ewater guidelines rrm (v1 0 interim dec 2011)

7/27/2019 eWater Guidelines RRM (v1 0 Interim Dec 2011)

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eWater CRC

Copyright Notice

eWater Ltd 2011

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This work is copyright. You are permitted to copy and reproduce the information, in an

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the use of information contained in this document. In no event shall the publisher and the

author be liable for any loss of profit or any other commercial damage caused or alleged to

have been caused directly or indirectly by this document.

Citing this document

Vaze, J ., J ordan, P., Beecham, R., Frost, A., Summerell, G. (eWater Cooperative Research

Centre 2011) Guidelines for Rainfall-Runoff Modelling: Towards Best Practice ModelApplication.

Publication date: December 2011 (Interim Version 1.0)

ISBN 978-1-921543-51-7

Acknowledgments

eWater CRC acknowledges and thanks all partners to the CRC and individuals who have

contributed to the research and development of this publication.

We acknowledge the inputs from the hydrology group in DERM, Queensland, and Mark

Alcorn from SA Department for Water. We thank Matthew Bethune, Peter Wallbrink, DugaldBlack, J in Teng, J ean-Michel Perraud, Melanie Ryan, Bill Wang, David Waters, Richard

Silberstein, Geoff Podger, David Post, Cuan Petheram, Francis Chiew and Andrew Davidson

for useful discussions.

eWater CRC gratefully acknowledges the Australian Governments financial contribution to

this project through its agencies, the Department of Innovation, Industry, Science and

Research, the Department of Sustainability, Environment, Water, Population and

Communities and the National Water Commission.

For more information:

Innovation Centre, Building 22

University Drive South

Bruce, ACT, 2617, Australia

T: 1300 5 WATER (1300 592 937)

T: +61 2 6201 5834 (outside Australia)

E: [email protected]

www.ewater.com.au


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Best Practice Guidelines for Rainfall-Runoff Modelling

Table of Contents

Table of Contents .......................................................................................................................... 21 Introduction ............................................................................................................................ 5

1.1 Background ................................................................................................................... 51.2 Definition of Best Practice ............................................................................................. 51.3 Scope ............................................................................................................................ 6

2 Overview of procedure for rainfall-runoff modelling .............................................................. 82.1 Problem definition .......................................................................................................... 8

2.1.1 Problem statement and setting objectives ............................................................ 82.1.2 Understanding the problem domain ...................................................................... 82.1.3 Metrics and criteria and decision variables ........................................................... 92.1.4 Performance across multiple catchments and subcatchments ............................. 9

2.2 Option modelling ........................................................................................................... 92.2.1 Methodology development .................................................................................... 92.2.2 Collate and review data ....................................................................................... 102.2.3 Setting up and building a model .......................................................................... 102.2.4 Calibration and Validation ................................................................................... 102.2.5 Sensitivity/uncertainty analysis............................................................................ 122.2.6 Documentation and Provenance ......................................................................... 122.2.7 Model acceptance and accreditation ................................................................... 132.2.8 Use of accepted/accredited model ...................................................................... 13

3 Model choice ....................................................................................................................... 143.1 Model selection ........................................................................................................... 143.2 Available models ......................................................................................................... 15

3.2.1 Empirical methods ............................................................................................... 153.2.2 Large scale energy-water balance equations ..................................................... 163.2.3 Conceptual Rainfall-Runoff Models ..................................................................... 163.2.4 Landscape daily hydrological models ................................................................. 173.2.5 Fully distributed physically based hydrological models which explicitly modelhillslope and catchment processes ..................................................................................... 17

4 Collate and Review Data ..................................................................................................... 214.1 Catchment details ........................................................................................................ 22

4.1.1 Location of gauges (streamflow, rainfall and evaporation) ................................. 224.1.2 Topography and Catchment Areas ..................................................................... 224.1.3 Soil types ............................................................................................................. 224.1.4 Vegetation ........................................................................................................... 224.1.5 Water Management Infrastructure ...................................................................... 23

4.2 Flow data ..................................................................................................................... 234.3 Rainfall ......................................................................................................................... 244.4 Evapotranspiration ...................................................................................................... 25

5 Statistical Metrics for Testing Performance......................................................................... 26

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6 Calibration and validation .................................................................................................... 286.1 Calibration ................................................................................................................... 286.2 Validation ..................................................................................................................... 286.3 Calibration and Validation of Models to Single Gauge Sites, Multiple Gauge Sites andRegionalisation of Model Parameter Sets ............................................................................... 306.4 Automated, Manual and Hybrid Calibration Strategies ............................................... 31

6.4.1 Manual Calibration .............................................................................................. 316.4.2 Automated Calibration ......................................................................................... 326.4.3 Hybrid Calibration Strategies............................................................................... 336.4.4 Selection of Objective Functions for Automated and Hybrid Calibration ............ 346.4.5 Further Guidance on Calibration and Validation of Conceptual Rainfall RunoffModels 36

6.5 Calibration of Regression Models ............................................................................... 397 Uncertainty and Sensitivity Analysis ................................................................................... 40

7.1 Sensitivity Analysis ...................................................................................................... 417.2 Application of Multiple Parameter Sets ....................................................................... 427.3 More Advanced Quantitative Uncertainty Analysis ..................................................... 427.4 Consideration of Uncertainty in Practical Applications of Rainfall Runoff Models ...... 43

8 Concluding remarks ............................................................................................................ 449 References .......................................................................................................................... 45


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1 Introduction

1.1 Background

Reliable estimates of stream flow generated from catchments are required as part of

the information sets that help policy makers make informed decisions on water

planning and management. The characteristics of the streamflow time series that

influence water resources system modelling and planning can include the sequencing

of flows on daily and longer time steps, spatial and temporal variability of flows,

seasonal distribution and characteristics of high and low flows.

The best available estimate of streamflow would be expected to come from water level

observations made at a gauging station, converted to flow estimates using a well

defined and stable rating curve. However, these observations are only available for

limited number of gauging locations and for limited time span. Estimates for ungauged

locations and for a much longer time period are needed for contemporary watermanagement, and ways to make estimates for future possible conditions are also

required.

A range of methods are available to estimate streamflow from catchments, using

observed data wherever possible, or estimating by empirical and statistical techniques,

and more commonly using rainfall-runoff models. The modelling approach used to

estimate streamflow by different water agencies and consultants varies across

Australia and depends on the purpose of the modelling, time and money available, and

the tools and skills available within the organisation. With increasing levels of inter-

agency collaboration in water planning and management, development of a best

practice approach in rainfall runoff modelling is desirable to provide a consistent

process, and improve interpretation and acceptability of the modelling results.

The purpose of this document is to provide guidance on the best practice for

implementing fit for purpose rainfall-runoff models, covering topics such as setting

modelling objectives, identifying data sources, quality assuring data and understanding

its limitations, model selection, calibration approaches, and performance criteria for

assessing fitness for purpose

1.2 Defini tion of Best Practice

Best Practice Modelling can be defined as a series of quality assurance principles and

actions to ensure that model development, implementation and application are the best

achievable, commensurate with the intended purpose (Black et al., 2011).

