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Living in the Water Living in the Water EnvironmentEnvironment
How can we protect ecosystems and better How can we protect ecosystems and better manage and predict water availability and manage and predict water availability and quality for future generations, given changes quality for future generations, given changes to the water cycle caused by human activities to the water cycle caused by human activities and climate trends?and climate trends?
Jeff Dozier, UC Santa BarbaraJeff Dozier, UC Santa Barbara
WATERSWATERS = WATWATer and EEnvironmental RResearch SSystems
Dynamic nature of atmospheric waterDynamic nature of atmospheric water
• Atmospheric precipitable water ~25 mm, annual precipitation/evapotranspiration ~1000 mm– So atmospheric residence time for water ~9
days– Climate models show much greater
agreement on temperature in a 2x CO2 environment than on precipitation• Coquard et al. [2004, Climate Dynamics]: 15 GCMs,
downscaled to western U.S., don’t agree on the sign of a future precipitation change
Water-energy nexusWater-energy nexus
• Including hydroelectric, US water withdrawals for energy are 80% of US agriculture’s withdrawal– Much non-consumptive (although
perhaps at higher temperature)– But consumptive withdrawals for energy
are ~20% of non-agricultural use
• Life cycle assessment of biofuels?
Coupled human-environment problemCoupled human-environment problem
• Incentives and institutional/governmental arrangements to manage water?– Not much behavioral information about response
to drought in early 1990s– Why is worldwide foreign aid directed toward
water quality ~4% of the bottled water industry?
• Water demand not as well understood as supply
• Value (saliency) of information – what decision can you make based on information, vs based on statistics?
Grand ChallengesHydrologic Sciences:
Closing the water balance
Social Sciences: People, institutions, and their
water decisions
Engineering: Integration of built environment
water system
Measurement of stores, fluxes, flow paths and
residence times
Water quality data throughout natural and
built environment
Synoptic scale surveys of human behaviors and
decisions
How is fresh water availability changing, and
how can we understand and predict these changes?
How can we engineer water infrastructure to be
reliable, resilient and sustainable?
How will human behavior, policy design and institutional decisions affect and be affected by changes
in water?
Resources needed to answer these questions and transform water science to address the Grand Challenges
Status as NSF MREFC “horizon” Status as NSF MREFC “horizon” project project ((Major Research Equipment and
Facilities Construction))• This year: produce science plan
– 15th May, in review by NRC, briefing on 15th June
• Conceptual design (2 years)
– Requirements definition, prioritization, review
– Identify critical enabling technologies and high risk items
– Top-down parametric cost and contingency estimates and risk assessment
– Draft Project Execution Plan
• Preliminary design/ readiness stage (2-3 years)
– Site selections in this stage
• National Science Board approves – final design
• Construction and Construction and
CommissioningCommissioning
– From MREFC accountFrom MREFC account
• Operation and maintenance
– From Directorates
• Renewal/termination7
Context: the NSF budgetContext: the NSF budget
Account FY09 ($M) Stimulus ($M)
FY10 request
($M)
Research & related activities
$5,183 $2,500 $5,733
Education & human resources
845 100 858
MREFC 152 400 117
Operations & management 294 318
National Science Board 4 4
Inspector General 12 2 14
TotalTotal $6,490$6,490 $3,002$3,002 $7,045$7,045
8
NSF FY2010 Budget Summary http://tinyurl.com/qebc7u
Accumulation and ablation inferred from snow pillow Accumulation and ablation inferred from snow pillow data, Tuolumne Meadows (TUM) and Dana Meadows data, Tuolumne Meadows (TUM) and Dana Meadows (DAN)(DAN)
10
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Total phosphorus and total suspended Total phosphorus and total suspended solids loadingsolids loading• TSS and TP
from turbidity using surrogate relationships
• ~50-60% of the annual load occurs during one month of the year
• Provides information about flow pathways Horsburgh 2009
Urban stormwater and wastewaterUrban stormwater and wastewater
Field scale green infrastructure (e.g.,
EPA’s Urban Watershed Research Facility in
Edison, NJ)
Field scale sewershed management
Alcosan Wastewater Treatment Facility
Pittsburgh Drinking Water Treatment Facility
Ohio River BasinDrinking Water Intake Sensors
Pittsburgh RegionUrban Water Treatment Plants
Pittsburgh RegionWastewater Collection System and Drinking
Water Distribution System
Pittsburgh Drinking Water Treatment FacilityNested sensor design Nested sensor design for drinking water for drinking water
systemssystems
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The water information value ladder
Monitoring
Collation
Quality assurance
Aggregation
Analysis
Reporting
Forecasting
Distribution
Done poorly
Done poorly to well
Generally done well, by many groups, butcould be vastly improved with new IT
>>> Incr
easing v
alue >
>>
Integration
Data >
>> Info
rmatio
n >>> In
sight
Slide Courtesy CSIRO, BOM, WMO
The data cycle perspective, from The data cycle perspective, from creation to curationcreation to curation
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• The science information user:— I want reliable, timely, usable
science information products• Accessibility• Accountability
• The funding agencies and the science community:— We want data from a network of
authors• Scalability
• The science information author:— I want to help users (and build my
citation index)• Transparency• Ability to easily customize and
publish data products using research algorithms
The Data CycleThe Data Cycle
Collect
Store
Search
Retrieve
Analyze
Present
Organizing the data cycleOrganizing the data cycle
• Progressive “levels” of data– EOS, NEON, WATERS
Network0 Raw: responses directly
from instruments, surveys1 Processed to minimal
level of geophysical, engineering, social information for users
2 Organized geospatially, corrected for artifacts and noise
3 Interpolated across time and space
4 Synthesized from several sources into new data products
• System for validation and peer review– To have confidence in
information, users want a chain of validation
– Keep track of provenance of information
– Document theoretical or empirical basis of the algorithm that produces the information
• Availability– Each dataset, each
version has a persistent, citable DOI (digital object identifier)
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Structure: similar environmental Structure: similar environmental themes for sampling designthemes for sampling design• Objectively identify “similar” thematic places
that are comparable and can be intensively studied at a few (1-4) “observatories” in each– Capture the diverse hydrologic, engineering and
social conditions that exist across the U.S.– Set of variables that quantify hydrologic setting,
both physical and human-influenced
• Example: ISODATAclustering based on theHuman-Influenced WaterEnvironmentClassification(HIWEC)
Hutchinson et al. 200926
Human-impacted water environment classes USGS hydrologic regions
US EPA ecoregions NEON domains
Example of virtual sewershed rainfall sensorExample of virtual sewershed rainfall sensor
NEXRAD reflectivity data on local server
Temporal averaging of sewershed rainfall rate produces 15 minute sewershed average rainfall data
Use Z-R relationship to convert average reflectivity (Z) into sewershed rainfall rate (R)
Spatially interpolate NEXRAD reflectivity from polar grid to polygon sample points. Then average over sample points.
