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Dataset results
9 results for “Terrestrial ecosystem model”
Embracing fine-root system complexity in terrestrial ecosystem modelling
<p>Model outputs for manuscript " <strong>Embracing fine-root system complexity in terrestrial ecosystem modelling</strong>".</p>
Dynamic Vegetation Model Dynamic Organic Soil Terrestrial Ecosystem Model (DVM-DOS-TEM) simulations focused on Eight Mile Lake, Alaska and Imnavait Creek, Alaska [2000-2015]
<p>This set of files store model simulations using the biosphere model Dynamic Vegetation Model Dynamic Organic Soil Terrestrial Ecosystem Model (DVM-DOS-TEM), developed to simulate biophysical and biogeochemical interactions between the soil, vegetation and atmosphere. To improve predictions of net carbon releases from thawing permafrost, we tested the sensitivity of a suite of model parameters. We analyzed the responses of ecosystem carbon balances to permafrost thaw by running site-level simulations at two long-term tundra ecological monitoring sites in Alaska: Eight Mile Lake (EML) and Imnavait Creek watershed (IMN). These sites are characterized by similar tussock tundra vegetation but differing soil drainage conditions and climate, IMN consists of well-drained soils, and EML has historically well-drained soils, however permafrost thaw has altered drainage conditions to wetter soils. Simulations were conducted at a 1km resolution, over a 1,000 km2 area (10x10 km square) centered on two long term ecological research sites in Alaska: Eight Mile Lake located in Interior Alaska (63.8900° N, 149.2535° W), and Imnavait creek watershed located on the northern foothills of the Brooks range (68°37′ N, 149°18′ W).</p> <p>Historical simulations are spanning the 2000 to 2015, and forced using climate simulations from the Climate Research Unit, time series 4.0. We ran 1,000 site level simulations for each model variable. The variables that are produced are gross primary productivity (GPP, in gC.m-2.m-1), net ecosystem exchange (NEE, gC.m-2.m-1), ecosystem respiration (RECO, gC/m2/m-1), active layer thickness (ALT, m), soil temperature (TLAYER,°C) at 5, 10, 40 cm depths, soil moisture (LWCLAYER, m-3/m-3) at 5, 10 cm depths, and snow depth (SNOWDEPTH, m), evapotransipiration(EET, mm/m2/time), potential evapotransipiration (PET, mm/m2/time), leaf area index (LAI, m2/m2), organic layer thickness (OLT, m). The data are stored as compiled csv files, with time as the index, and each model sample output stored in the columns. In addition, there is a postprocessing python script to demonstrate the step and workflow used to generate the individual csv files post processed from the raw model outputs stored as netcdfs.</p>
Model outputs and observation data for "Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)"
<p>Model outputs and observation data for paper "Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)".</p>
Observation‐based global soil heterotrophic respiration indicates underestimated turnover and sequestration of soil carbon by terrestrial ecosystem models
<p><span>Soil heterotrophic respiration (R<sub>h</sub>) refers to the flux of CO2 released from soil to atmosphere as a result of organic matter decomposition by soil microbes and fauna. As one of the major fluxes in the global carbon cycle, the estimation of global R<sub>h</sub> still exists large uncertainties, which further limited our current understanding of the carbon accumulation in soils. Here, we applied a Random Forest algorithm to create a global dataset of soil R<sub>h</sub>, by linking 761 field observations with both abiotic and biotic predictors. We estimated that the global R<sub>h</sub> was 48.8 ± 0.9 Pg C yr<sup>-1</sup> for 1982–2018, which was 16% less than the ensemble mean (58.6 ± 9.9 Pg C yr<sup>-1</sup>) of 16 terrestrial ecosystem models. By integrating our observational R<sub>h</sub> with independent soil carbon stock datasets, we obtained a global mean soil carbon turnover time of 38.3 ± 11 yr. Using observation-based turnover times as a constraint, we found that terrestrial ecosystem models simulated faster carbon turnovers, leading to a 30% (74 Pg C) underestimation of terrestrial ecosystem carbon accumulation for the past century, which was especially pronounced at high latitudes. This underestimation is equivalent to 45% of the total carbon emissions (164 Pg C) caused by global land use change at the same time. Our analyses highlight the need to constrain ecosystem models using observation-based and locally adapted R<sub>h</sub> values to obtain reliable predictions of the carbon sink capacity of terrestrial ecosystems. </span></p>
Observation‐based global soil heterotrophic respiration indicates underestimated turnover and sequestration of soil carbon by terrestrial ecosystem models
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Gross primary production and ecosystem respiration measurments based on the Terrestrial Ecosystem Model (TEM)
Gross primary production and ecosystem respiration measurments based on the Terrestrial Ecosystem Model (TEM).
