So how is Central America working to expedite intra-regional assistance to benefit disaster-affected populations?
The coordinated response among the region’s countries and the agile and timely arrival of humanitarian assistance in the context of disasters and emergencies can make the difference between life and death for many communities . This is especially relevant in Central America, the second most exposed and vulnerable region in the world to disasters, which often affect several countries simultaneously, as was the case after Hurricanes Eta and Iota in 2020.
Although the Government of Tajikistan will determine which road resilience measures to prioritize and pursue, a World Bank and GFDRR report also recommends that investment in improved emergency preparedness and response, including search and rescue capabilities, continue.
In Morocco, cities are exposed to risks arising from fire, unregulated construction, industry, epidemics and natural hazards. The effects of natural hazards alone, covering floods, earthquakes, tsunamis and droughts, are estimated to cost Morocco an average US$800 million a year, and pose significant threats to Moroccan citizens and their livelihoods. Other factors exacerbate these risks in Morocco, including rapid urbanization, aging building stock and climate change.
In the Caribbean, the second most disaster-prone region in the world, integrating gender equality in disaster preparedness and recovery on a national level has progressed greatly, particularly in the past five years . Yet, there are still considerable variations between countries. This called for a country-specific analysis to understand the context, and to be able to develop gender-informed climate resilience solutions and disaster recovery activities.
In 2016, Category 4 Hurricane Matthew made landfall in the south of Haiti, causing destruction unseen in more than a decade during the hurricane season, with winds as high as 230 kilometers per hour and 600 millimeters of rain in less than 24 hours. The toll was very high, with a total of 546 victims, 128 missing, 439 injured, and 2.1 million people affected.
From mapping hazard-prone urban areas in Tanzania to providing drought early warnings, disaster risk management professionals are finding new applications of machine learning (ML) at a rapid pace.
In disaster risk management, ML can help create actionable information faster and at lower cost: whether evaluating satellite imagery to determine flooded areas; processing street-level photography to identify structural characteristics of buildings; or assessing urban growth patterns to understand future vulnerabilities.
Road infrastructure is vulnerable to geohazards. Imagine you’re driving on a single lane rural road in Himalayas where you have a steep hill on your left and deep cliff on your right. It’s raining and the road is covered with mud eroded from the hill side.
Over the past 20 years, floods have displaced more Indonesians than any other disaster type, causing significant damage and disrupting local economies. The poor and vulnerable often bear the brunt of flood hazards and are affected disproportionately. They tend to live in hazardous areas such as dense settlements situated below flood levels, highly-exposed coastal areas, and along riverbanks that often overflow. They also often have limited access to financial services and basic support to cope with the aftermath of flood events.