Publicacións

@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{tamagnone_rainwater_2020,

title = {Rainwater Harvesting Techniques to Face Water Scarcity in African Drylands: Hydrological Efficiency Assessment},

author = {Paolo Tamagnone and Luis Cea-Gómez and Elena Comino and Maurizio Rosso},

url = {https://www.mdpi.com/2073-4441/12/9/2646},

doi = {10.3390/w12092646},

issn = {2073-4441},

year  = {2020},

date = {2020-09-01},

urldate = {2026-08-06},

journal = {Water},

volume = {12},

number = {9},

pages = {2646},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {The sub-Saharan climate is experiencing a marked increase in temperature and intensification of precipitation intensity and variability. Besides, longer dry spells are compromising the reliability of local agricultural practices. The present study provides a comprehensive investigation about the benefits induced by using indigenous rainwater harvesting techniques (RWHT) against hydrometeorological threats affecting the Sahelian areas. Different RWHT have been tested in term of runoff retention, infiltration increase into the root zone, and soil water stress mitigation. To achieve these purposes, hydrological processes at the field scale have been investigated using a two-dimensional distributed hydrological model. To make the study representative of the whole Sahelian areas, several simulations were carried out adopting a wide range of input parameters based on conventional values of those areas. The results reveal that RWHT may lead to a runoff retention up to 87% and to double the infiltration. Intercepting and storing runoff, RWHT increase the water content in the root zone and the right design can diminish the crop water stress. Furthermore, the results show that adopting RWHT makes it possible to extend the growing season up to 20 days, enhancing the yield. These benefits contribute to the reduction of the climate-related water stress and the prevention of crop failure.},

keywords = {climate-smart agriculture, crop water stress, Hydrological modeling, rainwater harvesting techniques, Sahel, sustainable land and water management, water balance},

pubstate = {published},

tppubtype = {article}

}

@article{cea_incorporating_2018,

title = {Incorporating Antecedent Moisture Conditions and Intraevent Variability of Rainfall on Flood Frequency Analysis in Poorly Gauged Basins},

author = {Luis Cea-Gómez and Ignacio Fraga},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2018WR023194},

doi = {10.1029/2018WR023194},

issn = {1944-7973},

year  = {2018},

date = {2018-01-01},

urldate = {2026-08-04},

journal = {Water Resources Research},

volume = {54},

number = {11},

pages = {8774–8791},

abstract = {Rainfall-runoff models are frequently used for the prediction of flood discharges from extreme rainfall data in poorly gauged basins. However, the conventional methods used to estimate the frequency of flood discharges from precipitation data do not incorporate a realistic representation of the spatial and temporal structure of rainfall at the study site nor of the antecedent soil moisture content. Temporally and spatially idealized rainfall patterns that do not represent properly the intraevent variability of rainfall are in general assumed. In this paper, we propose a methodology for flood frequency analysis based on the hydrological simulation of potentially hazardous real storm events. The aim of the proposed method is to incorporate the impact of the spatial and temporal intraevent variability of rainfall on flood discharge estimates without making strong assumptions about the spatial and temporal patterns of storms. It also includes a calibrated relation between the antecedent rainfall depth and the soil infiltration capacity, in order to consider the combined effect of antecedent soil moisture content and rainfall intensity. The flood frequency estimates obtained with the proposed methodology are compared to those estimated with a conventional method based on the definition of synthetic design hyetographs from local intensity-duration-frequency curves. The analysis is presented in two watersheds of 84 and 353 km2 located in the NW of Spain. Results highlight the role of the antecedent soil moisture in flood frequency analysis and show that the conventional methodology tends to overestimate the flood discharges when compared to the proposed approach.},

keywords = {antecedent soil moisture, flood frequency analysis, Hydrological modeling, poorly gauged basins, rainfall variability, storm rainfall},

pubstate = {published},

tppubtype = {article}

}