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

Speaker: Luciana Cunha, Research Associate
Series: EEWR Brown Bag Seminars
Location: Engineering Quad E225
Date/Time: Friday, October 26, 2012, 12:00 p.m. - 1:00 p.m.


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 physicallybased hydrological model, CUENCAS, with parameters estimated from readily available data and without fitting model output to discharge observations. We need to avoid calibrating the model’s parameters to discharge data because such fitting would compensate for the uncertainty due to the rainfall input and thereby compromise our objectives. To mimic radar-rainfall uncertainty, we applied a recently proposed statistical model of radar-rainfall error structure to produce input ensembles of 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. In particular, we found that random errors that are uncorrelated in space produce high peak flow variability for small scales basins, but uncertainties decrease rapidly as drainage area increases. In contrast, spatially correlated errors produce less scatter in peak flows for small scales, but uncertainty reductions are slower 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.