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ABSTRACT By altering hydrological and geomorphological processes at watershed scales, humans have substantially influenced the movement of sediment on Earth's surface. Despite widespread recognition of human impacts on erosion and deposition, few studies have assessed the magnitude of change in watershed‐scale sediment fluxes before and after the implementation of industrial agriculture and how agricultural development has altered the spatial distribution of sediment fluxes throughout watersheds. This study uses a modeling approach to explore changes in sediment fluxes before and after agricultural development in the upper Sangamon River Basin—an agricultural watershed in the midwestern United States. Comparison of model predictions with river hydrological and sediment data and with information on soil erosion and floodplain sedimentation shows the model accurately captures contemporary fluxes of water and sediment. To assess human impact, native land‐cover conditions are used to estimate the magnitude and spatial distribution of sediment fluxes before the landscape was transformed by farming practices. Results suggest that sediment delivery from hillslopes to streams in this low relief watershed has increased 11‐fold and the sediment load in streams has increased eight‐fold since European settlement. Floodplain sedimentation has also increased dramatically, a finding consistent with recent estimates of post‐settlement alluvium accumulation rates. The proportion of sediment exported from the basin is now slightly greater than it was in the 1800s. Overall, the model results indicate that humans have greatly enhanced the movement and storage of sediment within the upper Sangamon River basin. 

Abstract Relatively little is known about the geomorphological characteristics of floodplain secondary channels and the potential for floodplain flows to mobilize bed material within these channels. This study examines the geomorphological characteristics (channel form, material properties, wood jams) and bed‐material mobilization potential of secondary channels on the floodplain of a meandering river in Illinois, USA. It also compares these attributes to those of the main channel. Results show that secondary channels are at most about one‐third the size of the main channel but also vary in size over distance. Channel dimensions tend to be greatest near the proximal connection of secondary channels to the main channel, suggesting that flow from the main channel is effective in producing scour where it enters secondary channels. The beds of secondary channels consist mainly of mud in contrast to sand and gravel on the bed of the main channel, implying that secondary channels do not convey bed material from the main channel onto the floodplain. Secondary channels connected to the main channel at both ends have more abundant active wood jams than those connected only at the proximal end. Flow from the main channel enters secondary channels at sub‐bankfull stages, but maximum mobilization of cohesive bed material in secondary channels only occurs during flows that exceed the average bankfull stage in the main channel. Overall, secondary channels are active conduits of flow, sediment, and large wood on floodplains and can contribute to floodplain sediment fluxes through entrainment of bed material. 

Abstract Surface runoff and infiltrated water en route to the stream interact with dynamic landscape properties, ranging from vegetation and microbial activities to soil and geological attributes. Stream solute concentrations are highly variable and interconnected due to these interactions, flow paths, and residence times, and often exhibit hysteresis with flow. Significant unknowns remain about how point measurements of stream solute chemistry reflect interdependent hydrobiogeochemical and physical processes, and how signatures are encapsulated as nonlinear dynamical relationships between variables. We take a Machine Learning (ML) approach to understand and capture these dynamical relationships and improve predictions of solutes at short and long time scales. We introduce a physical process‐based “flow‐gate” into an Long Short‐Term Memory (LSTM) model, which enables the model to learn hysteresis behaviors if they exist. Further, we use information‐theoretic metrics to detect how solutes are interdependent and iteratively select source solutes that best predict a given target solute concentration. The “flow‐gate LSTM” model improves model predictions (1%–32% decreases in RMSE) relative to the standard LSTM model for all nine solutes included in the study. The predictive improvements from the flow‐gate LSTM model highlight the importance of lagged concentration and discharge relationships for certain solutes. It also indicates a potential limitation in the traditional LSTM model approach since flow rates are always provided as input sources, but this information is not fully utilized. This work provides a starting point for a predictive understanding of geochemical interdependencies using machine‐learning approaches and highlights potential improvements in model architecture. 

ABSTRACT Floodplains along low‐gradient, meandering river systems contain diverse hydrogeomorphic features, ranging from isolated depressions to hydrologically‐connected channels. These ephemerally‐flooded features inundate prior to river water overtopping all banks, enhancing river‐floodplain connectivity during moderately high flow stages. Predicting when and where ecological functions occur in floodplains requires understanding the dynamic hydrologic processes of hydrogeomorphic features, including inundation and exchange. In this study, we examined storm event‐scale inundation and exchange dynamics along a lowland, meandering river system in central Illinois (USA). We monitored surface water presence/absence, surface water level, and groundwater level across floodplain hydrogeomorphic feature types (i.e., isolated depression, backwater channel, and flow‐through channel). Using these data, we evaluated inundation onset and recession characteristics, drivers of groundwater‐surface water interactions, and direction of hydrologic exchange with the river channel. Surface water presence/absence patterns suggested inundation onset timescales were primarily controlled by microtopography and recession timescales were correlated with floodplain elevation. Employing a novel hysteresis approach for characterising groundwater‐surface water interactions, we observed distinct patterns indicating differences in water sources across hydrogeomorphic units and event characteristics. Finally, differences in hydraulic head along floodplain channels revealed that channels with multiple inlets/outlets (i.e., flow‐through channels) conveyed down‐valley flow and channels with single inlets primarily functioned as sinks of river‐derived water to the floodplain with short source periods. These results highlight the heterogeneity of hydrologic processes that occur along lowland, meandering river‐floodplains, and more specifically, point to the important role hydrogeomorphic features play in controlling dynamic connectivity within the river corridor. 

