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.

This study examines how greenhouse gas (GHG) production and organic matter (OM) transformations in coastal wetland soils vary with the availability of oxygen and other terminal electron acceptors. We also evaluated how OM and redox-sensitive species varied across different size fractions: particulates (0.45-1μm), fine colloids (0.1-0.45μm), and nano particulates plus truly soluble (<0.1μm; NP+S) during 21-day aerobic and anaerobic slurry incubations. Soils were collected from the center of a freshwater coastal wetland (FW-C) in Lake Erie, the upland-wetland edge of the same wetland (FW-E), and the center of a saline coastal wetland (SW-C) in Washington state. Anaerobic methane production for FW-E soils were 47 and 27,537 times greater than FW-C and SW-C soils, respectively. High particulate Fe2+ and dissolved sulfate concentrations in FW-C and SW-C soils suggest that iron and/or sulfate reduction inhibited methanogenesis. Aerobic CO2 production was highest for both freshwater soils, which had a higher proportion of OM in the NP+S fraction (64±28% and 70±10% for FW-C and FW-E, respectively) and C:N ratios reflective of microbial detritus (1.7±0.2 and 1.4±0.3 for FW-E and FW-C, respectively) compared to SW-C, which had a higher fraction of particulate (58±9%) and fine colloidal (19±7%) OM and C:N ratios reflective of vegetation detritus (11.2 ± 0.5). The variability in GHG production and shifts in OM size fractionation and composition observed across freshwater and saline soils collected within individual and across different sites reinforce the high spatial variability in the processes controlling OM stability, mobility, and bioavailability in coastal wetland soils.

Authors: Peter Regier1, Kyongho Son2, Xingyuan Chen2, Yilin Fang2, Peishi Jiang2, Micah Taylor2, Wilfred M Wollheim3, James Stegen2Affiliations 1Marine and Coastal Research Laboratory, Pacific Northwest National Laboratory, Sequim, WA, United States2Pacific Northwest National Laboratory, Richland, WA, United States3University of New Hampshire, Durham, NH, United StatesAbstract: Hyporheic zones regulate biogeochemical processes in streams and rivers, but high spatiotemporal heterogeneity makes it difficult to predict how these processes scale from individual reaches to river basins. Recent work applying allometric scaling (i.e., power-law relationships between size and function) to river networks provides a new paradigm to develop a scalable understanding of hyporheic biogeochemical processes. We used reach-scale hyporheic aerobic respiration estimates to explore allometric scaling patterns across two basins, and related these patterns to watershed characteristics. We found consistent scaling behaviors at lowest and highest exchange flux (HEF) quantiles, and consistent but HEF-dependent relationships to watershed elevation, precipitation, and land-cover. Our results also suggest variability of hyporheic respiration allometry for middle exchange flux quantiles, and in relation to land-cover. Our findings provide initial evidence that allometric scaling may be useful for predicting hyporheic biogeochemical dynamics across watersheds from reach to basin scales.Scientific Significance Statement: The hyporheic zone is a biogeochemical control point in streams and rivers, and processes like hyporheic respiration are important determinants of how watersheds move and process carbon and nutrients. However, the hyporheic zone is also characterized by high spatial heterogeneity, which makes it difficult to predict how hyporheic functions like respiration change across watersheds from reach to basin scales. This study applies allometric scaling theory, which suggests that function scales in a predictable way with size, to determine if hyporheic respiration scales with watershed area in two basins with contrasting watershed characteristics. We found some consistent patterns between basins that suggest allometric scaling of hyporheic respiration may be a tool for transferable knowledge of hyporheic function between basins, but also note some site-specific relationships may constrain the generalizability of this method to other regions and watersheds.

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.

