Handling Interdependencies in Climate Change Risk Assessment
2. Mechanisms of Interdependency
- Functional interdependencies—arise when one system is connected to, and relies on, another system to operate (e.g., water to cool power stations, energy to pump water through the water distribution infrastructure). Computerisation and automation of many systems has led to the proliferation of ICT related interdependencies.
- Physical interdependencies—arise when systems interact through a physical process (e.g., hydrological processes), or through shared physical attributes (e.g., freight and passenger travel on railways is limited by a maximum capacity).
- Geographic interdependencies—occur when geographic properties, such as proximity, lead to correlated responses in multiple systems. For example, a flood might impact upon multiple systems simultaneously within the floodplain, whilst damage to a single system might lead to wider disturbances as a result of geographical interdependencies (e.g., if a bridge collapses leading to the failure of ICT and electricity cables running over the bridge).
- Economic and financial interdependencies—Shared markets result in sectors interacting through the same economic system which influences investment cycles, bond markets, pricing structures and the availability of credit to create “top down” economic interdependencies. Conversely, multiple sectors converge at the point of end-users (individuals, buildings, etc.) whose behaviour (e.g., demand for services such as energy and water, or potential to take certain actions during an extreme event) is likely to be subject to budgetary constraints—creating “bottom up” economic interdependencies.
- Institutional and policy interdependencies—A shared regime (e.g., decarbonisation policy) where agencies may control and impact systems through policy, legal or regulatory means creates “top down” interdependencies amongst societal agents.
- Social interdependencies—Conversely, from the “bottom-up”, individuals and organisations interact locally, for example to communicate risks and build adaptive capacity.
|Characteristic||Implications for climate change risk assessment|
|Spatial scale||Phenomena relevant to climate risks take place over a range of scales. Greenhouse gas emissions are altering climatic processes at global scales. Global teleconnections such as the El Nino Southern Oscillation (ENSO) influences extreme events around the world . Other processes (e.g., the urban heat island) become more important for risk assessment at the scale of catchments, cities or lower spatial scales. Similarly, non-climatological processes play out at a range of spatial scales, including, for example, geopolitical issues such as competition for water resources, or availability of sea lanes for transport.|
|Temporal scale||The dynamics of processes and systems relevant to a climate risk assessment may vary from seconds (e.g., disturbances to power transmission systems) or individual events (e.g., a flood and associated responses) through decades (e.g., construction of major infrastructure) or centuries (e.g., commitment to sea level rise even if greenhouse gas emissions were to be ceased immediately ). Modelling and assessing risks for interdependent systems that interact over different timescales requires careful consideration.|
|Interaction strength||The nature of the interdependencies which include the strength of coupling, the directness of coupling and the (non-)linearity of interactions influences the propagation of climate risks through a system and the options available and priorities for adaptation. Tight coupling indicates a high degree of interaction between systems and typically a rapid response to changes. For example, a system reliant on electricity, with no backup generator, will immediately cease to operate if there is a failure in the power supply. Provision of onsite storage or generation capacity loosens the coupling of these systems.|
|Interaction complexity||The directness of coupling considers whether two systems interact directly or indirectly through one or more systems and processes. Complex interactions, in contrast to linear interdependencies, are those of unfamiliar sequences, or unplanned and unexpected sequences, and either not visible or not immediately comprehensible . These complex sequences can lead to cascading failures across systems, or a gradual failure that is incrementally exacerbated by multiple system interactions.|
|System state||Social, environmental and engineered systems exhibit a range of behaviours according to their state and interconnectivity. Their response and interactions—and, therefore, the magnitude of climate risk, will be altered if systems (and their interconnections) are degraded or stressed. It is therefore crucial to analyse risk over a wide range of events and system conditions.|
|Socio-economic context||Climate risks are mediated by the attitudes, motivations, culture, values and different sets of concerns of individuals, organisations, government and society more generally. These can lead to different policy, procedural and behavioural responses to climate risks and acceptance of policies to manage these risks.|
3. Case Studies
3.1. Flood Risk from Multiple Loading Sources
- Identify the drivers of flood risk and construct appropriate systems model (see Figure 2a)
- Identify the components in the urban drainage system, shown in Figure 2b, (and associated model parameters) to which risk is to be attributed.
