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Abstract. Watersheds are the fundamental Earth surface functioning units that connect the land to aquatic systems. Many watershed-scale models represent hydrological processes but not biogeochemical reactive transport processes. This has limited our capability to understand and predict solute export, water chemistry and quality, and Earth system response to changing climate and anthropogenic conditions. Here we present a recently developed BioRT-Flux-PIHM (BioRT hereafter) v1.0, a watershed-scale biogeochemical reactive transport model. The model augments the previously developed RT-Flux-PIHM that integrates land-surface interactions, surface hydrology, and abiotic geochemical reactions. It enables the simulation of (1) shallow and deep-water partitioning to represent surface runoff, shallow soil water, and deeper groundwater and of (2) biotic processes including plant uptake, soil respiration, and nutrient transformation. The reactive transport part of the code has been verified against the widely used reactive transport code CrunchTope. BioRT-Flux-PIHM v1.0 has recently been applied in multiple watersheds under diverse climate, vegetation, and geological conditions. This paper briefly introduces the governing equations and model structure with a focus on new aspects of the model. It also showcases one hydrology example that simulates shallow and deep-water interactions and two biogeochemical examples relevant to nitrate and dissolved organic carbon (DOC). These examples are illustrated in two simulation modes of complexity. One is the spatially lumped mode (i.e., two land cells connected by one river segment) that focuses on processes and average behavior of a watershed. Another is the spatially distributed mode (i.e., hundreds of cells) that includes details of topography, land cover, and soil properties. Whereas the spatially lumped mode represents averaged properties and processes and temporal variations, the spatially distributed mode can be used to understand the impacts of spatial structure and identify hot spots of biogeochemical reactions. The model can be used to mechanistically understand coupled hydrological and biogeochemical processes under gradients of climate, vegetation, geology, and land use conditions. 

Abstract Most earth system models fail to capture the seasonality of carbon fluxes in radiation‐limited tropical evergreen forests (TEF) in the Amazon. Kim et al. (2012,https://doi.org/10.1111/j.1365-2486.2011.02629.x) first statistically incorporated a light‐controlled phenology module into an ecosystem model to improve carbon flux simulations at one TEF site. However, it is not clear how their approach can be extended to other TEF sites with different climatic conditions. Here we evaluated temporal variability in plant functional traits at three different TEF sites using a data‐conditioned stochastic parameterization method. We showed that previously studied links—between seasonal photosynthetically active radiation (PAR) and the traitsV_cmax25and leaf longevity—occur across sites. We further determined that seasonal PAR could similarly drive variations in the stomatal conductance slope parameter. Differences found in temporal trait estimates among sites indicate that dynamic trait parameters cannot be applied uniformly over space, but it may be possible to extrapolate them based on climatic factors. Motivated by recent observations that physiological capacity develops as leaves mature, we built new regression models for predicting traits that not only include PAR but also an autoregressive lag term to capture observed physiological delays behind PAR‐driven phenology shifts. With our stochastic parameterization, we predicted the three sites to be carbon neutral or carbon sinks under the RCP 8.5 future climate scenario. In contrast, projections using standard static trait parameters show most of the Amazonian TEF region becoming a carbon source. We further approximated that variable traits may allow at least a third of the radiation‐limited TEF region in the Amazon to serve as a future net carbon sink.