Science to Support Nutrient Management in San Francisco Bay. David Senn San Francisco Estuary Institute
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1 Science to Support Nutrient Management in San Francisco Bay David Senn San Francisco Estuary Institute
2 Outline - Background Activities Develop Nutrient Science Strategy High priority (funded) projects Source: C. Benton
3 San Francisco Bay - Large nutrient loads ~40 POTW AG urban runoff
4 San Francisco Bay Paradox? National Estuarine Experts Workgroup (2010) Resilience of San Francisco Bay 1) High turbidity 2) Strong tidal mixing Subject to change? 3) Filter-feeding clams
5 South Bay Past 20 years +105% - decreased clam abundance, lower grazing rates Cloern et al (2007)
6 - low Chl-a food limitation - Past 20 yrs +32% Source: J. Cloern, USGS Suisun Bay Corbula clam invasion - NH + 4 impacts - impairing primary production? - toxicity to copepods? South Bay Past 20 years +105% - decreased clam abundance, lower grazing rates Cloern et al (2007)
7 Need for a Bay-Wide Nutrient Strategy Nutrient objectives on the horizon: Nutrient Numeric Endpoint (NNE) Consensus among scientific community: Bay conditions are changing - increasing chl-a, harmful algal blooms, other roles of NH 4 + (?) No regionally-administered water quality monitoring program - uncertain future for USGS research program (40 yr record) Early-draft Nutrient Strategy Sep. 2011, RMP Nutrient Workgroup
8 Key Management Questions Is there a nutrient problem, and how is it defined? - Now? Future? Under what scenarios? Most important sources, pathways, and processes? What loads can be assimilated without impairing beneficial uses? What are appropriate guidelines for identifying a problem? - assessment framework and monitoring
9 Nutrient Work in 2012 Develop Nutrient Science Strategy Final draft November 2012 Communication: stakeholder meetings, website, list-serve
10 Nutrient Work in 2012 Develop Nutrient Science Strategy Final draft November 2012 Communication: stakeholder meetings, website, list-serve Begin work on high priority (funded) projects 1. Define the problem: Conceptual models and scenarios 2. Assess nutrient loads and sources 3. Numeric modeling: Suisun Bay, South Bay 4. Suisun Bay: potential impacts of nutrients and NH Phytoplankton assessment framework
11 Draft Nutrient Strategy (Sep 2011) Assessment Framework, WQ objectives Define the Problem Modeling Monitoring Program Control Strategies GOAL: Beneficial Use Protection Regulatory Approaches
12 Draft Nutrient Strategy (Sep 2011) Assessment Framework, WQ objectives Define the Problem Modeling Monitoring Program Control Strategies GOAL: Beneficial Use Protection Regulatory Approaches Bay Entities with nutrient interests RB2, USEPA, BACWA, Regional Monitoring Program (RMP), USACE, BASMAA, Interagency Ecological Program (IEP), Delta Stewardship Council, Water Contractors, NGOs
13 Joint Fact-Finding Science for Nutrient Management Key Parties BACWA RB2 Technical Team SFEI Technical Lead SCCWRP Regional/National Experts RMP USEPA Other Parties IEP/DSC, BASMAA, RB5, Water Contractors, NGOs SFEI Science coordination: - relevant: science policy/regulation - high caliber Communication: - bi-directional information exchange
14 Joint Fact-Finding Science for Nutrient Management Key Parties BACWA RB2 Technical Team SFEI Technical Lead SCCWRP Regional/National Experts External Peer Review RMP USEPA Other Parties IEP/DSC, BASMAA, RB5, Water Contractors, NGOs Problem Define, Conc. Model Monitoring Program Work Elements Assessment framework Modeling Control Strategies Regulatory Framework Develop Work Elements, scopes, timelines - Funding plan - Communication: website, list-serve
15 Define the problem Monitor Conceptual models Quantify Loads Suisun Synthesis conceptual models and data synthesis Develop monitoring program RMP BACWA SWQCB Implement program Nutrient water quality objectives Phytoplank. Assessment Framework D.O. and NH 4 + objectives Seagrass and intertidal frameworks Modeling Modeling Strategy Numeric box modeling Load Models Suisun Bay modeling Bay-wide, 3D hydrodynamic and WQ Models
16 1. Problem Definition: Conceptual Models, Scenarios (2012) RMP What problems, or future scenarios are most concerning? What information do we need to evaluate these scenarios? How do we detect current problems or the onset of future problems?
