AI & Environment Resource HubN'da Jocelyne Maryse Christine Amichiatchi; Jean Hounkpè; Gneneyougo Emile Soro; Ojelabi Oluwatoyin Khadijat; Isaac Larbi; Andrew Manoba Limantol; Abdul-Rauf Malimanga Alhassan; Tie Albert Goula Bi; Agnidé Emmanuel Lawin · 2024-02-01
Analysis of rainfall extremes in Côte d'Ivoire's watersheds (1976–2050) reveals decreasing drought indices in historical data but increasing flood-related indices under RCP 4.5 and RCP 8.5 scenarios, highlighting growing climate-induced disaster risks.
N'da Jocelyne Maryse Christine Amichiatchi; Jean Hounkpè; Gneneyougo Emile Soro; Ojelabi Oluwatoyin Khadijat; Isaac Larbi; Andrew Manoba Limantol; Abdul-Rauf Malimanga Alhassan; Tie Albert Goula Bi; Agnidé Emmanuel Lawin. 2024-02-01. Analysis Of Past And Projected Changes In Extreme Precipitation Indices In Some Watersheds In Côte D'Ivoire. https://iwaponline.com/jwcc/article/15/2/392/99469/Analysis-of-past-and-projected-changes-in-extreme (AI & Environment Resource Hub; record paper-090; collection snapshot 2026-09-15).
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Source sheet: Scientific Paper · Row 94 · Original ID: paper-090.
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Shared topics: Climate and greenhouse gases; Water; Oceans and coasts; Weather, hazards, and adaptation
Severe flash floods in Charikar, Afghanistan, on August 26, 2020, were driven by extreme atmospheric instability, deep low-level convergence, and local topography, with climate change intensifying frontal activity and baroclinicity in the region.
Shared topics: Climate and greenhouse gases; Water; Oceans and coasts; Weather, hazards, and adaptation
Machine learning models predict hydrological changes in the 3S River Basin under climate change scenarios, showing increased flood risk in some areas and reduced streamflow in others.
Shared topics: Climate and greenhouse gases; Water; Oceans and coasts; Weather, hazards, and adaptation
Future rainfall data in Korea may underestimate multi-year droughts, highlighting limitations for evaluating long-term water supply resilience, particularly in the Boryeong Dam basin.
Shared topics: Climate and greenhouse gases; Water; Oceans and coasts; Weather, hazards, and adaptation
This study demonstrates that reinforcement learning (RL) can enhance climate adaptation decision-making by dynamically optimizing coastal flood risk mitigation strategies for Manhattan, NYC, significantly reducing expected costs and improving resilience to uncertainties compared to conventional methods.