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Presentazione Pierluigi Cau, 24-05-2012
 

Presentazione Pierluigi Cau, 24-05-2012

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Nel seminario viene descritta una piattaforma informatica integrata, basata su tecnologie GIS, generatori di griglia, simulatori numerici e visualizzatori, finalizzata ad indagare l'impatto sulla ...

Nel seminario viene descritta una piattaforma informatica integrata, basata su tecnologie GIS, generatori di griglia, simulatori numerici e visualizzatori, finalizzata ad indagare l'impatto sulla qualità delle acque derivante da fonti di inquinamento localizzate e diffuse e a quantificare l'incertezza nell'applicazione dei modelli.

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    Presentazione Pierluigi Cau, 24-05-2012 Presentazione Pierluigi Cau, 24-05-2012 Presentation Transcript

    • Modeling tools and Web based technologies to support water recourses managementPierluigi CauEnergy and Environment ProgramCenter for Advanced Studies, Research andDevelopment in Sardiniaplcau@crs4.itCRS4Sardegna Ricerche, 09010 Pula CA, Italyhttp://www.crs4.it
    • The mission of the E &E programCRS4 Mission and the Grand Challenges in the Environmental Sciences• Development of physical and numerical models implemented on HPC platforms for high resolution simulations• Software tools development for the analysis and management of environmental data, integration of information systems and numerical applications
    • Expertise: Environmental Science• Hydrological (SWAT, T-RIBS, MIKE SHE, Qual 2K) – Groundwater (CODESA 3D, Modflow, Feflow) – Ocean Modeling (GETM, GOTM)• HPC platforms, Cloud and Distributed Computing, Virtualization technologies in the field of Environmental management and monitoring• WEB based information systems that relies on a geographically distributed GIS, RDBMS, complex models
    • Objectives of the presentationThe aim is to present:1. the application of ICT numerical tools to study water dynamics for:- Groundwaters - the Oristano and the Portoscuso case studies,- Surface water - The Cedrino, San Sperate, ….. Case studies- Marine waters - The Orosei and Asinara case study2 .The challenges in the environmental science3. Future work
    • ISSUES: Environmental Science Complexity of environmental issues- multimedia environment,- multi scale (time and spatial) dynamics- complexity of the description of the system (lack of quality data) - characterization of the territory and the interaction with atmosphere: - complexity of anthropogenic pressures: • agricultural, zootechnical, civil, industrial pollution - Complexity of environmental dynamics- climate change • The Intergovernmental Panel on Climate Change predicts a further rise of the air temperature between 1.4°C and 5.8°C by the end of the century and as a consequence a sea level rise of about 1 to 2 mm/year.- EU/National/Regional Directives (EU WFD, MSFD, etc.) There is a need to improve comprehension and modeling technique at scales relevant to decision making of climate induced changes
    • ISSUES: Environmental ScienceToolsData, expertise, numerical codes, analysis and visualization tools, etc.ObjectivesImprove the wise management of water and natural resources by: • Predict the impact of environmental changes, such as climate or land use changes, on water resources; • Better comprehend the cause-effect relationship on the local and large scale (natural and anthropogenic stresses versus ecosystem responses) • ….Improve the usability of models and the interoperability between systemsthrough mesh up of web applicationsFill the gap between research and production (PA, economic operators,etc.)
