River flooding is a key topic for water managers because of social and economic losses it can cause. The complex topography and dynamics of mountain rivers has limited the analysis of their behavior during flood events (e.g., sediment transport, flooding). This study aims to test the performance of three hydraulic 1D models (HEC-RAS, MIKE 11, and Flood Modeller) to estimate inundation water levels for a mountain river. The evaluation of these models was performed considering steady state conditions through 10 scenarios, i.e. five discharge return periods, and two types of cross sections data: (a) type I, a detailed field survey complemented with information extracted from DEM, derived from LiDAR; and (b) type II, cross sections exclusively derived from the DEM. The research was conducted for a reach of 5 km of the Santa Bárbara River, with an average slope of 0.25%. HEC-RAS model results for cross sections type I, were previously validated and therefore used as reference for comparison between other models and scenarios. The goodness-of-fit between models was measured based on the Nash-Sutcliffe model efficiency coefficient (EF). The main goal of the current study was to determine the variability of inundation levels results compared with a validated model as reference, using the same input data for the three modelling packages. Our analysis shows that, when using cross sections type I, the evaluated modeling packages yield similar results (EF were between 0.94 and 0.99). On the other hand, the goodness of fit decreased when using type II data, with an average EF of 0.98 (HEC-RAS), 0.88 (Flood Modeller) and 0.85 (MIKE 11) when compared to the reference model. The authors conclude that it is highly recommend for practitioners to use geometric data type I instead of type II in order to obtain similar performance in the tested models. Only HEC-RAS type II has the same performance as type I models (average EF of 0.98).

The behavior of many physical systems is described by means of differential equations. These equations are usually derived from balance principles and certain modelling assumptions. For realistic situations, the solution of the associated initial boundary value problems requires the use of some discretization technique, such as finite differences or finite volumes. This research tackles the numerical solution of a 1D differential equation to predict water surface profiles in a river, as well as to estimate the so-called roughness parameter. A very important concern when solving this differential equation is the ability of the numerical model to capture different flow regimes, given that hydraulic jumps are likely to be observed. To approximate the solution, Physics-Informed Neural Networks (PINN) are used. Benchmark cases with different bed profile shapes, which induce different flows types (supercritical, subcritical, and mixed) are tested first. Then a real mountain river morphology, the so-called Step-pool, is studied. PINN models were implemented in Tensor Flow using two neural networks. Different numbers of layers and neurons per hidden layer, as well as different activation functions (AF), were tried. The best performing model for each AF (according to the loss function) was compared with the solution of a standard finite difference discretization of the steady-state 1D model (HEC-RAS model). PINN models show good predictability of water surface profiles for slowly varying flow cases. For a rapid varying flow, the location and length of the hydraulic jump is captured, but it is not identical to the HEC-RAS model. The predictability of the tumbling flow in the Step-pool was good. In addition, the solution of the estimation of the roughness parameter (which is an inverse problem) using PINN shows the potential of this methodology to calibrate this parameter with limited cross-sectional data. PINN has shown potential for its application in open channel studies with complex bed profiles and different flow types, having in mind, however, that emphasis must be given to architecture selection.

Precipitation event samples and weekly based water samples from streams and soils were collected in a tropical montane cloud forest catchment for 2 years and analyzed for stable water isotopes in order to understand the effect of sampling frequency in the performance of three lumped-parameter distribution functions (exponential-piston flow, linear-piston flow and gamma) which were used to estimate mean transit times of waters. Precipitation data, used as input function for the models, were aggregated to daily, weekly, bi-weekly, monthly and bi-monthly sampling resolutions, while analyzed frequencies for outflows went from weekly to bi-monthly. By using different scenarios involving diverse sampling frequencies, this study reveals that the effect of lowering the sampling frequency depends on the water type. For soil waters, with transit times on the order of few weeks, there was a clear trend of over predictions. In contrast, the trend for stream waters, which have a more damped isotopic signal and mean transit times on the order of 2 to 4 years, was less clear and showed a dependence on the type of model used. The trade-off to coarse data resolutions could potentially lead to misleading conclusions on how water actually moves through the catchment, notwithstanding that these predictions could reach better fitting efficiencies, fewer uncertainties, errors and biases. For both water types an optimal sampling frequency seems to be 1 or at most 2 weeks. The results of our analyses provide information for the planning of future fieldwork in similar Andean or other catchments.

In many medium-sized irrigation systems water is wasted during the night because demand is low and supply is not reduced accordingly. A hydrodynamic model was applied, using MIKE 11 as a software tool, to simulate abrupt discharge changes and their travel times along small irrigation canals. Filling and emptying of the canal were also analyzed. The model was calibrated with data of a field experiment that included startup, positive, and negative surges. A technique was developed to take into account the considerable water losses in the canal. Performance indicators efficiency, adequacy, equity, and dependability, proposed by Molden and Gates in 1990, were redefined using the concept of usefully delivered discharge. The newly defined indicators were consequently used to find the gate operation scenario that meets optimally the target day and night discharges. It was found that it is feasible to implement night delivery reduction. The calibrated model was used to develop guidelines for the operation of the canal with daily flow variation, resulting in considerable water savings during the night.