This study presents the first long-term hydro-sediment balance of the Ucayali River using observations, remote sensing, and SWAT-Amazon. The river exports 455 Mt yr⁻¹ of sediment, 36 % trapped in the floodplain. Floodplain recycling contributes 22 %, while a newly identified sand deposition process during floods decouples sediment from water fluxes. The study highlights the need for robust hydrological networks and regional assessments to better capture Amazonian sediment dynamics.

We present smash, an open-source framework for high-resolution hydrological modeling and data assimilation. It combines process-based models with neural networks for regionalization, enabling accurate simulations from the catchment scale to the country scale. With an efficient, differentiable solver, smash supports large-scale calibration and parallel computing. Tested on open datasets, it shows strong performance in river flow prediction, making it a valuable tool for research and operational use.

Understanding and modeling flash-flood-prone areas remains challenging due to limited data and scale-relevant hydrological theory. While machine learning shows promise, its integration with process-based models is difficult. We present an approach incorporating machine learning into a high-resolution hydrological model to correct internal fluxes and transfer parameters between watersheds. Results show improved accuracy, advancing the development of learnable and interpretable process-based models.

The proposed flood frequency model accounts for uncertainty in the perception threshold S and the starting date of the historical period. Using a 500-year-long case study, inclusion of historical floods reduces the uncertainty in flood quantiles, even when only the number of exceedances of S is known. Ignoring threshold uncertainty leads to underestimated flood quantile uncertainty. This underlines the value of using a comprehensive framework for uncertainty estimation.

Suspended-sand fluxes in rivers vary with time and space, complicating their measurement. The proposed method captures the vertical and lateral variations of suspended-sand concentration throughout a river cross-section. It merges water samples taken at various positions throughout the cross-section with high-resolution acoustic velocity measurements. This is the first method that includes a fully applicable uncertainty estimation; it can easily be applied to any other study sites.