Key Points: Remote sensing validation of a hydrologic‐hydrodynamic model of the Amazon Uncertainty of precipitation and river‐floodplain parameters cause model errors Importance of floodplains and backwater effects on flood waves traveling

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).

Increased energy demand has led to plans for building many new dams in the western Amazon, mostly in the Andean region. Historical data and mechanistic scenarios are used to examine potential impacts above and below six of the largest dams planned for the region, including reductions in downstream sediment and nutrient supplies, changes in downstream flood pulse, changes in upstream and downstream fish yields, reservoir siltation, greenhouse gas emissions and mercury contamination. Together, these six dams are predicted to reduce the supply of sediments, phosphorus and nitrogen from the Andean region by 69, 67 and 57% and to the entire Amazon basin by 64, 51 and 23%, respectively. These large reductions in sediment and nutrient supplies will have major impacts on channel geomorphology, floodplain fertility and aquatic productivity. These effects will be greatest near the dams and extend to the lowland floodplains. Attenuation of the downstream flood pulse is expected to alter the survival, phenology and growth of floodplain vegetation and reduce fish yields below the dams. Reservoir filling times due to siltation are predicted to vary from 106–6240 years, affecting the storage performance of some dams. Total CO₂ equivalent carbon emission from 4 Andean dams was expected to average 10 Tg y^-1 during the first 30 years of operation, resulting in a MegaWatt weighted Carbon Emission Factor of 0.139 tons C MWhr^-1. Mercury contamination in fish and local human populations is expected to increase both above and below the dams creating significant health risks. Reservoir fish yields will compensate some downstream losses, but increased mercury contamination could offset these benefits.

We develop the first global ‘Bare-Earth’ Digital Elevation Model (DEM) based on the Shuttle Radar Topography Mission (SRTM) for all landmasses between 60N and 54S. Our new ‘Bare-Earth’ SRTM DEM combines multiple remote sensing datasets, including point-ground elevations from NASA's laser altimeter ICESat, a database of percentage of tree cover from the MODIS satellite as a proxy for penetration depth of SRTM and a global vegetation height map in order to remove the vegetation artefacts present in the original SRTM DEM. We test multiple methods of removing vegetation artefacts and investigate the use of regionalization. Our final ‘Bare-Earth’ SRTM product shows global improvements greater than 10m in the bias over the original SRTM DEM in vegetated areas compared with ground elevations determined from ICESat data with a significant reduction in the root mean square error from over 14m to 6m globally. Therefore, our DEM will be valuable for any global applications, such as large scale flood modelling requiring a ‘Bare-Earth’ DEM