CUAHSI Hydrologic Observatories and the Neuse Basin Prototype

1. CUAHSI Hydrologic Observatories and the Neuse Basin Prototype Kenneth Reckhow UNC Water Resources Research Institute

2. 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

3. 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.

4. 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

5. 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?

6. 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

7. 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

8. Utility of Characterization • Hydrologic Characterization • Fluxes across Interfaces • Flowpaths • Residence time • Water Resource Issues • Non-point source controls • Risk assessment for floods/droughts • Wetland reconstruction • Interdisciplinary Linkages • Biogeochemical Cycles • Ecosystems • Atmospheric Interaction • Geomorphology

9. 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?

10. 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

11. Need for Design Balance Investigator Control Network Objectives • Creativity • Hypothesis-testing • Consistency • Cross-site comparison

12. 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

13. 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)

14. 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

15. Long-term Resource Allocation • Core: Characterization data • Design: Proprietary data to test PI’s hypotheses • Network: Non-PI investigations for cross-site hypotheses

16. 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

17. 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

18. 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

19. 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.

20. 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

21. CUAHSI Services • On-site staff and facilities • Common data model specified up front (HIS) • Instrumentation support through HMTF • Synthesis support through HSC • Data publishing

22. Neuse Prototype Team • In essence, one team of PI’s writing proposal • Determine characterization and design portions of observatory • Specify benchmarks

23. Next Steps • Develop science questions (PI-design portion) • State benchmark models

24. Current Status of Design • Completed first iteration of network for each discipline • Assembling cost estimates • Must iterate design to assess interactions

26. 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?