Estimated quantiles for the pour points of 9,203 level-12 hydrologic unit codes in the southeastern United States, 1950--2009
Dates
Publication Date
2019-03-29
Start Date
1950
End Date
2009
Citation
Worland, S.C., Knight, R.R., and Asquith, W.H., 2019, Estimated quantiles for the pour points of 9,203 level-12 hydrologic unit codes in the southeastern United States, 1950--2009: U.S. Geological Survey data release, https://doi.org/10.5066/P9YGKZZV.
Summary
This page contains 15 estimated quantiles for 9,203 level-12 Hydrologic Unit Code in the Southeastern United States for the decades 1950-1959, 1960-1969, 1970-1979, 1980-1989, 1990-1999, and 2000-2009. A multi-output neural network was used to generate the estimated quantiles (Worland and others, 2019). The R scripts that generated the predictions are also included along with a README file. The 15 quantiles are associated with the following 15 non-exceedance probabilities (NEPs): 0.0003, 0.0050, 0.0500, 0.1000, 0.2000, 0.3000, 0.4000, 0.5000, 0.6000, 0.7000, 0.8000, 0.9000, 0.9500, 0.9950, and 0.9997. The quantiles were calculated using the Weibull plotting position (more details can be found in the accompanying manuscript). In addition [...]
Summary
This page contains 15 estimated quantiles for 9,203 level-12 Hydrologic Unit Code in the Southeastern United States for the decades 1950-1959, 1960-1969, 1970-1979, 1980-1989, 1990-1999, and 2000-2009. A multi-output neural network was used to generate the estimated quantiles (Worland and others, 2019). The R scripts that generated the predictions are also included along with a README file. The 15 quantiles are associated with the following 15 non-exceedance probabilities (NEPs): 0.0003, 0.0050, 0.0500, 0.1000, 0.2000, 0.3000, 0.4000, 0.5000, 0.6000, 0.7000, 0.8000, 0.9000, 0.9500, 0.9950, and 0.9997. The quantiles were calculated using the Weibull plotting position (more details can be found in the accompanying manuscript). In addition to the median estimate of the quantiles, 68th, 95th, and 99.7th percentile intervals are also included in .csv file. The percentile intervals were estimated using Monte-Carlo dropout for 500 forward passes of the neural network. The intervals are represented in the .csv file as p0.0015, p0.0250, p0.1600, p0.5000, p0.8400, p0.975, and p0.9985 which indicates the 68th, 95th, and 99.7th percentile intervals. The median (p0.5000) and the mean estimate should be used if only a single realization of the estimated quantiles is needed. The neural network was trained using streamflow data at sites with records that contained only non-zero streamflow values. However, the model was used to make predictions for every HUC12 pour point. Some of these predictions are likely for sites that have streamflow values equal to zero.
Worland, S.C., Steinschneider, S., Asquith, W., Knight, R. and Wieczorek, M., 2019, Prediction and inference of flow-duration curves using multi-output neural networks, Water Resources Research, submitted.
Click on title to download individual files attached to this item.
meta_data_huc12_fdc_final.xml “metadata” Original FGDC Metadata
View
18.89 KB
application/fgdc+xml
util_funs.R “utility functions”
3.88 KB
text/x-rsrc
fdc_predictions_huc12s.csv “FDC estimates”
171.96 MB
text/csv
fdc_est.R “main R script”
19.2 KB
text/x-rsrc
plot_funs.R “plotting functions”
19.15 KB
text/x-rsrc
tune_layers.R “tune neural network layers”
4.46 KB
text/x-rsrc
thumbnail.png “example of estimated FDCs (with uncertainty bounds) for 3 HUC12s and 6 decades”
269.26 KB
image/png
README.pdf “README”
1.69 MB
application/pdf
Related External Resources
Type: Related Primary Publication
Worland, S. C., Steinschneider, S., Asquith, W., Knight, R., & Wieczorek, M.. ( 2019), Prediction and inference of flow‐duration curves using multioutput neural networks. Water Resources Research, 55, 6850– 6868. https://doi.org/10.1029/2018WR024463