Living in the Water Environment How can we protect ecosystems and better manage and predict water av

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Living in the Water EnvironmentHow can we
protect ecosystems and better manage and predict
water availability and quality for future
generations, given changes to the water cycle
caused by human activities and climate
trends?Jeff Dozier, UC Santa Barbara
WATERS WATer and Environmental Research Systems
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Dynamic 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., dont agree on the
  sign of a future precipitation change

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Water-energy nexus

• Including hydroelectric, US water withdrawals for
  energy are 80 of US agricultures 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?

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Coupled 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?

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Protect ecosystems and better manage and predict
water availability and quality
Grand Challenges
Social Sciences People, institutions, and their
water decisions
Engineering Integration of built environment
water system
Hydrologic Sciences Closing the water balance
WATERS Network science questions
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
Measurement of stores, fluxes, flow paths and
residence times
Synoptic scale surveys of human behaviors and
decisions
Water quality data throughout natural and built
environment
Observatories, Experimental Facilities,
Cyberinfrastructure
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Status as NSF MREFC horizon project (Major
Research Equipment and Facilities Construction)

• Preliminary design/ readiness stage (2-3 years)
• Site selections in this stage
• National Science Board approves final design
• Construction and Commissioning
• From MREFC account
• Operation and maintenance
• From Directorates
• Renewal/termination

• 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

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Context the NSF budget
NSF FY2010 Budget Summary http//tinyurl.com/qebc7
u
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Examples
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Accumulation and ablation inferred from snow
pillow data, Tuolumne Meadows (TUM) and Dana
Meadows (DAN)
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Snow Redistribution and Drifting
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Global hypoxia
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Hypoxic volume per unit N is increasing
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Total phosphorus and total suspended solids
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
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Effects of sampling frequency
Spring 2006
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Urban stormwater and wastewater
Field scale sewershed management
Field scale green infrastructure (e.g., EPAs
Urban Watershed Research Facility in Edison, NJ)
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Nested sensor design for drinking water systems
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Network considerations
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Science progress vs funding (conceptual)
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The water information value ladder
Slide Courtesy CSIRO, BOM, WMO
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The data cycle perspective, from creation to
curation

• 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 Cycle

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Organizing the data cycle

• Progressive levels of data
• EOS, NEON, WATERS Network
• Raw responses directly from instruments, surveys
• Processed to minimal level of geophysical,
  engineering, social information for users
• Organized geospatially, corrected for artifacts
  and noise
• Interpolated across time and space
• 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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Observatories and facilities
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Structure similar environmental themes 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 WaterEnvironmentClassificat
  ion(HIWEC)
• Hutchinson et al. 2009

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Human-impacted water environment classes
USGS hydrologic regions
US EPA ecoregions
NEON domains
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Information products, hydrologic example
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Example of virtual sewershed rainfall sensor
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.
Community workflow publication provenance
enable customized virtual sensors using
experimental algorithms and alternative
data/resolutions
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Information products, social science example
Data available operationally now, but dispersed
Census-derived Demographics
Agency Budgets, Policies, and Authorities
Water Uses and Withdrawal Rates
Survey data on agency and key actor interaction
experimental data on management decisions
Survey data on water information sources,
attitudes, values, and behaviors
experimental data on decisions
WATERS Network original data
Catalog of watershed-level interest groups and
politicians
Spatially distributed information and management
networks
Spatially distributed politics
Spatially distributed water user data
Interpolate network data with geospatial data
Interpolate political data with geospatial data
Interpolate user data with geospatial data
Geography of Water Information Sources
Attitudes, Behaviors, and Decisions
Spatial Distribution of Socio-Political Water
Systems
Geography of Water Information Networks
Resulting high-level data products
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Network of sites, sensors, facilities, surveys,
tools, and people, and the links 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

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Hydrologic Information System Service Oriented
Architecture
Global search (Hydroseek)
Deployment to test beds
Customizable web interface (DASH)
Other popular online clients
HTML - XML
Desktop clients
Data publishing
HIS CentralRegistry Harvester
Water Data Web Services, WaterML
WSDL - SOAP
Controlled vocabularies
Metadatacatalogs
Ontology
ETL services
ArcGIS
WSDL and ODM registration
Matlab
IDL, R
Ontology tagging (Hydrotagger)
MapWindow
ODM DataLoader
Excel
Streaming Data Loading
Programming (Java, C, VB,)
QA/QC
Modeling (OpenMI)
Server config tools
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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

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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