Long-term hydro-sediment dynamics of the Ucayali River (Amazon Basin) revealed through combined observations, remote sensing, and SWAT-Amazon modelling

Abstract. Since the early 1970s, the Amazon basin has experienced growing local and global changes, potentially reaching a climatic tipping point in the coming decades. However, due to cost constraints and limited access, conventional hydrological networks in the basin struggle to provide the spatial resolution and temporal extent required for accurate quantification of water and sediment budgets, which are essential for understanding biogeochemical cycles.

Focusing on the Ucayali River, a major Amazonian foreland tributary, this study provides the first long-term hydro-sediment balances in this region at sub-basin scale, distinguishing fine sediments from sand loads (37 years for water and sands, 20 years for fine sediments). It is achieved by the integration of remote sensing and hydrological-hydraulic modelling using a modified SWAT model, SWAT-Amazon. A new hydraulic module for water routing was implemented in SWAT-Amazon to suit the Amazon diffusive flood wave, representing floodplains as reservoirs. Fine sediment loads were estimated using satellite-derived concentrations and simulated discharges, while suspended sand loads were simulated within SWAT-Amazon.

Results indicate that the Andean Ucayali River exports 455 10⁶ t yr⁻¹ of suspended sediment (40 % sand). As the floodplain traps 36 % of the Andean sediments (65 % sand), mostly by tectonic subsidence, the Ucayali delivers 290 10⁶ t yr⁻¹ of total suspended sediment to the Amazon River, 26 % as sand. Floodplain recycling plays a crucial role as a secondary sediment source (22 % of the Ucayali load), with a water storage that peaks at 19.1 km³ in March (38 % of discharge). A previously undocumented sand sedimentation process is identified during the flooding period, capturing 14 % of the sand flux at peak discharge and thus decorrelating sediment transport from water discharge. No significant long-term trends in flood duration, discharge, or sediment fluxes were detected, suggesting contrasted evolution patterns of the precipitations in the basin due to its particular position in the Amazon Basin. This study emphasizes the need to rethink hydrological network management with robust and long-term conventional data at ‘super’ stations to support the calibration of remote sensing and modelling at ‘virtual’ stations. Extending this approach to other Amazonian basins could significantly enhance hydro-sediment and biogeochemical cycle research in large river systems. Additionally, it highlights the importance of regionally focused over large-scale assessments, which often carry high uncertainties and may mislead mitigation strategies.