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@Article{FalckMTDMBHNCR:2018:ImUsGr,
               author = "Falck, A. S. and Maggioni, Viviana and Tomasella, Javier and 
                         Diniz, F{\'a}bio Luis Rodrigues and Mei, Y. and Beneti, C. A. and 
                         Herdies, Dirceu Luis and Neundorf, R. and Caram, R. O. and 
                         Rodriguez, Daniel Andr{\'e}s",
          affiliation = "{George Mason University} and {George Mason University} and 
                         {Centro Nacional de Monitoramento e Alertas de Desastres Naturais 
                         (CEMADEN)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {George Mason University} and {Sistema Meteorol{\'o}gico do 
                         Paran{\'a} (SIMEPAR)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Sistema Meteorol{\'o}gico do Paran{\'a} 
                         (SIMEPAR)} and {Centro Nacional de Monitoramento e Alertas de 
                         Desastres Naturais (CEMADEN)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Improving the use of ground-based radar rainfall data for 
                         monitoring and predicting floods in the igua{\c{c}}u river 
                         basin",
              journal = "Journal of Hydrology",
                 year = "2018",
               volume = "567",
                pages = "626--636",
             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 
                         Igua{\c{c}}u 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 Igua{\c{c}}u 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.",
                  doi = "10.1016/j.jhydrol.2018.10.046",
                  url = "http://dx.doi.org/10.1016/j.jhydrol.2018.10.046",
                 issn = "0022-1694",
                label = "lattes: 3752951275341381 7 FalckMTDMBHNCR:2018:ImUsGr",
             language = "en",
           targetfile = "falck_improving.pdf",
        urlaccessdate = "04 dez. 2020"
}


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