Open AccessProbabilistic flood forecasting in the Doce Basin in Brazil: Effects of the basin scale and orientation and the spatial distribution of rainfall
Author(s) -
Tomasella J.,
Sene Gonçalves A.,
Schneider Falck A.,
Oliveira Caram R.,
Rodrigues Diniz F.L.,
Rodriguez D.A.,
Rodrigues do Prado M.C.,
Negrão A.C.,
Sueiro Medeiros G.,
Chagas Siquiera G.
Publication year - 2019
Publication title -
journal of flood risk management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.049
H-Index - 36
ISSN - 1753-318X
DOI - 10.1111/jfr3.12452
Subject(s) - streamflow , flood forecasting , environmental science , flood myth , climatology , predictability , scale (ratio) , probabilistic logic , meteorology , drainage basin , range (aeronautics) , structural basin , spatial ecology , geography , statistics , geology , mathematics , cartography , archaeology , paleontology , ecology , materials science , composite material , biology
We critically examined the performance of probabilistic streamflow forecasting in the prediction of flood events in 19 subbasins of the Doce River in Brazil using the Eta (4 members, 5 km spatial resolution) and European Centre for Medium‐Range Weather Forecasts (ECMWF; 51 members, 32 km resolution) weather forecast models as inputs for the MHD‐INPE hydrological model. We observed that the shapes and orientations of subbasins influenced the predictability of floods due to the orientation of rainfall events. Streamflow forecasts that use the ECMWF data as input showed higher skill scores than those that used the Eta model for subbasins with drainage areas larger than 20,000 km 2 . Since the skill scores were similar for both models in smaller subbasins, we concluded that the grid size of the weather model could be important for smaller catchments, while the number of members was crucial for larger scales. We also evaluated the performance of probabilistic streamflow forecasting for the severe flood event of late 2013 through a comparison of observations and streamflow estimations derived from interpolated rainfall fields. In many cases, the mean of the ensemble outperformed the streamflow estimations from the interpolated rainfall because the spatial structure of a rainfall event is better captured by weather forecast models.