The hyporheic zone is a hotspot for biogeochemical cycling where interactions with mineral metals preserve the release and biodegradation of organic matter (OM). A small fraction of OM can still be exchanged between localized sediments and the overlying water column, and recent evidence suggest there exists a longitudinal structuring in sediment dissolved OM (DOM) chemistry across the continental United States (CONUS). In this study, we tested a hypothesis that water extractable sediment DOM chemistry could be explained by sediment metal contents and integrative watershed scale features at the CONUS scale. Crowdsourced samples were characterized for high resolution mass spectrometry and coupled with sediment metals determined via x-ray fluorescence as well as with land cover and soil elemental information obtained from national databases. Our results highlight weak relationships between DOM chemistry and elemental composition at the CONUS scale indicating limited transferability of organo-metal linkages into multi-scale hydrobiogeochemical models.

1 INTRODUCTION:Climate change is driving earlier seasonal onset of wildfire, increased fire frequency, and larger fires in many regions globally (Flannigan et al., 2009; Westerling, 2016). Wildfires induce changes in ecohydrological processes, including reduced infiltration from increased soil hydrophobicity (DeBano, 2000), and reduced canopy cover that diminishes evapotranspiration and interception of precipitation (Guo et al., 2023; Wine et al., 2018). The resulting changes in streamflow and terrestrial-aquatic connectivity from these shifts in ecohydrological processes influence the composition and fluxes of materials to stream networks, with the potential to degrade downstream water quality (Ball et al., 2021; Dahm et al., 2015; Hohner et al., 2019; Jones et al., 2022; Paul et al., 2022; Rust et al., 2018; Santos et al., 2019). Thus, it is important to improve our understanding of the spatio-temporal drivers of water quality responses to wildfires (Raoelison et al., 2023).Across spatial scales, wildfire has been documented to increase solute concentrations by orders of magnitude in some receiving streams (Hickenbottom et al., 2023; Murphy et al., 2018), but lead to little response or decline in others (Abbott et al., 2021; Oliver et al., 2012). This may be due to differences in wildfire and/or watershed characteristics. For example, previous literature has identified a threshold of ~20% burn extent needed to trigger a hydrologic response across different ecoregions (Hallema et al., 2018), yet identification of such responses for water quality parameters is nascent (Richardson et al., 2024). While several previous studies have documented the effect of wildfire on water quality parameters and biogeochemical processes across broad spatial scales (e.g., Hampton et al., 2022; Raoelison et al., 2023; Rust et al., 2018), few have sought to link observed responses across time, climate, burn, and watershed characteristics.In particular, nitrate (NO3_) and dissolved organic carbon (DOC) are key nutrients that underpin global biogeochemical cycles and have the potential to degrade water quality with increasing wildfire activity. For example, excess nitrate can lead to downstream eutrophication (Mast et al., 2016), while DOC compositional shifts may influence water treatment processes (Hohner et al., 2019). Relationships with burn severity and extent have been observed in some systems for nitrate (Bladon et al., 2008; Rhoades, Chow, et al., 2019), however, for DOC, little to no relationships have been consistently observed across studies and systems (Santos et al., 2019a; Wei et al., 2021).Observed differences in nitrate and DOC concentrations pre- and post-fire were most pronounced in the first five years following wildfire (Rust et al., 2018). However, the persistence of fire effects on hydrologic and biogeochemical processes are moderated by the rate of post-wildfire vegetation recovery which can vary by ecosystem (Guo et al., 2023; Wine et al., 2018). Nitrate responses, for example, may lag due to the shift in nitrogen speciation during combustion creating conditions that increase nitrification post-fire (Gustine et al., 2022; Hanan, Schimel, et al., 2016). The magnitude and length of DOC responses are likely a result of heterogeneous burn conditions that can decrease and alter the chemistry of source pools (Santín et al., 2016).Responses may be linked to changes in streamflow (Richardson et al., 2024), which is highly variable across climates post-fire (Hallema et al., 2017). This variability may co-vary with additional drivers, such as drought (Murphy et al., 2018; Newcomer et al., 2023) resulting in shifts in nitrate and DOC export. For example, while the directionality of the relationship between concentration and