A multi-scale urban integrated assessment framework for climate change studies: A flooding application

In order to assess the potential future impacts of climate change on urban areas, tools to assist decision-makers to understand future patterns of risk are required. This paper presents a modelling framework to allow the downscaling of national- and regional-scale population and employment projections to local scale land-use changes, providing scenarios of future socio-economic change. A coupled spatial interaction population model and cellular automata land development model produces future urbanisation maps based on planning policy scenarios. The framework is demonstrated on Greater London, UK, with a set of future population and land-use scenarios being tested against flood risk under climate change. The framework is developed in Python using open-source databases and is designed to be transferable to other cities worldwide.     

Integrated assessment model of society-biosphere-climate-economy-energy system

The feedback based integrated assessment model ANEMI_2 represents the society-biosphere-climate-economy-energy system of the earth and biosphere. The ANEMI_2 model is based on the system dynamics simulation approach that (a) allows for the understanding and modeling of complex global change and (b) assists in the investigation of possible policy options for mitigating, and/or adapting to changing global conditions within an integrated assessment modeling framework. This paper outlines the ANEMI_2 model and its nine system components: climate, carbon cycle, land-use, population, food production, hydrologic cycle, water demand, water quality, and energy-economy. To evaluate market and nonmarket costs and benefits of climate change, the ANEMI_2 model integrates an economic optimization approach, with a focus on the international energy stock and fuel price, climate interrelations and temperature change. The model takes into account all major greenhouse gases (GHG) influencing global temperature and sea-level variation. Results from several scenarios (a) compare well with other information available in the scientific literature, (b) present comprehensive response of the society-biosphere-climate-economy-energy system to the selected scenarios, and (c) confirm the support role of the ANEMI_2 model in the policy development and analyses.     

Robust optimization to secure urban bulk water supply against extreme drought and uncertain climate change

Urban bulk water systems supply water with high reliability and, in the event of extreme drought, must avoid catastrophic economic and social collapse. In view of the deep uncertainty about future climate change, it is vital that robust solutions be found that secure urban bulk water systems against extreme drought. To tackle this challenge an approach was developed integrating: 1) a stochastic model of multi-site streamflow conditioned on future climate change scenarios; 2) Monte Carlo simulation of the urban bulk water system incorporated into a robust optimization framework and solved using a multi-objective evolutionary algorithm; and 3) a comprehensive decision space including operating rules, investment in new sources and source substitution and a drought contingency plan with multiple actions with increasingly severe economic and social impact. A case study demonstrated the feasibility of this approach for a complex urban bulk water supply system. The primary objective was to minimize the expected present worth cost arising from infrastructure investment, system operation and the social cost of “normal” and emergency restrictions. By introducing a second objective which minimizes either the difference in present worth cost between the driest and wettest future climate change scenarios or the present worth cost for driest climate scenario, the trade-off between efficiency and robustness was identified. The results show that a significant change in investment and operating strategy can occur when the decision maker expresses a stronger preference for robustness and that this depends on the adopted robustness measure. Moreover, solutions are not only impacted by the degree of uncertainty about future climate change but also by the stress imposed on the system and the range of available options.     

Combining narratives and modelling approaches to simulate fine scale and long-term urban growth scenarios for climate adaptation

Although climate scientists explore the effects of climate change for 2100, it is a challenging time frame for urban modellers to foresee the future of cities. The question addressed in this paper is how to improve the existing methodologies in order to build scenarios to explore urban climate impacts in the long term and at a fine scale. This study provides a structural framework in six steps that combines narratives and model-based approaches. The results present seven scenarios of urban growth based on land use strategies and technological and socio-economic trends. These contrasted scenarios span the largest possible world of futures for the city under study. Urban maps for 2010, 2040 and 2100 were used to assess the impacts on the Urban Heat Island. The comparison of these scenarios and related outputs allowed some levers to be evaluated for their capacity to limit the increase of air temperature.     

Climate and human development impacts on municipal water demand: A spatially-explicit global modeling framework

Municipal water systems provide crucial services for human well-being, and will undergo a major transformation this century following global technological, socioeconomic and environmental changes. Future demand scenarios integrating these drivers over multi-decadal planning horizons are needed to develop effective adaptation strategies. This paper presents a new long-term scenario modeling framework that projects future daily municipal water demand at a 1/8° global spatial resolution. The methodology incorporates improved representations of important demand drivers such as urbanization and climate change. The framework is applied across multiple future socioeconomic and climate scenarios to explore municipal water demand uncertainties over the 21st century. The scenario analysis reveals that achieving a low-carbon development pathway can potentially reduce global municipal water demands in 2060 by 2–4%, although the timing and scale of impacts vary significantly with geographic location.     

