Strikes & Gutterballs

Strikes & Gutterballs Modeling, monitoring, and bio-assessment techniques used in 2 flow ecology studies in Virginia.

Topics • Virginia and InstreamFlows • Modeling Approach • Space for Time • Resolution/Pour Point • Analysis Approach • Data Source & Quality • Challenges • Takeaways

Virginia & Instream Flows • Unique Regulatory Structure: • DEQ provides permits with consultation, comment to State Water Control Board • Commenting/Consulting Agencies staffed with instream flow experts • Public comment (NGO’s, citizens) can necessitate Water Control Board hearing • Instream flow recommendations in every single permit • Still a “water rich” state ~10% overall wd/Q, with isolated high allocated streams

Virginia Goals • Expand & Solidify Scientific Basis for Instream Flow Recs • Provide basin specific impact estimation and resource valuation. • A “3-tiered” approach to developing flow ecology relationships • Tier 1 – Continuous curves describing the incremental relationship between biological health and flow alteration • Tier 2 – Binary curves ,dividing the alteration spectrum along the line after which substantial degradation would be expected to occur. • Tier 3 – Best professional judgment, and non-site specific model curves. May be binary or continuous. Tier 1: E = f(h) Tier 2: Binary Tier 3: Best Prof. Judge.

Modeling: Space for Time Challenge: Scant before-after data (long-term) Hypothesis: Areas with low hydro alteration should have less hydro-biological impacts & represent the “pre-condition” of altered areas • Step 1: Create a hydrologic model of existing conditions • Step 2: Revert model to some “pre-development” state • Remove Impoundments • Remove withdrawals • Remove Discharges • Step 3: Calc. % Alteration of Hydro Indices

Hydro Modeling: Under the Hood • Rainfall Run-Off Simulation • HSPF-based • 26 Land-Uses: • But really, only about 5 that are truly hydrologically distinct: • Forest • Impervious • Crop Land • Hay • Pasture Land (similar to Urban Pervious) • Land Use Can Be Time-Varying/Customized

Flow Routing Used a physical “storage routing” model which considers channel slope, cross-sectional geometry and roughness. USGS regression relationships to estimate channel geometry by physiographic province and drainage area. Runs on user-defined time-step, base model has 1 hour time-step*.

ICPRB Modeling Version • Very Small Watersheds • “Sub-Resolution” • Use unit-area runoff from larger scale model • Route through small channel • Performed Well at original resolution • At Lower Resolution: • Low – OKMedian - Good • High - NSG Excerpted From Potomac River Small Watersheds Study, ICPRB 2011 (draft)

HWIModeling Version • 5-200 sqmi watersheds (mean ~70 sqmi) • Model calibrated to USGS gages • Model Performance: • Low – over 85% w/in 15% for low 10%Median – Very Good • High – Very Good Excerpted From Virginia HWI Study, Tetratech 2012 (draft)

Flow Alteration Models Jennings Randolph Flow Augmentation Reservoir(above) Beyond land-use: withdrawals, point source, reservoir operations

Model Assumption & Verification Excerpted From Potomac River Small Watersheds Study, ICPRB 2011 (draft) • The models may not get the flow exactly right, • The models will characterize the nature of the alteration. • Dperviousness • wd/ps

Model Resolution Pour Points – How Close is Close Enough? Thousands and thousands to 137  Excerpted From Virginia HWI Study, Tetratech 2012 (draft)

Flow Metrics: ICPRB (Potomac) Excerpted From Potomac River Small Watersheds Study, ICPRB 2011 (draft)

Flow Metrics: HWI (Virginia-Wide) 1-Day Maximum (M) 1-Day Minimum (M) August Low Flow (M) 7Q10 (M) Number of Reversals (F) High Flow Rise Rate (R) Richard-Bakers Index Flashiness 90 Day Maximum (M) 90 Day Minimum (M) High Flow Timing (T) Date of Minimum (T) Base Flow Index (M) * (M=magnitude, D=duration, F=frequency, T=timing, R=rate of change)

Biometrics • Use what you have, strengths and limitations: • Provided good coverage • “But it’s not made to do that” • Devise new methods to overcome the limitations of old metrics • Flow Preference • Benthics & Fish

Biometrics: ICPRB (Potomac)

Biometrics: HWI (Virginia-Wide) • Fish • Number individuals - total • Number taxa- benthic insectivores, benthic, Centrarchidae, darters, flow preference, fast, flow preference, moderate, flow preference, slow, intolerant suckers, native benthic, native Centrarchidae, native Cyprinidae, native insectivorous Cyprinidae, native, native round-bodied suckers, native sunfish, suckers, sunfish, total • Percent individuals - Cottidae, dace, dominant 01 taxon, flow preference, fast, flow preference, moderate, flow preference, slow, game fish, insectivore, insectivorous Cyprinidae, invertivore and piscivore, lithophils, non native, omnivores, round-bodied suckers, tolerant, top carnivores • Index - evenness, Shannon Wiener (log base e)

