%0 Journal Article
%4 sid.inpe.br/mtc-m21c/2019/03.01.17.16
%2 sid.inpe.br/mtc-m21c/2019/03.01.17.16.33
%@doi 10.1111/jfr3.12452
%@issn 1753-318X
%T Probabilistic flood forecasting in the Doce Basin in Brazil: Effects of the basin scale and orientation and the spatial distribution of rainfall
%D 2019
%8 mar.
%9 journal article
%A Tomasella, Javier,
%A Gonçalves, A. Sene,
%A Falck, Aline Schneider,
%A Caram, R. Oliveira,
%A Diniz, Fábio Luiz Rodrigues,
%A Rodriguez, Daniel Andres,
%A Prado, Maria Cecília Rdorigues do,
%A Negrão, Anne Caroline,
%A Medeiros, Gustavo Sueiro,
%A Siqueira, Gracielle Chagas,
%@affiliation Centro Nacional de Monitoramento e Alertas de Desastres Naturais (CEMADEN)
%@affiliation Centro Nacional de Monitoramento e Alertas de Desastres Naturais (CEMADEN)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Centro Nacional de Monitoramento e Alertas de Desastres Naturais (CEMADEN)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress javier.tomasella@cemaden.gov.br
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress gustavo.medeiros@inpe.br
%B Journal of Flood Risk Management
%V 12
%N 1
%P e12452
%K extreme events, forecasting and warning, natural flood management.
%X 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.
%@language en
%3 tomasella_probabilistic.pdf