EcoService Models Library (ESML)
Home
Search EMs
My
EMs 
Learn about
ESML
View ESML Data Maploading
Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480 
|
EM-593
|
EM-1008 |
|
EM Short Name
em.detail.shortNameHelp
?
|
EnviroAtlas - Natural biological nitrogen fixation | Plant species diversity, Central French Alps | Divergence in flowering date, Central French Alps | InVEST carbon storage and sequestration (v3.2.0) | Decrease in erosion (shoreline), St. Croix, USVI | Yasso07 - Land use SOC dynamics, China | DayCent N2O flux simulation, Ireland | Water quality land-sea planning submodel |
|
EM Full Name
em.detail.fullNameHelp
?
|
US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Plant species diversity, Central French Alps | Functional divergence in flowering date, Central French Alps | InVEST v3.2.0 Carbon storage and sequestration | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Yasso07 - Land use dynamics of Soil Organic Carbon in the Loess Plateau, China | DayCent simulation N2O flux and climate change, Ireland | A technical guide to the integrated land-sea planning toolkit |
|
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
US EPA | EnviroAtlas | EU Biodiversity Action 5 | EU Biodiversity Action 5 | InVEST | US EPA | None | None | None |
|
EM Source Document ID
|
262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
260 | 260 | 315 | 335 | 344 | 358 | 473 |
|
Document Author
em.detail.documentAuthorHelp
?
|
US EPA Office of Research and Development - National Exposure Research Laboratory | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | The Natural Capital Project | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Crist, P., Madden, K., Varley, I., Eslinger, D., Walker, D., Anderson, A., Morehead, S. and Dunton, K., |
|
Document Year
em.detail.documentYearHelp
?
|
2013 | 2011 | 2011 | 2015 | 2014 | 2015 | 2010 | 2009 |
|
Document Title
em.detail.sourceIdHelp
?
|
EnviroAtlas - National | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Carbon storage and sequestration - InVEST (v3.2.0) | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Integrated Land-Sea Planning: A Technical Guide to the Integrated Land-Sea Planning Toolkit. |
|
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
|
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report |
Software and Access
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
| https://www.epa.gov/enviroatlas | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | https://repositories.lib.utexas.edu/bitstreams/3dee92a8-9373-4bcc-be25-eda74e81fabf/download | |
|
Contact Name
em.detail.contactNameHelp
?
|
EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Sandra Lavorel | Sandra Lavorel | The Natural Capital Project | Susan H. Yee | Xing Wu | M. Abdalla |
Patrick Crist ?Comment:No contact information provided |
|
Contact Address
|
Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Chinese Academy of Sciences, Beijing 100085, China | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | None provided |
|
Contact Email
|
enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | invest@naturalcapitalproject.org | yee.susan@epa.gov | xingwu@rceesac.cn | abdallm@tcd.ie | patrick@planitfwd.com |
EM Description
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
|
Summary Description
em.detail.summaryDescriptionHelp
?
|
DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of abiotic variables." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | ABSTRACT: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. | To make this toolkit a true integrated land-sea planning toolkit it is necessary to model the input of terrestrial-originated pollution into the marine environment. The physical, geological, chemical, and biological characteristics of coastal/near shore ecosystems greatly affect how pollutants from terrestrial runoff are distributed and deposited within the marine environment. Therefore, it was not practical or advisable to select a single tool that could model these processes for all potential planning locations and applications. We advise that users select a regionally appropriate/calibrated tool or spatial modeling process to model the location and concentration of pollutants. Ideally, this tool should be able to directly use the N-SPECT outputs to facilitate its integration in the toolkit. The role of a marine plume model in the land-sea toolkit is to model the location and concentration of pollutants in the marine ecosystem coming from runoff (as modeled by N-SPECT). |
|
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None Identified | None identified | None identified | None identified | None identified | None identified | climate change | None provided |
|
Biophysical Context
|
No additional description provided | Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Not applicable | No additional description provided | Agricultural plain, hills, gulleys, forest, grassland, Central China | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Not applicable |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | No scenarios presented | No scenarios presented | Optional future scenarios for changed LULC and wood harvest | No scenarios presented | Land use change | air temperature, precipitation, Atmospheric CO2 concentrations | No scenarios presented |
EM Relationship to Other EMs or Applications
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
?
