Climate Change Preparedness: Comparing Future Urban Growth and Flood Risk in Amsterdam and Houston

Climate Justice Implications of Natech Disasters: Excess Contaminant Releases during Hurricanes on the Texas Gulf Coast

Extreme weather events are becoming more severe due to climate change, increasing the risk of contaminant releases from hazardous sites disproportionately located in low-income communities of color. We evaluated contaminant releases during Hurricanes Rita, Ike, and Harvey in Texas and used regression models to estimate associations between neighborhood racial/ethnic composition and residential proximity to hurricane-related contaminant releases. Two-to-three times as many excess releases were reported during hurricanes compared to business-as-usual periods. Petrochemical manufacturing and refineries were responsible for most air emissions events. Multivariable models revealed sociodemographic disparities in likelihood of releases; compared to neighborhoods near regulated facilities without a release, a one-percent increase in Hispanic residents was associated with a 5 and 10% increase in the likelihood of an air emissions event downwind and within 2 km during Hurricanes Rita and Ike (odds ratio and 95% credible interval= 1.05 [1.00, 1.13], combined model) and Harvey (1.10 [1.00, 1.23]), respectively. Higher percentages of renters (1.07 [1.03, 1.11], combined Rita and Ike model) and rates of poverty (1.06 [1.01, 1.12], Harvey model) were associated with a higher likelihood of a release to land or water, while the percentage of Black residents (0.94 [0.89, 1.00], Harvey model) was associated with a slightly lower likelihood. Population density was consistently associated with a decreased likelihood of a contaminant release to air, land, or water. Our findings highlight social inequalities in the risks posed by natural-technological disasters that disproportionately impact Hispanic, renter, low-income, and rural populations.

Adapting to Climate Change: Leveraging Systems-Focused Multidisciplinary Research to Promote Resilience

Approximately 2000 official and potential Superfund sites are located within 25 miles of the East or Gulf coasts, many of which will be at risk of flooding as sea levels rise. More than 60 million people across the United States live within 3 miles of a Superfund site. Disentangling multifaceted environmental health problems compounded by climate change requires a multidisciplinary systems approach to inform better strategies to prevent or reduce exposures and protect human health. The purpose of this minireview is to present the National Institute of Environmental Health Sciences Superfund Research Program (SRP) as a useful model of how this systems approach can help overcome the challenges of climate change while providing flexibility to pivot to additional needs as they arise. It also highlights broad-ranging SRP-funded research and tools that can be used to promote health and resilience to climate change in diverse contexts.

Modeling Urban Growth and the Impacts of Climate Change: The Case of Esmeraldas City, Ecuador

This research has been developed in the city of Esmeraldas, which is one of the poorest urban centers of Ecuador. Historically, the economic dynamics of the city have been related to the extraction of natural resources, but little has been invested in local populations. The objectives of this paper are, first, to create a predictive scenario of urban growth linked to future climate projections for Esmeraldas, with a focus on vulnerability to landslides and flooding; and second, to generate methodological advances related to the linkage between urban growth simulation and the downscaling of global models for climate change. This paper is based on spatially explicit simulation, Cellular Automata (CA), to capture the dynamics of urban processes. CA is linked to the analysis of vulnerability to climate change based on socioeconomic conditions and is focused on flooding- and landslide-exposed areas. We found that the proportion of Afro-Ecuadorian people and the risk of landslides and flooding are positively related to urban growth. Based on our future scenarios, the urban growth area in Esmeraldas will increase 50% compared to the year 2016. Moreover, if the existing trends continue, natural vegetation—including mangroves—will be removed by that time, increasing the vulnerability to climate change.

