progetto inno ed esempi di applicazioni nel campo della geomatica - p.cau

P. Cau, S. Manca, D. Muroni, C. Soru, P. A. Marras, R. Demontis, L. Muscas & E. Lorrai

Center for Advanced Studies, Research and Development in Sardinia Email: [email protected]

CRS4 Sardegna Ricerche, 09010 Pula CA, Italy http://www.crs4.it

Progetto INNO ed esempi di

applicazioni nel campo della

GEOMATICA.

mailto:[email protected]

CRS4 Mission and the Grand Challenges in Environmental, ICT, and Imaging 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

The mission of the E &E program

• Il progetto INNO:

• Obiettivi

• I WP

• Esempi di applicazioni di GEOMATICA sviluppate al CRS4

• Oilspill e ocenografia (il caso dell’Asinara)

• Il caso di Portoscuso

• Il bilancio idrologico della regione Sardegna

Outline della presentazione

Obiettivi

• INNO intende sviluppare e promuovere una serie

prodotti altamente innovativi per lo sviluppo di

servizi orientati alla Geomatica sul WEB e al

mondo delle applicazioni “Mobile”

• La proposta è migliorare la scalabilità delle

applicazioni, la fruibilità del dato, il rendering dei

dati geografici e l’ottimizzare l’archiviazione di

dati a componente spaziale tramite soluzioni

sviluppate ad hoc

Il progetto INNO

Organizzazione del progetto

WP1 - Back-End - SDI in ambiente Cloud

WP2 - Front-End - Client AJAX e Mobile

WP3 - Sviluppo applicazione di test

WP4 - Animazione, comunicazione e trasferimento

WP0 - Gestione del progetto e processi di qualità

Il progetto INNO

Mapping: Stato dell'Arte

WMS e WFS WTMS

• Il Web Tile Map Service (TMS) è

uno Standard OGC recente,

ispirato alla tecnologia Google

Maps

• Supera i problemi di scalabilita`

utilizzando tasselli precalcolati

(rendering statico) e strutturati

• I dati visualizzati sono “statici”,

non hanno la dimensione

alfanumerica del GIS

• E` molto efficace per

rappresentare strati coreografici

• Il Web Map Service (WMS) è il

primo standard OGC per il GIS-

WEB

• Il rendering viene eseguito Server

side (rendering dinamico), quindi

soffre di problemi di scalabilita` al

crescere del numero di utenti

• Problemi nella gestione di dataset

di grosse dimensioni

• Il Web Feature Service (WFS)

supera alcuni limiti del WMS. I dati

sono “vettoriali” e possono essere

interrogati gli attributi alfanumerici.

Il progetto INNO

7

INNO: Filosofia della Cloud

• Viene usata la mosaicatura e la piramide dei Tile secondo lo standard TMS

• Il tassello contiene dati vettoriali (geometrie) e alfanumerici in formato JSON

• I tasselli sono precalcolati e contengono una semplificazione delle

geometrie in funzione del livello di zoom che si rappresenta, in modo da

rendere l'esperienza dell'utente fluida e garantire una buona percezione dei

dettagli geometrici

• I tasselli sono archiviati nel back-end in un sistema Cloud no-SQL che

garantisce scalabilita` del servizio al crescere del numero di utenti

• Si rinuncia al livello relazionale SQL in cambio della filosofia chiave-valore

orientata agli oggetti

• I tasselli vengono renderizzati nel client, tipicamente con tecnologia

Canvas/HTML5

L'idea e` di realizzare una Cloud “ibrida”, che superi I limiti del

WMS/WFS e del WTMS

Il progetto INNO

8

Il progetto INNO

WP1

Nel WP1 viene svolta la fase di progettazione e

definizione dell’ambiente Back-end.

La proposta è l’utilizzo di servizi e tecnologie per

l’archiviazione, la gestione e l’interrogazione di dati

Geografici con un approccio basato su motori No-SQL.

