Bringing new hypotheses and models into CBP monitoring how do we better tie monitoring, modeling and management?

Transcription

1 Bringing new hypotheses and models into CBP monitoring how do we better tie monitoring, modeling and management? Monitoring Monitoring Modeling Management Modeling Management Pete Kleinman and Kathy Bailey-Boomer

2 Appearances perspective from the outside Limited collaboration across agencies. Monitoring tied to CBP related models (CBM, SPARROW) Model, disciplinary and scale biases Post-hoc approach to explaining monitoring data. measure first, explain trends later Water quality monitoring requires more than measuring what s in or under water. Links to landscape management, modeling and monitoring? Links to fisheries, wildlife management, monitoring and modeling?

3 Could different monitoring have prevent this? ChesapeakeStat.net, verified April 1, 2014

4 Soil test P summary Restricted to fields where the Maryland P Index was run (FIV > 150 only) Garrett Allegany (no data) Washington Frederick Carroll Baltimore Harford Cecil Mean Maryland P Fertility Index Value Montgomery Howard (no data) Prince George s Charles Anne Arundel St. Mary s Calvert Kent Queen Anne s Talbot Dorchester Caroline Wicomico Worcester Somerset Courtesy J. McGrath, Univ. Maryland Average P FIV = 374

5 P Loss (kg/ha) Precip (mm) Drainage losses from non-point sources Variability in flow determines annual load mg/kg 421 mg/kg 2 1 Kleinman et al., 2007 (J. Soil and Water Conserv.)

6 Mehlich-3 soil P (mg/kg) Kleinman et al., 2010 (Canadian J. Soil Science) Legacy P No change after one decade Lime Dissolved P in ditch flow (mg/l)

7 OLD Dissolved P, kg/ha How did scenario builder lose soil P? What role could monitoring play? What role could other models play? NEW Dissolved P, kg/ha Days Old Routines New Routines

8 Bringing new hypotheses and models into CBP monitoring how do we better tie monitoring to management? Next steps: Identify opportunities for targeted monitoring monitoring design and implementation based on a priori hypotheses and associated models of system responses to management (Nichols & Williams 2006)

9 Model Weight Eastern Population Breeding Pop n Survival Rate Targeted Monitoring Example: Adaptive Management of Mallards I. Define Alternative Hypotheses (models): Reproduction Survival Survival Breeding Pop n Harvest Rate II. Observe Outcomes & Adjust Management SaRw ScRs Cooch et al 2014

10 Targeted Monitoring Example: Estimation of Wetland Water Quality Benefits Sweeney and Newbold STREAMSIDE FOREST BUFFER WIDTH NEEDED TO PROTECT STREAM WATER QUALITY, HABITAT, AND ORGANISMS: A LITERATURE REVIEW. JAWRA 50(3): Model Hypothesis: E N = 100(1 exp(-λ bw idth / wf lux )

11 Targeted Monitoring Example: Estimation of Wetland Water Quality Benefits ESTIMATED TN RETENTION EFFICIENCY Function of Wetland Area? H A1 : 28.7 lbs TN/acre-year (CBT) Function of Incoming Load? H A2 : 25% of local delivered load (MD DNR) H A3 : 25% of load delivered by four acres (CBP4) Function of Watershed Position? H A4 : 1 - exp (-7.9 wetland:area) (STAC) Function of Wetland Width? H A5 : 39.5 * rip.width.m (USEPA)

12 Targeted Monitoring Opportunity: Bay Water Quality Management CBP Goals: DO, Chl, Clarity Standards Management Options: Manage tributary water quality (and quantity) Estuarine restoration Oyster reefs SAV beds Conowingo Discharge Evaluating Alternatives:

13 Targeted Monitoring Opportunity: Bay Water Quality Management Considerations to Advancing Chesapeake Monitoring Needs from a Manager s Perspective: Watershed Inputs Practice outcomes Tributary response Estuarine circulation Mainstem vs Trib influence Estuarine restoration effects (underwater wetland bmp s?) Oyster reefs SAV restoration Bay Monitoring Strategies CBP Model Results: Alternative Models? Wind, Shed Landscape Setting?

14 Preliminary Workshop Proposal: Developing Targeted Monitoring Programs to Support CBP Goals Objectives: Develop recommendations for targeted monitoring needs to evaluate and improve management outcomes toward CBP goals Potential Agenda Topics: Overview of Embracing Uncertainty in Resource Management Revisiting the CBP Management Recommendations: Overview of Goals and Objectives, Potential Management Actions Predicting Outcomes of Potential Management Strategies: Utility of multiple models to evaluate system dynamics Identifying Key Uncertainties Posing Greatest Risk to Management Developing monitoring programs to confront evaluate management efficacy an and model performance.