Base flow estimation via optimal hydrograph separation at CONUS watersheds and comparison to the National Hydrologic Model - Precipitation-Runoff Modeling System by HRU calibrated version
Dates
Publication Date
2019-08-08
Start Date
1900-09-01
End Date
2018-04-01
Citation
Foks, S.S., Raffensperger, J.P., Penn, C.A., and Driscoll, J.M., 2019, Base flow estimation via optimal hydrograph separation at CONUS watersheds and comparison to the National Hydrologic Model - Precipitation-Runoff Modeling System by HRU calibrated version: U.S. Geological Survey data release, https://doi.org/10.5066/P9XF3C11.
Summary
Optimal hydrograph separation (OHS) is a two-component, hydrograph separation method that uses a two-parameter, recursive digital filter (RDF) constrained via chemical mass balance to estimate the base flow contribution to a stream or river (Rimmer and Hartman, 2014; Raffensperger et al., 2017). A recursive digital filter distinguishes between high-frequency and low-frequency discharge data within a hydrograph, where high-frequency data corresponds to quick flow or storms and low-frequency data corresponds to base flow. The two parameters within the RDF are alpha and beta, both are unitless. Alpha is defined as the recession constant and typically found through recession analysis. For the purposes of this data release and study, we [...]
Summary
Optimal hydrograph separation (OHS) is a two-component, hydrograph separation method that uses a two-parameter, recursive digital filter (RDF) constrained via chemical mass balance to estimate the base flow contribution to a stream or river (Rimmer and Hartman, 2014; Raffensperger et al., 2017). A recursive digital filter distinguishes between high-frequency and low-frequency discharge data within a hydrograph, where high-frequency data corresponds to quick flow or storms and low-frequency data corresponds to base flow. The two parameters within the RDF are alpha and beta, both are unitless. Alpha is defined as the recession constant and typically found through recession analysis. For the purposes of this data release and study, we derived alpha from a groundwater flow coefficient (gwflow_coef) defined in the National Hydrologic Model Infrastructure run with the Precipitation-Runoff Modeling System (NHM-PRMS) (Regan et al., 2018). The second parameter, beta, is defined as the maximum value of the base flow index (Eckhardt, 2005). Beta is optimized using specific conductance and mass balance techniques, where a hydrograph is split into quick flow and base flow and specific conductance values are proposed for these streamflow components. OHS uses two model types to estimate base flow specific conductance from stream specific conductance, referred to as 'SCfit' and 'sin-cos' model types. The 'SCfit' model type uses a peak-fitting algorithm to define time periods where the stream is entirely comprised of base flow, whereas the 'sin-cos' model type emulates seasonal variation in streamflow specific conductance with a sine-cosine function to pinpoint when base flow contributes to streamflow. For more information and equations regarding model type and OHS methods, please see the associated publication (Foks et al., 2019).
OHS was applied to 1076 stream gages within the conterminous United States (CONUS) where daily streamflow and daily or discrete measurements of specific conductance were collected. Gages were selected for this method if they were of "reference quality" as defined by the Geospatial Attributes of Gages for Evaluating Streamflow (GAGES-II) dataset (Falcone, 2011). Of these 1076 sites, 825 had "successful" OHS models - implying good agreement between observed and simulated stream specific conductance.
This data release contains the results of applying OHS to hundreds of stream gages of varying watershed characteristics, summary of watershed and hydro-climatological characteristics for each site (Falcone, 2011; USGS, 2003), and a comparison of OHS-defined base flow to base flow -analogous flow components within the NHM-PRMS (gwres_flow and slow_flow) (Regan et al., 2018; Regan et al., 2019). For this data release and study, comparisons of OHS-defined base flow were made to the "by HRU" calibration of the NHM-PRMS (Hay, 2019).
