We used the lumped rainfall–runoff hydrologic models Génie Rural à 4, 5, 6 paramètres Journalier (GR4J, GR5J and GR6J) to evaluate the most robust model for simulating discharge on four forested small catchments (<40 ha) in south-central Chile. Different evapotranspiration methods were evaluated: Oudin, Hargreaves–Samani and Priestley–Taylor. Oudin’s model allows the achievement of the highest efficiencies in the flow simulation. The more sensitive parameters for each model were identified through a Generalized Probability Uncertainty Estimation (GLUE) model. Our results demonstrate that the three hydrological models were capable of efficiently simulating flow in the four study catchments. However, the GR6J model obtained the most satisfactory results in terms of simulated to measured streamflow closeness. In general, the three models tended to underestimate peak flow, as well as underestimate and overestimate flow events in most of the in situ observations, according to the probability of non-exceedance. We also evaluated the models’ performance in a simulation of summer discharge due to the importance of downstream water supply in the months of greatest scarcity. Again, we found that GR6J obtained the most efficient simulations.

This study evaluated the Snowmelt-Runoff Model (SRM) and the Génie Rural à X Paramètres Journalier (GRxJ) model family, analyzing the latter both independently and in combination with the CemaNeige snow module. SRM and GRxJ represent snowmelt-runoff and rainfall-runoff hydrological models, respectively. Accurate streamflow estimation in snow- and rain-dominated basins is crucial for water resource management, especially in the Andes where climate variability and glacier retreat threaten long-term water availability. The analysis was conducted in two Chilean watershed basins with contrasting regimes: the snow-dominated Aconcagua and the mixed rain–snow Duqueco basins. Daily data (2012–2020) of precipitation, temperature, evapotranspiration, snow cover (MODIS), and streamflow were used. Models were calibrated and validated with optimization algorithms and evaluated using $NSE$, $RMSE$, $R^2$, $PBIAS$, $KGE$, $MAE$, $logNSE$ and $APFB$. The results show that SRM effectively reproduces variability and, in the case of the rain–snow regime basin, extreme events, with $NSE$ ranging from $0.70$ to $0.78$ (Aconcagua) and $0.93$ to $0.94$ (Duqueco). Model selection should take into account the dominant hydrological processes. In this study, SRM showed the best performance in both analyzed catchments, although with limitations in reproducing extreme streamflow events. In contrast, the GRxJ models did not adequately capture the hydrological dynamics of the snow-dominated Aconcagua catchment. However, their performance improved considerably when applied to the mixed regime of the Duqueco River. These findings highlight the importance of adapting modeling strategies to local hydrological conditions and limited data availability, offering practical guidance for water management and climate change adaptation in Andean catchments.

Hyperbolic partial differential equations play a crucial role in modeling phenomena such as fluid dynamics, traffic flow, and wave interactions; due to the complexity of these equations, numerical methods are essential for obtaining solutions, particularly with the advent of parallel computing. Graphics Processing Units (GPUs), combined with NVIDIA's CUDA programming model, have enabled significant advancements in scientific computing. This work focuses on solving hyperbolic PDEs using CUDA to exploit the massive parallel processing power of GPUs. Specifically, we address Burgers' equation, the batch monodisperse sedimentation equation, and the polydisperse sedimentation model. Different numerical approaches are implemented, considering the architecture of NVIDIA GPUs and the parallel computing model. Numerical simulations are carried out to assess the efficiency and computational performance of each proposed method.

A three-dimensional model of polydisperse reactive sedimentation is developed by means of a multilayer shallow water approach. The model consists of a variety of solid particles of different sizes and densities, and substrates diluted in water, which produce biochemical reactions while the sedimentation process occurs. Based on the Masliyah-Lockett-Bassoon settling velocity, compressibility of the sediment and viscosity of the mixture, the system of governing equations is composed by non-homogeneous transport equations, coupled to a momentum equation describing the mass-average velocity. Besides, the free-surface depicted by the total height of the fluid column is incorporated and fully determined through the multilayer approach. A finite volume numerical scheme on Cartesian grids is proposed to approximate the model equations. Numerical simulations of the denitrification process exemplify the performance of the numerical scheme and model under different scenarios and bottom topographies.