Home / Topics Water & environment · AI research Explore data-driven flood forecasts, streamflow prediction, water resources and groundwater research.
Questions to bring to the papers Which data and tasks are used? How are independent tests designed? Where do the reported results apply, and where are their limits? These questions guide critical reading; they are not claims about every paper listed below.
Research records · 2 Research article Nature CAS Tier 1 · Top (2025) 2024.03.20
Authors Grey Nearing · Deborah Cohen · Vusumuzi Dube · Martin Gauch · Oren Gilon · Shaun Harrigan · Avinatan Hassidim · Daniel Klotz · Frederik Kratzert · Asher Metzger · Sella Nevo · Florian Pappenberger · Christel Prudhomme · Guy Shalev · Shlomo Shenzis · Tadele Yednkachw Tekalign · Dana Weitzner · Yossi Matias
AI summary Uses learning across watersheds to forecast extreme floods in ungauged basins and evaluates reliability across regions and lead times, addressing forecasting needs where observations are scarce.
Perspective Nature Reviews Earth & Environment CAS Tier 1 · Top (2025) 2023.07.11
Authors Chaopeng Shen · Alison P. Appling · Pierre Gentine · Toshiyuki Bandai · Hoshin Gupta · Alexandre Tartakovsky · Marco Baity-Jesi · Fabrizio Fenicia · Daniel Kifer · Li Li · Xiaofeng Liu · Wei Ren · Yi Zheng · Ciaran J. Harman · Martyn Clark · Matthew Farthing · Dapeng Feng · Praveen Kumar · Doaa Aboelyazeed · Farshid Rahmani · Yalan Song · Hylke E. Beck · Tadd Bindas · Dipankar Dwivedi · Kuai Fang · Marvin Höge · Chris Rackauckas · Binayak Mohanty · Tirthankar Roy · Chonggang Xu · Kathryn Lawson
AI summary Reviews how differentiable computation can jointly train physical components and neural networks to support process understanding, parameter estimation and geoscientific prediction.