What is in practice best achievable, commensurate with the intended purpose may be

subject to data availability, time, budget and other resourcing constraints. Hence, what

is meant by the term Best Practice Modelling can vary. Not only does it depend on

the circumstances of the project, particularly providing results that are fit for the

intended purpose, but it also depends to a great degree on interpretation in peer

review. This, in turn, will be influenced by the general state of knowledge and

technology in the modelling field, which can be expected to progressively develop overtime (such as new remote sensing data sources coming on line, and new computing


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hardware and software), as well as data, time, budget and resourcing constraints. Best

Practice Modelling provides for a strategic approach to modelling which enables the

trade-offs that may be imposed by these constraints to be better managed, and assists

in identifying priorities for addressing model and data limitations.

1.3 Scope

The eWater CRC has prepared generic Best Practice Modelling guidelines (Black et

al., 2011). They aim to provide for an integrated approach that enables interactions

and feedbacks between all domains relevant to water management (e.g. hydrological,

ecological, engineering, social, economic and environmental) to be considered.

The procedure in that guidance is intended to be flexible enough to accommodate

variations in the meaning of the term Best Practice Modelling and also allow for

continuous improvement as the state of knowledge and technology in the modelling

field develops and improves.

The eWater CRC will also provide guidelines to support the BPM guidelines in specificareas of hydrological modelling that relate to the products that they are developing.

This guideline is intended to address rainfall-runoff model application with the

objectives being to provide water managers, consultants and research scientists with

information on rainfall-runoff models and how to choose one that is fit for purpose, the

data available to develop them, and the calibration and validation methodologies.

There are a number of different purposes that a rainfall runoff model may be applied

within an overall water resources or catchment modelling framework, such as eWater

Source IMS. Most of these purposes relate to providing information to support decision

making for some water management policy. In particular, this can involve:

Understanding the catchment yield, and how this varies in time and space,

particularly in response to climate variability: seasonally, inter-annually, and

inter-decadally.

Estimating the relative contributions of individual catchments to water

availability over a much larger region, e.g. valley or basin scale.

Estimating how this catchment yield and water availability might change over

time in response to changes in the catchment, such as increasing development

of farm dams, or changes in land-use and land management.

In some instances with a high quality network of long term stream gauges, most of thistype of information can be estimated from the observations. However, the more

common case is where there is some combination of short term stations, variable

quality data, and gaps in spatial coverage. In these cases, spatial and temporal gaps in

the information can be estimated using rainfall runoff models to:

Infill gaps caused by missing or poor quality data in an observed data series for a

gauged catchment.

Estimate flows for a gauged catchment for periods before the observed flow record

started or after when the observed flow record ends.

Estimate flows for an ungauged catchment.

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Estimating flows from ungauged subcatchments within an overall gauged

catchment.

Forecast flows for some immediate future period (typically for a period of between

a few days and a few months), conditioned on current (or recent) observations of

the catchment state.

Assess the impacts of human influences within a gauged catchment (for examplelanduse or vegetation cover change) and simulating the flows that would have

occurred for the historical climate sequence with modified catchment conditions.

This may include assessment of catchment conditions that may be non-stationary

in either the observed record or for the simulation.

Assess the potential impact of climate variability and/or climate change on flows

from a gauged catchment.

In some cases, several of the above purposes may be satisfied by rainfall runoff

modelling for the same catchment. There are similarities in the approach that is taken

to rainfall runoff modelling, even though the purpose may differ.


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2 Overview of procedure for rainfall-runoff modelling

The generic guidelines (Black et al., 2011) outline a procedure for applying ahydrological model. This can be summarised as occurring in 4 phases:

1. Project management,

2. Problem definition,

3. Option modelling,

4. Compare Options and select the best.

This guidance deals only with problem definition and option modelling because the first

and last phases are discussed sufficiently for the purpose of rainfall-runoff modelling in

the generic guidelines. A further reason is that rainfall-runoff modelling is usually only a

part of a larger hydrological modelling project and these phases would be most

appropriately considered in the context of that larger project. Specific aspects of project

management and option comparison that are directly applicable to the development of

a rainfall-runoff model, such as accreditation, are dealt with at appropriate points in this

guidance.

2.1 Problem definit ion

2.1.1 Problem statement and setting objectives

The problem to be addressed must be clearly articulated to minimise the risk that thewrong tool will be used for the job. The problem statement will give direction on what

objectives will be considered in developing the rainfall-runoff model. As many water

management decisions will often have more than one goal it will be important to ensure

these are all identified.

Sometimes it can be useful to express objectives in a hierarchy that shows primary

objectives, secondary objectives and so on. In this regard, consideration should also

be given to possible additional future objectives and goals that could be met based on

the current project or on future projects that build upon the model established in the

current project. The decision on which option offers the best solution should be based

upon whether, or how well, each option meets the agreed objectives (also see section

2.2.1 and 2.2.2 in the generic guidelines).

2.1.2 Understanding the problem domain

The choice of the rainfall-runoff model will vary based on the purpose the modelling is

being done for, e.g., to understand seasonal low flow characteristics for an in-stream

environmental need; or to assess over-bank flow frequency; or to estimate overall

catchment yield on an average annual basis. The model selected, data required, and

calibration approach adopted should reflect this requirement. Where the same model

may be used for two or more different purposes, there may also be a need to focus thecalibration on a number of different flow regimes. If rough flow estimates are required

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over large areas and the runoff generation methodology should be consistent then the

data and modelling process will differ again.

2.1.3 Metrics and criteria and decision variables

Model calibration is a process of systematically adjusting model parameter values to

get a set of parameters which provides the best estimate of the observed streamflow(in the case of rainfall-runoff models). The process of determining which particular set

of parameter values are best for the intended purpose depends on what comparison

metrics are used. Metrics should be used to quantify the acceptability of the developed

model. In all cases graphical assessment and statistical results of some sort will be

assessed to identify the ability of the calibrated model to reproduce the flows calibrated

against.

Different metrics will be more effective in determining model appropriateness to meet

different objectives. What these are should be considered when the problem is being

defined. Understanding appropriate metrics allows model acceptance criteria to be

identified.

2.1.4 Performance across multiple catchments and subcatchments

In some situations, the purpose of rainfall runoff modelling is to produce an estimate of

the runoff at a single location where there is a streamflow gauge. If this is the case, the

calibration and validation process may be performed for the single gauged catchment.

This approach is justifiable in situations where gauged data is available for most of the

period that flow results are required for and the purpose of the rainfall runoff model is to

infill missing data during the period of record. It may also be justifiable where there is a

requirement to extend the period of record at the single gauge.

A much more common situation is that flow time series estimates are required at

several locations and that gauged streamflow data is also available at several

locations. The locations where flow estimates are required may or may not overlap with

the locations where the flow data is also available. An objective of any project that

involves the application of rainfall runoff models to multiple catchments or

subcatchments should be to demonstrate consistency in the rainfall runoff model

response between those catchments and to explain systematic differences in the

hydrological response between catchments and subcatchments in an appropriate and

logical manner.

2.2 Option modelling

This section describes the process of developing a rainfall-runoff model, further details

on key components are provided in later sections.

2.2.1 Methodology development

The models and methodology employed should be appropriate for the purpose that the

model will be used for. The choices made will be directed by the problem definition

developed and any other information at hand to the modeller. Detail on the models


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available and guidance on selecting models and methodology that is fit for purpose is

provided in Section 3.

