ABSTRACT:

There is a global abundance of non-perennial rivers and streams, of which are predicted to increase due to environmental change and anthropogenic influences. However, most modeled representations of streamflow have been constructed with perennial systems in mind, leaving a gap in our understanding and representation of non-perennial systems. To adapt to future challenges, there is a need to determine what modeled representations of low- and no-flow in non-perennial rivers and streams do well and where uncertainties may lie in the internal representations of hydrologic processes. Here we compare four publicly available process-based hydrologic models: Variable Infiltration Capacity (VIC), Precipitation Runoff Modeling System (PRMS), and National Water Model (NWM) versions 2.1 and 3.0, in their ability to represent non-perennial streamflow regimes across 156 streamgages that experience non-perennial streamflow behavior in the Pacific Northwest. Our results show that process-based models are largely unable to capture non-perennial streamflow behavior, and that simulation skill decreases as a function of increasing aridity of a streamgage location. Most simulations underestimate the number of no- and low-flow days a streamgage experiences and overestimates the magnitude of low-flows. The ability to accurately model non-perennial systems is paramount to draw inferences about the connections between hydrologic characteristics of low- and no-flow and the potential ecological, biogeochemical, and societal implications of these important systems. Our findings suggest that improving our predictive understanding of non-perennial streamflow of rivers and streams within the Pacific Northwest will fill critical gaps and better target the timing and location of future research, management, and conservation efforts as well as improve the usability of these models for a wider audience of practitioners across fields.

ABSTRACT:

This resource contains the data supporting the paper "The drying regimes of non-perennial rivers" currently in preparation. The data provided with this release contains streamflow drying characteristics for over 25,000 discrete drying events at 894 non-perennial U.S. Geological Survey GAGES-II (Falcone, 2011) gaging stations for years 1979 to 2019.

The columns of the dataset associated with stream drying are described below:

gage = USGS station ID (STAID)
event_id = unique drying event identifier
dec_lat_va = Latitude in decimal degrees of streamgage location
dec_long_va = Longitude in decimal degrees of streamgage location
peak_date = Day of year that peak occurred marking the beginning of drying event
peak_value = Discharge value in cubic feet per second of peak marking the beginning of drying event
peak_quantile = Discharge quantile value of peak marking the beginning of drying event
peak2zero = Number of days from peak_date to dry_date_start
drying_rate = The streamflow recession rate defined as the slope in log-log space of −d(discharge)/d(time) plotted against discharge
p_value = P-value reported from the fit of a linear model for discharge and time in log-log space
calendar_year = The calendar year in which the first no flow of the drying event occurred
season = The season in which the first no flow of the drying event occurred (April, May, June = spring; July, August, September = summer; October, November, December = fall; January, February, March = winter)
meteorologic_year = The meteorologic year in which the first no flow of the drying event occurred. Meteorologic years begin April 1 and conclude Mach 30.
dry_date_start = Julian day of the first no flow occurrence associated with the drying event
dry_date_mean = Julian day at the center of continuous no flow associated with the drying event
dry_dur = Duration (in days) of continuous no flow associated with the drying event

For information on the additional columns of data supplied that were used to run random forest models please see the section below "Additional Metadata."

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