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Modeling Support for Monitoring Design Using Land Use Data to Evaluate Multiple-Objective Monitoring Designs John W. Hunt University of California, Davis.

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Presentation on theme: "Modeling Support for Monitoring Design Using Land Use Data to Evaluate Multiple-Objective Monitoring Designs John W. Hunt University of California, Davis."— Presentation transcript:

1 Modeling Support for Monitoring Design Using Land Use Data to Evaluate Multiple-Objective Monitoring Designs John W. Hunt University of California, Davis Department of Environmental Toxicology Marine Pollution Studies Laboratory at Granite Canyon

2 California’s Surface Water Ambient Monitoring Program Statewide Assessment Framework (Stressors)

3 SWAMPers: Val Connor, Emilie Reyes, Karen Worcester, Dave Paradies, Karen Taberski, Tom Suk, Rusty Fairey, Max Puckett, Cassandra Lamerdin, Bev van Buuren, Terry Flemming, Rainer Hoenicke UC Davis: Brian Anderson, Bryn Phillips, Ron Tjeerdema UC Santa Cruz: Brent Haddad, Brian Fulfrost, Karen Holl, Carol Shennan, Russ Flegal

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5 Nutrients Pesticides Sediment Pathogens Industrial Metals

6 Urban Grazing Fertilizer and Pesticide Applications Poultry Nutrients Pesticides Sediment Pathogens Hg Mines Industrial Metals

7 Complexity Precipitation Hydrology Terrain Soils Vegetation Land Cover Land Management

8 California NPS Program Plan: 28 State Agencies State Water Resources Control Board 9 Regional Water Quality Control Bds CALFED Bay-Delta Program California Coastal Commission Santa Monica Mountains Conservancy SF Bay Conservation and Development Commission State Coastal Conservancy State Lands Commission California Integrated Waste Management Board US Environmental Protection Agency Region 9 California Departments of Boating and Waterways Conservation Fish and Game Food and Agriculture Forestry and Fire Protection Health Services Parks and Recreation Pesticide Regulation Toxic Substances Control Transportation Water Resources Bond Fund Grantees SWAMP

9 All of these agencies use water quality information to make resource management decisions. Monitoring to meet multiple objectives

10 Water Quality Information  Decision : What? Who? How? When?  Assessment questions  Ecological attributes  Spatial and temporal scales  Indicators and benchmarks  Data quality and level of uncertainty  Monitoring objectives  Monitoring designs  Sampling plans

11 Assessment Questions and Legal (Public) Mandates  Beneficial use benchmarks (CWA § 303[c])  Standards attainment (§ 305[b])  Impaired water body listing (§ 303[d])  Cause & source identification (§ 303[d], 305[b])  Management implementation (§ 303, 314, 319)  Program effectiveness (§ 303, 305, 402, 314, 319)  Basin planning activities (California Water Code)

12 Assessment Questions  Status of waterways (SWRCB)  Trends over time (SWRCB)  Causes of impairment (Reg Bds)  Sources of stressors (Reg Bds)  Program evaluation (All)

13 Assessment Questions  Status of waterways (statewide)  Trends over time (statewide)  Causes of impairment (local)  Sources of stressors (watershed)  Program evaluation (All)

14 Assessment Questions  Status of waterways (probabilistic)  Trends over time (fixed site)  Causes of impairment (gradient)  Sources of stressors (tributary network)  Program evaluation (All, over time)

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16 Control (N+1) Focal (N) Mechanistic (N+1) Hierarchies based on Process Rates Fast Slow SmallLarge Time Space Organism Landscape Ecosystem Community Population Species

17 Fast Slow SmallLarge Time Space Organism Landscape Ecosystem Community Population Species Flow of Inference

18 Hierarchies based on Process Rates Fast Slow SmallLarge Time Space Furrow Hydrologic Region Watershed River Tributary Stormdrain Flow of Inference

19 Integrate regional data into statewide assessments Status and Trends probabilistic sampling: stratification, clustering, proportional, spatially balanced Regional Cause and Source: gradients and networks arrayed around probability sites from statewide design Design criteria for regional assessments?

20 AggregateUp

21 Testing Candidate Designs against Expected Values from Models

22 EPA BASINS software system  SWAT: predicts pollutant yields from land use  WinHSPF: water concentrations from NPS loadings  PLOAD: annual average NPS loads per chemical  QUAL2E: pollutant transport within stream channels.

23 Testing Candidate Designs against Expected Values from Models EPA BASINS software system  SWAT: predicts pollutant yields from land use  WinHSPF: water concentrations from NPS loadings  PLOAD: annual average NPS loads per chemical  QUAL2E: pollutant transport within stream channels. Georeferenced Georeferenced Calibration Calibration Validation Validation

24 Target Stressors  Copper in streambed sediment  Chlordanes in streambed sediment  Nitrate in stream water  Diazinon in water and sediment

25 Target Stressors  Copper in streambed sediment  Chlordanes in streambed sediment  Nitrate in stream water  Diazinon in water and sediment frequently on 303[d] lists throughout the state; frequently on 303[d] lists throughout the state; commonly measured in monitoring programs; commonly measured in monitoring programs; range of physico-chemical properties; range of physico-chemical properties; multiple source activities; multiple source activities; previous water quality modeling studies. previous water quality modeling studies.

26 Fill the Reach File 3 stream segments with expected stressor concentrations.

27 Fill the Reach File 3 stream segments with expected stressor concentrations. Virtual sampling: Apply iterations of monitoring designs m

28 Fill the Reach File 3 stream segments with expected stressor concentrations. Virtual sampling: Apply iterations of monitoring designs m

29 Fill the Reach File 3 stream segments with expected stressor concentrations. Virtual sampling: Apply iterations of monitoring designs m

30 Monitoring Design Evaluation Compare known impairment (model derived) with observed impairment (from virtual sampling):  What proportion of “known” standards exceedences were observed?  What proportion of “known” tributary pathways were discovered?

31 Intended Benefits of this Approach  Process to consolidate disparate types of data: land use layers with water quality measurements;  Maps to target future monitoring;  Evaluation of potential monitoring designs.

32 Pilot Study Land Use: Pesticide Application Water Quality: In-stream pesticides and toxicity Central Coast

33 0.001 0.01 0.1 1 10 100100010000 Log Chlorpyrifos plus Diazinon Applied (kg) Log OPs in Water (ug/L) SA Land Use and Water Chemistry

34 0.00 0.20 0.40 0.60 0.80 1.00 020004000600080001000012000 Diazinon + Chlorpyrifos Applied (lbs) C. Dubia Survival Land Use and Water Toxicity

35 0.00 0.20 0.40 0.60 0.80 1.00 1.20 020004000600080001000012000 Organophosphate Pesticides Applied (lbs) Hyalella Survival Land Use and Sediment Toxicity

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