Abstract
Post-processing bias correction of streamflow forecasts can be useful in the hydrologic modeling workflow to fine-tune forecasts for operations, water management, and decision-making. Hydrologic model runoff simulations include errors, uncertainties, and biases, leading to less accuracy and precision for applications in real-world scenarios. We used random forest regression to correct biases and errors in streamflow predictions from the U.S. National Water Model (NWM) long-range streamflow forecasts, considering U.S. Geological Survey (USGS) gauge station measurements as a proxy for true streamflow. We used other features in model training, including watershed characteristics, time fraction of year, and lagged streamflow values, to help the model perform better in gauged and ungauged areas. We assessed the effectiveness of the bias correction technique by comparing the difference between forecast and actual streamflow before and after the bias correction model was employed. We also explored advances in hydroinformatics and cloud computing by creating and testing this bias correction capability within the Google Cloud Console environment to avoid slow and unnecessary data downloads to local devices, thereby streamlining the data processing and storage within the cloud. This demonstrates the possibility of integrating our method into the NWM real-time forecasting workflow. Results indicate reasonable bias correction is possible using the random forest regression machine learning technique. Differences between USGS discharge and NWM forecasts are less than the original difference observed after being run through the random forest model. The main issue concerning the forecasts from the NWM is that the error increases further from the reference time or start of the forecast period. The model we created shows significant improvement in streamflow the further the times get from the reference time. The error is reduced and more uniform throughout all the time steps of the 30-day long-range forecasts.
Degree
MS
College and Department
Ira A. Fulton College of Engineering; Civil and Environmental Engineering
Rights
https://lib.byu.edu/about/copyright/
BYU ScholarsArchive Citation
Anderson, Jacob Matthew, "Post-Processing National Water Model Long-Range Forecasts with Random Forest Regression in the Cloud to Improve Forecast Accuracy for Decision-Makers and Water Managers" (2024). Theses and Dissertations. 10635.
https://scholarsarchive.byu.edu/etd/10635
Date Submitted
2024-12-19
Document Type
Thesis
Handle
http://hdl.lib.byu.edu/1877/etd13472
Keywords
streamflow, forecasts, bias correction, machine learning, random forest regression, Google BigQuery, cloud
Language
english
