1.      Meta-analysis of past datasets of BMPs

a.       Add data to Art’s existing meta-analysis of bioreactors. - output is removal/time/volume, effect of T°C, design, hardwood/softwood, where they are functioning better/worse,

b.      do we include peer-reviewed journals or other non-public datasets?

Easily attainable

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Desirable

But not easy

2.      Critique of monitoring: are we measuring the right thing? Are we considering the right factors for monitoring?

a.       Edict BMP monitoring guidelines

b.      Case studies are valid way of sharing results in other disciplines - is this an appropriate approach for a monitoring critique?

c.       High intensity sampling vs. low frequency: what are we really gaining by one or the other

d.      What can one do/obtain with EC, Q, pH, etc.?

3.      Editorial article on BMPs in series

a.       When used in series to BMPs function synergistically?

b.      Are there rules for BMP placement in watersheds?

4.      New proposal effort: What is the impact of climate change on BMP design guidelines and predicted success

a.       Governing agencies do not measure each BMP but assign % removal based on design. What are the effects of changing precipitation patterns on this removal efficiency?

b.      How resilient are BMPs? If not resilient, what are the consequences of failure?

c.       Updating return intervals continuously so question related to older BMPs using “old” return intervals

1.      Review paper – What current model does what BMP? Using what concept/simplification? Do current models sufficiently represent BMPs? (pick 10 models?)

a.       How does one modify design criteria in light of climate change? How robust are BMPs? What consequences of climate change on BMPs, and can they be modeled?

Easily attainable

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Desirable

But not easy

2.      Produce new modules within existing models - use benchmark datasets to test these

b.      Most new modules created on modeling plateforms (e.g. SWAT) should be validated on benchmark datasets

c.       Generate a modeling QA/QC and accreditation

d.      Benchmark datasets require creating database for public access with citations

3.      BMPs in series/cumulative impacts– ties back to same issue raised during discussion of Objective 1.

e.       How do you modify design criteria in light of climate change?

f.       How are BMP spatial placement, cumulative vs. counteractive effects taken into account?

4.      Review of realtime datasets with new sensor data (see later discussion of this in Objective 3)

1.      Quality control methodology to reduce uncertainty in sampling collection and holding.

a.       Bruce suggested a standards/protocol: "ASABE engineering practice"

b.      EPA 319 QA/QC document designed for discrete sampling. needs to include sensors as well.

Easily attainable

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Desirable

But not easy

2.      SW BMP sampling - composite vs discretized data

3.      Effect of sampling frequency on monitoring uncertainty and model output

a.       A lot of high temporal frequency sensor data within the group

b.      Run existing engines to quantify uncertainties and extract rules

c.       Outliers - do they cluster by region, seasonality,

d.      Run Global Sensitivity Analysis (GSA) to apportion the source of uncertainty for monitoring

4.      Model calibration - broad opinion paper: uncertainty bands in model input and output to see if they overlap.

a.       paper on calibration ignores uncertainty in input; next paper went a little further (basic guidelines) - issues of temporal/spatial scales, constituent specific.

5.      Land use / climate change projections, can use techniques such as GSA to apportion sources of error to LU and CC.

6.      Paper on protocol (scholarship of sharing data) - thought piece that lays this out. Many papers on this already, our contribution to this literature would be specific to the high resolution sensor data