advancing hydrologic ensemble forecasting using distributed watershed models
DESCRIPTION
Advancing Hydrologic Ensemble Forecasting using Distributed Watershed Models. NWS Talk May 2006. Thorsten Wagener, Chris Duffy, Patrick Reed, Yong Tang, Katie Goodwin and Maitreya Yadav. http://www.engr.psu.edu/ce/Divisions/Hydro/hydro.html. Overall Objective. - PowerPoint PPT PresentationTRANSCRIPT
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Advancing Hydrologic Ensemble Forecasting using Distributed
Watershed Models
Thorsten Wagener, Chris Duffy, Patrick Reed, Yong Tang, Katie Goodwin and Maitreya Yadav
NWS Talk May 2006
http://www.engr.psu.edu/ce/Divisions/Hydro/hydro.html
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Overall Objective
To provide reliable forecasts of hydrologic variables for different water resources tasks, at gauged and ungauged locations, including estimates of uncertainty.
Understanding how to build and work with a new generation of more complex distributed hydrologic models.
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Outline
1. Background
2. Model Building
3. Calibration
4. Observations/Hydrologic Theory
5. Simulation
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DYNAMICRESPONSEBEHAVIOR
INPUTSTATE
OUTPUT
MODEL
CONCEPTUAL STRUCTUREFUNCTIONAL FORM
PARAMETER VALUES
DATA ASSIMILATION & MODEL CALIBRATION
WATERSHEDWATERSHED CHARACT.SYSTEM INVARIANTS
A PRIORI KNOWLEDGE
MODEL BUILDINGOBSERVATIONS & HYDROLOGIC THEORY
FORECASTING
STREAMFLOWINUNDATED AREAS
WATER QUALITY
SIMULATION
UNCERTAINTY
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WATERSHED
MODELDYNAMIC
RESPONSEBEHAVIOR
CONCEPTUAL STRUCTUREFUNCTIONAL FORM
PARAMETER VALUES
WATERSHED CHARACT.SYSTEM INVARIANTS
A PRIORI KNOWLEDGE
MODEL BUILDING
FORECASTING
UNCERTAINTY
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Model Building Questions
• How to build distributed watershed models?• What is the necessary degree of coupling of
processes?• What are appropriate levels of complexity for
different water resources tasks?• What are efficient ways of domain
decomposition?
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Duffy et al. Approach
To develop physically-based, multi-scale model for water, solute, sediment, and energy budgets in complex large-scale hydrologic systems
MOTIVATION:• to simplify complex, large-scale spatio-temporal models • to study or uncover new and emergent physical
phenomena in coupled hydrologic systems• to provide reliable water, solute and energy budgets• to estimate recharge, bank storage, ephemeral stream
losses, climate and landuse effects across river basins• to provide predictive tools for water resource forecasting
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Integrated Hydrologic Model (PIHM)
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Unstructured Grid - TINs
Flexibility in fitting a complex-shaped domain
– Ability to grade from small to large elements over a relatively short distance
– Decrease in number of nodes
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Nested Triangulation
Seamless assimilation of forcings and parameters at different resolutions
Combine large-scale simulations with nested mesoscale forecasts
Weber River Watershed
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General System
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Modular Modeling System (MMS)
• Background: In 1992, The USGS (George Leavesley) released a Unix-based Modular Modeling System (MMS) that incorporated their Performance and Results Measurement System (PRMS) surface runoff model.
• A more generic framework, (Leavesley et al., 1996) where different modules and model structures can be selectively combined to form an ‘optimal’ integrated model for environmental and water resources analysis.
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Major Components of MMS
• pre-process component– tools to build and analyzes the input data.
• model component– tools to apply the different models.
• post-process component– tools to analyze the output statistically and
graphically and pass the output to the decision support system or other software.
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Set-up
A schematic showing the different conceptual components of the Modular Modeling System (MMS) (Adapted from Leavesley et al. 1983)
Data Storage
GISWeasel
DataCollection
GUI
DMI DMI
Pre-Process
ModuleLibrary
Modular Model
GUI
DMI
Model
XmBuild
GUI
Visualization
Statistics
DSS
GIS Weasel
DMI
Post-Process
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Susquehanna River Basin GeoDataBase• Climate
– Temperate (controlled by polar front, prevailing westerlies & Atlantic)
– Orographic effects (P:35-45”, ET:15-50”)• Drainage
– 71,410 km2
– Main channel: 714 km– Headwaters: Finger lake uplift and
Appalachian mountain and plateau– Mouth: Chesapeake Bay, MD
• Physiography– Appalachian plateau– Ridge & Valley– Piedmont
• Hydrogeology– Flat/folded sandstone and shale– Some carbonate valleys– Some igneous dikes, sills, and fractures
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Conceptual Hydrologic Model: Susquehanna River
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Initial Results
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Weak or Strong Coupling?• Interception• Snowmelt• Evapotranspiration• Overland flow• Subsurface• Channel routing
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WATERSHED
MODELDYNAMIC
RESPONSEBEHAVIOR
INPUTSTATE
OUTPUT
CONCEPTUAL STRUCTUREFUNCTIONAL FORM
PARAMETER VALUES
DATA ASSIMILATION
& MODEL CALIBRATION
FORECASTING
UNCERTAINTY
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Background
timetime
: parameters: parameters
x : statex : state variablesvariables
z : output z : output u : input u : input
: uncertainty: uncertainty
zzttcompcomp
Model Model f f ( )( )
xxoo
uuttobsobs zztt
obsobs
Real WorldReal World
Court
esy
of
S. P
inker
uutttruetrue
zztttruetrue
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Model Calibration/D.A. Questions
• What are efficient optimization algorithms for highly complex models?