2.2.2 Collate and review data

The amount and quality of data available to develop a model should be determined at

the outset of the project. This can influence the selection of models, the performancecriteria, and the approach to calibrate models. A bare minimum data set sufficient to

make an approximate estimate of mean annual catchment yield would include

catchment area along with spatial and temporal characteristics of rainfall and potential

evapotranspiration (PET). A comprehensive data set would include long-term

streamflow measurements and rainfall and PET data collected at one or more locations

within the catchment along with land use coverage, vegetation cover and impervious

area information, including changes over time.

The quality of the data should be reviewed prior to using to detect errors, non-

stationarity if any, and understand uncertainties that may influence estimates. Some

methods are discussed in section 4.

2.2.3 Setting up and building a model

The catchment characteristics are considered along with the knowledge on data

available and any other information available to the modeller. The rainfall-runoff model

chosen is conceptualised and an initial parameter set is identified.

When the model is first set up consideration should be given to all constraints which

are limiting and their effects on the modelling. Section 5 provides more details

associated with this step.

2.2.4 Calibration and Validation

Model calibration is a process of systematically adjusting model parameter values to

get a set of parameters which provides the best estimate of the observed streamflow

(in the case of rainfall-runoff models).

The term validation, as applied to models, typically means confirmation to some

degree that the calibration of the model is acceptable for the intended purpose

(Refsgaard and Henriksen, 2004). In the context of rainfall runoff modelling, validation

is a process of using the calibrated model parameters to simulate runoff over anindependent period outside the calibration period (if enough data is available) to

determine the suitability of the calibrated model for predicting runoff over any period

outside the calibration period. If there is not enough data available, the validation may

be performed by testing shorter periods within the full record.

It is normal in research studies to split the observed data sets into calibration and

validation period prior to the study, to demonstrate the performance of the model under

both sets of conditions. Use of this approach can cause problems in practical

applications if a model demonstrates acceptable performance for the calibration data

set but produces unsatisfactory results for the validation data set. An alternative

approach in this situation is to calibrate the rainfall runoff model to all available data but

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to demonstrate that the performance of the model is satisfactory over different sub-sets

of the period that observed data is available.

Further discussion of model calibration and validation is provided in Section 6.

It is a very common situation in a project that involves rainfall runoff modelling for flow

time series to be required for several catchments or subcatchments within the model

domain and for data to be available from two or more stream flow gauges to facilitate

calibration and validation. At locations where gauged flows are available and flow

estimates are required, two options are available to the modeller:

The rainfall runoff models may be calibrated independently for each gauged

catchment. In this case, independent parameter sets will be derived for the

rainfall runoff models of each catchment; or

A joint calibration may be performed, with rainfall runoff models calibrated with

consistent parameters to fit to the gauge records from two or more gauges. In

this case, a single set of rainfall runoff model parameters will be produced for all

of the catchments that represent a compromise to fit the flows at all of the

gauges within that group.

Consideration should be given at the outset of modelling to the approach that will be

used for dealing with flows from multiple catchments and subcatchments and from

multiple gauges. The strategy for dealing with this issue should be documented at this

point and revised, if necessary, during the process of calibrating and validating the

models.

Calibration of a rainfall runoff model normally involves running the model may times,

trialling different values of parameters, with the aim of improving the fit of the model to

the calibration data. Calibration can be facilitated:

Manually, with the modeller setting the parameter values, running the model to

inspect the results and then repeating this process many times;

Using automated optimisation, with an optimiser algorithm running the model

hundreds or thousands of times with different parameter values; or

Using a hybrid approach of automated optimisation phases, interspersed with

manually implemented trials of parameter sets.

Defining the calibration and validation approach before commencing a modelling

project can maximise the efficiency of the calibration process, whilst avoiding the

temptation to overfit the model to noise in the observational data. A calibration

strategy should therefore outline the:

gauge locations where model calibration and validation will be performed;

viable or allowable ranges for each model parameter value;

known constraints, dependencies or relationships between parameter values

(for example, the total of the three partial area parameters in AWBM, A1, A2

and A3 must sum to 1);

period for calibration at each gauge location;

period for validation at each gauge location;


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expected level of uncertainty in observations introduced by measurement

uncertainty;

metrics to be used to test calibration and validation performance;

whether manual or automated calibration strategy will be adopted, or how a

hybrid strategy of progressive manual and automated calibration will be

implemented. If an automated or hybrid optimisation strategy is to be used, further details

should be defined at the outset of the calibration process on:

algorithms to be used for optimisation of parameter values;

objective function(s) that will be used to test the calibration performance;

weightings that may be applied in computation of objective functions, to

encourage fitting to different parts of the flow regime (typically the relative

weightings to high, medium and low flows); and

the set of model parameters that will be optimised during calibration and

constraints on the allowable range of values for each parameter.Ideally, calibration strategy should be documented prior to the commencement of the

calibration process. It may be appropriate for the calibration strategy to be reviewed

during the calibration.

2.2.5 Sensitivity/uncertainty analysis

Relevant sources of uncertainty in typical order of importance include:

5. Model input data including parameters, constants and driving data sets,

6. Model assumptions and simplifications of what the model is representing,

7. The science underlying the model,

8. Stochastic uncertainty (this is addressed under variability below),

9. Code uncertainty such as numerical approximations and undetected software

bugs.

The potential impacts of the above sources of uncertainty on the decisions that will be

made using the model should be considered early in the modelling process and then

re-examined once the model has been calibrated, validated and applied for scenario

runs. Uncertainty becomes more important for estimation of events in the tails of the

probability distribution, floods and droughts, than it is for consideration of events that

are closer to the centre of the probability distribution (such as estimation of the meanannual runoff from a catchment).

2.2.6 Documentation and Provenance

Documentation is an important requirement for model acceptance. Its role is:

1. To keep a record of what was done so that it can be reviewed and reproduced,

2. To provide source or background material for further work and research,

3. To effectively communicate the results from models, and

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4. To effectively communicate the assumptions made during the modelling

process and the decisions made by the modeller during implementation of the

model.

Good documentation supports a study and it will also enable someone coming along

later to see what decisions were made, what was done to underpin the decisions and

why, particularly if aspects of the project are revisited at some later time.

Provenance, as it might relate to hydrological modelling studies simply means the

ability to trace the source/lineage of the data, model and modelling results. Reasons for

providing provenance in rainfall runoff modelling include:

1. Accountability and a full audit trail for all modelled results.

2. Repeatability; ability to re-create a results data set using current data or better

understanding.

3. Reproducibility; ability to re-create a results data set exactly using all original

data, workflow ordering, assumptions and parameters.

4. Versioning of both entire workflow and systems implementation. Versioning ofthe subcomponents and data sets will be the responsibility of those who govern

them but must be captured by the system.

The degree of provenance required depends on the application and/or how the

modelling system is intended to be used by the individual or organisation in future.

Current best practise provenance is to save all input data and model/parameters

version and workflow history such that the outputs can be reproduced in future if

required. In the future the ability to register and resolve the type and identity of objects

within the modelling process should reduce the requirement to capture and archive

these objects, especially as modellers take greater advantage of services based point

of truth data streams and modelling systems, and rely less on ad hoc locally managedresources.

2.2.7 Model acceptance and accreditation

The aim of model acceptance is to gain agreement that the model is fit for purpose.

Information available from the model accreditation process (Reporting, QA

documentation, Peer review) provides model development details and review results

which will enhance model acceptance.

