Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “vegetation dynamics”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Model code and data: biomass allocation adjustments induced by elevated CO2 and warming in a C3 brackish marsh, 2017-2022, Maryland

This dataset and R script accompany the published paper Bruns et al. (2024) in Geophysical Research Letters. The data are from the first six years of a field manipulation of whole-ecosystem warming and elevated CO2 experiment (Salt Marsh Accretion Response to Temperature eXperiment, or SMARTX) in the Smithsonian's Global Change Research Wetland (GCReW), a brackish, microtidal wetland site on a subestuary of the Chesapeake Bay. These data were generated to understand how warming and elevated CO2 interact to structure ecosystem-level responses to global change, particularly in terms of carbon sequestration. The dataset covers 2017-2022 and includes peak annual above ground biomass, annual belowground fine root productivity, and porewater NH4 for each experimental plot. The overall experiment is replicated in two locations on the marsh, a lower elevation zone dominated the C3 sedge S. Americanus and a higher elevation plot dominated by the C4 species. This paper and its data release is only for the C3 plot. Variable descriptions for data file is available in variable_descriptions.pdf. The R script Bruns_et_al_2024_GRL_make_figures.Rmd contains model code and other scripts used to generate all paper figures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine learning models inaccurately predict current and future high-latitude C balances

The high-latitude carbon (C) cycle is a key feedback to the global climate system, yet because of system complexity and data limitations, there is currently disagreement over whether the region is a source or sink of C. Recent advances in big data analytics and computing power have popularized the use of machine learning (ML) algorithms to upscale site measurements of ecosystem processes, and in some cases forecast the response of these processes to climate change. Due to data limitations, however, ML model predictions of these processes are almost never validated with independent datasets. To better understand and characterize the limitations of these methods, we develop an approach to independently evaluate ML upscaling and forecasting. We mimic data-driven upscaling and forecasting efforts by applying ML algorithms to different subsets of regional process-model simulation gridcells, and then test ML performance using the remaining gridcells. In this study, we simulate C fluxes and environmental data across Alaska using ecosys, a process-rich terrestrial ecosystem model, and then apply boosted regression tree ML algorithms to training data configurations that mirror and expand upon existing AmeriFLUX eddy-covariance data availability. We first show that a ML model trained using ecosys outputs from currently-available Alaska AmeriFLUX sites incorrectly predicts that Alaska is presently a modeled net C source. Increased spatial coverage of the training dataset improves ML predictions, halving the bias when 240 modeled sites are used instead of 15. However, even this more accurate ML model incorrectly predicts Alaska C fluxes under 21st century climate change because of changes in atmospheric CO 2 , litter inputs, and vegetation composition that have impacts on C fluxes which cannot be inferred from the training data. Our results provide key insights to future C flux upscaling efforts and expose the potential for inaccurate ML upscaling and forecasting of high-latitude C cycle dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Assessment of U.S. Urban Surface Temperature Using GOES-16 and GOES-17 Data: Urban Heat Island and Temperature Inequality

Abstract This study utilizes hourly land surface temperature (LST) data from the Geostationary Operational Environmental Satellite (GOES) to analyze the seasonal and diurnal characteristics of surface urban heat island intensity (SUHII) across 120 largest U.S. cities and their surroundings. Distinct patterns emerge in the classification of seasonal daytime SUHII and nighttime SUHII. Specifically, the enhanced vegetation index (EVI) and albedo (ALB) play pivotal roles in influencing these temperature variations. The diurnal cycle of SUHII further reveals different trends, suggesting that climate conditions, urban and nonurban land covers, and anthropogenic activities during nighttime hours affect SUHII peaks. Exploring intracity LST dynamics, the study reveals a significant correlation between urban intensity (UI) and LST, with LST rising as UI increases. Notably, populations identified as more vulnerable by the social vulnerability index (SVI) are found in high UI regions. This results in discernible LST inequality, where the more vulnerable communities are under higher LST conditions, possibly leading to higher heat exposure. This comprehensive study accentuates the significance of tailoring city-specific climate change mitigation strategies, illuminating LST variations and their intertwined societal implications.

