Multi-hydro hydrological modelling of a complex peri-urban catchment with storage basins comparing C-band and X-band radar rainfall data

Souza, Bianca Alves de; Paz, Igor; Ichiba, Abdellah; Willinger, Bernard; Gires, Auguste; Amorim, José Carlos Cesar; Reis, Marcelo de Miranda; Tisserand, Bruno; Tchiguirinskaia, Ioulia; Schertzer, D.

doi:10.1080/02626667.2018.1520390

Cited by 16 publications

(8 citation statements)

References 43 publications

(45 reference statements)

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“…This therefore validates the whole catchment at this time, and not only the previous 60% of it, as was the case in Gires et al [25]. (Figure 7) was generated by considering only the priority rule [24,56]. The storm water sewer system (Figure 8) is discharged into the Bièvre River, which was also modelled as a pipe.…”

Section: Multi-hydro Description and Implementationsupporting

confidence: 71%

“…On the other hand, the mean singularity C 1 estimates are very similar for both the C-and X-band radar data per event, which would mean similar rainfall cumulus over the 64 km × 64 km area. However, the lower rainfall cumulus, especially for the last two events, for the X-band compared to the C-band radar data, might be related to a difference in linear pre-factors, as suggested by Alves de Souza et al [56].…”

Section: Multifractal Analysismentioning

confidence: 71%

“…The Jouy-en-Josas case study was modelled with a 10-m spatial resolution; the land use area ( Figure 7) was generated by considering only the priority rule [24,56]. The storm water sewer system (Figure 8) is discharged into the Bièvre River, which was also modelled as a pipe.…”

Section: Multi-hydro Description and Implementationmentioning

confidence: 99%

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Small-Scale Rainfall Variability Impacts Analyzed by Fully-Distributed Model Using C-Band and X-Band Radar Data

Paz

Willinger

Gires

et al. 2019

Water

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Recent studies have highlighted the need for high resolution rainfall measurements for better modelling of urban and peri-urban catchment responses. In this work, we used a fully-distributed model called “Multi-Hydro” to study small-scale rainfall variability and its hydrological impacts. The catchment modelled is a semi-urban area located in the southwest region of Paris, an area that has been previously partially validated. At this time, we make some changes to the model, henceforth using its drainage system globally, and we investigate the influence of small-scale rainfall variability by modelling three rainfall events with two different rainfall data inputs: the C-band radar data provided by Météo-France at a 1 km × 1 km × 5 min resolution, and the new X-band radar (recently installed at Ecole des Ponts, France) data at a resolution of 250 m × 250 m × 3.41 min, thereby presenting the gains of better resolution (with the help of Universal Multifractals). Finally, we compare the Multi-Hydro hydrological results with those obtained using an operational semi-distributed model called “Optim Sim” over the same area to revalidate Multi-Hydro modelling, and discuss the model’s limitations and the impacts of data quality and resolution, observing the difficulties associated with semi-distributed models when accounting the spatial variability of weather radar data. This work concludes that it may be useful in future to improve rainfall data acquisition, aiming for better spatio-temporal resolution (now achieved by the weather dual-polarized X-band radars) and data quality when considering small-scale rainfall variability, and to merge deterministic, fully-distributed and stochastic models into a hybrid model which would be capable of taking this small-scale rainfall variability into account.

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Section: Multi-hydro Description and Implementationsupporting

confidence: 71%

Section: Multifractal Analysismentioning

confidence: 71%

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Small-Scale Rainfall Variability Impacts Analyzed by Fully-Distributed Model Using C-Band and X-Band Radar Data

Paz

Willinger

Gires

et al. 2019

Water

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“…Traffic accidents have an adverse effect both on an individual and societal level resulting in costs due to person injuries and property damage, increased travel times, and emissions due to congestion. Indeed, the safety impact of weather conditions has been actively studied in different studies [26][27][28], which conclude that accident risks are significantly heightened during rainy, snowy and icy road conditions. According to Salli et al [29] the accident risk is more than four times higher for these conditions compared to bare road surface.…”

