Water quality data sets of the Gozyo catchment and the four U.S. watersheds

This zipped file contains three excel files. Table GZ_Solute.xls and Table GZ_Tb.xls are high-frequency water quality data in the stream of the small forested catchment, Gozyo cattchment (12.14ha), Nara, Japan. The high-frequency on-site Water quality (WQ) measurements of K, Cl, and Na in the stream using flow-injection potentiometry method with 15-minute interval were interpolated to 10-minute interval data. The SS concentration in the stream was estimated by linear transformation from the observed 10-minute turbidity value. Table GZ_Solute.xls gives observed 10-minute solutes data with discharge, rainfall, and temperature from 2009 to 2011, and Table GZ_Tb.xls provides the observed 10-minute turbidity data from 2012 to 2014. SS value (mg/l) is calculated as 1.09 Turbidity + 6.19. The data value of -999 means missing observation. Tables US_daily_WQ_datasets.xls contains daily water quality (WQ) and discharge data from the four watersheds in the United States. The data value of -999 also means missing observation. The names (USGS station numbers) of four WQ monitoring sites are Muskingum River (03150000), Rock Creek (04197170), Skunk River (05474000), and Vermilion River (04195000). Daily WQ data were composed by following the procedure described in the Appendix S1 in Hirsch (2014). The WQ data for Muskingum, Rock, and Vermilion river were retrieved from Heidelberg University's National Center for Water Quality Research site (https://ncwqr.org/monitoring/data) and suspended sediment discharge data were downloaded from the USGS National Water Information System (http://waterdata.usgs.gov/nwis/ or https://doi.org/: 10.5066/F7P55KJN). All the daily discharge data were acquired via the USGS National Water Information System. These data were used to evaluate the performance of the unbiased load estimates and confidence intervals of river loads based on the Rating curve method using importance sampling in the listed article below. To maintain the traceability of the proposed load estimation method and replicability the results in the article, the authors of the article upload the data used in this repository. References Hirsch, R. M. (2014). Large biases in regression-based constituent flux estimates: causes and diagnostic tools. <em>Journal of the American Water Resources Association</em>, 50(6), 1401-1424. https://doi.org/10.1111/jawr.12195 Tada, A. and H. Tanakamaru. (Submitted) Unbiased estimates and confidence intervals for riverine loads, <em>Water Resources Research</em>.