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-598 | EM-979 |
EM Short Name
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DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | Predicting ecosystem service values, Bangladesh |
EM Full Name
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DeNitrification-DeComposition simulation of N2O flux Ireland | Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh |
EM Source or Collection
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None | None |
EM Source Document ID
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358 | 457 |
Document Author
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Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Morshed, S. R., Fattah, M. A., Haque, M. N., & Morshed, S. Y. |
Document Year
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2010 | 2022 |
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 | Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh |
Document Status
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Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript |
EM ID
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EM-598 | EM-979 |
http://www.dndc.sr.unh.edu | Not applicable | |
Contact Name
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M. Abdalla | Syed Riad Morshed |
Contact Address
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Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, Bangladesh |
Contact Email
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abdallm@tcd.ie | riad.kuet.urp16@gmail.com |
EM ID
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EM-598 | EM-979 |
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. | Land Use/Land Cover (LULC) provides provisional, supporting, cultural, and regulating ecosystem services that contribute to ecological environments, enhance human health and living, have economic advantages for sustaining living organisms. LULC transformation due to enormous urban expansion diminishing Ecosystem Services Values (ESVs) and discouraging sustainability. Though unplanned LULC transformation practice became more prevalent in developing countries, comprehensive assessment of LULC changes and their influences in ESVs are rarely attempted. This study aimed to illustrate and forecast the LULC changes and their influences on ESVs change in Jashore using remote sensing technologies. ESVs estimation and change analysis were conducted by utilizing -derived LULC data of the year 2000, 2010, and 2020 with the corresponding global value coefficients of each LULC type which are previously published. For simulating future LULC and ESVs, Land Change Modeler of TerrSet Geospatial Monitoring and Modeling Software was used in Multi-Layer Perceptron-Markov Chain and Artificial Neural Network method. The decline of agricultural land by 13.13% and waterbody by 5.79% has resulted in the reduction of total ESVs US$0.23 million (24.47%) during 2000–2020. The forecasted result shows that the built-up area will be dominant LULC in the future, and ESVs of provisioning and cultural services will be diminished by $0.107 million, $63400.3 by 2050 with the declination of agricultural, waterbody, vegetation, and vacant land covers. The study signifies the importance of a strategic rational land-use plan to strictly monitor and control the encroachment of built-up areas into vegetation, waterbodies, and agricultural land in addition to scientific mitigative policies for ensuring ecological sustainability. |
Specific Policy or Decision Context Cited
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climate change | N/A |
Biophysical Context
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Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Jashore city, Bangladesh |
EM Scenario Drivers
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fertilization | No scenarios presented |
EM ID
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EM-598 | EM-979 |
Method Only, Application of Method or Model Run
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Method + Application | 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-598 | EM-979 |
Document ID for related EM
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None | None |
EM ID for related EM
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EM-593 | None |
EM Modeling Approach
EM ID
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EM-598 | EM-979 |
EM Temporal Extent
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1961-1990 | 2000-2050 |
EM Time Dependence
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time-dependent | time-dependent |
EM Time Reference (Future/Past)
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both | both |
EM Time Continuity
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discrete | discrete |
EM Temporal Grain Size Value
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1 | 10 |
EM Temporal Grain Size Unit
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Day | Year |
EM ID
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EM-598 | EM-979 |
Bounding Type
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Point or points | Geopolitical |
Spatial Extent Name
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Oak Park Research centre | Jashore city, Bangladesh |
Spatial Extent Area (Magnitude)
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1-10 ha | 1000-10,000 km^2. |
EM ID
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EM-598 | EM-979 |
EM Spatial Distribution
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spatially lumped (in all cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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Not applicable | map scale, for cartographic feature |
Spatial Grain Size
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Not applicable | 30m |
EM ID
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EM-598 | EM-979 |
EM Computational Approach
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Numeric | Analytic |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-598 | EM-979 |
Model Calibration Reported?
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Yes | Yes |
Model Goodness of Fit Reported?
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Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Yes |
Goodness of Fit (metric| value | unit)
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Model Operational Validation Reported?
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Yes | Yes |
Model Uncertainty Analysis Reported?
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No | Unclear |
Model Sensitivity Analysis Reported?
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No | Unclear |
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-598 | EM-979 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-598 | EM-979 |
None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-598 | EM-979 |
Centroid Latitude
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52.86 | 23.95 |
Centroid Longitude
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6.54 | 89.12 |
Centroid Datum
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None provided | other |
Centroid Coordinates Status
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Provided | Provided |
EM ID
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EM-598 | EM-979 |
EM Environmental Sub-Class
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Agroecosystems | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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farm pasture | Urban city |
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-598 | EM-979 |
EM Organismal Scale
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Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-598 | EM-979 |
None Available | None Available |
EnviroAtlas URL
EM-598 | EM-979 |
GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day) | GAP Ecological Systems |
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-979 |
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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-979 |
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