Problems Associated with Comparing In Situ Water Quality Measurements to Pollution Model Output for Geographic Analyses Presentation to the Annual Meeting.

Slides:



Advertisements
Similar presentations
1 EUROPEAN TOPIC CENTRE ON WATER WATERBASE Rivers Content and structure of Waterbase Rivers Update procedure Products based on Waterbase Rivers Future.
Advertisements

Spatial Modeling of Soil Heterogeneities and their Impacts on Soil-Phosphorus Losses in a Quebec Watershed By Alaba Boluwade Department of Bioresource.
5. Final Remarks Information and the GIS package developed will be used to evaluate the effectiveness of implemented watershed management practices in.
An open source version of the Nonpoint-Source Pollution and Erosion Comparison Tool Climate Tools Café Webinar Dave Eslinger, Ph.D. 3 May, 2012.
Developing Modeling Tools in Support of Nutrient Reduction Policies Randy Mentz Adam Freihoefer, Trip Hook, & Theresa Nelson Water Quality Modeling Technical.
REMM: Riparian Ecosystem Management Model USDA-Agricultural Research Service University of Georgia California State University – Chico USDA-Natural Resources.
Minnesota Watershed Nitrogen Reduction Planning Tool William Lazarus Department of Applied Economics University of Minnesota David Mulla Department of.
Introducing Web-Based Decision Tools for Environmental Management To Lake Michigan Communities Bernie Engel Long-Term Hydrological Impact Assessment (L-THIA)
Fort Bragg Cantonment Area Background The USGS is working with the U.S. Army at Fort Bragg to develop a Storm Water Pollution Prevention Plan (SWP3). The.
Land Use Change and Its Effect on Water Quality: A Watershed Level BASINS-SWAT Model in West Georgia Gandhi Raj Bhattarai Diane Hite Upton Hatch Prepared.
0 The National Hydrography Dataset Plus a tool for SPARROW Watershed Modeling Richard Moore (presented by Alan Rea)
KELLY HAYDEN Applying GIS to Watershed Pollution Management.
Impact of Sampling Frequency on Annual Load Estimation Amber Spackman Jones Utah Water Research Lab Nancy Mesner Watershed Science Jeff Horsburgh Utah.
Modeling Water Quality. Special reference of this work to….
Remote Mapping of River Channel Morphology March 9, 2003 Carl J. Legleiter Geography Department University of California Santa Barbara.
Introducing Web-Based Decision Tools for Environmental Watershed Management Bernie Engel and Roxanne Mitchell Agricultural & Biological Engineering Purdue.
Geospatial Modeling Maps and Animated Geography E. Lynn Usery Professor, University of Georgia Research Geographer, U.S. Geological Survey.
GIS for Faster Analysis of Dam-Break Flows Steve Pitman GIS in Water Resources – Fall 2003 Dr. David Maidment – UT Austin.
An Internet/GIS-Based Tool to Assist Community Planners Bernie Engel Jon Harbor Don Jones and many others.
Nonpoint Source Pollution Reductions – Estimating a Tradable Commodity Allen R. Dedrick Associate Deputy Administrator Natural Resources & Sustainable.
Impact of Climate Change on Flow in the Upper Mississippi River Basin
Chesapeake Bay and Land Use. Land Use Issues in Bay Watershed Herbicides and Pesticides Herbicides and Pesticides Fertilizer Fertilizer Sediment Runoff.
Developing Health Geographic Information Systems (HGIS) for Khorasan Province in Iran (Technical Report) S.H. Sanaei-Nejad, (MSc, PhD) Ferdowsi University.
Community-wide urban stormwater planning utilizing LiDAR, the WinSLAMM model and GIS Dan Murphy Rebecca Gronewold UNI GeoTREE Center April 4, 2013.
United States Department of Agriculture Cooperative State, Research, Education and Extension Service Impacts of Agriculture on Water Quality: The role.
Options for Identifying & Quantifying Pollutant Loads
The University of Mississippi Geoinformatics Center NASA RPC – March, Evaluation for the Integration of a Virtual Evapotranspiration Sensor Based.
Introduction SPATSIM is a system that makes use of shapefiles
Predicting Sediment and Phosphorus Delivery with a Geographic Information System and a Computer Model M.S. Richardson and A. Roa-Espinosa; Dane County.
Science Assessment to Support an Illinois Nutrient Reduction Strategy Mark David, George Czapar, Greg McIsaac, Corey Mitchell March 11,
U.S. Department of the Interior U.S. Geological Survey Analysis of Resolution and Resampling on GIS Data Values E. Lynn Usery U.S. Geological Survey University.
GIScience 2000 Raster Data Pixels as Modifiable Areal Units E. Lynn Usery U.S. Geological Survey University of Georgia.
Water Quality Data, Maps, and Graphs Over the Web · Chemical concentrations in water, sediment, and aquatic organism tissues.
