This review examined 272 publications on climate hazards in African cities from 2015 to 2025. Research is growing rapidly but concentrates on large cities, flooding and heat. Local observations and city-scale modelling remain limited, and few studies combine future climate change with urban growth. Broader coverage of cities and hazards, improved high-resolution observations and stronger integration of future urban development are needed to support adaptation planning.

Mountain flash floods pose growing risks as climate and landscapes change. We reviewed 1,967 studies from 2000 to 2025 and found research concentrated in Europe and Asia. Rainfall and local conditions shape floods, while their impacts depend on where people live and their ability to respond. We propose combining warnings, planning, engineering and natural protection in a flexible approach to prevent new risks, reduce existing risks and manage those that remain.

This review examines how machine learning methods can improve the assessment of interacting climate hazards and their risks, covering data processing, hazard prediction, risk assessment, and future scenarios. Machine learning is widely used, from analysing satellite data to predicting extreme events and estimating impacts. Key research gaps include better modelling of hazard interactions and changing vulnerability, transparent model outputs, and frameworks including uncertainty quantification.

Berlin’s parks are vital for recreation, biodiversity, and climate resilience, yet they face growing challenges from socio-economic inequalities and climate change. Our review examines how factors like gentrification and extreme weather impact access to and sustainability of these parks. By analysing over 200 studies, we highlight the need for inclusive policies, community engagement, and climate-adaptive park designs to ensure that Berlin’s parks remain accessible, resilient, and socially just.

This paper critically reviews 250 studies (2010–2023) on how social media are used to manage disasters triggered by natural hazards. Supported by a newly created Social Media Literature Database, it identifies trends, data collection and analysis strategies, actionable information types, and major research gaps. Best practices are proposed for community use of social media during disasters and for researchers seeking to enhance its integration into disaster management and resilience strategies.

In order to elucidate the potential for integrating deep learning with potential landslide identification, this paper focuses on four key dimensions: (1) Summarising data sources for potential landslide identification. (2) Compare the roles of commonly used deep learning models. (3) Analyse the practical applications of deep learning in early landslide detection. (4) Investigate key challenges and propose future priorities for potential landslide identification.

Indicators contain observable and measurable characteristics to understand the state of a concept or phenomenon and/or monitor it over time. There have been limited efforts to understand how indicators are being used in multi-hazard and multi-risk contexts. We find most of existing indicators do not include the interactions between hazards or risks. We propose a set of recommendations to enable the development and uptake of multi-hazard and multi-risk indicators.