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The water storage capacity of the root zone can determine whether plants survive dry periods and control the partitioning of precipitation into streamflow and evapotranspiration. It is currently thought that top‐down, climatic factors are the primary control on this capacity via their interaction with plant rooting adaptations. However, it remains unclear to what extent bottom‐up, geologic factors can provide an additional constraint on storage capacity. Here we use a machine learning approach to identify regions with lower than climatically expected apparent storage capacity. We find that in seasonally dry California these regions overlap with particular geologic substrates. We hypothesize that these patterns reflect diverse mechanisms by which substrate can limit storage capacity, and highlight case studies consistent with limited weathered bedrock extent (melange in the Northern Coast Range), toxicity (ultramafic substrates in the Klamath‐Siskiyou region), nutrient limitation (phosphorus‐poor plutons in the southern Sierra Nevada), and low porosity capable of retaining water (volcanic formations in the southern Cascades). The observation that at regional scales climate alone does not “size” the root zone has implications for the parameterization of storage capacity in models of plant dynamics (and the interrelated carbon and water cycles), and also underscores the importance of geology in considerations of climate‐change induced biome migration and habitat suitability. Plain Language Summary What determines how much water plants can store in their root zone? One school of thought posits that plants “size” the root‐zone capacity to survive a drought of a particular return period. In this scenario, plants extend their roots into the subsurface in response to climate drivers (e.g., precipitation magnitude‐frequency and atmospheric water demand). This worldview neglects the potential for geology to restrict root access to water. “Bottom‐up” limitations on storage capacity have been described at individual field sites, but it has been unclear how to identify geologic limitations at large scales. Here, we introduce an approach that quantifies differences between the climatically expected and locally observed apparent storage capacity, and relate these spatial patterns to geologic substrate. Importantly, we quantify apparent storage capacity via a method that includes water below the upper 1.5 m, within weathered bedrock, which is an important water source in seasonally dry climates and is typically excluded from traditional soil texture databases. We find that geology limits storage capacity at regional scales, and synthesize existing field evidence to hypothesize mechanisms of bottom‐up control. Our findings have important implications for water‐carbon cycle modeling efforts and the prediction of plant biome migration in response to climate change. Key Points Regionally extensive areas of low apparent root‐zone storage capacity for a particular climate coincide with particular geologic substrates Hypothesized geologic controls include water storage capacity limitation, nutrient limitation, and toxicity

In forested, seasonally dry watersheds, winter rains commonly replenish water storage deficits in the vadose zone before recharging underlying hillslope groundwater systems that sustain streamflow. However, the relative inaccessibility of the subsurface limits our understanding of how groundwater recharge is moderated by vadose zone storage deficits generated by plant‐water uptake. Here, we compare groundwater recharge inferred from the storage‐discharge relationship with independent, distributed estimates of deficits across 12 undisturbed California watersheds. We find accrued dry season deficits primarily driven by evapotranspiration insufficiently explain inter‐annual variability in the amount of precipitation required to generate groundwater recharge due to continued deficit accumulation between wet season storms. Tracking the deficit at the storm event‐scale, however, reveals a characteristic response in groundwater to increasing rainfall not captured in the seasonal analysis that may improve estimates of the rainfall required to generate recharge and streamflow on a per‐storm basis. Our findings demonstrate the potential for existing public data sets to better capture water partitioning within the subsurface and thus improve the prediction of rainfall‐runoff behavior and summer water availability in rainfall‐dominated, seasonally dry basins using a combined deficit‐recharge approach.

Water in rivers is delivered via the critical zone (CZ)—the living skin of the Earth, extending from the top of the vegetation canopy through the soil and down to fresh bedrock and the bottom of significantly active groundwater. Consequently, the success of stream‐rearing salmonids depends on the structure and resulting water storage and release processes of this zone. Physical processes below the land surface (the subsurface component of the CZ) ultimately determine how landscapes “filter” climate to manifest ecologically significant streamflow and temperature regimes. Subsurface water storage capacity of the CZ has emerged as a key hydrologic variable that integrates many of these subsurface processes, helping to explain flow regimes and terrestrial plant community composition. Here, we investigate how subsurface storage controls flow, temperature, and energetic regimes that matter for salmonids. We illustrate the explanatory power of broadly applicable, storage‐based frameworks across a lithological gradient that spans the Eel River watershed of California. Study sites are climatically similar but differ in their geologies and consequent subsurface CZ structure that dictates water storage dynamics, leading to dramatically different hydrographs, temperature, and riparian regimes—with consequences for every aspect of salmonid life history. Lithological controls on the development of key subsurface CZ properties like storage capacity suggest a heretofore unexplored link between salmonids and geology, adding to a rich literature that highlights various fluvial and geomorphic influences on salmonid diversity and distribution. Rapidly advancing methods for estimating and observing subsurface water storage dynamics at large scales present new opportunities for more clearly identifying landscape features that constrain the distributions and abundances of organisms, including salmonids, at watershed scales.

