CUAHSI Hydrologic Observatories and the Neuse

Basin Prototype

Kenneth ReckhowUNC Water Resources Research

Institute

Design Concepts for HO’s Community Resource

Not controlled by PI Support for remote investigators

Sufficiently Large Explore all interfaces, include LS/Atm Contribute to hydrologic improvement in GCM’s

National-scale Network Comparable data across observatories Test hypotheses in different hydrologic settings

Hydrologic Observatories• Hydrologic observatories are conceived as major research facilities that will be available to the full hydrologic community, to facilitate comprehensive, cross-disciplinary and multi-scale measurements necessary to address the current and next generation of critical science and management issues. • A network of hydrologic observatories is proposed that both develop national comparable, multidisciplinary data sets and provide study areas to allow scientists, through their own creativity, to make scientific breakthroughs that would be impossible without the proposed observatories.

Original Approach Science Drivers

Small set of ‘robust’ (resonant) hypotheses Specific enough for design Basis for network design (applies to all HO’s) Develops data sufficient to answer a question

Sequential application of Drivers to Neuse Identify scientific synergies Demonstrate cost efficiencies

Major Science Drivers Land-surface/atmosphere. Does water cycling within a basin

contribute significantly to the precipitation that falls in the basin, and do these feedbacks intensify wet and dry periods?

Land-surface/groundwater. How do atmospheric and surficial processes control groundwater recharge and how can this knowledge be used to develop quantitative estimates of recharge at the scale of thousands of square kilometers?

Groundwater/surface water. How can the exchange of water between the regional aquifer, alluvial aquifer and surface water be quantified and its residence time in each domain estimated, as these properties control many biogeochemical properties and influence aquatic ecosystems?

Hydrologic Extremes. How do human modifications of the local hydrologic system (both directly and indirectly by changing the land surface) influence the likelihood and intensity of drought and floods relative to global climatic phenomena such as ENSO?

Land use effects on biogeochemistry. How does land cover and use influence the loading, transport and transformation of biogeochemicals in large watersheds?

Initial Attempt Science drivers, as posed, were not

sufficiently specific for design Attempted to develop more

specific hypotheses that were applicable to Neuse

Found that all hypotheses required similar hydrologic characterization

Hydrologic Characterization Three properties:

Residence time distribution within “stores” Fluxes across interfaces/stores Flowpaths between stores

Conceptual model Include atmosphere as part of model Elaboration of stores (boxes) and structure

must be reconciled among disciplines

Utility of CharacterizationHydrologic Characterization

• Fluxes across Interfaces

• Flowpaths

• Residence time

Interdisciplinary Linkages• Biogeochemical Cycles

• Ecosystems

• Atmospheric Interaction

• Geomorphology

Water Resource Issues• Non-point source controls

• Risk assessment for floods/droughts

• Wetland reconstruction

Conceptual Problems with Science Drivers In a non-PI model, who tests the

hypotheses? How do hypotheses evolve? What is incentive for local scientists to

design study if they don’t control HO? Can one develop compelling

hypotheses without local knowledge?

Conceptual Problems with Characterization Need context for characterization

How precise do these measures need to be?

Is any measure sufficient to advance science?

Conceptual model dependent upon question.

Does not provide basis for network design

Need for Design Balance

Network Objectives

Investigator Control

• Creativity

• Hypothesis-testing

• Consistency

• Cross-site comparison

Current Approach for HO’s State broad scientific objectives

(like science drivers) Allow greater PI creativity Impose consistency constraints

after proposal received Specify proportions of resources

for competing activities

Overall Scientific Objective – Core Data Improve predictive understanding of fluxes of

Water Sediment Nutrients [Selected Contaminants]

Across spatial scales, including catchment outlet

Riverine fluxes, at a minimum, but also fluxes across other interfaces (PI-specified)

Observatory Operation Objective: Support of remote Investigators Resident staff of ~10 FTE’s

PhD-level Site Director (CUAHSI employee) Visiting Post-docs Master’s Level and Bachelor’s level technicians

Dormitory, Laboratory and Workshop space, Vehicles

“Resources” include allocation of staff time, equipment resources, etc. not just money

Long-term Resource Allocation

Core: Characterization data

Design: Proprietary data to test PI’s hypotheses

Network: Non-PI investigations for cross-site hypotheses

Core

Design

Network

Examples of Core Data Standard data—protocols established,

relatively inexpensive (e.g., sw network, gw levels), directly measuring state of system

Index data—for expensive data (e.g., eddy covariance towers in representative settings)

Metadata for monitoring—to enhance utility of agency collected data (e.g., USGS water levels)

Survey data—one-time (topography) and repeated (vegetation maps) at large scale

Characterization Objectives Designed to achieve:

Fluxes across interfaces Residence time/residence-time spectra Flowpaths

PI-designed, but CUAHSI influenced CUAHSI reviews and can modify PI request

for consistency across sites CUAHSI sets data standards CUAHSI publishes data

Design Pool PI-designed hypotheses to be tested,

responsive to CUAHSI science drivers PI directs CUAHSI staff on what needs to

be done, as well as own work. Proprietary period for PI use of data Five-year project cycle with explicit

benchmark

Specified Benchmark PI must specify Benchmark within 1st

year of proposal against which improvement will be judged.

Any objective benchmark can be used Simulation model Regression model, etc.

Test of understanding (such as manipulation) up to PI; this is an important criterion for evaluation.

Network Pool Objective: Ensure community

access to site Support for non-PI investigators Focus on cross-site comparison Encourage use of site to meet non-

CUAHSI objectives by outside funding agencies

CUAHSI Services On-site staff and facilities Common data model specified up

front (HIS) Instrumentation support through

HMTF Synthesis support through HSC Data publishing

Neuse Prototype Team In essence, one team of PI’s

writing proposal Determine characterization and

design portions of observatory Specify benchmarks

Next Steps Develop science questions (PI-

design portion) State benchmark models

Current Status of Design Completed first iteration of

network for each discipline Assembling cost estimates Must iterate design to assess

interactions

Beyond the Prototype Implementation plans

Include core data, proprietary data design

Benchmark specification Plans for leveraging existing data

Final network design After implementation plans complete Panel or another competition?