Jake Cavaiani

and 8 more

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.

Yunxiang Chen

and 16 more

Streambed grain sizes and hydro-biogeochemistry (HBGC) control river functions. However, measuring their quantities, distributions, and uncertainties is challenging due to the diversity and heterogeneity of natural streams. This work presents a photo-driven, artificial intelligence (AI)-enabled, and theory-based workflow for extracting the quantities, distributions, and uncertainties of streambed grain sizes and HBGC parameters from photos. Specifically, we first trained You Only Look Once (YOLO), an object detection AI, using 11,977 grain labels from 36 photos collected from 9 different stream environments. We demonstrated its accuracy with a coefficient of determination of 0.98, a Nash–Sutcliffe efficiency of 0.98, and a mean absolute relative error of 6.65% in predicting the median grain size of 20 testing photos. The AI is then used to extract the grain size distributions and determine their characteristic grain sizes, including the 5th, 50th, and 84th percentiles, for 1,999 photos taken at 66 sites. With these percentiles, the quantities, distributions, and uncertainties of HBGC parameters are further derived using existing empirical formulas and our new uncertainty equations. From the data, the median grain size and HBGC parameters, including Manning’s coefficient, Darcy-Weisbach friction factor, interstitial velocity magnitude, and nitrate uptake velocity, are found to follow log-normal, normal, positively skewed, near log-normal, and negatively skewed distributions, respectively. Their most likely values are 6.63 cm, 0.0339 s·m-1/3, 0.18, 0.07 m/day, and 1.2 m/day, respectively. While their average uncertainty is 7.33%, 1.85%, 15.65%, 24.06%, and 13.88%, respectively. Major uncertainty sources in grain sizes and their subsequent impact on HBGC are further studied.