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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An Online Approach to Solve the Dynamic Vehicle Routing Problem with Stochastic Trip Requests for Paratransit Services

Many transit agencies operating paratransit and microtransit services have to respond to trip requests that arrive in real-time, which entails solving hard combinatorial and sequential decision-making problems under uncertainty. To avoid decisions that lead to significant inefficiency in the long term, vehicles should be allocated to requests by optimizing a non-myopic utility function or by batching requests together and optimizing a myopic utility function. While the former approach is typically offline, the latter can be performed online. We point out two major issues with such approaches when applied to paratransit services in practice. First, it is difficult to batch paratransit requests together as they are temporally sparse. Second, the environment in which transit agencies operate changes dynamically (e.g., traffic conditions can change over time), causing the estimates that are learned offline to become stale. To address these challenges, we propose a fully online approach to solve the dynamic vehicle routing problem (DVRP) with time windows and stochastic trip requests that is robust to changing environmental dynamics by construction. We focus on scenarios where requests are relatively sparse—our problem is motivated by applications to paratransit services. We formulate DVRP as a Markov decision process and use Monte Carlo tree search to evaluate actions for any given state. Accounting for stochastic requests while optimizing a non-myopic utility function is computationally challenging; indeed, the action space for such a problem is intractably large in practice. To tackle the large action space, we leverage the structure of the problem to design heuristics that can sample promising actions for the tree search. Our experiments using real-world data from our partner agency show that the proposed approach outperforms existing state-of-the-art approaches both in terms of performance and robustness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

AMF diversity promotes plant community phosphorus acquisition and reduces carbon costs per unit of phosphorus

Plants may benefit from more diverse communities of arbuscular mycorrhizal fungi (AMF), as functional complementarity of AMF may allow for increased resource acquisition, and because a high AMF diversity increases the probability of plants matching with an optimal AMF symbiont. Here, we repeatedly radiolabeled plants and AMF in the glasshouse over c. 9 months to test how AMF species richness (SR) influences the exchange of plant C ( 14 C) for AMF P ( 32 P & 33 P) and resulting shoot nutrients and mass from a biodiversity–ecosystem functioning perspective. Plant P acquisition via AMF increased with sown AMF SR, as did shoot biomass, shoot P, and shoot N. The rate of plant C transferred to AMF for this P (C:P) decreased with sown AMF SR. Plants in plant communities benefit from inoculation with a variety of AMF species via more favorable resource exchange. Surprisingly, this effect did not differ among functionally distinct communities comprised entirely of either legumes, nonlegume forbs, or C3 grasses.

arbuscular mycorrhizal fungi↗
DOE OSTI2025

A Portfolio Approach to Massively Parallel Bayesian Optimization

One way to reduce the time of conducting optimization studies is to evaluate designs in parallel rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions of Bayesian optimization have been proposed. They work by building a surrogate model of the black-box to simultaneously select multiple designs via an infill criterion. Still, despite the increased availability of computing resources that enable large-scale parallelism, the strategies that work for selecting a few tens of parallel designs for evaluations become limiting due to the complexity of selecting more designs. It is even more crucial when the black-box is noisy, necessitating more evaluations as well as repeating experiments. Here we propose a scalable strategy that can keep up with massive batching natively, focused on the exploration/exploitation trade-off and a portfolio allocation. We compare the approach with related methods on noisy functions, for mono and multi-objective optimization tasks. These experiments show orders of magnitude speed improvements over existing methods with similar or better performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

EVSE DERMS Controls [SWR-26-010]

An MQTT (Message Queuing Telemetry Transport) and OCPP (Open Charge Point Protocol) based remote smart charging controller framework for AC Electric Vehicle Supply Equipments (EVSEs). The code in this repo allows for the National Laboratory of the Rockies (NLR) controls to interface with the real Distributed Energy Resource Management System (DERMS) and EVSEs in NLR's ESIF Optimization and Control Laboratory (OCL). Different charge management algorithms can be tested to determine which power allocation method is most effective with the overall goal of demonstrating clear and well documented test results as well as providing functional control algorithms which could be utilized to provide effective smart charge management (SCM) at EV charging stations. Different power allocation methods are programmed in lab_demo_controller.py and include allocation based on first come first served, equal sharing, state of charge (SOC), priority factors, and behind the meter control methods.