Biometrics: HWI (Virginia-Wide) • Benthic • Number individuals - total • Number taxa- Bivalvia, collectors, climbers, clingers, Coleoptera, Diptera, Ephemeroptera, EPT, predators, filterers, Gastropoda, intolerant, Plecoptera, predators, scrapers, shredders, sprawlers, swimmers, tolerant, total, Trichoptera • Percent individuals - Amphipoda,ratioBaetidae to Ephemeroptera, Bivalvia, Chironomidae, collectors, climbers, clingers, Coleoptera, Corbicula, Crustacea, Decapoda, Diptera, dominant 01 taxon, dominant 02 taxa, Ephemeroptera, EPT, Ephemeroptera & Tricoptera (no Hydropsychidae), predators, filterers, Gastropoda, ratio Hydropsychidae to EPT, ratio Hydropsychidae to Trichoptera, intolerant, Mollusca, non Insecta, Odonata, Oligochaeta, Plecoptera, predator, Plecoptera & Trichoptera (no Hydropsychidae), scrapers, shredders, sprawlers, swimmers, tolerant, Trichoptera • Index - Beck's, evenness, Gomphidae, Oligochaeta, Diptera, Hilsenhoff, Shannon Wiener (log base e), Coastal Plain Multimetric Index (genus), Stream Condition Index (family)

Analysis: Methods, Expectations, Statistics & Covariates • Creating a Living System: • “Open-Source” approach to tools, data sets and deliverables • Require contractors to deliver analysis routines, and use Open Source analyssis systems (“R” is your friend) • Understanding the System • Managing the Expectations of contractors, scientists and policy makers • Understanding the use of statistics, and making sure that analysts do as well

Flow Alteration Ecological Health Ecology = f (Flow) Water Quality Stream Class Ecology = f(Quality) Community = f (Class) Ecological Health Modeling System • Main Drivers of Ecological Health: • Native/Naturalized Community (stream class/location dependent) • Extent of detrimental flow alteration • Water Quality • Without knowing all three of the above, we face greater (sometimes unacceptable) uncertainty

Expectations & Covariates • “A” not “The” • The expectation of flow being a sole cause and effect is only valid in streams without any other controlling factors • Land Use/Habitat • Water Quality • Covariate analysis is essential to: • Verify that relationships demonstrate causation, not just correlation • Provide cleaner graphs

Tempering Expectations Sounds like "it’s not that great” But it IS that great, its just not that straight-forward It Might not look like Figure A – but At least Figure B

Establishing Flow-Ecology Hypotheses (FE-Hype) • Seemed to be disagreement in process, or perhaps miscommunications/semantic misunderstandings: • Do we just mine for significant stats? • Do we ONLY check for flow metrics and bio indicators that we think SHOULD have merit? • Is the reality actually somewhere in between?

FE-Hype: Points of View • Points of View: • Our use of IHA metrics is an implicit flow-ecology hypothesis: these are ecologically important flows, so… • But, just because a bio-metric shows some correlation with a ecological-flow metric doesn’t mean there is any causal relationship • Ultimately, both POV are true

FE-Hype: But Wait, There’s More • Sometimes, it is just as important to evaluate situations where you thought there should be a relationship that failed to materialize • Both flow regimes, and ecological indices are models – we might actually have some error here.

Act Now and Get This Bonus In the end, we cannot make a ruling about resource allocation based on a relationship that seems to have statistical significance, but for which we have no flow-ecology hypothesis to explain.

Ways of Looking at Data Linear Regression Quantile Regression Pearson Ranking Probability of “Adverse Impact”

How Significant is the Relationship? • The use and mis-use of R2 • R2 shows us % variation explained by x-y • The p-value • p tells us probability of being illusory • What % of health change do we expect a single flow metric to control? Excerpted From Virginia HWI Study, Tetratech 2012 (draft)

How Much Alteration is Enough? • Too much of the "wrong kind" (urbanization) • Not enough of the "right kind" (things we have regulatory control over) • But this is usable: • Maybe +/-20% is not the kiss of death? • Beyond +/-20% we start to scrutinize heavily. Excerpted From Virginia HWI Study, Tetratech 2012 (draft)

Flow-Preference Metrics Excerpted From Virginia HWI Study, Tetratech 2012 (draft)

The Y-axis: A Glass Half Full Metrics Aren’t Always Numerical ICPRB Team Used “Probability of Fair or Better” Good, but alas, Urban Signature “Risk Management” approach that works for managers Excerpted From Potomac River Small Watersheds Study, ICPRB 2011 (draft)

Our Takeaways • Highly urbanized systems provide a great challenge for developing F-E relationships • Space for Time shows great promise • Choosing hydrologic resolution is very important in maximizing use of data • The corollary: you must have a resolution that provides data coverage that fulfills statistical assumptions • Operational rules, withdrawals and discharges are all very potent sources of alteration • Flow-preference metrics show promise for “y-axis” • Some traditional metrics are not a “no-go”

Strikes & Gutterballs