The aim of this research is the detection and analysis of existing trends in the Meta River, Colombia, based on the streamflow records from seven gauging stations in its main course, for the period between June 1983 to July 2019. The Meta River is one of the principal branches of the Orinoco River, and it has a high environmental and economic value for this South American country. The methods employed for the trend detection and quantification were the Mann–Kendall (MK) test, the modified MK (MMK) test, and the Sen’s slope (SS) estimator. Statistically significant trends (at a 95% level of confidence) were detected in more than 30% of the 105 evaluated datasets. The results from the MK test indicate the presence of statistically significant downward trends in the upstream stations and upward trends in the downstream stations, with the latter presenting steep positive slopes. The findings of this study are valuable assets for water resources management and sustainable planning in the Meta River Basin.

Numerical models are important tools for analyzing and solving water resources problems; however, a model’s reliability heavily depends on its calibration. This paper presents a method based on Design of Experiments theory for calibrating numerical models of rivers by considering the interaction between different calibration parameters, identifying the most sensitive parameters and finding a value or a range of values for which the calibration parameters produces an adequate performance of the model in terms of accuracy. The method consists of a systematic process for assessing the qualitative and quantitative performance of a hydromorphological numeric model. A 75 km reach of the Meta River, in Colombia, was used as case study for validating the method. The modeling was conducted by using the software package MIKE-21C, a two-dimensional flow model. The calibration is assessed by means of an Overall Weighted Indicator, based on the coefficient of determination of the calibration parameters and within a range from 0 to 1. For the case study, the most significant calibration parameters were the sediment transport equation, the riverbed load factor and the suspended load factor. The optimal calibration produced an Overall Weighted Indicator equal to 0.857. The method can be applied to any type of morphological models.

Fluid Mechanics courses comprise both theoretical and laboratory modules. In developing nations, computer‐assisted techniques are not commonly applied in Fluid Mechanics instruction. Forced by the COVID‐19 pandemic, South American universities are, however, using them for online teaching. This contribution presents an 8‐semester (2016–2019) educational intervention over an undergraduate Fluid Mechanics course. It mainly blends physical (hands‐on) and virtual experiments (computer fluid dynamics‐based simulations) for the laboratory module, which are complemented by flipped classroom‐based prompts for the theoretical module. The intervention follows design‐based research as a research method and is guided via conjecture mapping and fidelity of implementation standards. Our results suggest that the intervention improves fluid mechanics laboratory instruction, although improvements depend upon the participation of other educational actors such as teaching assistants and laboratory technicians to some extent. Laboratory report grades (the assessment instrument) follow the Gompertz probability distribution. Following UNESCO standards, a portion of the intervention output is shared as open educational resources. This contribution encourages upscaling the educational intervention through the formation of cooperative clusters to build common‐pool Fluid Mechanics resources. Learning scientists have underlined the need to better understand laboratory instruction processes. They have been addressed in very few instances in developing countries. We believe that this study has the potential to provide valuable insights on the matter.

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Global reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, reanalysis datasets require detailed validation to determine their accuracy and reliability. This paper evaluates the performance of MERRA2 and ERA5 regarding their monthly rainfall products, comparing their areal precipitation averages with estimates based on ground measurement records from 49 rain gauges managed by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) and the Thiessen polygons method in the Sinu River basin, Colombia. The performance metrics employed in this research are the correlation coefficient, the bias, the normalized root mean square error (NRMSE), and the Nash–Sutcliffe efficiency (NSE). The results show that ERA5 generally outperforms MERRA2 in the study area. However, both reanalyses consistently overestimate the monthly averages calculated from IDEAM records at all time and spatial scales. The negative NSE values indicate that historical monthly averages from IDEAM records are better predictors than both MERRA2 and ERA5 rainfall products.

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