presentazione pierluigi cau, 24-05-2012
DESCRIPTION
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.TRANSCRIPT
Pierluigi Pierluigi CauCauEnergy and Environment Program
Modeling tools and Web based
technologies to support water
recourses management
Energy and Environment Program
Center for Advanced Studies, Research and Development in Sardinia
CRS4CRS4Sardegna Ricerche, 09010 Pula CA, Italy http://www.crs4.it
CRS4 Mission and the Grand Challenges in the
Environmental Sciences
• Development of physical and numerical models
implemented on HPC platforms for high resolution
The mission of the E &E program
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
• 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
Expertise: Environmental Science
The 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
Objectives of the presentation
3. Future work
- The Cedrino, San Sperate, ….. Case studies
- Marine waters
- The Orosei and Asinara case study
2 .The challenges in the environmental science
3. 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 Science
ToolsTools
Data, expertise, numerical codes, analysis and visualization tools, etc.
ObjectivesObjectives
Improve 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 • 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 systems
through mesh up of web applications
Fill 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
DPSIR: a causal framework for describing
the interactions between society and the
environment:
� Driving forces (e.g. industrial production)
� 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)
definitionPossible alternatives
Development &Implementation
Performance evaluation
From Modeling to WEB Services
A problem-solving cloud platform for the
integration, through a computing portal, of� resources for
� communication
� computation
� data storage
� visualization
� simulation software
� instrumentation� human know-how
in Environmental Sciences
The virtual organization acts as a
service provider while each
partner becomes the recipient of the WEB services
A cloud is an infrastructure that allows
the integrated and collaborative use of
virtualized resources owned and
managed by one or more entities
in Environmental Sciences
PdTA – 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=1
Datacrossing / Climi AridiWeb based tools for groundwater management and monitoring
http://datacrossing.crs4.it
Climb
Some Projects: 2002-2010
ClimbIntegration of climate and hydrological model
www.climb-fp7.eu/
EnviroGRIDS - NuvolaWeb based Information System and tools to model superficial waters
http://www.envirogrids.net
MOMARWeb tools to model the water cycle: from the watershed to the marine environment
http://www.mo-mar.net
Conceptual model – coastal shallow aquifer case
Groundwaters
Dirichlet BC Neumann BC
ChallengesChallenges: Model setModel set--up, calibration and uncertainty.up, calibration and uncertainty.
Groundwaters
�Kh and Kv are assumed deterministic for the phreatic aquifer on the basis of limited field data
� lateral inflow and vertical recharge boundary conditions for the groundwater model are indirect measure (e.g. calculated by the SWAT code)
� the geometry has been built on the basis of heterogeneous data (geologic � the geometry has been built on the basis of heterogeneous data (geologic map, boreholes and geophysical data)
� uncertainty of the interactions between the superficial water bodies and the groundwater 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
3 Muravera (Italy)1
23
3 Muravera (Italy)-Seawater intrusion
4 Oued Laou (Marocco)Aquifer management
5 Corba (Tunisia)Aquifer management
2
4
5
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 aquitard�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 study
Alternative aquifer exploitation schemesAlternative aquifer exploitation schemes
The Monte Carlo simulationMonte Carlo simulation has been run for each of the following scenarios:
A. A. Pumping from the phreaticphreatic aquifer only;aquifer only;
B. B. Pumping from the deep aquifer only;deep aquifer only;
C. C. Pumping from both aquifers together.aquifers together.
Groundwaters: the Oristano Case study
An example of a ln(K) synthetic realization (σσσσ2 = 10)
Methodology:1. Generate NSIM synthetic realizations of the K field by means of a stochastic (HYDRO_GEN) model;
Aquitard hydraulic conductivity K is assumed as the sole source of uncertainty. K is modeled as a stationary random function with a lognormal distribution y = ln(K) with K=10-8 m/s, s2(y) = 10 and an exponential covariance function.
Lighter colors represent aquitard “holes”
a stochastic (HYDRO_GEN) model;2. Simulate the NSIM correspondent pressure heads and concentrations using the coupled flow & transport CODESA-3D model; 3. Perform a probabilistic threshold analysis and evaluate the performance of the system by means of ensemble indicators.
