Data and information management

Study reveals a hidden problem: Temperature proxies used in many climate-impact studies can contain substantial measurement error, leading to misestimation of true impacts of temperature and, potentially understate climate impacts by at least 15%.

The DisasterAWARE ecosystem was not introduced in the middle of a crisis—it was already part of the international operating environment that bridges communities. It has been exercised through continuous engagements.

This guidance note provides a framework for evaluating whether climate and disaster risk models are fit for climate and disaster risk financing decisions, covering model transparency, hazard, exposure, vulnerability, loss and uncertainty assessment.

Two smoke-damage bills awaiting the governor’s signature address a longstanding issue brought to the fore by the Los Angeles County fires: When is it safe for wildfire survivors to move back into their homes?

This study develops a standardised, reproducible, and operational methodology to screen tsunami risk at the community scale across the Caribbean Basin, based exclusively on open and globally consistent datasets

More than half of reporting companies provide no core quantitative metrics, raising concerns over their ability to measure nature-related risks and potential greenwashing.

This study presents a detailed and refined glacial lake outburst floods (GLOFs) risk assessment using a data-driven machine learning framework.

This paper introduces the FLOSEV flood severity scale, an impact-based classification system using fatalities, affected municipalities and damage indicators. Tested on more than a century of Italian floods, it enables comparison of flood impacts.

This study examined the sensitivity of flood-frequency estimates to the length of hydrometric records using the annual maximum series (AMS) and partial value series (PVS) approaches at 15 hydrometric stations in the Karkheh Basin, Iran.

This study uses observations and machine learning to estimate bankfull river discharge globally. Results show that bankfull return periods vary significantly across climate zones, challenging the common 2-year assumption used in global flood models.

This paper presents LiveCyc, a machine learning system that converts tropical cyclone forecasts into probabilistic 1-km wind forecasts. Evaluation across 16 U.S. hurricane landfalls shows well-calibrated results and supports optimized warning triggers.

This paper analyses 150 years of census and hazard data to assess how extreme weather affected population trends in Northern Australia. It finds floods and cyclones can disrupt population growth highlighting need for resilience and adaptation planning.

This toolkit examines how extreme weather events fuel mis- and disinformation and provides frameworks for assessing readiness, triaging threats, building partnerships and strengthening information resilience before, during and after disasters.

A deadly Nepal-Tibet border flood highlights how limited cross-border data sharing, restricted access to Tibet and weak early warning systems can hinder disaster preparedness. The article calls for greater regional cooperation on climate and flood risks.

XAIDA is designing and applying AI-based methodologies to reveal how climate change impacts extreme weather events and to foresee their occurrence in the future.

The ENSO Outlook, developed by IWMI, Sri Lanka, a first-of-its-kind ENSO Digital Public Infrastructure, is designed to turn El Niño and La Niña climate signals into timely, actionable measures, focusing on farm-level preparedness and response to shocks.

This paper introduces a climate impact taxonomy linking IPCC climate impact-drivers with key climate risks, creating a practical framework for adaptation planning and decision-making.

HERA’s first-of-its-kind tool and analysis estimates how extreme heat affects economic output and mortality in cities around the world. It shows the likely impact on workers today and how those impacts could grow by 2050.

The University of Cumbria is leading an international partnership behind the launch of Apada Sahayak, a new bilingual app designed to help communities across the Himalayan region strengthen their preparedness for natural hazards and disasters.

The authors conducted a systematic review of 264 studies on flood risk assessment in data-scarce regions.

The University of Tokyo (UTokyo-IIS) developed a real-time data assimilation system that has significantly improved streamflow and flood forecasting accuracy across Japan and outperformed the country’s current early warning systems.

Disaster communications experts discuss how maintaining reliable communications for first responders is essential during emergencies, and how every disaster demands flexibility, coordination and close partnerships to keep response efforts connected.

Historical observations are our best way of filling crucial gaps in Australia’s climate records, allowing us to sharpen our forecasts of future weather extremes in a warmer world.

This study proposes a methodology to characterise land degradation and desertification in the European Union

This study addresses the limitations of using social media analytics in disaster management, arguing that human analysis remains indispensable, especially when dealing with vulnerable populations.

The climate monitoring developed by CMCC for Rome uses ultra-high-resolution datasets to provide robust and reliable territorial information, useful for urban planning and evaluating adaptation measures.

A new study shows the pros and cons of different model training methods.

A new online tool out of the Yale Center on Climate Change and Health (YCCCH) strives to provide hyperlocal information about the range of extreme weather risks on mortality, including the potential impact on older adults.

Climate change adaptation ‘success’ is being defined and measured through metrics that often fail the communities they are meant to support.

This article gives insight into the implementation of European Drought Impact Database (EDID), its structure and attributes, and provides an analysis of the content.

Using data from FIFA, Brown epidemiologists developed a tracking tool aimed at assisting public health experts in the event of an infectious disease outbreak.

There is a quiet contradiction at the heart of natural hazard science. The regions most exposed to multi-hazard events are precisely the regions where we know the least.

Beyond helping emergency managers better alert downstream communities to flooding events, tracking rapid runoff allows water managers to improve reservoir management.

Syria became one of the earliest countries to implement UNDRR's DELTA Resilience system, which will help it better understand and reduce the disaster risks it faces as it recovers from years of war.

ClimWeb is a digital platform that supports Africa's National Meteorological and Hydrological Services to strengthen early warning systems and climate services.

Researchers at the Institute of Industrial Science, The University of Tokyo (UTokyo-IIS) have developed a real-time data assimilation system that substantially improves streamflow and flood forecasting accuracy across Japan.

This brief supports members of national adaptation plan (NAP) teams involved directly or indirectly in MEL systems, and those supporting them, in identifying and using adaptation indicators for their national MEL systems effectively and efficiently.

Our novel artificial intelligence model can predict extreme storm surges with high accuracy, including under future climate conditions.

Maximum Information today announces the launch of PsiClone, a new live event monitoring platform that helps insurers, reinsurers and underwriting teams monitor portfolio exposure and act earlier during active tropical cyclone events.