Gestão da Água. Tecnologias virtuais

Transcription

1 Gestão da Água Tecnologias virtuais J. Gomes Ferreira Universidade Nova de Lisboa

2 Lecture outline Definitions Databases Geographic information systems Remote sensing Databases and GIS Remote sensing Dynamic models

3 More Less None Societal investment The Holy Grail Rare and misunderstood Consolidated experience Wisdom Expensive to process Knowledge Expensive to acquire Information Data Time Days Years Decades Centuries Somewhere between information and knowledge, things start to get useful.

4 D chlorophyll %) Raw data Water quality samples Bathymetry Remote Sensing Literature From data to information Data-oriented Processing GIS Georeferenced Databases Ecological models Model-oriented Processing Management models Box 23 Box 25 Box 27 Box 30 Box 33 Box Time (years)

5 What is virtual technology? Virtual Technology is defined as any artificial representation of ecosystems, whether directly (in situ) or indirectly (remote sensing). Such representations are designed to help measure, understand, and predict the underlying variables and processes. Simulation of wild species distribution, Loch Creran, Scotland. (Aquaculture, 274, ) Distinguish between: Tools which allow measurements to be made and translate data into information (Information and Communication Technology); Modelling tools (the way in which information is used for a given purpose) and the link to data collection technology.

6 Types of virtual technology Objective and issues Technology Scale 1 Knowledge gathering 2 Map resources and environment 3 Assess system changes Database (monitoring, expert knowledge, literature) GIS, remote sensing System approach, Mathematical models Micro(local) to macroscale (national, transboundary) Mesoscale (coastal to national boundaries) Meso- (regional) to macroscale 4 Optimise production Mathematical models Microscale to mesoscale 5 Control production Information technology, sensors Microscale (e.g. aquafarms) 6 Risk assessment Handbooks, models, expert knowledge, literature, monitoring 7 Build indicators of sustainability Stakeholder forums, enquiries, indicator databases, LCA Micro to macroscale (transboundary) Mesoscale (economic sector) 8 Communication and learning Web technologies, e-learning, forums, technical networks, demonstration tools Meso- (regional) to macroscale (national, transboundary)

7 Data and information Issue Morphology and climate Water availability, inputs, and exchange Water quality Environmental interactions Key variables Geometry, bathymetry, rainfall distribution, air temperature, wind speed, relative humidity Volume, seasonal and annual hydrographs, tidal range and prism, current velocities, residence time Temperature, salinity, suspended matter, nutrients, organic detritus, oxygen, chlorophyll, submerged aquatic vegetation, xenobiotics, microbiology Fouling, pathogens, extent of submerged aquatic vegetation, benthos Thematic data collection for use of virtual tools, applied on scales ranging from local to watershed.

8 Databases Data collection and analysis Data storage Types and volume of data Typical storage approaches Where do the problems begin? How do we move from data to information? Databases What makes a good database? Advantages and requirements Case studies Desks do not self-tidy. Data do not self-organize.

9 Databases Data types Metadata Institutions Teams Projects Systems Campaigns Products Formats Availability Cost Raw data Station name Station coordinates (...) Sample date Sample time Sample depth (...) Parameter name Parameter units (...) Measured value (result) (...) Data types exhibit wide variation.

10 Databases Data volumes From kilobytes to terabytes A typical one-year data collection cycle historically included seasonal to monthly sampling, a maximum of dozens of stations, some vertical resolution and a maximum of hundreds of parameters, particularly if species identification was included This typically resulted in 50, ,000 data items. Tagus (UNDP, 1980s: 68,000 items; Sanggou Bay (INCO, late 1990s): 52,000 items. Automated acquisition including moorings and remote sensing have increased this (already substantial) load by orders of magnitude Storage of images and video quickly takes storage to the petabyte range The more data there are, the more challenging the storage and retrieval problem becomes.

