risk management – Cabot Institute for the Environment blog

Tackling urban landslides in an uncertain future

Posted on September 21, 2021March 30, 2023 by janet.crompton

One of the challenges of the 21st century is how to reconcile global urban growth with the prevention and mitigation of environmental disasters, such as those caused by landslides. Every year 300 million people are exposed to landslides worldwide, with over 4,000 fatalities, 250,000 of people affected, and billions of US dollars of economic damage. However, impacts might be worse in the future for two main reasons. First, severe precipitations might become more frequent under climate change, causing more rainfall-triggered landslides. Second, growing urban population will lead more people to live in areas exposed to landslides globally, and in particular in developing countries where low-income dwellers are starting to overcrowd landslide-prone areas such as steep slopes. With more hurricanes to come and more people at risk, understanding where and when landslides might occur is becoming increasingly crucial.

Current predictions are too uncertain to support decisions

One method to predict landslides in the future is to look at landslides in the past. The analysis of historical records allows the identification of those hillslopes that have failed in the past. Currently stable hillslopes where similar conditions exist (for example, similar slope gradients) are ‘tagged’ with high landslide probability. These areas might be then excluded for construction development or might be the first to be alerted when a severe precipitation is expected.

This approach to landslide prediction is, however, often insufficient. Landslides and rainfall records as well as data on hillslope properties are often affected by large errors or unavailable in sufficient detail. In addition, what happened in the past might not be representative of what may happen in the future, making historical records less useful for long-term projections. Climate and socio-economic models can be used to build scenarios of how rainfall patters and cities might look like in the future. Unfortunately, these scenarios can vary significantly because they depend on highly uncertain factors such as future carbon emissions. As a result, landslide estimates can also be very different and sometimes even contradictory – some predicting an increase and others a decrease in landslides occurrence – undermining their practical use for risk management.

From ‘predict then act’ to ‘act now with low regrets’

Instead of trying to predict how climate and urban expansion will evolve in the future, I used a different approach centred on decision making. I ask the question: how much climate and/or urban expansion needs to change before landslide hazard significantly increases?

The scientific method behind my analysis (Bozzolan et al. 2020, NHESS) first generates thousands of synthetic but realistic hillslopes representations of the study area. Then, it imposes hypothetical scenarios of increasing rainfall severities and urban expansion, also considering different construction features that could affect slope stability (for example, the presence or not of adequate slope drainage such as roof gutters on houses).

Finally, it uses a computer model to assess the stability of these virtual hillslopes, generating a new synthetic library of landslide records. By exploring the library is now possible to identify those combinations of rainfall and urban development conditions (e.g., with or without roof gutters) for which hillslopes are most likely to fail. ‘Low-regret’ mitigation actions will be those that perform well across scenarios and therefore should be prioritised even if future rainfall and urban predictions remain unknown.

A practical tool for decision makers

This new method which explores many ‘what if’ scenarios is a useful tool for decision makers in landslide risk management and reduction. For example, figure 1 shows how a map of landslide probability in Saint Lucia (Eastern Caribbean) might look like if the severity of a destructive rainstorm such as the 2010 Hurricane Tomas were to increase under climate change or if unregulated housing expanded on slopes susceptible to failure. The analysis also shows that when both scenarios are included landslide probability disproportionally increases, revealing that ‘the whole is greater than the sum of its parts’. This information could be used to assess the risk and damages associated with each scenario and to identify low-regret nation-wide risk reduction and risk transfer strategies.


Figure 1: Maps of landslide probability in Saint Lucia under different ‘what if’ scenarios. The percentage (+%) indicates the increase of areas with high landslide probability.

The same method can also be applied to quantify the cost-benefit ratio of different landslide mitigation options, such as improving urban drainage or tree planting at the community/household scale. In Freetown (Sierra Leone), for example, I collaborated with the engineering firm Arup to identify those landslide hazard mitigation actions that would lead to the largest reduction in landslide probability for certain locations or types of slopes, and should thus be prioritised. The information generated through this analysis not only provides evidence to governments and investors for informing urban planning, but it might also encourage landslide probability from low to high micro-insurance in disaster prevention, where insurers offer lower premiums to reward risk-reducing behaviours.

