Holly R. Barnard

Water availability governs soil organic matter (SOM) decomposition, yet how fine‐scale hydrologic variability shapes seasonal SOM processing and persistence remains unclear. In seasonally dry forests, rewetting can trigger rapid mineralization, but antecedent moisture may leave biogeochemical legacies that modulate this response and its significance for carbon and nutrient cycling. We examined whether subsurface water storage and antecedent moisture regulate SOM processing and carbon trajectories. We leveraged a natural gradient in plant‐accessible water across two Douglas‐fir‐dominated hillslopes in Oregon's western Cascade Mountains (USA). These sites experience similar climate, vegetation, and soils, but differ in subsurface water‐storage capacity. We compared dry‐season (June) soils with wet‐season (December) soils, and paired continuous soil‐moisture profiles with high‐resolution mass spectrometry of water‐extractable (WEOM) and mineral‐associated organic matter (MAOM), bulk SOM pools, and microbial‐biomass. SOM chemistry was similar in June but diverged in hillslope‐specific ways by December. Soils from the high‐water‐storage hillslope accumulated OM in WEOM and MAOM and gained microbial biomass carbon, shifting toward more oxidized, relatively complex compounds. Soils from the low‐water‐storage hillslope lost OM across fractions and microbial biomass carbon, and retained more reduced, relatively simple compounds. High‐water‐storage profiles remained within water‐holding capacity longer—an optimum for microbial activity—whereas low‐water‐storage sites dried faster and experienced larger moisture extremes. Plant‐mediated hydraulic redistribution occurred on both hillslopes, but was not a key mechanism maintaining optimal soil water‐holding capacity. These insights support a hillslope storage framework: subsurface storage can imprint biogeochemical legacies of moisture conditions, shaping decomposer responses to cool‐season wetting in seasonally dry landscapes.

Silicate weathering produces solutes, consumes CO ₂ , and regulates Earth's long‐term climate. Rates of silicate weathering are changing, particularly in rapidly warming mountains with dwindling snowpacks. Although hydrologic shifts in warming mountains have been extensively studied, their impacts on water chemistry and weathering have been sparsely examined. Here we ask the question: How does changing mountain hydrology, in particular shrinking snowpacks and early snowmelt, alter silicate weathering, solute export, and stream chemistry? We answer this question using a catchment‐scale reactive transport model (BioRT‐HBV) calibrated with streamflow and chemistry data (2010–2020) from the Sagehen Creek Experimental Forest Catchment, a snow‐dominated headwater catchment in Sierra Nevada Mountains, CA, USA. We show that snow‐dependent water storage controls silicate weathering and solute export, especially in the shallow subsurface, contrasting the general perception that silicate weathering is slow and kinetically controlled with limited dependence on water storage. In the driest year with the least snow water equivalent ( SWE , 10% of the wettest year), rates of silicate weathering and export rates of weathering products (such as calcium) were about half and a third of those in the wettest year with the highest SWE . In fact, annual rates of weathering and solute export scale linearly with annual total precipitation and maximum SWE . The timing of weathering and solute export closely followed that of snowmelt, with rates peaking about 3 months earlier in the driest years compared to those in the wettest years. Mean stream calcium concentrations however were about 1.6 times higher in the driest year, with highest concentrations in the dry summer. These results suggest that as mountains shift to a low‐to‐no snow future, catchments transition from exporters to reactors with a lower weathering capacity and even lower export capacity, leading to higher solute concentration in streams.

Forest sustainability is regulated by the interplay between water and biochemical fluxes within the soil–tree continuum. However, capturing the daily to seasonal interplay of these ecohydrological and biological processes remains a major challenge. Instrumentation is typically compartmentalized—soil, roots, or trunk—hindering a unified understanding of the continuum. Here, we explore the potential of passive electrical methods to concurrently track water and biochemical fluxes across the soil–tree interface. Self and electrodic potential, depending on electrode type, respond to water flow, chemical and thermal diffusion, and redox gradients. We propose an electro‐hydro‐biogeochemical conceptual model linking electrical potential generation to tree transpiration, water uptake, hydraulic redistribution, and soil respiration. Using electrical potential monitored within the soil and across the soil‐root‐trunk continuum at the H.J. Andrews Experimental Forest, Oregon, USA, during summer 2023, we tested this model via daily correlations with sap flow, soil moisture, and carbon dioxide. Despite disturbances caused by wildfire smoke, electrical signals revealed strong correlations with daily patterns and event‐driven perturbations (e.g., wildfire smoke) of hydrological fluxes and biological activity in both trees and soil. Water fluxes emerge as the primary driver of self and electrodic potentials, with redox gradients also playing a significant role across the soil–tree continuum. This study presents a framework for using passive electrical methods as proxies for monitoring forest ecohydrological resilience.

