\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{Neglecting model parametric uncertainty can drastically underestimate
flood risks}



\author[1,1,1]{Sanjib Sharma}%
\author[2,2,2]{Benjamin Seiyon Lee}%
\author[1,1,1]{Iman Hosseini-Shakib}%
\author[1,1,1]{Murali Haran}%
\author[3,3,3]{Klaus Keller}%
\affil[1]{Pennsylvania State University}%
\affil[2]{George Mason University}%
\affil[3]{Thayer School of Engineering at Dartmouth College}%


\vspace{-1em}



  
  \date{May 19, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Floods drive dynamic and deeply uncertain risks for people and
infrastructures. Uncertainty characterization is a crucial step in
improving the predictive understanding of multi-sector dynamics and the
design of risk-management strategies. Current approaches to estimate
flood hazards often sample only a relatively small subset of the known
unknowns, for example the uncertainties surrounding the model
parameters. This approach neglects the impacts of key uncertainties on
hazards and system dynamics. Here we mainstream a recently developed
method for Bayesian inference to calibrate a computationally expensive
distributed hydrologic model. We compare three different calibration
approaches: (1) stepwise line search, (2) precalibration or screening,
and (3) the new Fast Model Calibrations (FaMoS) approach. FaMoS deploys
a particle-based approach that takes advantage of the massive
parallelization afforded by modern high-performance computing systems.
We quantify how neglecting parametric uncertainty and data discrepancy
can drastically underestimate extreme flood events and risks.
Precalibration improves prediction skill score over a stepwise line
search. The Bayesian calibration improves the uncertainty
characterization of model parameters and flood risk projections.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

