Abstract
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).
Highlights
Large-scale hydrological research relies on data from large samples of catchments to formulate general conclusions on hydrological processes and models (Gupta et al, 2014; Addor et al, 2019)
Large-sample hydrology is needed for evaluation of continental to global hydrological models; to identify limitations in model structure, parameterization, and forcing according to geographic and climatic regions (Haddeland et al, 2011; Gudmundsson et al, 2012; Beck et al, 2017a; Zhao et al, 2017; Siqueira et al, 2018; Veldkamp et al, 2018); to estimate uncertainty in model estimates (e.g., Müller Schmied et al, 2014; Beck et al, 2016; Hirpa et al, 2018); and to make use of data assimilation techniques (e.g., Wongchuig et al, 2019)
The first and second most common geologic class, their fractions, and the percentage of the catchment covered by carbonate rocks were extracted from the Global Lithological Map (GLiM; Hartmann and Moosdorf, 2012)
Summary
Large-scale hydrological research relies on data from large samples of catchments to formulate general conclusions on hydrological processes and models (Gupta et al, 2014; Addor et al, 2019). Studies in Brazil generally include only a reduced number of stream gauges and catchment attributes and are restricted to specific regions, such as the Amazon (e.g., Tomasella et al, 2011; Paiva et al, 2013; Latrubesse et al, 2017; Levy et al, 2018) or the La Plata basin (e.g., Collischonn et al, 2001; Pasquini and Depetris, 2007; Melo et al, 2016; Lima et al, 2017; Chagas and Chaffe, 2018) To overcome these limitations, we produced and made publicly available a new dataset for large-sample hydrological studies in Brazil, CAMELS-BR.
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