Introduction to Hydroinformatics - presentation for UNESCO-IHE students (v16)

1

Dr. Dimitri P. SolomatineProfessor of Hydroinformatics

“Programme with a difference”

HydroinformaticsModelling and information systems for integrated water management

D.P. Solomatine. Introduction to Hydroinformatics 2

Water

Water is an important constituent of the meteorological cyclePressure on water resourcesConsequences of climate changeNeed for conservation and sustainability of potable water resourcesNeed for better information and predictions - to understand and to manage water


Managing water resources

water-related decisions is difficult to test on large-scale experiments, hence importance of computer-based modelling and forecastingcontrol of water resources must be based on optimal solutionsmanagement of water needs a lot of data and informationfrom various sourcesneed for Computer-based modelling, Information and Communication Technology (ICT) tools


Hydroinformatics

modelling, information and communication technology, computer sciences

applied to problems of aquatic environment

with the purpose ofproper management


Modelling

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Modelling

Computer-based model isa simplified description of realityan encapsulation of knowledge about a particular physical or social process in electronic form

Hydroinformatics integratesdata,

models, people


Generations of modelling: a bit of history…

1. Computers used as calculation devices of analytical expressions – 1950s

no friendly interfaces

2. Mainframe computers used to solve differential equations numerically – 1960-70s

custom-built models


Generations of modelling

3. Production of modelling packages/systems for wide class of problems – 1980-90s

developed user interfaces“production lines” of models (modelling shells)refinement of solution methodspromotion of standardsmore clients more profits more enhancements


Generations of modelling

4. “Mass” production of modelling systems for PCs – 1990sprovision of products, not projectsaccess by non-specialistshigh standards of robustness and consistencyease of use via the sophisticated user interfacesinvolvement of software engineering and IT specialistsintegration with supporting tools and facilities


Generations of modelling. The fifth generation as we understand it now

5. Hydroinformatics systems – 1990s-Modelling as a central interface between

domain data (monitoring stations, weather radars, remote sensing)and human decision maker

Domain knowledge encapsulatorsIntegration of various types of modelsAlternative, non-process-based modelling paradigms (data-driven modelling)Potential of integration with artificial intelligence

5th generation modelling = CH AND AI(but this has not happen yet)


Hydroinformatics system: typical architecture

Data, information, knowledgePhysically-based

models

Data-drivenmodels

Decision support systems for management

Real world

Observations, Communication

User interface

Fact engines

Judgement engines

Knowledge-basesystems

Knowledge inference engines


Position of Hydroinformatics

Water EngineeringHydrology

Environment

Management

ICTComputingAI

Modelling

Systemssciences,

Optimisation

Instrumentation


Encapsulation of knowledge related to water

Tacit (implicit) knowledge embedded within a personWords, texts, images

printedstored in electronic media

Mathematical modelsformulasalgorithmsalgorithms encapsulated in computer programs (software)

Integrated systems encapsulating all of above –Hydroinformatics systems


Hydroinformatics system: flow of information

Earth observation, monitoring

Numerical Weather Prediction Models

Data modelling, integration with hydrologic and hydraulic models

Access to modellingresults

Data Models Knowledge Decisions

Decision support

Map of flood probability


Hydroinformatics systems in flood management

Data Models Knowledge Decisions

Map of flood probability


Models are indispensable in dealing with floods


Sobek modelling software (Delft Hydraulics) in designing urban master plan

visulaizing potential floodings:


Hydroinformatics systems for flood warning: MIKE FloodWatch

MIKE Flood Watch (Danish Hydraulic Institute), a decision support system for real-time flood forecasting:

advanced time series data base MIKE 11, for hydrodynamic modelingMIKE 11 FF, real-time forecasting system, ArcView, Geographical Information System (GIS)


Hydroinformatics systems for flood warning: MIKE FloodWatch


Flood warning systems: Piemonte case study


Architecture of a hydroinformatics system for flood warning in Athens, Greece (IHE project)

DSSMAIN

INTERFACE

Connector toTelemetricdata storage

Connector toHydrologic/Hydraulic

Model

CommunicationModule

(Email, FTP)

GISModule Database

Module

DecisionTree

Module

Post floodevaluation anddocumentation

module

Trigger

MeteorologicalModels

Hydrologic/HydraulicModels

TelemetricData

Internet, telephone lines


Flood warning system interface, developed by Hydroinformatics participant


Warragamba Dam, Australia

Warragamba Dam - 65 km west of Sydney in the Burragorang Valley

provides the major water supply for SydneyWarragamba River flows through a 300-600 m wide gorge, about 100 m deep before opening out into a large valley. This allows a relatively short and high dam to impound a vast quantity of water.