The streambed is the critical interface between the aquatic and terrestrial systems and hosts important biogeochemical hot spots within river corridors. Although the streambed characteristics are significantly different from those of its surrounding soil, the streambed itself has not been explicitly represented in watershed models. We developed an integrated hydrologic model that explicitly incorporated a streambed layer to examine the hydrological effects of streambed characteristics including hydraulic conductivity (K), layer thickness, and width on the exchange fluxes across the streambed as well as the streamflow at the watershed outlet. The numerical experiments were performed in the American River Watershed, a headwater, mountainous watershed within the Yakima River Basin in central Washington. Despite having a negligible effect on the watershed streamflow, an explicit representation of the streambed with distinctive properties dramatically changed the magnitude and variability of the exchange flux. In general, larger streambed K along with a thicker streambed layer induced larger exchange fluxes. The exchange flux was most sensitive to the streambed width or the mesh resolution of the streambed. A smaller streambed width (or a finer streambed resolution) increases exchange fluxes per unit area while reducing the overall exchange volumes across the entire streambed. The amount of baseflow decreased by 6% as the streambed width decreased from 250 m to 50 m. This finding is important because these hydrological changes may in turn affect the exchange of nutrients and contaminants between surface water and groundwater and the associated biogeochemical processes. Our work demonstrated the importance of representing streambed in fully distributed, process-based watershed models in better capturing the exchange flow dynamics in river corridors.

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.

Denitrification in the hyporheic zone (HZ) of river corridors is crucial to removing excess nitrogen in rivers from anthropogenic activities. However, previous modeling studies of the effectiveness of river corridors in removing excess nitrogen via denitrification were often limited to the reach-scale and low-order stream watersheds. We developed a basin-scale river corridor model for the Columbia River Basin with random forest models to identify the dominant factors associated with the spatial variation of HZ denitrification. Our modeling results suggest that the combined effects of hydrologic variability in reaches and substrate availability influenced by land use are associated with the spatial variability of modeled HZ denitrification at the basin scale. Hyporheic exchange flux can explain most of spatial variation of denitrification amounts in reaches of different sizes, while among the reaches affected by different land uses, the combination of hyporheic exchange flux and stream dissolved organic carbon (DOC) concentration can explain the denitrification differences. Also, we can generalize that the most influential watershed and channel variables controlling denitrification variation are channel morphology parameters (median grain size (D50), stream slope), climate (annual precipitation and evapotranspiration), and stream DOC-related parameters (percent of shrub area). The modeling framework in our study can serve as a valuable tool to identify the limiting factors in removing excess nitrogen pollution in large river basins where direct measurement is often infeasible.

Holistic approaches are needed to investigate the capacity of current water resource operations and infrastructure to sustain water supply and critical ecosystem health under projected drought conditions. Drought vulnerability is complex, dynamic, and challenging to assess, requiring simultaneous consideration of changing water demand, use and management, hydrologic system response, and water quality. We are bringing together a community of scientists from the U.S. Geological Survey, National Center for Atmospheric Research, Department of Energy, and Cornell University to create an integrated human-hydro-terrestrial modeling framework, linking pre-existing models, that can explore and synthesize system response and vulnerability to drought in the Delaware River Basin (DRB). The DRB provides drinking water to over 15 million people in New York, New Jersey, Pennsylvania, and Delaware. Critical water management decisions within the system are coordinated through the Delaware River Basin Commission and must meet requirements set by prior litigation. New York City has rights to divert water from the upper basin for water supply but must manage reservoir releases to meet downstream flow and temperature targets. The Office of the Delaware River Master administers provisions of the Flexible Flow Management Program designed to manage reservoir releases to meet water supply demands, habitat, and specified downstream minimum flows to repel upstream movement of saltwater in the estuary that threatens Philadelphia public water supply and other infrastructure. The DRB weathered a major drought in the 1960s, but water resource managers do not know if current operations and water demands can be sustained during a future drought of comparable magnitude. The integrated human-hydro-terrestrial modeling framework will be used to identify water supply and ecosystem vulnerabilities to drought and will characterize system function and evolution during and after periods of drought stress. Models will be forced with consistent input data sets representing scenarios of past, present, and future conditions. The approaches used to unify and harmonize diverse data sets and open-source models will provide a roadmap for the broader community to replicate and extend to other water resource issues and regions.

