U.S. Department of the Interior U.S. Geological Survey Linking County Data to Mapped Land Cover to Estimate Agricultural Sources of Chemicals Gail P. Thelin, Naomi Nakagaki, and Kerie J. Hitt U.S. Geological Survey th National Monitoring Conference, San Jose, CA
Overview Background and objective Data sources Method Limitations Applications Summary
Background Estimate agricultural sources of chemicals in study areas to: Interpret water-quality data Predict chemical levels in areas not sampled
Objective How do we estimate agricultural sources of chemicals in study areas using county-based chemical data? County A Study area County B County chemical use Study-area chemical use
Problem How do we estimate agricultural chemicals in counties that partially intersect a study area?
Possible Methods Allocate chemicals to mapped agricultural land in county Allocate chemicals uniformly across the county County ACounty B Study area County ACounty B
Data Sources Agricultural sources of chemicals by county GIS datasets: Land coverCounty boundariesStudy boundaries CountyKg A 1,000 B (watersheds, aquifers, well buffers)
Method Overview Estimate of chemical use on agricultural land in study area Land cover County boundaries Study area boundaries Study-area county weighting factors County agricultural chemical use Spatial overlays Tabular computations x
Step 1 Compute total area of agricultural land by county. Agricultural land, countywide CountySq km A 100 B 2, Land cover (agricultural land) County boundaries
Step 2 Compute area of agricultural land in study area by county. Agricultural land in study area Land cover Study area County boundaries Agricultural land B A CountySq km A 0 B 500 County A County B
Step 3 Compute the study area’s county weighting factors of agricultural land. County weighting factor of agricultural land = Area of agricultural land in study area, by county Area of agricultural land, countywide
Step 3 (continued) CountySq km A 0 B 500 CountySq km A 100 B 2, Agricultural land, countywide Agricultural land in study area County weighting factors of agricultural land CountyWeighting Factor A 0.00 B 0.25 } } B A
Step 4 Compute study area’s chemical estimate. a)Multiply each county chemical value by weighting factor. County weighting factors CountyWeighting Factor A 0.00 B 0.25 County Kg A 1,000 B 200 County chemical use } } County A: 0.00 x 1,000 = 0 County B: 0.25 x 200 = 50
Step 4 (continued) Study area’s estimate of chemical b) Sum for all counties in the study area. Compute study area’s chemical estimate. County A: 0.00 x 1,000 = 0 County B: 0.25 x 200 =
Limitations of Source Data County agricultural chemical inputs developed using many assumptions Land cover data derived from satellite imagery
Applications Characterizing study areas into national context Modeling nitrate and atrazine in unsampled areas Using weighting factors with other county agricultural data Applying method with other geographic units
Summary Addition of mapped land cover refines study-area estimates of agricultural sources of chemicals
Contact Information: Gail P. Thelin, Geographer U.S. Geological Survey, NAWQA Pesticide National Synthesis 6000 J Street, Placer Hall, Sacramento, CA (916) Naomi Nakagaki, U.S. Geological Survey, NAWQA Pesticide National Synthesis 6000 J Street, Placer Hall, Sacramento, CA (916) Kerie J. Hitt, U.S. Geological Survey, NAWQA Nutrients National Synthesis Sunrise Valley Drive, MS 413, Reston, VA (703)
Publications Method Estimating agricultural pesticide use using land cover maps and county pesticide data (Nakagaki and Wolock, 2005) Data sources County nutrient estimates (Ruddy and others, 2006) County pesticide estimates (Thelin and Gianessi, 2000) National Land Cover Dataset (Vogelmann and others, 2001)
Publications (continued) Applications Predicting nitrate contamination in shallow ground water (Nolan and others, 2002) Predicting concentrations of atrazine in streams (Larson and others, 2001) Predicting concentrations of dieldrin in fish (Nowell and others, 2005) Estimating occurrence of atrazine in shallow ground water (Stackelberg and others, 2006)