Environmental Sciences & Ecology↗
DOE OSTI2024

Data from "A Bayesian Record Linkage Approach to Applications in Tree Demography Using Overlapping LiDAR Scans"

Processed LiDAR data and environmental covariates from 2015 and 2019 LiDAR scans in the Vicinity of Snodgrass Mountain (Western Colorado, USA), in a geographic subset used in primary analysis for the research paper.This package contains LiDAR-derived canopy height maps for 2015 and 2019, crown polygons derived from the height maps using a segmentation algorithm, and environmental covariates supporting the model of forest growth. Source datasets include August 2015 and August 2019 discrete-return LiDAR point clouds collected by Quantum Geospatial for terrain mapping purposes on behalf of the Colorado Hazard Mapping Program and the Colorado Water Conservation Board. Both datasets adhere to the USGS QL2 quality standard. The point cloud data were processed using the R package lidR to generate a canopy height model representing maximum vegetation height above the ground surface, using a pit-free algorithm.This dataset was compiled to assess how spatial patterns of tree growth in montane and subalpine forests are influenced by water and energy availability. Understanding these growth patterns can provide insight into forest dynamics in the Southern Rocky Mountains under changing climatic conditions.This dataset contains .tif, .csv, and .txt files. This dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Energy Technology Innovation Partnership Project Strategic Energy Plan for Washington County, Maine

This Strategic Energy Plan (SEP) for Washington County, Maine was developed through the U.S. Department of Energy's Energy Technology Innovation Partnership Project (ETIPP) to address local challenges related to electricity reliability, affordability, and safety. Grounded in a "relational infrastructure" approach, the plan recognizes that improved energy outcomes depend not only on physical grid investments but also on strengthened coordination and trust between utilities and communities. Using utility-provided reliability data, public datasets, and community input, the plan identifies key risks, including vegetation-related outages, high energy burden, and vulnerability among elderly and medically dependent populations. The SEP establishes three priority areas: hazard mitigation for energy infrastructure, outage safety and emergency readiness, and energy assistance and thermal safety. It outlines community-driven actions designed to complement utility-led system improvements, including targeted outreach, municipal coordination, and expanded access to energy programs. The plan also situates local conditions within broader state policy, market dynamics, and funding opportunities. Intended as a living document, this SEP provides a scalable framework for collaborative action to improve resilience, safety, and affordability in rural energy systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Temporal Resource Continuity Increases Predator Abundance in a Metapopulation Model: Insights for Conservation and Biocontrol

The amount of habitat in a landscape is an important metric for evaluating the effects of land cover on biodiversity, yet it fails to capture complex temporal dimensions of resource availability that could be consequential for species population dynamics. Here, we use a spatially-explicit predator–prey metapopulation model to test the effect of different spatiotemporal resource patterns on insect predators and their prey. We examined population responses in model landscapes that varied in both the amount and temporal variability of basal vegetation. Further, we examined cases where prey comprised either a single generalist species or two specialist species that use different resources available either early or late in the growing season. We found that predators and generalist prey benefitted from lower temporal variance of basal resources, which increased landscape-scale abundances. However, increasing the amount of basal resources also increased the variability of generalist prey populations. Specialist prey, on the other hand, did not benefit from less temporally variable basal resources, as they were restricted by habitat type, while also suffering greater predation. Predators achieved greater prey suppression in landscapes with less temporally variable resources, but the overall effects on prey abundance depended on prey habitat specialization. Our simulations demonstrate the joint importance of both the amount and temporal variability of resources for understanding how landscape heterogeneity influences biodiversity and ecosystem services such as the biological control of agricultural pests.

