Hyporheic zones (HZ) can contribute substantially to total stream ecosystem respiration (ERtot). HZ-focused process-based models may, therefore, effectively predict ERtot across sites, yet this remains untested under variable environmental conditions. Here, we evaluate whether spatial variation in HZ respiration predicted via a process-based model explains spatial variation in field-estimates of ERtot across 33 sites in the Yakima River basin in Washington State, USA. We found that HZ respiration predictions did not explain spatial variation in field estimates of ERtot. To investigate further, we partitioned ERtot contributions into water-column respiration (ERwc) and sediment-associated respiration (ERsed). ERsed contributed >50% of ERtot at 88% of sites, though relative contributions varied substantially. Despite this dominance, modeled HZ respiration explained neither spatial variation in ERtot nor in ERsed, suggesting that the HZ model alone does not capture the drivers of sediment-associated respiration across these sites. Instead, ERsed spatial variation was primarily explained by gross primary production, stream slope, velocity, and total dissolved nitrogen rather than median grain size, a primary control of HZ respiration predicted by the process-based model. Consistent with recent studies, our results indicate that improving basin-scale ERtot predictions requires integrating hydrologic and biogeochemical processes across hyporheic, benthic, and water-column zones. Sediment-associated respiration accounts for most stream ecosystem respiration and its spatial variability, while modeled hyporheic respiration fails to explain these patterns, based on field measurements and process-based modeling across 33 sites in the Yakima River basin

Meteorological forcing data uncertainty is a major source of uncertainty in hydrologic models, with cascading effects on watershed model performance and parameterization. While direct impact on model outputs are well studied, the complex interaction between meteorological uncertainty, model performance, model parameter uncertainty and sensitivity remains underexplored. This study presents a diagnostic investigation into this uncertainty cascade. We systematically evaluate how the uncertainty of commonly used meteorological products (Daymet, Gridmet and NLDAS) propagates through the SWAT model to affect not only the watershed model performance-specifically streamflow, evapotranspiration (ET), and crop yield- but also the model parameter sensitivity and posterior parameter distributions. Also, we explore how these interactions are mediated by observed data used in calibration, and calibration methods.The evaluation of the meteorological forcing data showed that NLDAS exhibited the highest accuracy for measured precipitation compared to GHCNd, while Daymet had the highest accuracy for daily temperature. This translated into diagnostic trade-offs in model performance: Using Daymet resulted in the highest ET accuracy, whereas differences in flow and crop yield predictions among the meteorological-forcing data were minor. More importantly, the sensitivity of model parameters and resulting behavioral parameter sets are highly dependent on both the model variable and meteorological input. This provides direct evidence of meteorological input-driven equifinality, where the model compensates for different input bias by altering model parameterization. This study highlights a strong connection between the uncertainty of meteorological data and model performance and parameter uncertainty and sensitivity, and also emphasizes the importance of calibration methods and the observed data used in evaluating the impact of meteorological data uncertainty.

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 for understanding cumulative hyporheic biogeochemical processes. We used previously published model predictions of reach-scale hyporheic aerobic respiration to explore patterns in allometric scaling across two climatically divergent basins with differing characteristics in the Pacific Northwest, United States. In the model, hydrologic exchange fluxes (HEFs) regulate hyporheic respiration, so we examined how HEFs might influence allometric scaling of respiration. We found consistent scaling behaviors where HEFs were either very low or very high, but differences between basins when HEFs were moderate. Our findings provide initial model-generated hypotheses for factors influencing allometric scaling of hyporheic respiration. These hypotheses can be used to optimize new data generation efforts aimed at developing predictive understanding of allometries that can, in turn, be used to scale biogeochemical dynamics across watersheds.

