CABra: a novel large-sample dataset for Brazilian catchments

Abstract

Abstract. In this paper, we present the Catchments Attributes for Brazil (CABra), which is a large-sample dataset for Brazilian catchments that includes long-term data (30 years) for 735 catchments in eight main catchment attribute classes (climate, streamflow, groundwater, geology, soil, topography, land cover, and hydrologic disturbance). We have collected and synthesized data from multiple sources (ground stations, remote sensing, and gridded datasets). To prepare the dataset, we delineated all the catchments using the Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT DEM) and the coordinates of the streamflow stations provided by the Brazilian Water Agency, where only the stations with 30 years (1980–2010) of data and less than 10 % of missing records were included. Catchment areas range from 9 to 4 800 000 km2, and the mean daily streamflow varies from 0.02 to 9 mm d−1. Several signatures and indices were calculated based on the climate and streamflow data. Additionally, our dataset includes boundary shapefiles, geographic coordinates, and drainage area for each catchment, aside from more than 100 attributes within the attribute classes. The collection and processing methods are discussed, along with the limitations for each of our multiple data sources. CABra intends to improve the hydrology-related data collection in Brazil and pave the way for a better understanding of different hydrologic drivers related to climate, landscape, and hydrology, which is particularly important in Brazil, having continental-scale river basins and widely heterogeneous landscape characteristics. In addition to benefitting catchment hydrology investigations, CABra will expand the exploration of novel hydrologic hypotheses and thereby advance our understanding of Brazilian catchments' behavior. The dataset is freely available at https://doi.org/10.5281/zenodo.4070146 and https://thecabradataset.shinyapps.io/CABra/ (last access: 7 June 2021).

Are Brazilian catchments gaining or losing water? The effective area of tropical catchments

“…Furthermore, the increase in irrigated areas for food production in the Cerrado and Caatinga biomes has been recently related to an increase in evapotranspiration and baseflow reduction in these regions (Oliveira et al, 2019;Lucas et al, 2020) Our ECI results were positively correlated with mean slope and mean elevation, corroborating the gaining water condition found in 72% of the catchments located in the Atlantic Forest. Catchments in this biome presented the highest mean elevation and slope (Almagro et al, 2021). Indeed, high slopes and elevations were associated with larger effective areas (Figure 5b).…”

“…The country comprises six biomes with a climate that varies from semiarid to subtropical, with annual precipitation ranging from about 400 to 4000 mm. Elevation ranges from sea level up to 2900 m. In this study, we used the Catchment Attributes for Brazil dataset (CABra) (Almagro et al, 2021) for the analysis detailed throughout the next sections. CABra is a large‐scale dataset for catchment attributes, comprising a set of several attributes for 735 Brazilian catchments in multiple spatial and temporal scales.…”

“…ECI values that fall between these extreme ranges indicate either a small gaining or a small losing condition. The meteorological data used in this study, that is, precipitation ( P ), potential evapotranspiration (PET), and ET, with a climatological means over the 1980–2010 period came from the CABra dataset. As described in Almagro et al (2021), P is derived from an ensemble mean between a ground‐based reference dataset and the ERA5 reanalysis dataset. The PET is an estimation by the Priestley and Taylor method, while the ET is derived from the GLEAM v3 product.…”

Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023-2032

“…Automized sensors greatly improved the availability of in situ data, but they are still characterized by high costs and limited spatial coverage. New technologies such as remote sensing have provided us with better spatiotemporal data coverage, as well as measurements covering a larger part of the hydrological cycle, including for instance precipitation, evapotranspiration, snow, soil moisture, and water storage (Addor et al, 2017;Almagro et al, 2021;Arsenault et al, 2016;Cui et al, 2018;Klingler et al, 2021). Due to the size of these datasets, big data is a big topic in the environmental sciences including hydrology Wang, 2018, Gaffoor, et al 2020).…”

Interpreting extreme climate impacts from large ensemble simulations—are they unseen or unrealistic?

“…After Obidos, two tributaries from the south, Tapajos (grey circle) and Xingu (black circle in figure 2(a)), join the main Amazon River before the river reaches the outlet. For the period 1981-2010, streamflow data obtained using a rating curve are available for all three stations (figure 2(b)) with less than 10% missing from the catchments attributes for Brazil (CABra) series (Almagro et al 2021). In the CABra dataset, gauged daily streamflow from the Brazilian Water Agency are quality controlled to remove outliers, duplicate dates and values.…”