• How can parallel computing frameworks be used for efficient model calibration?
• What are appropriate calibration strategies for distributed watershed models?
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Comparison of 3 Multi-objective Population-based Search Algorithms
• Epsilon Nondominated Sorted Genetic Algorithm-II– Developed by Kollat and Reed (2005)– Extension of Deb et al. (2002)
• Multiobjective Shuffled Complex Evolution Metropolis – Developed by Vrugt et al. (2004)– Extension of Yapo et al. (1998)
• Strength Pareto Evolutionary Algorithm-II– Developed by Zitzler et al. (2001)– Extension of Zitzler & Thiele (1999)
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What are efficient optimization algorithms for highly complex models?
Testing of the efficiency and effectiveness of different multi-objective optimization algorithms resulted in improved understanding of how complex a problem can be solved in what time and with what reliability.
Test case: Sacramento model with Leaf River Data
RMSE(T): Box-Cox Transformed RMSERMSE(R): RMSE of raw data
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Reliability?
• Binary metric top ranking ratios– SPEA2 has the highest binary metric top ranking
ratio (i.e., it is the most reliable algorithm)
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Convergence?
• Dynamic unary metrics (best runs)– ε-NSGAII’s best trial run is superior to those of
SPEA2 and MOSCEM-UA
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How can parallel computing frameworks be used for efficient model
calibration?
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What are appropriate calibration strategies for distributed watershed models?
Main problem: The ‘open’ calibration of complex distributed hydrologic models is too complex. How can the calibration problem be simplified?
• For the Sacramento/HLRMS framework.
• For PIHM.
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Juniata River Basin
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Sacramento/HLRMS Model Scenarios
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Sensitivity Analysis
Which parameters dominate the model response?
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SensitivityAnalysis
Sacramento Model at Saxton
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Hierarchical Calibration
ANNUAL
HOURLY
COARSE
DETAILED
DATA MODEL
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WATERSHED
MODELDYNAMIC
RESPONSEBEHAVIOR
INPUTSTATE
OUTPUT
WATERSHED CHARACT.SYSTEM INVARIANTS
A PRIORI KNOWLEDGE
OBSERVATIONS & HYDROLOGIC
THEORY
FORECASTING
UNCERTAINTY
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Observations/Hydrol. Theory Questions
• How can our understanding about the link between watershed structure and watershed behavior be used to constrain hydrologic predictions?
• A new approach to the ungauged basins problem?
• How can we use our understanding about watershed function to decompose the model domain?
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Predictions in Ungauged Basins – October 1970
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0 100 200 300 40050Kilometers
0 100 200 300 40050Kilometers
0 100 200 300 40050Kilometers
0 100 200 300 40050Kilometers
J F M A M J J A S O N D0
0.5
1
1.5
2
Rai
n (m
m/d
)
Monthly Average Values (1980-1990)
J F M A M J J A S O N D0
0.5
1
1.5
Flo
w (
mm
/d)
J F M A M J J A S O N D0
0.2
0.4
0.6
Month
PE
(m
m/d
)
Flo
w (
mm
/d)
Percentage time flow is exceeded
Flow Duration Curve
0 20 40 60 80 10010
-2
10-1
100
101
102
0 2 40
0.5
1
P/PE
R/P
(a)
(b)
(c)
(d)(e)
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Regionalizing Watershed Behavior
BFIHOST
DLD
1 1.5 2 2.5 30
2
4
6
8
10
12
Prediction Limits
Confidance Interval
Line of Regression
R2 = 0.88195
y = -2.743.643 * x
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Ensemble Evaluation?
RELIABILITY: How much of the observations are contained by the ensemble?
SHARPNESS: How wide are the ensemble prediction ranges?
