Understanding the Mechanical Biases of Tipping-Bucket Rain Gauges: A Semi-Analytical Calibration Approach
Daniel A. Segovia-Cardozo,
Leonor Rodríguez-Sinobas,
Andrés Díez-Herrero,
Sergio Zubelzu,
Freddy Canales-Ide
Affiliations
Daniel A. Segovia-Cardozo
Research Group Hydraulics for Irrigation, Department Ingeniería Agroforestal, E.T.S.I. Agronómica, Alimentaria y Biosistemas, Universidad Politécnica de Madrid, Ciudad Universitaria s/n, 28040 Madrid, Spain
Leonor Rodríguez-Sinobas
Research Group Hydraulics for Irrigation, Department Ingeniería Agroforestal, E.T.S.I. Agronómica, Alimentaria y Biosistemas, Universidad Politécnica de Madrid, Ciudad Universitaria s/n, 28040 Madrid, Spain
Andrés Díez-Herrero
Geological Hazards Division, Geological Survey of Spain, Instituto Geológico y Minero de España (IGME-CSIC), Ríos Rosas 23, 28003 Madrid, Spain
Sergio Zubelzu
Research Group Hydraulics for Irrigation, Department Ingeniería Agroforestal, E.T.S.I. Agronómica, Alimentaria y Biosistemas, Universidad Politécnica de Madrid, Ciudad Universitaria s/n, 28040 Madrid, Spain
Freddy Canales-Ide
Research Group Hydraulics for Irrigation, Department Ingeniería Agroforestal, E.T.S.I. Agronómica, Alimentaria y Biosistemas, Universidad Politécnica de Madrid, Ciudad Universitaria s/n, 28040 Madrid, Spain
Tipping bucket rain gauges (TBR) are widely used worldwide because they are simple, cheap, and have low-energy consumption. However, their main disadvantage lies in measurement errors, such as those caused by rainfall intensity (RI) variation, which results in data underestimation, especially during extreme rainfall events. This work aims to understand these types of errors, identifying some of their causes through an analysis of water behavior and its effect on the TBR mechanism when RI increases. The mechanical biases of TBR effects on data were studied using 13 years of data measured at 10 TBRs in a mountain basin, and two semi-analytical approaches based on the TBR mechanism response to RI have been proposed, validated in the laboratory, and contrasted with a simple linear regression dynamic calibration and a static calibration through a root-mean-square error analysis in two different TBR models. Two main sources of underestimation were identified: one due to the cumulative surplus during the tipping movement and the other due to the surplus water contributed by the critical drop. Moreover, a random variation, not related to RI, was also observed, and three regions in the calibration curve were identified. Proposed calibration methods have proved to be an efficient alternative for TBR calibration, reducing data error by more than 50% in contrast with traditional static calibration.
