1.AGUA 2009Hydroinformaticsand some of its roles in the viewof climate variability Dr. Dimitri P. SolomatineProfessor of Hydroinformatics1 Quick start: role of uncertainty in flood management80So, issue a flood alarm or not?..70 Alarm levelO est i m eU neat pper bound Forecasted river dischargeLow bound er60Deterministic forecast50 Prediction intervalDi schar ge40 (uncertainty)302010 0 1 11 2131 41 51Ti me2 D.P. Solomatine. Hydroinformatics.

2. Climate is changing http://www.globalwarmingart.com/wiki/File:Holocene_Temperature_Variations_Rev_png3D.P. Solomatine. Hydroinformatics. Global warming4D.P. Solomatine. Hydroinformatics. 3. Variability in annual temperatures locallySource: www.john-daly.com, based on data from NASA Goddard Institute (GISS), USA,and Climatic Research Unit (CRU) of the University of East Anglia, Norwich, UK5 D.P. Solomatine. Hydroinformatics.Climate is changing There are many factors leading to changes in the rate of climate change Whatever the main reason is, the climate variations prompt for developing the water management strategies that take climate uncertainties into accountthe need for More observation systems Better predictive modelling tools Analytical methods to handle uncertainty Changes in design and adaptive management practices Changes in educational programmes at all levels These issues are the current focus of Hydroinformatics 6 D.P. Solomatine. Hydroinformatics. 4. Encapsulation of knowledgerelated to water Tacit (implicit) knowledge embedded within a person Words, texts, imagesprintedstored in electronic media Mathematical modelsformulas, algorithmsalgorithms encapsulated in computer programs(software) Integrated systems encapsulating all of above - Hydroinformatics systems7D.P. Solomatine. Hydroinformatics. Hydroinformatics modelling, information and communication technology, computer sciencesapplied to problems of aquatic environment1991 with the purpose of proper management 2008 8D.P. Solomatine. Hydroinformatics. 5. Flow of information in a Hydroinformatics systemDataModels KnowledgeDecisions Earth observation, Numerical Weather Data modelling, Access toDecision monitoring Prediction Models integration with modelling supporthydrologic and resultshydraulic models Map of flood probability 9 D.P. Solomatine. Hydroinformatics. Where is data coming from?10 D.P. Solomatine. Hydroinformatics. 6. Q Q 2 h+ A + gA x gAS o + gAS f = 0 t x Modellingis the heart of Hydroinformatics 11 D.P. Solomatine. Hydroinformatics. Modelling Model is a simplified description of reality an encapsulation of knowledge about a particular physical or social process in electronic form Goals of modelling are: understand the studied system or domain (the past) predict the future use the results of modelling for making decisions (change the future)12 D.P. Solomatine. Hydroinformatics. 7. Modelling is at heart of Hydroinformatics Hydroinformatics deals with the technologies ensuring the whole information cycle, and integrates data, models, people 13D.P. Solomatine. Hydroinformatics.Main modelling paradigms Physically-based model (process, simulation, numerical) is based on the understanding of the underlying processesData-driven model is based on the recorded values of variables characterising the system. They need less knowledge about the physical behaviourAgent-based model consists of dynamically interacting relatively simple rule-based computational codes (agents)14D.P. Solomatine. Hydroinformatics. 8. Applications of modelsRiver/urban flood forecasting and managementReservoir operationsSediment transport and morphologyEcology and water qualityStorm surges and coastal floodingDredging and reclamationUrban sewers and drainageWater distribution networksetc. 