Multi-scenario analysis in the Adriatic Sea: A GIS-based Bayesian network to support maritime spatial planning

Thriving arid oasis urban agglomerations: Optimizing ecosystem services pattern under future climate change scenarios using dynamic Bayesian network

The effects of global climate change and human activities are anticipated to significantly impact ecosystem services (ESs), particularly in urban agglomerations of arid regions. This paper proposes a framework integrating the dynamic Bayesian network (DBN), system dynamics (SD) model, patch generation land use simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for predicting land use change and optimizing ESs spatial patterns that is built upon the SSP-RCP scenarios from CMIP6. This framework is applied to the oasis urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang (UANSTM), China. The findings indicate that both the SD model and PLUS model can accurately forecast the distribution of future land use. The SD model shows a relative error of less than 2.32%, while the PLUS model demonstrates a Kappa coefficient of 0.89. The land use pattern displays obvious spatial heterogeneity under different climate scenarios. The expansion of cultivated land and construction land is the main form of land use change in UANSTM in the future. The DBN model proficiently simulates the interactive relationships between ESs and diverse factors. The classification error rates for net primary productivity (NPP), habitat quality (HQ), water yield (WY), and soil retention (SR) are 20.04%, 3.47%, 4.45%, and 13.42%, respectively. The prediction and diagnosis of DBN determine the optimal ESs development scenario and the optimal ESs region in the study area. It is found that the majority of ESs in UANSTM are predominantly influenced by natural factors with the exception of HQ. The socio-economic development plays a minor role in such urban agglomerations. This study offers significant insights that can contribute to the fields of ecological protection and land use planning in arid urban agglomerations worldwide.

Multi-model chain for climate change scenario analysis to support coastal erosion and water quality risk management for the Metropolitan city of Venice

Under the influence of anthropogenic climate change, hazardous climate and weather events are increasing in frequency and severity, with wide-ranging impacts across ecosystems and landscapes, especially fragile and dynamic coastal zones. The presented multi-model chain approach combines ocean hydrodynamics, wave fields, and shoreline extraction models to build a Bayesian Network-based coastal risk assessment model for the future analysis of shoreline evolution and seawater quality (i.e., suspended particulate matter, diffuse attenuation of light). In particular, the model was designed around a baseline scenario exploiting historical shoreline and oceanographic data within the 2015-2017 timeframe. Shoreline erosion and water quality changes along the coastal area of the Metropolitan city of Venice were evaluated for 2021-2050, under the RCP8.5 future scenario. The results showed a destabilizing trend in both shoreline evolution and seawater quality under the selected climate change scenario. Specifically, after a stable period (2021-2030), the shoreline will be affected by periods of erosion (2031-2040) and then accretion (2041-2050), with a simultaneous decrease in seawater quality in terms of higher turbidity. The decadal analysis and sensitivity evaluation of the input variables demonstrates a strong influence of oceanographic variables on the assessed endpoints, highlighting how the factors are strongly connected. The integration of regional and global climate models with Machine Learning and satellite imagery within the proposed multi-model chain represents an innovative update on state-of-the-art techniques. The validated outputs represent a good promise for better understanding the varying impacts due to future climate change conditions (e.g., wind, wave, tide, and sea-level). Moreover, the flexibility of the approach allows for the quick integration of climate and multi-risk data as it becomes available, and would represent a useful tool for forward-looking coastal risk management for decision-makers.

A Bayesian network-GIS probabilistic model for addressing human disturbance risk to ecological conservation redline areas

Population growth and associated ecological space occupation are posing great risks to regional ecological security and social stability. In China, "Ecological Conservation Redline" (ECR) that prohibited urbanization and industrial construction has been proposed as a national policy to resolve spatial mismatches and management contradictions. However, unfriendly human disturbance activities (e.g., cultivation, mining, and infrastructure construction) still exist within the ECR, posing a great threat to ecological stability and safety. In this article, a Bayesian network (BN)-GIS probabilistic model is proposed to spatially and quantitatively address the human disturbance risk to the ECR at the regional scale. The Bayesian models integrate multiple human activities, ecological receptors of the ECR, and their exposure relationships for calculating the human disturbance risk. The case learning method geographic information systems (GIS) is then introduced to train BN models based on the spatial attribute of variables to evaluate the spatial distribution and correlation of risks. This approach was applied to the human disturbance risk assessment for the ECR that was delineated in 2018 in Jiangsu Province, China. The results indicated that most of the ECRs were at a low or medium human disturbance risk level, while some drinking water sources and forest parks in Lianyungang City possessed the highest risk. The sensitivity analysis result showed the ECR vulnerability, especially for cropland, that contributed most to the human disturbance risk. This spatially probabilistic method can not only enhance model's prediction precision, but also help decision-makers to determine how to establish priorities for policy design and conservation interventions. Overall, it presents a foundation for later ECR adjustments as well as for human disturbance risk supervision and management at the regional scale.

