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

Abstract

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.

The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review

“…For instance, Thorndahl et al [18], in their review, identified a reduced need of high resolution radar rainfall for bigger urban catchments. As illustration, a comparison of the use of C-band and X-band radar data as rainfall inputs for rainfall-runoff models was performed by Paz et al [198] in an urbanized catchment (3 km 2 ) close to Paris. Results pointed to a better representation of X-band radar rainfall with a spatial resolution of 250 × 250 m 2 at 3.41 min frequency in contrast to the 1 × 1 km 2 spatial resolution of the C-band radar data at 5 min frequency.…”

“…The novelty of these scaling factors is that they allow the identification of the needed rainfall resolution in order to reach a given level of accuracy in model performance. Most studies (e.g., Anagnostou et al [199]; Paz et al [198]) in the literature have performed an evaluation of the impact of the radar rainfall resolution (i.e., spatial and temporal) in the hydrological response over a specific catchment, which impedes the generalization of their results. In this context, it is still difficult to assess if the findings in these studies respond to the size of the catchment, the variability of the rainfall event in time and space, the particularities of the terrain, etc.…”

Space variability impacts on hydrological responses of nature-based solutions and the resulting uncertainty: a case study of Guyancourt (France)

“…D F has been often used in catchment hydrology (e.g. Schertzer and Lovejoy, 1984, 1991Lavallée et al, 1993;Gires et al, 2013Gires et al, , 2017Ichiba et al, 2018;Paz et al, 2020;Versini et al, 2020). In this study, a standard box-counting technique was applied to estimate the D F of each NBS scenario (Hentschel and Procaccia, 1983;Lovejoy et al, 1987).…”

“…More precisely, the smaller D R is with respect to c N , the lesser extent to which the real field R is measured. Let us mention that Paz et al (2020) used this intersection theorem to determine when the adjustment of radar data by a rain gauge network becomes misleading instead of improving the data. The assessment of the performance of an NBS network cannot be reduced to the binary question of the presence of an NBS or not, like done for a rain gauge of a network.…”

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

“…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.…”