# Presentation: 351543

## 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 7-11 2006, Chicago, IL

- Michael P. Finn
- mfinn@usgs.gov

- http://carto-research.er.usgs.gov/

## 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**

## Slide 9

## Shoulder (or Knee) in Curve Example:Synthetic Data

| 0.16 | 9.84 |
| --- | --- |
| 0.20 | 9.80 |
| 0.25 | 9.75 |
| 4.00 | 9.00 |
| 5.00 | 5.00 |
| 6.00 | 1.00 |
| 9.75 | 0.25 |
| 9.80 | 0.20 |
| 9.84 | 0.16 |

- 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

- Direct match

## Measures of Accuracy

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

## Accuracy Curve6th Order Polynomial Example

| 1st: |
| --- |
|     y' = -1.2E-10x5 - 0.031x4 + 0.6208x3 - 3.7986x2 + 6.9548x - 2.2722 |
|     y' = 0 @ x = 9.68393, 7.48698, 2.44069, 0.414203 |
| 2nd: |
|    y'' = -6E-10x4 - 0.124x3 + 1.8624x2 - 7.5972x + 6.9548 |
|    y'' = 0 @ x = 8.74971, 4.98332, 1.28632 |
| 3rd: |
|    y''' = -2.4E-9x3 - 0.372x2 + 3.7248x - 7.5972 |
|    y''' = 0 @ x = 7.16098, 2.85193 |

## Accuracy CurveCubic Splines Example

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## Accuracy CurveLogistic Regression Example

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## 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 7-11 2006, Chicago, IL

- Michael P. Finn
- mfinn@usgs.gov

- http://carto-research.er.usgs.gov/