Dynamic water storage is the water that remains for enough time in watersheds to influence streamflow generation, chemically weather rock and drive the release of solutes, breakdown organic carbon (C) through microbial activity, and sustain vegetation between periods of precipitation. The amount and connectivity of dynamic water stores control critical zone processes, including evapotranspiration, vegetation productivity and mortality, streamflow, weathering and solute transport. Here, we present recent advances and identify frontiers in the study of dynamic water storage in the critical zone, focusing on observational techniques for quantifying dynamic storage, advances in conceptual and numerical models that capture dynamic storage, and emerging hypotheses that drive dynamic storage evolution. We specifically identify and focus on four primary dynamic water storages: snow, plant‐accessible water, groundwater, and surface water. While we use semi‐arid mountain environments as an exemplar of dynamic storage controls on critical zone processes, this work offers implications for a broad range of geoclimatic settings.

The interception of snow by the canopy is an important process in the water and energy balance in cold‐region coniferous forests. Direct measurements of canopy snow interception are difficult at scales larger than individual trees, requiring indirect methods such as eddy covariance, time‐lapse photography, or modeling. At the Niwot Ridge Subalpine Forest AmeriFlux site in the Colorado Front Range, USA, we compared methods that estimate or simulate the presence of snow interception. Timelapse photography images were analyzed using thresholding analysis and used to train a Convolutional Neural Network (CNN) model to estimate canopy snow presence. Interception was also estimated from eddy covariance measurements above and below the canopy, as well as from model simulations. These methods were applied over January 2019, with binarized results compared to a “ground truth” of human labeled images to calculate the Balanced Accuracy Score. The highest accuracy was achieved by the CNN predictions. Based on the Balanced Accuracy Scores, select methods were extended to estimate the presence of canopy snow for the 2018/2019 winter. All methods provided insight into the process of interception in a subalpine forest but presented challenges, including differing flux footprints of the above‐ and below‐canopy eddy covariance measurements and the inability of red‐green‐blue imagery to monitor snow interception at night, during sunrise, and during sunset.

Although the importance of dynamic water storage and flowpath partitioning on discharge behavior has been well recognized within the critical zone community, there is still little consensus surrounding the question, “ How do climate factors from above and land characteristics from below dictate dynamic storage, flowpath partitioning, and ultimately regulate hydrological dynamics ?” Answers to this question have been hindered by limited and inconsistent spatio-temporal data and arduous-to-measure subsurface data. Here we aim to answer this question above by using a semi-distributed hydrological model (HBV model) to simulate and understand the dynamics of water storage, groundwater flowpaths, and discharge in 15 headwater catchments across the contiguous United States. Results show that topography, precipitation falling as snow, and catchment soil texture all influence catchment dynamic storage, storage-discharge sensitivity, flowpath partitioning, and discharge flashiness. Flat, rain-dominated sites (< 30% precipitation as snow) with finer soils exhibited flashier discharge regimes than catchments with coarse soils and/or significant snowfall (>30% precipitation as snow). Rain-dominated sites with clay soils (indicative of chemical weathering) showed lower dynamic storage and discharge that was more sensitive to changes in dynamic storage than rainy sites with coarse soils. Steep, snowy sites with coarse soils (more mechanical weathering) had lowest dynamic storage and deep groundwater fed discharge that was less sensitive to changes in dynamic storage than fine-soil snowy or rainy catchments. These results highlight aridity and precipitation (snow versus rain) as the dominant climate controls from above and topography and soil texture as the dominant land controls from below. The study challenges the traditional view that climate controls water balance while subsurface structure dictates subsurface flow path. Rather, it shows that climate and land characteristics jointly regulate water balance, groundwater flowpath partitioning, and discharge responses. These findings have important implication for the projection of the future of water resources, especially as climate change and human activities continue to intensify.