Peer review plays an important part, especially with stakeholders that are external to

the organisation undertaking the model development. It is important for establishing

the credibility, reliability and robustness of results and the methodology used to obtain

the results. It is undertaken by people with specialist understanding in fields relevant to

the project.

2.2.8 Use of accepted/accredited model

Once a calibrated model is evaluated against good quality data and has undergone

thorough review process (model acceptance and accreditation), it can be used for

modelling to support water management planning and policy decisions (provided that

the model was accredited for similar purpose).


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3 Model choice

3.1 Model selection

Model selection is made based on an understanding of the objectives and the system

being modelled (http://www.toolkit.net.au/Tools/Category-Model_development;

CRCCH 2005a, b). The WMO (2008, 2009) report include the following factors and

criteria as being relevant when selecting a model:

1. The general modelling objective; e.g. hydrological forecasting, assessing

human influences on the natural hydrological regime or climate change impact

assessment.

2. The type of system to be modelled; e.g. small catchment, river reach, reservoir

or large river basin.

3. The hydrological element(s) to be modelled; e.g. floods, daily average

discharges, monthly average discharges, water quality, amongst others.

4. The climatic and physiographic characteristics of the system to be modelled.

5. Data availability with regard to type, length and quality of data versus data

requirements for model calibration and operation.

6. Model simplicity, as far as hydrological complexity and ease of application are

concerned.

7. The possible transposition of model parameter values from smaller sub

catchments of the overall catchment or from neighbouring catchments.

8. The ability of the model to be updated conveniently on the basis of currenthydrometeorological conditions.

Other things that should be considered are:

1. The level of modelling expertise available.

2. Whether the model is going to be used on its own, or if it is going to be used in

conjunction with other models.

3. Freedom of choice may be limited by a desire to minimise problems of different

models for much the same purpose in the same project area, or to avoid

problems of different models in adjoining project areas, particularly where the

models are linked in some way in the future or results compared in some way.

4. Whether uncertainty will be explicitly modelled. If uncertainty is to be explicitly

included, what types of uncertainty are to be modelled (e.g. climatic uncertainty,

uncertainty in climate change projections, uncertainty in rainfall runoff model

parameter values); what approaches will be used to generate the replicates to

represent uncertainty and how many replicates will be required to adequately

quantify uncertainty.

5. Whether simulation or optimisation, or a combination of both, is adopted.

6. Whether the model is to be used for hindcasting or forecasting when being

applied in predictive mode.

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In essence the governing principle in choosing a model should be that it should not have

more parameters requiring calibration or a greater level of detail than the available data can

support, to minimise problems of spurious results and false calibrations.

3.2 Available models

Rainfall runoff models can be represented by a range of approaches, in order ofincreasing complexity as:

simple empirical methods (e.g., curve number and regression equations);

large scale energy-water balance equations (e.g., Budyko curve);

conceptual rainfall-runoff models (e.g. SIMHYD, Sacramento, AWBM)

landscape daily hydrological models (e.g., VIC, WaterDyn);

fully distributed physically based hydrological models which explicitly model

hillslope and catchment processes (e.g., SHE, TOPOG).

These categories have been used for ease of description, and there is overlap betweenthese model types. Although these approaches vary in terms of the complexity with

which they represent the rainfall-runoff transformation processes, all of them

conceptualise the real processes using some sets of mathematical equations (and

hence are all conceptual models of the physical environment). Similarly, conceptual

rainfall-runoff models run in distributed mode can be classed as being landscape daily

hydrological models. This section provides a discussion of the characteristics of each

of these model types, along with a broad assessment of the strengths and weaknesses

of each approach for rainfall runoff modelling (Table 3-1).

3.2.1 Empirical methodsEmpirical methods to rainfall runoff modelling typically involve the fitting and application

of simple equation(s) that relate drivers of runoff response to flow at the catchment

outlet. Empirical equations are most often derived using regression relationships.

Common predictor variables may include rainfall for the catchment, flow observed at

another gauge in the vicinity, evapotranspiration, groundwater levels, vegetation cover

and the impervious area within the catchment. Where rainfall is used as a predictor

variable, regression relationships derived almost always include a non-linear

relationship between rainfall and runoff.

All catchments incorporate storage elements, including interception by vegetation,

storage within the soil column, groundwater storage and storage within stream

channels. Catchment storage typically results in runoff from the catchment being an

integrated function of the climatic conditions for the catchment over some period prior

to the period for which runoff is to be calculated by the model. Therefore, empirical

models that produce acceptably accurate simulations of runoff are either applied at

sufficiently long time steps that changes in internal water storage within the catchment

can be ignored (e.g. annual time step) or applied to represent an integration of the

climatic conditions that occurred over some time period prior. As a practical example,

for most catchments a regression model that only includes daily rainfall on the current

day is likely to produce a very poor estimation of daily runoff but a model for predicting


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daily runoff that used individual values of daily rainfall for several days prior may

produce acceptable runoff estimates.

Empirical regression relationships are often developed using spreadsheets. They can

also be fitted using more sophisticated statistical analysis packages, which may more

easily facilitate the investigation of predictor variables. For general information on the

development of regression relationships, the modeller is referred to NIST/SEMATECHe-Handbook of Statistical Methods (NIST and SEMATECH, 2010) or to a University

Level statistics text book.

Empirical regression equations are best suited to situations where there are two flow

gauges on the same stream with partially overlapping periods of record, which are

therefore subject to similar climatic drivers, and the regression equation is used to

extend the simulated flow to the combined period of record from both sites. They can

also produce adequate simulations for neighbouring gauged catchments with

overlapping periods of record in situations where the two catchments are subject to

similar rainfall timeseries and are relatively similar hydrologically.

3.2.2 Large scale energy-water balance equations

The large scale energy-water balance methods are based on the hypothesis of

available energy and water governing large scale water balance (precipitation,

evaporation and runoff). These are usually developed using large scale observed data

sets, eg. the Budyko curve (Budyko, 1958) was developed using mainly European

data, and numerous other forms have been proposed to improve estimates in local

regions and to account for different land cover types (Arora, 2002). One of the more

popular forms of the Budyko method is the Fu (1981) rational function equation (Zhang

et al., 2004) where a single parameter, , in the equation can be calibrated againstlocal data to tune the method for the local conditions. The inputs to these equations are

rainfall and potential evapotranspiration (PET) and the output is runoff at mean annual

time step.

3.2.3 Conceptual Rainfall-Runoff Models

Conceptual rainfall runoff models represent the conversion of rainfall to runoff,

evapotranspiration, movement of water to and from groundwater systems and change

in the volume of water within the catchment using a series of mathematical

relationships. Conceptual rainfall runoff models almost always represent storage of

water within the catchment using several conceptual stores (or buckets) that can

notionally represent water held within the soil moisture, vegetation, groundwater or

within stream channels within the catchment. Fluxes of water between these stores

and in and out of the model are controlled by mathematical equations.

Most applications of conceptual rainfall runoff models treat the model in a spatially

lumped manner, assuming that the time series of climatic conditions (notably rainfall

and potential evapotranspiration) and the model parameter values are consistent

across the catchment. There have been implementations in more recent times of

conceptual rainfall runoff models in semi-spatially distributed and spatially distributed

frameworks. In distributed application, the catchment is defined by grid cells orsubcatchments within the catchment that are assigned the same rainfall runoff

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parameter values but different time series of climatic inputs so that different grid cells

or subcatchments within the catchment produce different contributions to the overall

runoff. This is effectively a series of lumped rainfall runoff models, with lumped sets of

model parameters that are applied with spatially distributed rainfall.

