Involve to improve: A participatory approach for a Decision Support System for coastal climate change impacts assessment. The North Adriatic case

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A Bayesian network approach for multi-sectoral flood damage assessment and multi-scenario analysis

Extreme weather and climate related events, from river flooding to droughts and tropical cyclones, are likely to become both more severe and more frequent in the coming decades, and the damages caused by these events will be felt across all sectors of society. In the face of this threat, policy-and decision-makers are increasingly calling for new approaches and tools to support risk management and climate adaptation pathways that can capture the full extent of the impacts. In this frame, a GIS-based Bayesian Network (BN) approach is presented for the capturing and modelling of multi-sectoral flooding damages against future 'what-if' scenarios. Building on a risk-based conceptual framework, the BN model was trained and validated by exploiting data collected from the 2014 Secchia River flooding event, as well as other contextual variables. Moreover, a novel approach to defining the structure of the BN was performed, reconfiguring the model according to expert judgment and data-based validation. The model showed a good predictive capacity for damages in the agricultural, industrial and residential sectors, predicting the severity of damages with a classification accuracy of about 60% for each of these assessment endpoints. 'What-if' scenario analysis was performed to understand the potential impacts of future changes in i) land use patterns and ii) increasing flood depths resulting from more severe flood events. The output of the model showed a rising probability of experiencing high monetary damages under both scenarios. In spite of constraints within the case study dataset, the results of the appraisal show good promise, and together with the designed BN model itself represent a valuable support for disaster risk management and reduction actions against extreme river flooding events, enabling better informed decision making

Assessment of Climate Change Impacts in the North Adriatic Coastal Area. Part II: Consequences for Coastal Erosion Impacts at the Regional Scale

Coastal erosion is an issue of major concern for coastal managers and is expected to increase in magnitude and severity due to global climate change. This paper analyzes the potential consequences of climate change on coastal erosion (e.g., impacts on beaches, wetlands and protected areas) by applying a Regional Risk Assessment (RRA) methodology to the North Adriatic (NA) coast of Italy. The approach employs hazard scenarios from a multi-model chain in order to project the spatial and temporal patterns of relevant coastal erosion stressors (i.e., increases in mean sea-level, changes in wave height and variations in the sediment mobility at the sea bottom) under the A1B climate change scenario. Site-specific environmental and socio-economic indicators (e.g., vegetation cover, geomorphology, population) and hazard metrics are then aggregated by means of Multi-Criteria Decision Analysis (MCDA) with the aim to provide an example of exposure, susceptibility, risk and damage maps for the NA region. Among seasonal exposure maps winter and autumn depict the worse situation in 2070–2100, and locally around the Po river delta. Risk maps highlight that the receptors at higher risk are beaches, wetlands and river mouths. The work presents the results of the RRA tested in the NA region, discussing how spatial risk mapping can be used to establish relative priorities for intervention, to identify hot-spot areas and to provide a basis for the definition of coastal adaptation and management strategies.publishedVersio

Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks

[EN] With increasing evidence of climate change affecting the quality of water resources, there is the need to assess the potential impacts of future climate change scenarios on water systems to ensure their long-term sustainability. The study assesses the uncertainty in the hydrological responses of the Zero river basin (northern Italy) generated by the adoption of an ensemble of climate projections from 10 di erent combinations of a global climate model (GCM)¿regional climate model (RCM) under two emission scenarios (representative concentration pathways (RCPs) 4.5 and 8.5). Bayesian networks (BNs) are used to analyze the projected changes in nutrient loadings (NO3, NH4, PO4) in mid- (2041¿2070) and long-term (2071¿2100) periods with respect to the baseline (1983¿2012). BN outputs show good confidence that, across considered scenarios and periods, nutrient loadings will increase, especially during autumn and winter seasons. Most models agree in projecting a high probability of an increase in nutrient loadings with respect to current conditions. In summer and spring, instead, the large variability between di erent GCM¿RCM results makes it impossible to identify a univocal direction of change. Results suggest that adaptive water resource planning should be based on multi-model ensemble approaches as they are particularly useful for narrowing the spectrum of plausible impacts and uncertainties on water resources.Sperotto, A.; Molina, J.; Torresan, S.; Critto, A.; Pulido-Velazquez, M.; Marcomini, A. (2019). Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks. Sustainability. 11(17):1-34. https://doi.org/10.3390/su11174764S1341117RES/70/1. Transforming our World: The 2030 Agenda for Sustainable Developmenthttps://sustainabledevelopment.un.org/post2015/transformingourworldPasini, S., Torresan, S., Rizzi, J., Zabeo, A., Critto, A., & Marcomini, A. (2012). Climate change impact assessment in Veneto and Friuli Plain groundwater. Part II: A spatially resolved regional risk assessment. Science of The Total Environment, 440, 219-235. doi:10.1016/j.scitotenv.2012.06.096Iyalomhe, F., Rizzi, J., Pasini, S., Torresan, S., Critto, A., & Marcomini, A. (2015). Regional Risk Assessment for climate change impacts on coastal aquifers. Science of The Total Environment, 537, 100-114. doi:10.1016/j.scitotenv.2015.06.111Bussi, G., Whitehead, P. G., Bowes, M. J., Read, D. S., Prudhomme, C., & Dadson, S. J. (2016). Impacts of climate change, land-use change and phosphorus reduction on phytoplankton in the River Thames (UK). Science of The Total Environment, 572, 1507-1519. doi:10.1016/j.scitotenv.2016.02.109Huttunen, I., Lehtonen, H., Huttunen, M., Piirainen, V., Korppoo, M., Veijalainen, N., … Vehviläinen, B. (2015). Effects of climate change and agricultural adaptation on nutrient loading from Finnish catchments to the Baltic Sea. Science of The Total Environment, 529, 168-181. doi:10.1016/j.scitotenv.2015.05.055Carrasco, G., Molina, J.-L., Patino-Alonso, M.-C., Castillo, M. D. C., Vicente-Galindo, M.-P., & Galindo-Villardón, M.-P. (2019). Water quality evaluation through a multivariate statistical HJ-Biplot approach. Journal of Hydrology, 577, 123993. doi:10.1016/j.jhydrol.2019.123993Molina, J.-L., Zazo, S., & Martín, A.-M. (2019). Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence Rivers. Water, 11(5), 877. doi:10.3390/w11050877Beck, M., & Krueger, T. (2016). The epistemic, ethical, and political dimensions of uncertainty in integrated assessment modeling. Wiley Interdisciplinary Reviews: Climate Change, 7(5), 627-645. doi:10.1002/wcc.415Kundzewicz, Z. W., Krysanova, V., Benestad, R. E., Hov, Ø., Piniewski, M., & Otto, I. M. (2018). Uncertainty in climate change impacts on water resources. Environmental Science & Policy, 79, 1-8. doi:10.1016/j.envsci.2017.10.008Parker, W. S. (2013). Ensemble modeling, uncertainty and robust predictions. Wiley Interdisciplinary Reviews: Climate Change, 4(3), 213-223. doi:10.1002/wcc.220Hawkins, E., & Sutton, R. (2009). The Potential to Narrow Uncertainty in Regional Climate Predictions. Bulletin of the American Meteorological Society, 90(8), 1095-1108. doi:10.1175/2009bams2607.1Ajami, N. K., Hornberger, G. M., & Sunding, D. L. (2008). Sustainable water resource management under hydrological uncertainty. Water Resources Research, 44(11). doi:10.1029/2007wr006736Larson, K., White, D., Gober, P., & Wutich, A. (2015). Decision-Making under Uncertainty for Water Sustainability and Urban Climate Change Adaptation. Sustainability, 7(11), 14761-14784. doi:10.3390/su71114761Power, M., & McCarty, L. S. (2006). Environmental Risk Management Decision-Making in a Societal Context. Human and Ecological Risk Assessment: An International Journal, 12(1), 18-27. doi:10.1080/10807030500428538Uusitalo, L. (2007). Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling, 203(3-4), 312-318. doi:10.1016/j.ecolmodel.2006.11.033Wallach, D., Mearns, L. O., Ruane, A. C., Rötter, R. P., & Asseng, S. (2016). Lessons from climate modeling on the design and use of ensembles for crop modeling. Climatic Change, 139(3-4), 551-564. doi:10.1007/s10584-016-1803-1Tebaldi, C., & Knutti, R. (2007). The use of the multi-model ensemble in probabilistic climate projections. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1857), 2053-2075. doi:10.1098/rsta.2007.2076Martre, P., Wallach, D., Asseng, S., Ewert, F., Jones, J. W., Rötter, R. P., … Wolf, J. (2014). Multimodel ensembles of wheat growth: many models are better than one. Global Change Biology, 21(2), 911-925. doi:10.1111/gcb.12768Krishnamurti, T. N., Kishtawal, C. M., Zhang, Z., LaRow, T., Bachiochi, D., Williford, E., … Surendran, S. (2000). Multimodel Ensemble Forecasts for Weather and Seasonal Climate. Journal of Climate, 13(23), 4196-4216. doi:10.1175/1520-0442(2000)0132.0.co;2Xu, H., Brown, D. G., & Steiner, A. L. (2018). Sensitivity to climate change of land use and management patterns optimized for efficient mitigation of nutrient pollution. Climatic Change, 147(3-4), 647-662. doi:10.1007/s10584-018-2159-5Zuliani, A., Zaggia, L., Collavini, F., & Zonta, R. (2005). Freshwater discharge from the drainage basin to the Venice Lagoon (Italy). Environment International, 31(7), 929-938. doi:10.1016/j.envint.2005.05.004Facca, C., Ceoldo, S., Pellegrino, N., & Sfriso, A. (2014). Natural Recovery and Planned Intervention in Coastal Wetlands: Venice Lagoon (Northern Adriatic Sea, Italy) as a Case Study. The Scientific World Journal, 2014, 1-15. doi:10.1155/2014/968618Pesce, M., Critto, A., Torresan, S., Giubilato, E., Santini, M., Zirino, A., … Marcomini, A. (2018). Modelling climate change impacts on nutrients and primary production in coastal waters. Science of The Total Environment, 628-629, 919-937. doi:10.1016/j.scitotenv.2018.02.131Scoccimarro, E., Gualdi, S., Bellucci, A., Sanna, A., Giuseppe Fogli, P., Manzini, E., … Navarra, A. (2011). Effects of Tropical Cyclones on Ocean Heat Transport in a High-Resolution Coupled General Circulation Model. Journal of Climate, 24(16), 4368-4384. doi:10.1175/2011jcli4104.1Cattaneo, L., Zollo, A. L., Bucchignani, E., Montesarchio, M., Manzi, M. P., & Mercogliano, P. (2012). Assessment of COSMO-CLM Performances over Mediterranean Area. SSRN Electronic Journal. doi:10.2139/ssrn.2195524Sperotto, A., Molina, J. L., Torresan, S., Critto, A., Pulido-Velazquez, M., & Marcomini, A. (2019). A Bayesian Networks approach for the assessment of climate change impacts on nutrients loading. Environmental Science & Policy, 100, 21-36. doi:10.1016/j.envsci.2019.06.004MADSEN, A. L., JENSEN, F., KJÆRULFF, U. B., & LANG, M. (2005). THE HUGIN TOOL FOR PROBABILISTIC GRAPHICAL MODELS. International Journal on Artificial Intelligence Tools, 14(03), 507-543. doi:10.1142/s0218213005002235Bromley, J., Jackson, N. A., Clymer, O. J., Giacomello, A. M., & Jensen, F. V. (2005). The use of Hugin® to develop Bayesian networks as an aid to integrated water resource planning. Environmental Modelling & Software, 20(2), 231-242. doi:10.1016/j.envsoft.2003.12.021J. G. Arnold, D. N. Moriasi, P. W. Gassman, K. C. Abbaspour, M. J. White, R. Srinivasan, … M. K. Jha. (2012). SWAT: Model Use, Calibration, and Validation. Transactions of the ASABE, 55(4), 1491-1508. doi:10.13031/2013.42256Marcot, B. G. (2012). Metrics for evaluating performance and uncertainty of Bayesian network models. Ecological Modelling, 230, 50-62. doi:10.1016/j.ecolmodel.2012.01.013http://www.landscapelogic.org.au/publications/Technical_Reports/No_9_BNs_for_Integrated_Catchment_Management.pdfMolina, J.-L., Zazo, S., Rodríguez-Gonzálvez, P., & González-Aguilera, D. (2016). Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks. Water, 8(11), 484. doi:10.3390/w8110484Pollino, C. A., Woodberry, O., Nicholson, A., Korb, K., & Hart, B. T. (2007). Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment. Environmental Modelling & Software, 22(8), 1140-1152. doi:10.1016/j.envsoft.2006.03.006Pesce, M., Critto, A., Torresan, S., Giubilato, E., Pizzol, L., & Marcomini, A. (2019). Assessing uncertainty of hydrological and ecological parameters originating from the application of an ensemble of ten global-regional climate model projections in a coastal ecosystem of the lagoon of Venice, Italy. 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Multi-risk assessment in mountain regions: A review of modelling approaches for climate change adaptation