The study of single streamflow recession events is receiving increasing attention following the presentation of novel theoretical explanations for the emergence of power law forms of the recession relationship, and drivers of its variability. Individually characterizing streamflow recessions often involves describing the similarities and differences between model parameters fitted to each recession time series. Significant methodological sensitivity has been identified in the fitting and parameterization of models that describe populations of many recessions, but the dependence of estimated model parameters on methodological choices has not been evaluated for event-by-event forms of analysis. Here, we use daily streamflow data from 16 catchments in northern California and southern Oregon to investigate how combinations of commonly used streamflow recession definitions and fitting techniques impact parameter estimates of a widely used power law recession model. Results are relevant to watersheds that are relatively steep, forested, and rain-dominated. The highly seasonal mediterranean climate of northern California and southern Oregon ensures study catchments explore a wide range of recession behaviors and wetness states, ideal for a sensitivity analysis. In such catchments, we show the following: (i) methodological decisions, including ones that have received little attention in the literature, can impact parameter value estimates and model goodness of fit; (ii) the central tendencies of event-scale recession parameter probability distributions are largely robust to methodological choices, in the sense that differing methods rank catchments similarly according to the medians of these distributions; (iii) recession parameter distributions are method-dependent, but roughly catchment-independent, such that changing the choices made about a particular method affects a given parameter in similar ways across most catchments; and (iv) the observed correlative relationship between the power-law recession scale parameter and catchment antecedent wetness varies depending on recession definition and fitting choices. Considering study results, we recommend a combination of four key methodological decisions to maximize the quality of fitted recession curves, and to minimize bias in the related populations of fitted recession parameters.

A common parameter in hydrological modeling frameworks is root zone water storage capacity (SR[L]), which mediates plant water availability during dry periods as well as the partitioning of rainfall between runoff and evapotranspiration. Recently, a simple flux-tracking-based approach was introduced to estimate the value of SR (Wang-Erlandsson et al., 2016). Here, we build upon this original method, which we argue may overestimate SR in snow-dominated catchments due to snow melt and evaporation processes. We propose a simple extension to the method presented by Wang-Erlandsson et al. (2016) and show that the approach provides a lower estimate of SR in snow-dominated watersheds. This SR dataset is available at a 1 km resolution for the continental USA, along with the full analysis code, on the Google Colab and Earth Engine platforms. We highlight differences between the original and new methods across the rain–snow transition in the Southern Sierra Nevada, California, USA. As climate warms and precipitation increasingly arrives as rain instead of snow, the subsurface may be an increasingly important reservoir for storing plant-available water between wet and dry seasons; therefore, improved estimates of SR will better clarify the future role of the subsurface as a storage reservoir that can sustain forests during seasonal dry periods and episodic drought.

Across many upland environments, soils are thin and plant roots extend into fractured and weathered bedrock where moisture and nutrients can be obtained. Root water extraction from unsaturated weathered bedrock is widespread and, in many environments, can explain gradients in vegetation community composition, transpiration, and plant sensitivity to climate. Despite increasing recognition of its importance, the “rock moisture” reservoir is rarely incorporated into vegetation and Earth system models. Here, we address this weakness in a widely used dynamic global vegetation model (DGVM; LPJ-GUESS). First, we use a water flux-tracking deficit approach to more accurately parameterize plant-accessible water storage capacity across the contiguous United States, which critically includes the water in bedrock below depths typically prescribed by soil databases. Secondly, we exploit field-based knowledge of contrasting plant-available water storage capacity in weathered bedrock across two bedrock types in the Northern California Coast Ranges as a detailed case study. For the case study in Northern California, climate and soil water storage capacity are similar at the two study areas, but the site with thick weathered bedrock and ample rock moisture supports a temperate mixed broadleaf–needleleaf evergreen forest, whereas the site with thin weathered bedrock and limited rock moisture supports an oak savanna. The distinct biomes, seasonality and magnitude of transpiration and primary productivity, and baseflow magnitudes only emerge from the DGVM when a new and simple subsurface storage structure and hydrology scheme is parameterized with storage capacities extending beyond the soil into the bedrock. Across the contiguous United States, the updated hydrology and subsurface storage improve annual evapotranspiration estimates as compared to satellite-derived products, particularly in seasonally dry regions. Specifically, the updated hydrology and subsurface storage allow for enhanced evapotranspiration through the dry season that better matches actual evapotranspiration patterns. While we made changes to both the subsurface water storage capacity and the hydrology, the most important impacts on model performance derive from changes to the subsurface water storage capacity. Our findings highlight the importance of rock moisture in explaining and predicting vegetation structure and function, particularly in seasonally dry climates. These findings motivate efforts to better incorporate the rock moisture reservoir into vegetation, climate, and landscape evolution models.

