fundamentals of modelling: the process and tools · fundamentals of modelling: the process and...

Fundamentals of modelling:The process and toolsWater Modelling Overview

BASIN MANAGEMENT OUTCOMES – LAND AND WATER

Geoff Podger10:00-10:45 (4:00-4:45) am Friday, 4 Nov 2016

• Best Practice Modelling– 10 basic steps to problem definition

• Modelling tools and data• Building confidence and model acceptance• Interpreting outputs and telling the narrative

Contents

Fundamentals of Modelling | Geoff Podger2 |

Best Practice Modelling Guidelines

• 4 four major components• Project administration• Problem definition• Option modelling• Preferred option

• Needs to be done in the knowledge of the issues to be addressed

• Needs to be done before considering modelling and solutions

• It guides the nature and form of the conceptual model

• Stakeholder engagement is key


Best practice modelling framework


Problem definition

1. Problem statement2. Objectives3. Understanding the

problem domain4. System definition5. Conceptual models6. Metrics and criteria7. Decision variables8. Uncertainty and risk9. Preliminary

assessment10. Getting the narrative

right


Problem definition

• Defining the purpose and scope of work• Setting performance criteria• Getting early stakeholder buy-in and support

• Required for both new projects or an extension to existing work• Is likely to be an iterative process

• Budget and Terms of Reference may be provided but are rarely sufficient for a project to be implemented


1. Problem statement

Defining the problem is the most important step in any solution finding strategy• Requires consultation with water managers/policy officers,

modellers and the wider community• Should be initially considered free of time resource and budget

constraints so all problems and solutions are considered• The constraints on possible ways forward can be clearly explained• Technical• Budget• Time• Resource

• Transparent priority setting for multiple options• Is there a role for modelling in the project and what is this role?


2. Objectives

• Project goals and objectives should be identified in a consultative process

• There may be multiple goals

• It might be useful to describe objectives in a hierarchy• Consideration should be given to possible future objectives and

goals as this might influence the design of the initial project.

• The decision on the best option will be guided by how well it meets the agreed objectives


3. Understanding the problem domain

• The range of disciplines required to the understand the problem space needs to be identified and agreed:• Water managers and policy officers• Discipline experts• Modellers• Wider community/stakeholders

• Need to consider the environmental and economic impacts• Social acceptance and adoption: just because the modelling says it

is a good option does not mean that it will be adopted


4. System definition

• Should be done in consultation with stakeholders• Is critical to model applicability• Requires identification of system• Components• Behaviour• Feedbacks• Boundaries• Forcing states• Outputs

• Need to decide what is in and out of scope• Selection of the appropriate temporal and spatial scale and extent,

which may be constrained by available data• Documented in the conceptual model


5. Conceptual model

• An agreed understanding of how the system works and the relative importance of different aspects

• Assumptions should be clearly stated (simplifications, exclusions)

• It is important that agreement is reached with stakeholders on the conceptual model. This may be an iterative procedure.

• Should consider multiple alternative conceptual models to understand model uncertainty

• Existing models should be considered in the context of the required conceptual model. What are limitations and how can these be addressed?


Qin(obs)

Qloss(sim)

Qout(sim) vs Qout(obs)

Qin-trib(obs)

Qin-rain(obs)-Qout-evp(obs) (in-stream)

Qin-ug-tribs(sim)

Loss & delay due to dead storage

u/s gauge

d/s gauge

Loss & delay due to over-bank flow(Evap & GW) (above threshold)

Conceptual Model Design

• Climate• Rainfall/Snow/Ice• Surface/groundwater• Water quality• River systems modelling• Biophysical modelling

• Ecosystems• Socio-economic modelling

• Potable water• Hydropower• Agriculture• Ecosystem services• Social benefits• Gender

Geoff Podger | Fundamentals of Modelling | Geoff Podger12 |

Integrating Conceptual Framework

Hydrological systems and assetsServices

WellbeingBio-physical Modelling

Threats

Levers for trade-off scenarios

Risk management

Socio-economicModelling

SocialDecisionModelling

Integrating Conceptual Framework

Cyrosphere / Snow melt

models

River Flows Groundwater

Sediment

Nutrients

Rainfall / Runoff models

Assets

Hydropower

Food production

Ecosystem Services

Hazard/Risk

Services

Energy production

Water Security

Economic Security

Food Security

Wellbeing

Energy Security

Sense of Security / Place

Bio-physical Modelling

Climate• Change• Variability

Population Growth

Land use• Forest cover• Landslides• Bushfires

Threats

Environmental Security

Storages Floodplains

Water Quantity Water Quality

Irrigation

Environment

Levers for trade-off scenariosPolicy/Management, Build Infrastructure, Treatment, Targets/Standards, Education

