Abstract

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.

Highlights

  • Watersheds are the fundamental Earth surface units that receive and process water, mass, and energy (Li, 2019; Li et al, 2021; Hubbard et al, 2018)

  • The deep subsurface zone is the less weathered layer that harbors the old groundwater that contributes to stream flow. Note that these definitions differ from those in the hydrology community, which often refers to the shallow soil lateral flow as groundwater, in a way that distinguishes it from the surface runoff (Dingman, 2015)

  • When the model is used in a spatially distributed form, the model domain is set up using elevation, land cover, soil, and geology maps supplied by the user

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Summary

Introduction

Watersheds are the fundamental Earth surface units that receive and process water, mass, and energy (Li, 2019; Li et al, 2021; Hubbard et al, 2018). Complex biogeochemical interactions occur among soil, water, roots, and microbes along water’s flow paths, regulating gas effluxes (e.g., CO2) and solute export (Fatichi et al, 2019; van der Velde et al, 2010; Benettin et al, 2020; Adler et al, 2021) These hydrological and biogeochemical processes determine how Earth surface systems respond to hydroclimatic forcing and human perturbations. Biogeochemical processes are highly variable with seasonal dynamics and depend on local environments such as substrate availability, soil temperature, and soil moisture (Li et al, 2017a; Davidson and Janssens, 2006) These models cannot capture the temporal variations in environmental factors that regulate soil biogeochemical reactions and stream and water chemistry.

Model overview
Water equations
Reactive transport equations
Biogeochemical processes
Reaction kinetics in natural soils
Plant-related processes: root uptake of nitrate as an example
Numerical scheme and model verification
Model structure
Data needs
Domain setup: spatially lumped and distributed domains
Model applications
Example 1: shallow and deep-water interactions
Example 2: nitrate dynamics in a spatially implicit domain
Example 3
Temporal and spatial patterns of DOC production and export
C–Q patterns
Findings
Discussion
Summary and conclusion

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