Terrestrial production and export of dissolved organic and inorganic carbon (DOC and DIC) to streams depends on water flow and biogeochemical processes in and beneath soils. Yet, understanding of these processes in a rapidly changing climate is limited. Using the watershed‐scale reactive‐transport model BioRT‐HBV and stream data from a snow‐dominated catchment in the Rockies, we show deeper groundwater flow averaged about 20% of annual discharge, rising to ∼35% in drier years. DOC and DIC production and export peaked during snowmelt and wet years, driven more by hydrology than temperature. DOC was primarily produced in shallow soils (1.94 ± 1.45 gC/m²/year), stored via sorption, and flushed out during snowmelt. Some DOC was recharged to and further consumed in the deeper subsurface via respiration (−0.27 ± 0.02 gC/m²/year), therefore reducing concentrations in deeper groundwater and stream DOC concentrations at low discharge. Consequently, DOC was primarily exported from the shallow zone (1.62 ± 0.96 gC/m²/year, compared to 0.12 ± 0.02 gC/m²/year from the deeper zone). DIC was produced in both zones but at higher rates in shallow soils (1.34 ± 1.00 gC/m²/year) than in the deep subsurface (0.36 ± 0.02 gC/m²/year). Deep respiration elevated DIC concentrations in the deep zone and stream DIC concentrations at low discharge. In other words, deep respiration is responsible for the commonly‐observed increasing DOC concentrations (flushing) and decreasing DIC concentrations (dilution) with increasing discharge. DIC export from the shallow zone was ~66% of annual export but can drop to ∼53% in drier years. Numerical experiments suggest lower carbon production and export in a warmer, drier future, and a higher proportion from deeper flow and respiration processes. These results underscore the often‐overlooked but growing importance of deeper processes in a warming climate.

Two major barriers hinder the holistic understanding of subsurface critical zone (CZ) evolution and its impacts: (a) an inability to measure, define, and share information and (b) a societal structure that inhibits inclusivity and creativity. In contrast to the aboveground portion of the CZ, which is visible and measurable, the bottom boundary is difficult to access and quantify. In the context of these barriers, we aim to expand the spatial reach of the CZ by highlighting existing and effective tools for research as well as the “human reach” of CZ science by expanding who performs such science and who it benefits. We do so by exploring the diversity of vocabularies and techniques used in relevant disciplines, defining terminology, and prioritizing research questions that can be addressed. Specifically, we explore geochemical, geomorphological, geophysical, and ecological measurements and modeling tools to estimate CZ base and thickness. We also outline the importance of and approaches to developing a diverse CZ workforce that looks like and harnesses the creativity of the society it serves, addressing historical legacies of exclusion. Looking forward, we suggest that to grow CZ science, we must broaden the physical spaces studied and their relationships with inhabitants, measure the “deep” CZ and make data accessible, and address the bottlenecks of scaling and data‐model integration. What is needed—and what we have tried to outline—are common and fundamental structures that can be applied anywhere and used by the diversity of researchers involved in investigating and recording CZ processes from a myriad of perspectives.

Summer streamflow predictions are critical for managing water resources; however, warming‐induced shifts from snow to rain regimes impact low‐flow predictive models. Additionally, reductions in snowpack drive earlier peak flows and lower summer flows across the western United States increasing reliance on groundwater for maintaining summer streamflow. However, it remains poorly understood how groundwater contributions vary interannually. We quantify recession limb groundwater (RLGW), defined as the proportional groundwater contribution to the stream during the period between peak stream flow and low flow, to predict summer low flows across three diverse western US watersheds. We ask (a) how do snow and rain dynamics influence interannual variations of RLGW contributions and summer low flows?; (b) which watershed attributes impact the effectiveness of RLGW as a predictor of summer low flows? Linear models reveal that RLGW is a strong predictor of low flows across all sites and drastically improves low‐flow prediction compared to snow metrics at a rain‐dominated site. Results suggest that strength of RLGW control on summer low flows may be mediated by subsurface storage. Subsurface storage can be divided into dynamic (i.e., variability saturated) and deep (i.e., permanently saturated) components, and we hypothesize that interannual variability in dynamic storage contribution to streamflow drives RLGW variability. In systems with a higher proportion of dynamic storage, RLGW is a better predictor of summer low flow because the stream is more responsive to dynamic storage contributions compared to deep‐storage‐dominated systems. Overall, including RLGW improved low‐flow prediction across diverse watersheds.