Conceptual rainfall-runoff models have been widely used in Australia for water

resources planning and operational management because they are relatively easilycalibrated and they provide good estimates of flows in gauged and ungauged

catchments, provided good climate data is available.

In Australia there are six widely used conceptual rainfall-runoff models; AWBM

(Boughton 2004), IHACRES (Croke et al. 2006), Sacramento (Burnash et al. 1973),

SIMHYD (Chiew et al. 2002), SMARG (Vaze et al., 2004) and GR4J (Perrin et al.

2003). The input data into the models are daily rainfall and PET, and the models

simulate daily runoff. The models are typical of lumped conceptual rainfall-runoff

models, with interconnected storages and algorithms that mimic the hydrological

processes used to describe movement of water into and out of storages. They vary in

terms of the complexity of the catchment processes that they try to simulate and in

terms of the number of calibration parameters which vary from four to eighteen.

3.2.4 Landscape daily hydrological models

These models are based on the concept of landscape processes and they model the

typical landscape processes using simplified physical equations (VIC model, Liang et

al., 1994; 2CSALT, Stenson et al., 2011; AWRA-L, Van Dijk, 2010). A catchment is

usually conceptualised as a combination of landscapes which are delineated using

some combination of outputs from digital elevation model analysis, underlying geology,

soil types and land use. Often these models have been designed to reproduce othervariables in addition to streamflow (e.g. distributed evapotranspiration, soil moisture,

recharge, salinity), and as a result have a greater complexity to methods that target

streamflow alone.

3.2.5 Fully distributed physically based hydrological models which explicitly

model hillslope and catchment processes

The physically based models are based on our understanding of the physics of the

hydrological processes which control the catchment response and use physically

based equations to describe these processes. A discretisation of spatial and temporalcoordinates is made at a very fine scale for the entire catchment and the physical

equations are solved for each discretised grid to obtain a solution.


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Table 31 Assessment of Strengths and Weaknesses of Different Rainfall RunoffModel Structures

Criteria Model Type

Empirical Large Scale

Energy-Water

Balance

Conceptual Landscape

Daily

Fully

Distributed

Physically

Based

Typical Run

Time Step

Can be

daily if

daily flow

from

another

gauge is

used as a

predictor

variable.

Otherwisetypically

only

applied at

annual (or

longer)

time scale

Typically only

applied for

mean annual

runoff,

although

pattern of

flows from a

nearby gauge

may be used

todisaggregate

annual totals

to monthly or

daily time

steps

Daily,

although

shorter run

time steps

are

possible if

sufficient

climatic

data is

available atthis shorter

time step

Daily, although

shorter run

time steps are

possible if

sufficient

climatic data is

available at

this shorter

time step

Minutes to

hours to

maintain

numerical

stability,

although often

forced with

daily data and

assumed

patterns usedto

disaggregate

to shorter time

steps

Typical

Number of

Parameters

1 to 5 2 to 4 4 to 20 10 to 100 10 to 1000's

Risk of over-

fitting or over-parameterising

the model.

Low Very Low Moderate High Very High

Need for high

resolution

spatial data

layers

None to

Moderate

Low to

Moderate

Low High Very High

Strength of

Apparent

Connection

between Model

Parametersand

Measurable

Physical

Catchment

Characteristics

None None Weak for

most

parameters

(although

imperviousarea or

interception

may be

exceptions)

Moderately

weak

Claimed to be

strong by

proponents

but can be

difficult tovalidate this

claim

Run time on

typically

available

computer

platforms for

100 years ofdaily data


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Criteria Model Type


Energy-Water

Balance


Daily

Fully

Distributed

Physically

Based

Ability toimplement

multiple runs

for automated

calibration

Nottypically

required -

optimum

parameters

can be

obtained

by least

squares

fitting that

does not

require

multiple

runs

Not typicallyrequired

Very Good.Run times

are

typically

sufficiently

low to

facilitate

this and

tools are

available

(Rainfall

Runoff

Library and

Source

IMS) to

facilitate

this

Good. Runtimes likely to

be sufficiently

low to facilitate

this in most

circumstances,

however tools

for calibrating

such models

using

automated

routines are

not as widely

available

Poor. Runtimes are

generally too

long to

consider

automated

calibration

Typical

Performance in

Regionalisation

Moderate

at annual

time steps.

Usually

very poor

at shorter

time steps

(e.g. Daily)

Good at

annual time

steps. Usually

very poor at

shorter time

steps (e.g.

Daily)

Moderate

at daily

time steps

Proponents

claim to be

superior for

regionalisation

to conceptual

rainfall runoff

models

Proponents

claim this to

be a strength

of distributed

models but in

reality the

large number

of parameters

required may

compromise

the application

of distributed

models to

regionalisation

Representation

of non-

stationarity in

catchmentconditions

Not

possible

Often applied

to explicitly

represent

non-stationarity in

vegetation

cover for

mean annual

runoff signal

Usually

difficult,

due to lack

of physicalmeaning

for many

model

parameters

Possible Possible


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Criteria Model Type


Energy-Water

Balance


Daily

Fully

Distributed

Physically

Based

Typicalperformance of

model when

applying to a

very different

climatic period

to that used for

calibration

Poor Moderatewhen used to

estimate

impact on

mean annual

flow but flows

disaggregated

to shorter

time steps are

likely to be

poorly

estimated

Variable -can be

good in

some

catchments

but poor in

others

Variable - canbe good in

some

catchments

but poor in

others

Variable - canbe good in

some

catchments

but poor in

others

Typical level of

expertise with

this type of

model within

Australian

water industry

Strong Moderate Strong Weak Very weak

Likelihood that

previously

calibrated

models are

available for

catchment to

be modelled.

Moderate

to Low

Moderate Very High Low Very low

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4 Collate and Review Data

Climatic data is the most important driver of any rainfall runoff modelling process. The

calibration and validation of models also involves comparison to observed streamflowdata. Major causes of difficulty in calibrating rainfall-runoff models are errors and

uncertainties in the input data (see Kavetski et al, 2003). A discussion of these

problems can be found in the collection of papers in Duan et al 2003. Checks should

therefore be performed on the input data and the comparison data set for calibration

and validation to be used in rainfall runoff modelling before any attempt is made to

apply or calibrate the models. The intent here is to investigate the integrity of the data,

whether observations are in the first instance plausible (e.g., is the volume in a

hydrograph less than the product of the rainfall and catchment area). Investigations

into data to be used for rainfall runoff modelling should include checks of:

Stationarity of the data time series , i.e. has there been any systematic or step

change in the statistical properties over the time of data collection, and if so

why;

Spatial coherence of data, i.e., is the data consistent with regional spatial and

temporal patterns and trends;

Accuracy of the spatial location of the gauging site;

Consistency in the approach used to date and time stamp the data, particularly

for data provided by different agencies;

Procedures use for spatially interpolation of point observations to gridded data

estimates or estimated series across catchment areas

e.g., time series plots at different levels of temporal aggregation, ranked plots, residual

mass curves, double mass curves, etc. This will pick up patterns as well as identify

anomalies which may be potential data QA issues.

