Imagining how models and modelers advance understanding of
the SFE
Chris EnrightDelta Science Program
April 24, 2013
Main ideas
• Modeling is not about answers—it’s about insight, learning, and better questions
• We don’t have a modeling problem, rather it’s a sociology problem
• Modeling helps‐‐– contend with complexity– cope with non‐stationarity– “glue” between disciplines
THeat Content
Heat LossRate (Btu/min)
sink
Mass of Coffee
Coffee TempAir Temp
TempDifference
Mug insulation
Why model?• The Delta is a complex, connected, changing system
Courtesy RMA
Changes—intentional and unintentional—have Delta‐scaleconsequences for restorations and conveyance
Why model?• The Delta is a complex, connected, changing system• The future is non‐stationary. Nothing will hold still
June06
Breach occursJuly 8, 2006
Blacklock Stage ‐‐ June 18, 2006 to September 1, 2006
July06 Aug06 Sept06
Why model?• The Delta is a complex, connected, changing system• The future is non‐stationary. Nothing will hold still• “Answers” becoming increasingly provisional
June06
Breach occursJuly 8, 2006
Blacklock Stage ‐‐ June 18, 2006 to September 1, 2006
July06 Aug06 Sept06
Why model?• The Delta is a complex, connected, changing system• The future is non‐stationary. Nothing will hold still• “Answers” becoming increasingly provisional• Signal‐to‐noise is THE science issue for the future
Why model?• The Delta is a complex, connected, changing system• The future is non‐stationary. Nothing will hold still• “Answers” becoming increasingly provisional• Signal‐to‐noise is THE science issue for the future• Models are complexity holders, not answer givers
Why model?• The Delta is a complex, connected, changing system• The future is non‐stationary. Nothing will hold still• “Answers” becoming increasingly provisional• Signal‐to‐noise is THE science issue for the future• Models are complexity holders, not answer givers• Modeling is cheap
– We should run novel ideas to ground– “What if’s” should be routine– Probe sensitivity and uncertainty
Courtesy Stuart Siegel
1. Modeling is not about answers—it’s about insight and better questions
A complex system
Insight from a complex system: cooling coffee
T
Heat Content
Heat LossRate (Btu/min)
sink
Cooling Coffee
Insight from a complex system: cooling coffee
Management question:How long until I can drink it?
Cooling Coffee
T
Heat Content
Heat LossRate (Btu/min)
sink
Insight from a complex system: cooling coffee
Cooling CoffeeToo hot
Drinkable
Room Temp
Management question:How long until I can drink it?
T
Heat Content
Heat LossRate (Btu/min)
sink
Time
Insight from a complex system: cooling coffee
Cooling CoffeeToo hot
Drinkable
Room Temp
Management question:How long until I can drink it?
T
Heat Content
Heat LossRate (Btu/min)
sink
Time
Insight from a complex system: cooling coffee
Cooling CoffeeToo hot
Drinkable
Room Temp
Management question:How long until I can drink it?
T
Heat Content
Heat LossRate (Btu/min)
sink
TimeAnswer?
Insight from a complex system: cooling coffee
Cooling CoffeeToo hot
Room Temp
Management question:How long until I can drink it?
T
Heat Content
Heat LossRate (Btu/min)
sink
This model is wrong. Temp can go arbitrarily low.Needs other processes. May be somewhat useful though.
Drinkable
TimeAnswer?
Insight from a complex system: cooling coffee
Cooling Coffee
T
Heat Content
Heat LossRate (Btu/min)
sink
Mass of Coffee
Coffee TempAir Temp
TempDifference
Mug insulation
Mug shape
Too hot
Room Temp
Drinkable
Time
Insight from a complex system: cooling coffee
Cooling Coffee
T
Heat Content
Heat LossRate (Btu/min)
sink
Mass of Coffee
Coffee TempAir Temp
TempDifference
Mug insulation
Mug shape
Too hot
Room Temp
Answer?Time
Drinkable
Insight from a complex system: cooling coffee
Cooling Coffee
T
Heat Content
Heat LossRate (Btu/min)
sink
Mass of Coffee
Coffee TempAir Temp
TempDifference
Mug insulation
Mug shape
Too hot
Room Temp
Time
Drinkable
Answer?
