- Authors
- Frederik Kratzert; Daniel Klotz; Claire Brenner; Karsten Schulz; Mathew Herrnegger
- Institution
- University of Natural Resources and Life Sciences, Vienna
- Journal
- Hydrology and Earth System Sciences 22, 6005–6022
- Geography
- United States catchments (CAMELS)
- Method
- LSTM; hydrological benchmarking; regional transfer
- DOI
- 10.5194/hess-22-6005-2018
Research question
Can a recurrent neural network learn rainfall–runoff behaviour from meteorological inputs and benefit from information across catchments?
Method and findings
Kratzert and colleagues evaluated LSTM models using 241 catchments in the CAMELS dataset. They compared performance with the Sacramento Soil Moisture Accounting model coupled to Snow-17, and examined regional learning and transfer to individual catchments. Their transfer approach improved performance relative to single-catchment LSTM training and the stated hydrological benchmark.
Limitations
The reported experiments establish performance for their data and evaluation setting. They do not by themselves validate extrapolation to a changed climate, a new catchment or a drought operating policy.
Engineering interpretation
Explore the practical modelling workflow.
Sources & further reading
- View original research: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks ↗Kratzert, Klotz, Brenner, Schulz & Herrnegger · 22 November 2018
External source · Checked 24 September 2026
Source findings are distinguished from editorial interpretation. Apply current local criteria and project evidence when making engineering decisions.