Evaluating the combined effect of climate and anthropogenic stressors on marine coastal ecosystems: Insights from a systematic review of cumulative impact assessment approaches

Cumulative impacts increasingly threaten marine and coastal ecosystems. To address this issue, the research community has invested efforts on designing and testing different methodological approaches and tools that apply cumulative impact appraisal schemes for a sound evaluation of the complex interactions and dynamics among multiple pressures affecting marine and coastal ecosystems. Through an iterative scientometric and systematic literature review, this paper provides the state of the art of cumulative impact assessment approaches and applications. It gives a specific attention to cutting-edge approaches that explore and model inter-relations among climatic and anthropogenic pressures, vulnerability and resilience of marine and coastal ecosystems to these pressures, and the resulting changes in ecosystem services flow. Despite recent advances in computer sciences and the rising availability of big data for environmental monitoring and management, this literature review evidenced that the implementation of advanced complex system methods for cumulative risk assessment remains limited. Moreover, experts have only recently started integrating ecosystem services flow into cumulative impact appraisal frameworks, but more as a general assessment endpoint within the overall evaluation process (e.g. changes in the bundle of ecosystem services against cumulative impacts). The review also highlights a lack of integrated approaches and complex tools able to frame, explain, and model spatio-temporal dynamics of marine and coastal ecosystems' response to multiple pressures, as required under relevant EU legislation (e.g., Water Framework and Marine Strategy Framework Directives). Progress in understanding cumulative impacts, exploiting the functionalities of more sophisticated machine learning-based approaches (e.g., big data integration), will support decision-makers in the achievement of environmental and sustainability objectives.

A Go/No-Go Decision-Making Model Based on Risk and Multi-Criteria Techniques for Project Selection

The realization of infrastructures and the deployment of processes can follow project formalism. Generally, a project goes through a design and a realization phase. Between these two phases, there is a crucial milestone: Launching the project. Making this decision is not easy at all, and constitutes a real problem-- the main reasons to this are the numerous numbers of criteria (for technical, economic, social, environmental dimensions) and risks in the sense of feared events. Criteria and risks are most of the time not considered due to lack of time (for formalization) and the difficulty to handle them. The objective of this paper is to propose a relevant approach to make the decision of launching the project or not. The proposal outlined is innovative in that it can consider indicators based on several appropriate criteria, the associated risks, and their ways of management. The fact of considering several criteria and risks increases the probability of making the good decision.

Research on the Influence Mechanism of Street Vitality in Mountainous Cities Based on a Bayesian Network: A Case Study of the Main Urban Area of Chongqing

As the main spatial carrier for people’s social activities, street space occupies an important position in the urban space. However, under the direction of traffic-driven urban planning, the social function of street space has been neglected, resulting in the gradual loss of vitality. In mountainous cities with rugged terrain, the factors influencing the vitality of streets may be different compared to those in plain areas. In order to explore the influence mechanism of street vitality in mountainous cities, a new quantitative research method based on the new data environment and a Bayesian network is proposed. In this study, Python and GIS are used to obtain spatial data of streets, and Bayesian networks are used to construct street vitality models to identify important influencing factors and causal relationships between influencing factors. The results demonstrate strong causal dependencies between the factors influencing street vitality in mountainous cities. The mechanism of influence of street vitality revolves around functionality and street texture in terms of its own environment and external environment, respectively. The combination of factor group with functional density as the root node achieved the maximum probability of high vitality of the street. The results of this study have implications for community or urban planners with respect to urban regeneration and street vitality promotion.

