A comparison of methods to predict historical daily streamflow time series in the southeastern United States
Links
- More information: USGS Index Page
- Document: Report (1.14 MB pdf)
- Appendixes:
- Appendix A (59.2 KB pdf) A summary of sites where small portions of the historical record were completed using alternative techniques.
- Appendix B (200 KB pdf) A description of all basin characteristics considered as potential explanatory variables in the various regressions conducted as part of the Southeast Model Comparison.
- Appendix C (309 KB pdf) Supplemental Data
- Appendix C Figures (184 KB pdf)
- Companion File: Tables 1-7 (185 KB pdf) Contains: Records for each streamgage used in the Southeast Model Comparsion, a listing of all names and abbreviations of prediction methods, root-mean-square error data, fitted coefficients and goodness-of-fit statistics, mean rank performance metric, and mean and standard deviation of average ranks for each method of prediction.
- Download citation as: RIS | Dublin Core
Abstract
Effective and responsible management of water resources relies on a thorough understanding of the quantity and quality of available water. Streamgages cannot be installed at every location where streamflow information is needed. As part of its National Water Census, the U.S. Geological Survey is planning to provide streamflow predictions for ungaged locations. In order to predict streamflow at a useful spatial and temporal resolution throughout the Nation, efficient methods need to be selected. This report examines several methods used for streamflow prediction in ungaged basins to determine the best methods for regional and national implementation. A pilot area in the southeastern United States was selected to apply 19 different streamflow prediction methods and evaluate each method by a wide set of performance metrics. Through these comparisons, two methods emerged as the most generally accurate streamflow prediction methods: the nearest-neighbor implementations of nonlinear spatial interpolation using flow duration curves (NN-QPPQ) and standardizing logarithms of streamflow by monthly means and standard deviations (NN-SMS12L). It was nearly impossible to distinguish between these two methods in terms of performance. Furthermore, neither of these methods requires significantly more parameterization in order to be applied: NN-SMS12L requires 24 regional regressions—12 for monthly means and 12 for monthly standard deviations. NN-QPPQ, in the application described in this study, required 27 regressions of particular quantiles along the flow duration curve. Despite this finding, the results suggest that an optimal streamflow prediction method depends on the intended application. Some methods are stronger overall, while some methods may be better at predicting particular statistics. The methods of analysis presented here reflect a possible framework for continued analysis and comprehensive multiple comparisons of methods of prediction in ungaged basins (PUB). Additional metrics of comparison can easily be incorporated into this type of analysis. By considering such a multifaceted approach, the top-performing models can easily be identified and considered for further research. The top-performing models can then provide a basis for future applications and explorations by scientists, engineers, managers, and practitioners to suit their own needs.
Study Area
| Publication type | Report |
|---|---|
| Publication Subtype | USGS Numbered Series |
| Title | A comparison of methods to predict historical daily streamflow time series in the southeastern United States |
| Series title | Scientific Investigations Report |
| Series number | 2014-5231 |
| DOI | 10.3133/sir20145231 |
| Year Published | 2015 |
| Language | English |
| Publisher | U.S. Geological Survey |
| Publisher location | Reston, VA |
| Contributing office(s) | Office of Surface Water |
| Description | Report: vi, 34 p.; Appendixes A-C; Tables 1-7 |
| Country | United States |
| Online Only (Y/N) | Y |
| Additional Online Files (Y/N) | Y |
| Google Analytic Metrics | Metrics page |