11 Typical storage approaches Organized in time: data grouped by date one set has multiple sampling stations Organized spatially: data grouped by location (station) one set has multiple dates

12 Data storage in spreadsheets Station Date Time Depth Secchi Dissolved oxygen Ammonia Chlorophyll (m) (m) (mg L-1) (umol L-1) (ug L-1) Flatfiles encourage redundancy and empty records this wastes memory, lengthens search times, and makes filters difficult.

13 Database models Hierarchical Distributed Relational

14 The relational database model Data are organized into tables: rows & columns Each row represents an instance of an entity Each column represents an attribute of an entity Relationships between entities are represented by values stored in the columns of the corresponding tables (keys) Metadata describe each table column Accessible through Standard Query Language (SQL) Describes the properties or characteristics of other data Does not include sample data Allows database designers and users to understand the meaning of the data Takes time to setup This is one of the most enduring computational models. The internet is full of examples, and you use them every single day.

15 RDBMS Advantages Controlling redundancy is one of most important feature in DBMS Improved data consistency & quality Access control Transaction control Improved accessibility & data sharing Increased productivity of application development Manipulation of the database: Retrieval: Querying, generating reports Modification: Insertions, deletions and updates to its content Accessing the database through Web applications Sharing a database allows multiple users and programs to access the database simultaneously From Amazon to Expedia, the web has brought RDBS to the masses.

16 Web databases Data are accessible directly through the internet with integrated DBMS Water quality - USGS Example of biological databases WoRMS ( Fishbase.org Scientific papers Web of Knowledge

17 Water quality of San Francisco Bay, U.S.A. South Bay Suisun San Pablo Agricultural and urban nutrient loading in northern California

18 USGS water quality database - San Francisco Bay Interface is not sexy (pretty much like a decade ago), but the system works.

19 USGS water quality database - San Francisco Bay Dataset has no save option, but at least you can copy-paste into Excel.

20 Web Of Knowledge

21 RDBMS Relational Database Management System Facebook-style RDBMS for storing friends.

22 Structured Query Language (SQL) SQL script for generating the Person and Friend tables.

23 Relational Databases Environmental management BarcaWin is an example of a relational database focused on water quality.

24 The BarcaWin water quality database wizard for extracting results Data retrieval usually follows a series of screens or is partly map-based.

25 Data and information Example for an estuary Dataset 1. Salinity at different stations and sampling dates 2. River flows, estuary volume 3. Tide gauge data 1. Suspended particulate matter 2. Primary production rate 3. Concentration of N and P 4. Nutrient loading 5. Bathymetry, tidal prism Information 1. Estuarine stratification 2. Tidal prediction 3. Water residence time 4. Estuary number 1. Estimated total productivity 2.Limiting nutrient for primary production (why is this important?) 3. Nutrient removal from system Your imagination is the limit in converting data to information but use your common sense.

26 Geographic information systems Bathymetric data from soundings, coastline and other contours Bathymetric raster data at 25m spatial resolution Information on: Sampling stations Aquaculture sites FOYLE LARNE BELFAST CARLINGFORD STRANGFORD GIS is the standard spatial support for water quality analysis.

27 Lough Foyle model box divisions CEFAS 2006 survey: Low density of cockles and clams: < 1 ind/m2 Very low when compared with cultivated species Shellfish density Cockles and clams unlikely to pose a serious competition to cultivated species GIS is used to examine and cross a range of criteria.

28 Lough Foyle model box divisions Water Framework Directive Simulation area limits: Outer: boundary with Portstewart bay (WFD) Inner: mid of the Foyle and Faughan estuary (following LA box division proposal) Roe estuary considered too small for inclusion Box division between Lough Foyle and the Foyle and Faughan estuary Spatial divisions in a model should match legal divisions.

29 Lough Foyle model box divisions Water quality criteria Respect patterns of nutrient and chlorophyll concentrations One new division of Lough Foyle: total of 12 boxes Differentiate between Chl at the NW and NE part of the Foyle Nutrients: no new boundaries need to be added Spatial divisions in a model must take water quality into account.