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
Doc-260 | Doc-260 | Doc-269 | Doc-309 | Doc-335 | Doc-343 | Doc-342 | None | Doc-473 |
|
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
None | EM-65 | EM-66 | EM-68 | EM-69 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | EM-349 | EM-447 | EM-448 | EM-466 | EM-467 | EM-469 | EM-485 | EM-598 | EM-1003 | EM-1005 | EM-1006 | EM-1007 |
EM Modeling Approach
EM Relationship to Time
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
2006-2010 | 2007-2009 | 2007-2008 | Not applicable | 2006-2007, 2010 | 1969-2011 | 1961-1990 | Not applicable |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-dependent |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable | Not applicable | future time | Not applicable | past time | both | Not applicable |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | discrete | other or unclear (comment) |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | 1 | Not applicable |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Not applicable | Year | Not applicable | Year | Day | Not applicable |
EM Spatial Extent
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
|
Bounding Type
em.detail.boundingTypeHelp
?
|
Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Not applicable | Physiographic or ecological | Watershed/Catchment/HUC | Point or points | Not applicable |
|
Spatial Extent Name
em.detail.extentNameHelp
?
|
counterminous United States | Central French Alps | Central French Alps | Not applicable | Coastal zone surrounding St. Croix | Yangjuangou catchment | Oak Park Research centre | Not applicable |
|
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
>1,000,000 km^2 | 10-100 km^2 | 10-100 km^2 | Not applicable | 100-1000 km^2 | 1-10 km^2 | 1-10 ha | Not applicable |
Spatial Distribution of Computations
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) ?Comment:Watersheds (12-digit HUCs). |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | other or unclear (comment) |
|
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable |
|
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
irregular | 20 m x 20 m | 20 m x 20 m | application specific | 10 m x 10 m | 30m x 30m | Not applicable | Not applicable |
EM Structure and Computation Approach
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic |
|
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
|
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
|
|
|
|
Model Checking Procedures Used
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
|
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | No | No | Not applicable | Yes | No | No | Not applicable |
|
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | Yes | Yes | Not applicable | No |
Yes ?Comment:p value: p<0.001 |
Yes ?Comment:for N2O fluxes |
Not applicable |
|
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None |
|
|
None | None |
|
|
None |
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | No | No | Not applicable | Yes | No | Yes | Not applicable |
|
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No | Not applicable | No | No | No | Not applicable |
|
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | No | No | Not applicable | No | No | No | Not applicable |
|
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
?
| EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
|
|
|
None | None |
|
|
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
?
| EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
| None | None | None | None |
|
None | None | None |
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
39.5 | 45.05 | 45.05 | -9999 | 17.73 | 36.7 | 52.86 | Not applicable |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
-98.35 | 6.4 | 6.4 | -9999 | -64.77 | 109.52 | 6.54 | Not applicable |
|
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | None provided | Not applicable |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Provided | Provided | Not applicable | Estimated | Provided | Provided | Not applicable |
Environments and Scales Modeled
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Not applicable | Near Coastal Marine and Estuarine | Agroecosystems | Agroecosystems | Not applicable |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Terrestrial | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows | Terrestrial environments, but not specified for methods | Coral reefs | Loess plain | farm pasture | None |
|
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
?
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Community | Community | Not applicable | Not applicable | Not applicable | Not applicable | Community |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
| EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
| None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
em.detail.cicesHelp
?
| EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
|
None | None |
|
|
|
|
|
<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
fegs1Help
?
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
?
| EM-63 | EM-70 | EM-79 | EM-374 | EM-449 |
EM-480
|
EM-593
|
EM-1008 |
|
None | None | None |
|
None |
|
None |