The effects of spatial variability of rainfall, geomorphology, and climatology of precipitation and temperature on the hydrologic response remain poorly understood. This study characterizes the catchment response in terms of a variable called flashiness, that describes the severity of the flood response as the rate of rise of the unit discharge. It overcomes limitations of prior works based on limited case studies or simulations by gathering information on basins of widely varying characteristics and by using a high‐resolution rainfall and flooding event data set spanning 10 years over the Continental United States. The objective is to develop a robust understanding of how rainfall spatial variability influences flash flood severity and to assess its contribution relative to basin physiography and climatology. This study explores the first‐order dependencies as well as the variability in these relationships and investigates the complex interactions using a multi‐dimensional statistical modeling approach. The results confirm that the spatial organization of rainfall influences the basin response on par with geomorphology and climatology. Basin physiography dampens the effect of lower rainfall intensities, while higher rainfall overwhelms other factors and primarily contributes to flashiness. Dispersion of precipitation with respect to the flow path decreases flood severity. An improved understanding of sub‐basin scale rainfall spatial variability aids in developing a robust flash flood severity index to identify and mitigate flash flooding situations as well as identifying basins which could most benefit from distributed hydrologic modeling.

Sewer systems affect urban soil characteristics and subsoil water flow. The direct connection observed between baseflow in sewer systems under drainage infiltrations and piezometric levels influences the hydrological behavior of urban catchments, and must consequently be considered in the hydrologic modeling of urban areas. This research studies the groundwater contribution to sewer networks by first characterizing the phenomenon using experimental data recorded on a small urban catchment in Nantes (France). Then, the model MODFLOW was used to simulate the infiltration of groundwater into a sewer network and model dry weather flows at an urban catchment scale. This application of MODFLOW requires representing, in a simplified way, the interactions between the soil and the sewer trench, which acts as a drain. Observed average groundwater levels were satisfactorily simulated by the model while the baseflow dynamics is well reproduced. Nonetheless, soil parameters resulted to be very sensitive, and achieving good results for joint groundwater levels and baseflow was not possible.

A method that formulates the retrieval of drop size distribution (DSD) parameters from polarimetric radar variables at attenuating frequency as the solution of an inverse problem is presented. The DSD in each radar bin is represented by a normalized Gamma distribution defined by three parameters ($D_m,N_0^{*},\mu$). The direct problem that describes polarimetric radar observables—scattering and propagation terms—and their dependency on DSD parameters is analyzed based on T-matrix scattering simulations. The inverse algorithm and its application to the DSD retrieval are then presented. The inverse method is applied to an African Monsoon Multidisciplinary Analysis (AMMA) field campaign that deployed an X-band dual-polarization Doppler radar and optical disdrometers in Benin, West Africa, in 2006 and 2007. The dataset is composed of X-band polarimetric radar PPIs and disdrometer data for 15 organized convective systems observed in 2006. A priori information on DSD parameters (benchmark method) is derived from the polarimetric radar observables by applying power law relationships. The proposed retrieval method of DSD parameters leads to the following results as compared to the benchmark: (i) we found a better spatial consistency of the retrieved parameters, (ii) the reconstructed polarimetric radar observables are closer to the observations, (iii) The validation with disdrometer data confirms an improved estimation of the DSD parameters.