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CABra: a novel large-sample dataset for Brazilian catchments

Authors: Almagro, Andre; Oliveira, Paulo Tarso Sanches; Meira Neto, Antonio Alves; Roy, Tirthankar; Troch, Peter;

CABra: a novel large-sample dataset for Brazilian catchments

Abstract

Hydrometeorological time series and catchment attributes from the CABra dataset. The manuscript of "CABra: a novel large-sample dataset for Brazilian catchments" is under review on Hydrology and Earth System Sciences (HESS) journal. Here 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-use and 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 and the coordinates of the streamflow stations provided by the Brazilian Water Agency (ANA), 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 day-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. Data can be also accessed at: thecabradataset.shinyapps.io/CABra

For any questions contact andre.almagro@gmail.com

Keywords

multiple-scale, dataset, streamflow, large-sample, catchment, climate

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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