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-379 | EM-940 | EM-962 | EM-1001 |
EM Short Name
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VELMA soil temperature, Oregon, USA | OpenNSPECT v. 1.1, California, U.S. | RZWQM2, Quebec, Canada | NBS benefits explorer |
EM Full Name
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VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | OpenNSPECT v. 1.1, California, U.S. | Root zone water quality model 2 mitigation of greenhouse gases, Quebec, Canada | Benefit Accounting of Nature-Based Solutions for Watersheds: Guide |
EM Source or Collection
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US EPA | None | None | None |
EM Source Document ID
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317 |
433 ?Comment:Additional source for this EM: NOAA, 2012. National Oceanic and Atmospheric Administration. Technical Guide for OpenNSPECT, Version 1.1, p. 44. http://www.csc.noaa.gov/digitalcoast/tools/opennspect. |
447 | 471 |
Document Author
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Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Morrison, K. D. and C. A. Kolden | Jiang, Q., Zhiming, Q., Madramootoo, C.A., and Creze, C. | Brill, G., T. Shiao, C. Kammeyer, S. Diringer, K. Vigerstol, N. Ofosu-Amaah, M. Matosich, C. Müller-Zantop, W. Larson and T. Dekker |
Document Year
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2013 | 2015 | 2018 | 2022 |
Document Title
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Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Modeling the impacts of wildfire on runoff and pollutant transport from coastal watersheds to the nearshore environment | Mitigating greenhouse gas emisssions in subsurface-drained field using RZWQM2 | Benefit Accounting of Nature-Based Solutions for Watersheds: Guide |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report |
EM ID
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EM-379 | EM-940 | EM-962 | EM-1001 |
Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | https://coast.noaa.gov/digitalcoast/tools/opennspect.html | Not applicable | https://nbsbenefitsexplorer.net/tool | |
Contact Name
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Alex Abdelnour | Crystal A. Kolden | Zhiming Qi | Gregg Brill |
Contact Address
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Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | Not reported | Department of Bioresource Engineering, McGill University, Sainte-Anne-de-Bellevue, QC H9X 3V9, Canada | Not reported |
Contact Email
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abdelnouralex@gmail.com | ckolden@uidaho. Edu | zhiming.qi@mcgill.ca | Not reported |
EM ID
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EM-379 | EM-940 | EM-962 | EM-1001 |
Summary Description
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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: "Wildfire is a common disturbance that can significantly alter vegetation in watersheds and affect the rate of sediment and nutrient transport to adjacent nearshore oceanic environments. Changes in runoff resulting from heterogeneous wildfire effects are not well-understood due to both limitations in the field measurement of runoff and temporally-limited spatial data available to parameterize runoff models. We apply replicable, scalable methods for modeling wildfire impacts on sediment and nonpoint source pollutant export into the nearshore environment, and assess relationships between wildfire severity and runoff. Nonpoint source pollutants were modeled using a GIS-based empirical deterministic model parameterized with multi-year land cover data to quantify fire-induced increases in transport to the nearshore environment. Results indicate post-fire concentration increases in phosphorus by 161 percent, sediments by 350 percent and total suspended solids (TSS) by 53 percent above pre-fire years. Higher wildfire severity was associated with the greater increase in exports of pollutants and sediment to the nearshore environment, primarily resulting from the conversion of forest and shrubland to grassland. This suggests that increasing wildfire severity with climate change will increase potential negative impacts to adjacent marine ecosystems. The approach used is replicable and can be utilized to assess the effects of other types of land cover change at landscape scales. It also provides a planning and prioritization framework for management activities associated with wildfire, including suppression, thinning, and post-fire rehabilitation, allowing for quantification of potential negative impacts to the nearshore environment in coastal basins." | Abstract: "Greenhouse gas (GHG) emissions from agricultural soils are affected by various environmental factors and agronomic practices. The impact of inorganic nitrogen (N) fertilization rates and timing, and water table management practices on N2O and CO2 emissions were investigated to propose mitigation and adaptation efforts based on simulated results founded on field data. Drawing on 2012–2015 data measured on a subsurface-drained corn (Zea mays L.) field in Southern Quebec, the Root Zone Water Quality Model 2 (RZWQM2) was calibrated and validated for the estimation of N2O and CO2 emissions under free drainage (FD) and controlled drainage with sub-irrigation (CD-SI). Long term simulation from 1971 to 2000 suggested that the optimal N fertilization should be in the range of 125 to 175 kg N ha−1 to obtain higher NUE (nitrogen use efficiency, 7–14%) and lower N2O emission (8–22%), compared to 200 kg N ha−1 for corn-soybean rotation (CS). While remaining crop yields, splitting N application would potentially decrease total N2O emissions by 11.0%. Due to higher soil moisture and lower soil O2 under CD-SI, CO2 emissions declined by 6% while N2O emissions increased by 21% compared to FD. The CS system reduced CO2 and N2O emissions by 18.8% and 20.7%, respectively, when compared with continuous corn production. This study concludes that RZWQM2 model is capable of predicting GHG emissions, and GHG emissions from agriculture can be mitigated using agronomic management." | Watersheds around the world are in peril and risk further decline from climate change and human impacts, like pollution, degrading landscapes, and unsustainable water use. These impacts can inhibit the ability of ecosystems to regulate water flows, sequester carbon to reduce atmospheric greenhouse gas levels, maintain biodiversity and healthy waterways, promote social well-being, offer economic opportunities, and sustain agricultural productivity. Climate change is exacerbating these impacts by shifting weather and precipitation patterns, degrading habitats, and increasing the recurrence and severity of natural disasters. Urgent action is needed to address these impacts by implementing nature-based solutions (NBS). NBS protect, sustainably manage, and restore natural or modified watersheds, to address societal challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits (IUCN, 2016). Investment in NBS offers a mechanism to restore degraded watersheds and protect intact ones, leading to improved water quality and quantity, improved carbon sequestration and increased biodiversity, among many other social and economic benefits. NBS also support climate mitigation and adaptation efforts and reduce the impacts from other shocks, such as floods, droughts, and extreme weather events. Implementing NBS can also help advance progress toward achieving the United Nations Sustainable Development Goals (SDGs), particularly SDG 2 (zero hunger), SDG 6 (water), SDG 11 (sustainable cities and communities), SDG 13 (climate action), and SDG 15 (life on land). NBS therefore support social, economic and environmental objectives, and may be particularly important in supporting vulnerable communities. |
