A multi-risk methodology for the assessment of climate change impacts in coastal zones

Climate change threatens coastal areas, posing significant risks to natural and human systems, including coastal erosion and inundation. This paper presents a multi-risk approach integrating multiple climate-related hazards and exposure and vulnerability factors across different spatial units and temporal scales. The multi-hazard assessment employs an influence matrix to analyze the relationships among hazards (sea-level rise, coastal erosion, and storm surge) and their disjoint probability. The multi-vulnerability considers the susceptibility of the exposed receptors (wetlands, beaches, and urban areas) to different hazards based on multiple indicators (dunes, shoreline evolution, and urbanization rate). The methodology was applied in the North Adriatic coast, producing a ranking of multi-hazard risks by means of GIS maps and statistics. The results highlight that the higher multi-hazard score (meaning presence of all investigated hazards) is near the coastline while multi-vulnerability is relatively high in the whole case study, especially for beaches, wetlands, protected areas, and river mouths. The overall multi-risk score presents a trend similar to multi-hazard and shows that beaches is the receptor most affected by multiple risks (60% of surface in the higher multi-risk classes). Risk statistics were developed for coastal municipalities and local stakeholders to support the setting of adaptation priorities and coastal zone management plans

Inventory of GIS-Based Decision Support Systems Addressing Climate Change Impacts on Coastal Waters and Related Inland Watersheds

A Decision Support System (DSS) is a computer-based software that can assist decision makers in their decision process, supporting rather than replacing their judgment and, at length, improving effectiveness over efficiency. Environmental DSS are models based tools that cope with environmental issues and support decision makers in the sustainable management of natural resources and in the definition of possible adaptation and mitigation measures [2]. DSS have been developed and used to address complex decision-based problems in varying fields of research. For instance, in environmental resource management, DSS are generally classified into two main categories: Spatial Decision Support Systems (SDSS) and Environmental Decision Supports Systems (EDSS) [3-5]. SDSS provide the necessary platform for decision makers to analyse geographical information in a flexible manner, while EDSS integrate the relevant environmental models, database and assessment tools – coupled within a Graphic User Interface (GUI) – for functionality within a Geographical Information System (GIS) [1,4-6]. In some detail, GIS is a set of computer tools that can capture, manipulate, process and display spatial or geo-referenced data in which the enhancement of spatial data integration, analysis and visualization can be conducted [8-9]. These functionalities make GIS-tools useful for efficient development and effective implementation of DSS within the management process. For this purpose they are used either as data managers (i.e. as a spatial geo-database tool) or as an end in itself (i.e. media to communicate information to decision makers)

Stochastic system dynamics modelling for climate change water scarcity assessment of a reservoir in the Italian Alps

Water management in mountain regions is facing multiple pressures due to climate change and anthropogenic activities. This is particularly relevant for mountain areas where water abundance in the past allowed for many anthropogenic activities, exposing them to future water scarcity. Here stochastic system dynamics modelling (SDM) was implemented to explore water scarcity conditions affecting the stored water and turbined outflows in the Santa Giustina (S. Giustina) reservoir (Autonomous Province of Trento, Italy). The analysis relies on a model chain integrating outputs from climate change simulations into a hydrological model, the output of which was used to test and select statistical models in an SDM for replicating turbined water and stored volume within the S. Giustina dam reservoir. The study aims at simulating future conditions of the S. Giustina reservoir in terms of outflow and volume as well as implementing a set of metrics to analyse volume extreme conditions.Average results on 30-year slices of simulations show that even under the short-term RCP4.5 scenario (2021-2050) future reductions for stored volume and turbined outflow are expected to be severe compared to the 14-year baseline (1999-2004 and 2009-2016; -24.9 % of turbined outflow and -19.9 % of stored volume). Similar reductions are expected also for the long-term RCP8.5 scenario (2041-2070; -26.2 % of turbined outflow and -20.8 % of stored volume), mainly driven by the projected precipitations having a similar but lower trend especially in the last part of the 2041-2070 period. At a monthly level, stored volume and turbined outflow are expected to increase for December to March (outflow only), January to April (volume only) depending on scenarios and up to +32.5 % of stored volume in March for RCP8.5 for 2021-2050. Reductions are persistently occurring for the rest of the year from April to November for turbined outflows (down to -56.3 % in August) and from May to December for stored volume (down to -44.1 % in June). Metrics of frequency, duration and severity of future stored volume values suggest a general increase in terms of low volume below the 10th and 20th percentiles and a decrease of high-volume conditions above the 80th and 90th percentiles. These results point at higher percentage increases in frequency and severity for values below the 10th percentile, while volume values below the 20th percentile are expected to last longer. Above the 90th percentile, values are expected to be less frequent than baseline conditions, while showing smaller severity reductions compared to values above the 80th percentile. These results call for the adoption of adaptation strategies focusing on water demand reductions. Months of expected increases in water availability should be considered periods for water accumulation while preparing for potential persistent reductions of stored water and turbined outflows. This study provides results and methodological insights that can be used for future SDM upscaling to integrate different strategic mountain socio-economic sectors (e.g. hydropower, agriculture and tourism) and prepare for potential multi-risk conditions