In Mediterranean-type climates, asynchronicity between energy and water availability means that ecosystems rely heavily on the water-storing capacity of the subsurface to sustain plant water use over the summer dry season. The root-zone water storage capacity ( Smax [L]) defines the maximum volume of water that can be stored in plant accessible locations in the subsurface, but is poorly characterized and difficult to measure at large scales. Here, we develop an ecohydrological modeling framework to describe how Smax mediates root zone water storage (S [L]), and thus dry season plant water use. The model reveals that where Smax is high relative to mean annual rainfall, S is not fully replenished in all years, and root-zone water storage and therefore plant water use are sensitive to annual rainfall. Conversely, where Smax is low, S is replenished in most years but can be depleted rapidly between storm events, increasing plant sensitivity to rainfall patterns at the end of the wet season. In contrast to both the high and low Smax cases, landscapes with intermediate Smax values are predicted to minimize variability in dry season evapotranspiration. These diverse plant behaviors enable a mapping between time variations in precipitation, evapotranspiration and Smax, which makes it possible to estimate Smax using remotely sensed vegetation data − that is, using plants as sensors. We test the model using observations of Smax in soils and weathered bedrock at two sites in the Northern California Coast Ranges. Accurate model performance at these sites, which exhibit strongly contrasting weathering profiles, demonstrates the method is robust across diverse plant communities, and modes of storage and runoff generation.

Background Recurrent laryngeal nerve (RLN) palsy ranks among the leading reasons for medicolegal litigation of surgeons because of its attendant reduction in quality of life. As a risk minimization tool, intraoperative nerve monitoring (IONM) has been introduced to verify RLN function integrity intraoperatively. Nevertheless, a systematic evidence-based assessment of this novel health technology has not been performed. Methods The present study was based on a systematic appraisal of the literature using evidence-based criteria. Results Recurrent laryngeal nerve palsy rates (RLNPR) varied widely after thyroid surgery, ranging from 0%–7.1% for transient RLN palsy to 0%–11% for permanent RLN palsy. These rates did not differ much from those reported for visual nerve identification without the use of IONM. Six studies with more than 100 nerves at risk (NAR) each evaluated RLNPR by contrasting IONM with visual nerve identification only. Recuurent laryngeal nerve palsy rates tended to be lower with IONM than without it, but this difference was not statistically significant. Six additional studies compared IONM findings with their corresponding postoperative laryngoscopic results. Those studies revealed high negative predictive values (NPV; 92%–100%), but relatively low and variable positive predictive values (PPV; 10%–90%) for IONM, limiting its utility for intraoperative RLN management. Conclusions Apart from navigating the surgeon through challenging anatomies, IONM may lend itself as a routine adjunct to the gold standard of visual nerve identification. To further reduce the number of false negative IONM signals, the causes underlying its relatively low PPV require additional clarification.

The liberalization of marijuana policies, including the legalization of medical and recreational marijuana, is sweeping the United States and other countries. Marijuana cultivation can have significant negative collateral effects on the environment that are often unknown or overlooked. Focusing on the state of California, where by some estimates 60%–70% of the marijuana consumed in the United States is grown, we argue that (a) the environmental harm caused by marijuana cultivation merits a direct policy response, (b) current approaches to governing the environmental effects are inadequate, and (c) neglecting discussion of the environmental impacts of cultivation when shaping future marijuana use and possession policies represents a missed opportunity to reduce, regulate, and mitigate environmental harm.