Abstract
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.
Highlights
An interest among humans in measuring precipitation started in ancient times since this atmospheric process is an important factor in the water cycle and in life, as it has a significant role in hydrological processes [1]
There are two main sources of surplus water, one caused by the time the bucket takes to tip (St) and the other generated by the critical drop (Sd); both depend on rainfall intensity (RI), the
Measurement errors would be higher in isolated high RI storms, which are frequently used for the analysis of statistical data; a very fine resolution of RI data would be recommended
Summary
An interest among humans in measuring precipitation started in ancient times since this atmospheric process is an important factor in the water cycle and in life, as it has a significant role in hydrological processes [1]. Modern TBRs have evolved to become one of the most widely used rain gauges worldwide, employed by governmental agencies, airports, industries, farmers, and private individuals [17,18] due to their simple manufacturing structure (based on an electric pulse, a magnet switch mechanism, and a counter), low cost of production, and energy saving (a key element for most of the manufacturing of weather instruments and automation) [19] Their main drawbacks are measurement error (which can be significant in heavy rainfall events or during light drizzle); losses from evaporation and wind effects [20,21,22,23,24]; onset time; the sampling procedure; inaccuracy in the determination of the bucket’s rain residue and the stopping of precipitation [25]; maintenance problems (cleaning, colonization by insects or birds); and random errors [26] Rainfall data are important in various human activities, such as irrigation management, hydrological monitoring, water resource management, flood prevention, flood alert systems, runoff models, the calibration of distributed precipitation measurements, flow early warning systems [3,4,5,6,7], the downscaling of remote sensing precipitation models [8,9], radar calibration–validation [10,11,12,13], basin water balance and flood models [14], structure design and profitability in different projects (hydroelectric generation, dam, irrigation, city planning, etc.) [15,16], and other fields where accurate rainfall data with high frequency are needed to improve applications, especially in atmospheric and hydrological management and forecasting.
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