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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Dynamic life-cycle carbon analysis for fast pyrolysis biofuel produced from pine residues: implications of carbon temporal effects

Abstract Background Woody biomass has been considered as a promising feedstock for biofuel production via thermochemical conversion technologies such as fast pyrolysis. Extensive Life Cycle Assessment studies have been completed to evaluate the carbon intensity of woody biomass-derived biofuels via fast pyrolysis. However, most studies assumed that woody biomass such as forest residues is a carbon–neutral feedstock like annual crops, despite a distinctive timeframe it takes to grow woody biomass. Besides, few studies have investigated the impacts of forest dynamics and the temporal effects of carbon on the overall carbon intensity of woody-derived biofuels. This study addressed such gaps by developing a life-cycle carbon analysis framework integrating dynamic modeling for forest and biorefinery systems with a time-based discounted Global Warming Potential (GWP) method developed in this work. The framework analyzed dynamic carbon and energy flows of a supply chain for biofuel production from pine residues via fast pyrolysis. Results The mean carbon intensity of biofuel given by Monte Carlo simulation across three pine growth cases ranges from 40.8–41.2 g CO 2 e MJ −1 (static method) to 51.0–65.2 g CO 2 e MJ −1 (using the time-based discounted GWP method) when combusting biochar for energy recovery. If biochar is utilized as soil amendment, the carbon intensity reduces to 19.0–19.7 g CO 2 e MJ −1 (static method) and 29.6–43.4 g CO 2 e MJ −1 in the time-based method. Forest growth and yields (controlled by forest management strategies) show more significant impacts on biofuel carbon intensity when the temporal effect of carbon is taken into consideration. Variation in forest operations and management (e.g., energy consumption of thinning and harvesting), on the other hand, has little impact on the biofuel carbon intensity. Conclusions The carbon temporal effect, particularly the time lag of carbon sequestration during pine growth, has direct impacts on the carbon intensity of biofuels produced from pine residues from a stand-level pine growth and management point of view. The carbon implications are also significantly impacted by the assumptions of biochar end-of-life cases and forest management strategies.

09 BIOMASS FUELS↗
DOE OSTI2020

Sustainable production of ammonia fertilizers from biomass

Nitrogen is the most limiting plant nutrient. Inexpensive natural gas has substantially reduced costs of ammonia fertilizer for intensive agriculture in the developed world, but its excessive use negatively impacts downstream ecosystems. By contrast, the availability of ammonia fertilizer is a major economic bottleneck for agriculture in developing countries. A dedicated lignocellulosic biomass crop could supply sufficient substrate to generate optimal nitrogen fertilizer on less than 5% of a grower’s food crop acreage. However, reimagining ammonia generation using biomass could significantly enhance sustainable agricultural productivity in developing countries when combined with emerging catalytic technologies.

09 BIOMASS FUELS↗
DOE OSTI2022

In silico evaluation of plant nitrification suppression effects on agroecosystem nitrogen loss

