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
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EM ID
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EM-598 | EM-858 |
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EM Short Name
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DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | ARIES Flood Reg, Santa Fe, NM |
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EM Full Name
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DeNitrification-DeComposition simulation of N2O flux Ireland | ARIES Flood regulation, Santa Fe, New Mexico |
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EM Source or Collection
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None | None |
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EM Source Document ID
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358 | 411 |
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Document Author
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Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. |
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Document Year
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2010 | 2018 |
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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 | Towards globally customizable ecosystem service models |
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Document Status
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Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript |
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EM ID
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EM-598 | EM-858 |
| http://www.dndc.sr.unh.edu |
https://integratedmodelling.org/hub/#/register ?Comment:Need to set up an account first and then can access the main integrated modelling hub page: |
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Contact Name
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M. Abdalla | Javier Martinez-Lopez |
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Contact Address
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Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain |
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Contact Email
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abdallm@tcd.ie | javier.martinez@bc3research.org |
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EM ID
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EM-598 | EM-858 |
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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. 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. | ABSTRACT: "Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed. " |
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Specific Policy or Decision Context Cited
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climate change | None identified |
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Biophysical Context
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Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Watersheds surrounding Santa Fe and Albuquerque, New Mexico |
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EM Scenario Drivers
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fertilization | N/A |
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EM ID
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EM-598 | EM-858 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application |
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New or Pre-existing EM?
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Application of existing model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-598 | EM-858 |
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Document ID for related EM
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None | None |
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EM ID for related EM
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EM-593 | EM-859 |
EM Modeling Approach
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EM ID
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EM-598 | EM-858 |
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EM Temporal Extent
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1961-1990 | 1981-2015 |
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EM Time Dependence
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time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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both | Not applicable |
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EM Time Continuity
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discrete | Not applicable |
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EM Temporal Grain Size Value
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1 | Not applicable |
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EM Temporal Grain Size Unit
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Day | Not applicable |
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EM ID
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EM-598 | EM-858 |
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Bounding Type
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Point or points | Watershed/Catchment/HUC |
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Spatial Extent Name
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Oak Park Research centre | Santa Fe Fireshed |
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Spatial Extent Area (Magnitude)
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1-10 ha | 100-1000 km^2 |
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EM ID
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EM-598 | EM-858 |
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EM Spatial Distribution
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spatially lumped (in all cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
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Not applicable | 30 m |
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EM ID
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EM-598 | EM-858 |
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EM Computational Approach
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Numeric | Analytic |
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EM Determinism
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deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-598 | EM-858 |
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Model Calibration Reported?
em.detail.calibrationHelp
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Yes | Unclear |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
No |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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Model Operational Validation Reported?
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Yes | No |
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Model Uncertainty Analysis Reported?
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No | No |
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Model Sensitivity Analysis Reported?
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No | No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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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-598 | EM-858 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-598 | EM-858 |
| None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-598 | EM-858 |
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Centroid Latitude
em.detail.ddLatHelp
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52.86 | 35.86 |
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Centroid Longitude
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6.54 | -105.76 |
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Centroid Datum
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None provided | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated |
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EM ID
em.detail.idHelp
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EM-598 | EM-858 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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farm pasture | watersheds |
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EM Ecological Scale
em.detail.ecoScaleHelp
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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
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EM ID
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EM-598 | EM-858 |
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EM Organismal Scale
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Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-598 | EM-858 |
| 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-598 | EM-858 |
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<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-598 | EM-858 |
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None |
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