Spiesman, Brian↗
DOE OSTI2021

Studying drought-induced forest mortality using high spatiotemporal resolution evapotranspiration data from thermal satellite imaging

Drought can have pervasive and wide-spread impacts to forest health, as evidenced in several severe events occurring over the recent decades. Extensive forest die-off due to drought can impair the ecological functioning of forests, impacting habitat, water yield and quality from forested lands, and altering forest fire dynamics and intensity. Satellite remote sensing provides an effective means for detecting and monitoring spatial patterns of forest mortality over large areas, exploiting free and open long-term image archives available at a range in spatial and temporal resolutions. While remotely sensed surface reflectances and vegetation indices have been widely used to study optical response of forest canopies to drought events, retrievals of evapotranspiration (ET) derived from thermal satellite imagery – particularly at resolutions approaching crown scale - can provide insights into cumulative tree stresses that can incite disease and trigger mortality. In this study, we applied a multi-sensor satellite data fusion approach to estimate daily 30-m resolution ET and an associated Evaporative Stress Index (ESI) to study drought-induced mortality in a temperate forest at the Missouri Ozark AmeriFlux (MOFLUX) site, located in the central United States. The study covered the period from 2010 to 2014, including an exceptional drought year of 2012. Modeled ET agreed well with eddy flux measurements from the MOFLUX tower, with average monthly relative errors of 15%. Plot-scale ESI, describing temporal anomalies in the ratio of actual-to-reference ET, was used as an index of relative forest health to investigate relationships between forest mortality and drought severity. ESI showed good agreement with observed predawn leaf water potential, especially during the drought year. Furthermore, plot-scale ESI was also correlated with the subsequent year's tree mortality, suggesting the importance of considering the forest health condition prior to drought when studying drought-induced forest impacts. This study demonstrates the utility of multi-year ET remote sensing data at the stand or plot scale as an indicator of forest health and as a predictor of future mortality due to drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Land cover change-induced decline in terrestrial gross primary production over the conterminous United States from 2001 to 2016

As one of the most dynamic aspects of global environmental change, land cover change (LCC) has a profound impact on terrestrial carbon sequestration. However, LCC-induced carbon fluxes are still the most uncertain terms in global and regional carbon budgets. Ecosystem gross primary production (GPP) is the total carbon uptake by vegetation through photosynthesis, serving as a major control on ecosystem function and land carbon balance during and after the modification of the land surface. However, accurately capturing LCC-induced GPP changes requires both high-quality land cover data and controlling for variation driven by other environmental factors such as climate. In this study, we comprehensively examined the effects of LCC on annual GPP trends over the conterminous United States (CONUS) from 2001 to 2016 using the USGS National Land Cover Database, a remote sensing-driven ecosystem model, and the Google Earth Engine cloud computing platform. We designed a series of model experiments to identify LCC effects on GPP by controlling climate effects. During the study period, LCC exerted a strong negative effect on total GPP across the CONUS ([-2.2, -1.8] Tg C yr -2 ), while climate had smaller positive effects ([0.17, 0. 92] Tg C yr -2 ). The LCC-induced reduction of GPP was mainly caused by net forest loss ([-1.98, -1.39] Tg C yr -2 ) and urban expansion ([-2.03, -1.92] Tg C yr -2 ), but was partially offset by increases in crop area ([+0.66, +0.79] Tg C yr -2 ). Ensemble simulations from TRENDY did not capture the strong negative LCC influences on GPP, likely due to limitations of the adopted land use/cover data. Overall, our study provides a novel perspective on LCC-induced GPP changes, which could help to improve our understanding of ecosystem function changes and constrain the estimation of land carbon balance in the context of anthropogenic activity and climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Detection and attribution of long-term and fine-scale changes in spring phenology over urban areas: A case study in New York State