Watershed Hydrology Modeling: What is Considered Calibrated? Presented by: Jeremy Wyss, HIT Tetra Tech Presented by: Jeremy Wyss, HIT Tetra Tech 27 th.
Modeling experience of non- point pollution: CREAMS (R. Tumas) EPIC (A. Povilaitis and R.Tumas SWRRBWQ (A. Dumbrauskas and R. Tumas) AGNPS (Sileika and.
1 Evaluating and Estimating the Effect of Land use Changed on Water Quality at Selorejo Reservoir, Indonesia Mohammad Sholichin Faridah Othman Shatira.
al-weather-gang/wp/2014/10/28/hawaii- lava-flow-advances-now-less-than-100- yards-from-nearest-home-in- pahoa/?hpid=z3http://
How Breakthroughs in Information Systems Can Impact Local Decisions Bruce Babcock Center for Agricultural and Rural Development Iowa State University.
Timeline Impaired for turbidity on Minnesota’s list of impaired waters (2004) MPCA must complete a study to determine the total maximum daily load (TMDL)
PROJECT TO INTERCOMPARE REGIONAL CLIMATE SIMULATIONS Carbon Dioxide and Climate Change Eugene S. Takle Agronomy Department Geological and Atmospheric Science.
Gulf of Mexico Hypoxia and Nutrient Management in the Mississippi River Basin Herb Buxton, U.S. Geological Survey.
BASINS 2.0 and The Trinity River Basin By Jóna Finndís Jónsdóttir.
Relating Surface Water Nutrients in the Pacific Northwest to Watershed Attributes Using the USGS SPARROW Model Daniel Wise, Hydrologist US Geological Survey.
Building an OpenNSPECT Database for Your Watershed Shan Burkhalter and Dave Eslinger National Oceanic and Atmospheric Administration (NOAA) Office for.
Answering the Question: Why? Factors Affecting Change in Water Quality Exceptional challenge to explain “why” Poor quality of pollution source information.
THE NATIONAL MAP: AN AGENT FOR ENVIRONMENTAL MODELING USING THE WORLD WIDE WEB 8 June 2004 Michael P. Finn Jeffrey D. Spooner David K. Shaver E. Lynn Usery.
Mobile GIS CHAPTER 1: GIS AND THE INFORMATION AGE The Information Age:  The world changing and the methods of meeting the needs of those changes are also.
INTRODUCTION TO THE UK’S NATIONAL RIVER FLOW ARCHIVE Matt Fry Systems Development Manager National River Flow Archive.
SPARROW: A Model Designed for Use With Monitoring Networks Richard A. Smith, Gregory E. Schwarz, and Richard B. Alexander US Geological Survey, Reston,
INTEGRATION OF THE NATIONAL MAP 21 July 2004 Michael P. Finn E. Lynn Usery Michael Starbuck Bryan Weaver Gregory M. Jaromack U.S. Department.
U.S. Department of the Interior U.S. Geological Survey Automatic Generation of Parameter Inputs and Visualization of Model Outputs for AGNPS using GIS.
Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs.
Controls on Catchment-Scale Patterns of Phosphorous in Soil, Streambed Sediment, and Stream Water Marcel van der Perk, et al… Journal of Environmental.
Effect of Potential Future Climate Change on Cost-Effective Nonpoint Source Pollution Reduction Strategies in the UMRB Manoj Jha, Philip Gassman, Gene.
Abstract Man-made dams influence more than just the flow of water in a river. The build up of sediments and organic matter, increased residence times,
National Assessment for Cropland. Analytical Approach Sampling and modeling approach based on a subset of NRI sample points. Farmer survey conducted to.
U.S. Department of the Interior U.S. Geological Survey Data Integration of Layers and Features for The National Map March 31, 2003 E. Lynn Usery Michael.
Corn Yield Comparison Between EPIC-View Simulated Yield And Observed Yield Monitor Data by Chad M. Boshart Oklahoma State University.
HEC-PrePro Workshop GIS Research Group Center for Research in Water Resources University of Texas at Austin Francisco Olivera HEC-PrePro v. 2.0 Workshop.
-gSSURGO- Using the Soil Data Management Toolbox Steve Peaslee USDA-NRCS National Soil Survey Center Lincoln, Nebraska March.
Modeling with WEAP University of Utah Hydroinformatics - Fall 2015.
Geocoding Chapter 16 GISV431 &GEN405 Dr W Britz. Georeferencing, Transformations and Geocoding Georeferencing is the aligning of geographic data to a.
The aim of this project is to estimate changes in runoff, and nonpoint source (NPS) pollution resulting from past land use changes in the Great Lakes Area.
Slide Template for Module 2: Types, Formats, and Stages of Data.
Using RMMS to Track the Implementation of Watershed-based Plans
Dave Clark and Michael Kasch
Brian Haggard Arkansas Water Resources Center University of Arkansas
Estimation of Runoff & nonpoint source pollution using GIS techniques
GIS FOR HYDROLOGIC DATA DEVELOPMENT FOR DESIGN OF HIGHWAY DRAINAGE FACILITIES by Francisco Olivera and David Maidment Center for Research in Water Resources.
Hydrology Modeling in Alaska: Modeling Overview
Presentation transcript:

Problems Associated with Comparing In Situ Water Quality Measurements to Pollution Model Output for Geographic Analyses Presentation to the Annual Meeting of the Association of American Geographers March , Chicago, IL Michael P. Finn

Authors  Michael P. Finn a  David D. Bosch b  E. Lynn Usery a  Austin D. Hartman a  a U. S. Geological Survey, National Geospatial Technical Operation Center  b U. S. D. A., Agricultural Research Service (ARS), Southeast Watershed Research Laboratory

Background  Agricultural Non-Point Source (AGNPS) Pollution Model  Usery et al., (2004) showed utility of using GIS databases to automatically generate input parameters for AGNPS  Automated input and output visualization through the ADGen program (described in Finn et al., 2006)  Current research is focused on quantifying model output as generated by ADGen –Today’s topic is one of three parallel investigations of the current research

Objective  Use output of previous research to quantify significance of various resolutions of spatial parameters on the model output values –More specifically, determine the accuracy of output values relative to in situ measurements over a range of spatial resolutions and identify threshold of diminishing returns (shoulder in the curve)

Shoulder (or Knee) in Curve Example: Synthetic Data

Study Area Little River Watershed, Georgia  ARS benchmark watershed for tillage management, pesticide management, and riparian restoration issues  Agricultural areas with some woodland, wetlands, and small urban areas

AGNPS Output  A non- point source (“.nps”) file –ASCII file (tabular, numeric)

ADGen Output ADGen Image of Phosphorous Output for the Little River, Georgia. Single Band: band 4, Total soluble phosphorous

Shoulder (or Knee) in Curve Example: Synthetic Data X Resolution Y Accuracy

In Situ Water Quality Measurements Hydrologic “field” data  Sources: literature, spreadsheets, websites, archived files, tables in docs, etc. Monthly Water Quality Averages - in spreadsheet

In Situ Water Quality Measurements Hydrologic “field” data  Sources: literature, spreadsheets, websites, archived files, tables in docs, etc. Daily Output - by sub-basins - text file

In Situ Water Quality Measurements Hydrologic “field” data  Sources: literature, spreadsheets, websites, archived files, tables in docs, etc. Streamflow archives - from webpage

In Situ Water Quality Measurements Hydrologic “field” data  Sources: literature, spreadsheets, websites, archived files, tables in docs, etc. Verification runs - random point capture and comparison

Problems Comparing Field Data to Model Output  Convoluted matching of field and model values  Wide, disparate sets of data sources  Only two parameters match directly (with unit conversions) and an additional 6 indirectly Direct match

Measures of Accuracy  51 model output values of sediment, nitrogen, phosphorus, and other nutrients  Curves representing the accuracy via mathematical means –N th order polynomials, cubic splines or logistic regression  Identify shoulder in curve -> quantify spatial resolution threshold

Accuracy Curve 6 th Order Polynomial Example 1st: y' = -1.2E-10x x x x x y' = x = , , , nd: y'' = -6E-10x x x x y'' = x = , , rd: y''' = -2.4E-9x x x y''' = x = ,

Accuracy Curve Cubic Splines Example

Accuracy Curve Logistic Regression Example

Overcoming these problems  Quantification of spatial threshold –Provide insight to the role of spatial resolution on the variance of output values –Ultimately, insight into geographic analyses of water-quality investigations

Conclusions  Resolution affects model results  Some promising techniques to determine the accuracy of output values relative to in situ measurements for the purpose of identifying threshold of diminishing returns  Issues remain in building “Accuracy Graphs” for 51 model output parameters

Problems Associated with Comparing In Situ Water Quality Measurements to Pollution Model Output for Geographic Analyses Presentation to the Annual Meeting of the Association of American Geographers March , Chicago, IL Michael P. Finn