Groundwaters: the Oristano Case study
Monte Carlo iterates to Monte Carlo iterates to garanteegarantee stationaritystationarity
0
2
4normalized avarage of the I moment versus number of iterates
normalized avarage of the II moment versus number of iterates
-6
-4
-2
0
0 10 20 30 40 50 60 70 80 90 100
Groundwaters: the Oristano Case study
Pumping schemes: AA and BB
Saltwater front ( c = 0.1 [/]) probability map
20
A B
Groundwaters: the Oristano Case study
5%<P<95%
A B
Pumping schemes: AA and BB
21
Groundwaters: the Oristano Case study
Time evolution of the
concentration nodal variance (4th layer) ∑=
=NSIM
1j
2iij2
i NSIM
)c - (cσ
Contaminated areas sensitive to aquitard heterogeneity
22
10 Years 25 Years 40 Years 50 Years
σ2(c)
Pumping case (A)
Groundwaters: the Oristano Case study
Main statistical indicatorsMain 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;
Computational domain
Groundwaters: Portoscuso
++∇⋅∇+−∇=∂
∂
+∂∂−⋅−∇=
∂∂
fqccDcvt
cS
qt
cSv
t
w
w
*
0
)()()(φ
ρρεφψσ
flowflowequationequation
transport transport equationequation
Optimal Water Resources Manager: from Field Data to the
Contamination Source (an Inverse Problem)
Groundwaters: Portoscuso
Groundwaters: Portoscuso
Optimal Water Resources Manager: from Field Data to the
Contamination Source (an Inverse Problem)
Optimal Water Resources Manager: from Field Data to the
Contamination Source (an Inverse Problem)
Groundwaters: Datacrossing
TheTheTheThe DSSDSSDSSDSS interpolatesinterpolatesinterpolatesinterpolates thethethethe simulatedsimulatedsimulatedsimulatednodalnodalnodalnodal concentrationsconcentrationsconcentrationsconcentrations generatedgeneratedgeneratedgenerated bybybyby thethethethegroundwatergroundwatergroundwatergroundwater applicationapplicationapplicationapplication andandandand visualizesvisualizesvisualizesvisualizes themthemthemthemusingusingusingusing MapServerMapServerMapServerMapServer andandandand msCrossmsCrossmsCrossmsCross fromfromfromfromDatacrossingDatacrossingDatacrossingDatacrossing
The most likely The most likely The most likely The most likely contamination sourcecontamination sourcecontamination sourcecontamination source
Groundwaters: Portoscuso
Optimal Water Resources Manager: from Field Data to the
Contamination Source (an Inverse Problem)
Montecarlo
(1 PP)
Sim
2238
Disk space
45 MB/sim
Total Disk Space
100 GB
Montecarlo
(1 PP)
Sim
2238
CPU time/sim
5 min-6 ore
Total CPU Time
about2 months(1 PP) 2238 5 min-6 ore about2 months
Groundwaters: monitoring wells
T= 0T= 12
T= 6The model is used to assess the
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.
Optimal Water Resources Manager: sea water intrusion
Groundwaters: Datacrossing
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– training for students and advanced training for researchers
– seminars and dissemination events
Modeling Environmental Dynamics
Hydrology: EnviroGRIDS/Nuvola
Objectives
• Analyze pressures, states and
impacts on the environment;
• Identify critical areas (e.g.
affected by desertification);
• Run scenarios on a multi model
& multi scale framework
Development and implementation of
mathematical methods and innovative WEB
based ICT tools to support adaptive
strategies to face issues of water and soil
resource vulnerability
& multi scale framework
• produce report on a friendly
environment;
• Improve model usability;
• Improve public consciousness.
THE SWAT Model
Hydrology: EnviroGRIDS / Nuvola
It is a hydrological watershed-scale model developed by the USDA Agricultural Research Service (ARS) and Texas A&M University.
SWAT aims at predicting the impact of land management practices on water, sediment, and agricultural chemical yields practices on water, sediment, and agricultural chemical yields in large complex watersheds with varying soils, land use, and management conditionsover long periods of time.
The water cycle (precipitation, run off, infiltration, evapotranspiration, etc.), sediment cycle, crop growth, nutrient (N, P) cycle are directly modelled by SWAT.
Hydrology: ISSUES
Hydrology: Case studyes
The Cedrino (Italy) Watershed The S. Sperate (Italy) Watershed
The Black Sea Watershed The Gange (India) Watershed
Hydrology: Cedrino
Virtual river network Land Cover Soil
DAILY PLUVIOMETRIC DATA 1955-2007 DAILY TERMOMETRIC DATA 1955-2007
Hydrology: Cedrino
HRU DOMINANT
HRU MULTIPLE
Calibration
The complexity of the
Calibration period (1957-1964)
Initial K NS -4,4
SWATCUP (1500 runs): NS finale 0,41
NASH-SUTCLIFFE INDEX [-∞,1]
The complexity of the
simulation has been increased
Hydrology: Scenarios assessment
Hydrology: Soil water stress
Modeling Environmental Dynamics: the agricultural
drought for the Black Sea catchment
The Yellow/orange
indicates
soil water deficit
Modeling Environmental Dynamics: the agricultural
drought for the Black Sea catchment
Hydrology: the Black sea Catchment
We assess and quantify complex environmental dynamics through the use of sophisticated,
reliable models.
