Presentation on theme: "Dr. Matthew Hipsey School of Earth & Environment"— Presentation transcript:
1 Adaptive modelling of water quality using real time data from lake observatories - can we do it? Dr. Matthew HipseySchool of Earth & EnvironmentUniversity of Western AustraliaGLEON8: New Zealand, Feb 2009.
2 Models as Virtual Environmental Laboratories Reconcile theory with observationImprove system understanding:Quantify processes and controls on variabilitiesRisk assessmentsConduct system budgets (eg. nutrients, metals)Assess management interventions (eg. scenarios):Flushing/diversionsChemical ameliorationEngineering interventionsReal-time prediction:AlertsFore-casting
5 CAEDYM Computational Aquatic Ecosystem Dynamics Model Developed since 1998A generic & versatile water quality model:Process-basedCouples to 1, 2 & 3D hydrodynamic modelsModular, easily extensibleLarge international user-base, research and industry
6 Numerical Models DYRIM DYRESM Quasi 2D: Rivers/ Floodplains 1D: Lakes / Reservoirs / WetlandsCAEDYMWater QualityDIVAST2D: Rivers/ FloodplainsFreewareAll models written in Fortran 95Models run on Windows, Linux and MacOSXELCOM3D: Estuaries / Lakes/ Coastal Oceans
9 The Importance of Monitoring Monitoring data is critical for establishing the baseline condition of an environmental system, and to observe how it is changing due to multiple pressuresCritical for the success of any modelling study - “rubbish in = rubbish out”Studies conducted with a paucity of available data are more uncertainMost ‘routine’ monitoring programs are good for long-term ecosystem observing, but poor for developing process understanding, or validating models - mismatch in time and space scales of the processes of interest and the observationsStrategic (‘targeted’) monitoring is necessary for process understanding and more useful for model validation (and parameterisation)
10 Required Data Inflow/Outflows: Meteorological Data: WQ Parameter Data: River inflows, water qualityGroundwater contributionTide (if connected to ocean)ExtractionsMeteorological Data:Solar radiation; Long-wave RadiationWind speed and directionAir temperature & humidityWQ Parameter Data:SOD, nutrient fluxesPhyto assemblageOrganic MatterValidation Data:Water levelsTemp, Salinity, DONutrients, ChemistryChl-a
11 (Near-) Real-time Monitoring Large array of sensors for in situ (and real-time) environmental measurementsPhysical:T, SVelocityWater levels etc.Chemical:Nutrients, DO, pHBio-sensors: metals, contaminantsBiological:Not much … chl-aRemote Sensing: Temp, Chl-a, Colour/Turbidity
12 WQ Model ValidationModels are complex assemblies of multiple, constituent hypotheses that must be tested against field dataLarge models and large data-sets make validation difficultMulti-parameter optimisationUnder-determined systemData aliasingMis-match of temporal and spatial scalesCurrent approach:Ad hoc manual calibrationMathematic optimisation routinesSplit hind-cast into “training” and “testing”Fixed parameters values over the life of a simulation
14 Strategic Approach to Validation Identify available dataFix ‘universal’ parametersMeasure site-specific parametersCross-system validationAssimilate available real-time data (‘machine-learning’)
15 Examples: Yeates and Imberger (2008) Adiyanti et al (2007) Thermistor chain assimilation into ELCOM to improve predictions of stratification and internal wave climateAdiyanti et al (2007)Changes in thermal structure linked to optical properties and used to back-calculate pigment densitiiesHigh resolution oxygen sensor data assimilationRespiration and photosynthesis estimates
16 Real-time Management Systems A Real-time Management Systems is the coordinator of the numerous data types (real and virtual) and provides a means of integrating diverse data sets into a central relational databaseAutomate the collection, storage and management of real-time dataProvide high temporal resolution environmental dataInterface with non-real-time data to provide common repository for all dataCan be used to prepare model files and run simulationsCalculate environmental and sustainability indicesOnline visualisation for all data and modelsSet-up to provide /SMS alerts and other useful information
18 ‘Hybrid’ Systems Data sensors Data assimilation and transmission Data checking, processing and in-fillingData visualisation (local and online)Virtual domains: ModelsAlerts, forecasts, summary reportsData and Model IntegrationData ‘drives’ modelsModel results used to adjust samplingReal-time data assimilated using computational intelligence algorithms to improve modelsKnowledge discovery (‘Hydro-informatics’)Online system configuration (and potentially control)System optimisation
20 Current Frontiers Improved sensing: ChemicalBiological (eg. cytometry)Improved mechanistic basis for process descriptions:Phytoplankton physiologyBacteria and micro-zooplankton – the microbial loopMeso-zooplankton and fish – agent based modelsSediment-water interactionBenthic ecology linkages‘Hybrid’ management systemsHydro-informatics - learning from enormous volumes of dataDozens of simulated variables – maybe up to 10 measured at one or two locations …Managing uncertaintyInterfacing science with social and environmental management objectivesAdaptive control
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