Understanding aquatic ecosystem metabolism involves the study of two key processes: carbon fixation via primary production and organic C mineralization as total ecosystem respiration (ERtot). In streams and rivers, ERtot includes respiration in the water column (ERwc) and in the sediments (ERsed). While literature surveys suggest that ERsed is often a dominant contributor to ERtot, recent studies indicate that the relative influence of sediment-associated processes versus water column processes can fluctuate along the river continuum. Still, a comprehensive understanding of the factors contributing to these shifts within basins and across stream orders is needed. Here, we contribute to this need by measuring ERwc and aqueous chemistry across 47 sites in the Yakima River basin, Washington, USA. We find that ERwc rates vary throughout the basin during baseflow conditions, ranging from 0 to −7.38 g O2 m−3 d−1, and encompass the entire range of ERwc rates from previous work. Additionally, by comparing to ERtot estimates for rivers across the contiguous United States, we suggest that the contribution of ERwc rates to reach-scale ERtot rates across the Yakima River basin is likely highly variable, but we do not test this directly. We observe that ERwc is locally controlled by temperature, dissolved organic carbon, total dissolved nitrogen, and total suspended solids, which explains 49 % of ERwc variability across the basin using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Our findings highlight the potential relevance of water column processes in aquatic ecosystem metabolism across the entire stream network and that these influences are likely not predictable simply by knowing the position in the stream network. Our results are generally congruent with previous work in terms of locally influential variables, suggesting that the observed variability and suite of associated environmental factors influencing ERwc are potentially transferable across basins.

IntroductionThe distribution of sediment grain size in streams and rivers is often quantified by the median grain size (D50), a key metric for understanding and predicting hydrologic and biogeochemical function of streams and rivers. Manual D50 measurements are time-consuming and ignore larger grains, while approaches to model D50 based on catchment characteristics may over-generalize and miss site-scale heterogeneity. Machine learning-enabled object detection methods like You Only Look Once (YOLO) provides an alternative that enables estimation of D50 that is faster than manual measurements and more site-specific than predictions based on catchment characteristics.MethodsTo understand the potential role of object detection methods for improving understanding of D50, we compared D50 estimates made manually, predicted from catchment characteristics, and using a YOLO-enabled approach across the Yakima River Basin.ResultsWe found distinct differences between methods for D50 averages and variability, and relationships between D50 estimates and basin characteristics.DiscussionWe discuss the advantages and limitations of object detection methods versus current methods, and explore potential future directions to combine D50 methods to better estimate spatiotemporal variation of D50, and improve incorporation into basin-scale models.

The rate of sediment supply has significant impacts on river morphology, making it crucial to understand the geomorphic changes and grain size distribution dynamics in rivers. However, the effects of varying grain size sediment input on morphological changes in braided channels remain poorly understood. This study is the first to investigate the bar development and sediment sorting processes in braided channels with non-uniform sediment inputs using both numerical and experimental approaches. We applied a two-dimensional numerical model, Nays2DH to confirm and generalize experimental results. The model reproduced key experiment results, including 1) stream elevation changes, and 2) grain size distribution. Using this validated model, we explored the morphological changes and sorting process in a braided river with sediment inputs. The numerical experiments demonstrate that sediment input controls the elevation of the stream bed and the grain size distribution. Notably, both the elevation and grain-size distribution become relatively stable in downstream of the channel. Additionally, the simulation results suggest that an increased sediment supply leads to greater channel complexity, with bed surface armoring decreasing.

Areas where groundwater and surface water mix (i.e., hyporheic zones, HZ) contribute substantially to stream ecosystem respiration (ERtot). We rely on reactive transport models to understand HZ respiration at large scales; however, model outputs have not been evaluated with field estimates of ERtot. Here we evaluate the degree to which spatial variation in model-predicted HZ respiration can explain spatial variation in field-estimated ERtot across 32 sites in the Yakima River basin (YRB). We find that predicted HZ respiration did not explain spatial variation in ERtot. We hypothesize that ERtot is influenced by processes that integrate contributions from sediments, such as benthic algae, submerged macrophytes, and shallow HZ. Our results indicate that sediment-associated processes hydrologically connected to the active channel are primary drivers of spatial variation in ERtot in the YRB. We encourage conceptual and physical models of stream ERtot to integrate shallow hyporheic exchange with sediment-associated primary production. ### Competing Interest Statement The authors have declared no competing interest.

