Research Article AssessingtheUncertaintyAssociatedwithFloodFeaturesdueto VariabilityofRainfallandHydrologicalParameters AhmadSharafati , 1,2,3 MohammadRezaKhazaei , 4 MohamedSalemNashwan , 5,6 NadhirAl-Ansari , 7 ZaherMundherYaseen , 8 andShamsuddinShahid 5 1 Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam 2 Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam 3 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran 4 Department of Civil Engineering, Payame Noor University, Tehran, Iran 5 Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Skudai, Johor 81310, Malaysia 6 Faculty of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo, Egypt 7 Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea 97187, Sweden 8 Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc ang University, Ho Chi Minh City, Vietnam Correspondence should be addressed to Zaher Mundher Yaseen; yaseen@tdtu.edu.vn Received 25 September 2019; Revised 16 July 2020; Accepted 10 August 2020; Published 25 August 2020 Academic Editor: Emilio Bastidas-Arteaga Copyright © 2020 Ahmad Sharafati et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. An assessment of uncertainty in flood hydrograph features, e.g., peak discharge and flood volume due to variability in the rainfall- runoff model (HEC-HMS) parameters and rainfall characteristics, e.g., depth and duration, is conducted. Flood hydrographs are generated using a rain pattern generator (RPG) and HEC-HMS models through Monte Carlo simulation considering uncertainty in stochastic variables. e uncertainties in HEC-HMS parameters (e.g., loss, base flow, and unit hydrograph) are estimated using their probability distribution functions. e flood events are obtained by simulating runoff for rainfall events using the generated model parameters. e uncertainties due to rainfall and model parameters on generated flood hydrographs are evaluated using the relative coefficient of variation (RCV). e results reveal a higher RCV index for flood volume (RCV � 153) than peak discharge (RCV � 116) for a 12-hr rainfall duration. e average relative RCV (ARRCV) index computed for hydrological component (e.g., base flow, loss, or unit hydrograph) indicates the highest impact of rainfall depth on flood volume and peak. e results indicate that rainfall depth is the main source of uncertainty of flood peak and volume. 1.Introduction Reliable estimation of flood characteristics is essential for flood mitigation planning and designing of urban hydraulic structures [1–5]. Numerous models have been developed to relate the rainfall event over a catchment to emanated runoff at catchment outlet [6, 7]. ese rainfall-runoff models are mainly used to predict streamflow and forecasting floods [8]. e prediction of floods using rainfall-runoff models are associated with uncertainty due to the uncertainty in input variables (i.e., rainfall), model parameters (i.e., loss), and model structure [3, 9–14]. e frequency analysis approach is generally used for the estimation of the probable maxi- mum flood (PMF) from the time series of observed peak discharge [15]. e inherent uncertainty of floods is con- sidered as the primary source of uncertainty in such pro- cedure [16–18]. However, a flood occurs for a probable maximum precipitation (PMP) event in a catchment having a favourable hydraulic condition such as saturated soil moisture condition [19]. e PMP is estimated by fitting probability distribution function (PDF) to annual maximum precipitation (AMP) series. e PMP values for different Hindawi Advances in Civil Engineering Volume 2020, Article ID 7948902, 9 pages https://doi.org/10.1155/2020/7948902