sparrow surface water quality workshop october 29-31, 2002 reston, virginia section 5. comparison of...
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SPARROW Surface Water Quality Workshop
October 29-31, 2002Reston, Virginia
Section 5. Comparison of GIS Approaches, Data Sources and
Management
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Network Approach ComparisonRF1 DEM
NHD
Attributes
Topology
Scale/Density
Watersheds
AvailabilityDifficultModerateFew Problems
100K 24K
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NutrientSources
Delivery
NetworkBased on Stream Reaches
and Watersheds
Predictions and Diagnostics
Modeling Process
GIS Arc/Info & ArcView SPARROWSAS & Fortran
Point Source
Manure
Land Use
AtmosphericDeposition
Fertilizer
Estimated Loads by Stream Reach Incremental, Delivered, and Total
All Sources
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Nutrient Source Data Source Data
• Nutrient Sources– Atmospheric Deposition (NADP)
– Septic Systems (Census)
– Point Sources (PCS)
– Fertilizer (Ag Census)
– Manure (NASS, Ag Census)
– Land Use (MRLC / NLCD and Census)• Urban• Agricultural
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Agricultural Sources• Fertilizer
– Sales by state• Disaggregated by county expenditures
– Application rates (from state) by land use (CB)• Hightill, Lowtill, Pasture, Hayland
• Manure– Animal census by county
• Estimate nutrient generation based on animal unit
• Some counties grouped
– Application rates by land use (CB)• Hightill, Lowtill, Pasture, Hayland
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Delivery Data
• Land-Water Delivery Factors– Slope (from DEM)
– Soil (STATSCO)
– Precipitation (NCDC) – Temperature (HCN)
– Physiography/Geology (state 1:250,000)
– Density (RF1, DEM, NHD)
– Land Use/Cover (NLCD)
• Forest area• Wetland area
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Data Availibility
• Network Related• NHD http://nhd.usgs.gov/index.html
• NED http://gisdata.usgs.net/ned/
• EDNA http://edcnts12.cr.usgs.gov/ned-h/
• USEPA RF1 http://www.epa.gov/nsdi/projects/rf1_meta.html
• ERF1-2 http://water.usgs.gov/lookup/getspatial?erf1-2
• USEPA STORET http://www.epa.gov/storet/
• HUC Boundary http://www.ftw.nrcs.usda.gov/huc_data.html
• NWIS WEB http://waterdata.usgs.gov/usa/nwis/nwis
• RESERVOIRS http://water.usgs.gov/lookup/getspatial?reservoir
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Data Availibility
• Source• Agriculture http://www.nass.usda.gov/census/
• NASS http://www.usda.gov/nass/
• PCS http://www.epa.gov/enviro/html/pcs/pcs_query_java.html
• Sewer Population http://www.census.gov/geo/www/cob/
• Atmospheric http://nadp.sws.uiuc.edu/
• NRI http://www.ftw.nrcs.usda.gov/nri_data.html
• CENSUS http://www.census.gov/
• Land Area http://landcover.usgs.gov/natllandcover.html http://landcover.usgs.gov/
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• Delivery• SSURGO http://www.ftw.nrcs.usda.gov/ssur_data.html
• STATSCO http://www.ftw.nrcs.usda.gov/stat_data.html
• National Soil Survey http://www.statlab.iastate.edu/soils/nsdaf/
• PRISM http://ocs.oce.orst.edu/prism/prism_new.html
• NCDC http://lwf.ncdc.noaa.gov/oa/ncdc.html
• Other• GIS DATA http://www.gisdatadepot.com/catalog/US/index.html
• NAWQA Ancillary Data http://wwwdcascr.wr.usgs.gov/pnsp/gis/data/natancil/
• NAWQA WATER http://water.usgs.gov/nawqa/
• USGS GIS Data http://gisdata.usgs.net/
Data Availibility
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Data ConsiderationsNetwork
• ERF1– Scale, density, dated attributes– Availability, working network
• NHD– Connectivity issues, no streamflow attributes– Higher resolution, networking tools
• NED/EDNA– No streamflow attributes, errors in connection to
watersheds– Reach associated with elevation
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Data ConsiderationsNutrient Sources
• Fertilizer– Sales by state– Disaggregated by county expenditures
• Manure– County estimates based on census, animal units– Some counties grouped
• Point Source (PCS)– Lat long errors– Nutrient estimates from design flows
• Atmospheric– Few collection sites nationally
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Data ConsiderationsNutrient Sources Cont.
