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-260 |
EM-593 ![]() |
EM-656 |
EM Short Name
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Coral taxa and land development, St.Croix, VI, USA | DayCent N2O flux simulation, Ireland | P8 UCM |
EM Full Name
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Coral taxa richness and land development, St.Croix, Virgin Islands, USA | DayCent simulation N2O flux and climate change, Ireland | P8 Urban Catchment model method |
EM Source or Collection
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US EPA | None | None |
EM Source Document ID
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96 | 358 |
377 ?Comment:Published to the web. Previously versions prepared for EPA. |
Document Author
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Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Walker, W. Jr., and J.D. Walker |
Document Year
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2011 | 2010 | 2015 |
Document Title
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Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | 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 | P8 Urban Catchment Model Version 3.5 |
Document Status
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Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published report |
EM ID
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EM-260 |
EM-593 ![]() |
EM-656 |
Not applicable | Not applicable | http://www.wwwalker.net/p8/v35/webhelp/splash.htm | |
Contact Name
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Leah Oliver | M. Abdalla | William Walker Jr., PhD |
Contact Address
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National Health and Environmental Research Effects Laboratory | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Concord, Massachusetts |
Contact Email
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leah.oliver@epa.gov | abdallm@tcd.ie | bill@wwwalker.net |
EM ID
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EM-260 |
EM-593 ![]() |
EM-656 |
Summary Description
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AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | 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. | Author description: " P8 simulates the generation and transport of stormwater runoff pollutants in urban watersheds. Continuous water-balance and mass-balance calculations are performed on a user-defined drainage system consisting of the following elements: - Watersheds (<= 250 nonpoint source areas) - Devices (<=75 runoff storage/treatment areas or BMP's) - Particles (<= 5 fractions with different settling velocities) - Water Quality Components (<= 10 associated with particles) Simulations are driven by hourly precipitation and daily air temperature time series. Runoff contributions from snowmelt are also simulated. 'P8' abbreviates "Program for Predicting Polluting Particle Passage Thru Pits, Puddles, and Ponds", which more or less captures the basic features and functions of the model. It has been developed for use by engineers and planners in designing and evaluating runoff treatment schemes for existing or proposed urban developments. Design objectives are typically expressed in terms of percentage reduction in suspended solids or other water quality component. Despite its limitations, P8 has been used by state and local regulatory agencies as a consistent framework for evaluating proposed developments. Depending on applications, other models could be either too simple (easily used, but ignoring important factors) or too complex (requiring considerable site-specific data and/or user expertise). P8 attempts to strike a balance to between those extremes. Predicted water quality components include total suspended solids (sum of the individual particle fractions), total phosphorus, total Kjeldahl nitrogen, copper, lead, zinc, and total hydrocarbons. Simulated BMP types include detention ponds (wet, dry, extended), infiltration basins, swales, buffer strips, or other devices with user-specified stage/discharge curves and infiltration rates. A simple water budget algorithm can be used to estimate groundwater storage and stream base flow in watershed-scale applications. Initial calibrations were based upon runoff quality and particle settling velocity data collected under the EPA's Nationwide Urban Runoff Program (Athayede et al., 1983). Calibrations to impervious area runoff parameters for Wisconsin watersheds have been subsequently developed. Inputs are structured in terms which should be familiar to planners and engineers involved in hydrologic evaluation. Several tabular and graphic output formats are provided. " |
Specific Policy or Decision Context Cited
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Not applicable | climate change | None identified |
Biophysical Context
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nearshore; <1.5 km offshore; <12 m depth | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Urban setting |
EM Scenario Drivers
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Not applicable | air temperature, precipitation, Atmospheric CO2 concentrations | N/A |
EM ID
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EM-260 |
EM-593 ![]() |
EM-656 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only |
New or Pre-existing EM?
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New or revised model | 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-260 |
EM-593 ![]() |
EM-656 |
Document ID for related EM
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None | None | None |
EM ID for related EM
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None | EM-598 | None |
EM Modeling Approach
EM ID
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EM-260 |
EM-593 ![]() |
EM-656 |
EM Temporal Extent
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2006-2007 | 1961-1990 | Not applicable |
EM Time Dependence
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time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | both | Not applicable |
EM Time Continuity
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Not applicable | discrete | discrete |
EM Temporal Grain Size Value
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Not applicable | 1 | 1 |
EM Temporal Grain Size Unit
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Not applicable | Day | Hour |
EM ID
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EM-260 |
EM-593 ![]() |
EM-656 |
Bounding Type
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Physiographic or Ecological | Point or points | Not applicable |
Spatial Extent Name
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St.Croix, U.S. Virgin Islands | Oak Park Research centre | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 1-10 ha | Not applicable |
EM ID
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EM-260 |
EM-593 ![]() |
EM-656 |
EM Spatial Distribution
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spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
Spatial Grain Type
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Not applicable | Not applicable | Not applicable |
Spatial Grain Size
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Not applicable | Not applicable | Not applicable |
EM ID
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EM-260 |
EM-593 ![]() |
EM-656 |
EM Computational Approach
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Analytic | Numeric | Numeric |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-260 |
EM-593 ![]() |
EM-656 |
Model Calibration Reported?
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Yes | No | Yes |
Model Goodness of Fit Reported?
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Yes |
Yes ?Comment:for N2O fluxes |
Not applicable |
Goodness of Fit (metric| value | unit)
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None |
Model Operational Validation Reported?
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No | Yes | Not applicable |
Model Uncertainty Analysis Reported?
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Yes | No | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-260 |
EM-593 ![]() |
EM-656 |
None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-260 |
EM-593 ![]() |
EM-656 |
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None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-260 |
EM-593 ![]() |
EM-656 |
Centroid Latitude
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17.75 | 52.86 | Not applicable |
Centroid Longitude
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-64.75 | 6.54 | Not applicable |
Centroid Datum
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NAD83 | None provided | Not applicable |
Centroid Coordinates Status
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Estimated | Provided | Not applicable |
EM ID
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EM-260 |
EM-593 ![]() |
EM-656 |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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stony coral reef | farm pasture | Urban catchments |
EM Ecological Scale
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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 |
Scale of differentiation of organisms modeled
EM ID
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EM-260 |
EM-593 ![]() |
EM-656 |
EM Organismal Scale
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Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-260 |
EM-593 ![]() |
EM-656 |
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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-260 |
EM-593 ![]() |
EM-656 |
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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-260 |
EM-593 ![]() |
EM-656 |
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None |