The weathering of silicate rock in the hillslopes that feed headwater streams sets the chemical characteristics of water draining catchments, the transport of mass from continents to oceans and critical feedbacks between atmospheric CO2 and the land surface. Yet quantitative models for the basic relationship between the rate of water discharge from a landscape (Q) and the concentration of solutes (C) within that water remains a significant challenge. This is in part due to close coupling between the solubilization of bedrock and the formation of new secondary minerals, which we term silicate weathering. At the core of this uncertainty is a practical issue: the rates of silicate weathering are slow. This means that typical flow-through columns built in laboratories cannot capture even a simplified representation of silicate weathering in upland watersheds. In contrast, natural hillslopes are complicated and difficult to constrain. In this work, investigators will overcome this disparity using the unique mesoscale Landscape Evolution Observatory (LEO), which affords three replicate convergent hillslopes constructed on the world's largest weighing lysimeters. The LEO facility is housed within the Biosphere 2 center, which translates Earth system science research into tractable examples and demonstrations for over 100,000 public visitors per year. This includes 10,000 students who use the Biosphere 2 as part of their STEM curriculum. The project will train two PhD students, thus forming a collaborative research group across three institutions, and produce 'on-display' projects as part of the Biosphere 2 educational tour including information about the purpose and status of the work. Finally, the reactive transport simulations developed and calibrated by this project will be leveraged as an example for a current NSF Research Coordination Network: Community-based educational infrastructure for numerical simulation in the Earth Sciences. Even in a perfectly homogeneous system, an infinite combination of these tandem dissolution and precipitation rates could lead to the same solute concentration. Further, these reactions occur through non-uniform flow paths subject to unsteady infiltration. Thus, a critical need to advance process-based understanding of the C-Q relationship is the provision of additional constraints which embed within the same model framework and reduce the number of free parameters. Here, the researchers will use the characteristic shifts in stable isotope and trace element ratios to diagnose the relationship between primary silicate weathering and secondary mineral precipitation. Specifically, they will pair silicon isotopes (delta 30Si) and germanium-silicon ratios (Ge/Si), which are each uniquely sensitive to the rate and nature of secondary mineral formation in weathering systems, to unmask the balance of secondary precipitation reactions contributing to C-Q observations through expansion to a C-R-Q (concentration ? isotope/element ratio ? discharge) framework. At present, laboratory characterization studies of the parameters which describe partitioning of delta 30Si and Ge/Si during secondary mineral growth are expanding, as well as datasets of these ratios versus discharge at the field scale. Yet a critical gap existing in pairing this information across a flow-through system with constrained fluid transit time distributions to verify appropriate model representation of observed behavior. This gap is contingent upon operational limitations. The slow weathering rates of silicate water-rock interactions impede the use of standard flow-through column designs at reasonable scales, while the complexity of natural systems limits the capacity to develop constrained relationships between reactivity and fluid travel time. Here, they will use LEO and employ a novel flux-weighted time approach to constrain transient fluid travel time distributions across the system. Through this combination of unique experimental facility, novel transient travel time constraint, reactive transport modeling, and (pseudo)isotopic tracers, they believe that a transformative advancement in process-level representation and prediction of C-R-Q relationships is achievable. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.