MIDAS: A New Integrated Flood Early Warning System for the Miño River

Abstract

Early warning systems have become an essential tool to mitigate the impact of river floods, whose frequency and magnitude have increased during the last few decades as a consequence of climate change. In this context, the Miño River Flood Alert System (MIDAS) early warning system has been developed for the Miño River (Galicia, NW Spain), whose flood events have historically caused severe damage in urban areas and are expected to increase in intensity in the next decades. MIDAS is integrated by a hydrologic (HEC-HMS) and a hydraulic (Iber+) model using precipitation forecast as input data. The system runs automatically and is governed by a set of Python scripts. When any hazard is detected, an alert is issued by the system, including detailed hazards maps, to help decision makers to take precise and effective mitigation measures. Statistical analysis supports the accuracy of hydrologic and hydraulic modules implemented to forecast river flow and flooded critical areas during the analyzed period of time, including some of the most extreme events registered in the Miño River. In fact, MIDAS has proven to be capable of predicting most of the alert situations occurred during the study period, showing its capability to anticipate risk situations.

Assessment of Risks Associated with Extreme Climate Events in Small-Scale Bivalve Fisheries: Conceptual Maps for Decision-Making Based on a Review of Recent Studies

“…Although INTECMAR manages more than 50 oceanographic stations along the Galician coast (www.intecmar.gal/informacion/fito/estacions/, accessed on 1 May 2023), only five of the stations record data continuously, and sediment temperature is not recorded at any of the stations. This type of data could be used in local-scale modelling to create a warning system for each species, such as that automatically running for the Miño River [96]. In this system an alert is issued, including detailed hazards maps, which help decision makers to take precise and effective mitigation measures.…”

Estimation of flood-exposed population in data-scarce regions combining satellite imagery and high resolution hydrological-hydraulic modelling: A case study in the Licungo basin (Mozambique)

“…It was initially developed to model river flow, but in recent years several numerical implementations have broadened its range of application to hydrological processes (Cea and Blade, 2015), allowing the simulation of rainfall-runoff transformation and river inundation processes simultaneously. The model has previously been applied and validated in rainfall-runoff computations at different spatial scales (Cea et al, 2016;Cea and Blade, 2015;Fraga et al, 2019;Sanz-Ramos et al, 2018;Sanz-Ramos et al, 2021;Tamagnone et al, 2020;Uber et al, 2021), and its reliability and computational efficiency has led to its implementation in several flood early warning systems (Fernández-Nóvoa et al, 2020;Fraga et al, 2020;González-Cao et al, 2019).…”

An Augmented Reality Facility to Run Hybrid Physical-Numerical Flood Models

“…The GPU parallelized version of the model (Iber+) decreases by two orders of magnitude the time required to perform a simulation, being able to produce results faster than real-time in real-world applications [20]. In fact, this software has recently been implemented in several flood early warning systems [31,32]. Thus, this high-speed computation makes Iber+ perfectly suitable for use in this facility.…”