Climate change has already led to a wide range of impacts on our society, the economy and the environment.According to future scenarios, mountain regions are highly vulnerable to climate impacts, including changes in the water cycle (e.g. rainfall extremes, melting of glaciers, river runoff), loss of biodiversity and ecosystems services, damages to local economy (drinking water supply, hydropower generation, agricultural suitability) and human safety (risks of natural hazards). This is due to their exposure to recent climate warming (e.g. temperature regime changes, thawing of permafrost) and the high degree of specialization of both natural and human systems (e.g. mountain species, valley population density, tourism-based economy). These characteristics call for the application of risk assessment methodologies able to describe the complex interactions among multiple hazards, biophysical and socio-economic systems, towards climate change adaptation.Current approaches used to assess climate change risks often address individual risks separately and do not fulfil a comprehensive representation of cumulative effects associated to different hazards (i.e. compound events). Moreover, pioneering multi-layer single risk assessment (i.e. overlapping of single-risk assessments addressing different hazards) is still widely used, causing misleading evaluations of multi-risk processes. This raises key questions about the distinctive features of multi-risk assessments and the available tools and methods to address them.Here we present a review of five cutting-edge modelling approaches (Bayesian networks, agent-based models, system dynamic models, event and fault trees, and hybrid models), exploring their potential applications for multi-risk assessment and climate change adaptation in mountain regions.The comparative analysis sheds light on advantages and limitations of each approach, providing a roadmap for methodological and technical implementation of multi-risk assessment according to distinguished criteria (e.g. spatial and temporal dynamics, uncertainty management, cross-sectoral assessment, adaptation measures integration, data required and level of complexity). The results show limited applications of the selected methodologies in addressing the climate and risks challenge in mountain environments. In particular, system dynamic and hybrid models demonstrate higher potential for further applications to represent climate change effects on multi-risk processes for an effective implementation of climate adaptation strategies

Cumulative Impact Index for the Adriatic Sea: Accounting for interactions among climate and anthropogenic pressures

