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In this study, rating curves (RCs) were determined by applying satellite altimetry to a poorly gauged basin. This study demonstrates the synergistic application of remote sensing and watershed modeling to capture the dynamics and quantity of flow in the Amazon River Basin, respectively. Three major advancements for estimating basin-scale patterns in river discharge are described. The first advancement is the preservation of the hydrological meanings of the parameters expressed by Manning's equation to obtain a data set containing the elevations of the river beds throughout the basin. The second advancement is the provision of parameter uncertainties and, therefore, the uncertainties in the rated discharge. The third advancement concerns estimating the discharge while considering backwater effects. We analyzed the Amazon Basin using nearly one thousand series that were obtained from ENVISAT and Jason-2 altimetry for more than 100 tributaries. Discharge values and related uncertainties were obtained from the rain-discharge MGB-IPH model. We used a global optimization algorithm based on the Monte Carlo Markov Chain and Bayesian framework to determine the rating curves. The data were randomly allocated into 80% calibration and 20% validation subsets. A comparison with the validation samples produced a Nash-Sutcliffe efficiency ( [ABSTRACT FROM AUTHOR]

In this paper, a hydrologic/hydrodynamic modeling of the Amazon River basin is presented using the MGB-IPH model with a validation using remotely sensed observations. Moreover, the sources of model errors by means of the validation and sensitivity tests are investigated, and the physical functioning of the Amazon basin is also explored. The MGB-IPH is a physically based model resolving all land hydrological processes and here using a full 1-D river hydrodynamic module with a simple floodplain storage model. River-floodplain geometry parameters were extracted from the SRTM digital elevation model, and the model was forced using satellite-derived rainfall from TRMM3B42. Model results agree with observed in situ daily river discharges and water levels and with three complementary satellite-based products: (1) water levels derived from ENVISAT altimetry data; (2) a global data set of monthly inundation extent; and (3) monthly terrestrial water storage (TWS) anomalies derived from the Gravity Recovery and Climate Experimental mission. However, the model is sensitive to precipitation forcing and river-floodplain parameters. Most of the errors occur in westerly regions, possibly due to the poor quality of TRMM 3B42 rainfall data set in these mountainous and/or poorly monitored areas. In addition, uncertainty in river-floodplain geometry causes errors in simulated water levels and inundation extent, suggesting the need for improvement of parameter estimation methods. Finally, analyses of Amazon hydrological processes demonstrate that surface waters govern most of the Amazon TWS changes (56%), followed by soil water (27%) and ground water (8%). Moreover, floodplains play a major role in stream flow routing, although backwater effects are also important to delay and attenuate flood waves. [ABSTRACT FROM AUTHOR]

A key aspect of large river basins partially neglected in large-scale hydrological models is river hydrodynamics. Large-scale hydrologic models normally simulate river hydrodynamics using simplified models that do not represent aspects such as backwater effects and flood inundation, key factors for some of the largest rivers of the world, such as the Amazon. In a previous paper, we have described a large-scale hydrodynamic approach resultant from an improvement of the MGB-IPH hydrological model. It uses full Saint Venant equations, a simple storage model for flood inundation and GIS-based algorithms to extract model parameters from digital elevation models. In the present paper, we evaluate this model in the Solimões River basin. Discharge results were validated using 18 stream gauges showing that the model is accurate. It represents the large delay and attenuation of flood waves in the Solimões basin, while simplified models, represented here by Muskingum Cunge, provide hydrographs are wrongly noisy and in advance. Validation against 35 stream gauges shows that the model is able to simulate observed water levels with accuracy, representing their amplitude of variation and timing. The model performs better in large rivers, and errors concentrate in small rivers possibly due to uncertainty in river geometry. The validation of flood extent results using remote sensing estimates also shows that the model accuracy is comparable to other flood inundation modelling studies. Results show that (i) river-floodplain water exchange and storage, and (ii) backwater effects play an important role for the Amazon River basin hydrodynamics. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Highlights • Hydrological Reanalysis HR is proposed using hydrologic model and data assimilation of in-situ observations. • Unbiased large dataset of climate reanalysis is used as forcing for the hydrologic model. • Local data assimilation technique is used to improve hydrologic spatio-temporal fields. • HR is used in the Amazon to validate and study their hydrology along the 20th century. Abstract Adequate and accurate hydrological data is necessary to manage water resources, which are critical in developing countries where such information is limited. In recent years, global reanalysis datasets have been developed to provide this information in climatic fields and, more recently, in hydrologic fields. Nevertheless, these latest efforts have been limited to temporal coverage (∼30 years) and mostly simplified hydraulic scheme routing in rivers, which can be inadequate for regional and long term scale objectives. In this article, a dataset (called hydrological reanalysis across the 20th century [HRXX] in the Amazon Basin) was developed as a case study spanning back to the year 1900 through the use of: 1) a large-scale hydrologic-hydrodynamic model (MGB) forced by a long-term climatic reanalysis of rainfall (ERA-20CM) with bias removed; and 2) a data assimilation (DA) technique coupled with a localization method (LEnKF) to use several ground observations of daily discharge within a radius of influence. Several tests were assessed to find the best bias removal method, the optimal radius of influence for the localization method and the final HRXX dataset. A total of 114 hydrological ground observations of daily information were used for assimilation and validation purposes, and several statistics indexes were employed to assess their performance. Results indicate that both bias correction and the DA with localization method greatly improved the simulations. Overestimations of the peaks in the open-loop (OL) (free run) simulation, mainly in the southern and northern regions of the Amazon Basin, were removed, and recession timing in the east-central region, as seen at the Óbidos gauge station, were corrected. An average performance of ∼0.6 and ∼0.7 of the Nash–Sutcliffe and Kling–Gupta indexes was reached, even when only a few of the longest ground observations were used, which can be representative of the oldest periods (since ∼1930). To assess extreme events, the Pearson correlation coefficient was used for maximum and minimum annual water level anomaly values, reaching 0.6 and 0.7, respectively, at the Manaus gauge station, which is remarkable considering that the analysis covers approximately 110 years. Considering the results of this case study and the global coverage of rainfall datasets, this methodology can be transferred to other regions in order to better estimate and create a hydrological reanalysis that adequately represents the hydrologic and hydraulic spatio-temporal fields. [ABSTRACT FROM AUTHOR]