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This blog is written by Cabot Institute for the Environment member, Dr Elisa Bozzolan from the School of Civil Engineering at the University of Bristol.

Making decisions in an environmentally uncertain world

Posted on October 30, 2013April 20, 2023 by janet.crompton

Improved decision making in the face of environmental uncertainty is at the heart of the Cabot Institute. Although individuals, businesses and society aspire to make logical decisions, informed by evidence and wisdom, we are also influenced by a complex mixture of emotions, ethics, political opportunism and personal beliefs. These murky waters become even more challenging to navigate when dealing with the inherent uncertainty in the basic evidence. And it becomes almost impossible when pre-conceived beliefs and opinions replace evidence. In such scenarios, uncertainty can be manipulated as a tool to undermine evidence and justify flawed decisions. This is the particular challenge of decision making in the context of complex environmental, economic and ecological issues.

To a scientist confronted with evidence that human activity is changing our environment at unprecedented rates, it is apparent that environmental uncertainty is rarely appropriately deployed in policy making. Most perniciously, it is commonly argued that the risk of an action (i.e. loss of biodiversity or increasing CO2 emissions) could be at the low end of the probability distribution – ‘the temperature might not warm that much’, ‘we might not get more hurricanes’. That is not proper governance; that is hiding behind uncertainty and hoping for the best. Nor is it appropriate to govern based on the worst-case scenario. But nor can we govern by solely considering the most likely outcome. We must recognise the range of possibilities and plan within it – strategically, flexibly, resiliently. In other words, the uncertainty brought about by ongoing environmental change is itself a profound cause for concern and a challenge for governance.

However, environmental uncertainty is not an opaque label for things ‘we do not understand’ and by an extension it is not a cause for inaction.

Rich Pancost’s old farm, US Midwest

I grew up on a farm in the US Midwest and so environmental uncertainty to me mainly concerns our food and the people who provide it. Anyone who has ever been involved in farming understands how uncertain our environment can be. And they understand how undermining and economically challenging that uncertainty is, especially with respect to the weather (weather is not the same as climate, but it makes for a useful environmental analogy).

We had about 30 head of cattle on our small Ohio dairy farm , and my brother, parents and I needed to put aside 4000 bales of hay every summer. I loved that job – I remember the smell of drying hay and the fat bumble bees buzzing in the clover. I remember being with my family, the satisfaction of completed work and the closeness that came from achieving things together. But it was hard and uncertain work, my father cutting the grass, raking it and baling it, quickly over successive hot days so that it would dry before a summer rain shower could strip away the nutrients. Or worse: before an extended few days of rain saturated the mowed hay on the ground, causing it to become fungus-ridden and rotting it away in the field. We could work with a prediction of rain and we could work with a prediction of no rain or even drought. But we could not work with an overly uncertain prediction. Even worse were wrong (i.e. overly certain) predictions. We navigated the probabilistic terrain of the daily weather forecasts somewhat by instinct, but the stakes were high, and just three or four bad decisions in a summer would have been financially catastrophic. The farm is long gone but my Mom is still addicted to the weather reports.

The barn

But uncertainty does not mean paralysis; it means risk management. We mitigated the risk of wasted crop by renting and working fields that could yield 4500 bales rather than 4000. And those 4000 bales of hay were themselves, risk management, exceeding our likely needs. Gathering the bales and storing them in our barn’s loft was hard, sticky, hot and gritty work. The hay was delivered to the loft by a metal elevator – metal plates carried by metal chains up a metal chute, all powered by our forty-year old International Harvester tractor’s power take-off shaft. I loved doing this work on the farm – its physicality and the stimulus of all of your senses – but I do not miss that tremendous rattling, clanging noise! The loft itself could reach temperatures of 110°F and was filled with clouds of dust and darting, irritated wasps. Our necks would burn and our forearms would be filled with tiny splinters of hay.