30 Detail of the Lough Foyle LF11 station from GIS Shellfish model trial setup at LF11 LF11 was chosen as a trial site for experimental WinShell runs.

31 Tagus Estuary Potential locations for oyster culture Coordinates UTM 29 WGS 84 M = m P = 0 Estações Ostras Depth (m) 3,56 > 0,87 (intertidal) 0,87 > 0 (rarely emersed) 0 > -2 (never emersed) -2 > > > > -150

32 F. Vazquez Tagus estuary reclassification based on legislation Reclassification Unsuitable zones Suitable zones

33 0.9 Reclassificação Tagus estuary - combination of water quality criteria MCE cobination Final water quality map Batimetria (m) Chl-a (mg L -1 ) Batimetria (m) T (º C) Zonas Z N o ão n A a d s equ A ad d a e s quadas (0,8 < B < 4) Zonas Adequadas (0.8 < B < 5) Zonas Não Adequadas + T (º C) Sal (psu) Adequa+da (30 > T > 10) Zonas Não Adequadas (Chl-a < 1) Zonas Adequadas (55 > Chl-a >1) Sal (psu) Não Adequado (Sal < 10) Adequado (40 > Sal > 10) Reclassificação Unsuitable zones Suitable zones

34 EcoWin.NET model TEASMILE Sample locations, Decorana groupings and sediment types Benthic species were associated with habitat types, which were used for adding detailed filtration per box and habitat in EcoWin.

35 EcoWin.NET model Shellfish aquaculture management and benthic biodiversity Sanggou Bay Wildspecies distribution 野生种分布 Xiangshan Gang 考虑自然条件下的底生多样性的贝类养殖管理 Wildspecies filtration 野生种滤食 Percentage of the system filtered Ecosystem food availability Wildspecies food removal 生态系统可利用的生物量 This approach combines GIS and ecological modelling to assess ecological carrying capacity.

36 Review of data available in SMILE BarcaWin search BarcaWin quickly retrieves thousands of records and saves them to Excel.

37 Hunting for shellfish growth drivers Lough Foyle project database (SMILE) was searched to extract shellfish growth drivers: temperature, salinity, chlorophyll, POM, TPM; The BarcaWin option show only if all exist was used to obtain a homogeneous dataset; The search yielded 289 records, no synoptic ones. Stations such as LF11 had 4 (incomplete) records; The procedure was repeated for the Foyle historical database (SMILE); The search yielded 1280 synoptic records. Station LF11 was chosen for a trial WinShell model run.

38 Reworked data for environmental drivers Shellfish model trial setup at LF11 Day Temperature Salinity Chlorophyll a POM SPM ºC psu ug l-1 mg l-1 mg l Data from 1997 (Foyle Historical DB) for WinShell drivers.

39 WinShell layout for Pacific oyster AquaShell oyster model for Lough Foyle (uncalibrated) Culture practice data from SMILE.

40 WinShell mass balance for Pacific oyster AquaShell oyster model for the Foyle A mass balance analysis helps understand the internal model dynamics.

41 Remote sensing in coastal zones Active (provide own energy source) or passive (use available energy) Data acquisition about an object without touching it (e.g.camera, scanner, radar) Processing of data Interpretation of data Solar energy Reflected (visible) or re-emitted (IR) Sensor energy e.g. Fluorosensor, synthetic aperture radar (SAR) Different types of sensors provide data on aquatic systems freshwater and estuarine systems are a challenge e.g. due to resolution and interference.

42 CZCS derived sea-surface pigments Mediterranean Sea 5 o W 0 o 5 o E 10 o E 15 o E 20 o E 25 o E 30 o E 35 o E 45 o N 45 o N 40 o N 40 o N 35 o N 35 o N 30 o N 5o W 0 o 5 o E 10 o E 15 o E 20 o E 25 o E 30 o E 35 o E 30 o N Since the construction of the Aswan dam, the eastern Mediterranean has become increasingly oligotrophic.