The Yellow/orange
indicates
soil water deficit
Modeling Environmental Dynamics: water quality and
quantity states
Hydrology: The Gange (India) river
Hydrology: Climate analysis
The Objective is to:
- check the atmospheric/climate model output and see if they are consistent with the SWAT model specification
- set up a semiautomatic procedure to gather meteorological data and produce climatic data fit for the SWAT Model
- analyze the effect of the spatial downscaling on the water balance for a case study
- Quantify the uncertainty of the meteo-hydrological model chain. What limitation/uncertainty do we expect to have by using the meteorological data to feed the hydrological model?
Hydrology: Climate analysis
The Objective is to:
- check the atmospheric/climate model output and see if they are consistent with the SWAT model specification
- set up a semiautomatic procedure to gather meteorological data and produce climatic data fit for the SWAT Model
- analyze the effect of the spatial downscaling on the water balance for a case study
- Quantify the uncertainty of the meteo-hydrological model chain. What limitation/uncertainty do we expect to have by using the meteorological data to feed the hydrological model?
The ensemble climate model
The Ensembles Prediction Systems is based on global Earth System Models (ESMs) developed in Europe for use in
the generation of multi-model simulations of future climate
The project provides improved climate model tools developed in the context of regional models, first at spatial scales of 50 in the context of regional models, first at spatial scales of 50
km at a European-wide scale and also at a resolution of 20 km for specified sub-regions.
The ensemble climate model
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)
Complete daily data Incomplete daily data Missing data
A comprehensive analysis has been carried out.
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: comparison
SAR-PCP
MPI climate model-PCP
PCP-SAR
Model result: comparison
MPI climate model-PCP
Modeling Marine Water Dynamics
Ocean dynamics: MOMAR
Objectives
• Analyze pressures on coastal
areas;
• Identify major pollution sources;
• Model the bio-geochemical
A multi-model and multi-scale WEB-basedenvironment for coastal protection
• 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 3D oceanographic model, most efficiently used to study shallow waters and natural processes in natural marine waters.
GETM simulates hydrodynamicGETM simulates hydrodynamicand thermodynamic processes in natural waters, like currents, sea level, temperature, salinity, andvertical / turbulent mixing.
The GETM workflow• a batch procedure downloads daily:
- updated meteorological/oceanographic data from regional models:
1. http://nomads.ncep.noaa.gov/
2.http://www.ifremer.fr/thredds/catalog.html• Boundary (BC) and Initial Condition (IC) are interpolated on the high resolution GRID from the
Ocean dynamics: GETM
interpolated on the high resolution GRID from the above data for the GETM oceanographic model. • a set of configuration files are updated to match each new operational condition;• GETM is run and produce outputs in NETCDF format (about 4 GB ). • Each output file is processed to produce a spatialite db file to be displayed on the WEB interface .
FROM MARS 3D to GETM/BASHYT
Orosei Gulf - Forcast 21-03-2011 18:00 - Salinity
distribution
Ocean dynamics: interoperability
MOMAR (INTERREG)
Oil Spill Model (Lagrangian approach)
MOMAR (INTERREG)
River impact
MOMAR (INTERREG)
MOMAR (INTERREG)
The Asinara CASE
ASINARA: Oil spill – Gennaio 2011Setup GETM 0.0016 con vento GFS
The Asinara CASE
ASINARA: Oil spill – Gennaio 2011Setup GETM 0.0016 con vento MARS3d
Environmental issues make necessary a strong integration of expertise from different
disciplines, made possible through the development of virtual organizations of federated
entities
Conclusion
Reliable model prediction is primarily based on the acquisition and the efficient use of large
quality dataset and the development of an interdisciplinary approach to the study.
Today SW technology makes almost transparent the operability of a cloud/grid
infrastructure (network, compute and data resources) for the sharing and the exploitation
of complex applications via Internet
Shifting environmental applications from the desktop oriented approach to the web based
paradigm enhances flexibility in the whole system, extends the use of data and the sharing
of experiences, fostering user participation.
With 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, Michele
Fiori, Elisaveta Peneva, Gian Piero Deidda, and many more!!!
Conclusion
With the support of:
Regione Autonoma della Sardegna, Climb project, Nuvola project,
EnviroGRIDS project, MOMAR project