Sharon A. Billings

Land surface models (LSMs), such as the Community Land Model version 5 (CLM5), represent complex vegetation processes; however, systematic biases persist between modeled and observed leaf area index (LAI) because of parameter uncertainties and knowledge gaps. The high computational cost of CLM5 is a barrier to extensive sensitivity analyses and ensemble simulations at the global scale. This study addresses these limitations by training an evidential deep neural network (EDNN) emulator on a 500‐member CLM5 perturbed parameter ensemble generated via Latin hypercube sampling of 32 key plant physiological parameters. The EDNN employs cyclic temporal encoding to preserve seasonal periodicity and to predict LAI anomalies, thereby emphasizing variability. It also quantifies predictive uncertainty by jointly learning aleatoric and epistemic components in a single forward pass, yielding well‐calibrated probabilistic outputs without requiring computationally intensive ensembles. Across the contiguous United States, the EDNN reproduces CLM5‐simulated LAI with a median on held‐out members and years while requiring substantially less computation. The EDNN emulator captures seasonal cycles, interannual variability in LAI, and uncertainties (aleatoric and epistemic) in a single pass. The sensitivity analysis highlights photosynthetic capacity and the leaf carbon‐to‐nitrogen ratio as dominant controls on LAI variability, with seasonal shifts in their influence reflecting phenological dynamics. These capabilities enable comprehensive parameter‐sensitivity studies and more efficient calibration and tuning of land surface models by supporting rapid probabilistic forecasting and adaptive model refinement, thereby paving the way for scalable Earth‐system modeling, robust parameter exploration, and uncertainty‐informed projections of land‐surface processes.

Soil structure describes the repeating units of peds—aggregated soil particles bound by planes of weakness and formed from soil forming processes—and the corresponding network of interpedal pores. Processes forming soil structure can be altered in soils above shallow lithic contacts (SLCs), making the soil-rock interface potentially important to the development and maintenance of soil structure. Despite the widespread occurrence of SLCs, the mechanisms controlling soil structural development and water flow remain poorly understood. We used a unique long-term experiment at the Konza Prairie Biological Station where limestones were buried at ~20-25 cm depth to mimic a shallow bedrock and compared them to control plots without rocks. After 25 years, four soil pits (two control and two limestone treatments) were excavated to 40 cm to assess aggregate-size distribution, macroporosity, ped morphology, root biomass, micromorphology, and hydraulic properties. Larger aggregates (>0.5 mm) and greater macroporosity were observed above limestones compared to control. In the upper 0–5 cm, limestone plots exhibited larger, rougher, and less rounded peds, along with higher root biomass. Micromorphological analyses revealed distinct pore types and abundances, indicating structures that formed during the experiment as well as structural pores that can facilitate water and fine particle transport. Soil moisture data showed higher macropore flow and more macropore peaks in interspace (space b/w the rocks) than in control and rock plots. These findings provide a foundation for developing and testing hypotheses in experimental and natural settings for how lithic contacts influence soil structural formation and pore network development.

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Plants allocate significant amounts of carbon belowground to support water and nutrient acquisition, and a large proportion of that carbon is allocated to the production of root exudates. One utility of exudates is to aid in phosphorus acquisition, but the amount of phosphorus that can be liberated by different exudates is largely unknown. We used three common exudates—oxalic acid, malic acid, and acid phosphatase (APase)—to extract phosphate from 25 soil profiles across the United States. These extracts represent the “maximum efficiency” of specific exudates at liberating phosphorus from a wide range of soil types, providing an upper boundary to constrain biogeochemical and land‐surface models aimed at representing exudate‐related nutrient fluxes. Our findings reveal significant variability in the efficiency of exudates to liberate phosphate across locations and soil depths. Oxalic acid exhibited the highest potential extraction efficiency overall, and its efficiency often increased in deeper horizons—likely driven by higher proportions of mineral‐bound phosphorus in deeper soil layers. APase had the lowest overall potential extraction efficiency, and its efficiency typically declined with depth and had stronger associations with soil organic carbon. Malic acid potential extraction efficiency was intermediate, and depth trends were often more similar to APase than oxalic acid. Malic acid and APase showed increasing extraction efficiency at sites with higher mean annual temperature and precipitation. This study highlights the varying efficiencies of exudate compounds in liberating soil phosphorus across ecosystems and provides a framework for understanding nutrient dynamics under changing climate or land‐use conditions.