Il progetto INNO

Il progetto INNO

Il progetto INNO

Particolarità dei dati Sono gestiti strati informativi di tipo lineare, poligonale e puntuale L'inserimento implica un pre-trattamento dei dati che viene eseguito su postgresql-postgis I tasselli sono dati vettoriali molto leggeri I dati di test avranno caratteristiche limite cioè conterranno molti elementi con un numero elevato di vertici.

Il progetto INNO

WP2

Il Front-End consiste in visualizzatori e librerie utili per lo

sviluppo di applicazioni.

Il modello proposto prevede che sia il client a effettuare il

rendering delle mappe, utilizzando tecnologia HTML5, JS, etc.

Le librerie saranno complete di API di sviluppo e

comprenderanno la gestione del protocollo di comunicazione

con il back-end.

Il progetto INNO

15

INNO Client

• Permette di accedere ai dati dell'infrastruttura Cloud di INNO

• La libreria e` sviluppata con tecnologia HTML5 / Javascript + jQuery come plugin di LeafLet

• LeafLet e` leggero e funziona

molto bene sui dispositivi

mobili

• Compatibile con i web

browser HTML5

• rendering GIS client-side

Il progetto INNO

16

WP2: INNO Client (2)

• Prime prove di performance e soglie di semplificazione delle geometrie

Il progetto INNO

17

WP2: INNO Mobile Client

• Porting del client INNO su dispositivi mobili

• Utilizza le stesse tecnologie (Leaflet + jQuery + HTML5 / Javascript)

• Testato su Android > 4

Il progetto INNO

WP3 e WP4

Viene sviluppata una o più applicazioni di esempio che

dimostrino il funzionamento dei prodotti sviluppati.

L’infrastruttura e le tecnologie sono messe a disposizione

delle aziende.

Nel WP4 è prevista una attività di promozione e diffusione

dei risultati.

La valorizzazione punta sulla diffusione delle informazioni e

l’attivazione di canali di sostegno alle attività di sviluppo,

mantenimento e promozione degli strumenti sviluppati.

Il progetto INNO

Modeling Environmental Dynamics with GIS

Objectives

• Run scenarios on a multi model & multi scale framework

• Analyze pressures, states and impacts on the environment;

• Identify critical areas (e.g. affected by desertification);

• Improve model usability;

• Improve public consciousness.

Development and implementation of Hydro-metereological-ocean models and innovative WEB based ICT tools based on CLOUD/CLUSTER/GRID infrastructures to support adaptive strategies to face issues of water and soil resource vulnerability

Develop reporting services on demand, and in particular to expose catalogs of geodata; expose WMS, WFS, WaterML, GML, and SWE services; expose chart, table, map engine as service on demand to ease the report production mechanism .

Esempi di applicazioni sviluppate al CRS4

The conceptual infrastructure

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)

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

alternatives

Development &

Implementation

Performance

evaluation


The general objective is to provide support to policy makers in the monitoring, management, mitigation and/or restoration actions related to marine water quality issues. In particular we are addressing the issue related to the risk of point pollution (e.g. from small/medium boats and rivers) on coastal marine areas using numerical models (General Estuarine Transport Model – GETM), satellite images and quality data.

Marine coastal area assessment


Ocean hydrodinamics and Oilspill (The Asinara test case)


GETM simulates hydrodynamic

and thermodynamic processes in

natural waters, like currents, sea

level, temperature, salinity, and

vertical / turbulent mixing.