A dam break of the WarragambaDam would be a major disaster. SOBEK (Delft Hydraulics) software was used for simulation


Warragamba Dam, AustraliaSimulation of the dam break with SOBEK, Delft

HydraulicsThe animation shows the simulation results. They may be used for disaster management, evacuation planning, flood damage assessment, urban planning


Use of 2-dimensional modellingin Jamuna bridge project, Bangladesh

construction of a 4 km bridge and several river training works for guiding the flow to pass under the bridgeDanish Hydraulic Institute (DHI) carried out a study of the river morphology to enable the contractor to take the preventive or remedial measures (MIKE 21 modelling system was used)


Eutrophication modelling of a tidal lagoon in Bali, Indonesia


Example: Eutrophication modelling of a tidal lagoon in Bali, Indonesia

Turtle Island is an enlargement of the existing SeranganIsland at the entrance to Benoa Bay; three artificial lagoons are planned for leisure crafts and beachesat the order of Penta Ocean Construction Co. LTD, DHI performed modelling for water quality in terms of rooted benthic vegetation, macroalgae, concentrations of phytoplankton, nutrients and oxygen MIKE 21 EU (eutrophication) model was used; various scenarios were analysed. The concentration field of Chlorophyll is shown; the area is strongly influenced by tidal flushing and dries out during each tidal cycle


Example of Integrated Modelling:a Case Study for Sonso Lake, Colombia

(Masters study of Mr. Carlos Velez performed together with the experts from Delft Hydraulics)

Problem: 70% of the surface area of this shallow lake is covered by an invasive macrophite Water HyacinthCauses:

Nutrients pollution from agricultural use of landLack of sustainable management of the lake

Methodology:Integrated modelling of Water Hyacinth growth (ecological – water flow model)

Results: the developed model makes it possible to analyse alternatives to manage the Water Hyacinth infestation


Ecosystem Integrated Model: a Case Study for Sonso Lake, Colombia

Ecosystem

Hydrodynamic

Water Quality

Sobek Rural1D2D

Sobek RuralDELWAQ

Water Hyacinth Model (coded using SOBEK RURAL Open

Process Library)

Water HyacinthNH4

NO3

Norg Porg

PO4

Organic Matter Settled

9 10

13

12

Solar Radiation

11

14

1615

7

8

6

5Water Volume

2 3

1

4VelocityWater Depth

Flow

SEDIMENT

WATER SURFACE56

9

1. Input / Output2. Rainfall3. Evapotranspiration4. Advection/Dispersion

5. Input / Output6. Input / Output7. Sedimentation8. Resuspension

13. Photosynthesis14. Respiration15. Mortality16. Losses

9. Resuspension10. Hydrolysis11. Oxidation 12. Uptake/Growth

PROCESSES

Developed by Carlos Velez. Supervisors: A. Mynett, L. Postma, A. v. Griensven


Importance of modelling

reduces complexityencapsulates knowledgeprovides a laboratory experiencerefines tacit knowledgeenables reasoned intervention by humansfacilitates communicationassists education and training


Decision making process

Modelling

Archiving and analysis

Decision making

Acquisition of

information

Feedback and control

Application and

Evaluation

“Side effect”: Knowledge discovery

Objectives

World of water


Beyond physically-based models:

Computational intelligenceOptimisation and integrationInternet-based computing


Case study SIEVE: flood management problem

mountaneous catchment in Southern Europearea of 822 sq. km


SIEVE: visualization of data

variables for building a decision tree model were selected on the basis of cross-correlation analysis and average mutual information:

inputs: rainfalls REt, REt-1, REt-2, REt-3, flows Qt, Qt-1

outputs: flows Qt+1 or Qt+3

FLOW1: effective rainfall and discharge data

0

100

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400

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800

0 500 1000 1500 2000 2500

Time [hrs]

Discharge [m3/s]

0

2

4

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18

20

Discharge [m3/s]Eff.rainfall [mm]

Effective rainfall [mm]


Process (physically-based) modelling of flow:river modelling context

Available data:rainfalls Rt lateral inflows QLcatchment and river physical properties (soil, geometry, roughnesses)initial and boundary conditions for flows Q 0(x,t)

Inputs: QL(x,t), Qup(t), Q 0(x,t) , system propertiesOutput: flow Q (x, t)Model:

Q (x, t)=F (QL(x,t), Qup(t), Q 0(x,t) , system properties)Questions:

are the physical properties of the catchment known?is F good enough ?

QQtt

QQttupup

RRtt

0=+thb

xQ

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∂∂

0)( 2

2

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KQQ

gAHhx

gAA

Qxt

Q∂∂β

∂∂

∂∂


Using data-driven methods in rainfall-runoff modelling

Available data:rainfalls Rt

runoffs (flows) Qt

Inputs: lagged rainfalls Rt Rt-1 Rt-2 …Output to predict: Qt+T

Model: Qt+T = F (Rt Rt-1 … Qt Qt-1 …Qtup Qt-1

up …)(routing)

Questions: how to find the appropriate lags? (lags embody the physical properties of the catchment)how to build non-linear regression function F ?

QQtt

QQttupup

RRtt


Artificial neural network: a universal function approximator

u F a a x

j= ,..., N

j oj ij ii

N

hid

inp

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1

1

y F b b u

k= ,..., N

k ok jk ji

N

out

hid

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1

1

There are (Ninp+1)Nhid + (Nhid+1)Nout weights to be identified

Hidden layer

a ij

Inputs

x 1x 2 x 3

x nOutputs

y1y2y3

ym

u 1x

u s

b jkweights weights

x

f(x)1

0 Binary Sigmoid : F(x) = 1/ (1 + e-x)


Neural network tool in predicting flows


ANN verification RMSE=11.353NRMSE=0.234COE=0.9452

MT verificationRMSE=12.548NRMSE=0.258COE=0.9331

SIEVE: Predicting Q(t+3) three hours ahead

Prediction of Qt+3 : Verification performance

0

50

100

150

200

250

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350

0 20 40 60 80 100 120 140 160 180t [hrs]

Q [m

3 /s]

ObservedModelled (ANN)Modelled (MT)

D.P. Solomatine. Hydroinformatics. 40

MULTI-OBJECTIVE OPTIMIZATION

Finding variables’ values that bring the value of the “objective function” to a minimumIn water resources many problems require solving an optimization problem


Floo

d D

amag

e

Costs

Urban drainage system rehabilitation:use of multi-objective optimization

rehabilitation: changing pipes, creating additional storagesoptimization by multi-objective genetic algorithm: find a compromise btw. min. cost and min. damage due to flooding

Wastewater System Pipe Network Model (MOUSE)

Optimization Procedure (GLOBE, NSGA-II)

Data Processor Data Processor

Compromise optimal solutions


Using Random Search Global Optimization methods in water distribution network rehabilitation

Optimization of Networks with Predetermined Topology

Number and length of pipesDemand at every node (including pressure)Other hydraulic elementsCommercially available pipe sizes

Decision VariablesDiameter of each pipe in the network

Result: with optimal pipe diameters costs are 20% lower


Computational intelligence in generating inundation maps, Yellow River Commission


UNCERTAINTY

Uncertainties associated with climate change are very highDifferent IPCC scenarios lead to very different results of water modelsAny study exploring the impacts of CC needs powerful tools for analysing and predicting uncertainty


Role of uncertaintyin water management

0

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30

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60

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1 11 21 31 41 51Ti me

Disc

harg

e

One est i mat eUpper boundLower bound

Alarm level

Prediction interval (uncertainty)

Deterministic forecast

Fore

cast

ed ri

ver d

isch

arge

So, issue a flood alarm or not?..


