This study examines the effects of climate and weather shocks on Afghanistan's agricultural economy, with an emphasis on food security, prices, and wages. By utilizing a dynamical model and a unique data...
This study, led by the Global Facility for Disaster Reduction and Recovery (GFDRR) teams working on the Disaster-FCV Nexus thematic area and the Hydromet Services and Early Warning Systems thematic area...
2022 was a year when Climate Risk and Early Warning Systems (CREWS) relevance and remit came into sharp focus. The UN Secretary General’s Early Warnings for All initiative to ensure every country has an...
As a region that is impacted by multiple shocks, multi-hazard early warning systems (MHEWS) that provide timely, actionable information are critical to protecting lives, assets and livelihoods in the Caribbean...
An implementation plan is presented for building capacity in developing and delivering Multi-hazard Impact-based forecast and warning services in the Caribbean region. The purpose of this document is to...
This report presents key lessons and areas of good practice from specific examples, along with recommendations and entry points for inclusive, accessible early warning systems (EWS). It is aimed at development...
The analysis of early warning systems (EWS) in Niger maps the users and suppliers of early warning data, both state and non-state actors. The inventory clarifies their roles and responsibilities, with...
"Rapport de l’enquête sur la vulnérabilité alimentaire en milieu urbain (VAMU)” presents survey findings on household-level food vulnerability in the cities of Ouagadougou and Bobo Dioulasso in Burkina...
The suggested Project Development Objective of the Corredor Seco Food Security Project of Honduras is to enhance food and nutritional security of vulnerable households in selected locations. Food security...
Across many low- and middle-income countries, a sizable share of young people drop out of school before completing a full course of basic education. Early warning systems that accurately identify students...