Identifying the trade-offs between climate change mitigation and adaptation in urban land use planning: An empirical study in a coastal city

Solving the spatial extrapolation problem in flood susceptibility using hybrid machine learning, remote sensing, and GIS

Floods are arguably the most impactful of natural hazards. The increasing magnitude of their effects on the environment, human life, and economic activities calls for improved management of water resources. Flood susceptibility modeling has been used around the world to reduce the damage caused by flooding, although the extrapolation problem still presents a significant challenge. This study develops a machine learning (ML) model utilizing deep neural network (DNN) and optimization algorithms, namely earthworm optimization algorithm (EOA), wildebeest herd optimization (WHO), biogeography-based optimization (BBO), satin bowerbird optimizer (SBO), grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO), to solve the extrapolation problem in the construction of flood susceptibility models. Quang Nam Province was chosen as a case study as it is subject to the significant impact of intense flooding, and Nghe An Province was selected as the region for extrapolation of the flood susceptibility model. Root mean square error (RMSE), receiver operating characteristic (ROC), the area under the ROC curve (AUC), and accuracy (ACC) were applied to assess and compare the fit of each of the models. The results indicated that the models in this study are a good fit in establishing flood susceptibility maps, all with AUC > 0.9. The deep neural network (DNN)-BBO model enjoyed the best results (AUC = 0.99), followed by DNN-WHO (AUC = 0.99), DNN-SBO (AUC = 0.98), DNN-EOA (AUC = 0.96), DNN-GOA (AUC = 0.95), and finally, DNN-PSO (AUC = 0.92). In addition, the models successfully solved the extrapolation problem. These new models can modify their behavior to evaluate flood susceptibility in different regions of the world. The models in this study distribute a first point of reference for debate on the solution to the extrapolation problem, which can support urban planners and other decision-makers in other coastal regions in Vietnam and other countries.

Spatiotemporal effects of urban ecological land transitions to thermal environment change in mega-urban agglomeration

Urban ecological land transitions (UELTs) have far-reaching effects on the thermal environment, but their dynamic effects in urban agglomerations remain poorly understood. This study defines the UELTs concept and quantifies its spatiotemporal effects and driving mechanisms on land surface temperature interdecadal variations (LSTIVs) in the Guangdong-Hong Kong-Macao Greater Bay Area using remote sensing, fuzzy overlay, shape-weighted landscape evolution index, and Geodetector methods. The results showed that UELTs shifted from degradation, increasing pressure, and decreasing vegetation proportion in the central city to scattered restoration, pressure relief, and increasing vegetation proportion in 2010-2020. LSTIVs simultaneously transitioned from rapid growth and contiguous expansion to reduction and dispersion. Moreover, the contribution of UELTs to LSTIVs increased by 19.49% from 2000 to 2020, and gradually shifted from being driven by dominant transition (isolating and adjacent degradation) (mean q = 0.58) to recessive transition (increased population and construction land pressure) (mean q = 0.62), where q is the determinant power. Interactions between edge-expansion and infilling restoration with the blue-green ratio (BGR; i.e., ratio of waterbodies to vegetation), habitat quality, and population layout had significant effects on LSTIVs. In addition, the relative magnitude of the effect of UEL restoration-degradation and BGR on LSTIVs was not fixed, but rather related to their interaction effect and the urban agglomeration development stage. Therefore, in addition to promoting an increase in UEL, optimizing the landscape structure of UEL (e.g., increasing aggregation and connectivity, adjusting BGR) and UEL distribution with other human factors are also crucial to reduce the urban thermal environment.

Urban planning in Swiss cities has been slow to think about climate change: why and what to do?

Recent years have been marked by a strong popular and political mobilization around climate change. However, to what extent does this mobilization lead to reduce greenhouse gas emissions or the vulnerability of our society to the effects of climate change? This question is at the heart of the research presented, which sought to identify the barriers and levers to the integration of climate issues into urban planning of Swiss cities. The literature review first situates the integration of climate change in Swiss cities in relation to the evolution of practices at the international level. It emerged that Swiss cities have generally been late in integrating climate issues into their public policies. Practices still focus strongly on energy policies aimed at reducing greenhouse gas emissions, but adaptation measures in urban planning are poorly implemented. In order to better understand the reasons for this slow and late integration of climate change into urban planning of Swiss cities, a survey was conducted among more than 200 professionals. It showed that the evolution of practices is generally driven by "pioneering" actors who are strongly mobilized by personal values and who use specialized and scientific sources of information. Finally, two focus groups with representative professionals were organized in order to deepen the barriers and levers observed and to formulate sound recommendations for integrating the climate issue into urban planning. Two lines of action emerged: prioritization (strengthening legal frameworks and organizational structures) and support (training and involvement of climate experts at all stages of urban planning).