Insight: • The process is non‐linear
Insight from a complex system: cooling coffee
Cooling Coffee
T
Heat Content
Heat LossRate (Btu/min)
sink
Mass of Coffee
Coffee TempAir Temp
TempDifference
Mug insulation
Mug shape
Too hot
Room Temp
Answer?Time
Drinkable
Insight: • The process is non‐linear• I can influence the response
Insight from a complex system: cooling coffee
Cooling Coffee
T
Heat Content
Heat LossRate (Btu/min)
sink
Mass of Coffee
Coffee TempAir Temp
TempDifference
Mug insulation
Mug shape
Too hot
Room Temp
Time
Drinkable
Enjoy it!
Insight: • The process is non‐linear• I can influence the response• “Drinkable” isn’t one moment
Insight from a complex system: cooling coffee
Cooling Coffee
T
Heat Content
Heat LossRate (Btu/min)
sink
Mass of Coffee
Coffee TempAir Temp
TempDifference
Mug insulation
Mug shape
Too hot
Room Temp
Insight: • The process is non‐linear• I can influence the response• “Drinkable” isn’t one moment Better question:
Maximize enjoyment time!Time
Drinkable
Enjoy it!
Insight from a complex system: cooling coffee
Cooling Coffee
T
Heat Content
Heat LossRate (Btu/min)
sink
Mass of Coffee
Coffee TempAir Temp
TempDifference
Mug insulation
Mug shape
Too hot
Room Temp
TimeBetter but…
Drinkable
Insight: • The process is non‐linear• I can influence the response• “Drinkable” isn’t one moment Better question:
Maximize enjoyment time!
Insight from a complex system: cooling coffee
Cooling Coffee
T
Heat Content
Heat LossRate (Btu/min)
sink
Mass of Coffee
Coffee TempAir Temp
TempDifference
Mug insulation
Mug shape
Too hot
Room Temp
TimeWay Better!
Drinkable
Insight: • The process is non‐linear• I can influence the response• “Drinkable” isn’t one moment Better question:
Maximize enjoyment time!
Flow Pattern
Delta currentsSediment supplyor whatever…
Turbidityor whatever…
HabitatQuantity
HabitatQuality
Delta Flows!
T sink
Delta Flow
SpeciesResponse
Insight from a complex system: Delta flowsOcean or Export
You pick!
Management question:“How much flow do fish need?”
Flow Pattern
Delta currentsSediment supplyor whatever…
Turbidityor whatever…
HabitatQuantity
HabitatQuality
Delta Flows!
T sink
Delta Flow
SpeciesResponse
Insight from a complex system: Delta flowsOcean or Export
You pick!
Better question:“How can we influence the system trajectory with flowsand connections to increase native species resilience?”
Delta currentsSediment supplyor whatever…
Turbidityor whatever…Delta Flows!
T sink
Delta Flow
SpeciesResponse
Insight from a complex system: Delta flowsOcean or Export
You pick!
Time
Models hold complexity: • Alternative trajectories• Response sensitivity (insight)• Indicate uncertainty
Flow Pattern
HabitatQuantity
HabitatQuality
Delta currentsSediment supplyor whatever…
Turbidityor whatever…Delta Flows!
T sink
Delta Flow
SpeciesResponse
Insight from a complex system: Delta flowsOcean or Export
You pick!
Time
Models hold complexity: • Alternative trajectories• Response sensitivity (insight)• Indicate uncertainty
Flow Pattern
HabitatQuantity
HabitatQuality
Delta currentsSediment supplyor whatever…
Turbidityor whatever…Delta Flows!
T sink
Delta Flow
SpeciesResponse
Insight from a complex system: Delta flowsOcean or Export
You pick!