Relative to the rest of the soil matrix, soil preferential flow (PF) disproportionately influences large-scale processes like groundwater recharge and solute flux via rapid transport of water through soil pore spaces. Despite the ubiquity of PF occurrence across land uses and climates, there is lack of understanding around the feedbacks between PF generation and carbon transport and transformation, both of which result from abiotic (e.g., water content, clay content) and biotic (e.g., root and microbial activity) processes. We can assess the depth distribution of soil carbon parameters in relation to PF occurrence through soil profiles to decipher the effects of PF generation on carbon dynamics. Recent work from our group shows that the probability of PF occurrence increases with rainfall intensity, net primary production, and clay content. In addition, total rainfall amount increases PF propagation to deeper soil layers. We hypothesize that these same factors increase the transport of carbon deeper into the subsoil and influence carbon transformation at depth. Specifically, we hypothesize that high peak rainfall intensity, net primary production, and clay content will be associated with greater availability of extractable organic carbon relative to total soil organic carbon (SOC) at depth due to rapid transport via PF, high root abundance, and high retention of mineral-associated SOC, respectively. To identify the dominant drivers of PF and the resulting dynamic feedbacks with soil carbon, we synthesized metrics of soil PF, carbon, and enzyme activity from observations at eight U.S. sites spanning climatic gradients, land-cover types, and soil orders. We derived PF metrics using a soil water velocity threshold method based on observations of high frequency soil moisture and precipitation data. PF occurred when the observed soil water flux exceeded the saturated hydraulic conductivity of the soil matrix estimated from a pedotransfer function. Preliminary findings indicate that land-cover is a primary control on extractable DOC availability and propagation with soil depth. PF may also be associated with DOC transformations, given the coupling of land cover and net primary production. Understanding which, and to what extent, abiotic and biotic factors promote generation of PF through feedbacks with soil carbon dynamics advances our knowledge of the potential effects of changing climate and land use on water and carbon fluxes through soils.

Forest plantations are widespread globally. Young forest plantations (hereafter young forests) differ from natural old‐growth forests (hereafter old forests) in above‐ and below‐ground structures, shaping water and carbon cycling processes. While above‐ground differences are well studied, below‐ground hydrology and biogeochemical processes remain poorly understood. Here we asked: How do hydrological flow paths and dissolved carbon processes belowground differ between young and old forests? Using a process‐based hydro‐biogeochemical model (BioRT‐HBV) constrained by streamflow and dissolved organic and inorganic carbon (DOC and DIC) data, we analyzed three pairs of young‐old forests at the H.J. Andrews Experimental Forest, Oregon, USA. Detailed simulations for a 57‐year‐old plantation (WS01) and a naturally regenerated ∼500‐year‐old forest (WS02) showed that the young forest had lower streamflow and smaller deep groundwater contributions (20%) than the old forest (30%). DOC was mainly produced in shallow soil but diverged with depth: transformed into DIC in the young forest and further produced in the old forest, yielding contrasting export patterns of flushing (DOC increases with discharge) and dilution (DOC decreases with discharge). These differences likely stem from variations in subsurface structures, supported by deeper, denser roots in old forest. Extending the analysis to two additional pairs showed (a) higher DOC and DIC concentrations in all old forests; (b) consistent DIC dilution patterns but variable DOC patterns. Numerical experiments indicate that these diverse DOC behaviors result from interactions among forest age, geology, and hydrological connectivity, and other factors, highlighting the overlooked role of forest development in subsurface carbon cycling.

Accurate streamflow prediction in snow-dominated, ungauged basins remains a persistent challenge in hydrology due to strong seasonal variability, sparse observations, and complex topography. Predicting streamflow in these regions is crucial for improving water resource management and climate adaptation. In this study, we employed a model emulation approach that combines physically-based hydrologic ensemble simulations with Evidential Deep Neural Networks (EDNNs) to deliver both accurate predictions and uncertainty estimates that are statistically consistent with observations. We trained EDNNs on simulated streamflow generated by ensemble simulations of the Community Land Model version 5 (CLM5), where each ensemble member (EM) represents a different parameter configuration designed to sample parametric uncertainty. We identified snow-dominated basins among the 627 catchments from the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) dataset in the contiguous United States using hydroclimatic thresholds, followed by unsupervised clustering based on seasonal streamflow signatures and physiographic descriptors. Initially, we trained the EDNN on simulated outputs from a single CLM5 EM in a basin cluster and tested its generalization to other clusters and parameter configurations. Subsequently, we incrementally increased the training dataset by progressively adding five additional EMs and approximately five catchments at each increment, without altering the EDNN architecture, to evaluate how increasing training data influenced predictive accuracy (e.g., Nash–Sutcliffe Efficiency) and uncertainty calibration. Our findings revealed that the EDNN successfully captured streamflow dynamics while decomposing predictive uncertainty into epistemic (model-driven) and aleatoric (data-driven) components. Both uncertainty calibration and predictive accuracy improved substantially as more EMs and catchments were included, underlining the benefits of physics-based ensemble training data in sparsely observed regions. This approach offers a scalable framework for streamflow prediction in snow-dominated basins, reduces the computational costs of ensemble-based calibration efforts, and enables parameter transfer to out-of-sample watersheds.