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The modeling of large wood (LW) transport in rivers has received increasing interest from researchers in the last decade due to the widely recognized role of LW concerning flooding risk. For this purpose, few 2D depth-averaged hydraulic models have been coupled with LW transport models. However, such models usually neglect the effects of secondary currents on LW trajectories in river bends. In this work, the model Iber-Wood was enhanced to simulate the effects of secondary currents in river bends on LW trajectories. The proposed methodology presents a new formulation for considering secondary current effects on the flow field derived from the Manning formula and considers a new approach for reproducing the surface flow field that develops at channel bends. The enhanced model was tested to reproduce a series of laboratory experiments on wood transport in a sharp channel bend. The methodology introduces two new parameters in the model related to the secondary current effects, that is, the secondary current intensity and the adaptation length. These parameters were calibrated using available data from laboratory experiments. The good agreement between observed and simulated dowel trajectories in a sharp channel bend validated the proposed approach to simulate LW transport in the case of secondary currents, Peer Reviewed, Postprint (published version)

The aim is the reconstruction of the October 2019 flash flood, that was documented through extensive field work: rainfall (300 mm in just a few hours), flood marks, times of flood passage and witnesses' snapshots and reports, channel changes, log drift (20,000 trees) and woody debris at bridges, as well as large damage and six fatalities. The methods are: hydrological model built for the rainfall-runoff in the basin and the flood routing in the river, use of hydraulic principles such as flow at waterfalls, flow against obstacles (trees), etc. and finally 1D/2D free surface numerical models. The uppermost 100 km2 produced discharges of 700 m3/s (up to 50 m3/s/km2, locally). Three bridges failed, but their cascading failure (when one failure triggers the next one downstream) was not proved. The main channel widened more than 10 times, dragging away soil and vegetation like a bulldozer. The resulting large wood debris that clogged two bridges worsened the inundation. An anomalous flow downstream, probably a surge of around 1090 m3/s, due to the failure of a woody jam at a narrow bridge, took two lives. Water Authority is now warning flood planners that vegetated, torrential basins may cause catastrophic floods in the valley towns, if their narrow bridges are sensitive to woody debris., Catalan Water Authority and its Tarragona officers, Meteoprades and Fons Signatus, Joan March and a number of witnesses. The UPC's contribution was funded by the contract CTN2000029 of the “Agència Catalana de l′Aigua”. The UB's contribution has been developed within the framework of the AGORA project, funded by the "Agència Catalana de l′Aigua". Our thanks to the "Museu de la Vida Rural", in l′Espluga de Francolí, and all the citizens who contributed to the return of experience. The UIB's research has been supported by the Ministerio de Ciencia, Innovación y Universidades (CGL2017-82868-R and PID2020-113036RB-I00/AEI/10.13039/501100011033 research projects, which are partially supported by the European Regional Development Funds)., Peer Reviewed, Postprint (published version)

Large wood (LW) accumulations can cause several damages, especially if the recruited wood is transported during floods down to urban areas, like Lourdes (France). One of the most serious problems concerning bridges and weirs all around the world is the formation of LW accumulations, that might be responsible for the structure’s failure. However, the transport and deposition of LW during floods are very complex processes, which make predictions of these accumulations’ locations and potential impacts very challenging. Thus, reducing the LW-related risks is difficult. Thereby, we are conducting a study to assess the LW-related hazards by applying a numerical model of the Gave-de-Pau River. The software called Iber-Wood is a hydrodynamic model based on the 2D Saint Venant equations coupled to a wood dynamics module that simulates wood transport explicitly. It allows analyzing the LW accumulations’ formation, depending on several input parameters. The aim of this study was to simulate wood transport during floods, in order to identify the potential risks created by the formation of LW accumulations in Lourdes’ city center. The paper first proposes a framework to calibrate the parameters of LW-supply (flux and size of logs) based on the typical dataset available in France. It then presents a preliminary study of the 2D hydrodynamic model showing its potential to identify preferential storage areas and bridges prone to the formation of LW accumulations. The calibration phase of the model is not finished yet, but we can highlight some recommendations on LW-transport modelling from these early stage results., The study was performed within the scope of the Research Project 2015/19/N/ST10/01526 financed by the National Science Centre of Poland., Postprint (published version)

