BioRT-Flux-PIHM v1.0: a watershed biogeochemical reactive transport model

Abstract

Abstract. Watersheds are the fundamental Earth surface functioning unit that connects the land to aquatic systems. Existing watershed-scale models typically have physics-based representation of hydrology process but often lack mechanism-based, multi-component representation of reaction thermodynamics and kinetics. This lack of watershed reactive transport models has limited our ability to understand and predict solute export and water quality, particularly under changing climate and anthropogenic conditions. Here we present a recently developed BioRT-Flux-PIHM (BFP) v1.0, a watershed-scale biogeochemical reactive transport model. Augmenting the previously developed RT-Flux-PIHM that integrates land-surface interactions, surface hydrology, and abiotic geochemical reactions (Bao et al., 2017, WRR), the new development enables the simulation of 1) biotic processes including plant uptake and microbe-mediated biogeochemical reactions that are relevant to the transformation of organic matter that involve carbon, nitrogen, and phosphorus; and 2) shallow and deep water partitioning to represent surface and groundwater interactions. 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 to understand reactive transport processes in multiple watersheds across different climate, vegetation, and geology conditions. This paper introduces the governing equations and model structure of the code. It also demonstrates examples that simulate shallow and deep water interactions, and biogeochemical reactive transport relevant to nitrate and dissolved organic carbon (DOC). These examples were illustrated in two simulation modes of varying complexity. One is the spatially implicit mode that focuses on processes and average behavior of a watershed. Another is in a spatially explicit mode that includes details of topography, land cover, and soil property conditions. The spatially explicit mode can be used to understand the impacts of spatial structure and identify hot spots of biogeochemical reactions.

Hydrologic connectivity and source heterogeneity control concentration–discharge relationships

“…If we assume that discharge is directly related to water level (Seibert et al, 2009), then the cQ slope indicates the vertical solute distribution within the hydrologically connected catchment area and its change from deeper sections activated at low discharge to shallower sections activated as discharge increases. Consequently, in a simplified view, cQ slope values reflect the concentration changes within activated concentration profiles from bottom to top by expressing changes in response to changing runoff and water levels (e.g., Zhi et al, 2020). Average streamwater chemistry, on the other hand, compares flux‐weighted concentration profiles among subsets, that is, integrating from the bottom to the top of the concentration profiles.…”

The Shallow and Deep Hypothesis: Subsurface Vertical Chemical Contrasts Shape Nitrate Export Patterns from Different Land Uses

“…To understand the drivers of the C – Q patterns under different land use conditions, we resort to a recently developed, process-based watershed reactive transport model BioRT-Flux-PIHM (BFP). , The model was set up first to reproduce hydrological and nitrate data in Conewago Creek, a watershed with ∼47% agricultural land in the Chesapeake Bay. The model has three major water components contributing to the stream: surface runoff, shallow soil water, and deep water.…”

“…88 Nitrate Export Patterns under Different Land Uses. 45,90 The model was set up first to reproduce hydrological and nitrate data in Conewago Creek, a watershed with ∼47% agricultural land in the Chesapeake Bay. The model has three major water components contributing to the stream: surface runoff, shallow soil water, and deep water.…”

The Limits of Homogenization: What Hydrological Dynamics can a Simple Model Represent at the Catchment Scale?

“…The Q G values were estimated through the conductivity mass balance hydrograph separation (Lim et al., 2005). There is a newer version of Flux‐PIHM with fully coupled soil‐bedrock hydrological interactions (Zhi et al., 2021; https://github.com/PSUmodeling/MM-PIHM). The stream discharge Q is the sum of surface runoff ( Q S ), soil water lateral flow ( Q L ), and groundwater flow ( Q G ) (Figure 1).…”