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S. Krishnan
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FutureScapes
A design thinking approach to blending computational models and scenario narratives for urban futures
Accelerating urbanization and the inherent uncertainty in urban planning are increasing the demand for approaches that meaningfully integrate qualitative insights with quantitative analysis. While scenarios are widely used to explore multiple urban futures, existing methods that combine narrative storylines with computational models face persistent challenges: narrative assumptions are often oversimplified during translation; model structures frequently lack transparency regarding their underlying assumptions; and integrative processes tend to prioritize consensus, often sidelining the specialized insights of practitioners essential for urbanization strategies. Design Thinking (DT) offers a promising framework to address these limitations through its iterative, non-linear structure that bridges creative and analytical reasoning. Yet, a systematic, reproducible workflow that operationalizes DT for urban scenario development remains underdeveloped. This paper introduces FutureScapes (FS), a stepwise Design Thinking methodology for blending computational models and scenario narratives that embeds expert feedback into the modelling process. FS centers the spatial reasoning of expert stakeholders and introduces semi-quantitative boundary objects—in the form of scenario design maps—to break the traditionally linear sequence from story to simulation. This enables a reflexive process where model outputs actively reshape qualitative scenario assumptions to inform policy-relevant outcomes. The study contributes a generalizable methodology that enhances the contextual relevance, transparency, and strategic utility of computational scenario modelling for metropolitan planning.
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Accelerating urbanization and the inherent uncertainty in urban planning are increasing the demand for approaches that meaningfully integrate qualitative insights with quantitative analysis. While scenarios are widely used to explore multiple urban futures, existing methods that combine narrative storylines with computational models face persistent challenges: narrative assumptions are often oversimplified during translation; model structures frequently lack transparency regarding their underlying assumptions; and integrative processes tend to prioritize consensus, often sidelining the specialized insights of practitioners essential for urbanization strategies. Design Thinking (DT) offers a promising framework to address these limitations through its iterative, non-linear structure that bridges creative and analytical reasoning. Yet, a systematic, reproducible workflow that operationalizes DT for urban scenario development remains underdeveloped. This paper introduces FutureScapes (FS), a stepwise Design Thinking methodology for blending computational models and scenario narratives that embeds expert feedback into the modelling process. FS centers the spatial reasoning of expert stakeholders and introduces semi-quantitative boundary objects—in the form of scenario design maps—to break the traditionally linear sequence from story to simulation. This enables a reflexive process where model outputs actively reshape qualitative scenario assumptions to inform policy-relevant outcomes. The study contributes a generalizable methodology that enhances the contextual relevance, transparency, and strategic utility of computational scenario modelling for metropolitan planning.
The Future Ground. Urban Planning Under Climate Uncertainty
Theoretical Contributions, Methodological Innovation, and Empirical Insights from Amsterdam and Mumbai
The Future Ground: Urban Planning Under Climate Uncertainty
As the focal point of the climate crisis, urban regions face a critical mismatch between the century-long lifecycles of physical infrastructure and the short-term institutional planning timeframes that govern them. This research addresses the dual constraints of temporal myopia (short-sightedness) and sectoral fragmentation, addressing the lack of mechanisms to systematically embed climate uncertainty into spatial masterplans.
Positioned within the emerging paradigm of climate urbanism, this dissertation examines how urban planning can operationalize resilience thinking and apply design thinking to steer transformation under uncertainty. The research adopts a mixed-methods design grounded in Multi-Case Theory building (MCT), enabling a rigorous exchange between theory and practice. The approach is empirically refined through two contrasting global contexts: the Metropolitan Region of Amsterdam (MRA) and the Mumbai Metropolitan Region (MMR).
The research culminates in a new theories and conceptual framework that connects resilience and uncertainty approaches to physical planning interventions and an advanced understanding of the temporal dynamics of urban systems. Furthermore, it introduces a methodological innovation: a stepwise Design Thinking methodology for blending computational models and scenario narratives that embeds expert feedback into the modelling process to navigate and design for complex urban futures.
...
As the focal point of the climate crisis, urban regions face a critical mismatch between the century-long lifecycles of physical infrastructure and the short-term institutional planning timeframes that govern them. This research addresses the dual constraints of temporal myopia (short-sightedness) and sectoral fragmentation, addressing the lack of mechanisms to systematically embed climate uncertainty into spatial masterplans.
Positioned within the emerging paradigm of climate urbanism, this dissertation examines how urban planning can operationalize resilience thinking and apply design thinking to steer transformation under uncertainty. The research adopts a mixed-methods design grounded in Multi-Case Theory building (MCT), enabling a rigorous exchange between theory and practice. The approach is empirically refined through two contrasting global contexts: the Metropolitan Region of Amsterdam (MRA) and the Mumbai Metropolitan Region (MMR).
