calibration, uncertainty and regional analysis of...

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GWADI Workshop Roorkee Feb-March 2005 Calibration, Uncertainty and Regional Analysis of Conceptual Rainfall - Runoff models Howard Wheater 1 , Neil McIntyre 1 and Thorsten Wagener 2 1 Department of Civil and Environmental Engineering Imperial College London 2 Department of Civil and Environmental Engineering Pennsylvania State University

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GWADI WorkshopRoorkee Feb-March 2005

Calibration, Uncertainty and Regional Analysis of Conceptual Rainfall -

Runoff models

Howard Wheater1 , Neil McIntyre1 and Thorsten Wagener2

1Department of Civil and Environmental EngineeringImperial College London

2Department of Civil and Environmental EngineeringPennsylvania State University

GWADI WorkshopRoorkee Feb-March 2005

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

GWADI WorkshopRoorkee Feb-March 2005

Conceptual Models

Assumed model formIn general, parameters

have no direct (measurable) physical significance

Optimisation required for parameter identification

GWADI WorkshopRoorkee Feb-March 2005

Fitting a conceptual model

• Manual, subjective

or

• Automatic, objective

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)

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

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

GWADI WorkshopRoorkee Feb-March 2005

The solutions:

• Reduce model complexity• Increase the information content of the

data• Abandon the concept of a unique best-fit

model

GWADI WorkshopRoorkee Feb-March 2005

Model structural analysis

Towards parsimoneous model structures

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

GWADI WorkshopRoorkee Feb-March 2005

HYBRID MODEL ARCHITECTUREOPTIMIZATION

MODULES

VISUALANALYSISMODULES

OFF-LINE DATAPROCESSING

MODULES

MOISTUREACCOUNTING

MODULE

ROUTINGMODULE

ERPT

PET

AET

MOISTURE STATUS

Q

GUI

Rainfall-Runoff Modelling Toolbox

GWADI WorkshopRoorkee Feb-March 2005

k(slow)

k(quick)

u1k

u2k

stor

age

capa

city

F(c)

sk

cmax

1 0

ck

rkaek

alpha

+ qk

cmd+

c3

c4 dk

rkaek

rk rk

uk=0.5(msk+msk-1)*rk

)(1

k

ktv

msτ

−msk-1/c

msk/c

rkaek

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

GWADI WorkshopRoorkee Feb-March 2005

CLASSPLOTS

GUI

DOTTYPLOTS

DYNAMICIDENTIFIABILITY

ANALYSIS

(GLUE)REGIONAL

SENSITIVITYANALYSIS

2-D AND 3-DSURFACE

PLOTS

A POSTERIORIPARAMETER

DISTRIBUTIONS

GLUECONFIDENCE

LIMITS

GLUEVARIABLE

UNCERTAINTY

MULTI-OBJECTIVE

(MO) ANALYSIS

(MO)PARETO

CONFIDENCELIMITS

(MO)PARAMETERRANKINGS

(MO)NORMALIZEDPARAMETER

RANGES

GWADI WorkshopRoorkee Feb-March 2005

MONTE CARLO ANALYSIS OF MODEL STRUCTURE AND PERFORMANCE

With current computing power, it is possible to use Monte Carlo simulation techniques to generate many realisations of model performance, by sampling at random from the feasible parameter space.

Using this approach, techniques are now available to explore model performance, parameter sensitivity and parameter identifiability

This allows the analysis of a given model structure, and a means of tailoring a model structure to a particular application.

GWADI WorkshopRoorkee Feb-March 2005

CLASSPLOTS

GUI

DOTTYPLOTS

DYNAMICIDENTIFIABILITY

ANALYSIS

(GLUE)REGIONAL

SENSITIVITYANALYSIS

2-D AND 3-DSURFACE

PLOTS

A POSTERIORIPARAMETER

DISTRIBUTIONS

GLUECONFIDENCE

LIMITS

GLUEVARIABLE

UNCERTAINTY

MULTI-OBJECTIVE

(MO) ANALYSIS

(MO)PARETO

CONFIDENCELIMITS

(MO)PARAMETERRANKINGS

(MO)NORMALIZEDPARAMETER

RANGES

Monte Carlo Analysis Toolbox

GWADI WorkshopRoorkee Feb-March 2005

DOTTY PLOTS AND IDENTIFIABILITY ANALYSIS

A simple plot of performance vs parameter value for a given parameter using all Monte Carlo results can show whether the parameter is identifiable, at least in a univariate sense (parameter interactions are considered separately).

