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Abstract Evaluating stream water chemistry patterns provides insight into catchment ecosystem and hydrologic processes. Spatially distributed patterns and controls of stream solutes are well‐established for high‐relief catchments where solute flow paths align with surface topography. However, the controls on solute patterns are poorly constrained for low‐relief catchments where hydrogeologic heterogeneities and river corridor features, like wetlands, may influence water and solute transport. Here, we provide a data set of solute patterns from 58 synoptic surveys across 28 sites and over 32 months in a low‐relief wetland‐rich catchment to determine the major surface and subsurface controls along with wetland influence across the catchment. In this low‐relief catchment, the expected wetland storage, processing, and transport of solutes is only apparent in solute patterns of the smallest subcatchments. Meanwhile, downstream seasonal and wetland influence on observed chemistry can be masked by large groundwater contributions to the main stream channel. These findings highlight the importance of incorporating variable groundwater contributions into catchment‐scale studies for low‐relief catchments, and that understanding the overall influence of wetlands on stream chemistry requires sampling across various spatial and temporal scales. Therefore, in low‐relief wetland‐rich catchments, given the mosaic of above and below ground controls on stream solutes, modeling efforts may need to include both surface and subsurface hydrological data and processes. 

ObjectiveOur objectives were to assess the efficacy of active inference models for capturing driver takeovers from automated vehicles and to evaluate the links between model parameters and self-reported cognitive fatigue, trust, and situation awareness. BackgroundControl transitions between human drivers and automation pose a substantial safety and performance risk. Models of driver behavior that predict these transitions from data are a critical tool for designing safer, human-centered, systems but current models do not sufficiently account for human factors. Active inference theory is a promising approach to integrate human factors because of its grounding in cognition and translation to a quantitative modeling framework. MethodWe used data from a driving simulation to develop an active inference model of takeover performance. After validating the model’s predictions, we used Bayesian regression with a spike and slab prior to assess substantial correlations between model parameters and self-reported trust, situation awareness, fatigue, and demographic factors. ResultsThe model accurately captured driving takeover times. The regression results showed that increases in cognitive fatigue were associated with increased uncertainty about the need to takeover, attributable to mapping observations to environmental states. Higher situation awareness was correlated with a more precise understanding of the environment and state transitions. Higher trust was associated with increased variance in environmental conditions associated with environmental states. ConclusionThe results align with prior theory on trust and active inference and provide a critical connection between complex driver states and interpretable model parameters. ApplicationThe active inference framework can be used in the testing and validation of automated vehicle technology to calibrate design parameters to ensure safety. 

Automation misuse and acceptance, influenced by trust, environmental conditions, and confidence, have hindered drivers from fully benefiting from partially automated vehicles. This study investigates how driver trust changes with AV reliance, differences in mental and physiological states, and continuous measures’ effectiveness. The takeover drivers reported lower trust than the non-takeover drivers in all scenarios. Nontakeover drivers’ elevated DLPFC activation aligns with trust networks and emotion regulation. The groups also differed in neural activation preand during scenarios with the takeover group showed more PFC, V2V3, and IFC engagement pre-scenario. Gaze revealed the takeover group fixated more on the AV button or dashboard, indicating readiness to take over, while non-takeover drivers focused on the rearview mirror, reflecting situational awareness. HRV responses showed higher physiological arousal in the takeover group pre-scenario. In summary, our multimodal approach reveals takeover behavior is associated with lower trust, cognitive unloading, increased stress, and anticipatory visual attention. 

This study employs a discrete event simulation (DES) model to understand the dynamic workload of remote truck operators managing partially-automated trucks. The DES model uses operator queues and event generators simulating automated truck events and leverages data from the California DMV’s disengagement database and driving simulation experiments. Disengagement data were partitioned into three groups by disengagement frequency: low, moderate, and high and separate arrival time distributions were developed for each group. Simulations from the model suggest that for companies with low disengagement rates, operator utilization will likely remain below minimal thresholds to prevent boredom. In contrast, companies with moderate or high disengagement rates both exceed operator utilization capacity and generate prolonged wait times as more trucks are controlled. These findings suggest that calibrating remote truck control to human capabilities will be challenging. A sensitivity analysis suggests that accurately estimating disengagement rates will be crucial for model accuracy and predictive performance. 

Driver reliance on automated vehicles (AV) is a critical component of safety particularly during high-risk traffic scenarios. Factors that influence reliance, including trust, situation awareness, fatigue, and demographics, have been independently explored; however, few analyses have investigated predicting AV reliance and compared factors comprehensively. The goals of this study were to develop a random forest (RF) model to predict reliance and to analyze the importance of factors for reliance decisions. We leveraged data from a driving simulation study where participants encountered four traffic events including responding to an illegal vehicle crossing, managing construction zones, stopping at a vandalized stop sign, and a pedestrian detection task. The dataset included reliance decisions and subjective assessments of dispositional trust, situational trust, fatigue, and workload. An RF model fit to the dataset using cross validation achieved an average AUC of 0.81 and accuracy of 0.77 and situational trust emerged as the most influential predictor. 

ABSTRACT Climate change is altering precipitation regimes that control nitrogen (N) cycling in terrestrial ecosystems. In ecosystems exposed to frequent drought, N can accumulate in soils as they dry, stimulating the emission of both nitric oxide (NO; an air pollutant at high concentrations) and nitrous oxide (N₂O; a powerful greenhouse gas) when the dry soils wet up. Because changes in both N availability and soil moisture can alter the capacity of nitrifying organisms such as ammonia‐oxidizing bacteria (AOB) and archaea (AOA) to process N and emit N gases, predicting whether shifts in precipitation may alter NO and N₂O emissions requires understanding how both AOA and AOB may respond. Thus, we ask: How does altering summer and winter precipitation affect nitrifier‐derived N trace gas emissions in a dryland ecosystem? To answer this question, we manipulated summer and winter precipitation and measured AOA‐ and AOB‐derived N trace gas emissions, AOA and AOB abundance, and soil N concentrations. We found that excluding summer precipitation increased AOB‐derived NO emissions, consistent with the increase in soil N availability, and that increasing summer precipitation amount promoted AOB activity. Excluding precipitation in the winter (the most extreme water limitation we imposed) did not alter nitrifier‐derived NO emissions despite N accumulating in soils. Instead, nitrate that accumulated under drought correlated with high N₂O emission via denitrification upon wetting dry soils. Increases in the timing and intensity of precipitation that are forecasted under climate change may, therefore, influence the emission of N gases according to the magnitude and season during which the changes occur.