Redox processes, aqueous and solid-phase chemistry, and pH dynamics are key drivers of subsurface biogeochemical cycling in terrestrial and wetland ecosystems but are typically not included in terrestrial carbon cycle models. These omissions may introduce errors when simulating systems where redox interactions and pH fluctuations are important, such as wetlands where saturation of soils can produce anoxic conditions and coastal systems where sulfate inputs from seawater can influence biogeochemistry. Integrating cycling of redox-sensitive elements could therefore allow models to better represent key elements of carbon cycling and greenhouse gas production. We describe a model framework that couples the Energy Exascale Earth System Model (E3SM) Land Model (ELM) with PFLOTRAN biogeochemistry, allowing geochemical processes and redox interactions to be integrated with land surface model simulations. We implemented a reaction network including aerobic decomposition, fermentation, sulfate reduction, sulfide oxidation, and methanogenesis as well as pH dynamics along with iron oxide and iron sulfide mineral precipitation and dissolution. We simulated biogeochemical cycling in tidal wetlands subject to either saltwater or freshwater inputs driven by tidal hydrological dynamics. In simulations with saltwater tidal inputs, sulfate reduction led to accumulation of sulfide, higher dissolved inorganic carbon concentrations, lower dissolved organic carbon concentrations, and lower methane emissions than simulations with freshwater tidal inputs. Model simulations compared well with measured porewater concentrations and surface gas emissions from coastal wetlands in the Northeastern United States. These results demonstrate how simulating geochemical reaction networks can improve land surface model simulations of subsurface biogeochemistry and carbon cycling.

The high computational cost of large-scale, process-based hydrological simulations can be approached using variable resolution meshes, where only the region around significant topographic features is refined. However, generating quality variable resolution meshes from digital elevation data is non-trivial. In literature, usually a slope or curvature-based criterion is defined to detect regions of refinement. These techniques often involve a number of free parameters that control the finest and coarsest resolutions, and the transition between fine resolution to coarse resolution. The influence of these parameters on the resulting mesh is usually not well-understood. In order to overcome the large number of free parameters involved, we propose to carry out the Mallat decomposition of the digital elevation data using the Haar wavelet. This gives a nested multilevel representation of the elevation data, split into average coefficients and detail coefficients. Applying hard-thresholding to these detail coefficients assigns a required level of refinement to each data point. This reduces the number of free parameters to exactly one: the acceptable error threshold. In this presentation, we focus on identifying which geomorphometric parameter(s) should be used to steer mesh refinement. We compare zero-inertia model simulation runs on meshes generated by decomposing elevation and slope. We hypothesize that because of the form of the Haar wavelet, the first mesh refinement essentially is using the gradient information, while the latter is using the curvature as refinement criterion. Our results suggest that in high-elevation catchments the curvature of the topography is a far better indicator for refinement than the slope. Using the Mallat decomposition on the tensor of the first derivative of the bed elevation (i.e., bed slope) for mesh refinement yields better agreement in the hydrograph compared to the decomposition of the bed elevation. We present surface runoff results for the Lower Triangle catchment, CO, USA, to illustrate the performance of the wavelet-based local mesh refinement.

We numerically study how the interactions between surface, subsurface, and rainfall create complex runoff behavior in a headwater-dominated high elevation, mountainous catchment in East River Watershed, Colorado, USA. In order to understand the effect of surface--subsurface interactions on the hydrological response, we compare model variations with different soil permeability. We compute hydrographs, hydrological indices, and spatio-temporal distributions of hydrological variables. The indices and distributions are then linked to the hydrograph at the outlet of the domain. Our results show that changing surface--subsurface interactions trigger different flow regimes, connectivity dynamics, and runoff generation mechanisms inside the catchment, and hence, affect the distributed hydrological response. Increased surface--subsurface interaction leads to a higher degree of connectivity in the catchment. Temporal dynamics of both distributed and aggregated hydrological response did not change significantly, which may be because the structural connectivity of the catchment remained untouched. Our results indicate that in higher order catchments, the effect of changes in distributed hydrological response may not always be observable through aggregated hydrological signatures.