Post-Processing a Conceptual Rainfall-Runoff Model with an LSTM

This is a Preprint and has not been peer reviewed. This is version 3 of this Preprint.


Download Preprint

Supplementary Files

Grey Stephen Nearing, Alden Keefe Sampson, Frederik Kratzert, Jonathan Frame 


Machine learning is becoming an increasingly important part of streamflow forecasting, but as these models to date lack a physical basis, there is a potential that they may produce values that are not realistic. We tested a simple post-processing strategy that uses the outputs from a calibrated conceptual model (the Sacramento Soil Moisture Accounting Model with Snow-17; SAC-SMA) as inputs into a a Long Short Term Memory Network (LSTM). Overall, the SAC-SMA model was improved substantially, while post-processing offered only minor improvements relative to the standalone LSTM. SAC-SMA performance was improved in catchments with more snow. The standalone LSTM was improved in terms of long-term bias, which is likely because the LSTM is not constrained by conservation principles.



Earth Sciences, Environmental Sciences, Hydrology, Physical Sciences and Mathematics



Published: 2020-07-10 03:13

Older Versions

GNU Lesser General Public License (LGPL) 2.1

Add a Comment

You must log in to post a comment.


There are no comments or no comments have been made public for this article.