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Abstract Evapotranspiration (ET) is co‐regulated by subsurface water availability, atmospheric demand for water, and radiation. Spatial differences in the limiting factors on ET that emerge along the soil‐plant‐atmosphere continuum result in distinct ecohydrological regimes with differing sensitivities to atmospheric and subsurface drivers. However, different components of the soil‐plant‐atmosphere continuum are not equally well understood. Deep subsurface water access is particularly difficult to measure and model, but can sustain ET under drought conditions when shallow soil moisture appears to be acutely limiting. Here, we exploited this principle to identify ecosystems that rely on deep subsurface water availability. We first used a plant hydraulic model to determine the expected ET behavior for plants with deep water access. We then examined 19 flux towers and found that responsiveness of ET to atmospheric conditions during dry periods was indicative of some ecosystems with deep water access. We used the divergent sensitivities of ET to vapor pressure deficit, radiation, and shallow soil moisture to identify distinct ecohydrological regimes in gridded data covering the continental U.S. We diagnosed deep water usage in ecosystems where ET remained sensitive to atmospheric conditions despite being insensitive to shallow soil moisture variability. Further, we found that drought stress, plant hydraulic traits, and ecosystem biophysical variables mediated the sensitivity of ET to aboveground and belowground conditions. 

Summary The potential for widespread sink‐limited plant growth has received increasing attention in the literature in the past few years. Despite recent evidence for sink limitations to plant growth, there are reasons to be cautious about a sink‐limited world view. First, source‐limited vegetation models do a reasonable job at capturing geographic patterns in plant productivity and responses to resource limitations. Second, from an evolutionary perspective, it is nonadaptive for plants to invest in increasing carbon assimilation if growth is primarily sink‐limited. In this review, we synthesize the potential evidence for and underlying physiology of sink limitation across terrestrial ecosystems and contrast mechanisms of sink limitation with those of source‐limited productivity. We highlight evolutionary restrictions on the magnitude of sink limitation at the organismal level. We also detail where mechanisms regulating sink limitation at the organismal and ecosystem scale (e.g. the terrestrial carbon sink) diverge. Although we find that there is currently no direct evidence for widespread organismal sink limitation, we propose a series of follow‐up growth chamber manipulations, systematized measurements, and modeling experiments targeted at diagnosing nonadaptive buildup of excess nonstructural carbohydrates that will help illuminate the prevalence and magnitude of organismal sink limitation. 

Over the past three decades, assessments of the contemporary global carbon budget consistently report a strong net land carbon sink. Here, we review evidence supporting this paradigm and quantify the differences in global and Northern Hemisphere estimates of the net land sink derived from atmospheric inversion and satellite-derived vegetation biomass time series. Our analysis, combined with additional synthesis, supports a hypothesis that the net land sink is substantially weaker than commonly reported. At a global scale, our estimate of the net land carbon sink is 0.8 ± 0.7 petagrams of carbon per year from 2000 through 2019, nearly a factor of two lower than the Global Carbon Project estimate. With concurrent adjustments to ocean (+8%) and fossil fuel (−6%) fluxes, we develop a budget that partially reconciles key constraints provided by vegetation carbon, the north-south CO₂gradient, and O₂trends. We further outline potential modifications to models to improve agreement with a weaker land sink and describe several approaches for testing the hypothesis. 

ABSTRACT The rapid increase in the volume and variety of terrestrial biosphere observations (i.e., remote sensing data and in situ measurements) offers a unique opportunity to derive ecological insights, refine process‐based models, and improve forecasting for decision support. However, despite their potential, ecological observations have primarily been used to benchmark process‐based models, as many past and current models lack the capability to directly integrate observations and their associated uncertainties for parameterization. In contrast, data assimilation frameworks such as the CARbon DAta MOdel fraMework (CARDAMOM) and its suite of process‐based models, known as the Data Assimilation Linked Ecosystem Carbon Model (DALEC), are specifically designed for model‐data fusion. This review, motivated by a recent CARDAMOM community workshop, examines the development and applications of CARDAMOM, with an emphasis on its role in advancing ecosystem process understanding. CARDAMOM employs a Bayesian approach, using a Markov Chain Monte Carlo algorithm to enable data‐driven calibration of DALEC parameters and initial states (i.e., carbon pool sizes) through observation operators. CARDAMOM's unique ability to retrieve localized model process parameters from diverse datasets—ranging from in situ measurements to global satellite observations—makes it a highly flexible tool for analyzing spatially variable ecosystem responses to environmental change. However, assimilating these data also presents challenges, including data quality issues that propagate into model skill, as well as trade‐offs between model complexity, parameter equifinality, and predictive performance. We discuss potential solutions to these challenges, such as reducing parameter equifinality by incorporating new observations. This review also offers community recommendations for incorporating emerging datasets, integrating machine learning techniques, strengthening collaboration with remote sensing, field, and modeling communities, and expanding CARDAMOM's relevance for localized ecosystem monitoring and decision‐making. CARDAMOM enables a deep, mechanistic understanding of terrestrial ecosystem dynamics that cannot be achieved through empirical analyses of observational datasets or weakly constrained models alone. 

Abstract Forest fire frequency, extent, and severity have rapidly increased in recent decades across the western United States (US) due to climate change and suppression‐oriented wildfire management. Fuels reduction treatments are an increasingly popular management tool, as evidenced by California's plan to treat 1 million acres annually by 2050. However, the aggregate efficacy of fuels treatments in dry forests at regional and multi‐decadal scales is unknown. We develop a novel fuels treatment module within a coupled dynamic vegetation and fire model to study the effects of dead biomass removal from forests in the Sierra Nevada region of California. We ask how annual treatment extent, stand‐level treatment intensiveness, and spatial treatment placement alter fire severity and live carbon loss. We find that a ∼30% reduction in stand‐replacing fire was achieved under our baseline treatment scenario of 1,000 km² year⁻¹after a 100‐year treatment period. Prioritizing the most fuel‐heavy stands based on precise fuel distributions yielded cumulative reductions in pyrogenic stand‐replacement of up to 50%. Both removing constraints on treatment location due to remoteness, topography, and management jurisdiction and prioritizing the most fuel‐heavy stands yielded the highest stand‐replacement rate reduction of ∼90%. Even treatments that succeeded in lowering aggregate fire severity often took multiple decades to yield measurable effects, and avoided live carbon loss remained negligible across scenarios. Our results suggest that strategically placed fuels treatments are a promising tool for controlling forest fire severity at regional, multi‐decadal scales, but may be less effective for mitigating live carbon losses.