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Abstract. Providing reliable estimates of streamflow and hydrological fluxes is a major challenge for water resources management over national and transnational basins in South America. Global hydrological models and land surface models are a possible solution to simulate the terrestrial water cycle at the continental scale, but issues about parameterization and limitations in representing lowland river systems can place constraints on these models to meet local needs. In an attempt to overcome such limitations, we extended a regional, fully coupled hydrologic–hydrodynamic model (MGB; Modelo hidrológico de Grandes Bacias) to the continental domain of South America and assessed its performance using daily river discharge, water levels from independent sources (in situ, satellite altimetry), estimates of terrestrial water storage (TWS) and evapotranspiration (ET) from remote sensing and other available global datasets. In addition, river discharge was compared with outputs from global models acquired through the eartH2Observe project (HTESSEL/CaMa-Flood, LISFLOOD and WaterGAP3), providing the first cross-scale assessment (regional/continental  ×  global models) that makes use of spatially distributed, daily discharge data. A satisfactory representation of discharge and water levels was obtained (Nash–Sutcliffe efficiency, NSE > 0.6 in 55 % of the cases) and the continental model was able to capture patterns of seasonality and magnitude of TWS and ET, especially over the largest basins of South America. After the comparison with global models, we found that it is possible to obtain considerable improvement on daily river discharge, even by using current global forcing data, just by combining parameterization and better routing physics based on regional experience. Issues about the potential sources of errors related to both global- and continental-scale modeling are discussed, as well as future directions for improving large-scale model applications in this continent. We hope that our study provides important insights to reduce the gap between global and regional hydrological modeling communities.

Digital filters are useful tools for assessing the contribution of groundwater to total river flow. Several of those filters have been proposed in the last decades. One of the last contributions on this subject was given by Ekchardt (2005) who proposed a more general form of a digital baseflow filter and showed that some of the most used filters are special cases of this general form. This new filter has the inconvenience of having two parameters, one of them may be obtained directly from recession analysis, but the other (maximum baseflow index (BFImax)) is routinely estimated by a priori defined values according to the predominant geological characteristics of the drainage basin. In this short communication, we propose a method to estimate BFImax by a backwards filtering operation. The method was applied using data from 15 gauging stations in Brazil, with a varied range of groundwater contribution to streamflow. Results of the new estimation method for the BFImax parameter are coherent with the values which would be adopted by considering geological characteristics of the river basins. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract. We introduce a new catchment dataset for large-sample hydrological studies in Brazil. This dataset encompasses daily time series of observed streamflow from 3679 gauges, as well as meteorological forcing (precipitation, evapotranspiration, and temperature) for 897 selected catchments. It also includes 65 attributes covering a range of topographic, climatic, hydrologic, land cover, geologic, soil, and human intervention variables, as well as data quality indicators. This paper describes how the hydrometeorological time series and attributes were produced, their primary limitations, and their main spatial features. To facilitate comparisons with catchments from other countries, the data follow the same standards as the previous CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets for the United States, Chile, and Great Britain. CAMELS-BR (Brazil) complements the other CAMELS datasets by providing data for hundreds of catchments in the tropics and the Amazon rainforest. Importantly, precipitation and evapotranspiration uncertainties are assessed using several gridded products, and quantitative estimates of water consumption are provided to characterize human impacts on water resources. By extracting and combining data from these different data products and making CAMELS-BR publicly available, we aim to create new opportunities for hydrological research in Brazil and facilitate the inclusion of Brazilian basins in continental to global large-sample studies. We envision that this dataset will enable the community to gain new insights into the drivers of hydrological behavior, better characterize extreme hydroclimatic events, and explore the impacts of climate change and human activities on water resources in Brazil. The CAMELS-BR dataset is freely available at https://doi.org/10.5281/zenodo.3709337 (Chagas et al., 2020).