The research culminates in a new theories and conceptual framework that connects resilience and uncertainty approaches to physical planning interventions and an advanced understanding of the temporal dynamics of urban systems. Furthermore, it introduces a methodological innovation: a stepwise Design Thinking methodology for blending computational models and scenario narratives that embeds expert feedback into the modelling process to navigate and design for complex urban futures.
...
The Future Ground: Urban Planning Under Climate Uncertainty
As the focal point of the climate crisis, urban regions face a critical mismatch between the century-long lifecycles of physical infrastructure and the short-term institutional planning timeframes that govern them. This research addresses the dual constraints of temporal myopia (short-sightedness) and sectoral fragmentation, addressing the lack of mechanisms to systematically embed climate uncertainty into spatial masterplans.
Positioned within the emerging paradigm of climate urbanism, this dissertation examines how urban planning can operationalize resilience thinking and apply design thinking to steer transformation under uncertainty. The research adopts a mixed-methods design grounded in Multi-Case Theory building (MCT), enabling a rigorous exchange between theory and practice. The approach is empirically refined through two contrasting global contexts: the Metropolitan Region of Amsterdam (MRA) and the Mumbai Metropolitan Region (MMR).
The research culminates in a new theories and conceptual framework that connects resilience and uncertainty approaches to physical planning interventions and an advanced understanding of the temporal dynamics of urban systems. Furthermore, it introduces a methodological innovation: a stepwise Design Thinking methodology for blending computational models and scenario narratives that embeds expert feedback into the modelling process to navigate and design for complex urban futures.
As the focal point of the climate crisis, urban regions face a critical mismatch between the century-long lifecycles of physical infrastructure and the short-term institutional planning timeframes that govern them. This research addresses the dual constraints of temporal myopia (short-sightedness) and sectoral fragmentation, addressing the lack of mechanisms to systematically embed climate uncertainty into spatial masterplans.
Positioned within the emerging paradigm of climate urbanism, this dissertation examines how urban planning can operationalize resilience thinking and apply design thinking to steer transformation under uncertainty. The research adopts a mixed-methods design grounded in Multi-Case Theory building (MCT), enabling a rigorous exchange between theory and practice. The approach is empirically refined through two contrasting global contexts: the Metropolitan Region of Amsterdam (MRA) and the Mumbai Metropolitan Region (MMR).
The research culminates in a new theories and conceptual framework that connects resilience and uncertainty approaches to physical planning interventions and an advanced understanding of the temporal dynamics of urban systems. Furthermore, it introduces a methodological innovation: a stepwise Design Thinking methodology for blending computational models and scenario narratives that embeds expert feedback into the modelling process to navigate and design for complex urban futures.
TIMEWISE: Temporal Dynamics for Urban Resilience
Theoretical insights and empirical reflections from Amsterdam and Mumbai
Increasing frequency of climate-related disruptions requires transformational responses over the lifecycles of interconnected urban systems with short- and long-term change dynamics. However, the aftermath of disruptions is often characterised by short-sighted decision-making, neglecting long-term urban shifts. In this study, we present a first attempt to develop the theoretical foundation for temporal dynamics for increasingly disrupted yet ”connecting and moving” cities that can be used in planning for urban resilience. Using the lens of climate urbanism, we conceptualise the interplay of temporal dynamics to empirically examine how planning practice perceives and addresses temporality in two regions - Amsterdam, the Netherlands, and Mumbai, India. Our findings reinforce that disruptions do not inform long-term planning. Endogenous and exogenous dynamics of change are not viewed together nor used to embed short-term planning goals within long-term resilience visions. To address the lack of systematic planning approaches that can leverage temporal dynamics, we propose two options for temporally flexible urban planning processes.
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Increasing frequency of climate-related disruptions requires transformational responses over the lifecycles of interconnected urban systems with short- and long-term change dynamics. However, the aftermath of disruptions is often characterised by short-sighted decision-making, neglecting long-term urban shifts. In this study, we present a first attempt to develop the theoretical foundation for temporal dynamics for increasingly disrupted yet ”connecting and moving” cities that can be used in planning for urban resilience. Using the lens of climate urbanism, we conceptualise the interplay of temporal dynamics to empirically examine how planning practice perceives and addresses temporality in two regions - Amsterdam, the Netherlands, and Mumbai, India. Our findings reinforce that disruptions do not inform long-term planning. Endogenous and exogenous dynamics of change are not viewed together nor used to embed short-term planning goals within long-term resilience visions. To address the lack of systematic planning approaches that can leverage temporal dynamics, we propose two options for temporally flexible urban planning processes.