Abstract Nitrification regulates the potential for nitrogen (N) loss from ecosystems because it converts ammonium to nitrate, which is susceptible to leaching and gaseous emissions. Crops can suppress the microbes that perform nitrification by exuding nitrification‐inhibiting compounds from their roots and taking up available ammonium. However, the effect of nitrification suppression on agroecosystem N losses remains poorly characterized, and a lack of temporal synchrony between nitrification, N losses, and nitrification suppression by plants could limit the effect of nitrification suppression. We used the DayCent‐CABBI model to evaluate the effectiveness of the suppression of nitrification by sorghum to reduce N 2 O emissions and nitrate leaching in an energy sorghum/soybean rotation at the Energy Farm in Urbana‐Champaign, IL. We simulated nitrification suppression at the measured levels (MeasNS) and at the maximum measured level applied to the entire growing season (MaxNS), and we also explored ways to better utilize nitrification suppression by altering the timing of urea–ammonium–nitrate fertilizer applications. Model experiments showed that most nitrification occurred immediately after fertilizer was applied, whereas nitrification suppression began to ramp up more than a month after planting. On an annual basis, MeasNS experiments showed a 1%–2% reduction in annual N 2 O emissions relative to no nitrification suppression (NoNS), and MaxNS experiments showed a 4%–9% reduction in annual N 2 O emissions relative to NoNS. Both nitrification suppression levels showed <1% reduction in nitrate leaching. Altering the timing of fertilizer applications to better synchronize nitrification suppression with high soil ammonium levels had mixed effects on annual N 2 O emissions and nitrate leaching and sometimes resulted in increased N losses. The timing of simulated N 2 O emissions shifted with the timing of fertilization, and N 2 O emissions from denitrification increased when N 2 O emissions from nitrification decreased. Increasing N retention during the non‐growing season may be more effective than growing‐season nitrification suppression for reducing annual N losses in the rainfed Midwest.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Aboveground woody biomass estimation of young bioenergy plantations of Populus and its hybrids using mobile (backpack) LiDAR remote sensing

Woody aboveground biomass (AGB) including short-rotation Populus is used as a feedstock for renewable and carbon-neutral bioenergy. While woody AGB can be estimated with allometric equations requiring labor-intensive field data, remote sensing technologies like mobile terrestrial light detection and ranging (LiDAR) can estimate woody AGB quickly and accurately. Therefore, the goals of this study were to develop a model to predict woody AGB of 2-year-old Populus spp. from three taxa (P. deltoides, P. deltoides × P. maximowiczii and P. deltoides × P. trichocarpa) using allometric (height and diameter at breast height (DBH)) or LiDAR-derived metrics from a mobile terrestrial (backpack) system. Likewise, we sought to compare LiDAR-estimated tree height and DBH with field-measured values. We found that a taxa-specific model containing LiDAR-measured tree height, crown volume, and taxa interactions with the height of the 10 th percentile, and the density of the lowest interval (density metric 0) explained 84 % of the variation in woody AGB with a root mean square error (RMSE) of 28.7 % and performed slightly better than the allometric model. The best model excluding taxa had a slightly higher RMSE but lower bias than the allometric model. LiDAR-derived tree heights were highly correlated with field-measured heights, but DBH could not be estimated accurately. Therefore, terrestrial mobile LiDAR systems can accurately estimate woody AGB and tree height of Populus in short rotation systems to aid in the fast and efficient quantification of woody bioenergy production and renewable energy resources.

AGB↗
DOE OSTI2023

Automatic Crack Segmentation and Feature Extraction in Electroluminescence Images of Solar Modules

The effect of cracks in solar cells on the long-term degradation of photovoltaic (PV) modules remains to be determined. To investigate this effect in future studies, it is necessary to quantitatively describe the crack features (e.g., length) and correlate them with module power loss. Electroluminescence (EL) imaging is a common technique for identifying cracks. However, it is currently challenging and time-consuming to identify cracks in a large number of EL images and quantify complex crack features by human inspection. This article introduces a fast semantic segmentation method (~0.18 s/cell) to automatically segment cracks from EL images and algorithms to extract crack features. Here we fine-tuned a UNet neural network model using pretrained VGG16 as the encoder and obtained an average F1 score of 0.875 and an intersection over union score of 0.782 on the testing set. With cracks and busbars segmented, we developed algorithms for extracting crack features, including the crack-isolated area, the brightness inside the isolated area, and the crack length. We also developed an automatic preprocessing tool for cropping individual cell images from EL images of PV modules (~0.72 s/module). Our codes are published as open-source an software, and our annotated dataset composed of various types of cells is published as a benchmark for crack segmentation in EL images.