1. General scheme

Meteorological forcing (GFS)

1st nest:

operational model

Sardinia Island

2 min (~3 km)

2nd nest Cagliari (~600 m)

2nd nest Asinara (~600 m)

2nd nest Orosei (~600 m)

2nd nest Oristano (~600 m)

2nd nest Carloforte (~600 m)

APPLICATIONS 1

AP

PL

ICA

TIO

NS

2

simulated

monitoring

network Legislation

support

MS BD

River

input

Climate

Forecast Climate Climate Forecast

Forecast

BIO Validation

Forecast

Oil

spills

Oil

spills

BIO

3. Application of the GETM to the Sardinian Sea

Results – Forecast maps of temperature, salinity and

currents – 6 days ahead


Small scale implementation

Results – Forecast maps for 5 model domains of the

temperature, salinity and currents – 6 days ahead


The module is based on the moving of a set of Lagrangian passive tracers. In

each moment the coordinates are calculated using the old ones and the

displacement due to advection and diffusion processes. The initial coordinates

are randomly distributed in a circle around the place of the oil release:

The Oil Spill module



The results are presented in 3 different forms:

- Probability map of oil spill distribution


Fiume Santo oil spill accident on 11 January 2011.

On 11 January 2011 during the works in the E.On terminal oil spill leakage has

occurred with about 18000 liters of oil. It is very probable that a consecutive

leakages occurred on 19 January 2011 at the industrial port with ~100 liters of

oil.

Images source: www.e-geos.it




We tested the described above algorithm to study the oil spill distribution. The

case was used also to tune the parameter of diffusivity Ah, which characterizes

the diffusion processes.

After the oil leak event the oil was reported to reach various places on Sardinian

coast:

11 Jan 2011 Porto Tores

12 Jan 2011 Platamona

15 Jan 2011 Castelsardo

24 Jan 2011 Stintino Torre Saline

28 Jan 2011 Stintino La Pelosa 12 Jan 2011

15 Jan 2011 24 Jan 2011

28 Jan 2011

11 Jan 2011


Management of soil and water resources


The objective is to develop methods and

tools, GIS based, to assess the impact of

waste on groundwater resources:

- detecting pollution sources

- planning monitoring networks

Waste production in Sardinia

(ton/year) Landfill Recycling

Municipal waste production 842639 54 % 42,5 %

Industrial waste production 1445090 78 % 16 %

Solid (524430 ton/year) 524430

Mud (911150 ton/year) 911150

Liquid (9510 ton/year) 9510

36%

63%

1%

Solid (524430 ton/year)

Mud (911150 ton/year)

Liquid (9510 ton/year)

The Portovesme site produces alone 75% of the total industrial waste of Sardinia. 22 % of the regional waste from the industrial compartment is classified as hazardous. This comes almost completely from the Portovesme site.


The area is on the list of the sites of national interest to undergo

remediation.

In 1990, the Italian Government

declared with a National

Directive the area to be

Under "high environmental

risk".

A remediation plan was

enacted, in 2003, by the national environmental authority.

We aim at setting up a modeling infrastructure to assess the

impact of industrial solid waste on soil and groundwater.

The Portovesme industrial site (Italy)

Because of improper management strategies, soil, groundwater and

air are being heavily contaminated.

Currently it is almost impossible to detect from which area the

groundwater is being polluted.

Large amounts of industrial

waste are stocked in the

area (from industrial activities

mainly operating for the

production of aluminum,

zinc, lead, cadmium, etc.).

In addition, a thermoelectric

factory produces electricity

from coal combustion.

The Portovesme site


Computational Domain

The 3D groundwater finite-element code, CODESA 3D, and post-

processing applications aims at evaluating the impacts of leakage events

occurring from waste stocked in the area.

The CODESA 3D groundwater model is employed to account for spatial

and temporal variability in parameters and boundary conditions.


A automatic procedure runs the model simulating that each element

of the computational domain is a possible source of pollution.

2200 simulations are run.

Simulated nodal concentrations are interpolated in the well

positions, compared to the real measured ones for each time step

and the cumulative error is calculated.

Such error evaluates the performance of each simulation in

reproducing the real contamination, is time dependent and varies on

the location where the waste is stoked (source pollution).

Such error is plotted on a GIS system to detect which elements are

the most likely sources of the observed contamination and estimates

the time of the leakage event.