Chapter Inventory of GIS-Based Decision Support Systems Addressing Climate Change Impacts on Coastal Waters and Related Inland Watersheds

Cosmology & the univers

On the Application of GIS-based Decision Support Systems to study climate change impacts on coastal systems and associated ecosystems

One of the most remarkable achievements by scientists in the field of global change in recent years is the improvedunderstanding of climate change issues. Its effects on human environments, particularly coastal zones and associated watersystems, are now a huge challenge to environmental resource managers and decision makers. International and regionalregulatory frameworks have been established to guide the implementation of interdisciplinary methodologies, useful toanalyse water-related systems issues and support the definition of management strategies against the effects of climatechange. As a response to these concerns, several decision support systems (DSS) have been developed and applied toaddress climate change through geographical information systems (GIS) and multi-criteria decision analysis (MCDA)techniques; linking the DSS objectives with specific functionalities leading to key outcomes, and aspects of the decisionmaking process involving coastal and waters resources. An analysis of existing DSS focusing on climate change impacts oncoastal and related ecosystems was conducted by surveying the open literature. Consequently, twenty DSS were identifiedand are comparatively discussed according to their specific objectives and functionalities, including a set of criteria (generaltechnical, specific technical and applicability) in order to better inform potential users and concerned stakeholders throughthe evaluation of a DSS’ actual application.Key words: Climate change, Decision support, GIS, regulations, Environmen

The KULTURisk Regional Risk Assessment methodology for water-related natural hazards – Part 1: Physical–environmental assessment

Abstract. In recent years, the frequency of catastrophes induced by natural hazards has increased, and flood events in particular have been recognized as one of the most threatening water-related disasters. Severe floods have occurred in Europe over the last decade, causing loss of life, displacement of people and heavy economic losses. Flood disasters are growing in frequency as a consequence of many factors, both climatic and non-climatic. Indeed, the current increase of water-related disasters can be mainly attributed to the increase of exposure (elements potentially at risk in flood-prone area) and vulnerability (i.e. economic, social, geographic, cultural and physical/environmental characteristics of the exposure). Besides these factors, the undeniable effect of climate change is projected to strongly modify the usual pattern of the hydrological cycle by intensifying the frequency and severity of flood events at the local, regional and global scale. Within this context, the need for developing effective and pro-active strategies, tools and actions which allow one to assess and (possibly) to reduce the flood risks that threatens different relevant receptors becomes urgent. Several methodologies to assess the risk posed by water-related natural hazards have been proposed so far, but very few of them can be adopted to implement the last European Flood Directive (FD). This paper is intended to introduce and present a state-of-the-art Regional Risk Assessment (RRA) methodology to appraise the risk posed by floods from a physical–environmental perspective. The methodology, developed within the recently completed FP7-KULTURisk Project (Knowledge-based approach to develop a cULTUre of Risk prevention – KR) is flexible and can be adapted to different case studies (i.e. plain rivers, mountain torrents, urban and coastal areas) and spatial scales (i.e. from catchment to the urban scale). The FD compliant KR-RRA methodology is based on the concept of risk being function of hazard, exposure and vulnerability. It integrates the outputs of various hydrodynamic models with site-specific bio-geophysical and socio-economic indicators (e.g. slope, land cover, population density, economic activities etc.) to develop tailored risk indexes and GIS-based maps for each of the selected receptors (i.e. people, buildings, infrastructure, agriculture, natural and semi-natural systems, cultural heritage) in the considered region. It further compares the baseline scenario with alternative scenarios, where different structural and/or non-structural mitigation measures are planned and eventually implemented. As demonstrated in the companion paper (Part 2, Ronco et al., 2014), risk maps, along with related statistics, allow one to identify and classify, on a relative scale, areas at risk which are more likely to be affected by floods and support the development of strategic adaptation and prevention measures to minimizing flood impacts. In addition, the outcomes of the RRA can be eventually used for a further socio-economic assessment, considering the tangible and intangible costs as well as the human dimension of vulnerability

Timbre brownfield prioritization tool to support effective brownfield regeneration.

