Near-term forecasts of stream temperature using process-guided deep learning and data assimilation

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

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.


Download Preprint


Jacob Zwart , Samantha Kay Oliver , William David Watkins , Jeffrey Michael Sadler , Alison Paige Appling , Hayley Rikert Corson-Dosch , Xiaowei Jia , Vipin Kumar, Jordan S. Read


Near-term forecasts of environmental outcomes can inform real-time decision making. Data assimilation modeling techniques can be used for forecasts to leverage real-time data streams, where the difference between model predictions and observations can be used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided deep learning and data assimilation approach to make 7-day forecasts of daily maximum water temperature in the Delaware River Basin. Our modeling system produced forecasts of daily maximum stream temperature with an average root mean squared error (RMSE) from 1.2 to 1.6°C for 1-day lead time across all sites. The data assimilation algorithm successfully adjusted the process-guided deep learning model states and marginally improved forecast performance when compared to forecasts produced using the process-guided deep learning model alone (7-13% lower RMSE with the data assimilation algorithm). Our model characterized forecast uncertainty relatively well as 57-80% of observations were within 90% forecast confidence intervals across all sites and lead times, and the uncertainty associated with our forecasts allow managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of deep learning models to be applied to various prediction problems shows promise for using these types of models to forecast many other important environmental variables and aid in decision making.



Computer Sciences, Hydrology, Statistics and Probability, Water Resource Management


data assimilation, stream temperature


Published: 2021-08-13 12:44


CC0 1.0 Universal - Public Domain Dedication

Additional Metadata

Conflict of interest statement:

Data Availability (Reason not available):