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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. 

Soil biota generate CO2 that can vertically export to the atmosphere, and dissolved organic and inorganic carbon (DOC and DIC) that can laterally export to streams and accelerate weathering. These processes are regulated by external hydroclimate forcing and internal structures (permeability distribution), the relative influences of which are rarely studied. Understanding these interactions is essential a hydrological extremes intensify in the future. Here we explore the question: How and to what extent do hydrological and permeability distribution conditions regulate soil carbon transformations and chemical weathering? We address the questions using a hillslope reactive transport model constrained by data from the Fitch Forest (Kansas, United States). Numerical experiments were used to mimic hydrological extremes and variable shallow-versus-deep permeability contrasts. Results demonstrate that under dry conditions (0.08 mm/day), long water transit times led to more mineralization of organic carbon (OC) into inorganic carbon (IC) form (>98\%). Of the IC produced, ~ 75\% was emitted upward as CO2 gas and ~ 25\% was exported laterally as DIC into the stream. Wet conditions (8.0 mm/day) resulted in less mineralization (~88\%), more DOC production (~12\%), and more lateral fluxes of IC (~50\% of produced IC). Carbonate precipitated under dry conditions and dissolved under wet conditions as the fast flow rapidly droves the reaction to disequilibrium. The results depict a conceptual hillslope model that prompts four hypotheses for our community to test. H1: Droughts enhance carbon mineralization and vertical upward carbon fluxes, whereas large hydrological events such as storms and flooding enhance subsurface vertical connectivity, reduce transit times, and promote lateral export. H2: The role of weathering as a net carbon sink or source to the atmosphere depends on the interaction between hydrologic flows and lithology: transition from droughts to storms can shift carbonate from a carbon sink (mineral precipitation) to carbon source (dissolution). H3: Permeability contrasts regulate the lateral flow partitioning via shallow flow paths versus deeper groundwater though this alter reaction rates negligibly. H4: Stream chemistry reflect flow paths and can potentially quantify water transit times: solutes enriched in shallow soils have a younger water signature; solutes abundant at depth carry older water signature. 

Abstract. Carbonate weathering is essential in regulating atmosphericCO2 and carbon cycle at the century timescale. Plant roots accelerateweathering by elevating soil CO2 via respiration. It however remainspoorly understood how and how much rooting characteristics (e.g., depth anddensity distribution) modify flow paths and weathering. We address thisknowledge gap using field data from and reactive transport numericalexperiments at the Konza Prairie Biological Station (Konza), Kansas (USA), asite where woody encroachment into grasslands is surmised to deepen roots. Results indicate that deepening roots can enhance weathering in two ways.First, deepening roots can control thermodynamic limits of carbonatedissolution by regulating how much CO2 transports vertical downward tothe deeper carbonate-rich zone. The base-case data and model from Konzareveal that concentrations of Ca and dissolved inorganic carbon (DIC) areregulated by soil pCO2 driven by the seasonal soil respiration. Thisrelationship can be encapsulated in equations derived in this workdescribing the dependence of Ca and DIC on temperature and soil CO2. The relationship can explain spring water Ca and DIC concentrations from multiple carbonate-dominated catchments. Second, numericalexperiments show that roots control weathering rates by regulating recharge(or vertical water fluxes) into the deeper carbonate zone and exportreaction products at dissolution equilibrium. The numerical experimentsexplored the potential effects of partitioning 40 % of infiltrated waterto depth in woodlands compared to 5 % in grasslands. Soil CO2 datasuggest relatively similar soil CO2distribution over depth, which in woodlands and grasslands leads only to 1 % to∼ 12 % difference inweathering rates if flow partitioning was kept the same between the two landcovers. In contrast, deepening roots can enhance weathering by ∼ 17 % to200 % as infiltration rates increased from 3.7 × 10−2 to 3.7 m/a. Weathering rates in these cases however are more than an order of magnitude higher than a case without roots atall, underscoring the essential role of roots in general. Numericalexperiments also indicate that weathering fronts in woodlands propagated> 2 times deeper compared to grasslands after 300 years at aninfiltration rate of 0.37 m/a. These differences in weathering fronts areultimately caused by the differences in the contact times of CO2-charged water with carbonate in the deep subsurface. Within the limitation of modeling exercises, these data and numerical experiments prompt the hypothesis that (1) deepening roots in woodlands can enhance carbonate weathering by promotingrecharge and CO2–carbonate contact in the deepsubsurface and (2) the hydrological impacts of rooting characteristics canbe more influential than those of soil CO2 distribution in modulatingweathering rates. We call for colocated characterizations of roots,subsurface structure, and soil CO2 levels, as well as their linkage to waterand water chemistry. These measurements will be essential to illuminatefeedback mechanisms of land cover changes, chemical weathering, globalcarbon cycle, and climate. 