43 Classification with ground truthing and habitat mapping in Mauritius Rodrigues St. Brandon St. Brandon Preliminary resource mapping of the Cargados Carajos (St Brandon) Archipelago and Rodrigues by remote sensing using Landsat 7 ETM +, SPOT 4 HRVIR and Aerial photography (E. hardman & O.Tyack.

44 Remote sensing classification Paula, J. et al, J. Plank. Res. Vol 20 Remote sensing classification Boer Wetlands Ecology and Management, Vol. 10 Maputo Bay: mangrove habitat classification Mangrove Degraded mangrove Dwarf mangrove Detail for Inhaca Island

45 Sanggou Bay, China Remote sensing for aquaculture 1. Dammed fresh water lake. 2. Dammed fresh water lake. 3. Several shrimp ponds. 5. Some pond culture. 6. Bare wetland. 10. Some pond cultures. 11. Fish cages. 12. Reservoir. 13. Some oyster cultures. Note: Limit inner edge of main culture to -5m isobath.

46 Sanggou Bay, China Aquatic resources location Supervised classification of satellite images Aquaculture zonation in Sanggou Bay Landsat image Kelp structures

47 AkvaVis Aquaculture Decision Support Applied for mussel and finfish farming Three modules share the same databases but apply information for different purposes Siting module identifies potential farm sites, simulates carrying capacity Management module compiles information needed by the authorities for aquaculture management Application module promotes efficient application and ensures that all relevant information is provided

48 Design Build Design Import SCI pubs Online Excel Test Test WATER - General concept and framework Flight plan Web Analysis and exploitation RDBMS Sources META online application META Maritime and Environmental Thresholds WATER online application NETCDF Environmental parameter files Thresholds for cultivated species Thresholds for infrastructure Environmental datasets Like any endeavour, ninety percent perspiration, ten percent inspiration.

49 META List thresholds Online database search to retrieve all thresholds for a species.

50 WATER Gilthead in the Greek EEZ Gilthead suitability shows the best areas are fairly close inshore the coastal zone is a complex multi-user seascape.

51 FARM model Application to Integrated Multi-Trophic Aquaculture (IMTA) FARM model for finfish, shellfish, seaweed, and deposit feeders. Ferreira et al., Cultivation of gilthead bream in monoculture and integrated multi-trophic aquaculture. Analysis of production and environmental effects by means of the FARM model. Aquaculture , p

52 Gulf of Guinea potential for offshore shellfish culture Cultivation structures are suspended from longlines moored to the bottom.

53 Gulf of Guinea Current velocity profiles Current speed conditions the food supply to bivalves and the dispersal of finfish waste products.

54 Gulf of Guinea Sea Surface Temperature Sea surface temperature determined using the CORSA-AVHRR weekly SST composites, (Hardman-Mountford, 2000).

55 Gulf of Guinea Remote sensing data for median chlorophyll An adequate supply of algae is essential for shellfish aquaculture.

56 Gulf of Guinea FARM model mass balance for Mediterranean mussel longline culture (Mytilus galloprovincialis) About one million dollars annualized revenue, half in products and half in services.

57 Allochtonous supply of organic material to benthic deposit-feeders below a fish cage Background organics A f Finfish cage z f Sea cucumber pens z S b S w S f S b : Background loading (g d -1 ) S w : Waste feed loading (g d -1 ) S f : Faecal loading (g d -1 ) A f : Area of polar cage (m 2 ) A d : Area of benthic footprint (m 2 ) z: Water column depth (m) Z f : fish cage depth A d The simplest model with no advection or dispersion considers A d = A f

58 Allochtonous supply of organic material to deposit-feeders under a fish cage Polar cage z A d Longitudinal (main) current axis Advection shifts the dispersion footprint as a function of the residual current.