Spring phenology plays an essential role in climate change, terrestrial ecosystem, and public health. Field-based monitoring and understanding of changes in spring phenology for long periods and in large regions are challenging due to the limited in-site observations. Space-based remotely sensed observations offer great potentials for monitoring decadal spring phenology changes from regional to global scales. However, the coarse-scale remotely sensed observations are insufficient to capture fine-scale spring phenology dynamics, especially in urban areas, and this makes it challenging for understanding the combined effects of climate change and urbanization on spring phenology. We derived the start of phenology season (SOS) in New York State using 30 m Landsat observations from 1990 to 2015 to understand the impact of the environment and urbanization on SOS. The results show that SOS for different years reveals heterogeneous spatial distribution. Most regions of New York State have been experiencing significant spring phenology changes in form of earlier onset of vegetation greening, ranging from 0.2 to 0.6 day/year during 1990 to 2015, and this trend varies slightly with latitudes and urbanization levels. Further, spatial correlation analysis shows that the increase in temperature and urbanization could both promote the advancement of SOS. However, the effect of urbanization (partial correlation coefficient (R) ranges from −0.289 to −0.542) on SOS is greater than the effect of temperature (R ranges from 0.006 to −0.192). The study generates a high spatio-temporal resolution spring phenology dataset for ecological, environmental and public health studies, especially in urban areas, and reveals the importance of better accounting for the urbanization effects when quantifying the SOS dynamics in phenology models.

Landsat↗
DOE OSTI2026

Composition and persistence of soil organic matter along eroding and depositional transects in buried vs. modern soil layers: A case of the Brady paleosol at Wauneta, Nebraska

Paleosols form when soils are buried through deposition by aeolian, colluvial, alluvial or other processes. Burial of former topsoil isolates soil organic matter (SOM) from surface conditions, allowing carbon to accumulate and potentially remain stable for millennia. In this study, SOM composition, distribution, and persistence were analyzed in the Brady Soil of Nebraska, USA to compare SOM spatial variability in modern and buried soils, as well as the impact of erosional exposure on SOM stability. The Brady Soil, formed as a surface soil during the Pleistocene-Holocene transition and now a paleosol buried up to 6 m deep (or more) by loess deposition during the Holocene, was sampled along burial (up to 5.8 m depth) and erosional (up to 1.8 m depth) transects to compare SOM dynamics in different geomorphic settings. Fourier Transform Infrared Spectroscopy (FTIR) and Fourier Transform ion cyclotron resonance mass spectrometry (FTICR-MS) were used to analyze SOM composition, while δ 13 C isotope analyses identified SOM sources and radiocarbon values were used to estimate turnover rates. Results confirmed a vegetation shift from C3 to C4 plants after Brady Soil formation, reflecting warming climatic conditions. Increasing SOM age and decreasing δ 13 C and δ 15 N values with depth indicated slowing of decomposition rate in buried soils. Higher pH in the Brady Soil suggested greater base cation content, supporting SOM stabilization through organo-mineral associations and aggregate formation. However, exposure of the Brady Soil due to surface erosion caused faster SOM turnover. This result suggested susceptibility of buried SOM to losses via decomposition upon erosional exposure, possibly accelerated by priming in response to modern SOM inputs. These findings highlight the potential loss of carbon stocks in buried soils under future climate change, as shifts in soil physicochemical properties may destabilize long-preserved SOM.

Environmental sciences↗
DOE OSTI2024

Systematic Underestimation of Canopy Conductance Sensitivity to Drought by Earth System Models

The response of vegetation canopy conductance (g c ) to changes in moisture availability ($γ^{m}_{gc}$) is a major source of uncertainty in climate projections. While vegetation typically reduces stomatal conductance during drought, accurately modeling how and to what degree stomata respond to changes in moisture availability at global scales is particularly challenging, because no global scale g c observations exist. Here, we leverage a collection of satellite, reanalysis and station-based near-surface air and surface temperature estimates, which are physically and statistically linked to $γ^{m}_{gc}$ due to the local cooling effect of g c through transpiration, to develop a novel emergent constraint of $γ^{m}_{gc}$ in an ensemble of Earth System Models (ESMs). We find that ESMs systematically underestimate $γ^{m}_{gc}$ by ~33%, particularly in grasslands, croplands, and savannas in semi-arid and bordering regions of the Central United States, Central Europe, Southeastern South America, Southern Africa, Eastern Australia, and parts of East Asia. We show that this underestimation occurs because ESMs inadequately reduce g c when soil moisture decreases. As g c controls carbon, water and energy fluxes, the misrepresentation of modeled $γ^{m}_{gc}$ contributes to biases in ESM projections of gross primary production, transpiration, and temperature during droughts. Our results suggest that the severity and duration of droughts may be misrepresented in ESMs due to the impact of sustained g c on both soil moisture dynamics and the biosphere-atmosphere feedbacks that affect local temperatures and regional weather patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Model Data Archive Associated with Manuscript "Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon"