    • From Modeling to Industrial Projects Environmental issues make necessary a strong integration of expertise from different disciplines, made possible through the development of virtual organizations of federated entities Decision makers Problem definition Possible alternativesDPSIR: a causal framework for describing Development &the interactions between society and the Implementationenvironment: Performance Driving forces (e.g. industrial production) evaluation Pressures (e.g. discharges of waste water) States (e.g. water quality in rivers and lakes) Impacts (e.g. water unsuitable for drinking) Responses (e.g. watershed protection)
    • From Modeling to WEB ServicesA problem-solving cloud platform for theintegration, through a computing portal, of The virtual organization acts as a resources for service provider while each communication partner becomes the recipient of computation the WEB services data storage visualization A cloud is an infrastructure that allows simulation software the integrated and collaborative use of instrumentation virtualized resources owned and human know-how managed by one or more entitiesin Environmental Sciences
    • Some Projects: 2002-2010PdTA – Piano di Tutela delle Acque secondo la 152/99Decision Support and Information System for water management http://www.regione.sardegna.it/j/v/25?s=26251&v=2&c=1260&t=1Datacrossing / Climi AridiWeb based tools for groundwater management and monitoring http://datacrossing.crs4.itClimbIntegration of climate and hydrological model www.climb-fp7.eu/EnviroGRIDS - NuvolaWeb based Information System and tools to model superficial waters http://www.envirogrids.netMOMARWeb tools to model the water cycle: from the watershed to the marine environment http://www.mo-mar.net
    • GroundwatersConceptual model – coastal shallow aquifer case Dirichlet BC Neumann BC
    • GroundwatersChallenges:Challenges Model set-up, calibration and uncertainty. set- Kh and Kv are assumed deterministic for the phreatic aquifer on the basis oflimited field data lateral inflow and vertical recharge boundary conditions for thegroundwater model are indirect measure (e.g. calculated by the SWAT code) the geometry has been built on the basis of heterogeneous data (geologicmap, boreholes and geophysical data) uncertainty of the interactions between the superficial water bodies and thegroundwater system: - disconnected (I conceptualization) - connected or partially connected (II conceptualization) lack of adequate control data (heads and concentrations) few control points - few measures
    • Groundwaters: case studies 1 Oristano (Italy) -Seawater intrusion 2 Portoscuso (Italy) -Industrial contamination 1 3 2 3 Muravera (Italy) -Seawater intrusion 5 4 Oued Laou (Marocco) Aquifer management4 5 Corba (Tunisia) Aquifer management
    • Groundwaters : the Oristano Case study Study the hydrodinamic and the seawater intrusion processof the aquifer; Quantify the effect of a possibly discontinuous aquitard onthe salt dispersion process; Identify contaminated areas more sensitive to aquitardheterogeneity; Evaluate the impact of alternative exploitation schemes onthe salt water intrusion;
    • Groundwaters : the Oristano Case study• soil surface 280 x106 m2 ~ 270 km2;• aquifer average thickness t =123 m, 18 m < t < 218 m;• aquifer volume 17.8 x109 m3•2D surface nodes 1873; 2D surface triangles 3618;• vertical layers 10;• 3D nodes 20603; 3D tetrahedra 108540 zoom A A
    • Groundwaters: the Oristano Case study
    • Groundwaters: the Oristano Case study
    • Groundwaters: the Oristano Case studyAlternative aquifer exploitation schemesThe Monte Carlo simulation has been run for each of the following scenarios: A. A. Pumping from the phreatic aquifer only; B. B. Pumping from the deep aquifer only; C. C. Pumping from both aquifers together.
    • Groundwaters: the Oristano Case studyAquitard hydraulic conductivity K is assumed as the sole source ofuncertainty. K is modeled as a stationary random function with a lognormaldistribution y = ln(K) with K=10-8 m/s, s2(y) = 10 and an exponentialcovariance function. An example of a ln(K) synthetic realization σ (σ2 = 10)Methodology:1. Generate NSIM syntheticrealizations of the K field by means ofa stochastic (HYDRO_GEN) model;2. Simulate the NSIM correspondentpressure heads and concentrationsusing the coupled flow & transportCODESA-3D model;3. Perform a probabilistic thresholdanalysis and evaluate the performanceof the system by means of ensembleindicators. Lighter colors represent aquitard “holes”
    • Groundwaters: the Oristano Case study Monte Carlo iterates to garantee stationarity normalized avarage of the I moment versus number of iterates4 normalized avarage of the II moment versus number of iterates20-2-4-6 0 10 20 30 40 50 60 70 80 90 100
    • Groundwaters: the Oristano Case studySaltwater front ( c = 0.1 [/]) probability map Pumping schemes: A and BA B 20
    • Groundwaters: the Oristano Case studyPumping schemes: A and B 5%<P<95% A B 21
    • Groundwaters: the Oristano Case studyContaminated areas sensitive to aquitard heterogeneity NSIM (cij - c i ) 2 Time evolution of the concentration nodal variance (4th layer) σ i2 = ∑j=1 NSIM 10 Years 25 Years 40 Years 50 Years σ2(c) Pumping case (A) 22
    • Groundwaters: the Oristano Case study Main statistical indicators∆c
    • Groundwaters : the Portoscuso case study Study the hydrodinamic and contamination of the aquifer; Set up a numerical procedure to find the most likely pollutionsources; Identify the area controlled by the monitoring wells Set up an interactive Information system to view result;
    • Groundwaters: PortoscusoComputational domain  ∂ψ ∂c ρ  σ ∂t = −∇⋅ v −φSwε ∂t + ρ0 q flow   equation φ ∂(Swc) = −∇(cv) + ∇ ⋅ (D∇c) + qc* + f  ∂t  transport equation
    • Groundwaters: PortoscusoOptimal Water Resources Manager: from Field Data to the Contamination Source (an Inverse Problem)
    • Groundwaters: PortoscusoOptimal Water Resources Manager: from Field Data to the Contamination Source (an Inverse Problem)
    • Groundwaters: DatacrossingOptimal Water Resources Manager: from Field Data to the Contamination Source (an Inverse Problem) The most likely contamination source The DSS interpolates the simulated nodal concentrations generated by the groundwater application and visualizes them using MapServer and msCross from Datacrossing
    • Groundwaters: Portoscuso Optimal Water Resources Manager: from Field Data to the Contamination Source (an Inverse Problem)Montecarlo Sim Disk space Total Disk Space (1 PP) 2238 45 MB/sim 100 GBMontecarlo Sim CPU time/sim Total CPU Time (1 PP) 2238 5 min-6 ore about 2 months
    • Groundwaters: monitoring wellsT= 0 T= 12 The model is used to assess the T= 6 effectiveness of the monitoring network in detecting contamination. The area of influence of 41 wells, at different time steps (from top to bottom: 0 months, 6 months, 12 months) is shown in light blue. Outside this area, within the same time period, contamination sources will not affect the water quality of the wells. The monitored areas are expected to become larger with time as shown in this figure.
