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.

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.

This paper presents a novel approach to assess spatial and temporal irrigation performance in urban parks and can assist park manager/operator decisions in irrigation management. First, irrigation needs are estimated by traditional irrigation scheduling and the irrigation zones with similar water needs that share the same electric valve (hydrozones) are identified. Then, irrigation performance is calculated using the relative water supply (RWS) indicator and mapped (GIS software). This approach can be adapted to various spatial and temporal scales. In this study, it was applied to the Valdebebas urban development VBB (Madrid) between the 2017 and 2019 irrigation seasons. The results for the VBB parks showed high spatio-temporal variation in irrigation performance among plant typologies within an irrigation season, which can be explained by the landscape coefficient K_L variation across the parks. Likewise, this variation was also observed among the three evaluated seasons; explained among other factors by differences in irrigation management. For each hydrozone, the estimation of the NDVI index by Sentinel-2A satellite images in 2019 showed a threshold on irrigation performance. Thus, the remote sensing data together with the proposed approach can be a valuable tool for helping park managers/technicians adopt better decisions on irrigation practices.

En los parques urbanos, plataformas deportivas y jardines privados, el principal reto en la programación del riego es el cálculo de las necesidades hídricas de las plantas ornamentales (ETj), que pueden variar significativamente entre las distintas zonas de un mismo parque. De las numerosas metodologías para estimar las necesidades de riego de las plantas, destacan por su aplicabilidad el método WUCOLS para estimar los coeficientes de jardín (Kj). En este trabajo, se desarrolla una metodología que combina la información climática y el cálculo del índice de la eficiencia en el uso del agua (suministro relativo de agua, ‘relative water supply’) RWS, visualizada mediante mapas SIG, para evaluar el manejo del riego atendiendo tanto a las necesidades hídricas de las especies vegetales como a la eficiencia del manejo del riego. Esta metodología pretende apoyar la toma de decisiones de los gestores de los sistemas de riego de los grandes parques urbanos relacionadas con el manejo del riego. La metodología se ha aplicado durante tres años (2017-2019) a 18 ha de parques dentro de la urbanización de Valdebebas (Madrid) de 1018 ha regadas con goteo subsuperficial, completamente automatizado. Los resultados muestran que las necesidades hídricas de las especies vegetales son bajas, debido a que corresponden a especies ornamentales adaptadas al clima semiárido. Así mismo, la eficiencia en el uso del agua en los parques, aunque tengan las similares necesidades hídricas, es heterogénea. El mapeo proporciona una gran cantidad de información, tanto a escala espacial como temporal (semanal, mensual) que ayudará al personal a cargo del manejo de riego de los parques urbanos a ajustar las decisiones del manejo del riego con mayor detalle, así como, a detectar y evaluar cualquier posible anomalía que pueda presentarse durante las sucesivas temporadas.