Abstract. Aerobic respiration of organic matter is a key metabolic process influencing carbon (C) biogeochemistry in aquatic ecosystems. Anthropogenic and environmental perturbations to stream ecosystem metabolism can have deleterious effects on downstream water quality. Various environmental features of rivers also influence stream metabolism, including physical (e.g., discharge, light, flow regimes) and chemical factors (nutrients, organic matter) and watershed characteristics (e.g., stream size or drainage area, land use). The relative proportion of surface water contact with benthic sediments has been considered the primary driver of ecosystem processes, including ecosystem respiration (ER). While aquatic ecosystem respiration occurs in the water column (ERwc) and in benthic sediments—including surficial and subsurface sediments (ERsed)—ERsed has long been assumed to be the primary contributor to whole-river ecosystem respiration (ERtot). Recent studies show, however, that somewhere along the river continuum (e.g., 5th–9th order), rivers transition from being dominated by benthic processes to being dominated by water column processes. Yet few metabolism studies have parsed contributions from the water column (ERwc) to ERtot, making it difficult to evaluate the relative magnitude and importance of ERwc across the river continuum and across biomes. In this study, we used the Yakima River basin, Washington, USA, to increase our understanding of basin-scale variation in ERwc. We collected ERwc data and water chemistry samples in triplicate at 47 sites in the Yakima River basin distributed across Strahler stream orders 2–7 and different hydrological and biophysical settings during summer baseflow conditions in 2021. We found that observed ERwc rates were consistently slow throughout the basin during baseflow conditions, ranging from −0.11–0.03 mg O2 L⁻1 d⁻1, and were generally at the very slow end of the range of published ERwc literature values. When compared to reach-scale ERtot rates predicted for rivers across the conterminous United States (CONUS), the very slow ERwc rates we observed throughout the Yakima River basin indicate that ERwc is likely a small component of ERtot in this basin. Despite these slow rates, ERwc nonetheless shows spatial variation across the Yakima River basin that was well explained by watershed characteristics and water chemistry. Multiple linear regression model results show that nitrate (NO3-N), dissolved organic carbon (DOC), and temperature together explained 41.5 % of the spatial variation in ERwc. Supporting the findings of other studies, we found that ERwc increased linearly with increasing NO3-N, increasing DOC, and increasing temperature. We hypothesize that low concentrations of nutrients, DOC, and low temperatures in the water column, coupled with low TSS concentrations, likely contribute to the slow ERwc rates observed throughout the Yakima River basin. Because ERtot measurements integrate contributions from water column respiration and sediment-associated respiration (ERsed), estimating ERtot in cold, clear, low nutrient rivers like those in the Yakima River basin with very slow ERwc will essentially measure contributions from ERsed.

In agriculture-dominated watersheds where natural drainage is poor, agricultural ditches (narrow engineered channels) and tile drains (perforated pipes) are widely employed to enhance surface and subsurface drainage, respectively. Despite their relatively small scale, these features exert substantial control over the hydro-biogeochemical function of watersheds and their effects need to be represented in the models. We introduce a novel strategy to incorporate the effects of artificial agricultural drainage into a fully distributed basin-scale integrated surface-subsurface hydrology models. In our approach, narrow agriculture ditches for surface drainage are resolved efficiently using ditch-aligned computational meshes that are hydrologically conditioned to ensure connectivity in the stream/ditch network. For tile drainage in the subsurface, we use the physically based Hooghoudt's drainage equation as a subgrid model and route the water drained through tiles to the nearest ditch. Without site-specific calibration, this model reproduced observed streamflow in the Portage River Watershed (>1,000 km2) as recorded by a USGS gauge with good accuracy (normalized KGE = 0.81) and outperformed a calibrated SWAT model (normalized KGE = 0.68). Numerical experiments confirm that artificial drainage reduces surface inundations and effectively controls the water table. At the watershed scale, artificial drainage increases baseflow but has little effect on watershed discharges above the 90th percentile. The strong physical underpinnings and reduced need for calibration allow us to study the impacts of artificial drainage on distributed hydrological response in terms of fluxes and states and provide a platform for investigating watershed-scale nutrient transport.

A large amount of dissolved organic matter (DOM) is transported to the ocean from terrestrial inputs each year (~0.95 Pg C per year) and undergoes a series of abiotic and biotic reactions, causing a significant release of CO 2 . Combined, these reactions result in variable DOM characteristics (e.g., nominal oxidation state of carbon, double-bond equivalents, chemodiversity) which have demonstrated impacts on biogeochemistry and ecosystem function. Despite this importance, however, comparatively few studies focus on the drivers for DOM chemodiversity along a riverine continuum. Here, we characterized DOM within samples collected from a stream network in the Yakima River Basin using ultrahigh-resolution mass spectrometry (i.e., FTICR-MS). To link DOM chemistry to potential function, we identified putative biochemical transformations within each sample. We also used various molecular characteristics (e.g., thermodynamic favorability, degradability) to calculate a series of functional diversity metrics. We observed that the diversity of biochemical transformations increased with increasing upstream catchment area and landcover. This increase was also connected to expanding functional diversity of the molecular formula. This pattern suggests that as molecular formulas become more diverse in thermodynamics or degradability, there is increased opportunity for biochemical transformations, potentially creating a self-reinforcing cycle where transformations in turn increase diversity and diversity increase transformations. We also observed that these patterns are, in part, connected to landcover whereby the occurrence of many landcover types (e.g., agriculture, urban, forest, shrub) could expand DOM functional diversity. For example, we observed that a novel functional diversity metric measuring similarity to common freshwater molecular formulas (i.e., carboxyl-rich alicyclic molecules) was significantly related to urban coverage. These results show that DOM diversity does not decrease along stream networks, as predicted by a common conceptual model known as the River Continuum Concept, but rather are influenced by the thermodynamic and degradation potential of molecular formula within the DOM, as well as landcover patterns.

