AOGS 2026 Call for Abstracts: AI-driven Observation, Modeling, and Prediction of Precipitation and Hydrological Extremes

[Guoqiang Tang]

Dear colleagues,

We are pleased to invite you to submit abstracts to the following session at AOGS 2026:

Session Title: AI-driven Observation, Modeling, and Prediction of Precipitation and Hydrological Extremes
Conference: AOGS 2026, Fukuoka, Japan, 2–7 August 2026
Abstract deadline: 23 January 2026
Account creation & abstract submission: https://meetmatt-svr.net

Precipitation and hydrological extremes—such as floods, droughts, and compound events—are posing increasing risks to societies under a changing climate. Their reliable observation, modeling, and prediction remain challenging due to data sparsity, multiscale variability, and complex physical processes. Recent advances in artificial intelligence (AI) offer powerful new tools to extract information from diverse observations, improve process representation, and enhance predictive capability.

We welcome contributions on, but not limited to, the following topics:

• AI-based retrieval, estimation, and prediction of precipitation from satellite, radar, and in-situ observations

• Evaluation, bias correction, and fusion of precipitation and hydrological datasets

• Data-driven and hybrid physics–AI approaches for modeling and prediction of floods, droughts, and compound extremes

• AI applications for climate change impact assessment of precipitation and hydrological extremes

Conveners:

• Guoqiang Tang (Wuhan University)

• Bin Yong (Hohai University)

• Yixin Wen (University of Florida)

• Li Zhou (Sichuan University)

• Gang Zhao (Institute of Science Tokyo)

Please feel free to forward this call to colleagues and students who may be interested.

Best regards,

Guoqiang Tang
Professor, School of Water Resources and Hydropower Engineering
Wuhan University