15 D.P. Solomatine. Hydroinformatics. Example: a physically-based model of openchannel flow: Saint Venant equations The 1D continuity and momentum equations for openchannel flow are also referred as Saint Venant equationForm a pair of non-linear hyperbolic partial differential equationsin Q (flow) and h (depth)A Q+= qLContinuity equationt xQ Q 2 h+ A + gA x gAS o + gAS f = 0 Momentum equationt x x=distance, t=time, A=cross-section, S0=bottom slope, Sf=energy grade line slope, B=widthAnalytically can not be solvedNumerically can be solved usingfinite differences (explicit, implicit schemes),finite elements16 D.P. Solomatine. Hydroinformatics. 9. Why 2D/3D modelling?Often 1D model is not enough Horizontal velocity fields Vertical velocity fields17D.P. Solomatine. Hydroinformatics.Some examples of using modelling in water-related issues 18D.P. Solomatine. Hydroinformatics. 10. Warragamba Dam, Australia Warragamba Dam - 65 km west of Sydney in the Burragorang Valleyprovides the major water supply forSydneyWarragamba River flows through a300-600 m wide gorge, about 100 mdeep before opening out into a largevalley. This allows a relatively shortand high dam to impound a vastquantity of water. A dam break of the Warragamba Dam would be a major disaster. SOBEK (Delft Hydraulics) software was used for simulation19 D.P. Solomatine. Hydroinformatics. Warragamba Dam, Australia Simulation of the dam break with SOBEK by DeltaresThe animation shows the simulation results. They may be used for disaster management, evacuation planning, flood damage assessment, urban planning20 D.P. Solomatine. Hydroinformatics. 11. Models are indispensable in dealing with floods21 D.P. Solomatine. Hydroinformatics. Example: Hydroinformatics systems for floodwarning MIKE FloodWatchMIKE Flood Watch (Danish Hydraulic Institute), a decision support system for real-time flood forecasting: advanced time series data base MIKE 11, for hydrodynamic modeling MIKE 11 FF, real-time forecasting system, ArcView, Geographical Information System (GIS)22 D.P. Solomatine. Hydroinformatics. 12. Hydroinformatics systems for flood warning: MIKE FloodWatch 23D.P. Solomatine. Hydroinformatics.Ecosystem Integrated Model: a Case Study for Sonso Lake, ColombiaProblem: 70% of the surface area of this shallow lake is covered by an invasive macrophite Water Hyacinth Causes: Nutrients pollution from agricultural use of land Lack of sustainable management of the lake Methodology: Ecological modelling of Water Hyacinth Its integration with hydrodynamic model Analysis of Alternatives to Manage the Water Hyacinth Infestation24D.P. Solomatine. Hydroinformatics. 13. Ecosystem Integrated Model:a Case Study for Sonso Lake, ColombiaRef: MSc study by Carlos Velez (Colombia), UNESCO-IHE & Delft HydraulicsSolar WATER SURFACE Radiation2 3 56 16 Sobek Rural Sobek Rural 1 Water Volume 151D2DDELWAQ513 Norg Porg7 9 10Water Velocity14 Hydro Water4NH4 Hyacinthdynamic Water Depth11 QualityPO4 12 Flow 6 NO38 9EcosystemSEDIMENTOrganic Matter SettledPROCESSES Water Hyacinth1. Input / Output5. Input / Output9. Resuspension 13. PhotosynthesisModel (coded 2. Rainfall6. Input / Output10. Hydrolysis14. Respirationusing SOBEK3. Evapotranspiration7. Sedimentation 11. Oxidation 15. MortalityRURAL Open 4. Advection/Dispersion8. Resuspension12. Uptake/Growth 16. Losses 25 Process Library) D.P. Solomatine. Hydroinformatics.Hydrodynamic Model 1D River and Nutrients Model (Phosphate PO4) 2D Lake (Water Level)Processes included: Growth and Mortality Respiration/Photosynthesis Transportation by flow and wind Uptake/release of Nutrients from the water Mechanical, Biological and Chemical Control Options Water Hyacinth Integrated Model(Plant Density) 26D.P. Solomatine. Hydroinformatics. 