GWADI WorkshopRoorkee Feb-March 2005

GWADI WorkshopRoorkee Feb-March 2005

DYNAMIC IDENTIFIABILITY ANALYSIS

GWADI WorkshopRoorkee Feb-March 2005

PURPOSE SYSTEM

CONCEPTUALIZATION DATA

MODEL COMPLEXITYREQUIRED SUPPORTED

PERFORMANCE UNCERTAINTY

SUFFICIENT ACCEPTABLE

GWADI WorkshopRoorkee Feb-March 2005

Model Performance Versus Parameter Uncertainty

GWADI WorkshopRoorkee Feb-March 2005

Increasing the information content of the data:

multi-criteria analysis

GWADI WorkshopRoorkee Feb-March 2005

MULTIPLE OBJECTIVE FUNCTIONS

A single objective function cannot capture all of the many performance attributes that an experienced hydrologist might look for in evaluating model performance, and uses only a limited part of the total information content of a hydrograph. When used in calibration it will tend to bias model performance to match a particular aspect of the hydrograph

GWADI WorkshopRoorkee Feb-March 2005

MULTIPLE OBJECTIVE FUNCTIONS

A single objective function:• cannot capture the many performance

attributes that an experienced hydrologist might look for

• uses only a limited part of the total information content of a hydrograph

• when used in calibration it will tend to bias model performance to match a particular aspect of the hydrograph

GWADI WorkshopRoorkee Feb-March 2005

GWADI WorkshopRoorkee Feb-March 2005

A multi-criteria approach overcomes these problems (Wheater et al., 1986, Gupta et al., 1998, Boyle et al., 2001, Wagener et al., 2001).

For a single output problem (e.g. a stream hydrograph), different parts of the time series can be selected, e.g. rising limb, recession, low flow periods, to examine different aspects of model performance. These can be matched to the sensitivity of different components of the model (Wheater et al, 1986), or arbitrarily selected (Boyle et al., 2001).

GWADI WorkshopRoorkee Feb-March 2005

0 0.2 0.4 0.6 0.8 10

0. 2

0. 4

0. 6

0. 8

1

0 0.2 0.4 0.6 0.8 10

0. 2

0. 4

0. 6

0. 8

1

0 0.2 0.4 0.6 0.8 10

0. 2

0. 4

0. 6

0. 8

1

0 0.2 0.4 0.6 0.8 10

0. 2

0. 4

0. 6

0. 8

1Good trade offPoor trade off

cwi_leak model structure

at North Esk at Dalmore Weir

pd4_2pmp model structure

at Bogie at Redcraig

NSENSE

FSB FSB

0 0.2 0.4 0.6 0.8 10

0. 2

0. 4

0. 6

0. 8

1

0 0.2 0.4 0.6 0.8 10

0. 2

0. 4

0. 6

0. 8

1

0 0.2 0.4 0.6 0.8 10

0. 2

0. 4

0. 6

0. 8

1

0 0.2 0.4 0.6 0.8 10

0. 2

0. 4

0. 6

0. 8

1Good trade offPoor trade off

cwi_leak model structure

at North Esk at Dalmore Weir

pd4_2pmp model structure

at Bogie at Redcraig

NSENSE

FSB FSB

GWADI WorkshopRoorkee Feb-March 2005

• Trade-offs generally occur• A successful model will minimise the

trade-offs between alternative criteria• The user must decide what compromise to

make

GWADI WorkshopRoorkee Feb-March 2005

Abandoning the concept of a unique best-fit model

• Consider a population of models• Define the likelihood that they are

consistent with the available data

GWADI WorkshopRoorkee Feb-March 2005

REGIONAL SENSITIVITY ANALYSISSpear and Hornberger (1980)

Spear and Hornberger classified the realisations as "behavioural" or "non-behavioural", and used this classification to explore parameter sensitivity. Parameters were sensitive if there was a significant difference between the set of behavioural and non-behavioural parameters

