[1] The goal of this study is to diagnose the manner in which radar-rainfall input affects peak flow simulation uncertainties across scales. We used the distributed physically based hydrological model CUENCAS with parameters that are estimated from available data and without fitting the model output to discharge observations. We evaluated the model's performance using (1) observed streamflow at the outlet of nested basins ranging in scale from 20 to 16,000 km 2 and (2) streamflow simulated by a well-established and extensively calibrated hydrological model used by the US National Weather Service (SAC-SMA). To mimic radar-rainfall uncertainty, we applied a recently proposed statistical model of radar-rainfall error to produce rainfall ensembles based on different expected error scenarios. We used the generated ensembles as input for the hydrological model and summarized the effects on flow sensitivities using a relative measure of the ensemble peak flow dispersion for every link in the river network. Results show that peak flow simulation uncertainty is strongly dependent on the catchment scale. Uncertainty decreases with increasing catchment drainage area due to the aggregation effect of the river network that filters out small-scale uncertainties. The rate at which uncertainty changes depends on the error structure of the input rainfall fields. We found that random errors that are uncorrelated in space produce high peak flow variability for small scale basins, but uncertainties decrease rapidly as scale increases. In contrast, spatially correlated errors produce less scatter in peak flows for small scales, but uncertainty decreases slowly with increasing catchment size. This study demonstrates the large impact of scale on uncertainty in hydrological simulations and demonstrates the need for a more robust characterization of the uncertainty structure in radar-rainfall. Our results are diagnostic and illustrate the benefits of using the calibrationfree, multiscale framework to investigate uncertainty propagation with hydrological models.Citation: Cunha, L. K., P. V. Mandapaka, W. F. Krajewski, R. Mantilla, and A. A. Bradley (2012), Impact of radar-rainfall error structure on estimated flood magnitude across scales: An investigation based on a parsimonious distributed hydrological model, Water Resour. Res., 48, W10515,

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We present a diagnostic framework to assess changes in flood risk across multiple scales in a river network, under nonstationary conditions or in the absence of historical hydro‐meteorological data. The framework combines calibration‐free hydrological and hydraulic models with urban development information to demonstrate altered flood risk. Our models utilize hydraulic geometric data and high‐resolution remote‐sensing information provided on a nearly global basis. The need for calibration is eliminated because model parameters are directly related to the physical properties of the system. We apply the methodology in a case study for Mecklenburg County, NC, in which we assess the effects of land cover changes on flood frequency. We obtained maps of expected inundated zones under different conditions of land cover and storm return periods and compared them with the 100‐year return period inundation maps developed by Federal Emergency Management Agency that are based on more complex hydraulic models. The close agreement supports our framework's applicability and generality.

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