17 1. Problem Definition: Conceptual Models, Scenarios DIN DIP Phytoplank. DO Diss. Silica Filter BACWA (2011)
18 1. Problem Definition: Conceptual Models, Scenarios Example Scenarios - 1% per year decrease in sediment load - decreased clam abundance - changing nutrient loads, NH4:NO3, N:P:Si - drought conditions - climate change effects Outcomes - Consensus statement on nutrient outlook for the Bay - Critical knowledge gaps - Science plan to address gaps - Monitoring prog. recommendations
19 2. Assess Nutrient Loads to the Bay ( ) RMP ~40 POTW urban runoff AG Very Rough Numbers Tons DIN/yr Bay POTWs 18,000 SacRegional 5,000 Sac+SJ Rivers (Agriculture *) 5,000 Urban runoff ** 1,000 Space/time will be important - Assess major nutrient loads (and composition) - Characterize variations in space and time * Kratzer et al. (2011) ** Gluchowski and McKee (2011) - Identify major uncertainties and data gaps, future work
20 3. Numeric Models: Suisun Bay, South Bay BACWA ( ) Staged approach to modeling Build upon conceptual models Model Technical Advisory Group - scope of work - processes to model (and how) - platform 2-year study - Assess relative importance of key processes/drivers - Sensitivity analysis, identify critical uncertainties and data gaps - Characterize system response (e.g., chl, O 2 ) under future scenarios
21 3. Numeric Models: Suisun Bay, South Bay BACWA - flow, tidal exchange (t res ) - light limitation - benthic grazing - potential inhibition of PP by NH budgets: transformations, sources, and sinks
22 3. Numeric Models: Suisun Bay, South Bay BACWA - flow, tidal exchange (t res ) - light limitation - benthic grazing - potential inhibition of PP by NH budgets: transformations, sources, and sinks
23 3. Numeric Models: Suisun Bay, South Bay BACWA Outcomes - Key inputs to advanced modeling - Relative importance of processes - Uncertainty/sensitivity analysis - Knowledge/data gaps field studies, monitoring - Narrowing scenarios of concern
24 4. Suisun Bay: evaluating potential impacts of nutrients and NH 4 + Complex management questions BACWA ( ) - Pelagic Organism Decline (POD) - Phytoplankton and zooplankton - Decreased abundance - Different community composition - Potential links to nutrients, with specific focus on NH + 4
25 4. Suisun Bay: evaluating potential impacts of nutrients and NH 4 + Multiple Drivers BACWA ( )? Outcomes - Synthesis Nutrient/NH 4 + role in - altered phytoplankton community composition? - low primary production rates? - copepod toxicity - Data gaps and future studies IEP (2010) - Modeling contribution of Suisun dischargers
26 5. Phytoplankton Assessment Framework SWRCB-FUNDED ( ) Phytoplankton: leading candidate indicator for assessment of Bay eutrophication What are the precise measures of phytoplankton that we need to assess? Biomass? Assemblage? Harmful algal species? What are the appropriate thresholds for regulatory action? What kind of monitoring data are needed to make an assessment?
27 5. Phytoplankton Assessment Framework SWRCB-FUNDED ( ) Phytoplankton: leading candidate indicator for assessment of Bay eutrophication Outcomes - Transparent decision framework to determine whether regulatory action is required - Numeric targets that can be used to inform decisions on load and waste load allocations
28 Define the problem Conceptual models Quantify Loads Suisun Synthesis Monitor conceptual models and data synthesis Develop monitoring program Implement program Nutrient water quality objectives Phytoplank. Assessment Framework D.O. and NH 4 + objectives Seagrass and intertidal frameworks Modeling Numeric box modeling Modeling Strategy Load Models Bay-wide, 3D hydrodynamic and WQ Models Suisun Bay modeling
29 Lots of work in 2012 and beyond Science Management/Regulatory Decisions Work Plan details being developed and refined - Regional Board - BACWA and its member agencies - other stakeholders
30 Acknowledgements: Funding: BACWA, SWQCB, RMP BACWA A Chastain, M Connor, J Kelly, B Horenstein, BACWA Board SFRWQCB N Feger, T Mumley RMP Nutrient Workgroup SFEI M Sedlak, J Davis, R Allen, L McKee, R Hoenicke, J Cloern (USGS), R Dugdale (SFSU), A Mueller-Solger (IEP) Source: C. Benton
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