Panossian, Nadia [National Laboratory of the Rocki↗
DOE OSTI2021

Trade-Offs Between Growth Rate and Other Fungal Traits

If we better understand how fungal responses to global change are governed by their traits, we can improve predictions of fungal community composition and ecosystem function. Specifically, we can examine trade-offs among traits, in which the allocation of finite resources toward one trait reduces the investment in others. We hypothesized that trade-offs among fungal traits relating to rapid growth, resource capture, and stress tolerance sort fungal species into discrete life history strategies. We used the Biolog Filamentous Fungi database to calculate maximum growth rates of 37 fungal species and then compared them to their functional traits from the fun fun database. In partial support of our hypothesis, maximum growth rate displayed a negative relationship with traits related to resource capture. Moreover, maximum growth rate displayed a positive relationship with amino acid permease, forming a putative Fast Growth life history strategy. A second putative life history strategy is characterized by a positive relationship between extracellular enzymes, including cellobiohydrolase 6, cellobiohydrolase 7, crystalline cellulase AA9, and lignin peroxidase. These extracellular enzymes were negatively related to chitosanase 8, an enzyme that can break down a derivative of chitin. Chitosanase 8 displayed a positive relationship with many traits that were hypothesized to cluster separately, forming a putative Blended life history strategy characterized by certain resource capture, fast growth, and stress tolerance traits. These trait relationships complement previously explored microbial trait frameworks, such as the Competitor-Stress Tolerator-Ruderal and the Yield-Resource Acquisition-Stress Tolerance schemes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Data from: "Warming of alpine tundra enhances belowground production and shifts community towards resource acquisition traits"

This archive contains data used to draw conclusions in “Warming of alpine tundra enhances belowground production and shifts community towards resource acquisition traits”, by Yang et al. 2020. Data were collected on Niwot Ridge, in an alpine meadow within the Alpine Treeline Warming Experiment (ATWE) field sites in Colorado, USA. Samples were also processed in the U.S. Geological Survey Forest and Rangeland Ecosystem Science Center, in Boise, Idaho. File formats in this archive include comma-separated values (.csv), portable document format (.pdf), Microsoft Excel (.xlsx), and two types of geospatial files: keyhole markup language (.kml), and ESRI shapefiles (.shp). Leaf scans are .jpg images, and root scans are .tiff/.tif images.The .csv files can be opened using R, Microsoft Excel, or any simple text-editing software such as TextEdit and Notepad. Microsoft Excel files can be opened using Microsoft Excel, and .pdf files can be opened with Adobe Acrobat Reader, Preview, or other compatible programs. Scanned images can be opened using any photo and/or picture viewing software.The .kml file can be opened using Google Earth and Google Maps, and the shapefiles can be opened by any programs compatible with shapefiles, such as the ArcGIS Desktop suite, and QGIS.------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Measures of belowground net primary productivity (BNPP) are required to understand whether aboveground net primary production (ANPP) changes reflect changes in allocation or are indicative of a whole plant NPP response. Plant functional traits provide a key way to scale from the individual plant to the community level, and provide insight into drivers of NPP responses to environmental change. We used infrared heaters to warm an alpine plant community at Niwot Ridge, Colorado, and applied supplemental water to compensate for soil water loss induced by warming. We measured ANPP, BNPP, and leaf and root functional traits across treatments after 5 years of continuous warming. Community-level ANPP and total NPP (ANPP + BNPP) did not respond to heating or watering, but BNPP increased in response to heating. Heating decreased community-level leaf dry matter content and increased total root length, indicating a shift in strategy from resource conservation to acquisition in response to warming.

13C/12C isotope ratio↗
DOE OSTI2024

Large Divergence of Projected High Latitude Vegetation Composition and Productivity Due To Functional Trait Uncertainty

Abstract Vegetation distribution and composition are expected to change in northern high latitudes under rapid warming, which regulates ecosystem functions but remains challenging to predict. Vegetation change arises from the interplay of chronic climate trends such as warming and transient demographic processes of recruitment, growth, competition, and mortality. Most predictive models overlooked the role of demographic dynamics controlled by plant traits. Here, we simulate vegetation dynamics at the Kougarok Hillslope site in Alaska under historical and future climates using the E3SM Land Model coupled to the Functionally Assembled Terrestrial Simulator (ELM‐FATES). To evaluate the roles of plant traits, we parameterize the model with 5,265 trait configurations representing diverse physiological and demographic strategies. Results show current modeled biomass, composition, and productivity are most sensitive to traits controlling photosynthetic capacity, carbon allocation, allometry, and phenology. Among all trait configurations, ∼5% reproduce in situ biomass and plant functional type (PFT) composition measured in 2016, that are indistinguishable from these two observed ecosystem states. Notably, these same trait configurations produce diverging biomass, composition, and productivity under future climate, where the uncertainty attributable to traits is twice the change attributable to climate change. The variation of projected productivity arises from emerging PFT composition under novel climate regimes, primarily explained by traits controlling cold‐induced mortality, recruitment, and allometry. Our findings highlight the importance and uncertainty of demographic dynamics and its interaction with climate change in shaping Arctic vegetation change. Improved model predictions will likely benefit from explicit consideration of vegetation demography and better constraints of critical traits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Warming of alpine tundra enhances belowground production and shifts community towards resource acquisition traits

