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).

Increasing floods and droughts are raising concerns of an accelerating water cycle, however, the relative contributions to streamflow changes from climate and land management have not been assessed at the continental scale. We analyze streamflow data in major South American tropical river basins and show that water use and deforestation have amplified climate change effects on streamflow extremes over the past four decades. Drying (fewer floods and more droughts) is aligned with decreasing rainfall and increasing water use in agricultural zones and occurs in 42% of the study area. Acceleration (both more severe floods and droughts) is related to more extreme rainfall and deforestation and occurs in 29% of the study area, including southern Amazonia. The regionally accelerating water cycle may have adverse global impacts on carbon sequestration and food security.

The replacement of natural forests with agriculture is generally associated with modifications in the hydrological behavior of a basin. This is particularly notable in the tropics and subtropics. Southern Brazil is a region with extensive agricultural production, forest conservation, and a vast unexplored streamflow data despite substantial rainfall trends observed in recent decades. In this work, we explore trends in the streamflow regime in the majority of the monitored basins in Southern Brazil. Additionally, we evaluate if pristine forested basins and agricultural nonforested basins have significantly different streamflow responses to changes in rainfall. We analyzed annual averages, maxima, minima, and seasonality of a 36‐year data set (1975–2010) of 675 rainfall and 140 streamflow gauges. Results reveal that large trends are widespread in Southern Brazil, especially in basins with areas smaller than 10,000 km². Changes in the rainfall regime did not directly translate into changes in the streamflow regime. Changes in the annual maximum flow of forested basins were not statistically significant even when the annual average and maximum rainfall increased significantly. Correlations between changes in rainfall and streamflow for two indices, namely, duration of low‐magnitude events and seasonality, were statistically significant (p < 0.05) only for agricultural basins. The results indicate a higher propagation of hydrological changes through anthropogenically modified systems, providing evidence that agricultural basins are more sensitive to changes in the rainfall regime.

Daily streamflow dynamics can be accurately simulated by conceptual models as simple as a single bucket in some catchments, while they require more complex configurations in other catchments. However, without resorting to calibration, anticipating where and why a given model structure may be appropriate remains difficult. In this work, we explored the feasibility of relating suitable model structures to the climate and streamflow characteristics of 508 catchments in Brazil. Specifically, we tested four model structures using up to three reservoirs, where each reservoir is intended to represent a catchment function: the rainfall‐runoff threshold, the fast, and the slow hydrograph response. We hypothesized a relationship between suitable model structures and hydrological signatures of aridity (I_A) and baseflow index (I_B). Our results show that different classes of signatures resulted in distinct patterns of model performance. Wet catchments (I_A < 0.9) with low baseflow (I_B < 0.4) were the easiest to model, with a single‐reservoir model presenting a relatively good performance. In the case of low baseflow, adding a rainfall‐runoff threshold reservoir resulted in better performance than adding a slow response reservoir, whereas in the case of high baseflow (I_B < 0.6) the opposite occurred. In the case of low baseflow, the inclusion of a slow response reservoir helped the simulation of dry catchments (I_A < 1.1), but not of wet ones, which we attributed to the impact of permeability in dry catchments. These results indicate a path toward model structure identification from streamflow signatures and potentially from landscape features.

Low flows result from the interplay of climatic variability and catchment storage dynamics, but it is unclear which of these variables is more relevant for explaining low flow spatial patterns. Here, we develop a new conceptual model that integrates process‐based hydrological knowledge with statistics and test it for 1,400 Brazilian catchments. Through comparative hydrology, we isolate the low flow generating mechanisms and estimate their components using linear model trees. The model explains 58% of the spatial variance in 7‐day minimum annual flows (Q_min) based on climate and catchment characteristics. The primary Q_min controls depend on the spatial scale of analysis. Catchment characteristics govern Q_min up to the continental scale (10⁷ km²), where their relative importance matches that of climate. At subcontinental scales, catchment characteristics are twice as important as climate in predicting Q_min, suggesting that low flows are governed by the catchment's capacity to attenuate the climatic variability through water storage. Geological properties are the most important catchment characteristics, particularly bedrock type, lithology and topographic slope, determining streamflow recession rates in the dry season. Soil properties, primarily soil class and depth, are half as important as geology. Climate impacts Q_min mainly through mean annual rainfall minus evaporation, representing the potential groundwater recharge, while dry‐season length has the lowest impact. These results hold mainly for highly seasonal and snow‐free climates. Low flow hydrology that combines statistics with process understanding offers a promising framework for understanding regional low flow generating mechanisms and could support other estimation models than that presented here.