14 SOLAR ENERGY↗
DOE OSTI2026

Dissecting the contributions to non-photochemical quenching in a land plant under fluctuating light

Photosynthetic organisms have evolved multiple non-photochemical quenching (NPQ) processes, providing photoprotection by safely dissipating excess excitation energy. These processes involve various molecular players functioning on overlapping timescales from seconds to days, making it challenging to isolate and quantify their individual kinetics. In this study, we perform whole-leaf chlorophyll fluorescence lifetime and xanthophyll concentration measurements on wild-type and various newly characterized NPQ mutants of Nicotiana benthamiana, a vascular land plant. Based on these measurements, we construct a fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant NPQ behaviors under various light-dark regimes. Additionally, the model quantifies the per-molecule quenching effectiveness of various xanthophylls and the contributions of six quenching components (qE V , qE A , qE Z , qE L , qZ, and qI) across different genotypes. It also suggests improved overall quenching efficiency at specific VDE:ZEP:PsbS overexpression stoichiometries, aligning with previous studies and supporting translational efforts to optimize photoprotection and enhance crop yields under dynamic light environments.

Lam, Lam [University of California, Berkeley, CA (↗
DOE OSTI2024

Abstract for CRADA between Mati Car bon PBC and NETL

The National Energy Technology Laboratory (NETL) will assist Mati Carbon PBC (participant) in the design and execution of their community benefits plan (CBP) for scaling Enhanced Rock Weathering (ERW) carbon dioxide removal (CDR) in Arkansas. ERW involves spreading finely crushed rocks in agricultural fields to speed up the dissolution and release of certain minerals that amend soil acidity, replenish soil nutrients, and simultaneously converting CO 2 from the atmosphere to Dissolved Inorganic Carbon (DIC). ERW not only removes CO 2 from the atmosphere but also has the potential to generate co-benefits for famers through cost reductions and increased crop yields. The collaboration between NETL and Mati Carbon, PBC (participant) will entail a detailed review of the economic, environmental and community impact of ERW in agricultural settings. This review will guide all project related decisions. It will provide a better understanding of the scale of these potential co-benefits and quantify community needs that ERW can address.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

yuwangcn/C4_dynamic_model

A dynamic systems model of C4 photosynthesis was developed based on the previous NADP-ME metabolic model for maize (Wang et al., 2014 ab). The NADP-ME metabolic model is an ordinary differential equation model including all individual steps in C4 photosynthetic carbon metabolism. Here, the model is extended to include posttranslational regulation and temperature response of enzyme activities, dynamic stomata conductance, and leaf energy balance. This model is written in Matlab (R2019a) Steady-state and dynamic gas exchange data for maize (B73), sugarcane (CP88-1762) and sorghum (Tx430) were measured in a greenhouse in Urbana, IL from July 25, 2019 through August 8, 2019. Data include: CO2 response curves Light response curves Photosynthetic induction curves measured in the transition from darkness to high light (1800 μmol m-2 s-1), data logged every 1 min. Photosynthetic induction in the transition from darkness to high light (1800 μmol m-2 s-1) to determine the kinetics of rubisco activation in these C4 crops (τ_Rubisco), data logged every 10 s. Gas exchange under fluctuating light. After dark adaptation, the leaves undergo three light change steps, light intensity was set as 1800 µmol m-2 s-1, 200 µmol m-2 s-1 and 1800 µmol m-2 s-1 for each 1800 s step.

Wang, Yu↗
DOE OSTI2026

Data for Impact of Vertical and Seasonal Variation in Leaf Traits on Simulating Soybean Canopy Photosynthesis via 1D and 3D Modeling

Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax25), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax25 profile achieved comparable performance to the scheme with the measured Vcmax25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.