Abstract Climate warming is expected to stimulate plant growth in high‐elevation and high‐latitude ecosystems, significantly increasing aboveground net primary production (ANPP). However, the effects of simultaneous changes in temperature, snowmelt timing, and summer water availability on total net primary production (NPP)—and elucidation of both above‐ and belowground responses—remain an important area in need of further study. In particular, measures of belowground net primary productivity (BNPP) are required to understand whether ANPP changes reflect changes in allocation or are indicative of a whole plant NPP response. Further, plant functional traits provide a key way to scale from the individual plant to the community level and provide insight into drivers of NPP responses to environmental change. We used infrared heaters to warm an alpine plant community at Niwot Ridge, Colorado, and applied supplemental water to compensate for soil water loss induced by warming. We measured ANPP, BNPP, and leaf and root functional traits across treatments after 5 yr of continuous warming. Community‐level ANPP and total NPP (ANPP + BNPP) did not respond to heating or watering, but BNPP increased in response to heating. Heating decreased community‐level leaf dry matter content and increased total root length, indicating a shift in strategy from resource conservation to acquisition in response to warming. Water use efficiency (WUE) decreased with heating, suggesting alleviation of moisture constraints that may have enabled the plant community to increase productivity. Heating may have decreased WUE by melting snow earlier and creating more days early in the growing season with adequate soil moisture, but stimulated dry mass investment in roots as soils dried down later in the growing season. Overall, this study highlights how ANPP and BNPP responses to climate change can diverge, and encourages a closer examination of belowground processes, especially in alpine systems, where the majority of NPP occurs belowground.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Rewiring the unfolded protein response for plant growth recovery after stress

The unfolded protein response (UPR) is a highly coordinated signaling network that alleviates endoplasmic reticulum (ER) stress, a condition induced by diverse environmental challenges in plants. Over the past two decades, substantial progress has been made in elucidating the genetic and molecular mechanisms of ER stress sensing and signal transduction in plants, largely through studies in the model plant Arabidopsis thaliana . These advances have established the UPR as a central regulator of proteostasis and underscored its broader relevance to plant growth and development and crop productivity under stress conditions. Despite this progress, critical knowledge gaps remain, particularly concerning the downstream biological processes required for growth recovery once ER stress has subsided and how these processes are coordinated by UPR regulators. Recent systems-level and integrative studies have begun to reveal critical roles of UPR signaling in pathways governing growth re-establishment and homeostasis of nutrient allocation and energy metabolism. In this review, we highlight recent findings on the functional roles of the plant UPR in recovery from ER stress, with a focus on mechanisms mediated by UPR regulators and downstream biological pathways that enable the transition from stress mitigation to growth restoration. Although this research area is still emerging, accumulating evidence supports a model in which the UPR functions as a dynamic regulatory network that actively coordinates post-stress physiological recovery to support plant fitness.

ER stress↗
DOE OSTI2022

Elastic Resource Management for Deep Learning Applications in a Container Cluster

The increasing demand for learning from massive datasets is restructuring our economy. Effective learning, however, involves nontrivial computing resources. Most businesses utilize commercial infrastructure providers (e.g., AWS) to host their computing clusters in the cloud, where various jobs compete for available resources. While cloud resource management is a fruitful research field that has made many advances in production, such as Kubernetes and YARN, few efforts have been invested to further optimize the system performance, especially for deep learning (DL) training jobs in a container cluster. This work introduces FlowCon, a system that is able to monitor the individual evaluation functions of DL jobs at runtime, and thus to make placement decisions on resource allocations elastically. Here, we present a detailed design and implementation of FlowCon and conduct intensive experiments over various DL models. The results demonstrate that FlowCon significantly improves DL job completion time and resource utilization efficiency, compared to default systems. According to the results, FlowCon is able to improve the completion time by up to 68.8% and meanwhile, reduce the makespan by 18.0%, in the presence of various DL job workloads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Analysis of Reactive Power Load Modeling Techniques for PV Impact Studies [Slides]

