The WATERS Network: Conceptual Design Update

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The WATERS Network Conceptual Design Update

• David Tarboton
• Utah State University
• Team Barbara Minsker (PI) Jerry Schnoor, Chuck
  Haas, Roger Bales, Rick Hooper, Jami Montgomery,
  John Braden, Nick Clesceri, Martha Conklin, Beth
  Eschenbach, Ilya Zaslavsky with contributions
  from many others

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WATer and Environmental Research Systems
Network WATERS Network
The Waters Network seeks to examine the
interactions between Earth Systems processes and
human systems involved with the cycling of water,
sediment, nutrients, and contaminants.
Understanding how natural processes,
technology, and humans interact in response to
climate and population changes will provide the
knowledge required to better manage water and the
broader environment.
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Outline

• Scaling as the integrative theme
• Requirements tracing
• Common observation requirements
• National Network Design
• Modeling platform for integration and hypothesis
  testing

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An Example of a Large-Scale Critical Water
Process Gulf of Mexico hypoxia caused by runoff
from Mississippi Basin In 2007, dead zone was
7,900 mi2
Mississippi River meets the Gulf of
Mexico (Source http//www.gulfhypoxia.net)
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Scaling Challenges (e.g. in the Mississippi River
Basin/Gulf Hypoxia)

• Spatial
• Our understanding of water comes primarily from
  small-scale experiments to understand individual
  processes.
• Large-scale water processes are dynamic,
  non-linear combinations of individual processes
  that are not well understood.
• How do localized actions (contaminant releases,
  water treatment, and management practices)
  accumulate at the river basin scale?
• Temporal
• Storms drive much of the runoff in short-term
  episodic events that are not well captured by
  current observations
• Long-term effects of climate and population
  changes on large-scale systems cannot be
  predicted
• Organizational
• Disconnections between perceptions and attitudes
  on one hand and behavior on the other challenge
  the design of policies for water use
• We know little about the dependence of effective
  institutional design and theory of adaptive
  organizational response to water problems

WATERS Network proposes to use emerging sensing
and information technology to integrate across
multiple scales.
Multi-scale water dynamics are a coupled human
and natural system interactions are not well
understood
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Typical Small-Scale Experimentation Preferential
Flow in Soils
(Markus Weiler, ETH Zurich)
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Patterns and self-organization in water systems
at large scales
LANDSAT image over coastal Sarawak, Malaysia, 1996
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The Importance of Scales in Time
Kirchner, HP Today, 2002
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Discovery Science, Nonlinearities, and Scaling
Night-time reaeration due to cooling temperature
in small streams!
W. Q. Standard
How do these effects integrate as small streams
converge to larger scales?
Instrumental Variation in D.O. at Clear Creek,
Iowa, 2006 (Schnoor)
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How Can Basin-Scale Policy Adapt to Small-Scale
Hydrologic Variation?
Brozovic et al. (2006)
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Integrated Infrastructure to Advance Knowledge
Informatics/Cyberinfrastructure
Observatories/ Experimental Field Facilities
Sensors Measurement Facility
Synthesis/Modeling
User Community
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Community Science Priorities

• CLEANER Committee Reports (http//www.watersnet.or
  g/plngdocs.html)
• CUAHSI Science Plan (http//www.cuahsi.org/SciPlan
  -20070402.html)
• NRC CLEANER Report (http//www.nap.edu/catalog/116
  57.html)
• Water, Earth, Biota (WEB) Report
  (http//cires.colorado.edu/science/groups/gupta/pr
  ojects/web/)
• CUAHSI vision and synthesis papers
  (http//www.cuahsi.org/news.html)
• Many others

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Lead with the Science
Precisely relate the measurements being proposed
to the questions being addressed

• Question/Topic
• Importance
• Barriers and Enabling Technology
• Observatory Design
• Network Design

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How do land-use changes affect sediment loading
and contaminant transport?

• Enabling technology
• Sediment source fingerprinting
• Water quality sondes
• Tunable diode laser absorption spectroscopy
• Design
• Observatory level
• Spatially dense sonde network nested within
  sparse TDL network
• Water flux infrastructure
• Network level
• Representative coverage of variability in
  climate, geology, population density, land use,
  etc.

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How does vegetation mediate hydrologic processes?

• Enabling technology
• LIDAR
• Eddy covariance
• Acoustic Doppler stream gauging
• Fiber optic temperature sensing
• Precipitation Radar
• Multispectral imagery
• Near surface geophysics
• Design
• Observatory level
• Nested instrument clusters in different
  vegetation classes and subwatersheds to provide
  measured closure of water balance
• Network level
• Representative coverage of variability in
  climate, geology, population density, land use,
  etc.