Specifically, this data release contains the following:
basin characteristics compiled from the GAGES-II dataset (Falcone, 2011) and the National Principal Aquifers (USGS, 2003) map for streamgage locations where OHS was applied ("gage_characteristics.csv")
a summary data file containing "best" or "ranked" OHS models for gages that met successful model output criteria ("Ranked_OHS_Models.csv")
comparisons between OHS output and NHM-PRMS parameter output:
monthly and annual long-term average contributions of base flow to streamflow ("Comparison_Long_Term_Averages.csv")
average difference between OHS output and NHM-PRMS output ("Comparison_Average_Difference.csv")
comparisons of monthly contribution to total flow among flow quartiles ("Comparison_Monthly_Difference_Flow_Quartiles.csv")
References:
Eckhardt, K. (2005). How to construct recursive digital filters for baseflow separation. Hydrological Processes: An International Journal, 19(2), 507-515. https://doi.org/10.1002/hyp.5675
Falcone, J. A. (2011). GAGES-II: Geospatial attributes of gages for evaluating streamflow. US Geological Survey. https://doi.org/10.3133/70046617
Foks, S.S., Raffensperger, J.P., Penn, C.A., and Driscoll, J.M. (2019). Estimation of Base Flow by Optimal Hydrograph Separation for the Conterminous United States and Implications for National-Extent Hydrologic Models. Water, 11(8), 1629. https://doi.org/10.3390/w11081629
Hay, L.E. (2019). Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS), by HRU Calibrated Version: U.S. Geological Survey data release, https://doi.org/10.5066/P9NM8K8W
Raffensperger, J. P., Baker, A. C., Blomquist, J. D., & Hopple, J. A. (2017). Optimal hydrograph separation using a recursive digital filter constrained by chemical mass balance, with application to selected Chesapeake Bay watersheds (No. 2017-5034). US Geological Survey. https://doi/org/10.3133/sir20175034
Regan, R. S., Markstrom, S. L., Hay, L. E., Viger, R. J., Norton, P. A., Driscoll, J. M., & LaFontaine, J. H. (2018). Description of the national hydrologic model for use with the Precipitation-Runoff Modeling System (PRMS) (No. 6-B9). US Geological Survey. https://doi.org/10.3133/tm6B9
Regan, R. S., Juracek, K. E., Hay, L. E., Markstrom, S. L., Viger, R. J., Driscoll, J. M., LaFontaine, J.H., & Norton, P. A. (2019). The US Geological Survey National Hydrologic Model infrastructure: Rationale, description, and application of a watershed-scale model for the conterminous United States. Environmental modelling & software. https://doi.org/10.1016/j.envsoft.2018.09.023
Rimmer, A., & Hartmann, A. (2014). Optimal hydrograph separation filter to evaluate transport routines of hydrological models. Journal of hydrology, 514, 249-257. https://doi.org/10.1016/j.jhydrol.2014.04.033
[USGS] U.S. Geological Survey. (2003) Principal Aquifers of the 48 Conterminous United States, Hawaii, Puerto Rico, and the U.S. Virgin Islands Version 1.0. U.S. Geological Survey: Reston, VA. URL https://water.usgs.gov/ogw/aquifer/map.html
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Related External Resources
Type: Related Primary Publication
Foks, S.S., Raffensperger, J.P., Penn, C.A., and Driscoll, J.M. (2019). Estimation of Base Flow by Optimal Hydrograph Separation for the Conterminous United States and Implications for National-Extent Hydrologic Models. Water, 11(8), 1629. https://doi.org/10.3390/w11081629
The purpose of this dataset is to provide results from an application of optimal hydrograph separation (OHS) to over 1000 reference-quality stream gages in the conterminous United States. Another purpose is to provide results comparing with base flow-like output components from the NHM-PRMS by HRU calibrated version (Regan et al., 2018; Regan et al., 2019; Hay, 2019) to OHS base flow estimation. The goal is to provide an application example, methodology, and a base flow dataset at spatially distributed stream gages across the CONUS.
Rights
Users are advised to read the data set metadata thoroughly to understand appropriate use and data limitations. Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty.
