Satellite rainfall products are one of the most valuable tools for water resources monitoring in data-scarce regions, due to their low latency and quasi-global range. However, there are still uncertainties associated with rainfall products performance used to estimate hydrologic signatures in several regions, such as Brazil. Here, we investigate the performance of three rainfall products in estimating daily precipitation, daily river discharge, and hydrologic signatures over Brazil: the SM2RAIN-ASCAT and the GPM+SM2RAIN satellite products, and the ERA5 reanalysis product. We used a subset of 520 catchments from the Catchments Attributes for Brazil (CABra) dataset and the hydrologic modeling was carried out using the MISDc hydrologic model. Satellite-based products performed better than ERA5 for most Brazilian biomes in estimating daily precipitation when compared with ground observations used as reference. Daily river discharge was also better modeled with SM2RAIN-ASCAT and GPM+SM2RAIN. Hydrologic modeling presented low values of bias and >80% of catchments with KGE > 0.5 in calibration. Lastly, hydrologic signatures were well estimated by SM2RAIN-ASCAT and GPM+SM2RAIN, and for some biomes (Atlantic Forest, Cerrado, and Caatinga) they are better predictors than ground-based observations. We showed that there is a significant added value when using SM2RAIN-ASCAT and GPM+SM2RAIN products in tropical catchments, allowing a high-quality continuous water resources monitoring even in data-scarce regions. Besides, our findings pave the way for a better understanding of hydrologic extremes (droughts and floods) using these satellite rainfall products on multiple spatial and temporal scales.

由于其低延迟和准全球范围，卫星降雨产品是数据稀缺地区最有价值的水资源监测工具之一。然而，在巴西等几个地区，用于估计水文特征的降雨产品性能仍然存在不确定性。在这里，我们调查了三种降雨产品在估算巴西日降水量、每日河流流量和水文特征方面的性能：SM2RAIN-ASCAT 和 GPM+SM2RAIN 卫星产品，以及 ERA5 再分析产品。我们使用了巴西集水区属性 (CABra) 数据集中 520 个集水区的子集，并使用 MISDc 水文模型进行了水文建模。与用作参考的地面观测相比，基于卫星的产品在估计日降水量方面对大多数巴西生物群落的表现优于 ERA5。使用 SM2RAIN-ASCAT 和 GPM+SM2RAIN 也可以更好地模拟每日河流流量。水文模型的偏差值较低，校准中 KGE > 0.5 的集水区超过 80%。最后，SM2RAIN-ASCAT 和 GPM+SM2RAIN 对水文特征进行了很好的估计，对于某些生物群落（大西洋森林、塞拉多和卡廷加），它们比地面观测更好地预测。我们表明，在热带集水区使用 SM2RAIN-ASCAT 和 GPM+SM2RAIN 产品具有显着的附加值，即使在数据稀缺的地区也能进行高质量的连续水资源监测。除了，