Open AccessA coupled probabilistic hydrologic and hydraulic modelling framework to investigate the uncertainty of flood loss estimates
Author(s) -
Ahmadisharaf Ebrahim,
Kalyanapu Alfred J.
Publication year - 2019
Publication title -
journal of flood risk management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.049
H-Index - 36
ISSN - 1753-318X
DOI - 10.1111/jfr3.12536
Subject(s) - flood myth , hydrograph , environmental science , watershed , hydrology (agriculture) , estimation , hydrological modelling , probabilistic logic , surface runoff , antecedent moisture , continuous simulation , computer science , statistics , runoff curve number , climatology , geology , mathematics , geography , geotechnical engineering , simulation , engineering , archaeology , ecology , systems engineering , machine learning , biology
Reliable estimation of flood loss is crucial for flood management. Since this estimation relies on models that are prone to uncertainty, it is vital to clearly understand how the uncertainties affect the appraised flood loss. This paper presents a coupled probabilistic hydrologic and hydraulic modelling framework to estimate the uncertainty of anticipated loss. A hydrologic model performs rainfall‐runoff transformation and a two‐dimensional unsteady hydraulic model simulates flood inundation. The outputs of the latter are fed into a loss estimation tool. Two sources of uncertainty—rainfall depth and antecedent moisture condition—are used to illustrate the framework on the Swannanoa River watershed in North Carolina, United States. The impact of the uncertainty of these two sources is tracked over the hydrologic model, hydraulic model and loss estimation tool. Our case study results illustrate that the estimations on the percent affected people can be four times more uncertain than the rainfall depth and two times more than the flood extent, but its uncertainty is comparable to hydrograph attributes. The appraised structural damages are nearly two times more uncertain than the affected people. These findings, however, may only be valid for a case study with hilly topography and should be cautiously extrapolated to other areas.