GWADI WorkshopRoorkee Feb-March 2005

θjθi

0

11

0 cum

ulat

ive

dist

ribu

tion

cum

ulat

ive

dist

ribu

tion

)B|(F iθ

)B|(F iθ

)(F iθ

)B|(F jθ

)B|(F jθ

)(F jθ

GWADI WorkshopRoorkee Feb-March 2005

This approach was extended by Freer et al. (1996); instead of 2 classes, the model realisations are split into 10 groups of equal number, ranked according to their objective function performance, and the cumulative distributions can be plotted to indicate parameter sensitivity

GWADI WorkshopRoorkee Feb-March 2005

PREDICTION UNCERTAINTY AND GENERALIZED LIKELIHOOD UNCERTAINTY ANALYSIS (GLUE)

Uncertainties arise in model structure, parameter values and data. A popular approach to estimate and propagate modelling uncertainty is the GLUE procedure (Beven and Binley, 1992; Freer et al., 1996; Beven, 1998), which extends the RSA method discussed above.

GWADI WorkshopRoorkee Feb-March 2005

The simulations are classified as behavioural or non-behavioural, and the latter are rejected.

The likelihood measures of the behavioural set are scaled and used to weight the predictions associated with individual behavioural parameter sets.

The modelling uncertainty is then propagated into the simulation results as confidence limits of any required percentile

GWADI WorkshopRoorkee Feb-March 2005

GWADI WorkshopRoorkee Feb-March 2005

Regional analysis

GWADI WorkshopRoorkee Feb-March 2005

LOCALMODEL

STRUCTURE

GAUGEDCATCHMENT 1

GAUGEDCATCHMENT 2

GAUGEDCATCHMENT N

REGIONALMODEL

STRUCTURE

UNGAUGEDCATCHMENT*

Q*

I*

θ*

Φ*

I 1

θ1

I2

θ2

INθN

θ1 ,Φ

1

θ2,Φ2

θN,ΦN

REGIONALISATION

GWADI WorkshopRoorkee Feb-March 2005

C01

C02

C03

C04

C05C06

C07

C08C1

2

C10C11C1

3C09C1

42

C152

C162

C172

C182

C192 C2

02C212

C222 C2

32

= GAUGING STATION

40 km

C01

C02

C03

C04

C05C06

C07

C08

C12

C10C11

C13 C09C14

C152

C162

C17

C18

C19C20 C21

C22C23

= GAUGING STATION

N

40 km

GWADI WorkshopRoorkee Feb-March 2005

NO. STATIONREF.

LOCATION RIVER IHDTMNGR

DTMAREA(km2)

MEANFLOW

(m3 s-1) *C01 39053 Horley Mole TQ 527 143 91.63 1.31

C02 39069 Kinnersley Manor Mole TQ 526 146 146.17 2.09

C03 39068 Dorking Castle Mole TQ 518 150 317.2 3.61

C04 39079 Weybridge Wey TQ 506 164 903.05 6.68

C05 39011 Tilford Wey SU 487 143 391.24 3.19

C06 39078 Farnham Wey (N) SU 483 146 192.59 0.71

C07 39016 Theale Kennet SU 464 170 1037.38 9.52

C08 39027 Pangbourne Pang SU 463 176 175.68 0.61

C09 39025 Brimpton Enbourne SU 456 164 141.98 1.29

C10 39114 Frilsham Pang SU 453 173 90.06 0.15

C11 39115 Bucklebury Pang SU 455 171 108.94 0.20

C12 39019 Shaw Lambourn SU 446 168 235.25 1.69

C13 39103 Newbury Kennet SU 447 167 543.73 4.52

C14 39028 Hungerford Dun SU 432 168 99.84 0.71

C15 39101 Ramsbury Aldbourne SU 428 171 53.03 0.20

C16 39077 Poulton Farm Og SU 419 169 63.98 0.30

C17 39037 Marlborough Kennet SU 418 168 136.43 0.83

C18 39073 Cirencester Churn SP 402 202 83.17 0.74

C19 39020 Bibury Coln SP 412 206 107.37 1.32

C20 39042 Lechdale Leach SU 422 199 77.61 0.75

C21 39006 Newbridge Windrush SP 440 201 361.97 3.24

C22 39034 Cassington Evenlode SP 444 209 427.25 3.66

C23 39105 Wheatley Thame SP 461 205 531.54 3.63

Catchment Details. (* data obtained from the National River Flow Archive at www.nerc-wallingford.ac.uk).

