Quantifying the importance of spatial resolution and other factors through global sensitivity analysis of a flood inundation model

Abstract

Where high-resolution topographic data are available, modelers are faced with the decision of whether it is better to spend computational resource on resolving topography at finer resolutions or on running more simulations to account for various uncertain input factors (e.g., model parameters). In this paper we apply global sensitivity analysis to explore how influential the choice of spatial resolution is when compared to uncertainties in the Manning's friction coefficient parameters, the inflow hydrograph, and those stemming from the coarsening of topographic data used to produce Digital Elevation Models (DEMs). We apply the hydraulic model LISFLOOD-FP to produce several temporally and spatially variable model outputs that represent different aspects of flood inundation processes, including flood extent, water depth, and time of inundation. We find that the most influential input factor for flood extent predictions changes during the flood event, starting with the inflow hydrograph during the rising limb before switching to the channel friction parameter during peak flood inundation, and finally to the floodplain friction parameter during the drying phase of the flood event. Spatial resolution and uncertainty introduced by resampling topographic data to coarser resolutions are much more important for water depth predictions, which are also sensitive to different input factors spatially and temporally. Our findings indicate that the sensitivity of LISFLOOD-FP predictions is more complex than previously thought. Consequently, the input factors that modelers should prioritize will differ depending on the model output assessed, and the location and time of when and where this output is most relevant.

Impact of the Rainfall Duration and Temporal Rainfall Distribution Defined Using the Huff Curves on the Hydraulic Flood Modelling Results

“…Input hydrographs are one of the most important parameters that can have a significant impact on the hydraulic flood modelling results [23]. Savage et al [23] have shown that input hydrographs have a significant influence on modelling results, especially during rising limb of the hydrograph.…”

“…Input hydrographs are one of the most important parameters that can have a significant impact on the hydraulic flood modelling results [23]. Savage et al [23] have shown that input hydrographs have a significant influence on modelling results, especially during rising limb of the hydrograph. During peak discharge, the channel friction parameter has the largest impact, whereas during the recession part of the hydrograph, the floodplain friction parameter plays an important role.…”

Epistemic uncertainties and natural hazard risk assessment. 2. What should constitute good practice?

“…different probability distribution functions, imprecise probabilities or process representations (e.g. Hall, 2006;Baroni and Tarantola, 2014;Savage et al, 2016) or that allow us to explore the impact of distributions that are practically unconstrained due to a lack of observations (e.g. Prudhomme et al, 2010;Singh et al, 2014;Almeida et al, 2016).…”

“…However, in all cases it will be good practice to assess the sensitivity of the outcomes to a range of feasible assumptions (e.g. Saltelli, 2002;Tang et al, 2007;Saltelli et al, 2008;Pianosi et al, 2015;Savage et al, 2016).…”

A comprehensive sensitivity and uncertainty analysis for discharge and nitrate-nitrogen loads involving multiple discrete model inputs under future changing conditions

“…(Sobol, 1993) to identify the relevant system processes for groundwater flow and reactive transport represented in different model structures. Savage et al (2016) applied GSA to identify the dominant controls in the calculation of flood inundation, to assess whether a high spatial resolution of the flood inundation model or the model parametrization is dominating the simulation. The mentioned studies illustrate the use of GSA with discrete and complex model inputs.…”