loading page

Curating 62 years of Walnut Gulch Experimental Watershed dataset: Improving QAQC methods of rainfall and runoff measurements.
  • +6
  • Menberu Bitew,
  • Eleonora M. C. Demaria,
  • Philip Heilman,
  • David C. Goodrich,
  • Mark Kautz,
  • Gerardo Armendariz,
  • Carl Unkrich,
  • Haiyan Wei,
  • Anandraj Perumal
Menberu Bitew
USDA Agricultural Research Services
Author Profile
Eleonora M. C. Demaria
USDA-ARS
Author Profile
Philip Heilman
USDA Agricultural Research Services
Author Profile
David C. Goodrich
USDA-ARS
Author Profile
Mark Kautz
USDA Agricultural Research Services
Author Profile
Gerardo Armendariz
USDA Agricultural Research Services
Author Profile
Carl Unkrich
USDA Agricultural Research Services
Author Profile
Haiyan Wei
University of Arizona
Author Profile
Anandraj Perumal
University of Arizona
Author Profile

Abstract

The Walnut Gulch Experimental Watershed (WGEW) is the primary outdoor hydrologic laboratory for the USDA-ARS’ Southwest Watershed Research Center (SWRC). This site represents the Southwest semiarid environment within the Long-term Agroecosystem Research (LTAR) network. The SWRC maintains a collection of long-term hydro-climatic measurements from WGEW, featuring an extensive archive of rainfall and runoff observations from an ephemeral network of streams within the 149 km2 watershed. The WGEW was established in 1953, and has continually developed and improved quality assurance and quality control procedures to aid in the accuracy and curation of the constantly growing datasets obtained from more than 100 rain gauges and 18 flumes, weirs, and gauged ponds. These efforts have led to the development of a state-of-the-art database and data visualization tools to aid in the curation of research-grade hydrometeorologic datasets. This required development of automated quality assurance and quality control tools to check and maintain the data for 21st century research needs. We developed five tools to improve the quality of rainfall and runoff database based on conventional hydrologic principles and the relationships between them: 1) precipitation is spatially correlated; 2) there is a temporal relation between rainfall and runoff; 3) runoff is only a limit portion of rainfall; and, 4) closer took of extreme events. Hence, we developed the following methods that included the analysis of interpolated rainfall maps at a daily time step, the association between rainfall and runoff events, lag time, runoff coefficients, and multiple regression methods to identify problematic events in the data archive. To visually inspect and verify the errors, we developed a graphical tool that displays relevant event hyetographs and hydrographs within a specific time window. After flagging anomalous events, we evaluated the types of errors using the original records and metadata information. The implementation of these approaches resulted in developing a suite of semi-automated QAQC tools that correctly detected 813 rainfall and 24 runoff events with erroneous timestamps that had passed all previous quality checks.

Peer review status:IN REVISION

27 Sep 2020Submitted to Hydrological Processes
29 Sep 2020Submission Checks Completed
29 Sep 2020Assigned to Editor
03 Oct 2020Reviewer(s) Assigned
21 Nov 2020Review(s) Completed, Editorial Evaluation Pending
26 Nov 2020Editorial Decision: Revise Major
13 Feb 20211st Revision Received
15 Feb 2021Submission Checks Completed
15 Feb 2021Assigned to Editor
15 Feb 2021Reviewer(s) Assigned
18 Apr 2021Review(s) Completed, Editorial Evaluation Pending
21 Apr 2021Editorial Decision: Revise Major