Monte Carlo simulation of parametric uncertaintyy = M(x, s, θ) + εs + εθ + εx + εy


750 775 800 825 8500

1000

2000

3000

4000

Time(day)

Obs

erve

d flo

w (m

3 /s)

90% prediction limitsObserved flow

Rainfall-Discharge plot

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1000

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3000

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6000

Jan-

88

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-88

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89O

ct-8

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-90

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0N

ov-9

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b-93

Jun-

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Time [days]

Run

off [

Cum

ec]

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50

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Prec

ipita

tion

[mm

]

Runoff [Cumec] Precipitation [mm]

Estimated prediction bounds: verification (Bagmati river basin, Nepal)

LZ

UZ

SM

RF

R

PERC

EA

Q=Q0+Q1Q1

Transformfunction

SP

Q0

SF

CFLUX

IN

SF – SnowRF – RainEA – EvapotranspirationSP – Snow coverIN – InfiltrationR – RechargeSM – Soil moistureCFLUX – Capillary transportUZ – Storage in upper reservoirPERC – PercolationLZ – Storage in lower reservoirQo – Fast runoff componentQ1 – Slow runoff componentQ – Total runoff

LZ

UZ

SM

RFRF

RR

PERCPERC

EAEA

Q=Q0+Q1Q1Q1

Transformfunction

SP

Q0Q0

SFSF

CFLUXCFLUX

ININ

SF – SnowRF – RainEA – EvapotranspirationSP – Snow coverIN – InfiltrationR – RechargeSM – Soil moistureCFLUX – Capillary transportUZ – Storage in upper reservoirPERC – PercolationLZ – Storage in lower reservoirQo – Fast runoff componentQ1 – Slow runoff componentQ – Total runoff


Internet-based computing and knowledge management


Access to knowledge via Internet

Support for Communities of Practice

Internet

Database andtransaction server

Web Application Server

Web-based platform for engineering services

Client's PC Mobile client's PC

Experts

Databases

Knowledgebases

Forum(bulleting board)

Authorization and support for E-commerce

MessagingComputer

conferencing

Documentbases

Access to data-,knowledge- anddocument base

Distance learning

Document base with intelligent searchCase studiesProjects descriptionsExpertise profiles

Fun

ctio

ns

Users Users

Software(modellingsystems)

Knowledgemaps

Experts' PCs

Conference tools

Intranet


Examples of systems developed by MSc and PhD participants

modelling across Internet (Delphi, PHP, Java)

water distribution system modelling (exercise, Delphi)free-surface modelling across Internet (individual study, Java)distrbuted database for the water authority of Egypt (individual study, thin-client DB, Delphi)flood warning system (PhD study, Delphi)


Example: water distribution modelling


Example: web-based decision support system


Hydroinformatics specialisation at IHE


Hydroinformatics programme at IHE

Fundamentals, hydraulic, hydrologic and environmental processes Fundamentals, hydraulic, hydrologic and environmental processes

PhysicallyPhysically--based based simulation modelling simulation modelling

and toolsand tools

Information systems, GIS, communications, InternetInformation systems, GIS, communications, Internet

DataData--driven modelling driven modelling and computational and computational intelligence toolsintelligence tools

Integration of technologies, project managementIntegration of technologies, project management

Elective advanced topics

Systems analysis, Systems analysis, decision support, decision support,

optimizationoptimization

•• ArcGISArcGIS•• AccessAccess

•• SOBEKSOBEK•• RIBASIMRIBASIM•• Delft 3DDelft 3D•• SWATSWAT•• EPANETEPANET•• MOUSEMOUSE•• AquariusAquarius

•• MIKE 11MIKE 11•• HECHEC--RASRAS•• MIKE 21MIKE 21•• MIKE SHEMIKE SHE•• RIBASIMRIBASIM•• WEST++WEST++•• MODFLOWMODFLOW

•• LINGOLINGO•• GLOBEGLOBE•• BSCW BSCW •• AquaVoiceAquaVoice

•• NeuroSolutionsNeuroSolutions•• NeuralMachineNeuralMachine•• AFUZAFUZ•• WEKAWEKA

