Surface water quality database for five watersheds in the Arequipa region of Southern Peru
Molly Noel Rymes,
Rebecca Rasmussen,
Pablo A. Garcia-Chevesich,
Katie Burgert,
Jean Long,
Elsie McBride,
Gisella Martínez,
Kattia Martínez,
Teresa Tejada,
Kyle E. Murray,
Gary Vanzin,
Jonathan O. Sharp,
John E. McCray
Affiliations
Molly Noel Rymes
Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA
Rebecca Rasmussen
Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA; Corresponding author.
Pablo A. Garcia-Chevesich
Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA; Intergobernmental Hydrological Programme, United Nations Educational, Scientific, and Cultural Organization (UNESCO), Montevideo, Uruguay
Katie Burgert
Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA
Jean Long
Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA
Elsie McBride
Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA
Gisella Martínez
Universidad Nacional de San Agustín de Arequipa, Santa Catalina 117, Arequipa, Perú
Kattia Martínez
Universidad Nacional de San Agustín de Arequipa, Santa Catalina 117, Arequipa, Perú
Teresa Tejada
Universidad Nacional de San Agustín de Arequipa, Santa Catalina 117, Arequipa, Perú
Kyle E. Murray
Murray GeoConsulting LLC, PO Box 150458, Denver, CO 80215, USA
Gary Vanzin
Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA
Jonathan O. Sharp
Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA
John E. McCray
Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA
Though surface water quality has been monitored in southern Peru over the past and current century, it has been implemented by multiple organizations. The data lacks a centralized repository and access requires logistical and temporal hurdles associated with official requests. A substantial portion of the data has not been quality assured and is in difficult-to-access formats such as scanned PDF documents. These obstacles collectively make it challenging to maximize the impact of these monitoring efforts such as efficiently evaluating long-term water quality trends. To address this opportunity, we gathered available surface water quality information from five watersheds in the Arequipa Region of southern Peru: Camaná, Chili, Ocoña, Tambo, and Yauca. The effort required entry of more than 130,000 records of water quality properties across 274 monitoring stations with data including the concentration of select nutrients, metals, organic compounds, and biological taxa. The water quality records in the Chili watershed go back as far as 1905, while data for the other watersheds was largely confined to the years 2012–2021. This document describes how the surface water quality information was assimilated with quality control and provides a centralized Excel database so that the data can be efficiently used for research and decision making purposes.