The montecarlo procedure


Montecarlo

(1 fonte)

N. Sim

2238

Total Simulation Time

123 hours

The most likely source after 1 month


The DSS interpolates the simulated nodal concentrations generated by the groundwater application and visualizes them using MapServer and msCross from Datacrossing

The most likely contamination source

Optimal Water Resources Manager: from Field Data to the Contamination Source (an Inverse Problem)


Assessment of the monitoring wells : The area of influence of the monitoring wells (light blue). Outside this area, within the same time period, contamination sources will not affect the water quality sampled in the wells. Monitored areas become larger with time

0 months,

1 month

2 months

6 months


Management of Water resources

Objectives:

Develop a regional observation and assessment system based on SWAT for the management of water resources in Sardinia taking into consideration the present situation and climate change.


Set up of the System

Land Cover Soil type DEM

Land cover 2008 (RAS) DEM 10x10 m (RAS) Sardinia (ITALY) is a semiarid region located in the Mediterranean, with a total area of 24090 km2. Mild temperatures all year around, one hot/dry season, and one wet season with a dominant north-westerly wind, makes its climate typical Mediterranean.


Main watersheds: 108 (Area > 10 km2)

Subbasins: 1356

Main issue concerns: -delineation of flat areas with low slopes where often the automatic delineation fails to recognize the watershed or sub-watershed limits; -presence of many artificial channels that cross several hydraulic limits; -Often river’s path was modified from their natural course. Solution: -manual reshape of watershed and sub watershed limits; -use of a optimized river network to condition the automatic delineation.

Watershed delineation Esempi di applicazioni sviluppate al CRS4

Simulation period 1922-2008

49 TMP gages

243 PCP gages (with missing

data)

27 stream flow monitoring

stations

- 23 for calibration

- 4 for validation

Climate date


The performance ranges from 0.46 to 0.88. NS mean 0.70

SISS STAZIONE RUN 22

9 LISCIA 0.60

146 COGHINAS A MUZZONE 0.72

160 BERCHIDDA 0.84

175 CONCABELLA 0.69

236 MANNU A PEDRA ALVAS 0.60

257 MANNU DI OZIERI FRAIGAS 0.75

317 RIO BUTTULE A BUTTULE 0.74

328 MANNU DI OZIERI PONTE LEGNA 0.72

381 TEMO DIGA 0.70

471 RIFORNITORE TIRSO 0.58

480 PONTE CEDRINO 0.71

601 TALORO A PASSERELLA GAVOI 0.59

703 ARAXISI A ORTO SCIAVICO 0.70

711 FLUMINEDDU (TIRSO) ALLAI 0.75

747 FODDEDDU A CORONGIU 0.57

755 FLUMENDOSA A GADONI 0.88

879 MOGORO A SANTA VITTORIA 0.76

889 FLUMINEDDU A STANALI 0.46

933 M. SCROCCA AGGREGATA 0.86

1050 SA PICCOCCA MONTE ACUTO 0.80

1070 MANNU DI S. SPERATE A MONASTIR 0.71

1113 CIXERRI A UTA 0.73

1175 MONTI PRANU 0.66

Average 0.70

Station ID: 1070. NS is 0.70 with a P-factor of 0.77 and a R-factor of 1.09 obtained after 300 iterations.

Station ID: 1070

Calibration - result Esempi di applicazioni sviluppate al CRS4

NS = 0.89

NS = 0.59

Validation


Example of a web application

The monthly regional water balance, computed on the basis of the calibrated SWAT run showed: - average precipitation of 62.57 mm/month - water yield of 28.89 mm - evapotranspiration 30.1 mm - transmission (e.g. leaching through streambed)

and evaporation losses directly from the stream 3 mm.


Black Sea Basin: agricultural drought We assess and quantify complex environmental dynamics through the use of sophisticated, reliable models exploiting huge dataset on distributed environments.

The Yellow/orange indicates

soil water deficit


Gange river (India): Stream flow


progetto inno ed esempi di applicazioni nel campo della geomatica - p.cau

Environment

e e program

mosaicatura e

leafletleaflet e

wmswfs e

linfrastruttura e

progetto e processi

qualitil progetto inno

wtmsil progetto inno