Improving the use of ground-based radar rainfall data for monitoring and predicting floods in the Iguaçu river basin

•Applied an ensemble rainfall error model to radar-based rainfall fields.•Generated an ensemble of rainfall with improved statistical metrics.•The ensemble of rainfall was used as input in a hydrological model.•The use of the rainfall ensemble reduced the overestimation of streamflow volumes. This s...

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Bibliographic Details
Published in:	Journal of hydrology (Amsterdam) Vol. 567; pp. 626 - 636
Main Authors:	Falck, A.S., Maggioni, V., Tomasella, J., Diniz, F.L.R., Mei, Y., Beneti, C.A., Herdies, D.L., Neundorf, R., Caram, R.O., Rodriguez, D.A.
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01-12-2018
Subjects:	Flood event Radar rainfall Streamflow ensemble Uncertainties precipitation Radar rainfall Streamflow ensemble Flood event Uncertainties precipitation
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Summary:	•Applied an ensemble rainfall error model to radar-based rainfall fields.•Generated an ensemble of rainfall with improved statistical metrics.•The ensemble of rainfall was used as input in a hydrological model.•The use of the rainfall ensemble reduced the overestimation of streamflow volumes. This study investigates the efficiency of correcting radar rainfall estimates using a stochastic error model in the upper Iguaçu river basin in Southern Brazil for improving streamflow simulations. The 2-Dimensional Satellite Rainfall Error Model (SREM2D) is adopted here and modified to account for topographic complexity, seasonality, and distance from the radar. SREM2D was used to correct the radar rainfall estimates and produce an ensemble of equally probable rainfall fields, that were then used to force a distributed hydrological model. Systematic and random errors in simulated streamflow were evaluated for a cascade of sub-basins of the Iguaçu catchment, with drainage area ranging from 1,808 to 21,536 km2). Results showed an improvement in the statistical metrics when the SREM2D ensemble was used as input to the hydrological model in place of the radar rainfall estimates in most sub-basins. Specifically, SREM2D was able to remove the relative bias (up to 50%) in the radar rainfall dataset regardless of the basin dimension, whereas the random error was reduced more prominently in the larger basins (up to 100 m3 s−1). An event scale evaluation was also performed for nine selected flood events in three sub-basins. SREM2D reduced the overestimation in the cumulative rainfall and streamflow volumes during these events.
ISSN:	0022-1694 1879-2707
DOI:	10.1016/j.jhydrol.2018.10.046