59 Mass balance for an Atlantic salmon growth cycle Matched FCR and end-point weight.

60 Feed Conversion Ratio (FCR) and mass apportionment Example for 1kg of fish, FCR = 1.12 FW to DW conversion Consider a moisture content of 73.65% for Salmo salar muscle (Atanasoff et al., 2013): 1.00 kg wet weight = kg DW. Feed 1120 g DW Fish intake 1033 g DW FCR 1.12 Fish production 1000 g WW Total loss 87 g DW Assimilation 83% Feed used 1033 g DW = Fish faeces 177 g DW + Metabolism + Equiv g DW Fish mass g DW FCR is the result of input/output. Input-Output = Total Loss

61 Organic Sedimentation Model - ORGANIX ORGANIX predicts the benthic loading footprint. Many other models (Gowen, Silvert, Cromey, Corner, and respective co-workers) do this; Dispersion in 2 dimensions is based on Gaussian distribution functions; Advection is based on residual circulation; Model algorithm determines time to settle based on fall velocity. Probability distribution (dispersion) and advective shift is determined at each timestep until the plume reaches the bottom; Loading from culture structures is distributed over the modelled surface; Calibration for Atlantic Salmon, experimental data from DFO and literature. feed pellets fall faster than faeces; ORGANIX does not account for physiological variation. Calculation of bottom loading and spatial distribution under different culture and environmental conditions is essential for deposit feeder model.

62 ORGANIX ORGANIC Sedimentation model Multiple deposition plumes of waste feed and faeces for 14 salmon cages

63 Live Weight (g) Simulation of sea cucumber growth in integrated culture under salmon farms Days 23 gpom m-2 d-1 9 gpom m-2 d gpom m-2 d-1

64 Mass balance for a four year sea cucumber growth cycle Parastichopus californicus weight data - large animals: g WW (Hannah et al, 2013), g WW (Hannah et al., 2012).

65 200 m Farm (zoomed view) FARM model IMTA layout Fallow Kelp Salmon Oysters Water flow 50 m 200 m Deposit feeders cover the whole bottom (40,000 m 2 per section) Water flow Farm (full view) FARM simulates changes to individual weight, harvest, environment, and income.

66 Synthesis of FARM outputs for deposit feeders Scenario Mono IMTA 1 5 fish m -2 IMTA 2 20 fish m -2 IMTA 3 Oysters Individual weight (g) IMTA 4 IMTA 2 + IMTA 3 IMTA 5 IMTA4 + seaweeds Length (cm) Harvest (t cycle -1 ) APP Profit (k ) as EBITDA POM removal ( gc m -2 y -1 ) Net POM loading (g C m -2 y -1 ) Populationequivalents (y -1 ) Scenarios for monoculture (20 ind. m -2 ), different finfish densities in IMTA, shellfish longline culture (100 ind. m -2 ), shellfish + finfish, and seaweeds (50 ind. m -2 ). IMTA6 (not shown) increases deposit feeders to 80 ind. m -2.

67 Kelp monoculture: final individual weight of 134 g Increases to 175 g in IMTA5 Two key questions Role of seaweed (winged kelp Alaria esculenta) culture 22% increase in total physical product (TPP) for plants of harvestable size from 153 to 214 t cycle -1 No significant effect on DIN concentration (P 90 decreases by 0.4 mm) Role of suspended shellfish (Pacific oyster C. gigas) culture Oyster individual weight increases from g to g TPP from to t cycle -1 Increase of ratio of suspended particles to 80% makes little difference (end points are 65.7 g and t) Shellfish suspended culture is not enhanced by salmon culture; seaweeds do not reduce DIN significantly. This is basin-scale IMTA.

68 Synthesis Data per se is of little value Models without data are also of little use One of the secrets to information is combination Social perception themes such as viewsheds from windparks are now modelled in GIS GIS benefits from links to dynamic modelling platforms Remote sensing is very useful, and cost-effective, but there are limitations Water quality assessment and management is going through a technological revolution and you are part of it All slides