introduction to the gwadi modelling...
TRANSCRIPT
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GWADI WorkshopRoorkee Feb-March 2005
Introduction to the GWADI Modelling Workshop
H.S.WheaterImperial College, [email protected]
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Motivation
• Widespread and routine use of models for flood design, real time flood management, water resources management, coupled modelling – flow, sediments, water quality, ecology
• Specific hydrological characteristics of arid and semi-arid areas
• Problems of data• Important developments – in data and modelling• Developing countries – practitioners limited access
to information and tools, need to disseminate through regional centres
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Aim of workshop
To produce a training package of lectures and tools that can be freely available through the web to support regional centres and individual practitioners
This workshop is the test bed – joint activity of lecturers and participants
We need to feed in your experience, concerns and interests….
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Modelling Hydrological Processes in Arid and Semi-Arid Areas
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I. Some observations on the hydrology of arid areas
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•Arid zone hydrology has important differences from humid zone hydrology
•Hydrological processes in arid and semi-arid areas are poorly understood
•Rainfall spatial and temporal variability and transmission losses are of particular importance for runoff and recharge processes
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Appropriate models and decision support tools are needed for hydrology and water resources management; these must be based on appropriate understanding of hydrological processes
Inappropriate models = inappropriate solutions
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II. An introduction to rainfall-runoff models
setting the scene:
model strengths and weaknesses
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RAINFALL- RUNOFF MODELS
• A model is a simplified representation of a real world system.
• A mathematical model consists of a set of simultaneous equations or a logical set of operations contained within a computer program.
• Models have parameters which are numerical measures of a property or characteristics that are constant under specified conditions
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Tasks for hydrological simulation models include:-• Modelling existing catchments for which
input-output data exist,e.g. Extension of data series, Flood design, Water resource evaluation, Operational flood forecasting, Water resource management
• Prediction of effects of catchment change e.g. land use change
• Runoff estimation on ungauged basins• Coupled hydrology and geochemistry • Coupled hydrology and meteorology
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CLASSIFICATION OF MODELSLinear/ Non-linearStructure
black box or empiricalconceptual or grey box or parametric physically-based
Spatial representationlumped, distributed, semi-distributed
Output Typedeterministic, stochastic
Output Frequencyevent-based, continuous
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Historical development of modelling methods
1930s Unit hydrograph (Metric models)II 1960s Conceptual modelsI II I 1970s Physics-based modelsI I II I I 1980s Stochastic analysisI I I I
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Metric Models‘Letting the data speak for themselves’
Limitations of simplicityAdvantages of identifiability
Widely used for regionalisationImportant benefits for real-time forecasting
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GWADI WorkshopRoorkee Feb-March 2005
Workshop Examples
• Unit hydrograph (Al-Weshah)• Time-series analysis (Young)• [IHACRES (Croke, Jakeman)]
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GWADI WorkshopRoorkee Feb-March 2005
Conceptual Models
Assumed model formIn general, parameters
have no direct (measurable) physical significance
Optimisation required for parameter identification
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GWADI WorkshopRoorkee Feb-March 2005
Workshop Examples
• Hughes• Leavesley• Sharma• Singh
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GWADI WorkshopRoorkee Feb-March 2005
snow accumulation(snow water storage)
interception and surface moistening
soil surface storage(depression storage...)
soil moisture recharge(capillary water)
gravity water storage and flowincluding macropore flow
groundwater in upper horizons(shallow, perched)
groundwater in deeper horizons(large scale aquifers...)
evapotranspiration precipitation(rain, snow)
channel storageand routing
basindischarge
overlandflow
interflow
baseflow
delayedbaseflow
snowmelt
soil water supply
infiltration
percolation
deep percolation
capillary rise
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GWADI WorkshopRoorkee Feb-March 2005
u1k
u2k
stor
age
capa
city
F(c)
sk
1 0
ck
rkaek
alpha
+ qk
k(quick)
k(slow)
cmax
b
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GWADI WorkshopRoorkee Feb-March 2005
Fitting a conceptual model
• Manual, subjective
or
• Automatic, objective
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GWADI WorkshopRoorkee Feb-March 2005
Automatic fitting of a conceptual model
• Specify performance measure(s) (objective functions OFs)
• For p model parameters pose problem of minimisation or maximisation of (p+1) dimensional response surface
• Classic optimisation methods include Rosenbrock, Simplex
• Advanced methods include Shuffled Complex Evolution (SCE-UA)
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GWADI WorkshopRoorkee Feb-March 2005
Problems in optimisation
• Multiple local optima on the objective function surface
• Interdependence of parameters gives difficulties due to production of valleys (or ridges) in objective function
• Insensitive directions in parameter space, e.g. if parameter redundant due to a threshold value
• Search hampered by boundaries in parameter values
• Saddle points • Different scales of parameters
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GWADI WorkshopRoorkee Feb-March 2005
The reason?Model complexity exceeds the information content of the data
The result?Non -uniqueness:
many combinations of parameter values provide equally good fits to the data
Hence model parameters cannot be uniquely associated with physical catchmentcharacteristics
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GWADI WorkshopRoorkee Feb-March 2005
Performance of PDM model in estimating flood magnitudes from generalised parameter estimates(after Lamb et al., 2000)
Return period (yrs) 1.0 2.0 2.33 5.0 10.0 20.0
Mean error (%) 22 23 24 25 26 27
S.D. (%) 18 18 19 20 21 23
% Error% Error
Cou
nt
Cou
nt
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GWADI WorkshopRoorkee Feb-March 2005
Physics-based modelsIn principle based on
measurable parameters, can be used to predict change
But:Is the right physics
represented?Do effective parameters exist
at grid scale?If so, how can they be
obtained
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GWADI WorkshopRoorkee Feb-March 2005
Limitations
‘Physical’ parameters not measurable at the scale of application (at least for the subsurface)
Hence model fitting is needed – but with a large number of parameters
The result - ambiguity of parameter values
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GWADI WorkshopRoorkee Feb-March 2005
Workshop Examples
• KINEROS (Semmens)
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GWADI WorkshopRoorkee Feb-March 2005
Physics-based model performance using prior parameter estimates e.g. Lukey et al, 2000SHETRAN4 key parameters, baseline/upper/lower bound
values81 combinations; 5 years’ data
Envelope of simulations (selecting discharges above a minimum threshold) contained observed streamflow data for 64% of time.
R2 value for individual years from best set 0.03 -0.41
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GWADI WorkshopRoorkee Feb-March 2005
Conclusions?
• Model types have different strengths and limitations
• Model selection and application should be made with these in mind
• Significant progress is being made
To be continued…..