EcoService Models Library (ESML)
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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.
EM Identity and Description
EM ID
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EM-593 ![]() |
EM-895 |
EM Short Name
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DayCent N2O flux simulation, Ireland | HWB indicator-College degree, Great Lakes, USA |
EM Full Name
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DayCent simulation N2O flux and climate change, Ireland | Human well being indicator-College degree, Great Lakes waterfront, USA |
EM Source or Collection
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None | US EPA |
EM Source Document ID
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358 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
Document Author
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Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick |
Document Year
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2010 | None |
Document Title
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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 | Human well-being and natural capital indictors for Great Lakes waterfront revitalization |
Document Status
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Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) |
Comments on Status
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Published journal manuscript | Journal manuscript submitted or in review |
EM ID
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EM-593 ![]() |
EM-895 |
Not applicable | Not applicable | |
Contact Name
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M. Abdalla | Ted Angradi |
Contact Address
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Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 |
Contact Email
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abdallm@tcd.ie | tedangradi@gmail.com |
EM ID
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EM-593 ![]() |
EM-895 |
Summary Description
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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. | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. " |
Specific Policy or Decision Context Cited
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climate change | None identified |
Biophysical Context
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Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Waterfront districts on south Lake Michigan and south lake Erie |
EM Scenario Drivers
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air temperature, precipitation, Atmospheric CO2 concentrations | N/A |
EM ID
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EM-593 ![]() |
EM-895 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application |
New or Pre-existing EM?
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Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-593 ![]() |
EM-895 |
Document ID for related EM
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None | Doc-422 |
EM ID for related EM
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EM-598 | EM-886 | EM-888 | EM-889 | EM-890 | EM-891 | EM-893 | EM-894 |
EM Modeling Approach
EM ID
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EM-593 ![]() |
EM-895 |
EM Temporal Extent
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1961-1990 | 2022 |
EM Time Dependence
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time-dependent | time-stationary |
EM Time Reference (Future/Past)
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both | Not applicable |
EM Time Continuity
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discrete | Not applicable |
EM Temporal Grain Size Value
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1 | Not applicable |
EM Temporal Grain Size Unit
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Day | Not applicable |
EM ID
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EM-593 ![]() |
EM-895 |
Bounding Type
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Point or points | Geopolitical |
Spatial Extent Name
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Oak Park Research centre | Great Lakes waterfront |
Spatial Extent Area (Magnitude)
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1-10 ha | 1000-10,000 km^2. |
EM ID
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EM-593 ![]() |
EM-895 |
EM Spatial Distribution
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spatially lumped (in all cases) | spatially lumped (in all cases) |
Spatial Grain Type
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Not applicable | Not applicable |
Spatial Grain Size
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Not applicable | Not applicable |
EM ID
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EM-593 ![]() |
EM-895 |
EM Computational Approach
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Numeric | Numeric |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-593 ![]() |
EM-895 |
Model Calibration Reported?
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No | No |
Model Goodness of Fit Reported?
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Yes ?Comment:for N2O fluxes |
No |
Goodness of Fit (metric| value | unit)
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None |
Model Operational Validation Reported?
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Yes | No |
Model Uncertainty Analysis Reported?
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No | No |
Model Sensitivity Analysis Reported?
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No | Yes |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-593 ![]() |
EM-895 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-593 ![]() |
EM-895 |
None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-593 ![]() |
EM-895 |
Centroid Latitude
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52.86 | 42.26 |
Centroid Longitude
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6.54 | -87.84 |
Centroid Datum
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None provided | WGS84 |
Centroid Coordinates Status
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Provided | Estimated |
EM ID
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EM-593 ![]() |
EM-895 |
EM Environmental Sub-Class
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Agroecosystems | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Tundra | Ice and Snow | Atmosphere |
Specific Environment Type
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farm pasture | Lake Michigan & Lake Erie waterfront |
EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-593 ![]() |
EM-895 |
EM Organismal Scale
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Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-593 ![]() |
EM-895 |
None Available | None Available |
EnviroAtlas URL
EM-593 ![]() |
EM-895 |
GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day) | Dasymetric Allocation of Population, GAP Ecological Systems, Enabling Conditions |
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-593 ![]() |
EM-895 |
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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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-593 ![]() |
EM-895 |
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None |