EcoService Models Library (ESML)
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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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-374 | EM-379 |
EM-467 
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EM-593
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EM-1017 |
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EM Short Name
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InVEST carbon storage and sequestration (v3.2.0) | VELMA soil temperature, Oregon, USA | Yasso07 v1.0.1, Switzerland | DayCent N2O flux simulation, Ireland | N-SPECT, Sediment and runoff, Isfahan, Iran |
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EM Full Name
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InVEST v3.2.0 Carbon storage and sequestration | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | Yasso07 v1.0.1 forest litter decomposition, Switzerland | DayCent simulation N2O flux and climate change, Ireland | Investigation of runoff and sediment yield using N-SPECT model in Pelasjan (Eskandari), Isfahan, Iran |
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EM Source or Collection
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InVEST | US EPA | None | None | None |
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EM Source Document ID
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315 | 317 | 343 | 358 | 480 |
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Document Author
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The Natural Capital Project | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Khalili, S., Jamali, A.A., Hasanzadeh, M. and Morovvati, A., |
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Document Year
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2015 | 2013 | 2014 | 2010 | 2015 |
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Document Title
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Carbon storage and sequestration - InVEST (v3.2.0) | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Validating tree litter decomposition in the Yasso07 carbon model | 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 | Investigation of runoff and sediment yield using N-SPECT model in Pelasjan (Eskandari), Isfahan, Iran. |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
| https://www.naturalcapitalproject.org/invest/ | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | https://coast.noaa.gov/digitalcoast/tools/qnspect.html | |
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Contact Name
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The Natural Capital Project | Alex Abdelnour |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
M. Abdalla | Ali Akbar Jamali |
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Contact Address
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371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Department of Watershed MGT, Maybod Branch, Islamic Azad University, Maybod, Iran |
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Contact Email
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invest@naturalcapitalproject.org | abdelnouralex@gmail.com | markus.didion@wsl.ch | abdallm@tcd.ie | jamaliaa@maybodiau.ac.ir |
EM Description
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EM ID
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EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
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Summary Description
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Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation" | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | 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. | Identifying and quantifying the runoff and Sediment yield are the necessary measures in the issues of soil erosion in a watershed. Pelasjan watershed located in West of Isfahan and it is one of the sub basins of Zayanderud which is taken as the study area. In this study the amount of runoff and Sediment yield has been evaluated using the Nonpoint-Source Pollution and Erosion Comparison Tools (N-SPECT) model which is an extension to ArcGIS software. The input layer maps in the GIS environment, including land use, the rain erosion, vegetation, soil erodibility, contour map and watershed boundary map were prepared. By entering the input data and running N-SPECT model, runoff and Sediment yield raster maps of the study area were obtained. To evaluate the model and data comparing, the values obtained from the model and the actual data values of runoff and Sediment yield were converted to the eigenvalues. Special amount of runoff from the model equals 1483 m3/ha/year and the actual runoff is equivalent to 1253 m3/ha/year for 21 water years ,from 1991 to 2012. From the values obtained by the model and the actual data it can be concluded that the model is sufficiently accurate for estimating runoff since the actual runoff value and the value obtained from the model are close to each other and statistically, there is no significant difference between them during this 21 water year. In relation to a Sediment yield, the amount obtained from the model was 7.8 ton/ha/year and the average amount of Sediment yield for 21 water years is 2.1 ton/ha/year, which by comparing with the values obtained for Sediment yield it can be concluded that the model overestimates about three times from the actual amount and there is a significant difference between the real data and data obtained by model so the model has not been very successful in Sediment yield estimating. One of the advantages of this model for estimating runoff and Sediment yield is point to point estimation of runoff and Sediment yield in output maps of the region. This model is particularly recommended for harsh and difficult access regions of the watershed. |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | climate change | None provided |
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Biophysical Context
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Not applicable | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Pelasjan watershed, Zagros mountain range |
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EM Scenario Drivers
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Optional future scenarios for changed LULC and wood harvest | No scenarios presented |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
air temperature, precipitation, Atmospheric CO2 concentrations | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
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Method Only, Application of Method or Model Run
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Method Only | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Method + Application (multiple runs exist) View EM Runs | Method + Application |
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New or Pre-existing EM?
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New or revised model | Application of existing model | Application of existing model | 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-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
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Document ID for related EM
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Doc-309 | Doc-13 | Doc-317 | Doc-342 | Doc-344 | None | Doc-473 |
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EM ID for related EM
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EM-349 | EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | EM-466 | EM-469 | EM-480 | EM-485 | EM-598 | EM-1007 | EM-1003 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
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EM Temporal Extent
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Not applicable | 1969-2008 | 1993-2013 | 1961-1990 | 1991-2012 |
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EM Time Dependence
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time-dependent | time-dependent | time-dependent | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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future time | future time | future time | both | Not applicable |
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EM Time Continuity
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discrete | discrete | discrete | discrete | Not applicable |
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EM Temporal Grain Size Value
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1 | 1 | 1 | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Year | Day | Year | Day | Not applicable |
EM Spatial Extent
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EM ID
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EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
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Bounding Type
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Not applicable | Watershed/Catchment/HUC | Geopolitical | Point or points | Watershed/Catchment/HUC |
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Spatial Extent Name
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Not applicable | H. J. Andrews LTER WS10 | Switzerland | Oak Park Research centre | Pelasjan watershed |
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Spatial Extent Area (Magnitude)
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Not applicable | 10-100 ha | 10,000-100,000 km^2 | 1-10 ha | 1000-10,000 km^2. |
Spatial Distribution of Computations
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EM ID
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EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:See below, grain includes vertical, subsurface dimension. |
spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | volume, for 3-D feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable |
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Spatial Grain Size
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application specific | 30 m x 30 m surface pixel and 2-m depth soil column | 5 sites | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
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EM Computational Approach
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Analytic | Numeric | Numeric | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | stochastic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
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Model Calibration Reported?
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Not applicable | No | No | No | Unclear |
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Model Goodness of Fit Reported?
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Not applicable | No | No |
Yes ?Comment:for N2O fluxes |
No |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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None |
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Model Operational Validation Reported?
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Not applicable | No | Yes | Yes | Unclear |
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Model Uncertainty Analysis Reported?
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Not applicable | No | No | No | Unclear |
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Model Sensitivity Analysis Reported?
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Not applicable | No | No | No | Unclear |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
| None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
| None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
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Centroid Latitude
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-9999 | 44.25 | 46.82 | 52.86 | 32.26 |
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Centroid Longitude
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-9999 | -122.33 | 8.23 | 6.54 | 50.22 |
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Centroid Datum
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Not applicable | WGS84 | WGS84 | None provided | WGS84 |
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Centroid Coordinates Status
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Not applicable | Provided | Estimated | Provided | Provided |
Environments and Scales Modeled
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EM ID
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EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
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EM Environmental Sub-Class
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Not applicable | Forests | Forests | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Terrestrial environments, but not specified for methods | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | forests | farm pasture | Desert mountains watershed |
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EM Ecological Scale
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Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
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EM Organismal Scale
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Not applicable | Not applicable | Community | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
| None Available | None Available | None Available | 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)
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| EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
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None |
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-374 | EM-379 |
EM-467
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EM-593
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EM-1017 |
| None | None | None |
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None |