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EM: UFORE-Hydro (Urban Forest Effects - Hydrology) v1, Dead Run Catchment, Baltimore, MD ?Comment:
UFORE-Hydro is now incorporated in the i-Tree suite of models as iTree-Hydro. (EM-109)
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Comment:
UFORE-Hydro is now incorporated in the i-Tree suite of models as iTree-Hydro.
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EM Identity and Description
EM Identification
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EM ID
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EM-109 |
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EM Short Name
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UFORE-Hydro, Baltimore, MD, USA |
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EM Full Name
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UFORE-Hydro (Urban Forest Effects - Hydrology) v1, Dead Run Catchment, Baltimore, MD ?Comment:UFORE-Hydro is now incorporated in the i-Tree suite of models as iTree-Hydro. |
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EM Source or Collection
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i-Tree | USDA Forest Service |
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EM Source Document ID
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190 |
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Document Author
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Wang, J., Endreny, T. A. and Nowak, D. J. |
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Document Year
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2008 |
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Document Title
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Mechanistic simulation of tree effects in an urban water balance model |
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Document Status
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Peer reviewed and published |
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Comments on Status
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Published journal manuscript |
Software and Access
| http://www.itreetools.org/ | |
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Contact Name
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Jun Wang |
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Contact Address
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Environmental Resources and Forest Engineering, Colecge of Environmental Science and Forestry, State University of New York, Syracuse, New York 13210 |
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Contact Email
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Not reported |
EM Description
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Summary Description
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ABSTRACT: "A semidistributed, physical-based Urban Forest Effects – Hydrology (UFORE-Hydro) model was created to simulate and study tree effects on urban hydrology and guide management of urban runoff at the catchment scale. The model simulates hydrological processes of precipitation, interception, evaporation, infiltration, and runoff using data inputs of weather, elevation, and land cover along with nine channel, soil, and vegetation parameters. Weather data are pre-processed by UFORE using Penman-Monteith equations to provide potential evaporation terms for open water and vegetation. Canopy interception algorithms modified established routines to better account for variable density urban trees, short vegetation, and seasonal growth phenology. Actual evaporation algorithms allocate potential energy between leaf surface storage and transpiration from soil storage. Infiltration algorithms use a variable rain rate Green-Ampt formulation and handle both infiltration excess and saturation excess ponding and runoff. Stream discharge is the sum of surface runoff and TOPMODEL- based subsurface flow equations. Automated calibration routines that use observed discharge has been coupled to the model." FURTHER DESCRIPTION: UFORE-Hydro was tested in the urban Dead Run catchment of Baltimore, Maryland, USA. |
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Specific Policy or Decision Context Cited
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None identified |
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Biophysical Context
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No additional description provided |
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EM Scenario Drivers
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Base case; increase pervious area tree cover to 40%; increase impervious area tree cover to 40%; double impervious area to 60%; halve pervious area tree cover to 6%; double pervious area tree cover to 24% and increase pervious area tree cover to 20%. ?Comment:Base case is existing conditions. |
EM Relationship to Other EMs or Applications
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
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New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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Document ID for related EM
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Doc-198 |
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EM ID for related EM
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EM-137 |
EM Modeling Approach
EM Relationship to Time
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EM Temporal Extent
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2000 |
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EM Time Dependence
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time-dependent |
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EM Time Reference (Future/Past)
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both |
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EM Time Continuity
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discrete |
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EM Temporal Grain Size Value
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1 |
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EM Temporal Grain Size Unit
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Hour |
EM Spatial Extent
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Bounding Type
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Watershed/Catchment/HUC |
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Spatial Extent Name
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Dead Run Catchement, Baltimore, MD |
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Spatial Extent Area (Magnitude)
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10-100 km^2 |
Spatial Distribution of Computations
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EM Spatial Distribution
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spatially distributed (in at least some cases) |
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Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) |
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Spatial Grain Size
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irregular topographically delineated similar units |
EM Structure and Computation Approach
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EM Computational Approach
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Numeric |
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EM Determinism
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deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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Model Calibration Reported?
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Yes |
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Model Goodness of Fit Reported?
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Yes |
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Goodness of Fit (metric| value | unit)
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Model Operational Validation Reported?
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Yes |
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Model Uncertainty Analysis Reported?
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Unclear |
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Model Sensitivity Analysis Reported?
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No |
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Model Sensitivity Analysis Include Interactions?
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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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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| None |
Centroid Lat/Long (Decimal Degree)
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Centroid Latitude
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39.31 |
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Centroid Longitude
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-76.74 |
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Centroid Datum
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WGS84 |
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Centroid Coordinates Status
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Provided |
Environments and Scales Modeled
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EM Environmental Sub-Class
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Rivers and Streams | Ground Water | Created Greenspace |
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Specific Environment Type
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Urban watershed |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM Organismal Scale
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Community |
Taxonomic level and name of organisms or groups identified
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| None Available |
EnviroAtlas URL
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| GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Percent Impervious Area, Water supply from NID reservoirs (million gallons) |
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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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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EM Variable Names (and Units)
Predictor
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Intermediate
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Intermediate (Computed) Variables (and Units)
view details (2 variables)
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Response
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Observed Response Variables (and Units)
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None |
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Computed Response Variables (and Units)
view details (6 variables)
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