High-resolution urban change modeling and flood exposure estimation at a national scale using open geospatial data: A case study of the Philippines

Many developing countries in Asia are experiencing rapid urban expansion in climate hazard prone areas. To support climate resilient urban planning efforts, here we present an approach for simulating future urban land-use changes and evaluating potential flood exposure at a high spatial resolution (30 m) and national scale. As a case study, we applied this model to the Philippines – a country frequently affected by extreme rainfall events. Urban land-use changes were simulated to the year 2050 using a trend-based logistic regression cellular automata model, considering three different scenarios of urban expansion (assuming low/medium/high population growth). Flood exposure assessment was then conducted by overlaying the land-use simulation results onto a global floodplain map. We found that approximately 6040–13,850 ha of urban land conversion is likely to be located in flood prone regions between 2019 and 2050 (depending on the scenario), affecting approximately 2.5–5.8 million additional urban residents. In locations with high rates of future urban development in flood prone areas (Mindanao Island, in particular), climate resilient land-use plans should be developed/enforced, and flood mitigation infrastructure protected (in the case of “nature-based” infrastructure) or constructed. The data selected for our land-use change modeling and flood exposure assessment were all openly and (near-)globally available, with the intention that our methodology can potentially be applied in other countries where rapid urban expansion is occurring. The 2050 urban land-use maps generated in this study are available for download at https://www.iges.or.jp/en/pub/ph-urban2050/en to allow for their use in future works.     

Numerical modelling for analysis of the effect of different urban green spaces on urban heat load patterns in the present and in the future

This paper focuses on urban green spaces in terms of climate and human thermal comfort containing their effect on heat load mitigation. It incorporates a modelling study in which the role of green spaces was investigated in terms of heat stress modification by applying MUKLIMO_3 model. During the experiment, the thermal effects of dense trees, scattered trees, grasslands and mixed green infrastructure has been investigated in the case of Szeged (Hungary) and assessed using different climate indices. The investigations encompassed 3 climatological time periods (1981–2010, 2021–2050 and 2071–2100) and two emission scenarios for future climate (RCP4.5 and RCP8.5). It was found that urban green spaces (e.g. parks) generally cool the environment, although, the cooling potential of the different green types differs. The highest reduction of heat load was found in the case of large urban parks comprising of dense trees near the downtown. The spatial extension of detected cooling was found small. However, it would increase during the future, especially in the case of grasslands. For urban planners, it is highly recommended to introduce new green sites within a city and to increase the spatial extension of the existing ones to mitigate and adapt to the impacts of climate change in the urban environment.     

Importance of scenario analysis in urban development for urban water infrastructure planning and management

Sealing of surfaces and land use change induced by population change puts pressure on urban water networks. Changes in paved areas can also increase the risk of pluvial flooding at places that have not been endangered before. For an anticipatory planning and adaptation of the existing water infrastructure to a dynamic and evolving system like a growing or shrinking city, a comprehensive urban development scenario analysis is essential. This work presents an urban development model designed especially for simplistic simulation of multiple predefined population and spatial scenarios and allowing for an integration with successive urban water network models.Results show that an analysis of different development scenarios can help to increase a city's resilience to unexpected changes. Hence it is crucial to simulate a variety of scenarios to cover as many future outcomes of city development as possible for a systematic and rigorous inquiry for problematic situations in the future.     

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.     

Integrating spatial nonstationarity into SLEUTH for urban growth modeling: A case study in the Wuhan metropolitan area

Accurate forecasting of future urban land expansion can provide useful information for policy makers and urban planners to better plan for the impacts of future land use and land cover change (LULCC) on the ecosystem. However, most current studies do not emphasize spatial variations in the influence intensities of the various driving forces, resulting in unreliable predictions of future urban development. This study aimed to enhance the capability of the SLEUTH model, a cellular automaton model that is commonly used to measure and forecast urban growth and LULCC, by embedding an urban suitability surface from geographically weighted logistic regression (GWLR). Moreover, to examine the performance of the loosely-coupled GWLR-SLEUTH model, a layer with only water bodies excluded and a layer combining the former with an urban suitability surface from logistic regression (LR) were also used in SLEUTH in separate model calibrations. This study was applied to the largest metropolitan area in central China, the Wuhan metropolitan area (WMA). Results show that the integrated GWLR-SLEUTH model performed better than either the traditional SLEUTH model or the LR-SLEUTH model. Findings demonstrate that spatial nonstationarity existed in the drivers' impacts on the urban expansion in the study area and that terrain, transportation and socioeconomic factors were the major drivers of urban expansion in the study area. Finally, with the optimal calibrated parameter sets from the GWLR-SLEUTH model, an urban land forecast from 2017 to 2035 was conducted under three scenarios: 1) business as usual; 2) under future planning policy; and 3) ecologically sustainable growth. Findings show that future planning policy may promise a more sustainable urban development if the plan is strictly obeyed. This study recommended that spatial heterogeneity should be taken into account in the process of land change modeling and the integrated model can be applied to other areas for further validation and forecasts.     