Time
Models hold complexity: • Alternative trajectories• Response sensitivity (insight)• Indicate uncertainty
Flow Pattern
HabitatQuantity
HabitatQuality
2. Not a modeling problem, a sociology problem
• Models are mature and improving• Compute power is less limiting• We should improve the modeling model• The issue is how we work together
– We should ask “what if” routinely– Models are insight drivers if we use ‘em that way
Sociology of system knowledge
• Nobody really knows what they want from “the modeling” at first…
• ...so interdisciplinary interaction is key:CASCaDE links models of climate‐>
hydrology‐> hydrodynamics‐> sediment‐> geomorphology‐>
phytoplankton‐> bivalves‐> contaminants‐> marsh accretion‐> fish
Delta Science Plan:“Modeling community”
• How do we organize data, tools, and people to gain modeling insight in support of Bay‐Delta problem solving?
Data Models
People(many disciplines)
Insight
A modeling community links scientists and modelers
• Continuous conceptual model• Formulate hypotheses• Choose parsimonious modeling
tools• Formulate the modeling
approach • Get the thing to work
• Post‐process for question‐relevant high‐level outputs
• ANALYSIS—“so what,” “what did we learn,” ask a better question
multidisciplinary debate multidisciplinary debatetechnical debate
technical multidisciplinary collaborationTechnical warrior work—Takes timetechnical work iteration
multidisciplinary debate
Do it again…
A modeling community links scientists and modelers
• Continuous conceptual model• Formulate hypotheses• Choose parsimonious modeling
tools• Formulate the modeling
approach • Get the thing to work
• Post‐process for question‐relevant high‐level outputs
• ANALYSIS—“so what,” “what did we learn,” ask a better question
multidisciplinary debate multidisciplinary debatetechnical debate
technical multidisciplinarycollaborationTechnical warrior work—Takes timetechnical work iteration
multidisciplinary debate
Do it again…
Modeling recommendations
National Research Council• RPA assessment “…lack of life‐cycle model with outputs that match scale of actions.”
• BDCP Effect Analysis “(Use) integrated models including life‐cycle models.”
Modeling recommendations
IEP Science Advisory Group• “…get interdisciplinary researchers to work together on integrated models.”
Modeling recommendations
Mike Healy: Workshop on linking hydrodynamics and ecosystem models.
• Need decision analysis models (e.g. Delta EFT; Bay‐Delta EDT).
• Establish a Science Center to … coordinate data management, model development, and training in the use of models
Modeling recommendations
Modeling Community Letter to CALFED• Invest is common data, model engines, visualization, interoperability…
• “We recommend …a collaborative, integrated program dedicated to advancing Delta modeling capabilities”
Modeling recommendations
Independent modeling team (SWRCB)• Change will occur—preparation is needed• Integrated understanding requires integrated models
• Invest in long‐term model development• Organize policy‐making to better employ modeling results
Modeling recommendations
CWEMF at Sudwerk last night—• Supporting the Delta Science Plan
Modeling centers are common
• Interagency Modeling Center (Everglades)• Center for Integrated Modeling (Gulf of Mexico)• Ecosystem Modeling Program (NOAA Chesapeake)• National Wetlands Research Center (USGS)• Resource Ecology and Ecosystem Modeling (NOAA Alaska)
• Conservation Biology Institute• Joint Ecosystem Modeling Laboratory (Florida USGS)• NOAA Fisheries Science Centers
What you would know upon visiting the nascent B‐D Modeling Center
• Culture of independence and excellence• Interdisciplinary interaction is valued• Incubating great ideas• Works on policy‐relevant problem solving• Communicates to managers and policy makers• Builds modeling capacity—a real career path
3. The modeling issues for the future‐‐ technical ‐‐
• Sequencing and landscape‐scaling restoration– Shallow, high friction, flow and transport (in vegetation);– Tidal energy dissipation– Sediment transport—channel response– Geomorphic change trajectories
• Residence time (flushing, age, exposure) • Food web• Top down—bottom up• Life‐history and behavior• Model interoperability—process cascade and feedback• Real‐time forecasting and data assimilation• Geochemical cycling
3. The modeling issues for the future‐‐ sociological ‐‐
• Build capacity—should be a career path• Models can facilitate trust building• Remove obstacles to collaboration• Routine “what if” and “gaming”—scientists, stakeholders, and policy makers
• Bring models into the room• Modeling community needs a focal point—a modeling center of excellence
Thank you
CASCaDE teamSWRCB Expert PanelSteve CulbersonJon BurauAnke Mueller‐SolgerWalter BourezJohn DeGeorgeRichard RachieleLenny GrimaldoPeter Goodwin
“Modeling Community”
• Most of what we need to understand is at disciplinary boundaries
• Modeling should be the “glue” between the science disciplines
• What is the “geometry” of interaction?• How do we build capacity?
