Design and Testing of a Proces

Design and Testing of a Proces

Groundwater Vulnerability Ass

Groundwater Vulnerability Ass

(P

-GWAVA) S GWAVA) System for Predic

ystem for Predic

Concentrations of Agrichemi

Concentrations of Agrichem

Groundwater Across the Contermi

Groundwater Across the Contermi

States Using Data from the NAWQ

States Using Data from the NAWQ

Exposure Modeling Public Meetin

U.S. Environmental Protection Agen

Office of Pesticide Programs

Arlington, VA - July 27, 2010

Jack Barbash and Frank Voss

National Water-Quality Assessment (NAWQA) P

U.S.Geological Survey

Tacoma, WA

(jbarbash@usgs.gov; fdvoss@usgs.gov)
Principal objective

To make the most informed prediction

agrichemical concentrations in gr

anywhere in the contermin

United States

• Incorporate as much process unde

possible

• Focus only on data sources coverin

the lower 48 states

• Use only public-domain models tha

extensively used, tested and docum
Approach

• Use simulated concentrations of agriche

in the vadose zone as an indicator ofconcentrations in shallow groundwater

- Phase 1 – atrazine

- Phase 2 – atrazine, DEA (atrazine degradate), 

• Two simulation models examined:

- Phase 1 - Pesticide Root-Zone Model (PRZM) 

- Phase 2 - Root-Zone Water-Quality Model (RZ

• Compare concentrations predicted in vad

those measured in shallow groundwate

• One single crop simulated at all sites - C
Some of the Major Differences Be

PRZM and RZWQM

Model

Processes Simulated

Time scale for 

precipitation

Simulation of 

water flow in 

vadose zone

Preferential 

transport?

Depende

transforma

on de

PRZM Daily Simple No No 

RZWQM Hourly Complex Yes Ye

System Overview System Overview

Arc Tools
Uses of CLIGEN and SSURGO Data for R

Uses of CLIGEN and SSURGO Data for R
Study Sites for Phase 1 (PRZM) Relative to 

(Agricultural settings only - 42 study areas, 1254 
Study Sites for Phase 2 (RZWQM) Relative 

(435 well sites in agricultural settings within the C
Soil column representati

Soil column representati

PRZM – Uniform composition

(Soils data taken from STATSGO, 

weighted by depth and area)

RZWQM – All layers retained

(Soils data taken from SSURGO, 

no aggregation)

1 m

3 
Model Residuals for Atrazine Conce

(Results for PRZM and RZWQM at 266 sites across the Corn Belt whe
Model Residuals for DEA Concen

(RZWQM simulated value minus NAWQA measure

at 379 agricultural sites across the Corn Belt
Model Residuals for Nitrate Conce

(RZWQM simulated value minus NAWQA measure

at 379 agricultural sites across the Corn Belt



Statistical Correlations Between P

and Observed Solute Concentr

Solute Model Spatial 

Domain

Number of 

Sites

R

Atrazine PRZM Nationwide 1220 0.0

RZWQM Corn Belt 378 0.0

DEA RZWQM Corn Belt 377 0.0

Nitrate RZWQM Corn Belt 378 0.0
Model Residuals for Atrazine Detection 

(Value predicted by PRZM minus value measured by 

at the 47 sampling networks with 10 or more wells sampled in 

PRZM over-predicts detection frequencyPRZM under-predicts detection frequency
Model Residuals for Atrazine Detection F

(Value predicted by RZWQM minus value measured by NAWQA at the 

with 10 or more wells sampled in agricultural areas; temporally varia

RZWQM under-predicts

detection frequency

RZWQM over-predicts

detection frequency
Model Residuals for DEA Detection Fre

(Value predicted by RZWQM minus value measured by NAWQA at the 

with 10 or more wells sampled in agricultural areas; temporally varia

RZWQM over-predicts

detection frequency

RZWQM under-predicts

detection frequency
Model Residuals for Nitrate Detection F

(Value predicted by RZWQM minus value measured by NAWQA at the 

with 10 or more wells sampled in agricultural areas; temporally vari

RZWQM over-predicts

detection frequency

RZWQM under-predicts

detection frequency
Summary Summary

• Feasibility demonstrated for constructing a process-bas

vulnerability assessment (P-GWAVA) system that 

– Makes maximal use of current understanding (espe– Uses exclusively nationwide sources of data, includ

detailed data available for soil properties (SSURGO

• Results 

– Concentrations

 Closer agreement with NAWQA measurements in gr

RZWQM than for PRZM (atrazine only)

 PRZM tended to over-estimate concentrations (atraz RZWQM tended to under-estimate concentrations (a

nitrate)

 Model predictions explained less than 5% of overall v

solute 

– Detection frequencies

 Under- and over-predictions more evenly distributed 

RZWQM (atrazine only)

 RZWQM tended to under-estimate detection frequen

solutes (atrazine, DEA and nitrate)
Implications Implications

• Process-based modeling systems 

constructed and used to examine gvulnerability to surface-derived conover regional to national scales

• NAWQA data provide a nationally c

set of data for testing the predictionmodels on a national basis

• Relatively poor correlation between 

and observations (less than 5% of tion variability explained) implies thmodels will continue to provide mopredictions of groundwater vulneraforeseeable future
Additional slides Additional slides

(if needed for explanatory pu

(if needed for explanatory p
Groundwater Vulnerability as water Vulnerability as 

(time, sp

(time, sp
Nitrogen Cycling in RZWQM Nitrogen Cycling in RZWQ






Phase 2 (RZWQM) Site Locations Relati Phase 2 (RZWQM) Site Locations Relative to 

ve to 

CLIGEN weather stationPhase 2 site where weather station used was the neaPhase 2 site where weather station used was not the