Influence of rainfall spatial variability on rainfall–runoff modelling: Benefit of a simulation approach?

Emmanuel, Isabelle; Andrieu, Hervé; Leblois, Étienne; Janey, Nicolas; Payrastre, Olivier

doi:10.1016/j.jhydrol.2015.04.058

Cited by 85 publications

(77 citation statements)

References 41 publications

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“…However, in mountain areas without lakes, the effects of spatio-temporal variability in precipitation patterns on flood losses may be even more accentuated. However, a modelling approach is needed to analyse these effects as stated by Emmanuel et al (2015).…”

Section: Discussionmentioning

confidence: 99%

“…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%

“…Ochoa-Rodriguez et al (2015) also stated that the temporal variation of rainfall inputs affects hydrodynamic modelling results remarkably. Emmanuel et al (2015) showed that the spatio-temporal organization of rainfall plays an important role in the discharge at the outlet of the catchment and stated that a simulation approach is needed to study the effects of rainfall variability in complex river basins. The effects vary with the catchment size and its characteristics.…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effects of variability in probable maximum precipitation patterns on flood losses

Zischg

Felder

Weingartner

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…They found that storm motion did not play a significant role in generating hydrologic response for a large storm event, in basins sized 8-623 km 2 . Emmanuel et al (2015) investigated the impacts of spatial rainfall variability on hydrological response using a model simulation approach and found significant dispersion in results obtained for events for different simulation scenarios, showing the need for studying larger sets of events to derive robust general conclusions. Modeling studies reported in the literature have remained inconclusive with respect to the interactions between rainfall and catchment scales (Ogden et al, 2011;Morin et al, 2006;Nicotina et al, 2008;Rafieeinasab et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

The role of storm scale, position and movement in controlling urban flood response

Veldhuis

Zhou

Yang

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. The impact of spatial and temporal variability of rainfall on hydrological response remains poorly understood, in particular in urban catchments due to their strong variability in land use, a high degree of imperviousness and the presence of stormwater infrastructure. In this study, we analyze the effect of storm scale, position and movement in relation to basin scale and flow-path network structure on urban hydrological response. A catalog of 279 peak events was extracted from a high-quality observational dataset covering 15 years of flow observations and radar rainfall data for five (semi)urbanized basins ranging from 7.0 to 111.1 km 2 in size. Results showed that the largest peak flows in the event catalog were associated with storm core scales exceeding basin scale, for all except the largest basin. Spatial scale of flood-producing storm events in the smaller basins fell into two groups: storms of large spatial scales exceeding basin size or small, concentrated events, with storm core much smaller than basin size. For the majority of events, spatial rainfall variability was strongly smoothed by the flowpath network, increasingly so for larger basin size. Correlation analysis showed that position of the storm in relation to the flow-path network was significantly correlated with peak flow in the smallest and in the two more urbanized basins. Analysis of storm movement relative to the flow-path network showed that direction of storm movement, upstream or downstream relative to the flow-path network, had little influence on hydrological response. Slow-moving storms tend to be associated with higher peak flows and longer lag times. Unexpectedly, position of the storm relative to impervious cover within the basins had little effect on flow peaks. These findings show the importance of observation-based analysis in validating and improving our understanding of interactions between the spatial distribution of rainfall and catchment variability.

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“…These models act as filters, that is, they can smooth the influence of temporal and spatial variability of rainfall forcing [27]. Due to the different degrees of the "smooth effect", the varying sensitivity of models to rainfall inputs lead to different evaluation results of the effects of TRR.…”

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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Influence of rainfall spatial variability on rainfall–runoff modelling: Benefit of a simulation approach?

Cited by 85 publications

References 41 publications

Effects of variability in probable maximum precipitation patterns on flood losses

Effects of variability in probable maximum precipitation patterns on flood losses

The role of storm scale, position and movement in controlling urban flood response

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

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