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Flo
w (
mm
/day
)
0 50 100 150 200 250 300 3500
10
20
30
Total Range Flow
Confidence Interval
Observed
Nor
mal
ized
Ran
ge
Time (days)0 50 100 150 200 250 300 350
0
0.2
0.4
0.6
0.8
1
(a)
(b)
Reliability and SharpnessF
low
(m
m/d
ay)
80 90 100 110 120 130 140 150
0
2
4
6
8
Nor
mal
ized
Ran
ge
Time (days)0 50 100 150 200 250 300 350
0
0.2
0.4
0.6
0.8
1
(a)
(b)
Coquet@Morwick
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10 20 30 40 50 60 70 80 90 100
F3 F2 R2
MA9MA8MA7MA6MA4MA3MA2
MA1ML1
MH8MH7MH6MH5MH4MH3MH2
Reliability for Confience intervals
Flow Percentiles
Res
pons
e C
hara
cter
istic
s
85 – 90%,80 – 85% 90 – 95% 95 – 100%
10 20 30 40 50 60 70 80 90 100
F3 F2 R2
MA9MA8MA7MA6MA4MA3MA2
MA1ML1
MH8MH7MH6MH5MH4MH3MH2
Sharpness for Confidence intervals
Flow Percentiles
Res
pons
e C
hara
cter
istic
s
18.5 – 26%11 – 18.5% 26 – 33.5% 33.5 – 41%
Reliability and Sharpness per Flow Percentile
Reliability Values Sharpness Values
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Time (days)
Flo
w (
mm
/day
)
0 100 200 300 400 500 600 7000
2
4
6
8
10
12
14
16
18
20
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Multiple Response Characteristics as Constraints
All 19 indices used as constraint on ensemble predictions
Dove@Kirkby Mills Reliability = 80%Sharpness = 75%Behavioral = 1%
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DPSBAR (Slope) – Median,Runoff ratio
J F M A M J J A S O N D0.5
1
1.5
2
Flo
w (
mm
/day
)
P/PE – Max Feb, Max Nov, Mean, Runoff ratio
J F M A M J J A S O N D0.5
1
1.5
2
Flo
w (
mm
/day
) Rainfall
Elevation
Hydrogeology
Tillingbourne@Shalford
BFIHOST – High flowDischarge, skewness and variability in flow,High pulse count
J F M A M J J A S O N D0.5
1
1.5
2
Flo
w (
mm
/day
)
Low Flows
Low Flows
Low and Mid Flows
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Next Step: Using U.S. MOPEX Data
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WATERSHED
MODELDYNAMIC
RESPONSEBEHAVIOR
FORECASTING
STREAMFLOWINUNDATED AREAS
WATER QUALITY
SIMULATION
UNCERTAINTY
CONCEPTUAL STRUCTUREFUNCTIONAL FORM
PARAMETER VALUES
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Simulation Questions
• How can we estimate the reliability of forecasts?
• How can we create ensemble (probabilistic) predictions of inundated areas?
• What is needed for long term simulations incl. climate change impacts (droughts & floods)?
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Probabilistic Inundation and Hazard Maps
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Partial support for this work was provided by SAHRA under NSF- STC grant EAR-9876800, and the National Weather Service Office of Hydrology under grant numbers NOAA/NA04NWS4620012,UCAR/NOAA/COMET/S0344674, NOAA/DG 133W-03-SE-0916. We thank The British Atmospheric Data Center for providing the temperature data (http://badc.nerc.ac.uk/home/index.html).
Acknowledgements
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References
Yang, T., Reed, P. and Wagener, T. 2006. How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration? Hydrology and Earth System Sciences. In Press.
Wagener, T. and Gupta, H.V. 2005. Model identification for hydrological forecasting under uncertainty. Stochastic Environmental Research and Risk Assessment. DOI 10.1007/s00477-005-0006-5.
Ajami, N.K., Gupta, H.V., Wagener, T. and Sorooshian, S. 2004. Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. Journal of Hydrology, 298(1-4), 112-135.
Yang, T., Reed, P. and Kollat, J. 2006. … . Advances in Water Resources, in Review.Yadav, M., Wagener, T. and Gupta, H.V. 2006. Regionalization of constraints on hydrologic
watershed behavior . Advances in Water Resources, in Preparation. Wagener, T. and Kollat, J. Visual and numerical evaluation of hydrologic and environmental models
using the Monte Carlo Analysis Toolbox (MCAT). Environmental Modeling and Software, in Press pending minor Revisions.
Vrugt, J.A., Gupta, H.V., Dekker, S.C., Sorooshian, S., Wagener, T. and Bouten, W. 2006. Confronting parameter uncertainty in hydrologic modeling: Application of the SCEM-UA algorithm to the Sacramento Soil Moisture Accounting model. Journal of Hydrology, In Press.
Wagener, T. and Wheater, H.S. 2006. Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty. Journal of Hydrology, 320(1-2), 132-154.