GWADI WorkshopRoorkee Feb-March 2005

CATCHMENTCHARACTERISTIC

UNIT DESCRIPTION

AREA km2 Catchment drainage areaLDP km Longest drainage pathBFIHOST - Baseflow index derived using the HOST classificationSPRHOST % Standard percentage runoff derived using the HOST classificationFARL - Index of flood attenuation due to reservoirs and lakesPROPWET - Index of proportion of time that soils are wetDPLBAR km Index describing catchment size and drainage path configurationDPSBAR mkm-1 Index of catchment steepnessASPBAR - Index representing the dominant aspect of catchment slopesASPVAR - Index describing the invariability in aspect of catchment slopesRMED-1D mm Median annual maximum 1-day rainfallRMED-2D mm Median annual maximum 2-day rainfallRMED-1H mm Median annual maximum 1-hour rainfallSAAR mm 1961-90 standard-period average annual rainfallSAAR4170 mm 1941-70 standard-period average annual rainfallURBEXT1990 - FEH index of fractional urban extent for 1990URBCONC - Index of concentration of urban and suburban land coverURBLOC - Index of location of urban and suburban land cover

Description of catchment characteristics (after Institute of Hydrology, 1999).

GWADI WorkshopRoorkee Feb-March 2005

CATCHMENTNO. LOCATION RIVER SPRHOST BFIHOST

CLAYC02 KinnersleyManor Mole 41.5 0.445C03 DorkingCastle Mole 40.7 0.436C01 Horley Mole 40.2 0.464C09 Wheatley Thame 38.2 0.485C23 Brimpton Enbourne 32.8 0.5

MIXED (1)C22 Cassington Evenlode 24.1 0.699C04 Weybridge Wey 23 0.723C08 Pangbourne Pang 22 0.72C14 Hungerford Dun 21.3 0.768

MIXED (2)C07 Theale Kennet 18.7 0.767C05 Tilford Wey 18.3 0.795C21 Newbridge Windrush 17.2 0.79C12 Shaw Lambourn 16.1 0.839

MIXED (3)C13 Newbury Kennet 13.6 0.848C18 Cirencester Churn 13.5 0.844C06 Farnham Wey (North) 12.8 0.865C20 Lechlade Leach 12.3 0.864C19 Bibury Coln 12.1 0.858

CHALKC15 Ramsbury Aldbourne 6.1 0.955C17 Marlborough Kennet 5 0.959C16 Poulton Farm Og 4.6 0.97

Catchment clusters.

GWADI WorkshopRoorkee Feb-March 2005

0.4

0.5

0.6

0.7

0.8

0.9

1

CWI_2PAR IC1_2PAR PDM_2PAR IC1_LEAK PDM_LEAK

Model Structure

Nash

-Sut

cliffe

Effi

cien

cy (N

SE)

Dorking Castle Pangbourne Tilford Marlborough

Model Structure Performance with Respect to the Nash-Sutcliffe Efficiency.

GWADI WorkshopRoorkee Feb-March 2005

rk, tkrk

uk=0.5(sk+sk-1)*rk

)(1

k

k

ttauvolcs⋅

rt(q)

rt(s)

%q Q

GWADI WorkshopRoorkee Feb-March 2005

Catchment Number tau Refp mf rt(q) rt(s) %(q) RMSE NSE

C02 38.06 1.33 0.44 1.84 218.23 0.78 1.175 0.730 C03 2.82 4.48 0.93 2.78 497.51 0.77 0.796 0.740 C01 0.55 9.71 0.48 1.64 251.43 0.71 1.131 0.740 C09 6.26 2.77 1.23 3.01 191.97 0.81 0.373 0.790 C23 6.98 2.66 1.35 7.42 53.61 0.89 0.657 0.730 C22 29.04 1.44 1.02 7.10 56.02 0.62 0.290 0.870 C04 15.38 3.41 0.62 3.90 241.36 0.56 0.244 0.800 C08 26.81 1.79 1.65 3.27 106.56 0.15 0.070 0.890 C14 30.83 1.10 1.52 21.51 135.08 0.35 0.108 0.920 C07 26.95 1.76 1.14 7.98 91.16 0.25 0.191 0.880 C05 35.21 1.47 0.88 2.56 173.76 0.40 0.239 0.760 C21 12.76 2.90 0.76 11.72 59.10 0.36 0.183 0.900 C12 20.66 1.81 1.38 92.42 108.46 0.23 0.108 0.910 C13 25.92 2.27 0.88 52.18 216.52 0.81 0.169 0.890 C18 12.32 1.97 1.25 24.89 35.98 0.31 0.247 0.900 C06 29.28 1.22 1.19 2.12 106.64 0.31 0.149 0.810 C20 36.00 1.90 0.66 13.46 22.30 0.24 0.412 0.770 C19 18.11 2.89 0.66 32.69 68.38 0.61 0.273 0.870 C15 4.01 1.43 17.27 51.31 73.29 0.95 0.218 0.750 C17 21.39 1.41 17.59 18.62 69.40 0.37 0.241 0.820 C16 2.68 1.52 15.08 38.31 53.60 0.07 0.265 0.780

