Reposted by Martin GauchFrederik Kratzert @kratzert.bsky.social · 12/03/2026Excited to announce Groundsource - an open-source dataset of historic flood events! This has easily been one of the coolest projects I've worked on recently! Thread 🧵 for details and all relevant links. 1/n 27339
Martin Gauch @gauchm.bsky.social · 14/01/2026New #NeuralHydrology release 🎉 Some news from v1.13.0: * CAMELS-IND & CAMELS-DE support * AORC hourly forcing support * xLSTM supportSupport for embedding layers in MTS-LSTMs ...and various other improvements and fixes. The full release notes: github.com/neuralhydrol... Thanks to all contributors!github.comRelease v1.13.0 · neuralhydrology/neuralhydrologySetup changes As of #279, NeuralHydrology switched from using conda environments to uv. This has several advantages (e.g., it's much faster to create environments, and we'll be able to get up-to-da... 150
Martin Gauch @gauchm.bsky.social · 03/12/2025Back by popular demand: At #EGU26 we'll organize another BUGS session: Blunders, Unexpected Glitches, and Surprises! Submit abstracts on ideas that seemed great but didn't work, errors and bugs that led to new insights (or funny stories), or any other unexpected results. www.egu26.eu/session/56997 1138
Martin Gauch @gauchm.bsky.social · 13/11/2025It's (finally) published: hess.copernicus.org/articles/29/... Looking forward to all the different ways the title will be messed up by indexing tools!hess.copernicus.orgHow to deal w___ missing input dataAbstract. Deep learning hydrologic models have made their way from research to applications. More and more national hydrometeorological agencies, hydro power operators, and engineering consulting comp... 130
Reposted by Martin GauchEGU Hydrological Sciences Division @hs.egu.eu · 01/05/2025Congratulations to Frederik Kratzert on winning this year's Arne Richter Award for outstanding research by an early career scientist. Fantastic presentation at #EGU25 this afternoon! 1295
Reposted by Martin GauchFrederik Kratzert @kratzert.bsky.social · 07/04/2025Now on HESSD for open discussion: egusphere.copernicus.org/preprints/20... They even let us keep the paper title (for now?!) 🙄egusphere.copernicus.orgHow to deal w___ missing input dataAbstract. Deep learning hydrologic models have made their way from research to applications. More and more national hydrometeorological agencies, hydro power operators, and engineering consulting comp... 1101
Martin Gauch @gauchm.bsky.social · 18/03/2025NeuralHydrology just got a little better, especially if you're building custom models :) 030
Martin Gauch @gauchm.bsky.social · 15/03/2025Starting on bsky with a new preprint: "How to deal w___ missing input data" doi.org/10.31223/X50... Missing input data is a very common challenge in deep learning for hydrology: weather providers have outages, some data products start later than others, some only exist for certain regions, etc. 1195