The increasing availability of advanced metering infrastructure (AMI) data has led to significant improvements in load modeling accuracy. However, since many AMI devices were installed to facilitate billing practices, few utilities record or store reactive power demand measurements from their AMI. When reactive power measurements are unavailable, simplifying assumptions are often applied for load modeling purposes, such as applying constant power factors to the loads. The objective of this work is to quantify the impact that reactive power load modeling practices can have on distribution system analysis, with a particular focus on evaluating the behaviors of distributed photovoltaic (PV) systems with advanced inverter capabilities. Quasi-static time-series simulations were conducted after applying a variety of reactive power load modeling approaches, and the results were compared to a baseline scenario in which real and reactive power measurements were available at all customer locations on the circuit. Overall, it was observed that applying constant power factors to loads can lead to significant errors when evaluating customer voltage profiles, but that performing per-phase time-series reactive power allocation can be utilized to reduce these errors by about 6x, on average, resulting in more accurate evaluations of advanced inverter functions.

14 SOLAR ENERGY↗
DOE OSTI2025

Valued peaks: Sustainable water allocation for small hydropower plants in an era of explicit ecological needs

Optimizing hydropower operations to balance economic profitability and support functioning ecosystem services is integral to river management policy. In this article, we propose a dynamic, constrained optimization framework for small hydropower plants (SHPs) to evaluate trade-offs between economic profitability and socio-ecological requirements. Specifically, we examine the balance between short-term losses in hydropower generation and the potential for compensatory benefits in the form of revenue from recreational ecosystem services, irrespective of the direct beneficiary. Our framework integrates a fish habitat model, a hydropower optimization model, and a recreational ecosystem service estimate to evaluate different environmental flow scenarios. The optimization process gives three outflow release scenarios, informed by previous streamflow realisations (dam inflow), and designed environmental flow constraints. The framework is applied and tested for the river Kuusinkijoki in North-eastern Finland, which is a habitat for migratory brown trout and grayling populations. We show that the revenue loss due to the environmental flow constraints arises through a reduction in revenue per generated energy unit and through a reduction in turbine efficiency. Additionally, the simulation results reveal that all the designed environmental flow constraints cannot be met simultaneously. Under the environmental flow scenario with both minimum flow and flow ramping rate constraints, the annual hydropower revenue decreases by 16.5 %. An annual increase of 8 % in recreational fishing visits offsets the revenue loss. In conclusion, the developed framework provides knowledge of the costs and benefits of hydropower environmental flow constraints and guides the prioritizing process of environmental measures.

13 HYDRO ENERGY↗
DOE OSTI2025

Experiences with SYCL on AMD GPUs with Kokkos

With the recent diversification of the hardware landscape in the high-performance computing (HPC) community, performance-portability solutions are becoming more and more important. One of the most popular choices is Kokkos, which recently became a Linux Foundation project. Most of its development is supported by the US Department of Energy and the French Alternative Energies and Atomic Energy Commission. Kokkos is implemented as a C++ library with multiple backends to support CPUs as well as various GPU architectures. These backends include OpenMP, CUDA, HIP, and also SCYL. This approach enables users to leverage the preferred vendor toolchain for the respective platform (e.g. CUDA, ROCm, OneAPI). The SYCL backend is used to target Intel GPUs, in particular to support the Aurora exascale supercomputer. However, SYCL itself also offers a large degree of portability, and in fact Kokkos’ CI for SYCL has been running on NVIDIA hardware due to a lack of access to Intel GPUs. In this report, we describe our experience with using Kokkos SYCL backend on AMD GPUs targeting the Frontier supercomputer at Oak Ridge National Laboratory. The two major SYCL implementations are DPC++ and AdaptiveCpp. While the Kokkos SYCL backend has been implemented using the former, the latter was the first implementation to target AMD GPUs. We will discuss the experience with both of these SYCL implementations in terms of functionality and performance. Using Kokkos to evaluate SYCL toolchains has a number of benefits. Kokkos’ use of SYCL is fairly complex, exercising features such as graphs, relocatable device functions, atomics – including for non-arithmetic types, as well as pinned and page migratable memory allocations. Kokkos also needs to implement capabilities such as Kokkos’ hierarchical parallelism that are not a straight-forward mapping to SYCL capabilities. Furthermore, a large number of libraries and applications that represent diverse use cases are implemented in Kokkos, providing readily available test cases for a toolchain evaluation. Preliminary results show that support for AMD GPUs in DPC++ is much less mature than for NVIDIA GPUs or Intel GPUs. While the situation has improved significantly over the last year, we still encounter many runtime failures, dispatching problems, and code generation issues. With AdaptiveCpp the challenges arise even earlier in the evaluation process. Since Kokkos’ SYCL implementation is largely focused on supporting Intel GPUs, we opted to leverage SYCL extensions which are available in DPC++ but not in AdaptiveCpp. Furthermore, AdaptiveCpp appears to be less conformant with the SYCL2020 standard which Kokkos relies on. In some cases, we are able to work around the lack of feature support, in other cases we have to disable certain Kokkos capabilities to evaluate the toolchain. Our evaluation will leverage Kokkos’ unit tests to establish basic functionality and feature completeness. We then use simple benchmarks for components of a CG implementation as a measure of usability and performance of the SYCL toolchains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Divergent trait controls on soluble sugars and starch underlie global strategies of tree carbohydrate storage