    • Groundwaters: DatacrossingOptimal Water Resources Manager: sea water intrusion
    • Groundwaters: Datacrossing /Climi Aridi The OUED LAOU test case (Marocco) Objectives of the project• Increasing the level of knowledge of the Mediterranean coastal aquifers developing the hydrogeological model of the Oued Lou;• Developing innovative procedures and tools and improve the understanding of geographically distributed hydro-geological, physical, and geo-chemical variables;• Increase cooperation between Sardinia and Marocco through: – training for students and advanced training for researchers – seminars and dissemination events
    • Hydrology: EnviroGRIDS/Nuvola Modeling Environmental Dynamics Development and implementation of Objectives mathematical methods and innovative WEB• Analyze pressures, states and based ICT tools to support adaptive impacts on the environment; strategies to face issues of water and soil• Identify critical areas (e.g. resource vulnerability affected by desertification);• Run scenarios on a multi model & multi scale framework• produce report on a friendly environment;• Improve model usability;• Improve public consciousness.
    • Hydrology: EnviroGRIDS / Nuvola THE SWAT ModelIt is a hydrological watershed-scale model developed by theUSDA Agricultural Research Service (ARS) and Texas A&MUniversity.SWAT aims at predicting the impact of land managementpractices on water, sediment, and agricultural chemical yieldsin large complex watersheds with varying soils, land use, andmanagement conditionsover long periods of time.The water cycle (precipitation, run off,infiltration, evapotranspiration, etc.),sediment cycle, crop growth,nutrient (N, P) cycle are directlymodelled by SWAT.
    • Hydrology: ISSUES
    • Hydrology: Case studyesThe Cedrino (Italy) Watershed The S. Sperate (Italy) Watershed The Black Sea Watershed The Gange (India) Watershed
    • Hydrology: CedrinoVirtual river network Land Cover Soil DAILY PLUVIOMETRIC DATA 1955-2007 DAILY TERMOMETRIC DATA 1955-2007
    • Hydrology: Cedrino Calibration NASH-SUTCLIFFE INDEX [-∞,1]Calibration period (1957-1964) HRUInitial K NS -4,4 MULTIPLE HRUSWATCUP (1500 runs): NS finale 0,41 DOMINANT The complexity of the simulation has been increased
    • Hydrology: Scenarios assessment
    • Hydrology: Soil water stressModeling Environmental Dynamics: the agricultural drought for the Black Sea catchment The Yellow/orange indicates soil water deficit
    • Hydrology: the Black sea Catchment Modeling Environmental Dynamics: the agricultural drought for the Black Sea catchmentWe assess and quantify complex environmental dynamics through the use of sophisticated,reliable models. The Yellow/orange indicates soil water deficit
    • Hydrology: The Gange (India) riverModeling Environmental Dynamics: water quality and quantity states
    • Hydrology: Climate analysisThe Objective is to:- check the atmospheric/climate model output and see if theyare consistent with the SWAT model specification- set up a semiautomatic procedure to gather meteorologicaldata and produce climatic data fit for the SWAT Model- analyze the effect of the spatial downscaling on the waterbalance for a case study- Quantify the uncertainty of the meteo-hydrological modelchain. What limitation/uncertainty do we expect to have byusing the meteorological data to feed the hydrological model?
    • Hydrology: Climate analysisThe Objective is to:- check the atmospheric/climate model output and see if theyare consistent with the SWAT model specification- set up a semiautomatic procedure to gather meteorologicaldata and produce climatic data fit for the SWAT Model- analyze the effect of the spatial downscaling on the waterbalance for a case study- Quantify the uncertainty of the meteo-hydrological modelchain. What limitation/uncertainty do we expect to have byusing the meteorological data to feed the hydrological model?