Projection of future temperature extremes, related mortality, and adaptation due to climate and population changes in Taiwan

BACKGROUND

Extreme temperature events have been observed to appear more frequently and with greater intensity in Taiwan in recent decades due to climate change, following the global trend. Projections of temperature extremes across different climate zones and their impacts on related mortality and adaptation have not been well studied.

METHODS

We projected site-specific future temperature extremes by statistical downscaling of 8 global climate models followed by Bayesian model averaging from 2021 to 2060 across Taiwan under the representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5, and RCP8.5. We then calculated the attributable mortality (AM) in 6 municipalities and in the eastern area by multiplying the city/county- and degree-specific relative risk of mortality according to the future population projections. We estimated the degree of adaptation to heat by slope reduction of the projected AM to be comparable with that in 2018.

RESULTS

The annual number of hot days with mean temperatures over 30 °C was predicted to have a substantial 2- to 5-fold increase throughout the residential areas of Taiwan by the end of 2060 under RCP8.5, whereas the decrease in cold days was less substantial. The decrease in cold-related mortality below 15 °C was projected to outweigh heat-related mortality for the next two decades, and then heat-related mortality was predicted to drastically increase and cross over cold-related mortality, surpassing it from 2045 to 2055. Adjusting for future population size, the percentage increase in heat-related deaths per 100,000 people could increase by more than 10-fold under the worst scenario (RCP8.5), especially for those over 65 years old. The heat-related impacts will be most severe in southern Taiwan, which has a tropical climate. There is a very high demand for heat-adaptation prior to 2050 under all RCP scenarios.

CONCLUSIONS

Spatiotemporal variations in AM in cities in different climate zones are projected in Taiwan and are expected to have a net negative effect in the near future before shifting to a net positive effect from 2045 to 2055. However, there is an overall positive and increasing trend of net effect for elderly individuals under all the emission scenarios. Active adaptation plans need to be well developed to face future challenges due to climate change, especially for the elderly population in central and southern Taiwan.

Coastal vulnerability to climate change in China's Bohai Economic Rim

Climate change and human activities exert a wide range of stressors on urban coastal areas. Synthetical assessment of coastal vulnerability is crucial for effective interventions and long-term planning. However, there have been few studies based on integrative analyses of ecological and physical characteristics and socioeconomic conditions in urban coastal areas. This study developed a holistic framework for assessing coastal vulnerability from three dimensions - biophysical exposure, sensitivity and adaptive capacity - and applied it to the coast of Bohai Economic Rim, an extensive and important development zone in China. A composite vulnerability index (CVI) was developed for every 1 km² segment of the total 5627 km coastline and the areas that most prone to coastal hazards were identified by mapping the distribution patterns of the CVIs in the present and under future climate change scenarios. The CVIs show a spatial heterogeneity, with higher values concentrated along the southwestern and northeastern coasts and lower values concentrated along the southern coasts. Currently, 20% of the coastlines with approximately 350,000 people are highly vulnerable to coastal hazards. With sea-level rises under the future scenarios of the year 2100, more coastlines will be highly vulnerable, and the amount of highly-threatened population was estimated to increase by 13-24%. Among the coastal cities, Dongying was categorized as having the highest vulnerability, mainly due to poor transportation and medical services and low GDP per capita, which contribute to low adaptive capacity. Our results can benefit decision-makers by highlighting prioritized areas and identifying the most important determinants of priority, facilitating location-specific interventions for climate-change adaptation and sustainable coastal management.

Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land- and water-related applications in coastal zones. Compared to optical satellites, cloud-cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all-weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud-prone tropical and sub-tropical climates. The canopy penetration capability with long radar wavelength enables L-band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change-induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L-band SAR data for geoscientific analyses that are relevant for coastal land applications.

How Vulnerable Are Urban Regeneration Sites to Climate Change in Busan, South Korea?

Research on the risks of climate change to urban regeneration projects has been insufficient to date. Therefore, this study aims to compare and analyze the degree of risk of climate change impact on areas with and without urban regeneration projects (for Eup, Myeon, and Dong regional units) in Busan, South Korea. In this study, (1) climate change risk indicators were extracted based on the concept of risk (hazard, vulnerability, and exposure), (2) a spatial analysis was performed using a graphic information system (GIS), and (3) the primary influencing factors were derived through a logistic regression analysis. The principal results show that urban regeneration areas have a higher risk of climate change impact than other areas. The results indicate that urban regeneration areas have a higher population density per area and more impermeable or flooded areas can increase the risk of climate change impacts. We also discuss strategies to develop resilient cities and climate change adaptation policies for future urban regeneration projects.