Hydrologic exchange flows (HEFs) across the river-aquifer interface have important implications for biogeochemical processes and contaminant plume migration in the river corridor, yet little is known about the hydrogeomorphic factors that control HEFs dynamics under dynamic flow conditions. Here, we developed a 3-D numerical model for a large regulated river corridor along the Columbia River to study how HEFs are controlled by the interplays between dam-regulated flow conditions and hydrogeomorphic features of such river corridor system. Our results revealed highly variable intra-annual spatiotemporal patterns in HEFs along the 75-km river reach, as well as strong interannual variability with larger exchange volumes in wet years than dry years. In general, the river was losing during late spring to early summer when the river stage was high, and river was gaining in fall and winter when river stage was low. The magnitude and timing of river stage fluctuations controlled the timing of high exchange rates. Both river channel geomorphology and the thickness of a highly permeable river bank geologic layer controlled the locations of exchange hot spots, while the latter played a dominant role. Dam-induced, sub-daily to daily river stage fluctuations drove high-frequency variations in HEFs across the river-aquifer interfaces, resulting in greater overall exchange volumes as compared to the case without high-frequency flows. Our results demonstrated that upstream dam operations enhanced the exchange between river water and groundwater with strong potential influence on the associated biogeochemical processes and on the fate and transport of groundwater contaminant plumes in such river corridors.

River corridors, the spatial domains around rivers in which river water interacts with surrounding sediment and rock, are important components of watersheds. They comprise extremely complex ecosystems: heterogeneous at all spatial scales with strong temporal dynamics, coupled biological, geochemical, and hydrologic processes, and ubiquitous human impacts. We present several ways that our project, focused around the 75 km Hanford Reach of the Columbia River but with multiple connections to other systems, is addressing this challenge. These include 1) deployment of intensive, automated sensor networks supplemented by data from the Hanford Environmental Information System (HEIS) for hyporheic zone monitoring 2) data assimilation of these and other data into models using joint hydrologic and geophysical inversion, 3) integrating MASS2 model outputs and bathymetry data using machine learning to classify hydromorphologic features, 4) a community-based effort to develop broad understanding of organic carbon biogeochemistry and microbiomes in diverse river systems, and 5) use of multi-‘omics data to develop new biogeochemical reaction networks. These underpin the incorporation of process understanding and diverse data into high-resolution mechanistic models, and employment of those models to develop reduced-order models that can be applied at large scales while retaining the effects of local features and processes. In so doing we are contributing to reduction of uncertainties associated with major Earth system biogeochemical fluxes, thus improving predictions of environmental and human impacts on water quality and riverine ecosystems and supporting environmentally responsible management of linked energy-water systems.

We demonstrate the application of two simple machine learning tools - regression tree and gradient boosting analyses - to a hydrologic inference problem to address two objectives. The first goal was to infer the flux between a river and the subsurface based on high temporal resolution (5-minute) observations of subsurface pressure and temperature. The second goal was to identify an optimal set of observations to support these inferences. Specifically, we examine how many and what type of observations (pressure and/or temperature) were necessary and at what depths. Using synthetic observations and surface fluxes provided by a fully resolved three-dimensional flow and heat transport model, we found that both machine learning tools could identify the flux well using pressure and temperature measurements collected at three depths, even when considerable noise was added to the synthetic observations. Neither method could provide reasonable flux estimates given only noisy temperature data. A shallow, collocated temperature and pressure observations performed as well as the complete data set. The results show the promise of using machine learning tools to design hydrologic measurement networks - both for determining whether a proposed data set can constrain inversion and for optimizing monitoring networks comprised of multiple measurement types.

The comparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty involved in climate change predictions. In this study, an advanced hierarchical uncertainty analysis framework incorporated with a variance-based global sensitivity analysis, was developed to quantify different sources of uncertainty in hydrological projections under climate change. The uncertainties considered in this research are from greenhouse gas emission scenarios (GGES), global climate models (GCMs), hydrological models (Xinanjiang and variable infiltration capacity (VIC) models) and hydrological parameters, and this new methodology was implemented in a humid subtropical basin in southern China. The results indicated that the GCMs and hydrological parameters (GGESs) are the main (least) contributor of uncertainty in the discharge projections at the interannual scale. At the intra-annual scale, GCMs contribute the largest uncertainty of the discharge predictions during summer season, whereas the uncertainty due to GGESs, hydrological model and parameters is generally larger in winter. It was also found that although there is a strong temporal and spatial variability of general sources of uncertainty, this heterogeneity does not affect the importance of uncertainty sources. This study provides a better understanding of the uncertainty sources in hydrological predictions in the context of climate change. And the uncertainty analysis framework used is mathematically rigorous and can be applied to a wide range of climate and hydrological models with different uncertainty sources.