Temporal and spatial variability of rainfall at the urban hydrological scale

Emmanuel, Isabelle; Andrieu, Hervé; Leblois, Étienne; Flahaut, Bernard

doi:10.1016/j.jhydrol.2012.02.013

Cited by 116 publications

(100 citation statements)

References 22 publications

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“…Based on this method, Berne et al [10] proposed the relationship between the required characteristic scales of rainfall and urban catchments, and then derived the required rainfall resolution for urban hydrological applications. Emmanuel et al [11] summarized the characteristic scales of rainfall of different types. Also, stochastic rainfall generator and statistics downscaling methods were widely used to obtain high temporal resolution rainfall series [12][13][14][15][16].…”

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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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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“…Weather radars are remote sensing devices providing comprehensive images of rain fields at the regional scale (coverage up to about 200km from the radar device), with a high resolution (in the present case, 10 min in time and 1km in space). The resulting rain rate estimates are known to be biased (Berndt et al, 2014), but in counterpart radar images are currently the most reliable and exhaustive source of information about the spatial structure of rain fields and their temporal evolution (Emmanuel et al, 2012;Thorndahl et al, 2017;Marra and Morin, 2018): this is why radar images are used 25 in the current section to illustrate rainfall structure, and in the following to extract rainfall space-time statistics.…”

Section: Overview Of Rainfall Space-time Patterns Observed In Radar Imentioning

confidence: 99%

“…-Well defined spatial patterns (Guillot, 1999;Emmanuel et al, 2012;Marra and Morin, 2018), which can be linked to the processes responsible for rainfall production such as rain cells, rain bands or rain storms. -A temporal behavior shaped by the advection and diffusion of spatial patterns along time (Lepioufle et al, 2012;Creutin et al, 2015).…”

Section: Overview Of Rainfall Space-time Patterns Observed In Radar Imentioning

confidence: 99%

“…(Leblois and Creutin, 2013;Paschalis et al, 2013;Benoit et al, In Press). At such scales, not only the marginal distribution of observed rain intensity matters, but the space-time dependencies within rain fields are also important features of the rain process (Emmanuel et al, 2012;Marra and Morin, 2018). In particular, the impact of the advection and diffusion of spatial rainfall patterns (e.g.…”

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confidence: 99%

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Dealing with non-stationarity in sub-daily stochastic rainfall models

Benoît¹,

Vrac²,

Mariethoz³

2018

Preprint

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Abstract.Understanding the stationarity properties of rainfall is critical when using stochastic weather generators. Rainfall stationarity means that the statistics being accounted for remain constant over a given period, which is required for both inferring model parameters and simulating synthetic rainfall. Despite its critical importance, the stationarity of precipitation statistics is often regarded as a subjective choice whose examination is left to the judgement of the modeler. It is therefore desirable to establish 5 quantitative and objective criteria for defining stationary rain periods. To this end, we propose a methodology that automatically identifies rain types with homogeneous statistics. It is based on an unsupervised classification of the space -time -intensity structure of weather radar images. The transitions between rain types are interpreted as non-stationarities.Our method is particularly suited to deal with non-stationarity in the context of sub-daily stochastic rainfall models. Results of a synthetic case study show that the proposed approach is able to reliably identify synthetically generated rain types. The 10 application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm. This highlights the need for a careful examination of the temporal stationarity of precipitation statistics when modelling rainfall at high resolution.

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“…Rainfall variability in space and time influences the hydrological response, especially in urban areas, where hydrological response is fast and flow peaks are high (Fabry et al, 1994;Faures et al, 1995;Smith et al, 2002;Emmanuel et al, 2012;15 Gires et al, 2012;Smith et al, 2012;Ochoa-Rodriguez et al, 2015;Thorndahl et al, 2017). Finding a proper match between rainfall resolution and hydrological model structure and complexity is important for reliable flow prediction (Berne et al, 2004;Ochoa-Rodriguez et al, 2015;Pina et al, 2016;Rafieeinasab et al, 2015;Yang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Critical scales to explain urban hydrological response

Cristiano

Veldhuis

Gaitan

et al. 2018

Preprint

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Abstract. Rainfall variability in space and time, in relation to catchment characteristics and model complexity, plays an important role in explaining the sensitivity of hydrological response in urban areas. In this work we present a new approach to classify rainfall variability in space and time and we use this classification to investigate rainfall aggregation effects on urban hydrological response. Nine rainfall events, measured with a Dual polarimetric X-Band radar at the CAESAR Site (Cabauw Experimental Site for Atmospheric Research, NL), were aggregated in time and space in order to obtain different resolution 5 combinations. The aim of this work was to investigate the influence that rainfall and catchment scales have on hydrological response in urban areas. Three dimensionless scaling factors were introduced to investigate the interactions between rainfall and catchment scale and rainfall input resolution in relation to the performance of the model. Results showed that (1) rainfall classification based on cluster identification well represents the storm core, (2) aggregation effects are stronger for rainfall than flow, (3) model complexity does not have a strong influence compared to catchment and rainfall scales for this study 10 case, (4) scaling factors allow to select the adequate rainfall resolution to obtain a given level of accuracy in the calculation of hydrological response.

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Temporal and spatial variability of rainfall at the urban hydrological scale

Cited by 116 publications

References 22 publications

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

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

Dealing with non-stationarity in sub-daily stochastic rainfall models

Critical scales to explain urban hydrological response

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