Understanding and predicting catchment responses to a regional disturbance is difficult because catchments are spatially heterogeneous systems that exhibit unique moderating characteristics. Changes in precipitation composition in the Northeastern U.S. is one prominent example, where reduction in wet and dry deposition is hypothesized to have caused increased dissolved organic carbon (DOC) export from many northern hemisphere forested catchments; however, findings from different locations contradict each other. Using shifts in acid deposition as a test case, we illustrate an iterative “process and pattern” approach to investigate the role of catchment characteristics in modulating the steam DOC response. We use a novel dataset that integrates regional and catchment-scale atmospheric deposition data, catchment characteristics and co-located stream Q and stream chemistry data. We use these data to investigate opportunities and limitations of a pattern-to-process approach where we explore regional patterns of reduced acid deposition, catchment characteristics and stream DOC response and specific soil processes at select locations. For pattern investigation, we quantify long-term trends of flow-adjusted DOC concentrations in stream water, along with wet deposition trends in sulfate, for USGS headwater catchments using Seasonal Kendall tests and then compare trend results to catchment attributes. Our investigation of climatic, topographic, and hydrologic catchment attributes vs. directionality of DOC trends suggests soil depth and catchment connectivity as possible modulating factors for DOC concentrations. This informed our process-to-pattern investigation, in which we experimentally simulated increased and decreased acid deposition on soil cores from catchments of contrasting long-term DOC response [Sleepers River Research Watershed (SRRW) for long-term increases in DOC and the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO) for long-term decreases in DOC]. SRRW soils generally released more DOC than SSHCZO soils and losses into recovery solutions were higher. Scanning electron microscope imaging indicates a significant DOC contribution from destabilizing soil aggregates mostly from hydrologically disconnected landscape positions. Results from this work illustrate the value of an iterative process and pattern approach to understand catchment-scale response to regional disturbance and suggest opportunities for further investigations. 

Research at long‐term catchment monitoring sites has generated a great volume, variety, and velocity of data for analysis of stream water chemistry dynamics. To harness the potential of these big data and extract patterns that are indicative of underlying functional relationships, machine learning tools have advantages over traditional statistical methods, and are increasingly being applied for dimension reduction, feature extraction, and trend identification. Still, as examples of complex systems, catchments are characterized by multivariate factor interactions and equifinality that are not easily identified by most machine‐learning methods. Using dissolved organic carbon (DOC) dynamics as an illustration, we applied a new evolutionary algorithm (EA) to extract geologic, topographic, meteorologic, hydrologic, and land use attributes that were correlated to mean stream DOC concentration in forested catchments distributed across the continental United States. The EA reduced dimensionality of our attribute dataset to identify the combination of factors, and their specific value ranges, that interacted to drive membership in High or Low mean DOC clusters. High mean DOC concentrations were associated with two distinct geographic locations of variable climatic and vegetative conditions, indicating equifinality. Our findings underscore the importance of critical zone structure in mediating hydrological and biogeochemical processes to govern DOC dynamics at the catchment scale. This multi‐scale, pattern‐to‐process approach is being applied to refine hypotheses for process‐based modeling of DOC dynamics in forested headwater streams at catchment to site scales.

 

Large‐scale models often use a single grid to represent an entire catchment assuming homogeneity; the impacts of such an assumption on simulating evapotranspiration (ET) and streamflow remain poorly understood. Here, we compare hydrological dynamics at Shale Hills (PA, USA) using a complex model (spatially explicit, >500 grids) and a simple model (spatially implicit, two grids using “effective” parameters). We asked two questions:What hydrological dynamics can a simple model reproduce at the catchment scale? What processes does it miss by ignoring spatial details?Results show the simple model can reproduce annual runoff ratios and ET, daily discharge peaks (e.g., storms, floods) but not discharge minima (e.g., droughts) under dry conditions. Neither can it reproduce different streamflow from the two sides of the catchment with distinct land surface characteristics. The similar annual runoff ratios between the two models indicate spatial details are not as important as climate in reproducing annual scale ET and discharge partitioning. Most of the calibrated parameters in the simple model are within the ranges in the complex model, except that effective porosity has to be reduced to 40% of the average porosity from the complex model. The form of the storage‐discharge relationship is similar. The effective porosity in the simple model however represents the dynamic and mobile water storage in the effective drainage area of the complex model that connects to the stream and contributes to high streamflow; it does not represent the passive, immobile water storage in the often disconnected uphill areas. This indicates that an additional uphill functioning unit is needed in the simple model to simulate the full spectrum of high‐low streamflow dynamics in natural catchments.