A framework approach for unravelling the impact of multiple factors influencing flooding

Abstract

To have a better understanding of the influence of topographic, climatic, and, especially, anthropogenic factors on hydrological discharge and flooding, this study proposes a new framework approach using a set of methods to answer the questions why, where, when, and how flooding occurs. Including conditional inference tree (CIT), cross‐correlation, and double‐mass curves analysis, the approach is demonstrated in an application to the Wei River Basin, China. From the CIT analysis, dam construction period was identified as the most important factor (why), and the sub‐catchment farthest upstream contributed the most to the flooding of the downstream floodplain (where). We then analysed the effect of the periods of dam construction on the time lag change (when) and the precipitation–discharge relationship (how) using cross‐correlation analysis and double‐mass curves analysis, respectively. The results suggested that the dam construction delayed the precipitation for 0.4 days on average compared to before the dam construction period, and the discharge at the outlet of the basin was reduced by 44%. This framework approach is promising as it can quantitatively evaluate the importance of multiple factors on multiple years of flooding, while many studies evaluate single flooding events.

Floods, landscape modifications and population dynamics in anthropogenic coastal lowlands: The Polesine (northern Italy) case study

“…by filtering small floods, thus encouraging human settlement in areas that, far from being protected, are vulnerable to destruction by low-probability, high-impact events (Ludy and Kondolf, 2012;McNamara and Werner, 2008;Werner and McNamara, 2007). Anthropogenic impacts on surface water management have been the subject of several studies, focusing on the effects of climate and land-use changes on frequency and magnitude of floods (Bronstert et al, 2002;Camorani et al, 2005;Ferrier and Jenkins, 2009;Fohrer et al, 2001;Hall et al, 2014;Huang et al, 2017;Kundzewicz et al, 2014;Li et al, 2009;Tomer and Schilling, 2009;Whitfield, 2012;Wooldridge et al, 2001;Zhang et al, 2018;Zope et al, 2016Zope et al, , 2017, on the impacts of engineering works aimed at flood control and navigation (Gai et al, 2017;Mitkova et al, 2005;Pattison and Lane, 2012;Spinewine and Zech, 2008;Surian and Rinaldi, 2003), and on coastal and estuarine dynamics as well (Nicholls and Hoozemans, 1996;Silvestri et al, 2018;Simeoni and Corbau, 2009). Less attention has been paid to the effect of anthropogenic landscape modifications on flooding dynamics, i.e., on the evolution and features of the flooding process, as it is affected by the interaction with the landscape (Carisi et al, 2016;Onishi et al, 2014).…”

Effects of variability in probable maximum precipitation patterns on flood losses

“…Besides uncertainties in hydrological modelling that are not considered in this study, other factors lead to uncertainties in inundation modelling and in flood loss estimation. Uncer-tainties in inundation modelling and flood risk analysis are addressed by Apel et al (2008), Di Baldassarre et al (2010, Gai et al (2017), Thieken (2009), andNeal et al (2013). Savage et al (2015) and Fewtrell et al (2008) describe the effects of spatial scale on inundation modelling.…”

Additional comment to Reviewer #2

“…However, the coupled hydrological-hydraulic model was validated against the observation at the catchment outlet. In the validation period 2011-2014, the coupled hydrological-hydraulic model has a NSE value of 0.85 (Nash-Sutcliffe efficiency;Nash and Sutcliffe, 1970), and a KGE value of 0.85 (Kling-Gupta efficiency;Gupta et al, 2009;Kling et al, 2012).…”