Nonstructural carbohydrate (NSC) stores buffer tree metabolism, osmotic regulation, and defense, thereby mediating tolerance and survival under climate extremes. Yet, the functional and evolutionary determinants of interspecific variation in NSC remain elusive, limiting understanding and prediction of forest carbon allocation and mortality under global change. Here, we present a cross-species synthesis of NSC concentrations across multiple organs for 281 woody species from 102 mixed forest communities worldwide, where we quantified species-specific deviations from community means to disentangle intrinsic trait effects from environmental and methodological variation. We found phylogenetic signals in NSC deviations, with coniferous gymnosperms and evergreen species consistently maintaining lower stem soluble sugars and starch concentrations than co-occurring angiosperms and deciduous species, respectively. A global pattern emerged where greater stomatal sensitivity to leaf water potential was associated with declines in the relative concentrations of both sugars and starch. In contrast, xylem hydraulic safety traits showed weak and organ-dependent relationships with NSC concentrations. Sugars increased with photosynthetic capacity and declined with wood density, whereas starch showed the reverse pattern, which aligned with the distinct functional-metabolic roles of sugars and starch. By integrating trait-based ecology with a community-centered framework, our study provides global evidence that stomatal regulation, photosynthetic capacity, specific leaf area, and wood density jointly govern interspecific NSC variation, through contrasting effects on sugars and starch. These are among the most broadly measured traits globally, thus the emergent carbohydrate–trait relationships can have broad applications toward understanding and predicting forest growth and survival under climate change.

tropic system↗
DOE OSTI2022

Radial–axial transport coordination enhances sugar translocation in the phloem vasculature of plants

Abstract Understanding mass transport of photosynthates in the phloem of plants is necessary for predicting plant carbon allocation, productivity, and responses to water and thermal stress. Several hypotheses about optimization of phloem structure and function and limitations of phloem transport under drought have been proposed and tested with models and anatomical data. However, the true impact of radial water exchange of phloem conduits with their surroundings on mass transport of photosynthates has not been addressed. Here, the physics of the Munch mechanism of sugar transport is re-evaluated to include local variations in viscosity resulting from the radial water exchange in two dimensions (axial and radial) using transient flow simulations. Model results show an increase in radial water exchange due to a decrease in sap viscosity leading to increased sugar front speed and axial mass transport across a wide range of phloem conduit lengths. This increase is around 40% for active loaders (e.g. crops) and around 20% for passive loaders (e.g. trees). Thus, sugar transport operates more efficiently than predicted by previous models that ignore these two effects. A faster front speed leads to higher phloem resiliency under drought because more sugar can be transported with a smaller pressure gradient.

