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Extreme rainfall events in Andean basins frequently trigger landslides, obstructing river channels and causing flash flows, loss of lives, and economic damage. This study focused on improving the modeling of these events to enhance risk management, specifically in the La Liboriana basin in Salgar (Colombia). A cascading modeling methodology was developed, integrating the spatially distributed rainfall intensities, hazard zoning with the SLIDE model, propagation modeling with RAMMS using calibrated soil rheological parameters, the distributed hydrological model TETIS, and flood mapping with IBER. Return periods of 2.33, 5, 10, 25, 50, and 100 years were defined and applied throughout the methodology. A specific extreme event (18 May 2015) was modeled for calibration and comparison. The spatial rainfall intensities indicated maximum concentrations in the northwestern upper basin and southeastern lower basin. Six landslide hazard maps were generated, predicting landslide-prone areas with a slightly above random prediction rate for the 2015 event. The RAMMS debris flow modeling involved 30 simulations, indicating significant deposition within the river channel and modifying the terrain. Hydraulic modeling with the IBER model revealed water heights ranging from 0.23 to 7 m and velocities from 0.34 m/s to 6.98 m/s, with urban areas showing higher values, indicating increased erosion and infrastructure damage potential.

Mine tailings are commonly stored in off-stream reservoirs and are usually composed of water with high concentrations of fine particles (microns). The rupture of a mine-tailings pond promotes, depending on the characteristics of the stored material, the fluidization and release of hyper-concentrated flows that typically behave as non–Newtonian fluids. The simulation of non–Newtonian fluid dynamics using numerical modelling tools is based on the solution of mass and momentum conservation equations, particularizing the shear stress terms by means of a rheological model that accounts for the properties of the fluid. This document presents the extension of Iber, a two-dimensional hydrodynamic numerical tool, for the simulation of non–Newtonian shallow flows, especially those related to mine tailings. The performance of the numerical tool was tested throughout benchmarks and real study cases. The results agreed with the analytical and theoretical solutions in the benchmark tests; additionally, the numerical tool also revealed itself to be adequate for simulating the dynamic and static phases under real conditions. The outputs of this numerical tool provide valuable information, allowing researchers to assess flood hazard and risk in mine-tailings spill propagation scenarios.

Floods in Villahermosa are events that have occurred frequently over the centuries, due to the city’s location at the mouth of two of the most powerful rivers in Mexico. Flooding effects on residents have become increasingly damaging over the years as a consequence of the increase in frequency and intensity of extreme weather phenomena, in addition to poor land-use planning policies. The increase in population and consequent urban expansion are certainly causes of the problem, which are reflected in poor urban planning policy and in an almost absent perception of risk. In this work, we present a methodology for the construction of flood risk maps based on a hydraulic study, analysis of social vulnerability indexes, calculation of severity indexes and construction of hazard maps. The results of the hydraulic simulations show that relatively frequent rainfall causes floods of the order of 2 m, in agreement with annual observations conducted in Villahermosa. More extreme rainfall can lead to flooding greater than 4 m in marginalized areas of the city. The areas at greatest risk are sections close to the rivers that cross the city, and the estimated economic damage is greater than USD 14 million. Risk maps presented here constitute the first effort of an integrated study to couple flood analysis with the calculation of economic damage in the city of Villahermosa, and provide important tools to conscientize populations in their perception of risk, but also create the basis for a conscious urbanization policy.

Off-stream reservoirs are hydraulic structures that might cause severe flood damages in case of failure or improper operation. Their safety regulations usually require hydraulic studies for flood hazard and inundation zone mapping. The selection of the break point is not trivial because the topography in its surroundings is commonly highly anthropic. A wrong selection would not provide the worst scenario in terms of maximum flood hazard extent. This work presents a probabilistic approach based on a stochastic definition of the break point along the dyke. A number of failure scenarios are generated automatically, corresponding to different breach formations. Then, an in-cascade calculation process simulates each scenario, providing a framework to carry out statistical analysis. The simulation of the breach formation and the flood wave propagation is performed through a GPU parallelised two-dimensional hydraulic numerical model, which provides a probabilistic inundation zone and flood hazard mapping of all scenarios simulated in a suitable timeframe.

Machine learning (ML) models have been shown to be valuable tools employed for streamflow prediction, reporting considerable accuracy and demonstrating their potential to be part of early warning systems to mitigate flood impacts. However, one of the main drawbacks of these models is the low precision of high streamflow values and extrapolation, which are precisely the ones related to floods. Moreover, the great majority of these models are evaluated considering all the data to be equally relevant, regardless of the imbalanced nature of the streamflow records, where the proportion of high values is small but the most important. Consequently, this study tackles these issues by adding synthetic data to the observed training set of a regression-enhanced random forest model to increase the number of high streamflow values and introduce extrapolated cases. The synthetic data are generated with the physically based model Iber for synthetic precipitations of different return periods. To contrast the results, this model is compared to a model only fed with observed data. The performance evaluation is primarily focused on high streamflow values using scalar errors, graphically based errors and errors by event, taking into account precision, over- and underestimation, and cost-sensitivity analysis. The results show a considerable improvement in the performance of the model trained with the combination of observed and synthetic data with respect to the observed-data model regarding high streamflow values, where the root mean squared error and percentage bias decrease by 23.1% and 38.7%, respectively, for streamflow values larger than three years of return period. The utility of the model increases by 10.5%. The results suggest that the addition of synthetic precipitation events to existing records might lead to further improvements in the models.