•• MatlabMatlab•• DelphiDelphiToolsTools

•• JAVA JAVA •• UltraDevUltraDev

with applications to:with applications to:-- River basin managementRiver basin management-- Flood managementFlood management-- Urban systemsUrban systems-- Coastal systemsCoastal systems-- Groundwater and Groundwater and catchment hydrologycatchment hydrology

-- Environmental systemsEnvironmental systems(options)(options)


Hydroinformatics Study Modules

Introduction to Water science and EngineeringApplied HydraulicsGeo-information systemsComputational Hydraulics and Information ManagementModelling theory and applicationsComputational Intelligence and Control SystemsRiver Basin ModellingEnvironmental systems modellingFieldtrip to Florida, USASelective modelling subjects:

Flood risk managementUrban water systems modellingEnvironmental systems modelling

Hydroinformatics for Decision SupportGroupworkResearch proposal drafting and Special TopicsMSc research


Examples of MSc topics

Flood modelling and forecasting for Awash river basin in EthiopiaHarmful Algal Bloom prediction, case study of Western Xiamen Bay, ChinaApplication of Neural Networks to rainfall-runoff modelling in the upper reach of the Huai river basin, ChinaHeihe River Basin Water Resources Decision Support SystemDecision Support System for Irrigation Management in VietnamHydroinformatics for real time water quality management and operation of distribution networks, case study Villavicencio, Colombia1D-2D Coupling Urban Flooding Model using radar data in BangkokUrban Flood Warning System with wireless technology, case Study of Dhaka City, BangladeshWater distribution modelling with intermittent supply: sensitivity analysis and performance evaluation for Bani-Suhila City, Palestine


using neural network model to replicate the behaviour of a complex hydrodynamic modelusing fuzzy rule-based system to restore the missing rainfall datausing neural networks and fuzzy systems for controlling water levels in polder areasusing data mining and chaos theory to predict the surge in the coastal areas of The Netherlands for ship guidance

Note: around 50% of Hydroinformatics participants continue on with PhD programmes

Examples of MSc topics

Short courses (typically 3 weeks)www.ihe.nl/Education/Short-courses

River Basin ModellingIntroduction to River Flood ModellingFlood Modelling for Management (Online course)Flood Risk ManagementUrban Flood Modelling and Disaster Risk ManagementUrban Water Systems ModellingEnvironmental Systems Modelling (2 weeks)Decision Support Systems in River Basin ManagementNew data sources to support flood modelling (1 week)

Links:http://www.ihe.nl/Education/Short-courses/Regular-short-courses/Urban-Flood-Modelling-and-Disaster-Risk-Managementhttp://www.ihe.nl/Education/Short-courses/Regular-short-courses/Urban-Water-Systems-Modellinghttp://www.ihe.nl/Education/Short-courses/Online-courses/Flood-Modelling-for-Managementhttp://www.ihe.nl/Education/Short-courses/Regular-short-courses/Flood-Risk-Managementhttp://www.ihe.nl/Education/Short-courses/Regular-short-courses/Introduction-to-River-Flood-Modellinghttp://www.ihe.nl/Education/Short-courses/Regular-short-courses/Environmental-Systems-Modellinghttp://www.ihe.nl/Education/Short-courses/Regular-short-courses/River-Basin-Modellinghttp://www.ihe.nl/Education/Short-courses/Online-courses/Decision-Support-Systems-in-River-Basin-Managementhttp://www.unesco-ihe.org/Education/Short-courses/Regular-short-courses/New-data-sources-to-support-flood-modelling


Conclusion

Hydroinformatics is a unifying approach to water modelling and managementHydroinformatics is technology driven, so it uses the most modern technologies and research resultsSpecialists in hydroinformatics play an integrating role linking various specialists and managersHydroinformatics specialists:

this is what the water sector needs


What Hydroinformatics alumni say...

the course opened the new horizons in my professional life

Documents

Introduction to Hydroinformatics - presentation for UNESCO-IHE students (v16)