Design and Field Implementation of a Low-Cost, Open-Hardware Platform for Hydrological Monitoring
Daniel A. Segovia-Cardozo,
Leonor Rodríguez-Sinobas,
Freddy Canales-Ide,
Sergio Zubelzu
Affiliations
Daniel A. Segovia-Cardozo
Research Group Hydraulics for Irrigation, Departament 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, Departament 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, Departament Ingeniería Agroforestal, E.T.S.I. Agronómica, Alimentaria y Biosistemas, Universidad Politécnica de Madrid, Ciudad Universitaria s/n, 28040 Madrid, Spain
Sergio Zubelzu
Research Group Hydraulics for Irrigation, Departament Ingeniería Agroforestal, E.T.S.I. Agronómica, Alimentaria y Biosistemas, Universidad Politécnica de Madrid, Ciudad Universitaria s/n, 28040 Madrid, Spain
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.