discharge may not be altered with wildfire, the strength of that relationship has been shown to change for both nitrate and DOC (Murphy et al., 2018; Richardson et al., 2024). While trends are emerging for streamflow across time since fire, climate, and burn characteristics (Hallema et al., 2017), such trends have not yet emerged for nitrate and DOC.Discerning biogeochemical responses post-fire are further complicated by heterogeneous watershed characteristics (Agbeshie et al., 2022; Hallema et al., 2018). For example, catchment slope has a dominant influence on biogeochemical linkages between terrestrial and aquatic systems, primarily due to longer residence times of water and constituents in lower-gradient catchments (Lintern et al., 2018). The biogeochemical signatures in steeper catchments typically reflect that of surficial pathways, especially during periods of enhanced hydrologic connectivity where a large proportion of material is mobilized from the terrestrial landscape into receiving streams (Laudon & Sponseller, 2018). Conversely, lower-gradient catchments are less responsive to periods of enhanced hydrologic connectivity due to the greater proportion of groundwater contributions (Laudon & Sponseller, 2018). Lower-gradient catchments also promote longer residence times that allow for transformations and provide a source of DOC available to leach into receiving streams (Tank et al., 2020). Additionally, topography heavily influences terrestrial species composition which influences carbon and nitrogen cycling, thus affecting solutes available for export (Weintraub et al., 2017).The objectives of this meta-analysis were to better constrain the controls on stream water chemistry across broad spatial scales post-fire. In this study, we synthesize biogeochemical responses of nitrate and DOC to wildfires using meta-analytical techniques to evaluate the effect sizes and the percent differences across reference and fire-impacted sites spanning 3 biomes and 62 watersheds. We chose to leverage reference-burn study designs to minimize the confounding influence of interannual climate variability on our results (Clausen & Spooner, 1993). We focused specifically on the importance of time-since-fire, climate, and burn extent as factors of interest to assess post-fire shifts in solute concentrations through space and time. Through time as ecosystems recover, we hypothesize that there will be a decrease in the effect size of wildfire impacts on nitrate and DOC, as concentrations begin to reflect those in non-fire impacted systems. Furthermore, we anticipate that there will be a systematic shift in nitrate and DOC post-fire related to ranges in aridity and mean annual precipitation with climate, which will be modulated by in-stream hydrologic responses to local catchment characteristics. Lastly, we hypothesized that the area of watershed burned will impact the relationships between watershed characteristics and nitrate and DOC responses, influencing the magnitude of wildfire effects on water quality.

River sediment microbial respiration is a key indicator of ecosystem functioning and the biogeochemical fluxes across this critical zone link surface and subsurface waters. As such, there is tremendous interest in measuring and mapping these respiration rates. Respiration observations are expensive and labor intensive; there is limited data available to the community. An open science, collaborative initiative is collecting samples for respiration rate analysis and multi-scale metadata; this evolving data set is being used for making machine learning (ML) predictions at unsampled sites to help inform continued community engagement. However, it is a challenge to find an optimum configuration for ML models to work with this feature-rich (i.e. 100+ possible input variables) data set. Here, we present results from a two-tiered approach to managing the analysis of this complex data set: 1) a stacked ensemble of models that automatically optimizes hyperparameters and manages the training of many models and 2) feature permutation importance to detect the most important features in the models. The major elements of this workflow are modular, portable, open, and cloud-based thus making this implementation a potential template for other applications. The models developed here predict that sediment organic matter chemistry is one of the most important features for predicting sediment respiration rate. Other larger-scale, important features fall into the categories of climatic, ecological, geological, and fluvial settings. Leveraging these larger-scale features to generate data-driven estimates of river sediment respiration rates reveals spatially consistent but heterogeneous patterns across the river network of the Columbia River Basin.

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.