Presentazione Pierluigi Cau, 24-05-2012

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

[email protected]

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



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.


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.


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


Pumping schemes: AA and BB

Saltwater front ( c = 0.1 [/]) probability map

20

A B


5%<P<95%

A B

Pumping schemes: AA and BB

21


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)


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: 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




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

Technology

Presentazione Pierluigi Cau, 24-05-2012