Checking for missing content type metadata ...
This resource contains content types with missing metadata required to make it public or discoverable. Show missing content type metadata.
Click on the edit button ( ) below to edit this resource.
Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...
This resource contains some files/folders that have non-preferred characters in their name. Show non-conforming files/folders.
This resource contains content types with files that need to be updated to match with metadata changes. Show content type files that need updating.
| Authors: |
|
|
|---|---|---|
| Owners: |
|
This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (help@cuahsi.org) for information on this resource. |
| Type: | Resource | |
| Storage: | The size of this resource is 162.5 MB | |
| Created: | Jul 17, 2026 at 4:15 p.m. (UTC) | |
| Last updated: | Jul 18, 2026 at 5:43 a.m. (UTC) | |
| Citation: | See how to cite this resource | |
| Content types: | CSV Content |
| Sharing Status: | Public |
|---|---|
| Views: | 263 |
| Downloads: | 207 |
| +1 Votes: | Be the first one to this. |
| Comments: | No comments (yet) |
Abstract
The National Water Model (NWM), NOAA's operational physics-based hydrologic model, provides continental streamflow estimates but carries a systematic local bias, a consistent over- or under-estimation that varies by site and season and limits local use. We tested whether different machine learning algorithms (a simple recurrent network, a gated recurrent unit, a long short-term memory network, and a Transformer) can reduce this bias and asked where correction is most useful across three sites in three states (NC, VA, and SD). We trained these four sequence models under two setups: a residual setup that learns the difference between NWM discharge and observed USGS discharge and adds the learned correction back to NWM, and a direct setup that predicts observed discharge directly rather than a correction to NWM, with NWM still among its inputs. Records were split 70% training, 15% validation, 15% testing. We combined the models by simple averaging, error-weighted averaging, and constrained stacking, tuned and scored with time-ordered (walk-forward) cross-validation on a withheld recent block. The workflow was applied to three unregulated USGS gauges spanning NWM skill (Kling-Gupta Efficiency, KGE) from 2010–2020 at hourly resolution: Watauga River (high skill), New River (moderate), and Little Spearfish Creek, a groundwater-fed karst spring (poor). Correction gains increased as NWM skill decreased: KGE improved by +0.12 at Watauga, +0.31 at New River, and +6.90 at Little Spearfish, with corrected KGE reaching 0.83, 0.78, and 0.50 respectively. The residual setup performed best where NWM was reliable. Overall, ML correction generalized across regimes and added the most value where NWM skill was lowest, though at the karst spring it hit a ceiling set by driving information absent from the inputs.
Subject Keywords
Coverage
Spatial
Temporal
| Start Date: | |
|---|---|
| End Date: |
Content
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
Comments
There are currently no comments
New Comment