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Summary: With the objective of understanding the potential and limitations of available precipitation products for hydrological studies, this paper compares six daily and sub-daily precipitation datasets and their impacts on the water balance of the Negro River basin in the Amazon basin. The precipitation datasets contain gauge-based data [data derived from the Hybam Observatory Precipitation (HOP) dataset and provided by the Climate Prediction Center (CPC)], satellite-based data [the Global Precipitation Climatology Project (GPCP) one-degree daily and TRMM Multisatellite Precipitation Analysis (TMPA) datasets] and model-based data [the NCEP-DOE AMIP-II re-analysis (NCEP-2) and 40-year ECMWF Re-Analysis (ERA-40) datasets]. Each dataset has a common set of meteorological forcing data which are used to run the MGB-IPH hydrological model for the period from January 1998 to August 2002. The average precipitation of all the datasets is 2542mm for the Negro River basin, with a standard deviation of 317mm. TMPA and NCEP-2 have the lowest (2216mm/year) and the highest (3065mm/year) precipitation rates, respectively. The HOP and CPC datasets agree best with observed discharge. GPCP gives the best results among the ungauged datasets, followed by ERA-40. TMPA and NCEP-2 are found to be the least accurate. TMPA can reproduce the water cycles reasonably well, but underestimates the precipitation fields and discharges over the basin, while NCEP-2 is unable to represent the rainfall quantity and cycles, and the water discharge. Results suggest that gauge-based data are still the most representative of the actual precipitation in the northern Amazon basin. However, some satellite and model-based can reproduce fairly well the water cycle at the basin scale and monthly time step. [ABSTRACT FROM AUTHOR]

Summary: The relations between hydrology, geomorphology and vegetation provide the basis for understanding the ecological processes in the floodplains of rivers. In this paper, long-term (1969–2009) local flood characteristics (magnitude, duration, frequency, return period) are quantified for the floodplain of the Cuiabá River (Pantanal wetland in Brazil), using a predictive model based on artificial neural networks (ANNs) with input given by the historic river-level record. Morphological features were then described and their relation to vegetation analyzed and used to reconstruct floods. In addition to the river-level data used to train and validate a three-layer ANN, data were also used from 11 floodplain gauges across a 12km transect lateral to the river, which were monitored during the annual floods of 2004–2007. The resulting neural network gave satisfactory estimates of water depth over the floodplain, with an absolute error of 0.09m not exceeded in 95% of occurrences. The ANN showed that in almost all years, the level reached by the river was high enough to flood the profile completely: the exceptions were 1971 and 2001 when only 50% and 58% was flooded, to mean depths of 0.34 and 0.48m, respectively. The highest flood was that of 1995 when the floodplain was flooded to a mean depth of 2.56m. The median flood event (return period 2years) produced a mean flood depth 1.80m and lasted 119days. Among the most important morphological features is the presence of palaeo-channels which provide hydrological connectivity between the river and the floodplain. The distribution of phytophysiognomic units is significantly influenced by local geomorphology, which determines spatial variation in the magnitude, duration, and frequency of flooding. [ABSTRACT FROM AUTHOR]

Summary: Rainfall measurements by conventional raingauges provide relatively accurate estimates at a few points of a region. The actual rainfield can be approximated by interpolating the available raingauge data to the remaining of the area of interest. In places with relatively low gauge density such interpolated rainfields will be very rough estimates of the actual events. This is especially true for tropical regions where most rainfall has a convective origin with high spatial variability at the daily level. Estimates of rainfall by remote sensing can be very useful in regions such as the Amazon basin, where raingauge density is very low and rainfall highly variable. This paper evaluates the rainfall estimates of the Tropical Rainfall Measuring Mission (TRMM) satellite over the Tapajós river basin, a major tributary of the Amazon. Three-hour TRMM rainfall estimates were aggregated to daily values and were compared with catch of ground-level precipitation gauges on a daily basis after interpolating both data to a regular grid. Both daily TRMM and raingauge-interpolated rainfields were then used as input to a large-scale hydrological model for the whole basin; the calculated hydrographs were then compared to observations at several streamgauges along the river Tapajos and its main tributaries. Results of the rainfield comparisons showed that satellite estimates can be a practical tool for identifying damaged or aberrant raingauges at a basin-wide scale. Results of the hydrological modeling showed that TRMM-based calculated hydrographs are comparable with those obtained using raingauge data. [Copyright &y& Elsevier]