EcoService Models Library (ESML)
loading
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
em.detail.idHelp
?
|
EM-593 ![]() |
EM-938 |
Not applicable | https://coast.noaa.gov/digitalcoast/tools/opennspect.html | |
Contact Name
em.detail.contactNameHelp
?
|
M. Abdalla | Not reported |
Contact Address
|
Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | NOAA Coastal Services Center, 2234 South Hobson Avenue Charleston, South Carolina 29405-2413 |
Contact Email
|
abdallm@tcd.ie | Not reported |
EM ID
em.detail.idHelp
?
|
EM-593 ![]() |
EM-938 |
Summary Description
em.detail.summaryDescriptionHelp
?
|
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. | "This open-source version of the Nonpoint Source Pollution and Erosion Comparison Tool is used to investigate potential water quality impacts from climate change and development to other land uses. The downloadable tool is designed to be broadly applicable for coastal and noncoastal areas alike. Tool functions simulate erosion, pollution, and the accumulation from overland flow. OpenNSPECT uses spatial elevation data to calculate flow direction and flow accumulation throughout a watershed. To do this, land cover, precipitation, and soils data are processed to estimate runoff volume at both the local and watershed levels. Coefficients representing the contribution of each land cover class to the expected pollutant load are also applied to land cover data to approximate total pollutant loads. These coefficients are taken from published sources or can be derived from local water quality studies. The output layers display estimates of runoff volume, pollutant loads, pollutant concentration, and total sediment yield. Requires MapWindow GIS v.4.8.8 (open source software)" |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
climate change | None identified |
Biophysical Context
|
Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | No additional description provided |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
air temperature, precipitation, Atmospheric CO2 concentrations | No scenarios presented |
EM ID
em.detail.idHelp
?
|
EM-593 ![]() |
EM-938 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application (multiple runs exist) View EM Runs | Method Only |
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
em.detail.idHelp
?
|
EM-593 ![]() |
EM-938 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
None | None |
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-598 | EM-940 |
EM Modeling Approach
EM ID
em.detail.idHelp
?
|
EM-593 ![]() |
EM-938 |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
1961-1990 | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-dependent | time-stationary |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
both | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
discrete | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
1 | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Day | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-593 ![]() |
EM-938 |
Bounding Type
em.detail.boundingTypeHelp
?
|
Point or points | Not applicable |
Spatial Extent Name
em.detail.extentNameHelp
?
|
Oak Park Research centre | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
1-10 ha | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-593 ![]() |
EM-938 |
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially lumped (in all cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
Not applicable | area, for pixel or radial feature |
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
Not applicable | 30 m |
EM ID
em.detail.idHelp
?
|
EM-593 ![]() |
EM-938 |
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Numeric | Analytic |
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
EM ID
em.detail.idHelp
?
|
EM-593 ![]() |
EM-938 |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
Yes ?Comment:for N2O fluxes |
Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
|
None |
Model Operational Validation Reported?
em.detail.validationHelp
?
|
Yes | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-593 ![]() |
EM-938 |
|
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-593 ![]() |
EM-938 |
None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
?
|
EM-593 ![]() |
EM-938 |
Centroid Latitude
em.detail.ddLatHelp
?
|
52.86 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
?
|
6.54 | Not applicable |
Centroid Datum
em.detail.datumHelp
?
|
None provided | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Provided | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-593 ![]() |
EM-938 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
farm pasture | Coastal and non-coastal |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
?
|
EM-593 ![]() |
EM-938 |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-593 ![]() |
EM-938 |
None Available | None Available |
EnviroAtlas URL
EM-593 ![]() |
EM-938 |
GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day) | Average Annual Precipitation |
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-938 |
|
|
<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-938 |
|
None |