- Identify the range of variation for each parameter.
- Sample a range of values for each parameter. Replicated Latin Hypercube Sampling (rLHS), described in full by  was used to produce correlated inputs to capture interdependencies between variables.
- Run the flood model for each sample and calculate the corresponding damage.
- Analyse the sensitivity of the system to each parameter and attribute the risk accordingly.
3.2. Sediment Movements Mediating Two Coastal Risks
3.3. Spatial Planning and Multiple Risks in an Urban Area
- Baseline: Population and employment tend towards existing settlements with transport infrastructure capacity increasing in response to demand only to ensure existing infrastructure is unhindered by capacity constraints.
- Eastern axis: The Olympics and Thames Gateway Development Corporation serve as stimulus for long term investment, including new transport infrastructure, in East London and along the estuary.
- Centralisation: High density living and working becomes the main style of new development with population and employment concentrating in the city of London and adjacent areas. Expansion of the greenbelt discourages further suburban sprawl.
- Suburbanisation: Employment remains strong in the centre, but growth is focused around major suburban hubs. New radial routes, and local walking and cycling infrastructure support shorter commutes. Restrictions on tall buildings limit population growth in central areas.
4. Guidance for Handing Interdependency in Climate Change Risk Assessment
- System definition—When assessing climate risks in interdependent systems and providing insights for climate risk management, the first stage is to define the system of interest and the policy questions to be addressed:
- Identify the associated metrics by which climate risk is to be assessed.
- Explore, and potentially expand, the boundary of the system being analysed to include other “soft” (e.g., regulation) as well as “hard” systems (e.g., flood defences) that are relevant to the risk(s) to be assessed.
- Identify the processes of long term change, including climate change, that will influence the risk(s) and associated systems.
- Scope interaction mechanisms—Crucial to reducing the complexity of this problem is the identification of the system interactions and processes most relevant to the objectives and decision-makers using the risk assessment. A number of approaches can support this process, including the use of influence diagrams (e.g., Figure 1) or matrix structures such as Lano’s N2 chart  that structure interactions as an array where each row and column could represent one of the nodes in Figure 1.
- Apply appropriate modelling tools—The three case studies used statistical and process based models to quantify interdependent relationships. Clearly, the modelling approach used must be appropriate to the system being analysed, and, in some instances, a combination of methods may be required. A range of approaches are available, including qualitative assessment , network analysis [44,45], dynamic simulations—for example of supply-demand  or system dynamics , Input-Output modelling  and agent-based modelling .
- Identify vulnerabilities and opportunities—Use the systems analysis to identify beneficial interdependencies between drivers and sectors, as well as potentially problematic interactions (e.g., cross-sectoral antagonisms or vulnerabilities or pathways that could lead to cascading failures) that will need to be managed. As with any risk analysis, to identify and characterise a wide range of climate risks, the systems analysis must consider a full spectrum of threats, vulnerabilities and consequences:
- Subject the analysis to the full range of possible events and system states,
- Consider a range of possible futures, and alternative sectoral perspectives, and,
- Provide information on social, economic, environmental, technical and political risks.
- Assess the performance of adaptation measures—Identify and assess the potential benefits of adaptation options by considering their performance against the full range of threats and drivers.
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Dawson, R.J. Handling Interdependencies in Climate Change Risk Assessment. Climate 2015, 3, 1079-1096. https://doi.org/10.3390/cli3041079
Dawson RJ. Handling Interdependencies in Climate Change Risk Assessment. Climate. 2015; 3(4):1079-1096. https://doi.org/10.3390/cli3041079Chicago/Turabian Style
Dawson, Richard J. 2015. "Handling Interdependencies in Climate Change Risk Assessment" Climate 3, no. 4: 1079-1096. https://doi.org/10.3390/cli3041079