposure need to consider the timing of crop treatments and land management, including post harvest, to minimize exposure of aeroallergens and pathogens.