Toward high-resolution flash flood prediction in large urban areas – Analysis of sensitivity to spatiotemporal resolution of rainfall input and hydrologic modeling

Rafieeinasab, A.; Norouzi, Amir Mohammad; Kim, Sunghee; Habibi, Hamideh; Nazari, Behzad; Seo, Dong Jun; Lee, Haksu; Cosgrove, B.; Cui, Zhengtao

doi:10.1016/j.jhydrol.2015.08.045

Cited by 82 publications

(67 citation statements)

References 51 publications

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“…An important question concerns how the peak discharge and the volume of a flood depend on the intensity and track of the triggering precipitation events, i.e. the spatio-temporal pattern of precipitation (Adams et al, 2012;Bruni et al, 2015;Cristiano et al, 2017;Emmanuel et al, 2015Emmanuel et al, , 2016OchoaRodriguez et al, 2015;Paschalis et al, 2014;Rafieeinasab et al, 2015;Zhang and Han, 2017). In addition to the storm track dynamics, the peak flow depends on the watershed characteristics (Singh, 1997).…”

Section: Introductionmentioning

confidence: 99%

Effects of variability in probable maximum precipitation patterns on flood losses

Zischg

Felder

Weingartner

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. The assessment of the impacts of extreme floods is important for dealing with residual risk, particularly for critical infrastructure management and for insurance purposes. Thus, modelling of the probable maximum flood (PMF) from probable maximum precipitation (PMP) by coupling hydrological and hydraulic models has gained interest in recent years. Herein, we examine whether variability in precipitation patterns exceeds or is below selected uncertainty factors in flood loss estimation and if the flood losses within a river basin are related to the probable maximum discharge at the basin outlet. We developed a model experiment with an ensemble of probable maximum precipitation scenarios created by Monte Carlo simulations. For each rainfall pattern, we computed the flood losses with a model chain and benchmarked the effects of variability in rainfall distribution with other model uncertainties. The results show that flood losses vary considerably within the river basin and depend on the timing and superimposition of the flood peaks from the basin's sub-catchments. In addition to the flood hazard component, the other components of flood risk, exposure, and vulnerability contribute remarkably to the overall variability. This leads to the conclusion that the estimation of the probable maximum expectable flood losses in a river basin should not be based exclusively on the PMF. Consequently, the basin-specific sensitivities to different precipitation patterns and the spatial organization of the settlements within the river basin need to be considered in the analyses of probable maximum flood losses.

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Section: Introductionmentioning

confidence: 99%

Effects of variability in probable maximum precipitation patterns on flood losses

Zischg

Felder

Weingartner

et al. 2018

Hydrol. Earth Syst. Sci.

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“…For instance, Ochoa-Rodriguez et al [5] studied 7 urban catchments of areas between 3 and 8 km 2 and used 16 different combinations of TRR and SRR to investigate their effects on simulated flow. Rafieeinasab et al [18] studied 5 urban catchments with sizes varying from 3.4 to 54.6 km 2 and assessed the sensitivity of hydrological simulation to the temporal and spatial resolutions of precipitation and hydrologic modelling. Peleg et al [19] distinguished the effect of TRR and SRR with the help of a stochastic spatially distributed rainfall generator.…”

Section: Introductionmentioning

confidence: 99%

Effect of Temporal Resolution of Rainfall on Simulation of Urban Flood Processes

Lyu

Cao

et al. 2018

Water

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Rainfall exhibits substantial variability, and its temporal resolution considerably affects simulation of hydrological processes. This study aims to investigate the effect of the temporal resolution of rainfall (TRR) on urban flood modeling and to explore how high TRR is required. A routing-enhanced detailed urban stormwater (REDUS) model, which has four layers and accounts for complex urban flow paths, was developed and then applied to the campus of Tsinghua University, Beijing, China. For 30 rainfall events at 1-min resolution, the rainfall accuracy index (RAI) was used to describe the discrepancy of rainfall patterns by upscaling. Through hydrodynamic modelling, the effect of TRR was quantified by the relative error of flood volume and peak in typical areas. The results show that (1) flood peak is sensitive to TRR while flood volume is generally not; (2) with lower TRR, discharge peak is underestimated, and a power function is proposed to express the relationship between the effect of TRR and the characteristics of rainfall and underlying surfaces; and (3) rainfall data of 5-min resolution for urban areas smaller than 1 km 2 , or at least 15-min resolution for larger areas, are required to constrain the relative biases of flood peak within 10%.

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“…Previous studies have shown that a dependence exists between spatial and temporal resolution of rainfall inputs and model simulation results (Rafieeinasab et al, 2015;Ochoa-Rodriguez et al, 2015;Gires et al, 2015;Ichiba, 2016). Both rainfall phenomena and hydrological processes exhibit scale dependence, both with respect to their spatial and temporal resolution.…”

Section: Interactions Between Spatial and Temporal Resolutionmentioning

confidence: 98%

“…Mismatch between rainfall input resolution and model resolution: in this study, uniform rainfall input (rain gauge data) was applied to the catchment in all model simulations. Numerous authors have shown that model performance is strongly dependent on rainfall input resolution (Rafieeinasab et al, 2015;Ochoa-Rodriguez et al, 2015;Gires et al, 2015;Ichiba, 2016). Nevertheless, the aim of this study was to investigate the sensitivity of model performance to model resolution independently of rainfall resolution; therefore uniform rainfall was purposely input to the model.…”

Section: Specific Modelling Issues At Small Scalesmentioning

confidence: 99%

Scale effect challenges in urban hydrology highlighted with a distributed hydrological model

Ichiba

Gires

Tchiguirinskaia

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Hydrological models are extensively used in urban water management, development and evaluation of future scenarios and research activities. There is a growing interest in the development of fully distributed and grid-based models. However, some complex questions related to scale effects are not yet fully understood and still remain open issues in urban hydrology. In this paper we propose a twostep investigation framework to illustrate the extent of scale effects in urban hydrology. First, fractal tools are used to highlight the scale dependence observed within distributed data input into urban hydrological models. Then an intensive multi-scale modelling work is carried out to understand scale effects on hydrological model performance. Investigations are conducted using a fully distributed and physically based model, Multi-Hydro, developed at Ecole des Ponts ParisTech. The model is implemented at 17 spatial resolutions ranging from 100 to 5 m. Results clearly exhibit scale effect challenges in urban hydrology modelling. The applicability of fractal concepts highlights the scale dependence observed within distributed data. Patterns of geophysical data change when the size of the observation pixel changes. The multi-scale modelling investigation confirms scale effects on hydrological model performance. Results are analysed over three ranges of scales identified in the fractal analysis and confirmed through modelling. This work also discusses some remaining issues in urban hydrology modelling related to the availability of high-quality data at high resolutions, and model numerical instabilities as well as the computation time requirements. The main findings of this paper enable a replacement of traditional methods of "model calibration" by innovative methods of "model resolution alteration" based on the spatial data variability and scaling of flows in urban hydrology.

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Toward high-resolution flash flood prediction in large urban areas – Analysis of sensitivity to spatiotemporal resolution of rainfall input and hydrologic modeling

Cited by 82 publications

References 51 publications

Effects of variability in probable maximum precipitation patterns on flood losses

Effects of variability in probable maximum precipitation patterns on flood losses

Effect of Temporal Resolution of Rainfall on Simulation of Urban Flood Processes

Scale effect challenges in urban hydrology highlighted with a distributed hydrological model

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