Maintaining natural streamflow variability along the drainage network is crucial for preserving ecological integrity, species diversity, and their abundance. Unplanned reservoir releases and climate change can alter the natural streamflow regime. A systematic assessment of streamflow alterations under reservoir operations and climate change scenarios along the river, across varying spatial scales, can offer crucial insights into the ecological vulnerability of the region. In this study, we analysed alterations to the natural streamflow regime driven by reservoir releases and an extreme future climate scenario (SSP5-8.5) in three nested basins of the Western Ghats, a recognised biodiversity hotspot in India. The objective was to assess whether reservoir operations or extreme climate conditions have a greater influence on natural flows and how the impacts vary across spatial scales. Our results indicate that reservoir operations after 2015 have substantially disrupted the natural streamflow regime, with effects that were largely consistent across all study basins. In contrast, under the projected extreme climate change scenario, alterations were comparatively lower and exhibited greater variability across basin scales. The largest basin showed minimal streamflow alteration, suggesting a higher resilience of larger basins to future climate change. Overall, our findings highlight that the reservoir operations in the study region need to be immediately regulated as they pose a threat to the natural streamflow regime, higher than that under the extreme climate change scenario.

Machine-learning models, particularly the Long Short-Term Memory (LSTM) networks, have recently shown strong performance in streamflow prediction, often outperforming process-based rainfall–runoff models. However, the causes of the comparatively weaker performance of process-based models remain insufficiently understood. In the present study, we hypothesize that deficiencies in channel routing module limit the ability of process based models to capture the nonlinear dynamics of runoff propagation. To test this hypothesis, we coupled the HBV rainfall-runoff model with the recently proposed Iterative Routing Model (IRM), which dynamically updates flow velocity as a function of streamflow. Across 64 CAMELS catchments, the HBV–IRM framework improved median NSE from 0.65 (HBV) to 0.72 and approached the global LSTM benchmarks (median NSE = 0.74). The findings highlight the importance of nonlinear routing in process-based models and demonstrate that improvements in the routing module can narrow the performance gap with machine-learning approaches.