@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 = "16 jun. 2024"
}