Impact of radar‐rainfall error structure on estimated flood magnitude across scales: An investigation based on a parsimonious distributed hydrological model

Abstract

[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,

Analyzing the effects of excess rainfall properties on the scaling structure of peak discharges: Insights from a mesoscale river basin

“…This means that the drainage network, which plays a fundamental role in translating, aggregating, and attenuating streamflow across scales, is central to the simulation framework. The model is shown to adequately reproduce observed streamflow time series [Ayalew et al, 2014b;Cunha et al, 2012] that are For any hillslope-channel-link unit, which is used as a control volume, the mass conservation equation can be written as…”

“…The availability of the radar-rainfall product limits our analysis to the 12 year period between 2002 and 2013. The radar-rainfall product has a spatial resolution of 4 3 4 km and a temporal resolution of 1 h. The product, which is provided nationally on the Hydrologic Rainfall Analysis Project (HRAP) grid, is extensively used with good success for hydrologic modeling purposes in the Iowa River basin and elsewhere [e.g., Ayalew et al, 2014aAyalew et al, , 2014bCunha et al, 2012;Kalin and Hantush, 2006].…”

A Time-Based Framework for Evaluating Hydrologic Routing Methodologies Using Wavelet Transform

“…The second routing component we evaluate in this study is the one implemented in the Hillslope-Link hydrologic Model (HLM) developed and used by the Iowa Flood Center (IFC). This routing component is nonlinear and accounts for the momentum equation in a simplified form ( [6] [7] and [8]), and the stream velocity is determined based on the nonlinear relationship between the discharge and the served area ( [9] [10]). Similar to [1] [11] and [5] we do not account in this study for lateral flows to the channel in our calculation for the sake of simplicity and since our sub-catchment sizes are small.…”

Radar–rain-gauge rainfall estimation for hydrological applications in small catchments

“…However, meteorological radar exhibits some disadvantages, namely the calibration of the Z-R relationship, whose parameters change from one area to another and depend upon the storm typology, and the absence of a single method universally valid for calibration (Scofield and Kuligowski, 2003). Other important error sources in radar rainfall estimation are ground clutter, rain-induced attenuation, bright-band contamination, beam broadening and anomalous propagation (Borga, 2002;Cunha et al, 2012;Berne and Krajewski, 2013;Park et al, 2016;van de Beek et al, 2016). The presence of complex topography can further amplify some of these uncertainties, and mountainous obstructions can significantly reduce the radar coverage and its monitoring capabilities (Young et al, 1999;Montopoli et al, 2017).…”