We worked hard and put away 4000 bales each summer even though we would probably only need 3500, because we had to err on the side of caution in case there was an early winter. Or a long winter.

That is environmental uncertainty – and risk management – to me. Cutting the hay when the forecast predicts a 35% chance of rain and watching 400 bales of alfalfa rot in the field. Renting more land than we would likely need. Working 20% harder than necessary – just in case.

All of us understand this, whether it be maintaining the garden, managing the allotment or planning a holiday. This is part of human history: sound, profitable, secure decision-making has always required a confrontation with environmental uncertainty; consequently, almost all societies have strived to mitigate risks by understanding the environment, managing essential resources, and building up our own resilience.

From IPCC 2013, Working Group 1

What is disturbing and unique about the 21st century is that we are no longing mitigating environmental uncertainty but instead, we are very rapidly increasing it. We are changing our planet and where and how we live upon it. Increasing carbon dioxide emissions might warm the planet by 1.5°C. Or 3°C. Or 5°C. Such warming will probably cause the Southwest of England to have wetter summers and the great food-supplying regions of the American Midwest to become drier. But there is a probability that the opposite will happen. How does the small farmer plan? For that matter, how does the huge international agritech firm plan? I would argue that the greatest challenge posed by our changing environment is not how much the Earth warms but the uncertainty in how much it will warm and the uncertainty associated with the consequences of that warming. Planning for our future – perhaps for the first time in human history – is actually becoming more uncertain every year.

But we are also learning much more about ourselves and our environment, and this perhaps makes the future a bit more certain than it might otherwise be. Currently the Met Office is improving our prediction tools and tailoring specific advice to farmers; engineers are learning how we might mitigate or even adapt to this uncertainty; and we are developing methods to limit our dependence on fossil fuel and thus the associated climate change. And we are learning how to make sound decisions in the face of it. To achieve these objectives, the Cabot Institute and similar entities are bringing together a wide variety of scientists, social scientists, managers and engineers, all of whom share expertise with the community and industry. That expertise includes those who deal specifically with quantifying uncertainty, the underlying psychology and sociology of decision making, and the clash of ethical and pragmatic ideas that inform policy making. The world’s population is growing and with it our basic food, water and energy needs; to provide for those needs, we must make our future more certain but also more resilient and adaptable.

This blog was written by Professor Rich Pancost, Cabot Institute Director, University of Bristol

Prof Rich Pancost

Learning lessons from Fukushima

Posted on February 2, 2012April 20, 2023 by janet.crompton

When disasters happen scientists pretty much have a duty to try to understand what happened and why, and to try to learn the lessons. This week the catastophist Gordon Woo of Risk Management Solutions gave a seminar here at the Cabot Institute and suggested that the question that we should really ask is not “why did this happen?” but “why did this not happen before?”. This is also one of the ideas that emerged from a recent exercise that we undertook to try to understand the recent events at the Fukushima nuclear power plant in Japan. The range of skills available within Cabot allowed us to take a fundamentally holistic approach to the analysis that wouldn’t have been possible for any single individual. The results of the analysis are here, but two main points emerge.

First, there is the need to tackle is “chained” or “cascaded” hazards, which, as very low probability events, have traditionally been treated as independent random events and hence have too low a likelihood of coinciding together. There may be hidden dependencies, which are not always either obvious or intuitive, requiring careful analysis to tease out or recognise. This is particularly the case for complex infrastructure like nuclear power stations.

Second, it is no longer adequate to rely on deterministic assessments of hazards and risks from natural hazards as these cannot account properly for uncertainty. Dealing with uncertainty requires a probabilistic analysis that looks at the full range of possible situations that may arise, not just a single one that a company or regulator has (perhaps somewhat arbitrarily) decided is the ‘worst case’. Probabilistic approaches should now be regarded as mandatory, and application of rigorous, structured approaches to assessing risk are needed. Such assessments must include evaluation of all credible alternative models for natural processes, rather than just adopting particular models that happen to support inherited views.