Other checks and analysis, including regional consistency of runoff depths, rain-runoff

ratios, rating confidence limits, period of record, whether rainfall and PET is observed

or interpolated, base-flow characteristics, checks for stationarity and variability over

time, etc would also be useful. It is important that prior knowledge is considered.

One major factor which will apply across all types of time series data used is that thetime base must be kept consistent so that the data applies to the same time period. An

example is where flow data time steps should be matched to the rainfall data time step.

In Australia, daily rainfall data is commonly recorded as the depth of rainfall that

occurred in the 24 hours preceding 9 am on the date of the recorded data. In contrast,

daily streamflow totals are often quoted for the 24 hour period commencing on the

nominated date, resulting in the recorded flow data being offset by 1 day forward of the

rainfall data. Where possible the flow data should be extracted at a time step to match

the rainfall dataset. HYDSTRA databases allow this where the records are at short time

intervals. In other cases shifting the recorded time series by one day for either the

rainfall or flow time series may be required to produce consistent time series for

modelling.


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The remainder of Section 4 outlines the data types, sources, availability, accuracy,

manipulations (such as gap filling) and any other issues.

4.1 Catchment details

4.1.1 Location of gauges (streamflow, rainfall and evaporation)

The streamflow recorded at the catchment outlet is a combined response to the spatial

distribution of rainfall and evaporation across the catchment. There are uncertainties

associated with the streamflow measurements due to rating curve errors as well as due

to extrapolation outside the limits of the rating curve. There is spatial variability in

rainfall (and to smaller extent evaporation) across a catchment which is not captured

when undertaking lumped catchment modelling using a single rain gauge. There might

be problems with the location of the rain gauge in terms of capturing a representative

rainfall for all the rainfall events especially for catchments with high rainfall gradients.

4.1.2 Topography and Catchment AreasThe catchment area for a catchment represents the contributing area to the catchment

outlet where the streamflow is measured. The catchment boundaries (and the

corresponding catchment area) can either be derived from topographic map layers or

using the catchment digital elevation model (DEM) and a standard package such as

ARCGIS. It is usually easier to determine catchment area for the catchments located in

steeper terrain compared to those located in very flat areas (especially when using

DEM).

Slope and dominant aspect may provide useful explanatory variables for estimating

routing parameters or for regionalisation of rainfall runoff parameters betweencatchments.

4.1.3 Soil types

A catchments rainfall-runoff response is related to the soil types in the catchment. The

surface soil characteristics determine the infiltration rates and so the contributions from

different flow components (surface runoff, throughflow and base flow). Soils information

can be obtained from any soils field work that has been undertaken in the catchment or

from large scale soil properties maps (e.g. Australian Soils Atlas, Northcote et al.,

1960). In most practical applications of conceptual rainfall-runoff models in Australia,

soil information is seldom directly used as input in the calibration process because the

inherent spatial variability in soil properties within a catchment is typically sufficiently

large that it has been difficult to demonstrate statistically robust relationships between

conceptual model parameters and soil properties.

4.1.4 Vegetation

Land cover/vegetation cover in a catchment can often be correlated with the amount of

interception storage/loss and actual evapotranspiration in a catchment. The land cover

across the catchment can be derived from large scale vegetation mapping based on

satellite imagery or remotely sensed data. Vegetation cover data has not typically beenused explicitly in directly determining rainfall runoff model parameters, although there

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have been some recent studies which have demonstrated the importance of catchment

land cover in rainfall-runoff model calibration and for predictions in ungauged basins

(Zhang and Chiew, 2008; Vaze et al., 2011c).

4.1.5 Water Management Infrastructure

Water management infrastructure within a catchment can allow humans to make verysubstantial modifications to flows within a catchment. Water management infrastructure

may include large dams, farm dams and off stream storages, extractions, man-made

canals or diversion pipelines and discharges from sewage treatment plants. Locations

of these infrastructures should be identified where they exist within the catchment so

that their potential impact on streamflows may be assessed. Recorded flows at the

catchment outlet may require adjustment to allow for the influence of water

management infrastructure located upstream of each of the flow gauging locations.

4.2 Flow data

Reliable measurements of streamflow data are critical for successfully calibrating arainfall-runoff model to a catchment. The streamflow data for all the gauged locations

can be obtained from the respective state government water management agencies or

from the Bureau of Meteorology (in Australia). Considerations in checking streamflow

data include:

the agency collecting the data and the quality assurance procedures (if any)

implemented by that organisation during data collection;

reliability of the rating of levels to flows for the gauge;

the accuracy, extent and currency of cross sections surveyed at the gauge site.

(Surveyed cross sections may only extent to the top of the stream bank and

gauging for flows extending onto the floodplain may use a cross section that is

inaccurate);

vegetation and substrate material for the channel bed, channel banks and

floodplain and the influence of assumptions made about these on gauged flows;

the ratio of the highest flow outputs to the highest flow that the gauge has been

rated for;

how hydraulically stable (variable over time) the rating site is;

examination of potential backwater effects for the site from influences that are

downstream of the site, such as stream confluences, bridge crossings, culverts,dams or weirs;

hysteresis effects leading to different flow rates for the same recorded level on

rising and falling limbs of hydrographs;

how well maintained the gauging site and instrumentation used for measuring

water levels has been;

any changes to the gauging instrumentation over time;

the length of time since the last rating at high flows;

length of record at the site;

availability of quality codes with the flow data record;


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proportion of missing data;

trends in when data is missing from the record (i.e. Is there any bias toward

high or low flow periods, particular seasons, or are the gaps just random?) and

how this might influence any infilling procedures; and

if there are a number of gauges closely located that basically represent the

same catchment the data sets may be able to be combined to give a longerrecord for the site.

Assessment of the above factors will inform whether the data is useful in calibration of

the model, independent validation of the model or whether the data should be ignored.

4.3 Rainfall

Rainfall is the main driver of runoff and so reliable measurements of rainfall are critical

for successfully calibrating a rainfall-runoff model to a catchment. There are several

sources for obtaining climatic data for a particular catchment:

Site observations from Bureau of Meteorology climate database.

Site observations taken from monitoring sites collected by other organisations

that may exist outside of the Bureau of Meteorology database. Many

jurisdictional databases contain rainfall records.

Gridded data products, such as the Bureau of Meteorologys Australian Water

Availability Project (AWAP) or Queensland Centre for Climate Applications

SILO data set.

It is important to be aware of how this data has been collected and what data quality

control methods have already been applied to the data prior provision of the data set

as this may influence the modelling results. This is particularly relevant to gridded

products, such as SILO and AWAP (SILO, J effrey et al., 2001; AWAP, J ones et al.,

2009), as these data sources generally use different algorithms to convert time-series

observations at data points to gridded data products.

In a small catchment, considerably better results may be obtained from using rainfall

station data from the BOM (http://www.bom.gov.au/climate/) or locally collected data

than a gridded data set that smoothes observations from a smaller number of more

sparsely located sites. In some cases it may be appropriate to adjust the station data,

normally by a percentage, if the mean catchment rainfall can be defined using other

sources e.g. isohyetal detail.