• Land Use/Cover– NLCD
• Questionable confidence in AG classifications
• Acceptable time period
– NRI• Limited spatially
– Ag Census• Every 5 years
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Data ConsiderationsDelivery
• SOILs– STATSCO - Aggregated characteristics, 1:250K– SSURGO - Very limited availability, County
• Geology– Limited availability, very general at regional scale
• Precipitation and Temperature– Limited sampling locations– PRISM cost $$
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Data Types, Processing Methods,and Aggregation
• Point– Discrete– Continuous– Within watershed
• GRID (Raster)– Average value – Summed value
• County Census– Area weight– By land use area
• Polygon• Linear (accumulation)
Reach 3124
ATD = 2.1 kg/ha-yr
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Processing Point Source data• Map of point source data• Overlay watersheds• Sum load by watershed (one time period)• Or average by site, then sum by watershed
– Be careful of no data and zero values
• AND/OR• Convert point to grid• Zonalmean to link site ID and reach
– Watershed as ZONE, Point source ID as value
• Average load by site (or not)• Sum by reach watershed
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Creating continuous data from points
NTN locations from web site
Included Site ID and Ion conc.
1982 – 2000
Linear spatial interpolation for wet-deposition for nitrate
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Creating continuous data from points
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Accumulation using the GIS• Climb and d-climb routines in SPARROW
– Allows for the accumulation of reach-associated data
– Information at any location on the network
• Using linear reach network and GIS– Move up or down network to accumulate
– NHD tools (flow tables)
– Arcplot • TRACE ACCUMULATE
• AML’s
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Accumulating area
Can accumulate any information associated to the reach.
Land use area
Fertilizer
Could also display as a percent
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Kg of N from Manure Recovered from Confined Operations
• Aggregation of county statistics• Manure by county• Manure by ag area in county
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County AgricultureLoad
Kg of N from Manure Recoverable from Confined Operations
WatershedLoad
CountyLoad
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Load weighted by AG area in county
Kg of N from Manure Recoverable from Confined Operations
Load weighted by county area only
County load in AG area
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TN load from manure in the watershed?
• Weight factor = AG area in county in Watershed Segment Divided by TOTAL Ag area in County
• Load in watershed = Weight factor * N Load by County• Then N is summed by watershed• Useful if have to weight other county data by ag area
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How much Fertilizer is in the watershed?
• Load per Agricultural pixel =
Fertilizer load in county Divided by Number of Ag Pixels in County
• Load in watershed = Sum N Load by Watershed• Can be obtained by using ZONALSUM (Grid)
– Watershed GRID is ZONE GRID– Fertilizer load in ag area is VALUE GRID
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A value per
county
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Focus on Ag area
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Divide load
value by number of AG
pixels in county
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Sum load by
watershed
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Kg of N from Manure Recovered from Confined Operations
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Data Management Issues
• Directory Structure • Units and Unit Conversions!!!!• What do you do with all this data?
– One load value per source per reach ID• Both input and output (Predictions)
– INFO files and or DBMS
• AV projects of Source and Delivery data• Updates and Corrections to data
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Directory structure
Organize by topic
Organize by data set source
Decide on UNITS of data
Naming conventions
Datasets
Column names in data base
DOCUMENT as you go!!!!!
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Preparing data for model
• 1 value per reach ID
• Extract from DBMS or data set
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Managing input and output data• AV 3.X supports multiple documents• AV/AM Project organization by contaminant• AV/AM View and layout organization• Creating Datasets (Coverage, Shapefile, or GRID)
of contaminant containing all predicted parameters• Using DBMS and Joining tables• Cloning Views and layouts
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INPUT source Data
Spatial Data ManagementSpatial Data Management
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Views of Source data in native form
Source data aggregated to watersheds
Delivery data too
Spatial Data ManagementSpatial Data Management
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Spatial Data ManagementSpatial Data Management
Organize AV projects by contaminate
Organize Views by Prediction
Allows for comparison
Incremental vs Delivered
Individual sources
Individual source and All sources
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Spatial Data ManagementSpatial Data Management
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Spatial Data ManagementSpatial Data Management
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Joined table with all information allows for display and analysis
Can also save as data set (shapefile) maintaining joined table
JOINITEM does same thing, so does joining tables in AM
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Diagnostics
• Mapping Residuals (observed minus predicted) at calibration sites
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SUMMARY Section 5SUMMARY Section 5• A network…………
– Spatially references source, delivery, and prediction information– Requires connected stream reaches and watersheds with common ID– Requires streamflow and velocity for each reach– Requires associated water-quality and flow stations
• Several options have been presented regarding;– Network development and application– Data sets– Data management and aggregation
• Circumstances will vary regarding which techniques are better suited for model and network construction and application.
• Each method does not necessarily meet each circumstance entirely.
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SUMMARY Section 5SUMMARY Section 5
• Every data set has limitations– Understand the limitations
• Aggregation techniques– One value per reach to input into model– One value per reach for output
• Explore data management options– Document as you go– Decide on units and conversions