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.

Tipping bucket rain gauges (TBRs) continue to be one of the most widely used pieces of equipment for rainfall monitoring; they are frequently used for the calibration, validation, and downscaling of radar and remote sensing data, due to their major advantages—low cost, simplicity and low-energy consumption. Thus, many works have focused and continue to focus on their main disadvantage—measurement biases (mainly in wind and mechanical underestimations). However, despite arduous scientific effort, calibration methodologies are not frequently implemented by monitoring networks’ operators or data users, propagating bias in databases and in the different applications of such data, causing uncertainty in the modeling, management, and forecasting in hydrological research, mainly due to a lack of knowledge. Within this context, this work presents a review of the scientific advances in TBR measurement uncertainties, calibration, and error reduction strategies from a hydrological point of view, by describing different rainfall monitoring techniques, summarizing TBR measurement uncertainties, focusing on calibration and error reduction strategies, discussing the state of the art and providing future perspectives of the technology.

Hydrologic processes acting on catchments are complex and variable, especially in mountain basins due to their topography and specific characteristics, so runoff simulation models and water management are also complex. Nevertheless, model parameters are usually estimated on the basis of guidelines from user manuals and literature because they are not usually monitored, due to the high cost of conventional monitoring systems. Within this framework, a new and promising generation of low-cost sensors for hydrologic monitoring, logging, and transition has been developed. We aimed to design a low-cost, open-hardware platform, based on a Raspberry Pi and software written in Python 3, for measuring, recording, and wireless data transmission in hydrological monitoring contexts. Moreover, the data are linked to a runoff model, in real time, for flood prevention. Complementarily, it emphasizes the role of the calibration and validation of soil moisture, rain gauges, and water depth sensors in laboratories. It was installed in a small mountain basin. The results showed mean absolute errors of ±2.2% in soil moisture, ±1 mm in rainfall, and ±0.51 cm in water depth measurements; they highlight the potential of this platform for hydrological monitoring and flood risk management.