Urban growth modelling and social vulnerability assessment for a hazardous Kathmandu Valley

In our rapidly urbanizing world, many hazard-prone regions face significant challenges regarding risk-informed urban development. This study addresses this issue by investigating evolving spatial interactions between natural hazards, ever-increasing urban areas, and social vulnerability in Kathmandu Valley, Nepal. The methodology considers: (1) the characterization of flood hazard and liquefaction susceptibility using pre-existing global models; (2) the simulation of future urban built-up areas using the cellular-automata SLEUTH model; and (3) the assessment of social vulnerability, using a composite index tailored for the case-study area. Results show that built-up areas in Kathmandu Valley will increase to 352 km2 by 2050, effectively doubling the equivalent 2018 figure. The most socially vulnerable villages will account for 29% of built-up areas in 2050, 11% more than current levels. Built-up areas in the 100-year and 1000-year return period floodplains will respectively increase from 38 km2 and 49 km2 today to 83 km2 and 108 km2 in 2050. Additionally, built-up areas in liquefaction-susceptible zones will expand by 13 km2 to 47 km2. This study illustrates how, where, and to which extent risks from natural hazards can evolve in socially vulnerable regions. Ultimately, it emphasizes an urgent need to implement effective policy measures for reducing tomorrow's natural-hazard risks.

The projected impacts of smart decline on urban runoff contamination levels

There has been mounting interest about how the repurposing of vacant land (VL) through green infrastructure (the most common smart decline strategy) can reduce stormwater runoff and improve runoff quality, especially in legacy cities characterized by excessive industrial land uses and VL amounts. This research examines the long-term impacts of smart decline on both stormwater amounts and pollutants loads through integrating land use prediction models with green infrastructure performance models. Using the City of St. Louis, Missouri, USA as the study area, we simulate 2025 land use change using the Conversion of Land Use and its Effects (CLUE-S) and Markov Chain urban land use prediction models and assess these change's probable impacts on urban contamination levels under different smart decline scenarios using the Long-Term Hydrologic Impact Assessment (L-THIA) performance model. The four different scenarios are: (1) a baseline scenario, (2) a 10% vacant land re-greening (VLRG) scenario, (3) a 20% VLRG scenario, and (4) a 30% VLRG scenario. The results of this study illustrate that smart decline VLRG strategies can have both direct and indirect impacts on urban stormwater runoff and their inherent contamination levels. Direct impacts on urban contamination include the reduction of stormwater runoff and non-point source (NPS) pollutants. In the 30% VLRG scenario, the annual runoff volume decreases by 11%, both physical, chemical, and bacterial pollutants are reduced by an average of 19%, compared to the baseline scenario. Indirect impacts include reduction of the possibility of illegal dumping on VL through mitigation and prevention of future vacancies.

A GIS-Cellular Automata-Based Model for Coupling Urban Sprawl and Flood Susceptibility Assessment

In Urban Planning (UP), it is necessary to take under serious consideration the inhibitors of the spread of a settlement in a specific direction. This means that all those parameters for which serious problems may arise in the future should be considered. Among these parameters are geo-hazards, such as floods, landslides, mud movement, etc. This study deals with UP taking into account the possibility of widespread flooding in settlement expansion areas. There is a large flooding history in Greece, which is accompanied by a significant number of disasters in different types of land use/land cover, with a large financial cost of compensation and/or rehabilitation. The study area is the drainage basin of Erasinos River in the Attica Region, where many and frequent flood events have been recorded. The main goal of this study is to determine the flood susceptibility of the study area, taking into account possible factors that are decisive in flood occurrence. Furthermore, the flood susceptibility is also determined, taking into account the scenarios of precipitation and the urban sprawl scenario in the area of reference. The study of flood events uses the Analytic Hierarchy Process (AHP) model and the urban sprawl model SLEUTH, which calibrates historical urban growth, using open and cost-free data and software. Eventually, flood susceptibility maps were overlaid with future urban areas to find the vulnerable areas. Following, three scenarios of flood susceptibility with the corresponding susceptibility maps and vulnerability maps, which measure the flood susceptibility of the current and future urban space of the study area, are presented. The results have shown significant peaks in the moderate class of flood susceptibility, while, in the third scenario, high values of flood susceptibility seem to appear. The proposed methodology and specifically the output maps can serve as a decision support tool to assist urban planners and hazard managers in making informed decisions towards sustainable urban planning.