Towards Sustainable Management of Anchoring on Mediterranean Islands—Concession Support Concept

The focus of this paper is to define anchorage management model for concession planning purposes to provide quality support to experts in spatial planning when developing maritime spatial plans. The research aim is to develop an anchorage management model that includes decision and concession support concept. Decision support concept is defined in order to support the processes of identifying potential anchorage locations, their evaluation and comparison, and finally, the priority ranking and selection of locations for their construction. The final step is modelling the concession support concept that includes financial analysis to concession parameters definition. The problem of decision making and concession of the anchorage location selection is complex and ill-structured because of the unsystematic and ad-hoc decisions by all included stakeholders. Additionally, the involvement of several stakeholders’ groups with different preferences and background knowledge, a large amount of conflicting and seemingly incomparable information and data, and numerous conflicting goals and criteria impact final decisions. The proposed concepts overcome the above obstacles in order to enable the construction of anchorages in a way of optimal use of maritime space. The model is tested on the island of Brač, Croatia. The methods used to solve the task are SWARA (The Stepwise Weight Assessment Ratio Analysis) for defining the criteria weights and ELECTRE (Elimination and Choice Expressing Reality) for ranking anchorage locations.

Optimization of the land use pattern in Horqin Sandy Land by using the CLUMondo model and Bayesian belief network

Land use and cover change is an important concept in the study of ecosystem services, especially in ecologically fragile areas. This study generated three scenarios, namely historical trend (HT), national planning (NP), and windbreak and sand fixation (WS), by using the CLUMondo model and Bayesian belief network (BBN) to explore land use with diverse demands. The CLUMondo model was utilized to simulate the land use probability surface of Horqin Sandy Land in 2025 under different scenarios. A BBN was constructed to investigate the net primary productivity (NPP), crop production (CP), and wind protection and sand fixation (WPSF) of Horqin Sandy Land in 2025 under uncertain land use to identify the short board areas of various services. The following results were obtained from the analysis. (1) The land use pattern of Horqin Sandy Land in 2025 under the HT scenario will be dominated by cultivated land expansion and grassland reduction. Under the NP scenario, forest will increase, and unused land and grassland will decrease considerably. Under the WS scenario, cultivated land will still maintain a similar growth state, but the difference is that forest and grassland will significantly increase. (2) NPP had the highest probability of being the Highest and the lowest probability of being Low, whereas CP and WPSF obtained the highest probability of being Medium and the lowest probability of being Higher. (3) Tuquan County and Wengniute Banner with a high probability of providing few ecosystem services should be regarded as key areas for ecological restoration. Kailu County and Horqin Left-wing Middle Banner can provide higher ecosystem services. The methodology adopted in this study establishes the connection between the land use probability surface and the optimized pattern of ecosystem services and can therefore be applied in areas where multi-objective comprehensive improvement of ecosystem services is expected.

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.

Spatial Conflict of Production–Living–Ecological Space and Sustainable-Development Scenario Simulation in Yangtze River Delta Agglomerations

Production–living–ecological space (PLES) is a recent research hotspot on land planning and regional sustainable development in China. Taking the Yangtze River Delta agglomerations as a case study, this paper establishes a spatial-conflict index to identify the PLES conflicts, and then builds a cellular-automaton (CA) Markov model to simulate the PLES pattern in 2030 and to evaluate the influence on PLES conflicts under two scenarios. Results showed that the ecological space (ES) and the living–productive space (LPS) of the Yangtze River Delta agglomerations showed a descending tendency in 2010–2015, whereas ecological–productive space (EPS) and productive–ecological space (PES) reflected a small increase. EPS and PES had squeezed ES and LPS with urbanization and industrial development in this region. Meanwhile, the spatial conflicts of PLES worsened during the period, with the average of the spatial-conflict index (SCI) shifting from 0.283 to 0.522, and seemed to gain momentum. On the basis of scenario analysis for 2030, it was concluded that the “ecological red line policy”, appropriate restriction of urban expansion, and ecological management of the bank of the Yangtze River are helpful in alleviating PLES conflicts, and contribute to spatial structure and harmonizing. The results of this study are expected to provide valuable implications for spatial planning and sustainable development in the Yangtze River delta agglomerations.