Pluvial flood risk assessment for 2021–2050 under climate change scenarios in the Metropolitan City of Venice

Pluvial flood is a natural hazard occurring from extreme rainfall events that affect millions of people around the world, causing damages to their properties and lives. The magnitude of projected climate risks indicates the urgency of putting in place actions to increase climate resilience. Through this study, we develop a Machine Learning (ML) model to predict pluvial flood risk under Representative Concentration Pathways (RCP) 4.5 and 8.5 for future scenarios of precipitation for the period 2021-2050, considering different triggering factors and precipitation patterns. The analysis is focused on the case study area of the Metropolitan City of Venice (MCV) and considers 212 historical pluvial flood events occurred in the timeframe 1995-2020. The methodology developed implements spatiotemporal constraints in the ML model to improve pluvial flood risk prediction under future scenarios of climate change. Accordingly, a cross-validation approach was applied to frame a model able to predict pluvial flood at any time and space. This was complemented with historical pluvial flood data and the selection of nine triggering factors representative of territorial features that contribute to pluvial flood events. Logistic Regression was the most reliable model, with the highest AUC score, providing robust result both in the validation and test set. Maximum cumulative rainfall of 14 days was the most important feature contributing to pluvial flood occurrence. The final output is represented by a suite of risk maps of the flood-prone areas in the MCV for each quarter of the year for the period 1995-2020 based on historical data, and risk maps for each quarter of the period 2021-2050 under RCP4.5 and 8.5 of future precipitation scenarios. Overall, the results underline a consistent increase in extreme events (i.e., very high and extremely high risk of pluvial flooding) under the more catastrophic scenario RCP8.5 for future decades compared to the baseline

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

Analysis of the Temporal and Spatial Distribution of Lake and Reservoir Water Quality in China and Changes in Its Relationship with GDP from 2005 to 2010

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SWOT Analysis of the Application of International Standard ISO 14001 in the Chinese Context. A Case Study of Guangdong Province

Industry has long been one of the most important drivers of Chinese economic growth. In order to improve the environmental footprint of industrial areas, Chinese authorities have established mechanisms of environmental control in the internal management processes of companies. In this regard, the international standard ISO 14001 for environmental management systems is the management tool that has had widest adoption among Chinese companies since its creation in 1996. The main purposes of the paper are to investigate the available international and national statistics on the adoptionof ISO 14001 in China, and present opinions on ISO 14001 of the 72 representatives of small and medium enterprises and multinational companies of Guangdong province that participated to the workshop New tools and standards to advance and measure corporate sustainability, held in Guangzhou on 26 January 2018. The analysis of strengths, weaknesses, opportunities and threats (SWOT) was adopted as the research method to collect opinions on the ISO 14001 standard. Participants were asked to discuss strengths, weaknesses, threats and opportunities considering four business aspects: sustainability, internal processes, stakeholder engagement, and resource management. Our findings indicate that companies fully embraced ISO 14001 and recognized the necessity of a standardized approach to identify environmental aspects. On the other hand, they also expressed concern about aspects such as the certification cost, the focus on certification itself and not on the improvement of environmental performance, and the lack of integration with sustainability tools such as life cycle assessment (LCA) and other sustainability paradigms such as circular economy and corporate social responsibility (CSR)

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. 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Multi-scenario analysis in the Apulia shoreline: A multi-tiers analytical framework for the combined evaluation and management of coastal erosion and water quality risks

Ongoing climate change is causing threats to coastal areas, according to scenarios of Intergovernmental Panel on Climate Change (IPCC). The coastal areas are becoming highly exposed to erosion as a direct consequence of natural and anthropogenic processes that occur at different spatial-temporal scales. Against this interplay, coastal managers are calling for integrated tools supporting a multi-scenario evaluation of risks arising from natural and anthropogenic stress. A multi-tier analytical framework, exploiting the openly available Earth Observation databases, was developed, allowing the combination of remote sensing, GIS and Bayesian Network to evaluate the probability and uncertainty of coastal erosion risks and connected water quality variation, against what-if scenarios, representing different management measures (i.e., Nature-Based Solution) and climate change impacts (e.g., higher incident waves due to increased storminess). Based on the available data for the Municipality of Ugento (Italy), the designed framework was applied over the 2009-2018 timeframe, allowing to capture local-scale shoreline erosion dynamics and driving forces. Results from scenario analysis revealed, to a minor extent, a nexus between oceanographic drivers, shoreline evolution, and water quality changes, with increasing probability of high erosion/accretion and higher turbidity under the simulated rising maximum significant wave height. However, the implementation of Nature-Based Solutions (i.e., circular approach exploiting beached Posidonia oceanica leaves) resulted in significant positive effects, stabilizing the shoreline by reducing high erosion and accretion rates. Despite data constraints, outcomes of the performed assessment could represent valuable information to drive adaptive policy pathways in the context of coastal management along the Ugento shoreline

SADA and DESYRE DSSs descriptive classification criteria (main issues related to DSS's)

Brownfield rehabilitation is an essential step for sustainable land-use planning and management in the European Union. In brownfield regeneration processes, the legacy contamination plays a significant role, firstly because of the persistent contaminants in soil or groundwater which extends the existing hazards and risks well into the future; and secondly, problems from historical contamination are often more difficult to manage than contamination caused by new activities. Due to the complexity associated with the management of brownfield site rehabilitation, Decision Support Systems (DSSs) have been developed to support problem holders and stakeholders in the decision-making process encompassing all phases of the rehabilitation. This paper presents a comparative study between two DSSs, namely SADA (Spatial Analysis and Decision Assistance) and DESYRE (Decision Support System for the Requalification of Contaminated Sites), with the main objective of showing the benefits of using DSSs to introduce and process data and then to disseminate results to different stakeholders involved in the decision-making process. For this purpose, a former car manufacturing plant located in the Brasov area, Central Romania, contaminated chiefly by heavy metals and total petroleum hydrocarbons, has been selected as a case study to apply the two examined DSSs. Major results presented here concern the analysis of the functionalities of the two DSSs in order to identify similarities, differences and complementarities and, thus, to provide an indication of the most suitable integration options