In large catchments there is spatial variability in rainfall across a catchment which is

not captured when undertaking rainfall-runoff modelling using rainfall time series from

the rain gauges. If using a single rain gauge, there might be problems with the location

of the rain gauge in terms of capturing a representative rainfall for all the rainfall

events. If using a spatial rainfall product (SILO or AWAP in Australia), there can be

uncertainties introduced because of the method used for interpolating rainfall between

rain gauges and changes in the rain gauge network over time. Interpolation methods

currently used are more suited to areas where rainfall varies less over space and in

time. They do not account well for orographic effects, and rainfall networks in Australia

historically have not captured the spatial and temporal variations in tropical andmonsoonal areas well.

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Vaze et al., 2010b discusses testing carried out considering the effects of using

different rainfall data sets on the calibration and simulation of conceptual rainfall-runoff

models. They conclude that considerable improvements can be made in the modelled

daily runoff and mean annual runoff with better spatial representation of rainfall. Where

a single lumped catchment-average daily rainfall series is used, care should be taken

to obtain a rainfall series that best represents the spatial rainfall distribution across the

catchment. However where only estimates of runoff at the catchment outlet arerequired, there is little advantage in driving a rainfall-runoff model with different rainfall

inputs from different parts of the catchment compared to using a single lumped rainfall

series for the catchment.

4.4 Evapotranspiration

The measured pan evaporation data can be obtained for all the locations with the

evaporation gauges installed (in Australia from the Bureau of Meteorology (BoM) basic

records). In Australia there are also some spatial climate products which use point

evaporation measurements recorded by the BoM and use an interpolation schemes to

produce spatial evaporation surfaces (SILO, J effrey et al., 2001; AWAP, J ones et al.,

2009).

The network of pan evaporation recording stations in Australia is sparse in comparison

to stream flow and rainfall networks, although there is some compensation in that

typically potential evapotranspiration exhibits substantially higher spatial correlation

than rainfall or stream flow. This limits the ability to accurately represent the true spatial

and temporal variability in evaporation in models however the spatial variability in

evaporation is much smaller compared to the variability in rainfall.

The BoM network records pan evaporation. Modelling requires potentialevapotranspiration (PET). There are a number of methods to convert pan evaporation

to PET including Penman Monteith, Mortons and accepted pan factors. These use

climatic variables in the conversion calculation including solar radiation, temperature,

vapour pressure, and wind speed which are recorded at some pan recording stations

but not all. This further limits the network available to draw data from.

When all the required data is available the conversion calculations will use the records

but often some variable is missing and estimates of that variable are made and used.

Where there is no data for the climatic variables, calculated pan to PET conversion

factors from a nearby station can be used to derive PET from pan evaporation.

Commonly the spatial products have interpolated layers for a range of climatic factors

and the spatial PET layer is calculated from data in these layers rather than

interpolating PET calculated at recording stations.


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5 Statistical Metrics for TestingPerformance

There are many performance measures used to consider the acceptability of a rainfall-runoff model. In all cases visual assessment and statistical results of some sort will be

assessed to identify the ability of the model to reproduce the flows it is calibrated or

validated against. All may contribute to best practice and which measures are more

appropriate will be directed by the modelling objective. A number of commonly used

visual assessment techniques are outlined inTable 51. Statistical performance

measures and their relevance in various study types are listed below inTable 52.

Table 51 Plots for assessing model performance

Plot Assessment and Purpose

Daily and monthly plots (linear

and log)

Used to check the general size, shape and timing of hydrographs.

Linear plots will better show medium and high flows and log plots low

flows. Baseflow and recession characteristics can be reviewed. If

recessions are frequently too flat then this can indicate that the

interflow and baseflow are not represented correctly.

Scatter Plot Scatter plots show the ability of the model to match flows on actual

time steps. They show the flow ranges where the model is more

accurate. Linear and log plots will show the variability across the

various flow ranges. Often a line of best fit is shown to indicate the

bias of the model in estimating flows.

Ranked Plots Commonly referred to as frequency of excedence or flow durationgraphs, they show the percentage of time a flow is exceeded over the

modelled period. They show whether the modelled output can

replicate the observed flow regime. Flow duration curves are effective

diagnostics to ensure that both the variability and the seasonal pattern

are captured.

Cumulative mass or cumulative

residual mass curves

Scatter plots and flow duration curves do not examine the time

sequence of events. A model could appear to be replicating the flow

regime however the replication of regimes during wet and dry periods

may not be adequate. A cumulative residual mass curve is a

cumulative plot of residuals (flow value - mean of all values). A

residual, and therefore slope of the curve, will be positive during wet

periods as flows are higher than average and during dry periods theslope will be negative. If the curves diverge there may be a data

issue. If they diverge consistently in all wet or all dry periods it is likely

that model parameterisation for wet periods or dry periods may not be

appropriate.

Plotting average daily or monthly

flows (average of all Days,

average of all J anuaries)

A simple diagnostic to ensuring that the model can replicate

seasonality characteristics.

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Table 52 Statistical performance measures (metrics) and their relevance in variousstudy types (Y Yes, N No)

Metric Purpose

Runoff

Yield

Climate

change

Landuse

change

Low

flow

Water

quality

Peak flow

/ floods

Difference in total runoff Y Y Y N N Y

Difference in total runoff over different

seasons of the year*Y

Y Y Y YY

Difference in total runoff contained

within high, medium and low parts of the

flow duration curve

Y

Y Y Y YY (high

flows)

Difference in proportion of time that

cease to flow occursN

Y Y Y YN

Difference in the slope of logarithm of

flow versus time for baseflow recessionperiods

N

N Y Y Y

N

Mean square error between observed

and modelled runoffY

Y Y N NY

Coefficient of determination (often

referred to as r)Y

Y Y N NY

Nash Sutcliffe coefficient of efficiency

on daily flowsY

Y Y N NY


on monthly accumulated flowsY

Y Y N NN


calculated using logarithm transformed

flows

N

Y Y Y Y

N

* Definition of seasons to be used will vary depending upon the climatic zone that the catchment is in. For

tropical areas, two seasons (a wet season from December-April and dry season from May-November)

may be appropriate. In Southern Australia, it may be appropriate to consider the four conventional

calendar seasons (Dec-Feb, Mar-May, J un-Aug and Sep-Nov).

** Definitions of high, medium and low flow ranges will depend upon the purpose of the study and the

catchment. Typical ranges might be High flows: days in observed data in the 0 to 20% probability of

exceedance range; Medium flows: days in observed data in the 20 to 80% probability of exceedance

range; Low flows: days in observed data with greater than 80% probability of exceedance and above the

cease to flow level at the gauge. Adjustment of the low and medium flow ranges may be required

particularly in response to the probability of cease to flow conditions at the gauge.


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recorded data should be checked using independent data sources (such as aerial

photography, satellite imagery, landuse, topographic or other spatial information).

A more sophisticated calibration approach can involve multiple calibration and

validation periods. As in the simple split sample approach, the model is calibrated to a

calibration period and then performance is tested over the validation period without

changing the model parameter values. This approach is then repeated multiple times,with each replicate using different start and end dates for the respective calibration and

validation periods. This allows a range of model performance statistics for calibration

and validation periods to be reported.

There will be some instances with this calibration and validation approach where the

calibrated parameters perform well against the calibration data set, but performs poorly

against the validation data set. In research type investigations, where the modeller may

be comparing different rainfall-runoff models, calibration methods, or objective

functions, the validation results can be used directly to help decide the best model or

method or objective function. However, in practical applications, a modeller may have

to decide either not to change the calibrated parameters and report the poor results, or

to recalibrate the model because the performance is unacceptable.