In Riohacha the La Niña, phenomenon generates intense rains with consequent serious flooding. To address this reality, MODCEL, a conceptual cell-based model, had been applied and calibrated in a previous project. In this research, we compare MODCEL with IBER, a well-known, physically based 2D hydraulic model. The purpose is twofold: (i) to illustrate how system schematization can be carried out in the two modeling frameworks, which is not a trivial task and implies several choices and assumptions; (ii) to point out the strengths and weaknesses of these two models in a comparative fashion. Here, IBER has been calibrated and validated with the same data used for MODCEL. MODCEL performs slightly better, both in calibration and validation possibly because of the low resolution of the topographic information, an essential element for IBER. Furthermore, in IBER it is not possible to represent adequately all the different hydraulic works spread across the town. MODCEL, in turn, is not easy to apply because it requires a deep insight into the actual behavior of the physical system and time-consuming schematization attempts where a deep experience is needed; furthermore, it is by far less user-friendly than IBER. In any case, the two models capture sufficiently well the behavior of urban flooding and its changes according to hypothetical interventions.

Approximately 70,000 Spanish off-stream reservoirs, many of them irrigation ponds, need to be evaluated in terms of their potential hazard to comply with the new national Regulation of the Hydraulic Public Domain. This requires a great engineering effort to evaluate different scenarios with two-dimensional hydraulic models, for which many owners lack the necessary resources. This work presents a simplified methodology based on machine learning to identify risk zones at any point in the vicinity of an off-stream reservoir without the need to elaborate and run full two-dimensional hydraulic models. A predictive model based on random forest was created from datasets including the results of synthetic cases computed with an automatic tool based on the two-dimensional numerical software Iber. Once fitted, the model provided an estimate on the potential hazard considering the physical characteristics of the structure, the surrounding terrain and the vulnerable locations. Two approaches were compared for balancing the dataset: the synthetic minority oversampling and the random undersampling. Results from the random forest model adjusted with the random undersampling technique showed to be useful for the estimation of risk zones. On a real application test the simplified method achieved 91% accuracy.

The floodplain of La Sabana River, Guerrero State, Mexico, was subject to disastrous floods due to the passage of extreme weather phenomena. This is a situation facing many ungauged rivers in Mexico, as well as in other developing countries, where increased urbanization and a lack of monitoring systems make many inhabited areas more vulnerable to flooding. The purpose of this work is to provide a tool for determining the flood terrain response to flooding based on a hydraulic study. This methodology combines a hydrological analysis of the river basin with the floodplain hydraulic study for the precise identification of overflow points and the resulting flood levels. Results show that, for an ungauged river, hydraulic analysis is an essential tool for determining the main potential flood points and establishing whether the river has the capacity to contain floods. Specifically, it is shown that La Sabana River is predisposed to overflow long before the river reaches its maximum flow, even in correspondence with more frequent flood scenarios. This study shows a further application that a hydraulic model can have to improve flood risk preparedness for ungauged rivers of regions where other types of monitoring tools cannot be used.

2D models based on the shallow water equations are widely used in river hydraulics. However, these models can present deficiencies in those cases in which their intrinsic hypotheses are not fulfilled. One of these cases is in the presence of weirs. In this work we present an experimental dataset including 194 experiments in nine different weirs. The experimental data are compared to the numerical results obtained with a 2D shallow water model in order to quantify the discrepancies that exist due to the non-fulfillment of the hydrostatic pressure hypotheses. The experimental dataset presented can be used for the validation of other modelling approaches.

Dual urban drainage models allow users to simulate pluvial urban flooding by analysing the interaction between the sewer network (minor drainage system) and the overland flow (major drainage system). This work presents a free distribution dual drainage model linking the models Iber and Storm Water Management Model (SWMM), which are a 2D overland flow model and a 1D sewer network model, respectively. The linking methodology consists in a step by step calling process from Iber to a Dynamic-link Library (DLL) that contains the functions in which the SWMM code is split. The work involves the validation of the model in a simplified urban street, in a full-scale urban drainage physical model and in a real urban settlement. The three study cases have been carefully chosen to show and validate the main capabilities of the model. Therefore, the model is developed as a tool that considers the main hydrological and hydraulic processes during a rainfall event in an urban basin, allowing the user to plan, evaluate and design new or existing urban drainage systems in a realistic way.