Papers

@article{farfan-duran_integrating_2025,

title = {Integrating net rainfall calculation in deep learning-based surrogate modeling frameworks for 2D flood prediction},

author = {Juan F. Farfán-Durán and Carlos I. Montalvo-Montenegro and Luis Cea-Gómez and João P. Leitão},

url = {https://www.sciencedirect.com/science/article/pii/S0022169425009709},

doi = {10.1016/j.jhydrol.2025.133632},

issn = {0022-1694},

year  = {2025},

date = {2025-11-01},

urldate = {2025-11-01},

journal = {Journal of Hydrology},

volume = {661},

pages = {133632},

abstract = {This study proposes a novel deep learning (DL)-based surrogate model that incorporates the calculation of net rainfall using the SCS-CN method, providing a flexible framework for evaluating the influence of rainfall events under different antecedent moisture conditions (AMC). The proposed framework involves establishing a ground truth model (Iber-SWMM) and defining the necessary terrain features and rainfall patterns for training the surrogate. A benchmark surrogate model using only gross rainfall, replicating methodologies from previous studies, is also developed for comparison. The trained models are then applied to predict water depth maps using test rainfall patterns under different scenarios, both with and without net rainfall. The results demonstrate that the proposed surrogate model reduces the computational times of Iber-SWMM by 2 to 4 orders of magnitude while outperforming the benchmark surrogate in all the measures. It presents satisfactory accuracy in water depth prediction, with 80% to 95% of predictions within a -0.2 to 0.2 m error range and hit ratios between 0.87 to 0.91 in terms of flooded pixels in the more extreme events. These outcomes are comparable to those achieved by a physics-based model on one of the test events. The study also suggests future lines for refinement.},

keywords = {Deep learning, Flood prediction, Hydrological modeling, Surrogate model, Urban hydrology},

pubstate = {published},

tppubtype = {article}

}

@article{sanudo_iberswmm_2025,

title = {IberSWMM+: A high-performance computing solver for 2D-1D pluvial flood modelling in urban environments},

author = {Esteban Sañudo-Costoya and Orlando García-Feal and L. Hagen and Luis Cea-Gómez and Jerónimo Puertas-Agudo and Carlos I. Montalvo-Montenegro and Reginald Alvarado-Vicencio and Julian Hofmann},

url = {https://www.sciencedirect.com/science/article/pii/S0022169424019991},

doi = {10.1016/j.jhydrol.2024.132603},

issn = {0022-1694},

year  = {2025},

date = {2025-04-01},

urldate = {2025-04-01},

journal = {Journal of Hydrology},

volume = {651},

pages = {132603},

abstract = {Urban drainage modelling is essential for effective city planning and flood management. The increasing complexity of urban environments and the growing availability of high-resolution data have led to the need to develop more sophisticated and freely accessible urban drainage models. This paper presents the parallel implementation of Iber-SWMM, a freely distributed integrated 2D/1D urban drainage model for modelling surface and sewer flows and their interactions. Iber-SWMM constitutes an advance in the field by incorporating a fully distributed hydrological approach, advanced roof modelling tools, and GIS interoperability, offering a comprehensive solution for urban hydrodynamics. Originally designed for research in small urban drainage models due to CPU limitations, Iber-SWMM has now been enhanced with High Performance Computing (HPC) techniques. This allows for the simulation of high-resolution urban models with fine meshes comprising millions of elements, essential for accurate representation of complex urban geometries. We validated the model through laboratory-scale tests and two city-scale scenarios, providing detailed input data and demonstrating the applicability of the model in real-world situations. Our results show that the GPU-accelerated version achieves simulation speeds up to 200 times faster than the sequential version for large models. For instance, in a city-scale scenario with approximately 6 million cells, 3000 nodes, and 3000 links, simulation time was reduced from 72 h to just 20 min. To ensure result consistency and assess convergence, we conducted simulations using low, medium, and high-resolution computational meshes for each case study. Our findings indicate that both parallel and sequential versions produce consistent results, with convergence typically achieved at medium to high resolutions. Notably, we observed that for very large models, the computation of the drainage network in SWMM can become a bottleneck, suggesting an area for future optimization. By enabling the simulation of high-resolution urban models with millions of elements up to 200 times faster than sequential versions, this study bridges the gap between academic research and practical urban planning, empowering stakeholders to conduct more detailed, city-wide simulations, and ultimately contributing to faster urban flood risk management.},

keywords = {Dual drainage modelling, High performance computing, Iber - SWMM, Urban drainage, Urban flooding, Urban hydrology},

pubstate = {published},

tppubtype = {article}

}

@article{sanudo_large-scale_2024,

title = {Large-scale physical facility and experimental dataset for the validation of urban drainage models},

author = {Esteban Sañudo-Costoya and Luis Cea-Gómez and Jerónimo Puertas-Agudo and Juan Naves-García-Rendueles and Jose Anta-Álvarez},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hyp.15068},

doi = {10.1002/hyp.15068},

issn = {1099-1085},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Hydrological Processes},

volume = {38},

number = {1},

pages = {e15068},

abstract = {Numerical models are currently the main tool used to simulate the effects of urban flooding. The validation of these models requires thorough and accurate observed data in order to test their performance. The current study presents a series of laboratory experiments in a large-scale urban drainage physical facility of approximately 100 m2 that includes roofs, streets, inlets, manholes and sewers. The facility is equipped with a rainfall simulator as well as a surface runoff and pipe inflows generators. The experiments were divided in two sets. In Set 1 the surface runoff was generated exclusively by the rainfall input, while in Set 2 the rainfall simulator was used in combination with the runoff generators. In all the tests the water discharge was measured at points on the inlets, roofs, and outfall. The water depth at different locations of the facility was also measured. The experimental tests were replicated numerically using the urban drainage model Iber-SWMM. Experimental results show that, even in a relatively small catchment the peaks in the hydrographs generated at each element of the facility during intermittent rainfalls are significantly attenuated at the catchment outlet. The agreement between the experimental and numerical results show that there are some differences in the hydrographs generated at each element, but that these differences compensate each other and disappear at the outfall. The results generated provide the research community with a thorough and high-resolution dataset obtained under controlled laboratory conditions in a large-scale urban drainage facility, something which has not previously been available.},

note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.15068},

keywords = {experimental dataset, physical model, rainfall simulator, rainfall-runoff experiments, Urban drainage, Urban hydrology},

pubstate = {published},

tppubtype = {article}

}