Gutterballs Modeling monitoring and bioassessment techniques used in 2 flow ecology studies in Virginia Topics Virginia and Instream Flows Modeling Approach Space for Time ResolutionPour Point ID: 531612
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Slide1
Strikes & Gutterballs
Modeling, monitoring, and bio-assessment techniques used in 2 flow ecology studies in Virginia.Slide2
Topics
Virginia and
Instream
Flows Modeling ApproachSpace for TimeResolution/Pour PointAnalysis ApproachData Source & QualityChallengesTakeawaysSlide3
Virginia & Instream Flows
Unique Regulatory Structure:
DEQ provides permits with consultation, comment to State Water Control Board
Commenting/Consulting Agencies staffed with instream flow expertsPublic comment (NGO’s, citizens) can necessitate Water Control Board hearingInstream flow recommendations in every single permitStill a “water rich” state ~10% overall wd/Q, with isolated high allocated streamsSlide4
Virginia Goals
Expand & Solidify Scientific Basis for
Instream
Flow RecsProvide basin specific impact estimation and resource valuation.A “3-tiered” approach to developing flow ecology relationshipsTier 1 – Continuous curves describing the incremental relationship between biological health and flow alterationTier 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.Slide5
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 areasStep 1: Create a hydrologic model of existing conditionsStep 2: Revert model to some “pre-development” stateRemove ImpoundmentsRemove withdrawals
Remove DischargesStep 3: Calc. % Alteration of Hydro IndicesSlide6
Hydro Modeling: Under the Hood
Rainfall Run-Off Simulation
HSPF-based
26 Land-Uses:But really, only about 5 that are truly hydrologically distinct:ForestImperviousCrop LandHayPasture Land (similar to Urban Pervious)Land Use Can Be Time-Varying/CustomizedSlide7
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*.Slide8
ICPRB Modeling Version
Very Small Watersheds
“Sub-Resolution”
Use unit-area runoff from larger
scale modelRoute through
small channel
Performed Well at
original resolution
At Lower Resolution:
Low – OK
Median - Good
High - NSG
Excerpted From Potomac River Small Watersheds Study, ICPRB 2011 (draft)Slide9
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)Slide10
Flow Alteration Models
Beyond land-use: withdrawals, point source, reservoir operations
Jennings Randolph Flow
Augmentation Reservoir
(above)Slide11
Model Assumption & Verification
The models
may not
get the flow exactly right, The models will characterize the nature of the alteration. D perviousnesswd/ps
Excerpted From Potomac River Small Watersheds Study, ICPRB 2011 (draft)Slide12
Model Resolution
Pour Points – How Close is Close Enough?
Thousands and thousands to 137
Excerpted From Virginia HWI Study,
Tetratech
2012 (draft)Slide13
Flow Metrics: ICPRB (Potomac)
Flow Range
Magnitude
Duration
Frequency
Rate of Change
High
Annual 3-day Maximum
High flow duration DH17
High flow volume index MH21
High Pulse
Count
Mid
Median
Flashiness
Low
Annual 3-day Minimum
Low Pulse
Duration
Low Pulse Count
Extreme Low Flow Frequency
Excerpted From Potomac River Small Watersheds Study, ICPRB 2011 (draft)Slide14
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)Slide15
BiometricsUse 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 metricsFlow PreferenceBenthics & FishSlide16
Biometrics: ICPRB (Potomac)
Biometrics
Ecological characteristic
Chesapeake Benthic Index of Biotic Integrity (BIBI)
Composite Index
Shannon-Weiner Index (SW)
Taxonomic diversity
% EPT
Composition; generally pollution sensitive
% Scraper
Feeding group
Hilsenhoff
Family Level Biotic Index (FBI)
Pollution Tolerance
%
Chironomidae
Composition; generally pollution tolerant
% Clingers
Habit groupSlide17
Biometrics: HWI (Virginia-Wide)
Fish
Number individuals
- totalNumber 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, totalPercent 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 carnivoresIndex - evenness, Shannon Wiener (log base e)Slide18
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,ratio Baetidae
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, TrichopteraIndex - Beck's, evenness, Gomphidae, Oligochaeta, Diptera, Hilsenhoff, Shannon Wiener (log base e), Coastal Plain Multimetric Index (genus), Stream Condition Index (family)Slide19
Analysis: Methods, Expectations, Statistics & Covariates
Creating a Living System:
“Open-Source” approach to tools, data sets and
deliverablesRequire contractors to deliver analysis routines, and use Open Source analyssis systems (“R” is your friend)Understanding the SystemManaging the Expectations of contractors, scientists and policy makersUnderstanding the use of statistics, and making sure that analysts do as wellSlide20
Ecological Health Modeling System
Main Drivers of Ecological Health:
Native/Naturalized Community (stream class/location dependent)
Extent of detrimental flow alterationWater QualityWithout knowing all three of the above, we face greater (sometimes
unacceptable) uncertainty
Ecological
Health
Water
Quality
Stream
Class
Flow
Alteration
Ecology
= f(Quality
)
Ecology
=
f (Flow)
Community
= f (Class)Slide21
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/HabitatWater QualityCovariate analysis is essential to:Verify that relationships demonstrate causation, not just correlationProvide cleaner graphsSlide22
Tempering Expectations
Sounds like "it’s not that great”
But it
IS that great, its just not that straight-forwardIt Might not look like Figure A – but At least Figure BSlide23
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?Slide24
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 relationshipUltimately, both POV are trueSlide25
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 materializeBoth flow regimes, and ecological indices are models – we might actually have some error here.Slide26
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.Slide27
Ways of Looking at Data
Linear Regression
Quantile
RegressionPearson RankingProbability of “Adverse Impact”Slide28
How Significant is the Relationship?
The use and
mis
-use of R2R2 shows us % variation explained by x-yThe p-valuep tells us probability of being illusoryWhat % of health change do we expect a single flow metric to control?
Excerpted From Virginia HWI Study,
Tetratech
2012 (draft)Slide29
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)Slide30
Flow-Preference Metrics
Excerpted From Virginia HWI Study,
Tetratech
2012 (draft)Slide31
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)Slide32
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 dataThe corollary: you must have a resolution that provides data coverage that fulfills statistical assumptionsOperational rules, withdrawals and discharges are all very potent sources of alterationFlow-preference metrics show promise for “y-axis”Some traditional metrics are not a “no-go”