Period Comparison
Choose two historical periods to compare. Period 2 will default to the same duration.Extreme Threshold
For monthly reconstructions, it is possible to focus on particular months. Selections on the left will populate all other tabs.
Reconstruction Location
Reconstruction Source
Recommended Citation:
Observation Source
Extreme Flows
Extreme Flow Distribution
Extreme Flow Details
Period Comparison
Extreme Flows
Threshold Exceedances
Flow Distribution Comparison
Flow distributions can be compared between the two periods. Plot will adjust automatically to changes in period.Goodness of Fit
Caution: The reconstruction explains less than 50% of the variance in flow (R2).
While this indicates a relatively poor calibration, it may be acceptable depending on your use case. Carefully consider how you will use the reconstruction and double-check the fit before proceeding.
Caution: The calibration Nash-Sutcliffe Efficiency (NSE) is less than 0.
This means the reconstruction performs worse than if the model had assumed the historical average for all years. While this indicates a relatively poor calibration, it may be acceptable depending on your use case. Carefully consider how you will use the reconstruction and double-check the fit before proceeding.
Caution: Validation statistics much worse than calibration.
There was a large decrease in predictive skill in the validation step, indicating possibility of an over-fit model that could perform poorly outside the calibration period. This may be acceptable depending on your use case. Carefully consider how you will use the reconstruction and double-check the fit before proceeding.
Reconstructed flows plotted against observed flows for the instrumental (calibration) period. A perfect calibration fit would be along the 1:1 line, shown in red.
Hover over a point to see its date and values. Click and drag to zoom in on an area. Right click to zoom out.
Calibration
Calibration statistics indicate the best expected model performance and should be complemented with validation.
Validation
If validation is much worse than calibration, it indicates the model is too sensitive to new data, or 'overfit'
It represents percent explained (%) and ranges from zero to one, with one being a perfect fit.
Reconstruction-Statistical Background
Within the reconstruction community, this metric is typically referred to as the Coefficient of Efficiency (CE) when applied to calibration and Reduction of Error (RE) when applied to validation.
Efficiency Criteria
RMSE is calculated as the square root of mean square of errors (predicted minus observed flow). A perfect model would have no errors, and therefore RMSE = 0.
Reconstruction-Statistical Background
MAE is similar in interpretation to RMSE and is the more intuitive of the two. Where RMSE handles negative values by squaring them, MAE simply applies the absolute value ||.
Mean Error should always be considered with other metrics because poor predictive models can have ME near zero if large positive and negative errors can cancel each other out.
'Split Sample' validation defines these training and validation sets explicitly when fitting the model.
'K-fold' cross-validation predicts part of the sample (typically 10%, referred to as 'k') and predicts using the remaining 90%. This is repeated k times until each point is predicted without being used in training.
Leave-One-Out is a commonly used method and is an extreme version of K-fold cross-validation, where each point is predicted using a model fit with all other data.
Comparison of Flow Distributions
Distribution of errors
Submit a Reconstruction
Thanks, your reconstruction was submitted successfully!
Submit another reconstructionTo submit a new reconstruction, first download the template (Step 1). Then upload the completed template (Step 2). Please include as much metadata in the CSV file as possible, along with entering a name for the reconstruction and a valid email address so that we may contact in case of questions (Step 3). Finally, push Submit (Step 4).
We strongly recommend that the original files submitted to PaleoFlow be housed in a standard repository, such as the International Tree-Ring Data Bank (ITRDB).
Download a blank template and insert data. All submissions must use the template provided. A completed file is provided as an example.
Submitting...
Error:
Push submit when finished. You will receive confirmation on the next screen.
About
The Reconstructed Streamflow Explorer was developed by James Stagge in conjunction with the Utah State University Water Research Lab and the Wasatch Dendroclimatology Research Group . It was funded in part by Utah Mineral Lease funds.
Citation
When using the Reconstructed Streamflow Explorer for research or reference, please cite as follows:
All code for this application is available as a GitHub repository , made available under the MIT license.
Contact Information
Please direct any questions to James Stagge .