RISE-UP: Resilience in urban planning for climate uncertainty
Empirical insights and theoretical reflections from case studies in Amsterdam and Mumbai
Climate change is one of the main drivers of uncertainty in urban planning, but only a few studies systematically address these uncertainties, especially in the long term. Urban resilience theory presents principles to manage uncertainty but largely focuses on individual urban systems rather than complex interdependent dynamics. Further, most planning and resilience theory originates from the Global North and is unsuitable for capturing the dynamics of the Global South. This study uses an exploratory multi-case analysis towards developing an enhanced understanding of urban planning for climate uncertainty. We argue that long-term urban planning for climate uncertainty can benefit from systematically integrating resilience principles. We use a two-step qualitative research approach: (1) To propose a conceptual framework connecting urban resilience principles, approaches to urban planning under uncertainty and planning responses in urban systems. (2) To use the conceptual framework to analyse climate-related planning responses in two contrasting case studies in the Global North (GN) and Global South (GS) (Amsterdam and Mumbai). We conclude with four propositions towards an enhanced understanding of urban planning for climate uncertainty by drawing upon the empirical insights from the two case studies.
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Climate change is one of the main drivers of uncertainty in urban planning, but only a few studies systematically address these uncertainties, especially in the long term. Urban resilience theory presents principles to manage uncertainty but largely focuses on individual urban systems rather than complex interdependent dynamics. Further, most planning and resilience theory originates from the Global North and is unsuitable for capturing the dynamics of the Global South. This study uses an exploratory multi-case analysis towards developing an enhanced understanding of urban planning for climate uncertainty. We argue that long-term urban planning for climate uncertainty can benefit from systematically integrating resilience principles. We use a two-step qualitative research approach: (1) To propose a conceptual framework connecting urban resilience principles, approaches to urban planning under uncertainty and planning responses in urban systems. (2) To use the conceptual framework to analyse climate-related planning responses in two contrasting case studies in the Global North (GN) and Global South (GS) (Amsterdam and Mumbai). We conclude with four propositions towards an enhanced understanding of urban planning for climate uncertainty by drawing upon the empirical insights from the two case studies.
Cities are complex socio-technical systems (STSs) under tremendous stress due to climate change. To incorporate resilience into urban plans and move towards evidence-based long-term decision-making, we must unravel complex land-use dynamics and the effect of climate uncertainties on cities. Currently, land-use dynamics are explored through Cellular Automata models to investigate the impacts of urban planning scenarios. What is, however, missing to support resilience decisions, is a systematic analysis of long-term climate uncertainties on land-use change. This study addresses this gap by analysing the effects of flood uncertainties on land-use patterns. While conventionally, urban planning decisions for climate uncertainty are based on a few scenarios, we use exploratory modeling to sample and combine uncertain climate variables to scenarios and understand the implications of the climate scenarios on land use via computational experiments. Specifically, we integrate flood probability maps into land-use maps to assess land suitability. Agglomerative clustering allows us to analyze the resulting land-use maps based on their similarity. Finally, we select representative maps from each cluster and compare them with the baseline map. We apply our integrated modeling approach in the Metropolitan Region of Amsterdam (MRA). Our results show spatially explicit alternatives for high-density residential development that is climate-resilient. The proposed framework can be applied to other cities to investigate the long-term impacts of climate uncertainties and adopt resilience-informed decision-making.
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Cities are complex socio-technical systems (STSs) under tremendous stress due to climate change. To incorporate resilience into urban plans and move towards evidence-based long-term decision-making, we must unravel complex land-use dynamics and the effect of climate uncertainties on cities. Currently, land-use dynamics are explored through Cellular Automata models to investigate the impacts of urban planning scenarios. What is, however, missing to support resilience decisions, is a systematic analysis of long-term climate uncertainties on land-use change. This study addresses this gap by analysing the effects of flood uncertainties on land-use patterns. While conventionally, urban planning decisions for climate uncertainty are based on a few scenarios, we use exploratory modeling to sample and combine uncertain climate variables to scenarios and understand the implications of the climate scenarios on land use via computational experiments. Specifically, we integrate flood probability maps into land-use maps to assess land suitability. Agglomerative clustering allows us to analyze the resulting land-use maps based on their similarity. Finally, we select representative maps from each cluster and compare them with the baseline map. We apply our integrated modeling approach in the Metropolitan Region of Amsterdam (MRA). Our results show spatially explicit alternatives for high-density residential development that is climate-resilient. The proposed framework can be applied to other cities to investigate the long-term impacts of climate uncertainties and adopt resilience-informed decision-making.
