Calculated streamflow metrics for machine learning regionalization across the conterminous United States, 1950 to 2018

Dates

Publication Date: 2022-02-15
Start Date: 1950-01-01
End Date: 2018-12-31

Citation

Eng, K., 2022, Calculated streamflow metrics for machine learning regionalization across the conterminous United States, 1950 to 2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9VQAZN7.

Summary

This metadata record describes 99 streamflow (referred to as flow) metrics calculated using the observed flow records at 1851 streamflow gauges across the conterminous United States from 1950 to 2018. Calculation of these metrics are often used as dependent variables in statistical models to make predictions of these flow metrics at ungaged locations. Specifically, this record describes (1) the U.S. Geological Survey streamgauge identification number, (2) the 1-, 7-, and 30-day consecutive minimum flow normalized by drainage area, DA (Q1/DA, Q7/DA, and Q30/DA [cfs/sq km]), (3) the 1st, 10th, 25th, 50th, 75th, 90th, and 99th nonexceedence flows normalized by DA (P01/DA, P10/DA, P25/DA, P50/DA, P75/DA, P90/DA, P99/DA [cfs/sq km]), (4) [...]

Summary

This metadata record describes 99 streamflow (referred to as flow) metrics calculated using the observed flow records at 1851 streamflow gauges across the conterminous United States from 1950 to 2018. Calculation of these metrics are often used as dependent variables in statistical models to make predictions of these flow metrics at ungaged locations. Specifically, this record describes (1) the U.S. Geological Survey streamgauge identification number, (2) the 1-, 7-, and 30-day consecutive minimum flow normalized by drainage area, DA (Q1/DA, Q7/DA, and Q30/DA [cfs/sq km]), (3) the 1st, 10th, 25th, 50th, 75th, 90th, and 99th nonexceedence flows normalized by DA (P01/DA, P10/DA, P25/DA, P50/DA, P75/DA, P90/DA, P99/DA [cfs/sq km]), (4) the annual mean flows normalized by DA (Mean/DA [cfs/sq km]), (5) the coefficient of variation of the annual minimums and maximum flows (Vmin and Vmax [dimensionless]), the average annual duration of flow pulses less than P10 and greater than P90 (Dl and Dh [number of days]), (6) the average annual number of flow pulses less than P10 and greater than P90 (Fl and Fh [number of events]), (7) the average annual skew of daily flows (Skew [dimensionless]), (8) the number of days where flow greater than the previous day divided by the total number of days (daily rises [dimensionless]), (9) the low- and high-flow timing metrics for winter, spring, summer, and fall (Winter_Tl, Spring_Tl, Summer_Tl, Fall_Tl, Winter_Th, Spring_Th, Summer_Th, and Fall_Th [dimensionless]), (10) the monthly nonexceedence flows normalized by DA (JAN, FEB, MAR, APR, MAY, JUN, JUL, AUG, SEP, OCT, NOV, and DEC P'X'/DA where the 'X'=10, 20, 50, 80, and 90 [cfs/sq km]), and (11) monthly mean flow normalized by DA (JAN, FEB, MAR, APR, MAY, JUN, JUL, AUG, SEP, OCT, NOV, and DEC mean/DA [cfs/sq km]). For more details for flow metrics related to (2) through (8) and (11), please see Eng, K., Grantham, T.E., Carlisle, D.M., and Wolock, D.M., 2017, Predictability and selection of hydrologic metrics in riverine ecohydrology: Freshwater Science, v. 36(4), p. 915-926 [Also available at https://doi.org/10.1086/694912]. For more details on (9), please see Eng, K., Carlisle, D.M., Grantham, T.E., Wolock, D.M., and Eng, R.L., 2019, Severity and extent of alterations to natural streamflow regimes based on hydrologic metrics in the conterminous United States, 1980-2014: U.S. Geological Survey Scientific Investigations Report 2019-5001, 25 p. [Also available at https://doi.org/10.3133/sir20195001]. For (10), all daily flow values for the month of interest across all years are ranked in descending order, and the flow values associated with 10, 20, 50, 80, and 90 percent of all flow values are assigned as the monthly percent values. The data are in a tab-delimited text format.

Contacts

Point of Contact :: Kenny Eng
Originator :: Kenny Eng
Metadata Contact :: Kenny Eng
Publisher :: U.S. Geological Survey
Distributor :: U.S. Geological Survey - ScienceBase
SDC Data Owner :: Integrated Modeling & Prediction Division
USGS Mission Area :: Water Resources

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KEng_Calculated_Streamflow_Metrics_Machine_Learning_Evaluation_Metadata.xml Original FGDC Metadata	View	74.48 KB	application/fgdc+xml
KEng_99_Streamflow_Metrics_1851_Gauges_US_MLEval_Data_Release_Revised.txt		1.95 MB	text/plain

Related External Resources

Type: Related Primary Publication

Eng, K., and Wolock, D.M., 2022, Evaluation of machine learning approaches for predicting streamflow metrics across the conterminous United States: U.S. Geological Survey Scientific Investigations Report 2022–5058, 27 p., https://doi.org/10.3133/sir20225058.	https://doi.org/10.3133/sir20225058

Purpose

The purpose of the dataset was to provide calculated flow metrics from observed flow records as dependent variables in machine learning methods and conventional linear regression for predicting the 99 streamflow metrics at ungaged stream locations.

Calculated streamflow metrics for machine learning regionalization across the conterminous United States, 1950 to 2018

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