A stochastically constrained cellular model of urban growth

Recent approaches to modeling urban growth use the notion that urban development can be conceived as a self-organizing system in which natural constraints and institutional controls (land-use policies) temper the way in which local decision-making processes produce macroscopic patterns of urban form. In this paper a cellular automata (CA) model that simulates local decision-making processes associated with fine-scale urban form is developed and used to explore the notion of urban systems as self-organizing phenomenon. The CA model is integrated with a stochastic constraint model that incorporates broad-scale factors that modify or constrain urban growth. Local neighborhood access rules are applied within a broader neighborhood in which friction-of-distance limitations and constraints associated with socio-economic and bio-physical variables are stochastically realized. The model provides a means for simulating the different land-use scenarios that may result from alternative land-use policies. Application results are presented for possible growth scenarios in a rapidly urbanizing region in south east Queensland, Australia.     

A framework for integrated assessment of food production economics in South Asia under climate change

Agriculture is a sector vulnerable to climate change. The potential decline of crop yields from this vulnerability has important policy implications for food security in South Asia. In this study an integrated assessment modelling framework is proposed to link a global economic model with global climate models via an econometric model of crop productivity. It is then used to examine the impact of climate change on food security in individual South Asian countries by exploring the interaction between climate-induced productivity change and changes in food production and food prices. The results of our simulations suggest that unfavourable climate change can reduce food production significantly from the historical trend and create upward pressure on food prices. This, in turn, will have serious adverse impacts on food security in the South Asian region.     

A spatial agent-based model for assessing strategies of adaptation to climate and tourism demand changes in an alpine tourism destination

A vast body of literature suggests that the European Alpine Region is amongst the most sensitive socio-ecosystems to climate change impacts. Our model represents the winter tourism socio-ecosystem of Auronzo di Cadore, located in the Dolomites (Italy), which economic and environmental conditions are highly vulnerable to climate variations. This agent-based model includes eight types of agents corresponding to different winter tourist profiles based on their socio-economic background and activity targets. The model is calibrated with empirical data while results are authenticated through direct interaction of local stakeholders with the model. The model is then used for assessing three hypothetical and contrasted infrastructure-oriented adaptation strategies for the winter tourism industry, that have been previously discussed with local stakeholders, as possible alternatives to the “business-as-usual” situation. These strategies are tested against multiple future scenarios that include: (a) future weather conditions in terms of snow cover and temperature, (b) the future composition and total number of tourists and (c) the type of market competition. A set of socio-economic indicators, which are strongly coupled with relevant environmental consequences, are considered in order to draw conclusions on the robustness of the selected strategies.     

Urban growth modeling of Kathmandu metropolitan region, Nepal

The complexity of urban system requires integrated tools and techniques to understand the spatial process of urban development and project the future scenarios. This research aims to simulate urban growth patterns in Kathmandu metropolitan region in Nepal. The region, surrounded by complex mountainous terrain, has very limited land resources for new developments. As similar to many cities of the developing world, it has been facing rapid population growth and daunting environmental problems. Three time series land use maps in a fine-scale (30 m resolution), derived from satellite remote sensing, for the last three decades of the 20th century were used to clarify the spatial process of urbanization. Based on the historical experiences of the land use transitions, we adopted weight of evidence method integrated in cellular automata framework for predicting the future spatial patterns of urban growth. We extrapolated urban development patterns to 2010 and 2020 under the current scenario across the metropolitan region. Depending on local characteristics and land cover transition rates, this model produced noticeable spatial pattern of changes in the region. Based on the extrapolated spatial patterns, the urban development in the Kathmandu valley will continue through both in-filling in existing urban areas and outward rapid expansion toward the east and south directions. Overall development will be greatly affected by the existing urban space, transportation network, and topographic complexity.     