A certain acceptable level of risk in a major drainage system must be established since urban areas cannot be made entirely free from pluvial flooding. Among the diversity of flood risks in urban areas, direct damage to property has been extensively studied. A novel model approach (SFLOOD) to estimate flood damage to property in urban areas has been developed and presented herein. The model was conceptualised according to the knowledge of an insurance surveyor, acquired over many years on flood economic losses appraisals. It is a micro-scale-, depth-damage- and GIS-based model where water depth is the only hydrodynamic variable considered as a damage driver. The model testing has been conducted through the direct comparison of computed damage and damage appraisals provided by the Spanish public insurance company, Consorcio de Compensación de Seguros (CCS), for three actual flood events that occurred in Barcelona (Spain). Although a variety of uncertainties related to the flood damage estimates have been revealed here, the model is able to predict the order of magnitude of the actual damages according to the results obtained., The authors thank the RESCCUE project, which is funded by the EU H2020 (Grant Agreement No. 700174), whose support is gratefully acknowledged. The authors are also grateful to BCASA for allowing the use of their drainage model of Barcelona and for providing rainfall records. METEOCAT also contributed to this study by providing rainfall records from the rain gages they own, and the authors are grateful to them too. Finally, this study was conducted appropriately thanks to the contribution of the Consorcio de Compensación de Seguros (CCS), the institution that kindly provided insurance data to validate the tool presented herein., Peer Reviewed, Postprint (author's final draft)

The impact of precise river flood forecasting and warnings in preventing potential victims along with promoting awareness and easing evacuation is realized in the reduction of flood damage and avoidance of loss of life. Machine learning models have been used widely in flood forecasting through discharge. However the usage of discharge can be inconvenient in terms of issuing a warning since discharge is not the direct measure for the early warning system. This paper focuses on water level prediction on the Storå River, Denmark utilizing several machine learning models. The study revealed that the transformation of features to follow a Gaussian-like distribution did not improve the prediction accuracy further. Additional data through different feature sets resulted in increased prediction performance of the machine learning models. Using a hybrid method for the feature selection improved the prediction performance as well. The Feed-Forward Neural Network gave the lowest mean absolute error and highest coefficient of determination value. The results indicated the difference in prediction performance in terms of mean absolute error term between the Feed-Forward Neural Network and the Multiple Linear Regression model was 0.003 cm. It was concluded that the Multiple Linear Regression model would be a good alternative when time, resources, or expert knowledge is limited.

During the past three years the WoodFlow project (2015-2019), funded by the Federal Office for the Environment) aimed to develop the knowledge and methods to analyse instream large wood dynamics and to mitigate potential wood-related hazards in Swiss rivers. This paper presents part of the relevant results related to the analysis of the spatial and temporal variability of large wood transport, which was done by remote sensed monitoring and numerical modelling., Postprint (published version)

Stormwater management is essential to reducing the occurrence of flooding events in urban areas and to adapting to climate change. The construction of stormwater Best Management Practices (BMPs) entails a series of life cycle environmental impacts but also implies avoided burdens, such as replacing urban infrastructure after flooding. The aim of this paper is to integrate flood damage prevention into the life cycle assessment (LCA) of BMPs for quantifying their net environmental impact (NEI) and environmental payback (EP) from a consequential LCA standpoint. As a case study, the application of a filter, swale and infiltration trench (FST) in a Brazilian neighborhood was assessed considering a high-intensity rainfall event. The potential avoided impacts were related to cars and sidewalks that were not destroyed due to flooding. In terms of CO2eq. emissions, the environmental investment related to the FST was recovered when the destruction of one car or 84 m2 of sidewalk was prevented. The NEI of the FSTs resulted in significant impact reductions (up to 700%) with respect to not accounting for the avoided products. This approach can be implemented to any type of BMP, and more accurate estimations can be made with data for different events and different types of material damage., Peer Reviewed, Postprint (author's final draft)

In terms of flood hazard, the presence of large wood (logs, trees, branches and roots) in rivers may aggravate the consequences of flood events. This material may affect infrastructures such as bridges, weirs, etc., especially those intersecting forested mountain rivers. Until recently, a widely accepted practice was to systematically remove wood debris from river channels as a preventive measure. However, studies have shown that this practice may be useless as the material is transported and deposited after each flood and may even not benefit the long term natural balance of the river ecosystem. Therefore, the presence of this woody material in rivers must be managed and included in flood hazard and risk analysis. In this paper we present a comprehensive methodological approach to study the role of large wood in rivers, with a focus on flood hazard. First, to understand the dynamics of wood recruitment, the contributing areas delivering wood to the streams have to be delineated and the recruitment mechanisms studied. Thus, an estimate can be obtained of the potential volume of deliverable wood. To analyse wood transport we present a numerical model, which allows simulates the behaviour of individual pieces of wood together with hydrodynamics. Finally, we analyse the impact of wood on the magnitude of flood events (in terms of water level, flow velocity or flooded areas), using as an example a flood which occurred in December 1997 in the Sierra de Gredos. The results allowed us to reproduce the wood deposit patterns during the event and to reconstruct the bridge blockage. This caused the upstream water level to rise by up to 2 meters and reduced the flow velocity, which favoured debris and sediment deposits. Consequently, the effects of flooding were equivalent to those of a greater magnitude event. This increase in the flood hazard has been numerically quantified., Postprint (published version)