Photosynthesis↗
DOE OSTI2020

BESS-STAIR: a framework to estimate daily, 30m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt

Abstract. With increasing crop water demands and drought threats, mapping andmonitoring of cropland evapotranspiration (ET) at high spatial and temporalresolutions become increasingly critical for water management andsustainability. However, estimating ET from satellites for precise waterresource management is still challenging due to the limitations in bothexisting ET models and satellite input data. Specifically, the process of ETis complex and difficult to model, and existing satellite remote-sensing datacould not fulfill high resolutions in both space and time. To address theabove two issues, this study presents a new high spatiotemporal resolution ETmapping framework, i.e., BESS-STAIR, which integrates a satellite-drivenwater–carbon–energy coupled biophysical model, BESS (Breathing Earth SystemSimulator), with a generic and fully automated fusion algorithm, STAIR(SaTallite dAta IntegRation). In this framework, STAIR provides daily 30'mmultispectral surface reflectance by fusing Landsat and MODIS satellite datato derive a fine-resolution leaf area index and visible/near-infrared albedo,all of which, along with coarse-resolution meteorological and CO 2 data, are used to drive BESS to estimate gap-free 30 m resolution daily ET.We applied BESS-STAIR from 2000 through 2017 in six areas across the US CornBelt and validated BESS-STAIR ET estimations using flux-tower measurementsover 12 sites (85 site years). Results showed that BESS-STAIR daily ETachieved an overall R2=0.75, with root mean square error RMSE=0.93 mm d -1 and relative error RE =27.9 % when benchmarkedwith the flux measurements. In addition, BESS-STAIR ET estimations capturedthe spatial patterns, seasonal cycles, and interannual dynamics well indifferent sub-regions. The high performance of the BESS-STAIR frameworkprimarily resulted from (1) the implementation of coupled constraints onwater, carbon, and energy in BESS, (2) high-quality daily 30 m data from theSTAIR fusion algorithm, and (3) BESS's applicability under all-skyconditions. BESS-STAIR is calibration-free and has great potentials to be areliable tool for water resource management and precision agricultureapplications for the US Corn Belt and even worldwide given the globalcoverage of its input data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Accelerating the development of a sustainable bioenergy portfolio through stable isotopes

Bioenergy could help limit global warming to 2°C above pre-industrial levels while supplying almost a fourth of the world's renewable energy needs by 2050. However, the deployment of bioenergy raises concerns that adoption at meaningful scales may lead to unintended negative environmental consequences. Meanwhile, the full consolidation of a bioenergy industry is currently challenged by a sufficient, resilient, and resource-efficient biomass supply and an effective conversion process. Here, we provide a comprehensive analysis of how stable isotope approaches have accelerated the development of a robust bioeconomy by advancing knowledge about environmental sustainability, feedstock development, and biological conversion. We show that advances in stable isotope research have generated crucial information to (1) gain mechanistic insight into the potential of bioenergy crops to mitigate climate change as well as their impact on water and nutrient cycling; (2) develop high-yielding, resilient feedstocks that produce high-value bioproducts in planta; and (3) engineer microbes to enhance feedstock conversion to bioenergy products. Further, we highlight knowledge gaps that could benefit from future research facilitated by stable isotope approaches. We conclude that advances in mechanistic knowledge and innovations within the field of stable isotopes in cross-disciplinary research actions will greatly contribute to breaking down the barriers to establishing a robust bioeconomy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

BTRIC Technical Support for Appalachia: FY 2024 Summary Report on Net Negative Carbon Building Demonstration with AgPod

This report investigates the effects of various concentrations of CO 2 on the photosynthesis and biomass accumulation of crops growing in an AgPod, using kale as the test crop. In the first year of the study, increasing the CO 2 concentration in the AgPod was found to promote higher photosynthetic rates in kale plants up to 1,300 ppm (the highest CO 2 concentration measured). An interesting finding was that net photosynthetic rates of kale leaves remain unsaturated until reaching unusually high levels of photosynthetically active radiation (PAR, > 1,200 µmol m -1 s -1 ) and CO 2 concentration (> 1,100 ppm). Such high levels of saturating PAR and CO 2 concentration values rarely occur under natural conditions, suggesting a promising potential for carbon capture via enriched controlled environment agriculture (ECEA). During the second year of the study, a second crop was planted to confirm the actual effects on growth with increased CO 2 concentrations. Our results suggest that growing kale plants in the AgPod system at higher CO 2 levels resulted in a successful and highly productive crop, up to three times the amount grown at 415 CO 2 . Further, the kale plants grown under higher CO 2 conditions (1000 ppm) are not CO 2 saturated and can potentially further improve yield at even higher CO 2 environments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Bioenergy landscapes drive trophic shifts in generalist ants