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Hydrology-Vegetation Interactions
How are water quantity, quality and related Earth
system processes coupled to natural and
human-induced changes in climate and the
landscape?
Variable
Measurement
Precipitation
NEXRAD
Landscape
Climate
Precipitation Gauge
Topography (Slope, Aspect)
LIDAR
Cover
Causes changes in
Multi-Spectral Imagery
Vegetation Class
Sonic depth sensor
Vegetation and land use
Snow Depth
Snow Pillow
Interception
Soil thermometers
Snow water Equivalent
Snowmelt
Eddy Covariance
Infiltration
Soil Temperatures
Cause changes in
Radiation
Sublimation
Vapor flux
Meteorology
Evapotranspiration
Soil Moisture
TDR/Capacitance Probes
Soil Water
Groundwater level
Piezometers
Ground Water
(Format from NEON)
Stream Gauge
Streamflow
Streamflow
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How does source water quality variability affect
water treatment?

• Enabling technology
• Pathogen and other water quality sensors
• Control algorithms
• Design
• Facility level
• Intake sensors
• Watershed instrumentation for intake water
  quality forecasting
• Multiple treatment trains for experimentation
• Network level
• Replication in different locales with differing
  source water qualities and influent
  characteristics

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How is the carrying capacity of a hydrologic
region affected by physical processes,
infrastructure investment and social organization?

• Enabling technology
• Near surface geophysics
• CFC and isotope dating and tracing technology
• Tracer injection discharge techniques
• Fiber optic temperature sensing
• ADCP stream gauging
• LIDAR
• Managed system and human response sensing
• Decision theatre

• Design
• Observatory level
• Stratification by geology, land use, human
  effects, land form
• Network level
• Stratification across climate and other strata
  constant at observatory level

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Common Requirements Allow a Regional Focus Across
National Scales

• Fluxes and stores of water and contaminants at
  various scales
• Meteorology (precip, evap, wind, solar, eddy)
• Groundwater piezometry and quality
• Soil moisture
• Flow discharge, velocities, stage, bathymetry
• Water quality (pH, temp, cond, turb,D.O.,chla)
• Nutrients (NO3-, PO43-, DOC) for mass fluxes
• Sediments (TSS, turbidity, bed load?)
• Pathogens (Enterococcus, E. Coli, others)

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National Network Design Enable multi-scale,
dynamic predictive modeling for water, sediment,
and water quality (flux, flow paths, rates),
including Near-real-time assimilation of
data High frequency measurements
Point- to national-scale prediction Observatory
provides data sets/framework to test
Sufficient data to test science hypotheses
Alternative model conceptualizations across
entire range of attributes (popn.,
hydrology) Master Design Variables
Scale Climate Hydrologic setting
Land form and geology Land use
population
Nested Observatories over Range of Scales Point
to Plot (100 m2) Subcatchment (2 km2) Catchment
(10 km2) single land use Watershed (10010,000
km2) mixed use Basin (10,000100,000
km2) Continental scale-up by HIS, models
remote sensing (MODIS, Landsat, flyovers)
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National Network Design
Human dominated Environment (Anthropogenic Lands
cape)
Concept design and construct an observing and
modeling system that addresses water dynamics
over the full range of human and naturally
dominated environments
Naturally dominated Environment (Hydrologic
Landscape)
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Population per unit area by county
1996 Population/Km2
0-5 5-10 10-20 20-50 gt 50
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Public supply water use intensity by county
Source USGS Estimated Water Use in the United
States in the year 2000 http//pubs.usgs.gov/circ/
2004/circ1268/
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Hydrologic Landscapes(Wolock et al., 2004)
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NETWORK DESIGN An example showing 9 categories of
granular precipitation and population for
conterminous U.S.
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Overlay Observatory 4-Digit Hydrologic Unit Codes
onto the geospatial polygons of granular
Popn/Precipitation
HUC-8 watersheds from USGS
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Hypothetical random examples from USGS 4 digit
HUCs that cover the Physiographic Attributes of
the Nation.
Statistical selection techniques indicate that 14
(or fewer) observatories can adequately cover all
the attributes of the nation.
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National Modeling Platform

• Models enable us to organize the information
  about very complex systems
• Catalyst for advancing knowledge
• Drive and exploit data fusion cyberinfrastructure
• Drive and exploit sensor system
• Drive and exploit experimental facility
• Integrate knowledge across disciplines

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Questions ?