The modeller may choose the latter option, and may then recalibrate and compare

against the validation data set several times until the calibrated parameters perform

acceptably against both data sets. However, as the validation data set has now been

used to change the calibrated parameters, it is no longer an independent data set and

has in effect indirectly become part of the calibration data set.

This risk of having much poorer performance in validation than calibration may be

mitigated by ensuring as far as possible both data sets have similar flow distributions,An arbitrary approach to splitting the data, e.g., at the midpoint, may result in half of the

data being in a much wetter period. A model calibrated to these conditions would not

be expected to perform well under the drier conditions in the validation data set. More

alternate approaches should be considered on how to split the data set, perhaps into

non-contiguous periods, to ensure overall flow distributions are similar in each period.

Data is a valuable resource, and should be used to greatest effect. In most Australian

conditions, long data sets are needed to adequately represent climatic variability. An

alternative approach to having split samples is to use the complete data set to calibrate

the model, then to report its performance for different sub-periods, e.g., first half and

last half, or decadally, or driest X year period and wettest X year period. The objectivewould be to have a comparatively persistent performance across all these periods. This

does not necessarily give you an independent assessment of performance, but does

report on performance under different conditions.

Transposition of model parameter values from gauged to ungauged catchments may

be tested using a spatial variant on split sample validation. Under this approach,

component models from a gauged catchment with the calibrated parameter values for

that catchment can be applied to another gauged catchment to test the uncertainty and

bias introduced from transposition. Uncertainty ranges can be established by testing

contributions flow series produced by model outputs with parameter sets adopted from


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several different gauged catchments. Examples of the performance of these

transposition approaches are discussed in Viney et al. (2009) and Chiew (2010).

Generally the same metrics used to assess the performance of the model during

calibration are also used to assess model performance during validation. The model

performance during validation is almost always poorer than during calibration because

model parameters are deliberately not specifically fitted to the data for the validationperiod.

6.3 Calibration and Validation of Models to Single Gauge

Sites, Mult iple Gauge Sites and Regionalisation of Model

Parameter Sets

It is a very common situation in a project that involves rainfall runoff modelling for flow

time series to be required for several catchments or subcatchments within the model

domain and for data to be available from two or more stream flow gauges to facilitate

calibration and validation. At locations where gauged flows are available and flowestimates are required, two options are available to the modeller:

The rainfall runoff models may be calibrated independently for each gauged

catchment. In this case, independent parameter sets will be derived for the

rainfall runoff models of each catchment; or

A joint calibration may be performed, with rainfall runoff models calibrated with

consistent parameters to fit to the gauge records from two or more gauges. In

this case, a single set of rainfall runoff model parameters will be produced for all

of the catchments that represents a compromise to fit the flows at all of the

gauges within that group.

The advantage of the joint calibration approach is that, assuming some degree of

homogeneity in the rainfall runoff response of the selected gauged catchments, the

parameter sets produced should be more robust when applied to other catchments

with similar response that were not used for the calibration.

If an automated calibration process is used for joint calibration of multiple catchments,

the objective function used for automated calibration to the gauged catchments will be

a weighted average of the objective function values produced at the individual gauges.

Options for selecting the weighting values are:

All gauged catchments contribute equally to the overall objective function;

Weights are assigned according to the length of available record (e.g. number

of days with data) at each site;

Weights are assigned according to the inverse of the correlation coefficient in

gauged flows between one gauge and one or more of the other gauges in the

set (i.e. gauges with strongly correlated recorded flows are assigned lower

weighting factors than gauges that have weaker correlations with other gauges);

Some combination of the above factors.

There are three main methods of developing flow data sets in residual ungauged

catchments between upstream and downstream gauges:

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1. Calibrate a model to the difference in flow between the gauged upstream flows

routed to downstream (adjusted for known transmission losses) and

downstream gauges.

2. Adjust a flow data set from a nearby catchment using either recorded or

generated data,

3. Apply parameter values from other calibrated models and use catchment

appropriate climate data.

There are two main methods of developing flow data sets in ungauged catchments:

1. Develop a regression equation between flows for the ungauged catchment and

gauged catchments and apply this equation to transpose the flow, or

2. Apply parameter values from other calibrated models and use catchment

appropriate climate data.

Generally in the second case parameters for a neighbouring or nearby catchment are

used but climate data and catchment characterises of the catchment of interest are

applied in the model. Many studies have shown that selecting a donor catchmentbased on spatial proximity gives similar or better results than selecting a donor

catchment based on catchment attributes (Merz and Bloschl 2004, Oudin et al 2008;

Parajka et al. 2005; Zhang and Chiew 2009).

6.4 Automated, Manual and Hybrid Calibration Strategies

Calibration of hydrological models can be conducted using manual or automated

methods, or a combination of the two approaches (see Boyle et al, 2000 and Brdossy,

2007 for frameworks for combining manual and automated methods of model

calibration). Calibration involves the adjustment of model parameter values to improve

the fit of model output data to observations to a level that is acceptable.

In case of manual calibration, definition of goodness of fit is usually produced as a

combination of statistical indices and visual inspection of the observed and simulated

hydrographs. Whereas in case of automated calibration, definition of goodness of fit

is usually produced using an objective function. The objective function translates the

observed and modelled outputs into a single number, so that the results of successive

calibration iterations can be compared. Automated calibration routines use a defined

algorithm that runs the model multiple times, adjusting model parameter values

according to a strategy that is intended to improve the value of the objective function.

The sections that follow give information on the calibration methods available and their

relevance in various study types (shown in Table 3-1) which dealt with model choice

appropriate for intended purposes.

6.4.1 Manual Calibration

Manual calibration involves the modeller selecting a set of parameters for their model,

running the model once and then examining the output statistics from the model (from

the list discussed in Section 5). The modeller would then revise the values of one or

more parameters and repeat the above process. This may continue many times until

the model achieves the desired level of performance.


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4. Repeatable. Different people will get same parameter values.

Weaknesses:

1. Is confined by the optimisation routine chosen and how the objectives are set.

2. Is dependent on the computer routine being set up accurately to reflect thechoices in 1.

3. Lack ability to check the relationships between the calibrated parameter values

produced as the calibration proceeds, which may cause investigation of sets of

parameters that are infeasible (unless appropriate checks are build within the

calibration algorithms).

4. Software is required to automate the optimisation process.

5. Parameter values commonly become trapped against the minimum and

maximum constraints of the allowable parameter ranges set by the user. If the

user does not check for this, the parameter set chosen may be sub-optimal as

the best parameter set may have parameter values that lie outside the

constraints set by the user at the time the optimisation is initiated.

There are two global optimisation methods included in Source: Shuffled Complex

Evolution (SCE) and Uniform random sampling. The analysis undertaken as part of the

testing with data from 200+ catchments in southeast Australia showed that there is an

advantage in following a global optimiser with a local optimiser to fine tune the

calibrated parameter values. The Rosenbrock method is included in the framework as

a local optimiser. The testing results suggest that the combination of SCE followed by

the Rosenbrock should be used (Vaze et al., 2011a,d).

6.4.3 Hybrid Calibration Str

ewater guidelines rrm (v1 0 interim dec 2011)

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