Changes in trophic niche—the pathways through which an organism obtains energy and nutrients—are a fundamental way in which organisms respond to environmental conditions. But the capacity for species to alter their trophic niches in response to global change, and the ways they do so when able, remain largely unknown. Here we examine food webs in three long–term and large–scale experiments to test how resource availability and nutritional requirements interact to determine an organism's trophic niche in the context of one of the largest global trends in land use—the rise in bioenergy production. Here, we use carbon and nitrogen stable isotope analyses to characterize arthropod food webs across three biofuel crops representing a gradient in plant resource richness (corn monocultures, fields dominated by native switchgrass and restored prairie), and to quantify changes in the trophic niche of a widespread generalist ant species across habitats. In doing so, we measure the effects of basal resource richness on food chain length, niche breadth and trophic position. We frame our results in the context of two hypotheses that explain variation in trophic niche—the niche variation hypothesis which emphasizes the importance of resource diversity and ecological opportunity, and the optimal diet hypothesis which emphasizes dietary constraints and the availability of optimal resources. Increasing plant richness lengthened food chains by 10%–20% compared to monocultures. Niche breadths of generalist ants did not vary with resource richness, suggesting they were limited by optimal diet requirements and constraints rather than by ecological opportunity. The ants instead responded to changes in plant richness by shifting their estimated trophic position. In resource–poor monocultures, the ants were top predators, sharing a trophic position with predatory spiders. In resource–rich environments, in contrast, the ants were omnivores, relying on a mix of animal prey and plant–based resources. In addition to highlighting novel ecosystem impacts of alternate bioenergy landscapes, our results suggest that niche breadth and trophic diversification depend more on the presence of optimal resources than on ecological opportunity alone.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Structural Diversity in Eukaryotic Photosynthetic Light Harvesting

Photosynthesis has been using energy from sunlight to assimilate atmospheric CO 2 for at least 3.5 billion years. Through evolution and natural selection, photosynthetic organisms have flourished in almost all aquatic and terrestrial environments. This is partly due to the diversity of light-harvesting complex (LHC) proteins, which facilitate photosystem assembly, efficient excitation energy transfer, and photoprotection. Structural advances have provided angstrom-level structures of many of these proteins and have expanded our understanding of the pigments, lipids, and residues that drive LHC function. In this review, we compare and contrast recently observed cryo-electron microscopy structures across photosynthetic eukaryotes to identify structural motifs that underlie various light-harvesting strategies. We discuss subtle monomer changes that result in macroscale reorganization of LHC oligomers. Additionally, we find recurring patterns across diverse LHCs that may serve as evolutionary stepping stones for functional diversification. Advancing our understanding of LHC protein–environment interactions will improve our capacity to engineer more productive crops.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Mineral nutrient signaling controls photosynthesis: focus on iron deficiency-induced chlorosis

Photosynthetic organisms convert light energy into chemical energy stored in carbohydrates. To perform this process, an adequate supply of essential mineral elements, such as iron, is required in the chloroplast. Because iron plays a crucial role during electron transport and chlorophyll formation, iron deficiency alters photosynthesis and promotes chlorosis, or the yellowing of leaves. Intriguingly, iron deficiency-induced chlorosis can be reverted by the depletion of other micronutrients [i.e., manganese (Mn)] or macronutrients [i.e., sulfur (S) or phosphorus (P)], raising the question of how plants integrate nutrient status to control photosynthesis. Here, in this work, we review how improving our understanding of the complex relationship between nutrient homeostasis and photosynthesis has great potential for crop improvement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