14. Beyond classical modelling: current developments in HydroinformaticsMachine learning in data-driven modellingMulti-objective optimisationInformation theoryPredicting models uncertaintyIntegration 27 Data-Driven Modelling Uses (numerical) data (time series) describing some physical process Establishes functions that link variables outputs = F (inputs) Valuable when physical processes are unknown Also useful as emulators of complex physically-based models (surrogate models)Actual (observed)Modelled output YInput data X(real) systemLearning is aimedat minimizing thisMachine differencelearning(data-driven) model Predicted output Y28 D.P. Solomatine. Hydroinformatics. 15. Example of a data-driven modelLinear regression modelY = a0 + a1 X observed data characterises theY input-output relationshipactualoutput(e.g., flow) X Yvaluemodel parameters are found by optimization modelpredicts new the model then predicts output output valuefor the new input without actual knowledge of what drives Y new input X value (e.g. rainfall)Which model is better: green, red or blue? 29 D.P. Solomatine. Hydroinformatics.Data-driven rainfall-runoff models: Case study Sieve (Italy) mountaneouscatchment in SouthernEuropearea of 822 sq. km 30 D.P. Solomatine. Hydroinformatics. 16. SIEVE: visualization of dataFLOW1: effective rainfall and discharge data Discharge [m3/s] Eff.rainfall [mm]8000 2700 4600Effective rainfall [mm]6500 840010Discharge [m3/s] 12300 14200 16100 180200 500100015002000 2500Time [hrs] 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 Solomatine. Hydroinformatics.D.P. 31 Using data-driven methods in rainfall-runoff modellingQtup Available data:rainfalls Rtrunoffs (flows) Qt Inputs: lagged rainfalls Rt Rt-1 Rt-LRt Qt Output to predict: Qt+TModel: Qt+T = F (Rt Rt-1 Rt-L Qt Qt-1 Qt-A Qtup Qt-1up ) (past rainfall) (autocorrelation)(routing)Questions: how to find the appropriate lags? how to build non-linear regression function F ?Linear regression, neural network, support vector machine etc.32 D.P. Solomatine. Hydroinformatics. 17. Artificial neural network: a universal functionapproximator (=non-linear regression model) weightsweights x1 a ijb jk y1 N hid x2 u 1x y2 yk = F bok + b jk u j i =1 x3y3 k=1,..., N outxnusymInputsHidden layer OutputsF(v) N inp1 uj= F aoj + aij xi i =1 0v j=1,..., N hid Non-linear sigmoid function: F(v) = 1/ (1 + e-v)There are (Ninp+1)Nhid + (Nhid+1)Nout parameters (weights) to be identified byoptimisation process (training)33D.P. Solomatine. Hydroinformatics. Neural network tool interface 34D.P. Solomatine. Hydroinformatics. 18. SIEVE: Predicting Q(t+3) three hours ahead(ANN learned the relationship btw rainfall and flow)Prediction of Qt+3 : Verification performance ANN verification350 RMSE=11.353 NRMSE=0.234300 ObservedModelled (ANN) COE=0.9452 250 Modelled (MT)Q [ m 3 /s ]200 MT verification RMSE=12.548150 NRMSE=0.258100 COE=0.9331 5000 20 4060 80100 120140 160180t [hrs] 35 D.P. Solomatine. Hydroinformatics. Use of machine learning (data-driven) modelsin water resourcesHydrological modelling Water demand forecasting Prediction of ocean surges Models of wind-wave interaction Sedimentation modellingMeta-models (emulating, fast models) of water systems to replace complex physically-based models 36 D.P. Solomatine. Hydroinformatics. 19. MULTI-OBJECTIVE OPTIMIZATIONFinding variables values that bring the value of the objective function to a minimum In water resources many problems require solving an optimization problem 37D.P. Solomatine. Hydroinformatics. Many optimization problems in waterresources are multi-objectivethere are several objectives that are to be optimized often they are in conflict, i.e. minimizing one does not mean minimizing another one a solution (the set of decision variables) is always a compromise Examples:multi-purpose reservoir operation electricity generation vs. irrigation vs. navigabilitymodels calibration (error minimization) models good "on average" vs. good for particular hydrologic conditions (floods)pipe networks optimization (design and rehabilitation) costs vs. reduction of flood damage 38D.P. Solomatine. Hydroinformatics. 