The complex interactions among soil, vegetation, and site hydrologic conditions driven by precipitation and tidal cycles control biogeochemical transformations and bi-directional exchange of carbon and nutrients across the terrestrial-aquatic interfaces (TAIs) in the coastal regions. This study uses a highly mechanistic model, ATS-PFLOTRAN, to explore how these interactions impact the material exchanges and carbon and nitrogen cycling along a TAI transect in the Chesapeake Bay region that spans zones of open water, coastal wetland and upland forest. Several simulation scenarios are designed to parse the effects of the individual controlling factors and the sensitivity of carbon cycling to reaction constants derived from laboratory experiments. Our simulations revealed a hot zone for carbon cycling under the coastal wetland and the transition zones between the wetland and the upland. Evapotranspiration is found to enhance the exchange fluxes between the surface and subsurface domains, resulting in higher dissolved oxygen concentration in the TAI. The transport of organic carbon decomposed from leaves provides additional source of organic carbon for the aerobic respiration and denitrification processes in the TAI, while the variability in reaction rates mediated by microbial activities plays a dominant role in controlling the heterogeneity and dynamics of the simulated redox conditions. This modeling-focused exploratory study enabled us to better understand the complex interactions of various system components at the TAIs that control the hydro-biogeochemical processes, which is an important step towards representing coastal ecosystems in larger-scale Earth system models.

Wildfires can induce an abundance of vegetation and soil changes that may trigger higher surface runoff and soil erosion, affecting the water cycling within these ecosystems. In this study, we employed the Advanced Terrestrial Simulator (ATS), an integrated and fully distributed hydrologic model at watershed scale to investigate post-fire hydrologic responses in a few selected watersheds with varying burn severity in the Pacific Northwest region of the United States. The model couples surface overland flow, subsurface flow, and canopy biophysical processes. We developed a new fire module in ATS to account for the fire-caused hydrophobicity in the topsoil. Modeling results show that the watershed-averaged evapotranspiration is reduced after high burn severity wildfires. Post-fire peak flows are increased by 21-34% in the three study watersheds burned with medium to high severity due to the fire-caused soil water repellency (SWR). However, the watershed impacted by a low severity fire only witnessed a 2% surge in post-fire peak flow. Furthermore, the high severity fire resulted in a mean reduction of 38% in the infiltration rate within fire-impacted watershed during the first post-fire wet season. Hypothetical numerical experiments with a range of precipitation regimes after a high severity fire reveal the post-fire peak flows can be escalated by 1-34% due to the SWR effect triggered by the fire. This study implies the importance of applying fully distributed hydrologic models in quantifying the disturbance-feedback loop to account for the complexity brought by spatial heterogeneity.

Streambed grain sizes and hydro-biogeochemistry (HBGC) control river functions. However, measuring their quantities, distributions, and uncertainties is challenging due to the diversity and heterogeneity of natural streams. This work presents a photo-driven, artificial intelligence (AI)-enabled, and theory-based workflow for extracting the quantities, distributions, and uncertainties of streambed grain sizes and HBGC parameters from photos. Specifically, we first trained You Only Look Once (YOLO), an object detection AI, using 11,977 grain labels from 36 photos collected from 9 different stream environments. We demonstrated its accuracy with a coefficient of determination of 0.98, a Nash–Sutcliffe efficiency of 0.98, and a mean absolute relative error of 6.65% in predicting the median grain size of 20 testing photos. The AI is then used to extract the grain size distributions and determine their characteristic grain sizes, including the 5th, 50th, and 84th percentiles, for 1,999 photos taken at 66 sites. With these percentiles, the quantities, distributions, and uncertainties of HBGC parameters are further derived using existing empirical formulas and our new uncertainty equations. From the data, the median grain size and HBGC parameters, including Manning’s coefficient, Darcy-Weisbach friction factor, interstitial velocity magnitude, and nitrate uptake velocity, are found to follow log-normal, normal, positively skewed, near log-normal, and negatively skewed distributions, respectively. Their most likely values are 6.63 cm, 0.0339 s·m-1/3, 0.18, 0.07 m/day, and 1.2 m/day, respectively. While their average uncertainty is 7.33%, 1.85%, 15.65%, 24.06%, and 13.88%, respectively. Major uncertainty sources in grain sizes and their subsequent impact on HBGC are further studied.