58 GEOSCIENCES↗
DOE OSTI2025

UBW (USLCI-Brightway2) [SWR-25-169]

Life cycle inventory (LCI) data are critical for robust life cycle assessment (LCA), yet many widely used datasets such as the U.S. Life Cycle Inventory (USLCI) are not natively compatible with advanced modeling frameworks like Brightway2. This work presents an automated pipeline to transform USLCI data into a fully functional Brightway2 project. The workflow performs systematic data cleaning, resolves duplicate process and exchange identifiers, and applies allocation to multi-output processes. Technosphere and biosphere flows are harmonized through unit conversions and a bridge mapping to the biosphere3 database, with comprehensive logging of missing flows and cutoff issues. The resulting Brightway2 database is validated using matrix diagnostics to ensure consistency of the technosphere, and is benchmarked via life cycle impact assessment (LCIA) methods such as ReCiPe and IPCC GWP. Outputs include reproducible CSV exports of corrected processes, elementary flows, characterization factors, and LCIA results, alongside backup utilities for project sharing. This pipeline lowers barriers for integrating USLCI data into open-source LCA workflows, enabling reproducible, validated LCA inventories within the Brightway 2 framework.

Ghosh, Tapajyoti [National Laboratory of the Rocki↗
DOE OSTI2025

Agile Allocation in the Tundra: A Single Growing Season of Warming Increases Nutrient Availability While Decreasing Fine-Root Length

The majority of plant biomass is located belowground in Arctic ecosystems and plant roots are responsible for the uptake of the nutrients that constrain plant growth in these infertile ecosystems. Despite performing a crucial role connecting primary producers to the soil, roots are relatively understudied in the Arctic and their functional response to a rapidly warming and increasingly variable climate is unknown. Here, we assessed whether one growing season with elevated temperatures would have an impact on nutrient uptake and allocation by applying a warming technique that increased daily air temperatures by 3.2 °C. Destructive sampling was performed at the peak of the growing season to quantify biomass pools of carbon (C) and nitrogen (N), root traits, and uptake of a 15 N tracer ( 15 NH 4 + ) for the dominant plant species, Arctagrostis latifolia. We found that soil nutrient availability increased with short-term warming, but A. latifolia NH 4 + uptake remained unchanged. Fine-root length density and root biomass within the soil profile, however, were both reduced by warming. N allocation patterns across plant tissues were also altered by warming. NH 4 + uptake was best fit with a logistic model that captured the spatial relationship between roots and soil (NH 4 + uptake expressed per length fine root and NH 4 + availability expressed per unit soil volume) rather than a traditional Michaelis–Menten model. Our results indicate that short-term experimental warming can shift plant–soil interactions, suggesting that the tundra’s belowground response to elevated temperatures may be more dynamic than previously recognized.

15N tracer↗
DOE OSTI2024

Allometric relationships and trade‐offs in 11 common M editerranean‐climate grasses

Abstract Biomass allocation in plants is the foundation for understanding dynamics in ecosystem carbon balance, species competition, and plant–environment interactions. However, existing work on plant allometry has mainly focused on trees, with fewer studies having developed allometric equations for grasses. Grasses with different life histories can vary in their carbon investment by prioritizing the growth of specific organs to survive, outcompete co‐occurring plants, and ensure population persistence. Further, because grasses are important fuels for wildfire, the lack of grass allocation data adds uncertainty to process‐based models that relate plant physiology to wildfire dynamics. To fill this gap, we conducted a greenhouse experiment with 11 common California grasses varying in photosynthetic pathway and growth form. We measured plant sizes and harvested above‐ and belowground biomass throughout the life cycle of annual species, while for the establishment stage of perennial grasses to quantify allometric relationships for leaf, stem, and root biomass, as well as plant height and canopy area. We used basal diameter as a reference measure of plant size. Overall, basal diameter is the best predictor for leaf and stem biomass, height, and canopy area. Including height as another predictor can improve model accuracy in predicting leaf and stem biomass and canopy area. Fine root biomass is a function of leaf biomass alone. Species vary in their allometric relationships, with most variation occurring for plant height, canopy area, and stem biomass. We further explored potential trade‐offs in biomass allocation across species between leaf and fine root, leaf and stem, and allocation to reproduction. Consistent with our expectation, we found that fast‐growing plants allocated a greater fraction to reproduction. Additionally, plant height and specific leaf area negatively influenced the leaf‐to‐stem ratio. However, contrary to our hypothesis, there were no differences in root‐to‐leaf ratio between perennial and annual or C 4 and C 3 plants. Our study provides species‐specific and functional‐type‐specific allometry equations for both above‐ and belowground organs of 11 common California grass species, enabling nondestructive biomass assessment in California grasslands. These allometric relationships and trade‐offs in carbon allocation across species can improve ecosystem model predictions of grassland species interactions and environmental responses through differences in morphology.

54 ENVIRONMENTAL SCIENCES↗