Abstract In a complex ecohydrologic system, vegetation and soil variables combine to dictate heat fluxes, and these fluxes may vary depending on the extent to which drivers are linearly or nonlinearly interrelated. From a modeling and causality perspective, uncertainty, sensitivity, and performance measures all relate to how information from different sources “flows” through a model to produce a target, or output. We address how model structure, broadly defined as a mapping from inputs to an output, combines with source dependencies to produce a range of information flow pathways from sources to a target. We apply information decomposition, which partitions reductions in uncertainty into synergistic, redundant, and unique information types, to a range of model cases. Toy models show that model structure and source dependencies both restrict the types of interactions that can arise between sources and targets. Regressions based on weather data illustrate how different model structures vary in their sensitivity to source dependencies, thus affecting predictive and functional performance. Finally, we compare the Surface Flux Equilibrium theory, a land‐surface model, and neural networks in estimating the Bowen ratio and find that models trade off information types particularly when sources have the highest and lowest dependencies. Overall, this study extends an information theory‐based model evaluation framework to incorporate the influence of source dependency on information pathways. This could be applied to explore behavioral ranges for both machine learning and process‐based models, and guide model development by highlighting model deficiencies based on information flow pathways that would not be apparent based on existing measures. 

Abstract At the edge of alpine and Arctic ecosystems all over the world, a transition zone exists beyond which it is either infeasible or unfavorable for trees to exist, colloquially identified as the treeline. We explore the possibility of a thermodynamic basis behind this demarcation in vegetation by considering ecosystems as open systems driven by thermodynamic advantage—defined by vegetation’s ability to dissipate heat from the earth’s surface to the air above the canopy. To deduce whether forests would be more thermodynamically advantageous than existing ecosystems beyond treelines, we construct and examine counterfactual scenarios in which trees exist beyond a treeline instead of the existing alpine meadow or Arctic tundra. Meteorological data from the Italian Alps, United States Rocky Mountains, and Western Canadian Taiga-Tundra are used as forcing for model computation of ecosystem work and temperature gradients at sites on both sides of each treeline with and without trees. Model results indicate that the alpine sites do not support trees beyond the treeline, as their presence would result in excessive CO$$_2$$ ${}_2$loss and extended periods of snowpack due to temperature inversions (i.e., positive temperature gradient from the earth surface to the atmosphere). Further, both Arctic and alpine sites exhibit negative work resulting in positive feedback between vegetation heat dissipation and temperature gradient, thereby extending the duration of temperature inversions. These conditions demonstrate thermodynamic infeasibility associated with the counterfactual scenario of trees existing beyond a treeline. Thus, we conclude that, in addition to resource constraints, a treeline is an outcome of an ecosystem’s ability to self-organize towards the most advantageous vegetation structure facilitated by thermodynamic feasibility. 

Abstract Vegetation optimizes its geochemical environment for resource management via root exudation. We refer to the soil zone where biogeochemical behavior is significantly influenced, directly or indirectly, by root processes as the vegetation induced reactive zone (VIRZ). Root exudates react with VIRZ soil substrates creating temporally variable chemical environments through depth that extend below the rooting zone, impacting weathering, and releasing solutes and gases. We present a new framework, REWTCrunch, to capture VIRZ dynamics by integrating three modeling advances: the multicomponent reactive transport model CrunchFlow, the root exudation model REWT, and the multilayer canopy‐root ecohydrologic model MLCan. REWTCrunch's high‐resolution, process‐based simulation of root exudation, and the transport and transformation of carbon (C) and nutrients according to mass‐balanced and charge‐balanced reaction networks gives new insight into vertically resolved root‐soil‐microbe‐water interactions and their influence on solute fluxes at a daily timescale. We benchmark REWT and CrunchFlow, illustrate coupling mechanisms, and present REWTCrunch simulations for an agricultural site in the US Midwest. Results demonstrate root‐sourced reactive C can augment or reduce solute concentrations in the soil by several orders of magnitude. Silicate weathering products illustrate after‐harvest effects of plant C inputs in leaching patterns. Calcium simulations reveal the development of a stable weathering front. Aluminum concentrations are particularly responsive to root‐sourced reactivity, and analysis of leaching concentration versus leaching flux indicates hysteresis behavior. REWTCrunch significant improves our ability to simulate the link between root processes and soil biogeochemistry, thereby filling an important gap in the numerical simulation of root processes, weathering, and long‐term soil health. 

