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-996 |
|
EM Short Name
em.detail.shortNameHelp
?
|
DayCent N2O flux simulation, Ireland | Co$ting Nature model method |
|
EM Full Name
em.detail.fullNameHelp
?
|
DayCent simulation N2O flux and climate change, Ireland | Co$ting Nature model method |
|
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
None | None |
|
EM Source Document ID
|
358 | 466 |
|
Document Author
em.detail.documentAuthorHelp
?
|
Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Mulligan, M. |
|
Document Year
em.detail.documentYearHelp
?
|
2010 | None |
|
Document Title
em.detail.sourceIdHelp
?
|
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 | Conservation prioritisation and Ecostystem services mapping with Co$ting Nature |
|
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published | Other or unclear (explain in Comment) |
|
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | Web page so cannot tell if documentation is reviewed |
|
EM ID
em.detail.idHelp
?
|
EM-593 |
EM-996 |
| Not applicable | http://www1.policysupport.org/cgi-bin/ecoengine/start.cgi?project=costingnature&version=3 | |
|
Contact Name
em.detail.contactNameHelp
?
|
M. Abdalla | Mark Mulligan |
|
Contact Address
|
Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | King's College London, Dept. of Geography |
|
Contact Email
|
abdallm@tcd.ie | mark.mulligan@kcl.uk |
|
EM ID
em.detail.idHelp
?
|
EM-593 |
EM-996 |
|
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. | ABSTRACT: " Co$tingNature is a sophisticated web-based spatial policy support system for natural capital accounting and analysing the ecosystem services provided by natural environments (i.e. nature's benefits), identifying the beneficiaries of these services and assessing the impacts of human interventions. This PSS is a testbed for the development and implementation of conservation strategies focused on sustaining and improving ecosystem services. It also focused on enabling the intended and unintended consequences of development actions on ecosystem service provision to be tested in silico before they are tested in vivo . The PSS incorporates detailed spatial datasets at 1-square km and 1 hectare resolution for the entire World, spatial models for biophysical and socioeconomic processes along with scenarios for climate and land use. The PSS calculates a baseline for current ecosystem service provision and allows a series of interventions (policy options) or scenarios of change to be used to understand their impact on ecosystem service delivery. We do not focus on valuing nature (how much someone is willing to pay for it) but rather costing it (understanding the resource e.g. land area and opportunity cost of nature being protected to produce the ecosystem services that we need and value). " |
|
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
climate change | Conservation priorities |
|
Biophysical Context
|
Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Woldwide coverage |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
air temperature, precipitation, Atmospheric CO2 concentrations | Policy decisions affecting future land use |
|
EM ID
em.detail.idHelp
?
|
EM-593 |
EM-996 |
|
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-996 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
None | None |
|
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-598 | None |
EM Modeling Approach
|
EM ID
em.detail.idHelp
?
|
EM-593 |
EM-996 |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
1961-1990 | Not applicable |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-dependent | time-dependent |
|
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-996 |
|
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-996 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially lumped (in all cases) | spatially lumped (in all cases) |
|
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
Not applicable | Not applicable |
|
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
Not applicable | Not applicable |
|
EM ID
em.detail.idHelp
?
|
EM-593 |
EM-996 |
|
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-996 |
|
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-996 |
|
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
|
EM-593 |
EM-996 |
| None | None |
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
?
|
EM-593 |
EM-996 |
|
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-996 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Agroecosystems | Terrestrial Environment (sub-classes not fully specified) |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
farm pasture | Non urban |
|
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-996 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
|
EM-593 |
EM-996 |
| 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-593 |
EM-996 |
|
|
<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-996 |
|
|
Home
Search EMs
My
EMs
Learn about
ESML
Show Criteria
Hide Criteria