1. Flow Estimation in Ungaged Basins
USGS Progress and Perspectives
Julie Kiang, Office of Surface Water
Stacey Archfield and Lauren Hay, National Research Program

2. Contributors Statistical estimation Stacey Archfield, MA WSC
Will Farmer, OSW
Tom Over, IL WSC
Riten Patel, IL WSC
David Eash, IA WSC
Jon Nania, IA WSC
Mike Linhart, IA WSC
PRMS development
Lauren Hay, NRP
Roland Viger, NRP
Steve Markstrom, NRP
Steve Regan, NRP
Andy Bock, NRP
Shannon Poole, NRP
David Bjerklie, CT WSC
John Risley, OR WSC
Jeff Tracey, CA WSC
PRMS applications
Jacob Lafontaine, GA WSC
Jeff Riley, GA WSC
Ana Maria Garcia, NC WSC
Rodney Knight, TN WSC
Dan Christiansen, IA WSC
Kasey Hutchinson, IA WSC
Eddie Haj, IA WSC
Parker Norton, ND WSC
Portal development
Dave Blodgett, CIDA
Jordan Walker, CIDA
Gap Analysis
Dave Stewart, OSW Stacey Archfield, MA WSC
Emily Osborne, OSW
Ken Eng, NRP
Comparisons
Jessica Thompson, CIDA
Dave Wolock, NAWQA
Ken Eng, NRP
Plus other WSC staff and students …

3. Current capabilities for ungaged locations
WaterWatch: monthly time series estimates at HUC-8 scale StreamStats: estimation of specific flow statistics Local and regional modeling studies Low-resolution national studies

4. Updating our capabilites
National Water Census
Objective: To place technical information and tools in the hands of stakeholders, allowing them to answer questions about water availability.
Streamflow estimates are needed at ungaged locations
National Streamflow Information Program (NSIP)
Objectives include: Perform regional streamflow assessments to estimate streamflow at ungaged locations, identify trends, and attempt to determine the cause of trends.
John Wesley Powell Center Workgroup
Objective: Develop an integrated model approach to estimate streamflow at ungaged locations in the United States

5. Daily Flow Estimation: Model Comparison
Goals for USGS work on ungaged catchments
Daily Flow Estimation: Model Comparison
Improve temporal and spatial resolution of natural streamflow estimates at ungaged locations throughout the U.S. - daily timestep - 30-year historical period - HUC-12 or better spatial scale Web portal for delivery of results Current work: - testing and enhancing current models - improve understanding of hydrologic similarity and scaling - characterize uncertainty in estimates

6. Models being considered
Type
Used Where
Dependency on Streamgage Density
Drainage Area Ratio
(baseline approach)
Statistical
Widely
High
Flow Duration Transfer (and variants)
MA, PA, NY
Analysis of Flow in Networks of Channels (AFINCH)
Great Lakes Basin
Water Availability Tools for Environmental Resources (WATER) very limited testing
Deterministic Watershed
KY, NJ, Great Lakes
Moderate
Precipitation Runoff Modeling System (PRMS)
Note: Regional regressions will also be used to estimate specific flow statistics.

7. All methods require transfer of information from gaged watersheds to ungaged watersheds. Step 1: look at gage network

8. Streamgage network: spatial analysis
Spatial Gap Analysis
Drainage area <=20,000 sq mi
9,674 streamgages
78% coverage

9. Streamgage network: spatial analysis
Spatial Gap Analysis
Drainage area <=2,000 sq mi
8,167 streamgages
53% coverage

10. Streamgage network: spatial analysis
Spatial Gap Analysis
Drainage area <=1,000 sq mi
7,466 streamgages
37% coverage

11. Streamgage network: spatial analysis
Spatial Gap Analysis
Drainage area <=500 sq mi
6,346 streamgages
23% coverage

12. Availability of long streamflow records
Temporal Gap Analysis
8.6% of continental U.S. is gaged by streamgages considered of “reference” quality.

13. Estimating Stats at Ungaged Locations
Estimating streamflow at ungaged sites
Estimating Stats at Ungaged Locations
Requires the transfer of information from a gaged to an ungaged location.
River location
Watershed
A key assumption: gaged catchments are available which are similar in some way to the ungaged locations.
Correlation – tells us about redundancy and where we may need gages (for map correlation)
Basin similarity – tells us what basin characteristics we are missing

14. Correlation
Correlation
The correlation between streamflow at two gages is one way to assess the potential for successful transfer of information.
r = 1: Perfect correlation
r = 0: No correlation
r = 1
r = 0
Gage B
Gage B
STREAMFLOW
STREAMFLOW
Gage A
Gage A

15. Maximum correlation between stations

16. Models being considered
Type
Used Where
Dependency on Streamgage Density
Drainage Area Ratio
(baseline approach)
Statistical
Widely
High
Flow Duration Transfer (and variants)
MA, PA, NY
Analysis of Flow in Networks of Channels (AFINCH)
Great Lakes Basin
Water Availability Tools for Environmental Resources (WATER) very limited testing
Deterministic Watershed
KY, NJ, Great Lakes
Moderate
Precipitation Runoff Modeling System (PRMS)
Note: Regional regressions will also be used to estimate specific flow statistics.

17. Southeast U.S. Model Comparison Study Domain

18. Lots of methods tested – some jump out as being better than others
Lots of methods tested – some jump out as being better than others. Note PRMS is very competitive in terms of NSE. Highlighting QPPQ and PRMS as the two models described earlier and the focus of future work.

19. Looked at lots of different metrics, this for example, NSE but for logs of flows. There we start to see more so that the stats models have advantages. Working on a report that fully documents the analysis.

20. Trade-off Plot of Model Rankings
PRMS
NN-SMS12R
AFINCH
NN-DAR
NN-SMS12L
NN-SM12
NN-QPPQ
Trade-off Plot of Model Rankings
Standard deviation of ranking
Average mean ranking
PRELIMINARY RESULTS

21. Preliminary results from model comparisons
For statistical methods, only able to provide historical estimates and highly dependent on gage network
Without calibration, PRMS and other rainfall-runoff models have larger errors
Rainfall-runoff models desirable for scenarios (land use change, climate change, etc.)
Need method for constraining model parameters at ungaged locations
What if we combined modeling approaches to constrain the PRMS model?

22. Next steps Explore improvements for PRMS
bring in information from stats models
better understanding of hydrologic similarity and scaling
Improve default parameter specification
Expand analysis of uncertainty of model results
Assess results throughout nation
WaterCensus
Web portal for streamflow and other components of the water budget
Provide tools for ecological analysis