Evaluating the benefits of national adaptation to reduce climate risks and contribute to the Sustainable Development Goals

Scaling up national climate adaptation under the Paris Agreement is critical not only to reduce risk, but also to contribute to a nation’s development. Traditional adaptation assessments are aimed at evaluating adaptation to cost-effectively reduce risk and do not capture the far-reaching benefits of adaptation in the context of development and the global Sustainable Development Goals (SDGs). By grounding adaptation planning in an SDG vision, we propose and demonstrate a methodological process that for the first time allows national decision-makers to: i) quantify the adaptation that is needed to safeguard SDG target progress, and ii) evaluate strategies of stakeholder-driven adaptation options to meet those needs whilst delivering additional SDG target co-benefits. This methodological process is spatially applied to a national adaptation assessment in Ghana. In the face of the country’s risk from floods and landslides, this analysis identifies which energy and transport assets to prioritise in order to make the greatest contribution to safeguarding development progress. Three strategies (‘built’, ‘nature-based’, ‘combined SDG strategy’) were formulated through a multi-stakeholder partnership involving government, the private sector, and academia as a means to protect Ghana’s prioritised assets against climate risk. Evaluating these adaptation strategies in terms of their ability to deliver on SDG targets, we find that the combined SDG strategy maximises SDG co-benefits across 116 targets. The proposed methodological process for integrating SDG targets in adaptation assessments is transferable to other climate-vulnerable nations, and can provide decision-makers with spatially-explicit evidence for implementing sustainable adaptation in alignment with the global agendas.     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.