In the last decade, the regeneration of derelict or underused sites, fully or partly located in urban areas (or so called “brownfields”), has become more common, since free developable land (or so called “greenfields”) has more and more become a scare and, hence, more expensive resource, especially in densely populated areas. Although the regeneration of brownfield sites can offer development potentials, the complexity of these sites requires considerable efforts to successfully complete their revitalization projects and the proper selection of promising sites is a pre-requisite to efficiently allocate the limited financial resources. The identification and analysis of success factors for brownfield sites regeneration can support investors and decision makers in selecting those sites which are the most advantageous for successful regeneration. The objective of this paper is to present the Timbre Brownfield Prioritization Tool (TBPT), developed as a web-based solution to assist stakeholders responsible for wider territories or clusters of brownfield sites (portfolios) to identify which brownfield sites should be preferably considered for redevelopment or further investigation. The prioritization approach is based on a set of success factors properly identified through a systematic stakeholder engagement procedure. Within the TBPT these success factors are integrated by means of a Multi Criteria Decision Analysis (MCDA) methodology, which includes stakeholders' requalification objectives and perspectives related to the brownfield regeneration process and takes into account the three pillars of sustainability (economic, social and environmental dimensions). The tool has been applied to the South Moravia case study (Czech Republic), considering two different requalification objectives identified by local stakeholders, namely the selection of suitable locations for the development of a shopping centre and a solar power plant, respectively. The application of the TBPT to the case study showed that it is flexible and easy to adapt to different local contexts, allowing the assessors to introduce locally relevant parameters identified according to their expertise and considering the availability of local data

Venice lagoon chlorophyll-a evaluation under climate change conditions: A hybrid water quality machine learning and biogeochemical-based framework

Climate change presents a significant challenge to lagoon ecosystems, which are highly valued coastal environments known for their provision of unique ecosystem services. As important as fragile, lagoons are vulnerable to both natural processes and anthropogenic activities, and this vulnerability is exacerbated by the impacts of climate change, which are likely to result in severe ecological consequences. The complexity of water quality (WQ) processes, characterized by compounding and interconnected pressures, highlights the importance of adequate sophisticated methods to estimate future ecological impacts on lagoon environments. In this setting, a hybrid framework is introduced where Machine Learning (ML) and biogeochemical (BGC) models are integrated in a sequential modelling approach. This integration exploits the unique strengths offered by both models. The ML model allows capturing and learning linear and nonlinear correlations from historical data; the BGC interprets and simulates complex environmental systems subject to compounded pressures, building on identified causal relationships. Multi-Layer Perceptron (MLP) and Random Forest (RF) ML algorithms are trained, validated and tested within the Venice lagoon case study to assimilate historical WQ data (i.e., water temperature, salinity, and dissolved oxygen) and spatio-temporal information (i.e., monitoring station location and month), and to predict changes in chlorophyll-a (Chl-a) conditions. Then, projections from the BGC model SHYFEM-BFM for 2019, 2050, and 2100 timeframes under RCP 8.5 are integrated into the ML model (composing the hybrid ML-BGC model) to evaluate Chl-a variations under future biogeochemical conditions forced by climate change projections. Moreover, the SHYFEM-BFM standalone Chl-a projections are also used to compare the hybrid and the BGC scenarios. Annual and seasonal Chl-a predictions are developed by classes based on two classification modes (median and quartiles) established on the descriptive statistics computed on historical data. Results from the case study showed as the RF successfully classifies Chl-a with an overall model accuracy of about 80% for the median and 61% for the quartiles modes. Concerning future climate change scenarios, results revealed a decreasing trend for the lowest Chl-a values (below the first quartile, i.e. 0.85 µg/l) moving to the far future (2100), with an opposite rising trend for the highest Chl-a values (above the fourth quartile, i.e. 2.78 µg/l). On the seasonal level, summer remains the season with the highest Chl-a values in all scenarios, although in 2100 a strong increase in higher Chl-a values is also expected during the springtime one. The proposed hybrid framework represents a valuable approach to strengthen both multivariate Chl-a modelling and scenarios analysis, by placing artificial intelligence-based models alongside biogeochemical models

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

With increasing evidences of climate change affecting coastal waters, there is a strong need to understand future climate conditions and assess the potential responses of delicate coastal ecosystems. Results of climate change studies based on only one GCM-RCM combination should be interpreted with caution as results are highly dependent on the assumptions of the selected combination. In this study we examined the uncertainty in the hydrological and ecological parameters of the Zero river basin (ZRB) – Palude di Cona (PDC) coastal aquatic ecosystem generated by the adoption of an ensemble of climate projections from ten different combinations of General Circulation Model (GCM) – Regional Climate Model (RCM) under two emission scenarios (RCP4.5 and RCP8.5) implemented in the hydrological model (SWAT) and the ecological model (AQUATOX). The baseline period of 1983–2012 was used to identify climate change variations in two future periods: mid-century (2041–2070) and late-century (2071–2100) periods. SWAT outputs from the ensemble indicate a summer reduction in inorganic nitrogen loadings of 1–22% and a winter increase of 1–19%. Inorganic phosphorus loadings indicate a yearly increase of 32–61%. AQUATOX outputs from the ensemble show major changes in the summer period, with an increase in Chl-a concentration of 9–56%, a decrease in diatoms of 74–98% and an increase in cyanobacteria of 421–3590%. Obtained results confirm that the use of multiple GCM-RCM projections can provide a more robust assessment of climate change impacts on the hydrology and ecology of coastal waters, but at the same time highlight the large uncertainty of climate change-related impact studies, which can affect the decision-making processes regarding the management and preservation of sensitive aquatic ecosystems such as those in coastal areas. © 2019 Elsevier B.V