Section: Discussionmentioning

confidence: 99%

Disdrometer Performance Optimization for Use in Urban Settings Based on the Parameters that Affect the Measurements

et al. 2020

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There are currently different types of commercial optical disdrometers to measure the rainfall intensity, of which many are commonly used for monitoring road conditions. Having information about the amount of rain, the composition of the precipitation particles and visibility are essential to avoid accidents, which requires intelligent systems that warn drivers and redirect traffic. However, few studies related to Intelligent Transport Systems (ITS) have been performed regarding why these devices are not optimized for this type of applications. Therefore, this paper analyzes and evaluates the operating mode of these equipment through their theoretical model, which will allow the design of prototypes of disdrometers with different characteristics. In addition, this model will be implemented in a simulation program, through which an exhaustive study analyzing how the type of precipitation and its intensity affect the measures provided by the model will be conducted. In this way, the results will help optimize its operation to be thus used in urban settings, which will allow obtaining more accurate real-time information, better traffic management, and a reduction in the number of accidents.

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“…The Multi-Hydro model is a fully-distributed and physically-based hydrological model, which has been developed by HM&Co/ENPC (El Tabach et al, 2009;Giangola-Murzyn, 2013;Ichiba, 2016;Ichiba et al, 2018). It has been successfully implemented and validated in several catchments (e.g., Versini et al, 2016;Ichiba et al, 2017;Gires et al, 2017;Gires et al, 2018;Alves de Souza et al, 2018;Versini et al, 2018;Paz et al, 2019). In this study, it is used for assessing hydrological responses of the NBS scenarios at the urban catchment scale.…”

Section: Multi-hydro Modelmentioning

confidence: 99%

Space variability of hydrological responses of Nature-Based Solutions and the resulting uncertainty

Qiu¹,

Paz²,

Chen³

et al. 2020

Preprint

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Abstract. During the last decades, the urban hydrological cycle has been strongly modified by the built environment, resulting in fast runoff and increasing the risk of waterlogging. Nature-Based Solutions (NBS), which apply green infrastructures, have been more and more widely considered as a sustainable approach for urban stormwater management. However, the assessment of NBS performance still requires further modelling development because of their hydrological responses sensitively depends on the representation of multiscale space variability of both the rainfall and the NBS distribution. Indeed, we initially argue this issue with the help of the multifractal intersection theorem. To illustrate the importance of this question, the spatial heterogeneous distributions of two series of NBS scenarios (porous pavement, rain garden, green roof, and combined) are quantified with the help of their fractal dimension. We point out consequences of their estimates. Then, a fully-distributed and physically-based hydrological model (Multi-Hydro) was applied to consider the studied catchment and these NBS scenarios with a spatial resolution of 10 m under two different types of rainfall: distributed and uniform, and for three rainfall events. These simulations show that the impact of spatial variability of rainfall on the uncertainty of peak flow of NBS scenarios ranges from about 8 % to 17 %, which is more pronounced than those of the total runoff volume. In addition, the spatial variability of the rainfall intensity at the largest rainfall peak responds almost linearly to the uncertainty of the peak flow of NBS scenarios. However, the hydrological responses of NBS scenarios are less affected by the spatial distribution of NBS. Finally, the intersection effects of the spatial variability of rainfall and the spatial arrangement of NBS seem more pronounced for the peak flow of green roof scenarios and the total runoff volume of combined scenarios.

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Multi-hydro hydrological modelling of a complex peri-urban catchment with storage basins comparing C-band and X-band radar rainfall data

Cited by 16 publications

References 43 publications

Small-Scale Rainfall Variability Impacts Analyzed by Fully-Distributed Model Using C-Band and X-Band Radar Data

Small-Scale Rainfall Variability Impacts Analyzed by Fully-Distributed Model Using C-Band and X-Band Radar Data

Disdrometer Performance Optimization for Use in Urban Settings Based on the Parameters that Affect the Measurements

Space variability of hydrological responses of Nature-Based Solutions and the resulting uncertainty

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