Risk management

Socio-economicModelling

Over-allocation• Surface water• Groundwater

Pollution• Point• Non point

Tran

spor

t and

fate

Trea

tmen

tOrganics

Pathogen

Heavy metals

Urban water

Industrial

Recreational

Navigation

SocialDecisionModelling

Potable WaterEnergy

Security

Model choice

• There are lots of different models available (too many acronyms to remember) and in many cases the underlying algorithms are similar

• There is no one model that can do everything• Model choice is a trade-off

• Parsimony: Choose the simplest model that best answers the question

• Take into consideration uncertainty. Is the model telling us something useful or is it noise?

ModelChoice

Problem Space

ComplexityData


Model choice questions

Problem Space (What are the issues to be considered?)Planning (scale, sectors, sharing rules, WQ, SW/GW)Operations (dams, structures, hydro, irrigation, environment, culture, WQ, GW

pumping)Forecasts (scale, lead time, extent, floods, allocations)

Data (What is needed and available?)Global/Local, Observed/Inferred, Historic/Real-timeWhat is the uncertainty in the data?

Complexity (What is justified given data and problem space?)Spatial and temporal scaleProcess descriptionRun timesNumber or parameters


Conceptual model check list

• Is the conceptual model domains, boundary conditions spatial/temporal scales and extents considered?

• Are all relevant processes/dependencies addressed and assumptions and limitations clearly stated?

• Has the need for an alternative conceptual model been addressed?

• Is the conceptual model sound and defensible?• Is the model complexity supported by the available data?• Has this conceptual model been applied elsewhere and lessons

learnt have been considered?• Is the conceptual model consistent with the project objectives?


6. Metrics and criteria

Model performance criteria and metrics need to be agreed up front else the goal post will change as results emerge• Performance criteria and indicators that demonstrate compliance with

agreed objectives need to be identified and agreed between modellers and stakeholders

• The metrics and criteria should reflect the purpose and objectives of the model e.g. different criteria for flood models compared to planning models

• Some performance criteria may be set by legislation e.g. drinking water quality standards

• A baseline period and associated development conditions needs to be agreed where performance against a baseline are considered

• Positive change might be a sufficient socially acceptable metric• Risk around meeting performance criteria needs to be considered


7. Decision variables

Decision variables include anything that the stakeholders can adjust to influence the performance of the system• Decision variables should be agreed early in the project in

consultation with stakeholders• Might include social and economic instruments and incentives, as

well as institutional arrangements and the biophysical components of the system

• Different solutions are generated by considering different states of decision variables


http://www.birdsinbackyards.net/sites/www.birdsinbackyards.net/files/factsheets/image/other/little_egret_2.jpg

http://www.scienceimage.csiro.au/index.cfm?event=site.image.detail&id=5239

8. Uncertainty and risk

Uncertainty• Sources of uncertainty should

be identified• Considered for deciding on

metrics and criteria• Considered for decision

variables• May be qualitative in the

problem definition phase

Risk• Considers the likelihood

(frequency/certainty) and consequence (impact on assets)

• Understanding uncertainty is a essential part of risk assessment

• Likelihoods may be expressed qualitatively (high, med, low) and derived from expert knowledge


9. Preliminary assessment

• Comprises a simple appraisal of the likely results from the planned modelling such that the results could be used as independent check on detailed modelling results.

• Where modelling results differ from the preliminary assessment further investigation of the model will be required to determine if the model is behaving correctly

• May lead to a review of project objectives and metrics


10. Getting the narrative rightSo what?• What does this mean for people• $ Impact for farmers• $ Recreation• Impact of energy on business• Loss/health of species• People flooded/loss of life• Sustainability of industry• Impacts on the vulnerable• Impacts on women and children


Cease to flow days

Flood level

Water Resource Management Technology

• Remote sensing (LIDAR, DEM, ET and Land use)

• Climate surfaces, GCMs and downscaling

• Water Resource Information Systems• Flood forecasting systems• Flexible hydrological modelling

frameworks• Workflow tools (integrating hydrological,

environmental and economic models)• Modelling uncertainty and risk• Technology in the cloud and web