Parameter values and model fits for the CWI_2PAR model.

CLAY

MIXED 1

CHALK

MIXED 2

MIXED 3

GWADI WorkshopRoorkee Feb-March 2005

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

34 35 36 37 38 39 40

SMDBAR

mf

Overall RMSE best fit mf versus SMDBAR.

GWADI WorkshopRoorkee Feb-March 2005

For a given model structure, parameter sets are generated (either based on random sampling of the feasible parameter space assuming a uniform distribution, or some known or assumed prior distribution) and the model is run using a Monte Carlo procedure

GWADI WorkshopRoorkee Feb-March 2005

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.4 0.5 0.6 0.7 0.8 0.9 1

BFIHOST

%(q

)

Overall RMSE best fit %(q) versus BFIHOST for all catchments.

GWADI WorkshopRoorkee Feb-March 2005

MODELPARAMETER

MOST SIGNIFICANT CATCHMENT DESCRIPTOR (CORRELATIONCOEFFICIENT)

tau DPSBAR (-0.27) BFIHOST (0.19) RMED-1H (0.13) ASPVAR (-0.12)refp BFIHOST (-0.53) PROPWET (0.42) FARL (-0.42) ASPVAR (0.32)mf BFIHOST (0.53) AREA (-0.32) FARL (0.54) PROPWET (-0.05)rt(q) BFIHOST (0.53) DPSBAR (0.47) PROPWET (-0.38) FARL (0.27)rt(s) BFIHOST (-0.62) PROPWET (-0.38) DPSBAR (-0.38) ASPBAR (0.37)%(q) BFIHOST (-0.54) ASPBAR (0.32) RMED-1H (-0.23)volc BFIHOST (0.58) DPSBAR (0.54) ASPBAR (-0.44) SAAR (0.25)

GWADI WorkshopRoorkee Feb-March 2005

tau = -0.4648 (DPSBAR) - 37.5915 (ASPVAR) - 4.2586 (RMED-1H) + 99.5594

(R2 = 0.514)

refp = -1.1997 (BFIHOST) - 12.7610 (PROPWET) + 0.2643 (ASPVAR) - 0.2687

(FARL) + 7.0517 (R2 = 0.342)

mf = -13.3104 (PROPWET) - 0.3199 (BFIHOST) + 0.00013 (AREA) + 4.9492

(FARL) + 0.8636 (R2 = 0.647)

rt(q) = 29.0766 (BFIHOST) - 0.1514 (DPSBAR) -14.1469 (PROPWET) - 3.8213

(R2 = 0.741)

rt(s) = -315.0290 (BFIHOST) + 1016.5670 (PROPWET) + 0.01487 (ASPBAR) -

0.0619 (DPSBAR) + 25.6877 (R2 = 0.685)

%(q) = -1.0597 (BFIHOST) + 0.00019 (ASPBAR) - 0.0031 (RMED-1H) + 1.3342

(R2 = 0.733)

volc = -0.00698 (BFIHOST) – 0.00004 (DPSBAR) – 0.000004 (ASPBAR) + 0.01065

(R2 = 0.754)

GWADI WorkshopRoorkee Feb-March 2005

PARAMETER CALIBRATED ESTIMATED

tau 29.2828 25.1805

refp 1.2182 1.2975

mf 1.1864 0.8528

rt(q) 2.1170 8.0050

rt(s) 106.6432 106.5143

%(q) 0.3130 0.3959

volc 0.0017 0.0021

Calibrated and regionally estimated parameter values for the Farnham catchment (C06).