Van Sickle Island‐‐2000 acres
100’ Breach
Thank you RMA
Van Sickle Island‐‐2000 acres
5000’ Breach
-6.50
-11.32 -0.64
-8.61
-9.48
-10.35 -6.65
-6.74
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Change from Base EC(%)
September 1, 2002 14.05
13.93 1.04
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10.74 7.10
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11.38
7.10
9.90
12.15
2.10
-0.89
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4.30 6.73 3.40
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7.24
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Change from Base EC(%)
September 1, 1992
September 1, 1992
All else equal: 7000 ac
restoration
Northern Suisun Marsh (away from the Bay)
Southern Suisun Marsh (on the Bay)
Salinity decreases5‐15% in Delta
Salinity increases5‐15% in Delta
Courtesy RMA
Future is now
• Exponential growth of compute power.• Massively parallel supercomputing and the next
• Expect “what‐if” results now.generation of atmospheric models
Van Sickle Is Levee BreachEffect on Stage and Salinity
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Van Sickle Is Levee BreachEffect on Stage and Salinity
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Van Sickle Is Levee BreachEffect on Stage and Salinity
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Stage (ft)
July 1992 Simulation Van Sickle Breach
Van Sickle Is Levee BreachEffect on Stage and Salinity
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Van Sickle IslandLevee Breach2000 acres
Stage
July 1992 Simulation Van Sickle Breach
Van Sickle Is Levee BreachEffect on Stage and Salinity
Nurse Slough restoration:Induced regional bed shear stress
Base
NurseDifferenceNurse ‐ Base
05 Jun 0100
05 Jun 0100
W a t e r S u r f a c e E l e v a t i o n a t S - 2 1 S u n r i s e C l u b i n S u i s u n M a r s h N o v e m b e r 1 9 9 8 - N o v e m b e r 2 0 0 0
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1 1 / 1 / 9 8 1 / 3 0 / 9 9 4 / 3 0 / 9 9 7 / 2 9 / 9 9 1 0 / 2 7 / 9 9 1 / 2 5 / 0 0 4 / 2 4 / 0 0 7 / 2 3 / 0 0 1 0 / 2 1 / 0 0
Sta
ge (f
t, N
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D)
S u n r i s e C l u b f l o o d e d
Chadbourne Slough Levee Breach Suisun Marsh
Tidal Range Reduction Due to Levee Breach
9.14.9
3.0 2.0
5.96.5
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1.21.31.3
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2.62.1
1.21.00.5
‐0.5‐0.1
‐0.6
‐0.3
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0.00.0 0.0 0.0
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‐0.2‐0.2 ‐0.4
0.0
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+10%
+5%
0
‐5%
Regional Salinity Impact of Sunrise Club Levee Breach
Average of DSM1 and DSM2 Simulation of July 2000
Percentage ChangeSunrise Club
‐0.1
1. Breaches damp tidal energy
Tidal Trapping may occur wherever standing waves and progressive waves are proximate.
|max'||max'|''huhuSSD
Standing Waves
ProgressiveWaves
Examples of Insight from modeling
• PTM used to estimate steelhead migration rate. Data later showed they go faster—not an answer, insight into behavior.
•
Uniquely available from models tie together what if baseline
• Residence time (exposure, flushing, age)• Particle history (amenable to productivity calculation)• Eulerian and Lagrangian excursion (stationary and dynamic habitat overlap model)
• Tidal range• Salinity gradient• Stage/current phasing (wave progressivity)• ubar/u’ (physical behavior cues, non‐flood sediment cues)
• Most importantly: change in these quantities within the scope of SWRCB power.