20. Model-based optimization of urban drainagenetworkMOUSE modelling system (DHI Water and Environment) 1D model of free-surface flow is used39D.P. Solomatine. Hydroinformatics.Urban drainage system rehabilitation: use of multi-objective optimizationrehabilitation: changing pipes, creating additional storagesoptimization by multi-objective genetic algorithm:find a compromise btw. min. cost and min. damage due to floodingCompromise Flood Damageoptimal solutionsWastewater System PipeNetwork Model (MOUSE) Data Processor Data ProcessorOptimization ProcedureCosts (GLOBE, NSGA-II)40D.P. Solomatine. Hydroinformatics. 21. INFORMATION THEORY Shannon entropy provides a mathematical framework to evaluate the amount of information contained in a data series H = p log2 p Average mutual information (AMI) is measure of information available from one set of data having knowledge of another set of data AMI can be used to investigate dependencies and lag effects in time series data PXY ( xi , y j ) AMI= PXY ( xi , y j ) log 2 i, j PX ( xi ) P ( y j ) Y 41 D.P. Solomatine. Hydroinformatics. Information theory and optimization for sensors locations for contaminant detection in water distribution systems Three criteria considered:ConcentrationVolume of contaminated water deliveredTime of detection PhD research of Mr. Leonardo Alfonso, UNESCO-IHE. L. Alfonso , A. Jonoski , D.P. Solomatine. Multi-objective optimisation of operational responses for contaminant flushing in water distribution networks. ASCE J. Water Res. Plan.Manag., 2009. 42 D.P. Solomatine. Hydroinformatics. 22. Multi-objective optimization of sensors locations to detect contamination Location of 5 sensors Scenario: 2 sources of pollution Time of Detection40 50 Contaminated Volume Contaminant concentration 501 Tank A 80140 6030 90 150170502 100Tank B 70160 130 500 20110120 SourceLocations found using different method43 D.P. Solomatine. Hydroinformatics.Average mutual information in optimizing thestructure of a Neural Network model Rainfall-runoff forecasting model: Rt Qt Qt+T = F (Rt Rt-1 Rt-L Qt Qt-1 Qt-A) (past rainfall) (autocorrelation)Finding optimal lags between Qt+T and rainfall Rt 1.00.300.80.25 0.20 Corr. Coef. 0.6AMI0.15 0.40.10 0.20.050.00.00 05 1015 20Time lags (hours)Cross-correlation AutocorrelationAMI44 D.P. Solomatine. Hydroinformatics. 23. UNCERTAINTY Uncertainties associated with climate change are very high Different IPCC scenarios lead to very different results of water models Any study exploring the impacts of CC needs powerful tools for analysing and predicting uncertainty 45D.P. Solomatine. Hydroinformatics.Uncertainty in flood management:evacuate? 8070 O est i m e neatUpper boundLow bound er 60 50 Di schar ge 4030 20 10 0111213141 51 Ti me46D.P. Solomatine. Hydroinformatics. 24. Point forecasts vs. Uncertainty bounds 400035003000Discharge(m3/s)25002000150010005000900 92094096098010001020 Time(days) 47D.P. Solomatine. Hydroinformatics.Sources of uncertainty in modellingy = M(x, s, ) + s + + x + y Inputs Model parameters Calibration data p X(t) Q(t)Model 48D.P. Solomatine. Hydroinformatics. 25. Monte Carlo simulation of parametric uncertaintyy = M(x, s, ) + s + + x + y49D.P. Solomatine. Hydroinformatics. 80 Uncertainty analysis: issues 70 O est i m e neatUpper boundLow bound er 60 50 Di schar ge 40 3020 1001 11 21 31 41 51Ti meMost methods are aimed at analysing average model uncertainty, butnot predicting it for the new inputsMost uncertainty analysis studies focus on the parametric uncertaintyonly. More has to be done to analyse and predict: Input data uncertainty Residual uncertainty (uncertainty associated with the deficiencies of the optimal model)Model uncertainty is estimated. What next?: Should we combine in an ensemble several good models, instead of using one calibrated model? How can we predict model uncertainty for the future situations? How to communicate uncertainty to decision makers?50D.P. Solomatine. Hydroinformatics. 