U.S. Agrivoltaics Irradiance Database

This is a foundational data set for research and deployment of agrivoltaics, which is the co-location of agriculture and solar power plants on the same land. This irradiance and shading dataset can be utilized to determine the suitability of agrivoltaics configurations for a given region and crop-type. The data is hourly, 4x4 km resolution across the contiguous United States and Hawaii. It is calculated from the National Solar Radiation Database sites, using the System Advisor Model (SAM) to simulate the shading patterns for 10 common agrivoltaics configurations. Sunlight availability data is reported for 10 locations on the ground between adjacent rows of solar panels, as well as averaged across areas of interest such as the average irradiance in the edge-to-edge open area or across 3-6 planting beds. Other available metrics include the input meteorological data from the NSRDB (e.g. global horizontal irradiance, wind speed, etc.) and estimates for comparing energy and agricultural characteristic across the 10 configurations, including power output per acre or per kW installed capacity and farmable land area per acre.

14 SOLAR ENERGY↗
DOE OSTI2020

Design Consideration for Solar Powered Wastewater Treatment Facility for Agriculture and Potable Usage on Acoma Pueblo Reservation

Environmental stewardship has long been a guiding principle among indigenous peoples. Acoma Pueblo, located approximately 45 miles west of Albuquerque, New Mexico, shares these values. They are exploring how to incorporate these values into future development projects. As such, Acoma recently created a wastewater treatment facility to provide irrigation water to nearby crops. Treatment facilities like this further sustainable practices and benefit the community but may draw power from the grid. This report looks at design considerations for possible photovoltaic utilization at the site to offset current power and monetary costs. Lessons learned from a larger, municipal wastewater treatment center will be applied with special considerations for Acoma Pueblo's values and unique wastewater treatment facility configuration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Daily evapotranspiration changes during heatwaves at 32 NEON sites, 2019-2021

This dataset provides partitioned evapotranspiration (ET, the combined loss of water from soil and plant surfaces) anomalies during heatwave events—soil evaporation (E) and transpiration (T)—for 268 heatwave events across 32 National Ecological Observatory Network (NEON) flux sites in the contiguous United States from 2019–2021. Using an ensemble of four high-frequency turbulence methods (Flux-variance Similarity, Conditional Eddy Covariance [CEC], CEC with Water-Use Efficiency, and Conditional Eddy Accumulation; see Zahn and Bou-Zeid 2024), half-hourly transpiration-to-evapotranspiration (T/ET) ratios were derived from 20 hertz (Hz, cycles per second) eddy covariance measurements of carbon dioxide (CO₂) and water vapor (H₂O) concentrations. The dataset spans six vegetation types including evergreen and deciduous forests, grasslands, cultivated crops, shrublands, and emergent herbaceous wetlands. Data Package Contents: The dataset includes a single CSV (comma-separated values) file containing daily anomalies (deviations from baseline conditions) for transpiration (Delta_T), evaporation (Delta_E), total evapotranspiration (Delta_ET), and T/ET ratio (Delta_T_ET) during each day of identified heatwave events. The file also includes site codes, dates, heatwave event identifiers, and day-of-heatwave indicators. The CSV file can be opened with spreadsheet software (Microsoft Excel, Google Sheets) or programming environments (Python, R, MATLAB). This resource enables researchers to investigate ecosystem-specific responses to thermal extremes, validate land surface model partitioning of ET fluxes, and examine feedbacks between water cycling and surface energy balance during heatwaves. The dataset is particularly valuable for studies linking vegetation hydraulic strategies to climate resilience, as it captures the divergent responses of shallow-rooted versus deep-rooted ecosystems. Potential applications include improving drought early warning systems, informing irrigation management strategies, and advancing our mechanistic understanding of land-atmosphere interactions under extreme heat conditions.

Day of Heatwave↗