Contents lists available at ScienceDirect Journal of Hydrology journal homepage: www.elsevier.com/locate/jhydrol Research papers Improving the use of ground-based radar rainfall data for monitoring and predicting floods in the Iguaçu river basin A.S. Falck a,b, ⁎ , V. Maggioni a , J. Tomasella b , F.L.R. Diniz c , Y. Mei a , C.A. Beneti d , D.L. Herdies c , R. Neundorf d , R.O. Caram b , D.A. Rodriguez e a Sid and Reva Dewberry Dept. of Civil, Environmental and Infrastructure Engineering, George Mason University, USA b National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), Brazil c Center for Weather Forecast and Climate Studies (CPTEC/INPE), Brazil d Paraná Meteorological System (SIMEPAR), Brazil e Earth System Science Center (CCST/INPE), Brazil ARTICLE INFO This manuscript was handled by Marco Borga, Editor-in-Chief, with the assistance of Francesco Marra, Associate Editor Keywords: Radar rainfall Streamflow ensemble Uncertainties precipitation Flood event ABSTRACT 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, season- ality, 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 km 2 ). 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 pro- minently in the larger basins (up to 100 m 3 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. 1. Introduction Minimizing the loss of human lives and mitigating socio-economic impacts associated with severe flooding depend on the ability to issue warnings with sufficient lead-times to enable preemptive mitigation actions. Flood prediction poses scientific and operational challenges to natural disasters centers mainly due to the difficulties in monitoring rainfall that directly impacts streamflow model simulations. These is- sues are aggravated in basins with short response times (few hours), where forecasting systems need to combine meteorological and hy- drological input at fine temporal and spatial scales (Caseri et al., 2016). Located in southern Brazil, the Iguaçu river basin has a long history of severe floods with significant socio-economic impacts (Garcia, 2016). Land use (deforestation) and climate changes in recent decades have contributed to worsen the effects of floods in the region. In addition, the disordered development of riverine areas and the increased land surface impermeability, mainly in the basin headwater areas where large urban centers are located, amplified both frequency and magnitude of flash floods (Pisani and Bruna, 2011). A recent survey of the Brazilian Water National Agency has identified five areas that are highly vulnerable to floods in the Iguaçu river basin (ANA, 2014; Fig. 1). The use of hydrological models in flood monitoring largely depends on the reliability and availability of real-time precipitation input data. This is due the fact that the quality of hydrological forecasts is strongly dependent on the initial moisture conditions in the basin, which is mostly determined by antecedent rainfall events. Due to their fine spatial and temporal resolution, ground radar precipitation estimates represent a viable option for monitoring and forecasting flood hazards. However, these estimates are affected by errors due to unwanted echoes from the local topography and the conversion of reflectivity into pre- cipitation rate (i.e., Z-R relationship), among others (Anagnostou et al., 1999, 2010, 2017, 2018). Despite several corrections in the calibration of the Z-R relationship and attempts to assess these uncertainties, error residuals are still present in the final radar precipitation products. For https://doi.org/10.1016/j.jhydrol.2018.10.046 Received 27 May 2018; Received in revised form 3 September 2018; Accepted 18 October 2018 ⁎ Corresponding author at: Sid and Reva Dewberry Dept. of Civil, Environmental and Infrastructure Engineering, George Mason University, USA. E-mail addresses: asfalck@gmail.com, aline.falck@cemaden.gov.br (A.S. Falck). Journal of Hydrology 567 (2018) 626–636 Available online 21 October 2018 0022-1694/ © 2018 Elsevier B.V. All rights reserved. T