This data package supports the publication “Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon” by Li et al. (2026). The package contains processed model inputs, configuration files, restart files, simulation outputs, scripts, and visualization products used to evaluate post-fire dissolved organic carbon (DOC) dynamics in the Naches River Watershed, Washington, USA, following the 2021 Schneider Springs Fire. The modeling workflow couples ELM-BGC, the biogeochemistry-enabled Energy Exascale Earth System Model Land Model; ATS, the Advanced Terrestrial Simulator for integrated surface-subsurface hydrology; and PFLOTRAN, a reactive transport model for multicomponent aqueous geochemistry. Together, these models simulate how wildfire-induced changes in vegetation, litter, coarse woody debris, and soil organic matter influence DOC production, transport, and reaction from burned hillslopes to stream networks. The archive includes preprocessed meteorological, geospatial, hydrologic, and biogeochemical forcing data; ELM-BGC-derived DOC source terms; ATS mesh files; PFLOTRAN reactive-transport inputs; model configuration files; spin-up and transient restart files; watershed-scale diagnostic outputs; stream concentration time series; and figures or visualization files used to inspect and reproduce key results. File types include Hierarchical Data Format 5 (HDF5) files for gridded forcing and model-coupling data, model input and configuration files for ELM-BGC, ATS, and PFLOTRAN, restart and simulation-output files generated by the modeling workflow, tabular or time-series diagnostic outputs, scripts for post-processing and figure generation, and image or visualization products associated with the manuscript. Use of the package depends on the intended task. Re-running the simulations requires the relevant modeling software, including ELM-BGC, ATS, and PFLOTRAN as ATS's geochemical engine. Inspecting outputs and reproducing figures requires Python with scientific plotting libraries such as Matplotlib, and three-dimensional model outputs may be viewed with ParaView. Geographic information system files or maps may be inspected with ArcGIS Pro or comparable GIS software. The data package is intended to enable traceability, reuse, and partial reproduction of the coupled land-to-watershed hydro-biogeochemical modeling workflow used to test how wildfire disturbance affects terrestrial carbon pools and downstream DOC dynamics.

ATS↗
DOE OSTI2021

North American boreal forests are a large carbon source due to wildfires from 1986 to 2016

Wildfires are a major disturbance to forest carbon (C) balance through both immediate combustion emissions and post-fire ecosystem dynamics. Here we used a process-based biogeochemistry model, the Terrestrial Ecosystem Model (TEM), to simulate C budget in Alaska and Canada during 1986–2016, as impacted by fire disturbances. We extracted the data of difference Normalized Burn Ratio (dNBR) for fires from Landsat TM/ETM imagery and estimated the proportion of vegetation and soil C combustion. We observed that the region was a C source of 2.74 Pg C during the 31-year period. The observed C loss, 57.1 Tg C year -1 , was attributed to fire emissions, overwhelming the net ecosystem production (1.9 Tg C year -1 ) in the region. Our simulated direct emissions for Alaska and Canada are within the range of field measurements and other model estimates. As burn severity increased, combustion emission tended to switch from vegetation origin towards soil origin. When dNBR is below 300, fires increase soil temperature and decrease soil moisture and thus, enhance soil respiration. However, the post-fire soil respiration decreases for moderate or high burn severity. The proportion of post-fire soil emission in total emissions increased with burn severity. Net nitrogen mineralization gradually recovered after fire, enhancing net primary production. Net ecosystem production recovered fast under higher burn severities. The impact of fire disturbance on the C balance of northern ecosystems and the associated uncertainties can be better characterized with long-term, prior-, during- and post-disturbance data across the geospatial spectrum. Our findings suggest that the regional source of carbon to the atmosphere will persist if the observed forest wildfire occurrence and severity continues into the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Tropical understory herbaceous community responds more strongly to hurricane disturbance than to experimental warming