The spectrum of DEM products

9 second SRTM 3 second DEM-H 1 second Lidar 5 mGeoff Podger | Fundamentals of Modelling | Geoff Podger24 |

Catchment delineation

Error in delineated watershed area

5%

0.5%

3 arcsec: 90 m1” : 30 m

DEM Cell Size

1990’s

2000’s

From: Maidment (2011)


Climate dataClimate variables• Precipitation (rainfall, snow)• Temperature• Potential evaporation

• Humidity• Solar radiation• Wind speed/run

Geoff Podger |

Data sources• BoM daily observations• BoM climate data sets• Silo rainfall

• Local data• Global products (NOAA)


Change in mean annual runoff for 2oC warming

2080 flood frequency for 20th century 100-year flood

Climate change impact on water


Water Resource Information Systems: Geofabric


• 3 month probabilistic outlook of unregulated total streamflow volumes

• Ensemble forecasts at 74 sites in 32 river basins

• Uses CSIRO developed statistical model (BJP)

• Further testing on sites in all states and territories

• Extend to 200 sites by the mid 2015

• www.bom.gov.au/water/ssf

Seasonal streamflow forecasting

http://www.bom.gov.au/water/ssf

Flood & short-term forecasting

Ensemble forecast of a flood event in the Stanley RiverGeoff Podger | Fundamentals of Modelling | Geoff Podger30 |

Short term streamflow forecasting

• Flow forecasts up to 10 days ahead

• Unregulated inflows to regulated systems

• Includes rainfall forecasts

• R&D conducted through CSIRO

• Ovens River pilot for registered users


CLIMATE

LAND USE

ECOLOGICAL ASSETS

DAMS & WEIRS

IRRIGATIONCITIES

Flexible hydrological modelling frameworks


Tamor daily rainfall runoff model

• Area: 4058 km2

• Elevation: 422 – 8505 m• Glacier area: 13%Rainfall runoff model• GR4J+snow+glacier• 7 parameters• 44 HRUs• Bias 2% NSE 0.87

Indus River System Model Structure

Dam break modelling

• CSIRO 2 and 3d hydrodynamic models • Geheyan Dam in China• Different dam break scenarios

Scenario 1 Scenario 2 Scenario 3

Scenario 4 Scenario 5 Scenario 6


Urban water management


Irrigation modelling

• Farm scale bio-physical models (e.g. APSIM) consider:• Physical properties of different crops over growing period• Soil type• Water and heat stress• Irrigation efficiency• Fertiliser application• Yield and production

• Regional scale crop models (e.g. Source) consider:• Supply storages both regional and local• Water access rules• Multiple water sources (surface and groundwater)• Distribution and return systems (irrigation districts)• FAO 56 crop factors to drive current use and future demands• Losses (channel, escapes, deep percolation)• Crude yield and production estimates


http://www.scienceimage.csiro.au/index.cfm?event=site.image.detail&id=5239

Floodplain modellinghydrologic vs 2d hydrodynamic

a) Simulated inundation by LiDAR based approach b) Simulated inundation by 2D HD model

22 month event10 minute run time

55 day event10 day run time


• Revised annualised Ungauged Losses and Adjusted Losses (ML): Willara to Wanaaring.

(Positive values are losses, negative values are gains)

-200000

-100000

0

100000

200000

300000

1975 1980 1985 1990 1995 2000 2005 2010

Ungauged Loss FunctionAdjusted Loss Function

0

20000

40000

60000

80000

1/01/2000 1/04/2000 2/07/2000 1/10/2000 1/01/2001-10000

-5000

0

5000

10000Raw Flow (Fit + UGL)Groundwater LossUngauged Loss FunctionAdjusted ULF (ULF - GWL)

Revised daily flow components for the year 2000 (ML/d), Willara to Wanaaring.River flow and GW loss are on left-axis, loss/gain functions on the right-axis.