As climate change is becoming a reality, there is an increasing demand to improve urban resilience. Planning Support Systems (PSS) enable climate-informed planning. However, previous research confirms difficulties in the uptake of PSS due to their resource-intensive nature and lack of awareness of their usefulness. This chapter aims to make a headway in understanding research priorities and gaps that need to be addressed for PSS to address climate resilience in the long run. To this end, we review the emerging body of knowledge in academia and practice, by conducting a text-mining analysis of academic (n = 36,405) and non-academic (practice) (n = 86) literature on urban planning and climate resilience. We extract trends in climate pressures, infrastructure drivers, and planning approaches. A key finding from the academic literature is that long-term planning continues to be limited to a few fixed scenarios and places a strong focus on single sector strategies. Practice documents continue to be designed to inform high-level policies, but not spatial plans that require integrated thinking. Our analysis concludes with a research agenda for improving PSS to (1) identify and integrate the full range of variables in the long-term; (2) support selection of appropriate planning responses across multiple infrastructure systems; and (3) improve flexibility in planning by a deeper understanding of temporal aspects such as planning timeframes.
...
As climate change is becoming a reality, there is an increasing demand to improve urban resilience. Planning Support Systems (PSS) enable climate-informed planning. However, previous research confirms difficulties in the uptake of PSS due to their resource-intensive nature and lack of awareness of their usefulness. This chapter aims to make a headway in understanding research priorities and gaps that need to be addressed for PSS to address climate resilience in the long run. To this end, we review the emerging body of knowledge in academia and practice, by conducting a text-mining analysis of academic (n = 36,405) and non-academic (practice) (n = 86) literature on urban planning and climate resilience. We extract trends in climate pressures, infrastructure drivers, and planning approaches. A key finding from the academic literature is that long-term planning continues to be limited to a few fixed scenarios and places a strong focus on single sector strategies. Practice documents continue to be designed to inform high-level policies, but not spatial plans that require integrated thinking. Our analysis concludes with a research agenda for improving PSS to (1) identify and integrate the full range of variables in the long-term; (2) support selection of appropriate planning responses across multiple infrastructure systems; and (3) improve flexibility in planning by a deeper understanding of temporal aspects such as planning timeframes.
Journal article
(2019)
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S. Krishnan, Jiabiao Lin, Johannes Simanjuntak, Fransje Hooimeijer, Jeremy Bricker, Maäyan Daniël, Yuka Yoshida
Engineering for flood resilience of dense coastal regions often neglects the resultant impact on urban design quality. Vital subsurface infrastructure such as hydraulic systems, water networks, civil construction, transport, energy supply and soil systems are especially important in shaping the urban environment and integrating resilience. However, the complexity and resource intensive nature of these engineering domains make it a challenge to incorporate them into design measures. In the process of planning, this impedes proactive collaboration between the design and engineering communities. This study presents a collaborative design engineering exercise undertaken to find spatial solutions to flood-prone Edogawa ward in Tokyo, Japan. The team included urbanists, hydraulic engineers, water resource managers, and landscape architects. Hydraulic engineering solutions were combined with spatial planning methods to deliver two alternative strategies for the chosen site. Each alternative was then evaluated for its urban design quality and effectiveness in reducing flood risk. The exercise highlighted that successful design requires comprehensive interdisciplinary collaboration to arrive at a sustainable bargain between hard and soft measures
...
Engineering for flood resilience of dense coastal regions often neglects the resultant impact on urban design quality. Vital subsurface infrastructure such as hydraulic systems, water networks, civil construction, transport, energy supply and soil systems are especially important in shaping the urban environment and integrating resilience. However, the complexity and resource intensive nature of these engineering domains make it a challenge to incorporate them into design measures. In the process of planning, this impedes proactive collaboration between the design and engineering communities. This study presents a collaborative design engineering exercise undertaken to find spatial solutions to flood-prone Edogawa ward in Tokyo, Japan. The team included urbanists, hydraulic engineers, water resource managers, and landscape architects. Hydraulic engineering solutions were combined with spatial planning methods to deliver two alternative strategies for the chosen site. Each alternative was then evaluated for its urban design quality and effectiveness in reducing flood risk. The exercise highlighted that successful design requires comprehensive interdisciplinary collaboration to arrive at a sustainable bargain between hard and soft measures
Current approaches to spatial planning for resilience involves adding a ‘protective’ layer based on a ‘return period’ to shield the landscape from threat and/or rehabilitation to ‘bounce back’ to the pre-existing condition. Escalating manifestations of climate risk often surpass the level of protection offered by this type of approach leading to massive economic losses and reconstruction.
