The linear relationship between gross primary productivity (GPP) and evapotranspiration (ET), evidenced by site-scale observations, is well recognized as an indicator of the close interactions between carbon and hydrologic processes in terrestrial ecosystems. However, it is not clear whether this relationship holds at the catchment scale, and if so, what are the controlling factors of its slope and intercept. This study proposes and examines a generalized GPP-ET relationship at 380 near-natural catchments across various climatic and landscape conditions in the contiguous U.S., based on monthly remote sensing-based GPP data, vegetation phenology, and several hydrometeorological variables. We demonstrate the validity of this GPP-ET relationship at the catchment scale, with Pearson’s r ≥ 0.6 for 97% of the 380 catchments. Furthermore, we propose a regionalization strategy for estimating the slope and intercept of the generalized GPP-ET relationship at the catchment scale by linking the parameter values a priori with hydrometeorological data. We validate the monthly GPP predicted from the relationship and regionalized parameters against remote-sensing based GPP product, yielding Kling-Gupta Efficient (KGE) values ≥ 0.5 for 92% of the catchments. Finally, we verify the relationship and its parameter regionalization at 35 AmeriFlux sites with KGE ≥ 0.5 for 25 sites, demonstrating that the new relationship is transferable across the site, catchment, and regional scales. The relationship will be valuable for diagnosing coupled water–carbon simulations in land surface and Earth system models and constraining remote-sensing based estimation of monthly ET.

Bedload flux is notoriously challenging to measure and model. The dynamics of bedload therefore remains largely unknown in most fluvial systems worldwide. We present a global scale bedload flux model as part of the WBMsed modeling framework. Our results show that the model can very well predict the distribution of water discharge and suspended sediment and well predict bedload. We analyze the model’s bedload predictions sensitivity to river slope, particle size, discharge, river width and suspended sediment. We found that the model is most responsive to spatial dynamics in river discharge and slope. We analyze the relationship between bedload and total sediment flux globally and in representative longitudinal river profiles (Amazon, Mississippi, and Lena Rivers). We show that while, as expected, the proportion of bedload is decreasing from headwater to the coasts, there is considerable variability between basins and along river corridors. The latter is largely responsive to changes in suspended sediment and river slope due to dams and reservoirs. We provide a new estimate of water and sediment fluxes to global oceans from 2,067 largest river outlets (draining 67% of the global continental mass). Estimated water discharge (30,579 km3/y) corresponds well to past estimates however sediment flux is considerably higher. Of the total 22 Gt/y estimated average sediment flux to global oceans, 19 Gt/y is transported as washload, 1 Gt/y as bedload, and 2 Gt/y as suspended bed material. The largest 25 rivers are predicted to transport over 55% of total sediment flux to global oceans.