26. UNEEC: Novel uncertainty prediction method D.P. Solomatine, D.L. Shrestha. A novel method to estimate model uncertainty using machine learning techniques. Water Resources Res., 45, W00B11, doi:10.1029/ 2008WR006839, 2009. A calibrated model M of a water system is considered M is run for the past hydrometeorological events It is assumed that the errors of model M characterize the residual uncertainty in different situations (events) This data is used to train the machine learning model U that predicts the error (uncertainty) of model M, which is specific for a particular hydrometeorological eventUNEEC-M: parametric and input uncertainty is added as well 51D.P. Solomatine. Hydroinformatics. UNEEC: fuzzy clustering and ANN in encapsulating the model uncertainty Error limitspast records Error distribution in cluster Error (or prediction(examples in the intervals) iN i i =1 space of inputs) N(1 / 2) i i =1 Flow Qt-1N / 2 i i =1Train regression (ANN)Prediction interval models:PIL = fL (X)PIU = fU (X) Rainfall Rt-2New record. The trained f L and f U models willestimate the prediction interval 52D.P. Solomatine. Hydroinformatics. 27. Estimated prediction bounds: verification(Bagmati river basin, Nepal)Rainfall-Discharge plot6000 050 5000100Precip itation [mm]Runoff [Cumec] 4000150 3000 200250 2000300 1000350 0400Jan-88 M ay-88 Sep-88Feb-89 Jun-89Oct-89 M ar-90 Jul-90Nov-90 Apr-91Aug-91 Jan-92M ay-92Sep-92 Feb-93Jun-93 Oct-93M ar-94Jul-94 Dec-94Apr-95 Aug-95Time [days]Runoff [Cumec] Precipitation [mm] 4000 90% prediction limits Observed flow (m /s) Observed flow30003 SF Snow RF Rain EA Evapotranspiration SP Snow coverSF RFIN Infiltration2000 EAR Recharge SM Soil moisture CFLUX Capillary transport SPUZ Storage in upper reservoirIN PERC Percolation 1000 SM LZ Storage in lower reservoir RCFLUXQo Fast runoff component Q0Q1 Slow runoff component UZQ Total runoff0 PERC Q1 Q=Q0+Q1750775800 825 850 LZTransformTime(day) 53function D.P. Solomatine. Hydroinformatics. Hydroinformatics is aboutINTEGRATIONof data, models and people54 D.P. Solomatine. Hydroinformatics. 28. Integration of atmospheric, hydro- and environmental models, data systems HBV 55 D.P. Solomatine. Hydroinformatics.Integration of models, communicationsand peopleInternet models on demand, distributed DSS Mobile telephony a channel for hazards warnings and advice systemsRef: MSc by L. Alfonso (Colombia), UNESCO-IHE 56 D.P. Solomatine. Hydroinformatics. 29. Integration of Hydroinformatics systems anddecision making Multi-criteria, multi-stakeholder80scenario analysis70 O est i m e neatUpper boundCommunication of modelLow bound er 60uncertainty to managers 50 Di schar ge 40 30 20 10 01 11 21 31 41 51Ti meMap of flood probability 57D.P. Solomatine. Hydroinformatics. Education: Hydroinformatics at UNESCO-IHE,Delft, The Netherlands 58D.P. Solomatine. Hydroinformatics. 30. Postgraduate Education, Training and Capacity Building in Water, Environment and Infrastructure59D.P. Solomatine. Hydroinformatics.UNESCO-IHE: 14,000 AlumniUNESCO-IHE Alumni Community0 - 50 51-150 151-300 301-500501-850 851-120060D.P. Solomatine. Hydroinformatics. 