• Establishing relationships in losing and gaining streams

• Validating the relationships

• Integrating with the river system model

Preliminary results from Paroo

SW-GW connectivity map (MDBSY)

SW-GW interaction


Physically based groundwater models


Ecological Systems: Environmental Flow Modelling

Understanding Ecosystem Complexity

• Species, habitats and refugia

Predicting Ecological Outcomes• Ecological Response Models

• Driver-Pressure-Stressor-Impact-Response

Integration: Models & Assessment• Scenario-based tools

• Optimisation-based tools

Time Since Last Wet (Low_Floodplain)currentup to 05ybtn 05 to 1btn 1 to 2btn 2 to 5btn 5 to 10greater 10

0 0 0

100 0 0 0

Suitabiliy_TSLW1Extreme DryDryOptimal

0 0

100

Redgum_ForestExtreme DryDryOptimalWet

0 0

100 0

Suitabiliy_Duration1Extreme DryDryOptimalWet

0 0

100 0

Duration1irrelevantup to 1moup to 2moup to 4moup to 6moup to 12mo

100 0 0 0 0 0

Suitabiliy_FF1Extreme DryDryOptimalWet

0 0

100 0

FF_last_ten_yrs (Low Floodplain)one in oneone in twoone in fiveone in tenless one in ten

0 100

0 0 0


Predicting Ecological Outcomes: Models that are ‘Fit for Purpose’

No Data, High uncertainty

Some Data, Some Knowledge

Lots of Data, Good Knowledge

Data complete, Systemwell defined

Conceptual, Hydrologic Alteration

Expert system, Habitat models, Uncertainty analysis

Population dynamics, Function dynamics

Dynamic ecosystem models

Part

icip

atio

n


Economic modelling

• Local scale (farm, hydro station)• Regional scale (irrigation districts, countercyclical trade)• National scale (computable general equilibrium)• Trans-boundary scale (trade between countries)

Pric

e, $

Quantity, GL

Supply

Demand

Qe

Pe

Pareto surface


Getting confidence

• Applies to both modellers and stakeholders• A model that is not agreed by all stakeholders is useless• Reproducing history may be an important step• Be careful not to overfit models• Model uncertainty vs data uncertainty

• Ensuring the model is robust• Model parameters are stable• There is no equifinality• Ensure parsimony: every parameter needs to prove its right to be there

• Calibration and validation• Uncertainty and sensitivity


Choosing an objective function

Need to reflect what the model is going to be used for• Flood models need to represent:• Level/depth• Extent• High Flows

• Operation models• Daily volume and flow• Low to medium flows

• Planning models• Overall mass balance• Flux attribution• Average flows


419053 Manilla R @ Black Springs

Quantifying rating uncertainty Quantifying river model uncertainty

Quantifying groundwater model uncertaintyBATEA Uncertainty analysis

Sensitivity and uncertainty

Quantifying climate uncertainty

Examples of custom interfacesBrahmani system interface: 16 scenarios + climate change

Indus system

Lower Murray operations support system


Interpreting outputs

• It is not just about the physical outputs• Flow• Groundwater level• Water quality• Hydropower and irrigation production• Reliability of supply

• It is not just about the environmental and economic outputs• Wetland health• Fish health• Economic benefit for irrigation and power production

• It is about people• The stories and narratives from their perspective


0.0001

0.01

1

100

10000

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

B0H MIN

B0M MIN

B0D MIN

B0H MED

B0M MED

B0D MED

BOH MAX

BOM MAX

BOD MAX

Thank you

CSIRO LAND AND WATER FLAGSHIP

Land and Water Basin Management OutcomesGeoff PodgerProject Director SDIPt +61 2 6246 5851e [email protected] http://www.csiro.au/people/Geoff.Podger.html

Problem Definition Check ListProblem statement Have the issues to be addressed been discussed internally?

Have the issues to be addressed been discussed with stakeholders?Is a model required?Have different modelling options been explored?

Objectives Have objectives been agreed internally?Have objectives been agreed with stakeholders?Is there a clear objective hierarchy?

Problem domain Have the important disciplines been identified?Is there a plan for the community acceptance process?

System definition System components/modules identified?Is the existing system behaviour understood?At the various biophysical and socioeconomic interactions understood?Have temporal scale data constraints been considered?Have spatial scale data constraints been considered?Has data analysis and quality assurance been undertaken?Has prior knowledge been elicited and used?

Conceptual model Has the relative importance of components been considered?Have functional relationships been considered?Are the model assumptions clearly stated?Do you have stakeholder agreement?Have alternatives been assessed and documented?Has model soundness been considered?Does the model have a track record?Does the conceptual model meet project objectives?Has model complexity and parsimony been considered with respect to data?

Metrics and criteria Have been defined and agreed?Match the project objectives?Have local and international standards been considered?Analysis periods have been defined and agreed?

Decision variables Clearly defined and agreed?Uncertainty and risk Was uncertainty considered in the conceptual model?

Has uncertainty been considered for option modelling (will the model tell us anything useful)?Has risk assessment be considered?

Preliminary assessment Are the expected model outcomes clearly defined?

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