The distribution of sediment grain size in streams and rivers is often quantified by the median grain size (d50), a key metric for understanding and predicting hydrologic and biogeochemical function of streams and rivers. Manual methods to measure d50 are time-consuming and ignore larger grains, while model-based methods to estimate d50 often over-generalize basin characteristics, and therefore cannot accurately represent site-scale heterogeneity. Here, we apply a machine learning photogrammetry methodology (You Only Look Once, or YOLO) for estimating d50 for grains > 2 mm based on images collected from streams and rivers throughout the Yakima River Basin (YRB). To understand how photogrammetric methods may help bridge the gaps in resolution and accuracy between manual and model-based d50 estimates, we compared YOLO d50 values to manual and model-based estimates across the YRB. We found distinct differences among methods for d50 averages and variability, and relationships between d50 estimates and basin characteristics. We discuss the advantages and limitations of the YOLO algorithm versus current methods, and explore potential future directions to combine d50 methods to better estimate spatiotemporal variation of d50, and improve incorporation into basin-scale models.

The Colorado River Basin (CRB) supports the water supply for seven states and forty million people in the Western United States (US) and has been suffering an extensive drought for more than two decades. As climate change continues to reshape water resources distribution in the CRB, its impact can differ in intensity and location, resulting in variations in human adaptation behaviors. The feedback from human systems in response to the environmental changes and the associated uncertainty is critical to water resources management, especially for water-stressed basins. This paper investigates how human adaptation affects water scarcity uncertainty in the CRB and highlights the uncertainties in human behavior modeling. Our focus is on agricultural water consumption, as approximately 80% of the water consumption in the CRB is used in agriculture. We adopted a coupled agent-based and water resources modeling approach for exploring human-water system dynamics, in which an agent is a human behavior model that simulates a farmer's water consumption decisions. We examined uncertainties at the system, agent, and parameter levels through uncertainty, clustering, and sensitivity analyses. The uncertainty analysis results suggest that the CRB water system may experience 13 to 30 years of water shortage during the 2019-2060 simulation period, depending on the paths of farmers' adaptation. The clustering analysis identified three decision-making classes: bold, prudent, and forward-looking, and quantified the probabilities of an agent belonging to each class. The sensitivity analysis results indicated agents whose decision making models require further investigation and the parameters with the higher uncertainty reduction potentials. By conducting numerical experiments with the coupled model, this paper presents quantitative and qualitative information about farmers' adaptation, water scarcity uncertainties, and future research directions for improving human behavior modeling.

Global sensitivity analysis (GSA) often is applied to assess the sensitivity of model outputs to their inputs using ensemble simulations. However, increasing model complexity and the associated computational cost have limited the use of most GSA approaches for process‐based watershed models. We propose to use mutual information (MI) as a computationally efficient GSA method for watershed modeling. Such MI computed from several hundred realizations usually can capture nonlinear relationships between inputs and outputs of interest. We perform MI‐based watershed sensitivity analyses in studies of the Portage River Watershed in Ohio and the American River Watershed in Washington. In these studies, MI is used to evaluate the sensitivity of river discharges simulated by the Soil and Water Assessment Tool to no less than 20 SWAT parameters for each watershed. Our MI‐based sensitivity analyses achieved convergence with about 300–500 realizations, a small fraction of the ensemble size (i.e., several thousands) required by the Sobol method. Nevertheless, the two‐dimensional MI yields similar sensitivity ranking compared to Sobol's total‐order sensitivity indices, especially for sensitive parameters. Our study thus sheds new light on the use of MI as an affordable GSA method for computationally intensive models such as the hyperresolution, watershed hydrobiogeochemical models.