Publicacións

@article{gonzalez-cao_enhancing_2025,

title = {Enhancing Flood Risk Management: A Review on Numerical Modelling of Past Flood Events},

author = {José González-Cao and Helena Barreiro-Fonta and Diego Fernández-Nóvoa and Orlando García-Feal},

url = {https://www.mdpi.com/2306-5338/12/6/133},

doi = {10.3390/hydrology12060133},

issn = {2306-5338},

year  = {2025},

date = {2025-06-01},

urldate = {2026-03-18},

journal = {Hydrology},

volume = {12},

number = {6},

pages = {133},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {Recent scientific literature has consistently highlighted a significant increase in both the frequency and intensity of flood events, primarily attributed to the effects of climate change. Projections suggest that this trend will likely intensify in the coming decades. In this context, enhancing our understanding of flooding dynamics becomes not only necessary but urgent. A critical component of this advancement lies in the numerical analysis of historical flood events, which provides valuable insights into flood behaviour across extended temporal and spatial scales. This approach enables two key outcomes: a significant improvement in conventional methods for estimating return periods and a reduction in the uncertainties associated with historical flood events by simulating multiple plausible scenarios to identify the most likely one. This paper presents a comprehensive review of the scientific literature focused on the numerical simulation and reconstruction of past flood events. Two main conclusions emerge from this review: First, the temporal scope of the studies is notably wide, covering events ranging from glacial periods to those occurring in the mid-20th century. Second, there exists a pronounced spatial imbalance in the geographical distribution of these studies, with certain regions significantly underrepresented. This review provides a valuable resource for researchers and practitioners working in flood risk assessment and hydrological modelling. By consolidating existing knowledge, it supports the development and refinement of decision-support tools aimed at improving mitigation strategies to reduce the impact of flooding on both populations and infrastructure.},

keywords = {Climate change, historical floods, numerical modelling, return period},

pubstate = {published},

tppubtype = {article}

}

@article{fernandez-novoa_neural_2025,

title = {Neural network approach for modeling future natural river flows: Assessing climate change impacts on the Tagus River},

author = {Diego Fernández-Nóvoa and Pedro M. M. Soares and Orlando García-Feal and Xurxo Costoya and Ricardo M. Trigo and Moncho Gómez-Gesteira},

url = {https://www.sciencedirect.com/science/article/pii/S2214581825000151},

doi = {10.1016/j.ejrh.2025.102191},

issn = {2214-5818},

year  = {2025},

date = {2025-04-01},

urldate = {2026-03-18},

journal = {Journal of Hydrology: Regional Studies},

volume = {58},

pages = {102191},

abstract = {Study region 

Tagus River basin (Iberian Peninsula). 

Study focus 

An innovative methodology is developed to analyze the impact of climate change on the hydrological cycle. Initially, natural river flow is reconstructed to address the challenge posed by river regulation, which complicates accurate hydrological modeling and can obscure the true impact of climate change. The Iber+ hydrodynamic model is applied to account for downstream reservoir contributions, which allows reversing their influence. Then, neural networks of varying configurations, with specific requirements such as data bucketing, are trained to replicate river flow utilizing recorded precipitation and temperature datasets, subjected to validation procedures. A multi-model ensemble is constructed to address uncertainties inherent in modeling future hydrological climate scenarios. This ensemble, supplied with climate model data, derives historical and projected river flows, allowing analysis of their temporal evolution. 

New hydrological insights for the region 

The findings affirm the efficacy of the proposed methodology and reveal, for the considered high-risk SSP5–8.5 scenario, the intensification of the Tagus hydrological cycle. Within the inherent uncertainty of climate models, average ensemble outputs indicate a reduction of about −20 % in available water at the end of the century, especially critical during summer, with an almost 600 % rise in dry months. Average ensemble results also indicate an increase in flooding events, with extreme floods that currently have five-year frequency, projected to double by the century’s end.},

keywords = {Climate change, Extreme events, Future scenarios, Hydrological procedure, Neural network ensemble, Tagus river flow},

pubstate = {published},

tppubtype = {article}

}

@article{bermudez_robust_2020,

title = {A Robust Method to Update Local River Inundation Maps Using Global Climate Model Output and Weather Typing Based Statistical Downscaling},

author = {María Bermúdez and Luis Cea-Gómez and E. Van Uytven and P. Willems and Juan F. Farfán-Durán and Jerónimo Puertas-Agudo},

url = {https://doi.org/10.1007/s11269-020-02673-7},

doi = {10.1007/s11269-020-02673-7},

issn = {1573-1650},

year  = {2020},

date = {2020-11-01},

urldate = {2026-08-06},

journal = {Water Resources Management},

volume = {34},

number = {14},

pages = {4345–4362},

abstract = {Global warming is changing the magnitude and frequency of extreme precipitation events. This requires updating local rainfall intensity-duration-frequency (IDF) curves and flood hazard maps according to the future climate scenarios. This is, however, far from straightforward, given our limited ability to model the effects of climate change on the temporal and spatial variability of rainfall at small scales. In this study, we develop a robust method to update local IDF relations for sub-daily rainfall extremes using Global Climate Model (GCM) data, and we apply it to a coastal town in NW Spain. First, the relationship between large-scale atmospheric circulation, described by means of Lamb Circulation Type classification (LCT), and rainfall events with potential for flood generation is analyzed. A broad ensemble set of GCM runs is used to identify frequency changes in LCTs, and to assess the occurrence of flood generating events in the future. In a parallel way, we use this Weather Type (WT) classification and climate-flood linkages to downscale rainfall from GCMs, and to determine the IDF curves for the future climate scenarios. A hydrological-hydraulic modeling chain is then used to quantify the changes in flood maps induced by the IDF changes. The results point to a future increase in rainfall intensity for all rainfall durations, which consequently results in an increased flood hazard in the urban area. While acknowledging the uncertainty in the GCM projections, the results show the need to update IDF standards and flood hazard maps to reflect potential changes in future extreme rainfall intensities.},

keywords = {Climate change, Extreme floods, rainfall variability, Statistical downscaling, Weather types},

pubstate = {published},

tppubtype = {article}

}