The effects of climate change on tropical forests may have global consequences due to the forests’ high biodiversity and major role in the global carbon cycle. In this study, we document the effects of experimental warming on the abundance and composition of a tropical forest floor herbaceous plant community in the Luquillo Experimental Forest, Puerto Rico. This study was conducted within Tropical Responses to Altered Climate Experiment (TRACE) plots, which use infrared heaters under free-air, open-field conditions, to warm understory vegetation and soils + 4°C above nearby control plots. Hurricanes Irma and María damaged the heating infrastructure in the second year of warming, therefore, the study included one pretreatment year, one year of warming, and one year of hurricane response with no warming. We measured percent leaf cover of individual herbaceous species, fern population dynamics, and species richness and diversity within three warmed and three control plots. Results showed that one year of experimental warming did not significantly affect the cover of individual herbaceous species, fern population dynamics, species richness, or species diversity. In contrast, herbaceous cover increased from 20% to 70%, bare ground decreased from 70% to 6%, and species composition shifted pre to posthurricane. The negligible effects of warming may have been due to the short duration of the warming treatment or an understory that is somewhat resistant to higher temperatures. Our results suggest that climate extremes that are predicted to increase with climate change, such as hurricanes and droughts, may cause more abrupt changes in tropical forest understories than longer-term sustained warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Active microbial biomass decreases, but microbial growth potential remains similar across soil depth profiles under deeply-vs. shallow-rooted plants

Climate-smart land management practices that replace shallow-rooted annual crop systems with deeply-rooted perennial plants can contribute to soil carbon sequestration. However, deep soil carbon accrual may be influenced by active microbial biomass and their capacity to assimilate fresh carbon at depth. Incorporating active microbial biomass, dormancy, and growth in microbially-explicit models can improve our ability to predict soil's capacity to store carbon. But, so far, the microbial parameters that are needed for such modeling are poorly constrained, especially in deep soil layers. Here, we used a lab incubation experiment and growth kinetics model to estimate how microbial parameters vary along 240 cm of soil depth in profiles under shallow- (soy) and deeply-rooted (switchgrass) plants 11 years after plant cover conversion. We also assessed resource origin and availability (total organic carbon, 14 C, extractable organic carbon, specific UV absorbance of K 2 SO 4 extractable organic C, total nitrogen, total dissolved nitrogen) along the soil profiles to examine associations between soil chemical and biological parameters. Even though root biomass was greater and rooting depth was deeper under switchgrass than soy, resource availability and microbial growth parameters were generally similar between vegetation types. Instead, depth significantly influenced soil chemical and biological parameters. For example, resource availability and total and relative active microbial biomass decreased with soil depth. Decreases in the relative active microbial biomass coincided with increased lag time (response time to external carbon inputs) along the soil profiles. Even at a depth of 210–240 cm, microbial communities were activated to grow by added resources within a day. Maximum specific growth rate decreased to a depth of 90 cm and then remained consistent in deeper layers. Our findings show that >10 years of vegetation and rooting depth changes may not be long enough to alter microbial growth parameters, and suggest that at least a portion of the microbial community in deep soils can grow rapidly in response to added resources. Our study determined microbial growth parameters that can be used in microbially-explicit models to simulate carbon dynamics in deep soil layers.