    • The ensemble climate model The Ensembles Prediction Systems is based on globalEarth System Models (ESMs) developed in Europe for use in the generation of multi-model simulations of future climateThe project provides improved climate model tools developed in the context of regional models, first at spatial scales of 50km at a European-wide scale and also at a resolution of 20 km for specified sub-regions.
    • The ensemble climate modelA comprehensive analysis has been carried out. Complete daily data Incomplete daily data Missing data Istitution Country Note CNRM-ARPEGE-new France No data – Only ancillary CNRM-ARPEGE-old France No data – Only ancillary– Lustrum step DMI Denmark DMI-BCM Denmark No data – Only ancillary – Start: 1961 DMI-ECHAM5 Denmark Last time interval: 2091-2099 (9 years instead of 10) ETHZ Switzerland Last time interval: 2091-2099 (9 years instead of 10) GKSS-IPSL Germany No Daily step HadRM3Q0 UK HadRM3Q16 UK HadRM3Q3 UK ICTP Italy KNMI Netherlands Is present a yearly simulation (1950-1950) METNO Norway Last time interval:2041-2050 METNO-HadCM3Q0 Norway Last time interval:2041-2050 MPI Germany SMHI-BCM Sweden Start: 1961-1970 SMHI-ECHAM5 Sweden SMHI-HadCM3Q3 Sweden VMGO Russia Last time interval: 2021-2030 (pr); 2011-2020 (tasmin, tasmax)
    • Model result: comparisonSAR-PCPMPI climate model-PCP
    • Model result: comparison PCP-SARMPI climate model-PCP
    • Ocean dynamics: MOMAR Modeling Marine Water Dynamics A multi-model and multi-scale WEB-based environment for coastal protection Objectives• Analyze pressures on coastal areas;• Identify major pollution sources;• Model the bio-geochemical status of the sea;• Run scenarios on a multi model & multi scale framework;• Produce report on a friendly environment;• Improve the monitoring network;• Improve model usability;• Improve public consciousness.
    • Ocean dynamics: GETM General Estuarine Transport Model (GETM)GETM is a Public Domain, finite difference numerical 3Doceanographic model, most efficiently used to study shallowwaters and natural processes in natural marine waters.GETM simulates hydrodynamicand thermodynamic processes innatural waters, like currents, sealevel, temperature, salinity, andvertical / turbulent mixing.
    • Ocean dynamics: GETM The GETM workflow• a batch procedure downloads daily: - updated meteorological/oceanographicdata from regional models: 1. http://nomads.ncep.noaa.gov/2.http://www.ifremer.fr/thredds/catalog.html• Boundary (BC) and Initial Condition (IC) areinterpolated on the high resolution GRID from theabove data for the GETM oceanographic model.• a set of configuration files are updated to matcheach new operational condition;• GETM is run and produce outputs in NETCDFformat (about 4 GB ).• Each output file is processed to produce aspatialite db file to be displayed on the WEBinterface .
    • Ocean dynamics: interoperabilityFROM MARS 3D to GETM/BASHYT Orosei Gulf - Forcast 21-03- 2011 18:00 - Salinity distribution
    • MOMAR (INTERREG)
    • MOMAR (INTERREG)Oil Spill Model (Lagrangian approach)
    • MOMAR (INTERREG)River impact
    • MOMAR (INTERREG)
    • The Asinara CASEASINARA: Oil spill – Gennaio 2011 Setup GETM 0.0016 con vento GFS
    • The Asinara CASEASINARA: Oil spill – Gennaio 2011 Setup GETM 0.0016 con vento MARS3d
    • ConclusionEnvironmental issues make necessary a strong integration of expertise from differentdisciplines, made possible through the development of virtual organizations of federatedentitiesReliable model prediction is primarily based on the acquisition and the efficient use of largequality dataset and the development of an interdisciplinary approach to the study.Today SW technology makes almost transparent the operability of a cloud/gridinfrastructure (network, compute and data resources) for the sharing and the exploitationof complex applications via InternetShifting environmental applications from the desktop oriented approach to the web basedparadigm enhances flexibility in the whole system, extends the use of data and the sharingof experiences, fostering user participation.
    • ConclusionWith the collaboration of:Simone Manca, Davide Muroni, Costantino Soru, Marco Pinna,Giuditta Lecca, Fabrizio Murgia, Antioco Vargiu, Gian Carlo Meloni,Carlo Milesi, Paolo Maggi, Stefano Amico, Ernesto Bonomi, MicheleFiori, Elisaveta Peneva, Gian Piero Deidda, and many more!!!With the support of:Regione Autonoma della Sardegna, Climb project, Nuvola project,EnviroGRIDS project, MOMAR project