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.

Sustainable Urban Drainage Systems (SUDS) represent a paradigm shift in stormwater management, offering holistic solutions to urban water challenges. This review examines SUDS principles, design strategies, effectiveness and barriers to implementation. SUDS prioritize infiltration and mimic natural hydrological processes to reduce flood risk, improve water quality and support ecosystems in urban environments. Effective SUDS design integrates different components such as permeable pavements, green roofs, and rain gardens, tailored to the local context. Evidence suggests that well-designed SUDS can mitigate peak flows, reduce runoff volumes, and purify water. However, barriers to widespread adoption include lack of awareness, upfront costs, and regulatory complexity. Overcoming these will require collaborative stakeholder action to prioritize education, policy support, and funding opportunities. Future research should focus on optimizing SUDS design, assessing long-term performance, and quantifying socio-economic benefits. By integrating SUDS into urban landscapes, cities can strengthen hydrological resilience, promote sustainability, and enrich urban life.

Tomato crop is grown worldwide and is considered a mass consumer product. In Ecuador, tomato growers face two major issues: water scarcity and water mismanagement, which cause a reduction in the framers’ gross income and ecosystem services. This paper is aimed at finding an optimal irrigation scheduling in greenhouse tomato crop to achieve a balance among production, fruit quality and water use efficiency. Thus, two experiments were settled. In the first experiment, four water doses (80, 100, 120 and 140% ETc) and two irrigation frequencies (one and two irrigations per day) were compared. The second experiment evaluated the two best water doses of the first one (100 and 120% ETc) and four irrigation frequencies (one and two irrigations per day, one irrigation every two days, one irrigation every three days). Each experiment monitored the variables for tomato production (plant height, stem diameter, fruits per plant, yield) and tomato quality (pH, total soluble solids, titratable acidity). The study concluded that water doses affected more than irrigation frequency to fruit quality and production. The dose of 100% ETc, applied in one irrigation per day, is suggested to obtain a balance between production, fruit quality and water use efficiency.

In this paper, we present an approach that combines data-driven and physical modelling for predicting the runoff occurrence and volume at catchment scale. With that aim, we first estimated the runoff volume from recorded storms aided by the Green-Ampt infiltration model. Then, we used machine learning algorithms, namely LightGBM (LGBM) and Deep Neural Network (DNN), to predict the outputs of the physical model fed on a set of atmospheric variables (relative humidity, temperature, atmospheric pressure, and wind velocity) collected before or immediately after the beginning of the storm. Results for a small urban catchment in Madrid show DNN performed better in predicting the runoff occurrence and volume. Moreover, enriching the input primary atmospheric variables with auxiliary variables (e.g., storm intensity data recorded during the first hour, or rain volume and intensity estimates obtained from auxiliary regression methods) largely increased the model performance. We show in this manuscript data-driven algorithms shaped by physical criteria can be successfully generated by allowing the data-driven algorithm learn from the output of physical models. It represents a novel approach for physics-informed data-driven algorithms shifting from common practices in hydrological modelling through machine learning.