Trend analysis of simulated streamflows via NARX-RNN and under CMIP6 climate scenarios in the Amazon River basin

This study aims to simulate streamflow using machine learning in a basin located in the Brazilian Amazon under two future climate scenarios from CMIP6, and analyze the impacts of climate change on streamflow until 2100 through trend analysis. A Nonlinear Auto Regressive Recurrent Neural Network with Exogenous Inputs (NARX) was trained to project streamflow under the SSP2-4.5 and SSP2-4.5 and SSP5-8.5 scenarios. Precipitation projected by the Global Circulation Models (GCMs) GFDL-ESM4, FGOALS-g3 e CESM2 was used as input to the model. The maximum streamflow simulated by NARX model in the reference period were underestimated. This underestimation was attributed to a systematic error in the precipitation projected by the GCMs, characterized by the delay in the peak of maximum. Therefore, the empirical quantile mapping EQM method was applied to correct the bias in the simulated streamflow using observed data from the reference period. The Mann-Kendall method was used to analyze future streamflow trends. The results show that the overall performance of the simulations was classified as good, with Kling-Gupta (KGE) values ranging from 0.73 to 0.77 in both scenarios. This performance was also reflected in the cumulative distribution function (CDF) curves of streamflow, which showed good agreement with the observed streamflow distribution. The Mann–Kendall test results for the simulated streamflow under the SSP2-4.5 scenario indicate stability in the streamflow regime. In contrast, for SSP5-8.5, a stronger signal appears mainly in GFDL-ESM4, which shows a significant decreasing trend (p = 0.0071) with Sen’s slope = − 0.313, indicating possible streamflow reduction. FGOALS-g3 (p = 0.5120) and CESM2 (p = 0.1527) showed no significant trends, although their slopes suggest opposite tendencies (0.0566 and − 0.1533, respectively). The MME also showed no significant trend (p = 0.1029), but its negative slope (− 0.1488) suggests a general tendency toward decreasing streamflow.