Uniformly sample area within polygon on 500 m by 500 m grid
Geospatial sewershed polygon encoded as KML
<Polygon><tessellate>1</tessellate><outerBoundaryIs><LinearRing><coordinates>-87.8891714325616,41.8093419197642,0-87.8858482809939,41.7695567017372,0 -87.8284450634905,41.7684213670785,0 -87.8462880396867,41.8051753689394,0 -87.8891714325616,41.8093419197642,0 </coordinates></LinearRing></outerBoundaryIs></Polygon>
Community workflow publication & provenance enable customized virtual sensors using experimental algorithms and alternative data/resolutions
Information products, social science Information products, social science exampleexample
30
Data available operationally
now, but dispersed
WATERS Network
original data
Resulting high-level
data products
Census-derived
Demographics
Agency Budgets, Policies,
and Authorities
Water Uses and
Withdrawal Rates
Catalog of watershed-
level interest groups and politicians
Spatially distributed
politics
Spatially distributed water user
data
Interpolate network data
with geospatial
data
Interpolate political data
with geospatial
data
Survey data on agency and key
actor interaction;
experimental data on
management decisionsSpatially
distributed information
and management
networks
Survey data on water
information sources,
attitudes, values, and
behaviors; experimental
data on decisions
Spatial Distribution of Socio-
Political Water
Systems
Geography of
Water Informati
on Networks
Geography of Water
Information Sources
Attitudes, Behaviors,
and Decisions
Interpolate user data
with geospatial
data
Network of Network of sites, sensors, facilities, sites, sensors, facilities, surveys, tools, surveys, tools, and and people,people, and the and the links among themlinks among them• Within a specific observational or experimental facility
– scale through a sampling design, which may contain elements of nested and gradient-based design
• In multiple places within a water environment class– extend from intensively observed places to similar environments– validate our capability to extend the knowledge gained from the
detailed sites or facilities to other places in the same class
• Throughout the set of water resource classes– test the generality of proposed hypotheses across
heterogeneous environments– provide multiple lines of evidence to constrain theory– accumulate community data, models, and information to
support multiple investigators working in interdisciplinary water science.
• Mesh with existing national-scale data– national water survey of human behavior– remotely sensed observations of continental or global extent– existing data of operational agencies
31
Test bed HISServers
Central HIS servers
ArcGIS
Matlab
IDL, R
MapWindow
Excel
Programming (Java, C, VB,
…)
Desktop clients
Customizable web interface (DASH)
HTML - XMLW
SD
L - SO
AP
Modeling (OpenMI)
Global search (Hydroseek)
Water Data Web Services, WaterML
HIS LiteServers
External data
providers
Deployment to test beds
Other popular online clients
ODM DataLoader
Streaming Data Loading
Ontology tagging
(Hydrotagger)
WSDL and ODM registration
Data publishing
QA/QC
Server config tools
HIS CentralRegistry & Harvester
Hydrologic Information System Service Oriented Architecture
Why now?Why now?
• Addresses Grand Challenges in environmental research and integrates natural, engineering, and social science
• Water couples humans and natural systems as a balancing mechanism between human activity and sustainability
• Given the current state of water issues, the need to understand and predict is urgent
• Other federal agencies are making investments, so leveraging opportunities exist over the next decade
• Because of community readiness and technological advances, the ability to address this need (finally) exists 33
What Next?What Next?
• Thematic division (the many-colored map)– Multi-disciplinary workshop to
get to community consensus
• Integration with mission agencies
• Review enabling technologies and high-risk items– Sensors, satellites, surveys
• Review & select models to use in network design– Review datasets, models, and
experience from testbeds & CZO investigations
• Education and outreach plan
• Cyberinfrastructure– Framework and facility to
support higher-level data products
– Community network– Identify research &
development needs for WATERS Network
• Execution plan and strategy for selection in Preliminary Design phase– Observatories, facilities, surveys
• Cost and contingency estimates
• Examine options and identify management structure for MREFC
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