@Article{TomasellaGFCDRPNMS:2019:EfBaSc,
author = "Tomasella, Javier and Gon{\c{c}}alves, A. Sene and Falck, Aline
Schneider and Caram, R. Oliveira and Diniz, F{\'a}bio Luiz
Rodrigues and Rodriguez, Daniel Andres and Prado, Maria
Cec{\'{\i}}lia Rdorigues do and Negr{\~a}o, Anne Caroline and
Medeiros, Gustavo Sueiro and Siqueira, Gracielle Chagas",
affiliation = "{Centro Nacional de Monitoramento e Alertas de Desastres Naturais
(CEMADEN)} and {Centro Nacional de Monitoramento e Alertas de
Desastres Naturais (CEMADEN)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Centro Nacional de Monitoramento e Alertas
de Desastres Naturais (CEMADEN)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "Probabilistic flood forecasting in the Doce Basin in Brazil:
Effects of the basin scale and orientation and the spatial
distribution of rainfall",
journal = "Journal of Flood Risk Management",
year = "2019",
volume = "12",
number = "1",
pages = "e12452",
month = "mar.",
keywords = "extreme events, forecasting and warning, natural flood
management.",
abstract = "We critically examined the performance of probabilistic streamflow
forecasting inthe prediction of flood events in 19 subbasins of
the Doce River in Brazil using theEta (4 members, 5 km spatial
resolution) and European Centre for Medium-RangeWeather Forecasts
(ECMWF; 51 members, 32 km resolution) weather forecastmodels as
inputs for the MHD-INPE hydrological model. We observed that
theshapes and orientations of subbasins influenced the
predictability of floods due tothe orientation of rainfall events.
Streamflow forecasts that use the ECMWF dataas input showed higher
skill scores than those that used the Eta model for subbasinswith
drainage areas larger than 20,000 km2. Since the skill scores were
similar forboth models in smaller subbasins, we concluded that the
grid size of the weathermodel could be important for smaller
catchments, while the number of memberswas crucial for larger
scales. We also evaluated the performance of
probabilisticstreamflow forecasting for the severe flood event of
late 2013 through a compari-son of observations and streamflow
estimations derived from interpolated rainfallfields. In many
cases, the mean of the ensemble outperformed the streamflow
esti-mations from the interpolated rainfall because the spatial
structure of a rainfallevent is better captured by weather
forecast models.",
doi = "10.1111/jfr3.12452",
url = "http://dx.doi.org/10.1111/jfr3.12452",
issn = "1753-318X",
language = "en",
targetfile = "tomasella_probabilistic.pdf",
urlaccessdate = "27 abr. 2024"
}