Uncertainty analysis of 100-year flood maps under climate change scenarios

Floods are natural disastrous hazards that throughout history have had and still have major adverse impacts on people’s life, economy, and the environment. One of the useful tools for flood management are flood maps, which are developed to identify flood prone areas and can be used by insurance companies, local authorities and land planners for rescue and taking proper actions against flood hazards. Developing flood maps is often carried out by flood inundation modeling tools such as 2D hydrodynamic models. However, often flood maps are generated using a single deterministic model outcome without considering the uncertainty that arises from different sources and propagates through the modeling process. Moreover, the increasing number of flood events in the last decades combined with the effects of global climate change requires developing accurate and safe flood maps in which the uncertainty has been considered. Therefore, in this thesis the uncertainty of 100-year flood maps under 3 scenarios (present and future RCP4.5 and RCP8.5) is assessed through intensive Monte Carlo simulations. The uncertainty introduced by model input data namely, roughness coefficient, runoff coefficient and precipitation intensity (which incorporates three different sources of uncertainty: RCP scenario, climate model, and probability distribution function), is propagated through a surrogate hydrodynamic/hydrologic model developed based on a physical 2D model. The results obtained from this study challenge the use of deterministic flood maps and recommend using probabilistic approaches for developing safe and reliable flood maps. Furthermore, they show that the main source of uncertainty comes from the precipitation, namely the selected probability distribution compared to the selected RCP and climate model.publishedVersio

Representation of 100-year design rainfall uncertainty in catchment-scale flood modeling: A MCMC Bayesian approach

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Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin

Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR

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Linear and non-linear approaches to predict the Darcy-Weisbach friction factor of overland flow using the extreme learning machine approach

(IF 1.97; Q3)International audienceIn modeling of overland flow and erosion, the overland flow friction factor (f), is a crucial factor. Due to the importance of a good understanding of f and its variability, the current study aimed to investigate the capability of non-linear approaches to estimate the Darcy-Weisbach friction factor of overland flow and its components (sediment transport, wave, form, and grain friction factors) through the Extreme Learning Machine (ELM) approach. Four datasets were used herein which were obtained from flume experiments done by different researchers. In order to investigate the effects of different parameters on the friction factor, numerous models consisting of various parameters were utilized to predict the friction factor using the ELM approach. The modeling procedure was established in two stages; the first stage aimed to model the overland flow friction factor and investigate the effect of the different parameters on the friction factor using non-linear separation via the ELM approach. In the second stage, the friction factor was linearly separated into different types of friction factors and then the separate components were estimated. Sensitivity analysis results confirmed the key role of Froude number (Fr) values for most of the models. On the other hand, the results obtained for estimated values of the friction factor were acceptable and outperformed available empirical approaches

A practical methodology to perform global sensitivity analysis for 2D hydrodynamic computationally intensive simulations

Abstract Sensitivity analysis is a commonly used technique in hydrological modeling for different purposes, including identifying the influential parameters and ranking them. This paper proposes a simplified sensitivity analysis approach by applying the Taguchi design and the ANOVA technique to 2D hydrodynamic flood simulations, which are computationally intensive. This approach offers an effective and practical way to rank the influencing parameters, quantify the contribution of each parameter to the variability of the outputs, and investigate the possible interaction between the input parameters. A number of 2D flood simulations have been carried out using the proposed combinations by Taguchi (L27 and L9 orthogonal arrays) to investigate the influence of four key input parameters, namely mesh size, runoff coefficient, roughness coefficient, and precipitation intensity. The results indicate that the methodology is adequate for sensitivity analysis, and that the precipitation intensity is the dominant parameter. Furthermore, the model calibration based on local variables (cross-sectional water level) can be inaccurate to simulate global variables (flooded area)