1. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Tom Harmon UC Merced School of Engineering and the Center for Embedded Networked Sensing (CENS) Environmental processes –Distributed parameter models Sensor network design – what level of detail do you need? Example: river hydrodynamics Other considerations with respect to sensor networks Using Environmental Process Models to Guide Sensor Network Sampling Design

2. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Generally interested in fluids (air, water)… …and transported chemicals, organisms (e.g., oxygen bacteria) Environmental matrices convey the flow –Usually complex geometry (e.g., coastal margin with inlets) –Often composed of nonhomogeneous materials (e.g., soil) Modeling Environmental Processes

3. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM By far the most common approach in environmental science Compartment (box) models –Materials and energy in each compartment are homogeneously distributed –Transfers between compartments are simple terms (yet can be tricky to estimate) –Useful to some extent and easily applied (systems of ODEs) –Can be designed, parameterized, tested using relatively sparse sensor networks Hydrodynamic models –Fluid flow, mass and energy transport models –Well-studied but more difficult to use –Complex geometries, distributed parameters –High granularity sensor networks will enhance the accuracy and usefulness of these models…and vice versa Environmental process models

4. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM The scientific questions we ask influence the spatial and temporal scales over which we observe… …and the length and timescales of our modeling approaches… …which (along with the budget!) are used to drive our sensor network design Scales and “scaleability”

5. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Questions and scales of observation Quick example: soil-water-plant system statistical spatial mapping of soil properties How are nutrients cycled between the soil and roots? How are water and nutrients optimally applied to a crop? How sure are we that we are avoiding over-irrigating/fertilizing (which can pollute groundwater)?

6. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Longer example: Consider a river… Ready?...then climb aboard!

7. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM What’s around the bend? Textbook rivers and network design Real river issues Example: San Joaquin- Merced Rivers confluence River sensor network design exercise

8. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Environmental issues and river mixing These are mainly centered around pollutants (like salinity, nutrients) and oxygen deficits (low dissolved oxygen) Pollutant mixing is governed by the turbulent and shear mixing processes described Dissolved oxygen (DO) would also be impacted by these processes, but also by: –Biochemical oxygen demand in the river and sediments (oxygen consumed) –Reaeration processes (atmosphere-river surface) gas transfer

9. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Textbook stuff: Water flows downhill… A A’ x z y d river bed …along rough, irregular boundaries The turbulence causes mixing of chemical species Local velocity differences serve to distribute mass carried by the fluid This is true for the individual profiles (turbulent mixing) Also true for the mean profile (shear mixing) horizontal velocity distribution

10. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Sampling implications “snapshot” velocity profile at a given time z/d surface = 1.0 bottom time-averaged velocity profile with depth If you need to know average behavior, then dwell –No problem with stationary sensors –An issue with mobile sensors in rivers (see Singh et al. ICRA 2007) river flow direction

11. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Consider a source flowing into a river Stationary flow from the pipe and in the river Temperature and chemical concentrations are not substantial (homogeneous flow) River geometry well-defined (banks, bottom bathymetry, etc) Sensors are available for fluid velocity, temperature, salinity Jason Fisher drawing

12. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Mathematical description of chemical transport (concentration, C) Transverse turbulent (or eddy) diffusion coefficients longitudinal turbulent (or eddy) diffusion coefficient; BUT, this is overshadowed by shear dispersion caused by the cross-channel velocity profile (as opposed to turbulent velocity fluctuations….so: dispersion coefficient In the “near-field,” we would need to describe this in 3D to be accurate

13. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Other extreme: Large scale river models This model can provide predictions of distributed water quality conditions given a well- defined source D typically overshadows  y in “far-field” observations (major stretches of a river) Thus, in far-field applications 1D models (advection-dispersion models) are the norm For recent example (Hudson R.): Ho et al., Environ. Sci. & Technol. 35(15), pp 3234-3241 (2002)

14. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Many science (and regulatory) drivers point to the need for more detail This model may be needed to resolve distributions in the mid-field 2D may be necessary in many applications, 3D in some, 1D for very coarse, large scale issues.

15. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Parameter estimates for turbulent diffusion Based on “shear velocity” Vertical diffusion (some theoretical basis) –Remember, this is generally not needed because d << W, so vertical mixing has ample time to occur –Could be necessary to incorporate in near-field studies (near the pipe…)

16. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Traditional estimates of turbulent diffusion parameters (ideal channels) Correlations developed for lab flumes Generally greater diffusion observed in a limited number of field studies River bank and bottom irregularities, river bends Classic experiments: –tracer releases with exhaustive measurements –days to weeks of boat time, sampling, analysis

17. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Let’s look at a confluence setting… (salt for example)

18. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM California’s San Joaquin Valley On-going test site for multi-scale embedded networked sensing

19. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM

20. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM San Joaquin-Merced River Confluence Looking upstream looking downstream (closeup) about 100 m about 7 m

21. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Confluence geometry via bathymetry

22. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM San Joaquin-Merced River Confluence

23. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Low Res vs High Res Velocity Cross-Sections Integration 4% below gauge stn Integration within 0.5% of gauge stn 10x vertical exaggeration in plots Harmon et al. Environ. Eng. Science, in press, 24(2), 2007 (March issue)

24. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Salinity distributions Day 2: 9.33 kg/s (after complete reassembly!) Day 1: 9.30 kg/s Harmon et al. Environ. Eng. Science, in press, 24(2), 2007 (March issue)

25. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Experimental design: Measuring mixing at different scales…mid-field confluence zone use historical mixing coefficients to place transects (e.g., 25% and 50% of complete mixing) Mixing will follow (roughly) Taylor dispersion principles… - spreading - variance - moments Influent concentrations 1 and 2 (well-mixed upstream of confluence?)

26. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Measuring mixing at different scales… (2) near- to mid-field observations = NIMS AQ transect Continuous injection of salt or rhodamine dye solution, oxygen-devoid water…

27. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Idealized models will only work for so much…river realities Flows can be highly variable with day, week, and (below) season August 31, 2005 April 9, 2006 Merced R-San Joaquin R confluence

28. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Water quality “perturbations” agro-industrial Discharges (surface and subsurface) livestock

29. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Generic tools observation network design –Ideal solutions to simple cases … ranging to numerical –Fast parameter optimization, model inversion strategies –Model structure identification –Statistical algorithms connected to network design Data assimilation strategies –Models and data from sensors –Incorporating sensor error, model error, fault detection, etc. to yield network integrity estimates Data fusion strategies –Remote sensing data  embedded sensing data –Legacy data  embedded sensing data Opportunities, needs, challenges…

30. CENTER FOR EMBEDDED NETWORKED SENSING UCLA USC UCR CALTECH UCM Concluding remarks… Sensor installation and network layout need to consider the environmental media and the science questions at hand (as well as networking technology issues) We (environmental scientists) have a good idea about how to model many of our systems –Parameterization should be eased by networked sensing –We need to begin to worry more about model structure (before not enough data to bother so much) Our first-cut models for systems can be used to design sensor network modalities and layout—but we must become more nimble in doing this –Laptop toolkits like “design interfaces” set in GIS-like context for better field transferability