31. Hydroinformatics Masters programmeFundamentals, hydraulic, hydrologic and environmental processesInformation systems, GIS, communications, Internet ArcGIS Matlab JAVA AccessTools Delphi UltraDev Physically-basedPhysically- SOBEK MIKE 11 simulation modelling RIBASIM Delft 3D HEC-RASHEC- MIKE 21with applications to: and tools SWAT MIKE SHE- River basin management EPANET RIBASIM MOUSE WEST++- Flood management Data-driven modelling Data- Aquarius MODFLOW- Urban systemsand computational NeuroSolutions - Coastal systems NeuralMachine intelligence tools AFUZ - Groundwater and WEKAcatchment hydrology Systems analysis, LINGO- Environmental systems decision support, GLOBE BSCW (options) optimization AquaVoice Integration of technologies, project managementElective advanced topics 61 D.P. Solomatine. Hydroinformatics. Hydroinformatics Study ModulesIntroduction to Water science and Engineering Applied Hydraulics and hydrology Geo-information systems Computational Hydraulics and Information Management Modelling theory and applications Computational Intelligence and Control Systems River Basin Modelling Fieldtrip to Florida, USA Selective modelling subjects (2 modules each): Flood risk management Urban water systems modelling Environmental systems modelling Hydroinformatics for Decision Support Groupwork Research proposal drafting and Special Topics MSc research62 D.P. Solomatine. Hydroinformatics. 32. Examples of MSc topics Hydroinformatics for real time water quality management andoperation of distribution networks, case study Villavicencio, ColombiaWater distribution modelling with intermittent supply: sensitivityanalysis and performance evaluation for Bani-Suhila City, PalestineUrban Flood Warning System with wireless technology, case study ofDhaka City, BangladeshFlood modelling and forecasting for Awash river basin in EthiopiaHarmful Algal Bloom prediction, study of Western Xiamen Bay, ChinaApplication of Neural Networks to rainfall-runoff modelling in theupper reach of the Huai river basin, ChinaHeihe River Basin Water Resources Decision Support SystemDecision Support System for Irrigation Management in Vietnam1D-2D Coupling Urban Flooding Model using radar data in BangkokUsing chaos theory to predict ocean surge 63D.P. Solomatine. Hydroinformatics. A new programme is planned: International Masters in HydroinformaticsUNESCO-IHE UniValle-Cinara Hidroinformtica modelacin y sistemas de informacin para la gestin del aguaPrograma Internacional de Maestra en Cienciajointly delivered byUNESCO-IHE Institute for Water Education, Delft, The NetherlandsandUniversidad del Valle (UNIVALLE, Cinara),Cali, Colombiaand leading to the degree of Master of Science in Water Science and Engineering, specialisation inHydroinformatics,accredited by the Dutch Ministry of Education Planned to start in September 2010 Fliers are available Hydroinformatics.D.P. Solomatine. 64 33. Programme structureTaught partBlock 1: Location: UNIVALLE, CaliECTSFundamental subjects for 15hydroinformatics Period: September-January Block 2: Location UNESCO-IHEHydroinformatics theory and Period: Mid-January end-August: 9applications modules of the existing UNESCO-IHEECTS WSE-HI programme (modules 4-12) 45Thesis partBlock 3: Location: Any of the core partners (inMSc thesis proposalthe beginning UNESCO-IHE)preparation + special topics Period: Begin-September Mid- October ECTS 10Block 4: Location: Any of the core or theMSc Thesis researchassociated partners (at least the last month at UNESCO-IHE) ECTS Period: Mid-October mid-April.36 Public MSc defence and graduation end of April65 D.P. Solomatine. Hydroinformatics.What Hydroinformatics alumni say...the course has opened the new horizonsin my professional life66 D.P. Solomatine. Hydroinformatics. 34. ConclusionHydroinformatics is a unifying approach to water modelling and management Specialists in hydroinformatics play an integrating role linking various specialists and decision makers Access to information by widening groups of stakeholders leads to democratisation of water services One of the roles of Hydroinformatics is developing analytical methods to deal with climatic variability in modelling and management practice Focus should be on education and training 67 D.P. Solomatine. Hydroinformatics. more data is needed68 D.P. Solomatine. Hydroinformatics.