Buy them out before they are built: evaluating the proactive acquisition of vacant land in flood-prone areas

Rising flood damages have prompted local communities to implement buyout and property acquisition programmes to eliminate repetitive losses for at-risk properties. However, buyouts are often costly to implement and are reactionary solutions to flooding. This study quantifies the benefits of acquiring vacant private properties in flood-prone areas rather than acquiring such properties after they are built up. Using a geodesign framework that integrates concepts and analytical approaches derived from geographical, spatial and statistical-based disciplines, we analyse vacant properties with high development potential that intersect current and future floodplain areas in Houston (TX, USA). We use geospatial proximity analysis to select candidate properties, land-use prediction modelling to estimate future development and sea-level rise and benefit-cost analysis to assess the economic viability of buyouts. The results indicate that cumulative avoided flood losses exceed the cost of vacant land acquisition by a factor of nearly two to one, and up to a factor of ten to one in selected areas. This study emphasizes the benefits of proactive property buyouts that focus on acquiring parcels before they are built up, while also avoiding the social and institutional problems associated with traditional buyout programmes.

A Review of Driving Factors, Scenarios, and Topics in Urban Land Change Models

Due to the increase in future uncertainty caused by rapid environmental, societal, and technological change, exploring multiple scenarios has become increasingly important in urban planning. Land Change Modeling (LCM) enables planners to have the ability to mold uncertain future land changes into more determined conditions via scenarios. This paper reviews the literature on urban LCM and identifies driving factors, scenario themes/types, and topics. The results show that: (1) in total, 113 driving factors have been used in previous LCM studies including natural, built environment, and socio-economic factors, and this number ranges from three to twenty-one variables per model; (2) typical scenario themes include “environmental protection” and “compact development”; and (3) LCM topics are primarily growth prediction and prediction tools, and the rest are growth-related impact studies. The nature and number of driving factors vary across models and sites, and drivers are heavily determined by both urban context and theoretical framework.

Advancing Scenario Planning through Integrating Urban Growth Prediction with Future Flood Risk Models

High uncertainty about future urbanization and flood risk conditions limits the ability to increase resiliency in traditional scenario-based urban planning. While scenario planning integrating urban growth prediction modeling is becoming more common, these models have not been effectively linked with future flood plain changes due to sea level rise. This study advances scenario planning by integrating urban growth prediction models with flood risk scenarios. The Land Transformation Model, a land change prediction model using a GIS based artificial neural network, is used to predict future urban growth scenarios for Tampa, Florida, USA, and future flood risks are then delineated based on the current 100-year floodplain using NOAA level rise scenarios. A multi-level evaluation using three urban prediction scenarios (business as usual, growth as planned, and resilient growth) and three sea level rise scenarios (low, high, and extreme) is conducted to determine how prepared Tampa's current land use plan is in handling increasing resilient development in lieu of sea level rise. Results show that the current land use plan (growth as planned) decreases flood risk at the city scale but not always at the neighborhood scale, when compared to no growth regulations (business as usual). However, flood risk when growing according to the current plan is significantly higher when compared to all future growth residing outside of the 100-year floodplain (resilient growth). Understanding the potential effects of sea level rise depends on understanding the probabilities of future development options and extreme climate conditions.

Risk Analysis Related to Impact of Climate Change on Water Resources and Hydropower Production in the Lusatian Neisse River Basin

Water resources are one of the most important issues affected by climate change. Climate scenarios show that in the upcoming decades, further climate change can occur. It concerns especially air temperature and sunshine duration, whose prognosis indicates a significant rising trend till the end of the century. The goal of the paper was the evaluation of water resources and hydropower production in the future, depending on climate scenarios with a consideration of risk analysis. The analysis was carried out on the basis of observation data for the Lusatian Neisse river basin (Poland) for 1971–2015 and climate projections till 2100 for the RCP2.6 and RCP8.5 (representative concentration pathways) scenarios. The results of the research showed that, especially in terms of RCP8.5, very high risk of decrease in water resources and hydropower production is expected in the future. Therefore, recommendations for mitigation of the possible effects are presented.