PalEON: Terrestrial Ecosystem Model Drivers for the Northeastern U.S., 0850-2010
This dataset from the PalEON Ecosystem Model Intercomparison Project (PEMIP) provides harmonized regional environmental and meteorological drivers at a resolution of 0.5 degrees for the North-central and Northeastern U.S. over the time period 0850-01-01 to 2010-12-31. This dataset consists of the regional environmental and meteorological drivers. The environmental drivers include (1) dominant biome type, (2) plant functional type, (3) annual carbon dioxide concentration, (4) monthly carbon dioxide concentration, (5) land use-land cover change, (6) nitrogen concentrations, and (7) soil measurements. The meteorological drivers include (1) incident longwave radiation, (2) incident shortwave radiation, (3) precipitation, (4) surface pressure, (5) specific humidity, (6) air temperature, and (7) wind speed. The PEMIP is a coordinated effort to develop a set of terrestrial ecosystem model simulations with the ability to evaluate high-resolution ecophysiological causes and consequences of forest responses to climatic variability and change over the past millennium.
Literature-Derived Parameters for the BIOME-BGC Terrestrial Ecosystem Model
Ecosystem simulation models use descriptive input parameters to establish the physiology, biochemistry, structure, and allocation patterns of vegetation functional types, or biomes. For single-stand simulations, it is possible to measure required data, but as spatial resolution increases, data availability decreases. Generalized biome parameterizations are then required. Undocumented parameter selection and unknown model sensitivity to parameter variation for larger-resolution simulations are currently the major limitations to global and regional modeling. We present documented input parameters for process-based ecosystem simulation models (specifically for the BIOME-BGC) for major natural temperate biomes. Parameter groups include the following: turnover and mortality; allocation; carbon to nitrogen ratios (C:N); the percent of plant material in labile, cellulose, and lignin pools; leaf morphology; leaf conductance rates and limitations; canopy water interception and light extinction; and the percent of leaf nitrogen in Rubisco (i.e., ribulose bisphosphate-1,5-carboxylase/oxygenase). Input parameters may also be used for other ecosystem models.
Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1
Biome-BGC is a computer program that estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems. The primary model purpose is to study global and regional interactions between climate, disturbance, and biogeochemical cycles.Biome-BGC represents physical and biological processes that control fluxes of energy and mass. These processes include: New leaf growth and old leaf litterfall Sunlight interception by leaves and penetration to the ground Precipitation routing to leaves and soil Snow accumulation and melting Drainage and runoff of soil water Evaporation of water from soil and wet leaves Transpiration of soil water through leaf stomata Photosynthetic fixation of carbon from CO2 in the air Uptake of nitrogen from the soil Distribution of carbon and nitrogen to growing plant parts Decomposition of fresh plant litter and old soil organic matter Plant mortality Fire The model uses a daily time-step. This means that each flux is estimated for a one-day period. Between days, the program updates its memory of the mass stored in different components of the vegetation, litter, and soil. Weather is the most important control on vegetation processes. Flux estimates in Biome-BGC depend strongly on daily weather conditions. Model behavior over time depends on climate--the history of these weather conditions.A companion file with more information about Biome-BGC and its components is available at ftp://daac.ornl.gov/data/model_archive/BIOME_BGC/biome_bgc_4.1.1/comp/BiomeBGC_v411_release.pdf .Biome-BGC, Version 4.1.1 was developed and is maintained by the Numerical Terradynamic Simulation Group, School of Forestry, The University of Montana, Missoula, Montana, USA. Additional information can be found on there web site at: http://www.ntsg.umt.edu/.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.