Specific Policy or Decision Context Cited
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None identified | None identified | None | None identified |
Biophysical Context
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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. | Central California coast includes twelve adjacent watersheds covering 87,638 ha and rises steeply from sea level to just below 1800 m within a few km from the coast, and experiences a Mediterranean climate, with fire season typically lasting from June to November. Precipitation is dependent on elevation ranging from 65 cm near the coast to over 130 cm at ridge top. Three ecological zones occur within the study area. These zones are comprised of grasslands, coastal sage scrub, chaparral, oak forests, mixed broadleaf evergreen forest, and coniferous forests. | None | NA |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | None | No scenarios presented |
EM ID
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EM-379 | EM-940 | EM-962 | EM-1001 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | None | Method Only |
New or Pre-existing EM?
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Application of existing model | Application of existing model | None | 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-379 | EM-940 | EM-962 | EM-1001 |
Document ID for related EM
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Doc-13 | Doc-317 | Doc-431 | None | None |
EM ID for related EM
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EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | EM-938 | None | None |
EM Modeling Approach
EM ID
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EM-379 | EM-940 | EM-962 | EM-1001 |
EM Temporal Extent
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1969-2008 | 2005-2008 | 2012-2015 | Not applicable |
EM Time Dependence
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time-dependent | time-stationary | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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future time | Not applicable | past time | Not applicable |
EM Time Continuity
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discrete | Not applicable | discrete | Not applicable |
EM Temporal Grain Size Value
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1 | Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Day | Not applicable | Year | Not applicable |
EM ID
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EM-379 | EM-940 | EM-962 | EM-1001 |
Bounding Type
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Watershed/Catchment/HUC | Watershed/Catchment/HUC | Point or points | Not applicable |
Spatial Extent Name
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H. J. Andrews LTER WS10 | Big Sur region, central California | Corn field | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 ha | 100-1000 km^2 | 1-10 ha | Not applicable |
EM ID
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EM-379 | EM-940 | EM-962 | EM-1001 |
EM Spatial Distribution
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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) |
Spatial Grain Type
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volume, for 3-D feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable |
Spatial Grain Size
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30 m x 30 m surface pixel and 2-m depth soil column | irregular | Not applicable | Not applicable |
EM ID
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EM-379 | EM-940 | EM-962 | EM-1001 |
EM Computational Approach
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Numeric | Analytic | * | Analytic |
EM Determinism
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deterministic | deterministic | None | deterministic |
Statistical Estimation of EM
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None |
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EM ID
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EM-379 | EM-940 | EM-962 | EM-1001 |
Model Calibration Reported?
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No | No | None | Not applicable |
Model Goodness of Fit Reported?
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No | No | None | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None | None |
Model Operational Validation Reported?
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No | No | None | Unclear |
Model Uncertainty Analysis Reported?
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No | No | None | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | None | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | None | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-379 | EM-940 | EM-962 | EM-1001 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-379 | EM-940 | EM-962 | EM-1001 |
None |
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None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-379 | EM-940 | EM-962 | EM-1001 |
Centroid Latitude
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44.25 | 35.96 | 45.32 | Not applicable |
Centroid Longitude
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-122.33 | -121.43 | 74.17 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | None provided | Not applicable |
Centroid Coordinates Status
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Provided | Estimated | Provided | Not applicable |
EM ID
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EM-379 | EM-940 | EM-962 | EM-1001 |
EM Environmental Sub-Class
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Forests | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | None | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland |
Specific Environment Type
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400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Coastal watersheds | None | None |
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 | None | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-379 | EM-940 | EM-962 | EM-1001 |
EM Organismal Scale
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Not applicable | Not applicable | None | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-379 | EM-940 | EM-962 | EM-1001 |
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)
EM-379 | EM-940 | EM-962 | EM-1001 |
None |
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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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-379 | EM-940 | EM-962 | EM-1001 |
None | None | None | None |