14C↗
DOE OSTI2023

The interactive indoor-outdoor building energy modeling for enhancing the predictions of urban microclimates and building energy demands

There is a lack of an urban building energy modeling framework that considers the influence of surrounding buildings and local urban climate on building thermal performance. This can lead to inaccurate results since the thermal performance of individual buildings is heavily influenced by their surrounding built and climatic environment. This study establishes an interactive indoor-outdoor building energy modeling method to enhance the predictions of urban microclimates and building energy demands by coupling an urban physics model with a physics-based building energy model. Validation of the interactive coupling scheme uses field measurement datasets. Parametric simulation and analysis are conducted to understand the influence of the roof-to-canyon width ratio, canyon orientation, and ground vegetation fraction on canyon temperature, building energy consumption, and energy demand. Furthermore, the impacts of building energy model complexity (e.g., detailed vs. simplified building models) and coupling approaches on canyon temperature and building energy profiles are demonstrated using two case study buildings. In comparison with the one-way coupling approach, cooling energy consumption predicted with the dynamic two-way coupling approach varies by 3.5% and 0.5% for the detailed medium office building model and high-rise building model, respectively, and peak cooling demand varies by 8.4% and 7.0% for the detailed medium office building model and high-rise building model, respectively. Here this study also suggests that adopting a complex two-way coupling approach with environmental data exchange at various elevations is necessary for modeling tall buildings at the urban scale.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Soil moisture-atmosphere interactions drive terrestrial carbon-water trade-offs

Soil moisture is coupled with vegetation and atmosphere, influencing global cycling of water, carbon, and energy. However, it remains unclear how soil moisture-atmosphere interactions affect land-atmosphere carbon and water exchanges simultaneously. Using Earth system model experiments, we show widespread carbon-water trade-offs between net ecosystem production and precipitation-minus-evapotranspiration driven by soil moisture dynamics. Soil moisture positively controls net ecosystem production and negatively affects precipitation-minus-evapotranspiration, through direct soil water stress and indirect soil moisture-atmosphere feedbacks. While soil moisture variability magnifies the interannual variability of net ecosystem production, it moderates that of precipitation-minus-evapotranspiration over land. These opposing effects lead to a pronounced carbon-water trade-off, which originates from the interplay between carbon acquisition through photosynthesis and water extraction through evapotranspiration. This trade-off is projected to intensify in a warming and drying future, as soil moisture increasingly regulates carbon and water exchanges, posing a serious challenge to sustaining both terrestrial carbon sink and water supply.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Stone Content Influence on Land Surface Model Simulation of Soil Moisture and Evapotranspiration at Reynolds Creek Watershed

Considerable advancement in spatiotemporal resolution of remote sensing and ground-based measurements has enabled refinement of parameters used in land surface models for simulating surface water fluxes. However, land surface modeling capabilities are still inadequate for accurate representation of subsurface properties and processes, which continue to limit the accuracy of land surface model simulation. Our objective in this study was to examine the performance of the variously parameterized Noah-Multiphysics (Noah-MP) land surface model and to improve simulations of evapotranspiration (ET) and soil moisture dynamics in stony soils using verification from eddy covariance ET and in-situ soil moisture data. Meteorological and eddy covariance data from the Lower Sheep sub-catchment within the Reynolds Creek Experimental Watershed in Southwestern Idaho were employed to drive Noah-MP and to assess simulation results. We evaluated the performance of Noah-MP considering four different scenarios: (1) A one-layer soil profile with Noah-MP default soil hydraulic parameters, followed by five-layer soil profiles using -(2) Noah-MP default soil hydraulic parameters -(3) soil hydraulic parameters derived from a pedotransfer function using field observations, and -(4) hydraulic parameters from (3) which also accounted for stone content in each layer. Each modeling experiment was forced with the same set of initial conditions, atmospheric input and vegetation parameters. Our results indicated that enhanced representation of soil profile properties and stone content information noticeably improve the Noah-MP land surface model simulation of soil moisture content and evapotranspiration.

Land surface models, Soil hydrology, Noah-MP, Evap↗