Impact of urban planning on household's residential decisions: An agent-based simulation model for Vienna

Interest in assessing the sustainability of socio-ecological systems of urban areas has increased notably, with additional attention generated due to the fact that half the world's population now lives in cities. Urban areas face both a changing urban population size and increasing sustainability issues in terms of providing good socioeconomic and environmental living conditions. Urban planning has to deal with both challenges. Households play a major role by being affected by urban planning decisions on the one hand and by being responsible – among many other factors – for the environmental performance of a city (e.g. energy use). We here present an agent-based decision model referring to the city of Vienna, the capital of Austria, with a population of about 1.7 million (2.3 million within the metropolitan area, the latter being more than 25% of Austria's total population). Since the early 1990s, after decades of negative population growth, Vienna has been experiencing a steady increase in population, mainly driven by immigration. The aim of the agent-based decision model is to simulate new residential patterns of different household types based on demographic development and migration scenarios. Model results were used to assess spatial patterns of energy use caused by different household types in the four scenarios (1) conventional urban planning, (2) sustainable urban planning, (3) expensive centre and (4) no green area preference. Outcomes show that changes in preferences of households relating to the presence of nearby green areas have the most important impact on the distribution of households across the small-scaled city area. Additionally, the results demonstrate the importance of the distribution of different household types regarding spatial patterns of energy use.     

Social Capital,Resilience and Accessibility in Urban Systems: a Study on Sweden

Spatial systems appear to exhibit often a complex pattern of socio-economic development, in terms of (un)employment, income, mobility, ethnic composition, and urbanisation rates. Their evolution is co-determined by such factors as: market proximity, labour and housing market developments, public amenities, use of and access to transport systems, socio-economic composition of the population, etc. In addition, a sine qua non for sustaining urban economic growth is the local or regional presence of individual and collective cognitive assets that favour knowledge acquisition and transfer, education, innovation, and creativity. In this context, social capital and spatial accessibility are critical factors. The present paper aims to provide an operational framework for mapping out and understanding the mechanisms which drive spatial systems from the perspective of the resilience of urban areas, in the light of their social capital in combination with accessibility. Particular attention is therefore given to the essential role of social capital and transport accessibility in shaping both the economic development of cities and the spatial pattern in the evolution of cities, in terms of, inter alia, education, age, ethnic composition, (un)employment, and socio-economic poverty. For our quantitative analysis of the Swedish urban system, a quantile regression model is introduced and applied in order to study which urban-economic factors in Sweden determine the different levels of social capital, which are seen here as the fundamental component of the resilience capacity of urban areas.     

The persistence of shocks in GDP and the estimation of the potential economic costs of climate change

Integrated assessment models (IAMs) typically ignore the impact climate change could have on economic growth. The damage functions of these models assume that climate change impacts have no persistence at all, affecting only the period when they occur. Persistence of shocks is a stylized fact of macroeconomic time series and it provides a mechanism that could justify larger losses from climate change than previously estimated. Given that the degree of persistence of climate impacts is unknown, we analyze the persistence of generic shocks in observed GDP series for different world regions and compare it to that of the leading IAMs. Under the working hypothesis of interpreting the direct impact of climate change as such shocks, the implications for growth are investigated for two RCP scenarios. The way of introducing climate shocks to GDP in most IAMs can be interpreted as assuming an autonomous, costless, large and effective reactive adaptation capacity.     

A microsimulation model of urban energy use: Modelling residential space heating demand in ILUTE

Rapid urbanization, climate change and energy security warrant a more detailed understanding of how cities today consume energy. Agent-based, integrated microsimulation models of urban systems provide an excellent platform to accomplish this task, as they can capture both the short- and long-term decisions of firms and households which directly affect urban energy consumption. This paper presents the current effort towards developing an urban energy model for the Integrated Land Use, Transportation, Environment (ILUTE) modelling system.     

An integrated modelling approach for the assessment of land use change effects on wastewater infrastructures

The simulation of sewage systems and wastewater treatment plants is strategic for assessing the effect of new dwellings on the existing water facilities. This paper introduces an integrated framework made by a land use change model, a sewage system simulator, and a wastewater treatment plant simulator. This is a complex system since each element is characterized by different dynamics. The land use change model simulates the annual expansion of an urban area according to planners’ guidelines; the sewage system simulator investigates the response of the drainage system to the expansion. The wastewater treatment plant is simulated in order to assess the impact of the new outflows on the existing plant. The three models are integrated into a Simulink model. Two components of the developed framework are based on models well established in literature. The proposed framework is tested on a simple case study of a small town located in south west of Scotland.