Abstract Threshold changes in rainfall‐runoff generation commonly represent shifts in runoff mechanisms and hydrologic connectivity controlling water and solute transport and transformation. In watersheds with limited human influence, threshold runoff responses reflect interaction between precipitation event and antecedent soil moisture. Similar analyses are lacking in intensively managed landscapes where installation of subsurface drainage tiles has altered connectivity between the land surface, groundwater, and streams, and where application of fertilizer has created significant stores of subsurface nitrogen. In this study, we identify threshold patterns of tile‐runoff generation for a drained agricultural field in Illinois and evaluate how antecedent conditions—including shallow soil moisture, groundwater table depth, and the presence or absence of crops—control tile response. We relate tile‐runoff thresholds to patterns of event nitrate load observed across multiple storm events and evaluate how antecedent conditions control within‐event nitrate concentration‐discharge relationships. Our results demonstrate that an event tile‐runoff threshold emerges relative to the sum of gross precipitation and indices of antecedent shallow soil moisture and antecedent below‐tile groundwater moisture deficit, indicating that both shallow soil and below‐tile storages must be filled to generate significant runoff. In turn, event nitrate load shows a linear dependence on runoff for most time periods, suggesting that subsurface nitrate export and storage can be estimated using runoff threshold relationships and long‐term average nitrate concentrations. Finally, within‐event nitrate concentration‐discharge relationships are controlled by event size and the antecedent tile flow state because these factors dictate the sequence of flow path activation and tile connectivity over a storm event. 

Abstract Hydraulic redistribution is the transport of water from wet to dry soil layers, upward or downward, through plant roots. Often in savanna and woodland ecosystems, deep‐rooted trees, and shallow‐rooted grasses coexist. The degree to which these different species compete for or share soil‐water derived from precipitation or groundwater, as well as how these interactions are altered by hydraulic redistribution, is unknown. We use a multilayer canopy model and field observations to examine how the presence of deep, but tree‐root accessible, groundwater impacts seasonal patterns of hydraulic redistribution, and interaction between coexisting vegetation species in a semiarid riparian woodland (US‐CMW). Based on the simulation, trees absorb moisture at the water table (∼10 m depth) and release it in the shallow soil depth (0–3 m) during the dry pre‐monsoon season. We observed the occurrence of a new convergent hydraulic redistribution pattern during the monsoon season, where moisture is transported from both the near‐surface (0–0.5 m) and the water table to intermediate soil layers (1–5 m) through tree roots. We found that hydraulic redistribution demonstrates a growth facilitation effect at this site, supporting 49% of growing season tree transpiration and 14% of the grass transpiration. Compared to a similarly structured upland savanna without accessible groundwater, the riparian site shows an increased amount of hydraulically redistributed water and more facilitative water use between coexisting grasses and trees. These results shed light on the linkage between accessible groundwater and the role of hydraulic redistribution on the interaction between deep‐rooted and shallow‐rooted vegetation. 

Satellite-based evapotranspiration (ET) products inform decision-making regarding water availability and plant water use from sub-field to watershed scales. These products are validated using eddy covariance flux towers, where observations are subject to the influence of landscape heterogeneity due to a constantly shifting contributing area, or flux footprint, governed by surface and atmospheric drivers. In agricultural regions with multiple crop types, local heterogeneity may amplify the importance of appropriate grid cell selection for satellite ET comparisons. We evaluate the extent to which different satellite ET products capture both ET magnitudes and spatial variability due to landscape heterogeneity. We compare ECOSTRESS 70m instantaneous and daily ET products to flux tower observations in central Illinois with measurements at two heights, representing different but overlapping flux footprint areas. We estimate satellite ET based on a flux footprint weighting, gridded averages around the tower, and for crop-specific corn or soybean grid cells. In this region, the disALEXI daily ET product has better overall performance relative to the PT-JPL daily product, which is more sensitive to overpass times and tends to overestimate instantaneous ET. However, the PT-JPL model reflects more variability at high spatial resolution. Specifically, a footprint-derived ET improves the data-model comparison relative to disALEXI, and PT-JPL more closely replicates crop-specific and field-specific differences as inferred from the 2-height experimental setup. This study highlights differences in how models integrate spatial inputs, which lead to different representations of spatial variability for the same nominal resolution. This can also have important implications for understanding and predicting field-level differences in land-atmosphere fluxes.