The shortsight in spatial decision making for climate resilience can be attributed to conflicting timelines of climate change research (100 years) ,urban planning practice (20 years) and political term (5 years) which makes long term consensus challenging. This paper looks at how we can sync the urban planning timeline with return periods of climate hazard to utilise land management as a tool to not just mitigate but better transition to adapt to intensifying risks. The principal objective is mainstreaming the role of spatial planning for risk reduction.
The method adopted is learning from urban crisis recovery patterns in space offered by critical infrastructure networks - transport, water, energy lines.
Utilising a spatial disaster event chain (informed by literature on causal chain effect, complexity theory and cascading effects), risks are projected in space. The test case is the East Bay of San Francisco which is at risk of a sea level rise by 2100 and is a hotbed of seismic activity. Risks from flooding and earthquake are projected iteratively in space to simulate obstruction in evacuation movement on road networks using ArcGIS Network Analyst. This informs the critical web of the region that forms the backbone to reconfigure land-use and densities. Understanding recovery patterns due to incremental risk on infrastructure helps derive the gradient of spatial vulnerability. A hierarchy of risk informs the adaptivity framework for resilient land parcels that absorbs intensive growth and vulnerable parcels that must transition to other functions. This defines a revised cohesive spatial morphology for the region.
In this paper, we develop a spatial planning approach to embrace uncertainties ‘proactively’ such that we allocate investment that are not solely focused on ‘reactively’ decoding the blind side of climate change. It provides the potential to bounce forward in space to better cope with a disaster which is essential for a robust environment that can contribute to resilient regional economic growth in the long run.
While the method utilises urban design as a qualitative tool , it draws from quantitative conclusions from transport, seismic and flood risk simulations. In doing so it highlights vital gaps between the two approaches and invites further interdisciplinary practitioners to refine the methodology for a more realistic implementation of long range urban planning for climate risk. It develops a ‘spatio-temporal’ scale in syncing decision making across different hierarchies of space, governance and risk. Thus, it wants to contribute towards a composite framework for better urban planning practices and a faster, more integrated decision making for implementation under deep uncertainty.
...
Current approaches to spatial planning for resilience involves adding a ‘protective’ layer based on a ‘return period’ to shield the landscape from threat and/or rehabilitation to ‘bounce back’ to the pre-existing condition. Escalating manifestations of climate risk often surpass the level of protection offered by this type of approach leading to massive economic losses and reconstruction.
The shortsight in spatial decision making for climate resilience can be attributed to conflicting timelines of climate change research (100 years) ,urban planning practice (20 years) and political term (5 years) which makes long term consensus challenging. This paper looks at how we can sync the urban planning timeline with return periods of climate hazard to utilise land management as a tool to not just mitigate but better transition to adapt to intensifying risks. The principal objective is mainstreaming the role of spatial planning for risk reduction.
The method adopted is learning from urban crisis recovery patterns in space offered by critical infrastructure networks - transport, water, energy lines.
Utilising a spatial disaster event chain (informed by literature on causal chain effect, complexity theory and cascading effects), risks are projected in space. The test case is the East Bay of San Francisco which is at risk of a sea level rise by 2100 and is a hotbed of seismic activity. Risks from flooding and earthquake are projected iteratively in space to simulate obstruction in evacuation movement on road networks using ArcGIS Network Analyst. This informs the critical web of the region that forms the backbone to reconfigure land-use and densities. Understanding recovery patterns due to incremental risk on infrastructure helps derive the gradient of spatial vulnerability. A hierarchy of risk informs the adaptivity framework for resilient land parcels that absorbs intensive growth and vulnerable parcels that must transition to other functions. This defines a revised cohesive spatial morphology for the region.
In this paper, we develop a spatial planning approach to embrace uncertainties ‘proactively’ such that we allocate investment that are not solely focused on ‘reactively’ decoding the blind side of climate change. It provides the potential to bounce forward in space to better cope with a disaster which is essential for a robust environment that can contribute to resilient regional economic growth in the long run.
While the method utilises urban design as a qualitative tool , it draws from quantitative conclusions from transport, seismic and flood risk simulations. In doing so it highlights vital gaps between the two approaches and invites further interdisciplinary practitioners to refine the methodology for a more realistic implementation of long range urban planning for climate risk. It develops a ‘spatio-temporal’ scale in syncing decision making across different hierarchies of space, governance and risk. Thus, it wants to contribute towards a composite framework for better urban planning practices and a faster, more integrated decision making for implementation under deep uncertainty.