The impacts of climate change on soil erosion may bring serious economic, social and environmental problems. However, few studies have investigated these impacts on continental scales. Here we assessed the influence of climate change on rainfall erosivity across Brazil. We used observed rainfall data and downscaled climate model output based on Hadley Center Global Environment Model version 2 (HadGEM2-ES) and Model for Interdisciplinary Research On Climate version 5 (MIROC5), forced by Representative Concentration Pathway 4.5 and 8.5, to estimate and map rainfall erosivity and its projected changes across Brazil. We estimated mean values of 10,437 mm ha−1 h−1 year−1 for observed data (1980–2013) and 10,089 MJ mm ha−1 h−1 year−1 and 10,585 MJ mm ha−1 h−1 year−1 for HadGEM2-ES and MIROC5, respectively (1961–2005). Our analysis suggests that the most affected regions, with projected rainfall erosivity increases ranging up to 109% in the period 2007–2040, are northeastern and southern Brazil. Future decreases of as much as −71% in the 2071–2099 period were estimated for the southeastern, central and northwestern parts of the country. Our results provide an overview of rainfall erosivity in Brazil that may be useful for planning soil and water conservation, and for promoting water and food security.

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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).

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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 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. The 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.4070147.

The Pantanal biome integrates the lowlands of the Upper Paraguay Basin (UPB), which is hydrologically connected to the biomes of the Cerrado and Amazon (the highlands of the UPB). The effects of recent land-cover and land-use (LCLU) changes in the highlands, combined with climate change, are still poorly understood in this region. Here, we investigate the effects of soil erosion in the Brazilian Pantanal under climate and LCLU changes by combining different scenarios of projected rainfall erosivity and land-cover management. We compute the average annual soil erosion for the baseline (2012) and projected scenarios for 2020, 2035, and 2050. For the worst scenario, we noted an increase in soil loss of up to 100% from 2012 to 2050, associated with cropland expansion in some parts of the highlands. Furthermore, for the same period, our results indicated an increase of 20 to 40% in soil loss in parts of the Pantanal biome, which was associated with farmland increase (mainly for livestock) in the lowlands. Therefore, to ensure water, food, energy, and ecosystem service security over the next decades in the whole UPB, robust and comprehensive planning measures need to be developed, especially for the most impacted areas found in our study.

Similar to most countries, the Brazilian water resources management considers topographically delineated catchment as a territorial unit for policy implementation. Yet, previous studies have shown that catchments are not hydrologically isolated, and topographic limits often neglect the groundwater boundaries. Thus, studies on effective catchment areas are promising for shedding light on inter‐catchment groundwater flow. Here, we investigated the deviation between the topographic and effective areas across Brazil. We applied the effective catchment area index (ECI) to 733 Brazilian catchments and identified the most influencing attributes on the ECI by using principal component and random forest analyses (PCA and RFA, respectively). Further analysis was carried out by contrasting the ECI values against the expected range of the Budyko curve, considering both topographic and effective catchment areas (classic and adjusted framework, respectively). We noted that nearly 32% of the Brazilian catchments presented more than 30% of difference between the effective area and its topographic boundaries. In general, the more arid biomes in Brazil—the Cerrado and Caatinga—are prone to have smaller effective areas while larger effective areas were mostly found in the Atlantic Forest biome, a humid tropical region with a higher mean elevation. Our findings indicate that the aridity index was the main driving factor and negatively correlated with ECI followed by mean slope, precipitation seasonality, and mean elevation. We highlight the potential of adopting a pooling of catchments based on their interconnectivity to minimize management costs while maximizing synergies and lessening trade‐offs between ecosystem functioning and water transfer processes. Our results contribute to a better country‐wide understanding of hydrological connectivity among catchments and highlight the need to consider the effective catchment area to overcome water‐food‐energy security challenges on multiple scales.

General Circulation and Earth System Models are the most advanced tools for investigating climate responses to future scenarios of greenhouse gas emissions, playing the role of projecting the climate throughout the century. Nevertheless, climate projections are model-dependent and may show systematic biases, requiring a bias correction for any further application. Here, we provide a dataset based on an ensemble of 19 bias-corrected CMIP6 climate models projections for the Brazilian territory based on the SSP2-4.5 and SSP5-8.5 scenarios. We used the Quantile Delta Mapping approach to bias-correct daily time-series of precipitation, maximum and minimum temperature, solar net radiation, near-surface wind speed, and relative humidity. The bias-corrected dataset is available for both historical (1980–2013) and future (2015–2100) simulations at a 0.25° × 0.25° spatial resolution. Besides the gridded product, we provide area-averaged projections for 735 catchments included in the Catchments Attributes for Brazil (CABra) dataset. The dataset provides important variables commonly used in environmental and hydroclimatological studies, paving the way for the development of high-quality research on climate change impacts in Brazil.