“My work deals with forecast evaluation, machine learning, forecast-based action and humanitarian applications, among other aspects,” says Andrea. “The goal is to advance the understanding of flood forecast predictability and flood drivers, and consequently improve resilience to natural hazards, in particular in communities in sub-Saharan Africa.”
More than 600 million people around the world live less than 10 meters above mean sea level; at the same time, climate change is accelerating sea-level rise and making coastal flooding more severe and destructive. Compound riverine and coastal flooding endangers the lives of millions of people in coastal areas and can wash away their habitats, destroy their livelihoods and damage infrastructures.
Andrea’s project will have a particular focus on Mozambique. Situated in Southern Africa, it is one of the world’s most natural disaster-prone countries, with a high risk of compound floods caused by tropical cyclones.
“Economic damages from global coastal floods and storm surges currently range between US$10 billion and US$40 billion a year. In the absence of solid adaptation measures, previous studies agree that these damages are expected to increase significantly, despite a wide range of uncertainties; considering major coastal cities only, damages are expected to rise to more than US$1 trillion annually by 2050,” he alerts.
In Mozambique, the government recently concluded that, on average, the country is affected by a tropical cyclone or flood event every two years. In 2019, cyclones Idai and Kenneth caused more than 700 fatalities, displaced some 420,000 and affected more than two million people. A case study led by the World Meteorological Organization determined that the loss of life and damage could have been reduced with better flood forecasting and improved warnings.
Faced with the inevitability of rising sea levels and episodic flooding events, local and national coastal authorities around the world have historically pursued two possible courses of action.
‘Soft-path’ measures, such as early warning and early action systems, real-time emergency management, insurance and disaster financial risk hedging mechanisms, are examples of short-term solutions to increase coastal communities’ resilience to climate change.
On the other hand, long-term solutions typically rely on ‘hard-path’ measures. These consist of coastal protection structures – barriers, seawalls and revetments – the reinforcement of houses and infrastructures, as well as the implementation of nature-based solutions, such as land-use planning to reduce impervious surfaces and restore coastal ecosystems. However, as sea levels continue to rise, so will the cost to maintain and improve those defenses, and so will the cost of failure.
“Hard infrastructures and nature-based solutions, though effective, face practical challenges,” says Andrea. “They necessitate huge and risky investments while being subject to significant uncertainty in terms of climate risk, government financial capacity, infrastructure investment decisions and local land-use regulation.”
Such challenges can be overcome by modulating investments over time, and integrating hard-path measures with soft-path solutions as hedging mechanisms, using decision analytics methods and climate data, to identify robust and optimal pathways.
During his two-year AXA Research Fund fellowship, Andrea will employ machine learning to better predict compound flood risk and identify high-risk areas. He will base his work on climate services; in other words, climate information and products generated to inform and assist in decision-making processes related to climate risk management.