Assessing and managing cumulative impacts produced by interactive anthropogenic and natural drivers is a major challenge to achieve the sustainable use of marine spaces in line with the objectives of relevant EU acquis. However, the complexity of the marine environment and the uncertainty linked to future climate and socio-economic scenarios, represent major obstacles for understanding the multiplicity of impacts on the marine ecosystems and to identify appropriate management strategies to be implemented. Going beyond the traditional additive approach for cumulative impact appraisal, the Cumulative Impact Index (CI-Index) proposed in this paper applies advanced Multi-Criteria Decision Analysis techniques to spatially model relationships between interactive climate and anthropogenic pressures, the environmental exposure and vulnerability patterns and the potential cumulative impacts for the marine ecosystems at risk. The assessment was performed based on spatial data characterizing location and vulnerability of 5 relevant marine targets (e.g. seagrasses and coral beds), and the distribution of 17 human activities (e.g. trawling, maritime traffic) during a reference scenario 2000-2015. Moreover, projections for selected physical and biogeochemical parameters (temperature and chlorophyll 'a') for the 2035-2050 timeframe under RCP8.5 scenario, were integrated in the assessment to evaluate index variations due to changing climate conditions. The application of the CI-Index in the Adriatic Sea, showed higher cumulative impacts in the Northern part of the basin and along the Italian continental shelf, where the high concentration of human activities, the seawater temperature conditions and the presence of vulnerable benthic habitats, contribute to increase the overall impact estimate. Moreover, the CI-Index allowed understanding which are the phenomena contributing to synergic pressures creating potential pathways of environmental disturbance for marine ecosystems. Finally, the application in the Adriatic case showed how the output of the CI-Index can provide support to evaluate multi-risk scenarios and to drive sustainable maritime spatial planning and management

KULTURisk regional risk assessment methodology for water-related natural hazards - Part 2: Application to the Zurich case study

The aim of this paper is the application of the KULTURisk regional risk assessment (KR-RRA) methodology, presented in the companion paper (Part 1, Ronco et al., 2014), to the Sihl River basin, in northern Switzerland. Flood-related risks have been assessed for different receptors lying on the Sihl River valley including Zurich, which represents a typical case of river flooding in an urban area, by calibrating the methodology to the site-specific context and features. Risk maps and statistics have been developed using a 300-year return period scenario for six relevant targets exposed to flood risk: people; economic activities: buildings, infrastructure and agriculture; natural and semi-natural systems; and cultural heritage. Finally, the total risk index map has been produced to visualize the spatial pattern of flood risk within the target area and, therefore, to identify and rank areas and hotspots at risk by means of multi-criteria decision analysis (MCDA) tools. Through a tailored participatory approach, risk maps supplement the consideration of technical experts with the (essential) point of view of relevant stakeholders for the appraisal of the specific scores weighting for the different receptor-relative risks. The total risk maps obtained for the Sihl River case study are associated with the lower classes of risk. In general, higher (relative) risk scores are spatially concentrated in the deeply urbanized city centre and areas that lie just above to river course. Here, predicted injuries and potential fatalities are mainly due to high population density and to the presence of vulnerable people; flooded buildings are mainly classified as continuous and discontinuous urban fabric; flooded roads, pathways and railways, most of them in regards to the Zurich central station (Hauptbahnhof) are at high risk of inundation, causing severe indirect damage. Moreover, the risk pattern for agriculture, natural and semi-natural systems and cultural heritage is relatively less important mainly because the scattered presence of these assets. Finally, the application of the KR-RRA methodology to the Sihl River case study, as well as to several other sites across Europe (not presented here), has demonstrated its flexibility and the possible adaptation of it to different geographical and socioeconomic contexts, depending on data availability and particulars of the sites, and for other (hazard) scenarios

Real-Life Experience of Molnupiravir in Hospitalized Patients Who Developed SARS-CoV2-Infection: Preliminary Results from CORACLE Registry