GWADI WorkshopRoorkee Feb-March 2005

Performance Measure Calibrated Estimated

RMSE 0.149 0.189

NSE 0.81 0.69

Model performance measures for the calibrated and estimated model parameters for the Farnham catchment.

GWADI Workshop RoorkeeFeb-March 2005

A Generic Framework for the Identification of Parsimonious Rainfall-Runoff Models

Thorsten Wagener and Howard S. Wheater

Imperial College

Regionalised model fits for the Farnham catchment (C06)

GWADI WorkshopRoorkee Feb-March 2005

In conclusion:

• recent developments in stochastic analysis, including multi-objective and dynamic analysis, are opening up new horizons in hydrological modelling

• these ideas were developed for simple models but are now being applied to complex physics based models

• significant progress is being made in the regionalisation of hydrological models - and catchment analysis

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

GWADI WorkshopRoorkee Feb-March 2005

Some current research challenges:

The spatial dimension:• Spatial-temporal rainfall modelling• Semi-distributed catchment modelling

(spatial structures and data conditioning)• Fully-distributed catchment modelling

(parameterisation and scale)

GWADI WorkshopRoorkee Feb-March 2005

Storm

Storm arrivals

Cell

Cell arrivals

Cell intensity

Cell duration

Single site rainfall models

…..total (observed) intensity is sum of cell intensities

GWADI WorkshopRoorkee Feb-March 2005

Extreme value analysis - B-L random eta - Heathrow, July

Hourly maxima

GWADI WorkshopRoorkee Feb-March 2005

Launching rain events over catchment

generate• sequence of durations of rain events (2 types) and inter-

event dry periods in semi-Markov process• orientations of leading and trailing edges of each event• other event parameters (including velocity) from fitted joint

distribution• rain band wide enough to cover catchment for given event

duration and velocitysimulate• ‘within-event’ model within rain band

GWADI WorkshopRoorkee Feb-March 2005

Data (lhs) and simulation (below) - winter

GWADI WorkshopRoorkee Feb-March 2005

Spatial-temporal disaggregation, river Lee daily to hourly data

GWADI WorkshopRoorkee Feb-March 2005

Rainfall-Runoff Modelling Toolbox RRMT

Barbara OrellanaDepartment of Civil and Environmental Engineering

Imperial College [email protected]

GWADI WorkshopRoorkee Feb-March 2005

HYBRID MODEL ARCHITECTUREOPTIMIZATION

MODULES

VISUALANALYSISMODULES

OFF-LINE DATAPROCESSING

MODULES

MOISTUREACCOUNTING

MODULE

ROUTINGMODULE

ERPT

PET

AET

MOISTURE STATUS

Q

GUI

Rainfall-Runoff Modelling Toolbox

GWADI WorkshopRoorkee Feb-March 2005

http://ewre.cv.imperial.ac.uk

The Matlab-based RRMT and MCAT Toolboxes can be downloaded free for research users. Please note that Matlabsoftware is required for their implementation.

GWADI Workshop RoorkeeFeb-March 2005

Catchment Wetness indexYe et al. Model StructurePenmanCatchment Moisture DeficitBucket StructurePenman VersionNo Soil Moisture AccountingProbability Distribution of Soil Moisture Stores

Conceptual ReservoirTwo Conceptual Reservoir in ParallelThree Conceptual Reservoir in ParallelLeaky Aquifer Model StructureNo Routing ComponentMacro-pore approach, single reservoirMacro-pore approach, parallel structureTransfer Function

Shuffled Complex Evolution AlgorithmUniform Random Search

Nash Sutcliffe EfficiencyCoefficient of DeterminationRoot Mean Squared ErrorAbsolute BiasHeteroscedastic Maximun Likelihood Est.RMSE Segmentation ARMSE Segmentation BRMSE High FlowsRMSE Low Flows…

GWADI Workshop RoorkeeFeb-March 2005

GWADI Workshop RoorkeeFeb-March 2005

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GWADI Workshop RoorkeeFeb-March 2005

GWADI WorkshopRoorkee Feb-March 2005

GWADI WorkshopRoorkee Feb-March 2005

GWADI WorkshopRoorkee Feb-March 2005

GWADI WorkshopRoorkee Feb-March 2005

GWADI WorkshopRoorkee Feb-March 2005