GROWTH AND SHRINKAGE PRE AND POST TSUNAMI IN FUKUSHIMA PREFECTURE, JAPAN

Depopulation is a severe problem in many urban areas globally. Massive population migrations can occur due to relocation after natural disasters and significantly change the demographic composition of regions and cities. The 2011 Great Tsunami in Japan resulted in a combined total of deaths and missing persons of more than 24,500. Post-tsunami recovery efforts resulted in widespread population relocation of high-risk communities into lower-risk areas. Using the Fukushima Prefecture in Japan as the study area, a region characterized by several depopulating cities both pre and post-tsunami, this research examines how the population relocation efforts have either exacerbated or assisted in lessening the effects of urban shrinkage and decline after the earthquake and tsunami of 2011. The results show that 30 municipalities have seen population and economic growth since 2011, and 12 municipalities are underdoing trends toward decline within Fukushima. Negatively affected cities tend to have larger populations than positively affected cities. Most of the small towns and villages closer to the inundation area are fall into the category of negatively affected areas. Moreover, the population increases in many post-disaster cities are primarily due to significant increases in elderly populations with minimal young persons that will inevitably decline in the next decade. By determining the effects of their relocation efforts, the government can better develop targeted strategies that good for the prosperity and development of the Fukushima Prefecture.

Citizen Science-Informed Community Master Planning: Land Use and Built Environment Changes to Increase Flood Resilience and Decrease Contaminant Exposure

Communities adjacent to concentrated areas of industrial land use (CAILU) are exposed to elevated levels of pollutants during flood disasters. Many CAILU are also characterized by insufficient infrastructure, poor environmental quality, and socially vulnerable populations. Manchester, TX is a marginalized CAILU neighborhood proximate to several petrochemical industrial sites that is prone to frequent flooding. Pollutants from stormwater runoff discharge from industrial land uses into residential areas have created increased toxicant exposures. Working with local organizations, centers/institutes, stakeholders, and residents, public health researchers sampled air, water, indoor dust, and outdoor soil while researchers from landscape architecture and urban planning applied these findings to develop a community-scaled master plan. The plan utilizes land use and built environment changes to increase flood resiliency and decrease exposure to contaminants. Using a combination of models to assess the performance, costs, and benefits of green infrastructure and pollutant load impacts, the master plan is projected to capture 147,456 cubic feet of runoff, and create $331,400 of annual green benefits by reducing air pollution and energy use, providing pollution treatment, increase carbon dioxide sequestration, and improve groundwater replenishment. Simultaneously, there is a 41% decrease across all analyzed pollutants, reducing exposure to and transferal of toxic materials.

Integrating Prediction and Performance Models into Scenario-based Resilient Community Design

Urban expansion can worsen climate change conditions and enlarge hazard zones. Sea level rise due to climate change makes coastal populations more susceptible to flood risks. The use of land change prediction modelling to inform scenario-based planning has been shown to help increase capabilities when dealing with uncertainties in urbanization such as urban growth and flood risk, when compared to singular comprehensive plans. This research uses the Land Transformation model to predict three different urban growth scenarios for Tampa, FL to determine how effective the current comprehensive plan is in adapting urban growth to decreasing flood risk and pollutant load. To achieve this, the research develops master plans according to each scenario then assesses their probable impact using the Long-Term Hydrologic Impact Analysis Low Impact Development Spreadsheet as a performance model. Findings show that the current future land use plan for Tampa, while it appears to be better than current patterns of development, has higher flood exposure, stormwater runoff, and pollutant discharge that current conditions but more than a purely resilient approach to future growth.

Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms

The estimation of an increase in sea level with sufficient warning time is important in low-lying regions, especially in the east coast of Peninsular Malaysia (ECPM). This study primarily aims to investigate the validity and effectiveness of the support vector machine (SVM) and genetic programming (GP) models for predicting the monthly mean sea level variations and comparing their prediction accuracies in terms of the model performances. The input dataset was obtained from Kerteh, Tioman Island, and Tanjung Sedili in Malaysia from January 2007 to December 2017 to predict the sea levels for five different time periods (1, 5, 10, 20, and 40 years). Further, the SVM and GP models are subjected to preprocessing to obtain optimal performance. The tuning parameters are generalized for the optimal input designs (SVM2 and GP2), and the results denote that SVM2 outperforms GP with R of 0.81 and 0.86 during the training and testing periods, respectively, at the study locations. However, GP can provide values of 0.71 and 0.79 for training and testing, respectively, at the study locations. The results show precise predictions of the monthly mean sea level, denoting the promising potential of the used models for performing sea level data analysis.