Real-life experience of molnupiravir treatment is lacking, especially in people hospitalized for underlying diseases not related to COVID-19. We conducted a retrospective analysis regarding molnupiravir therapy in patients with SARS-CoV-2 infection admitted for underlying diseases not associated with COVID-19. Forty-four patients were included. The median age was 79 years (interquartile range [IQR]: 51-93 years), and most males were 57,4%. The median Charlson Comorbidity Index and 4C score were, respectively, 5 (IQR: 3-10) and 9.9 (IQR: 4-12). Moreover, 77.5% of the patients had at least two doses of the anti-SARS-CoV-2 vaccine, although 10.6% had not received any SARS-CoV-2 vaccine. Frequent comorbidities were cardiovascular diseases (68.1%), and diabetes (31.9%), and most admissions were for the acute chronic heart (20.4%) or liver (8.5%) failure. After molnupiravir started, 8 (18.1%) patients developed acute respiratory failure, and five (11.4%) patients died during hospitalisation. Moreover, molnupiravir treatment does not result in a statistically significant change in laboratory markers except for an increase in the monocyte count (p = 0.048, Z = 1.978). Molnupiravir treatment in our analysis was safe and well tolerated. In addition, no patients' characteristics were found significantly related to hospital mortality or an increase in oxygen support. The efficacy of the molecule remains controversial in large clinical studies, and further studies, including larger populations, are required to fill the gap in this issue

Climate risk index for Italy.

We describe a climate risk index that has been developed to inform national climate adaptation planning in Italy and that is further elaborated in this paper. The index supports national authorities in designing adaptation policies and plans, guides the initial problem formulation phase, and identifies administrative areas with higher propensity to being adversely affected by climate change. The index combines (i) climate change-amplified hazards; (ii) high-resolution indicators of exposure of chosen economic, social, natural and built- or manufactured capital (MC) assets and (iii) vulnerability, which comprises both present sensitivity to climate-induced hazards and adaptive capacity. We use standardized anomalies of selected extreme climate indices derived from high-resolution regional climate model simulations of the EURO-CORDEX initiative as proxies of climate change-altered weather and climate-related hazards. The exposure and sensitivity assessment is based on indicators of manufactured, natural, social and economic capital assets exposed to and adversely affected by climate-related hazards. The MC refers to material goods or fixed assets which support the production process (e.g. industrial machines and buildings); Natural Capital comprises natural resources and processes (renewable and non-renewable) producing goods and services for well-being; Social Capital (SC) addressed factors at the individual (people's health, knowledge, skills) and collective (institutional) level (e.g. families, communities, organizations and schools); and Economic Capital (EC) includes owned and traded goods and services. The results of the climate risk analysis are used to rank the subnational administrative and statistical units according to the climate risk challenges, and possibly for financial resource allocation for climate adaptation.This article is part of the theme issue 'Advances in risk assessment for climate change adaptation policy'

Barriers and enablers for upscaling coastal restoration

Coastal restoration is often distrusted and, at best, implemented at small scales, which hampers its potential for coastal adaptation. Present technical, economic and management barriers stem from sectoral and poorly coordinated local interventions, which are insufficiently monitored and maintained, precluding the upscaling required to build up confidence in ecosystem restoration. The paper posits that there is enough knowledge, technology, financial and governance capabilities for increasing the pace and scale of restoration, before the onset of irreversible coastal degradation. We propose a systemic restoration, which integrates Nature based Solutions (NbS) building blocks, to provide climate-resilient ecosystem services and improved biodiversity to curb coastal degradation. The result should be a reduction of coastal risks from a decarbonised coastal protection, which at the same time increases coastal blue carbon. We discuss barriers and enablers for coastal adaptation-through-restoration plans, based on vulnerable coastal archetypes, such as deltas, estuaries, lagoons and coastal bays. These plans, based on connectivity and accommodation space, result in enhanced resilience and biodiversity under increasing climatic and human pressures. The paper concludes with a review of the interconnections between the technical, financial and governance dimensions of restoration, and discusses how to fill the present implementation gap