CITRIS Seminar: Measuring and Interpreting Fluxes of Trace Gases Across Local and Global Networks A Nexus between Biometeorology and Engineering Dennis Baldocchi Professor of Biometeorology Environmental Science, Policy and Mgt University of California, Berkeley April 25, 2012

Smart Dust Offers the Possibility of Spreading 100s and 1000s of Sensors across the Environment

Wireless Networks Offer Possibility of Distributing Many Nodes of Sensors Across a Landscape without Tethering to Wires

Measuring the State of the Environment has a Long History Rudolf Oskar Robert Williams Geiger (August 24, 1894 in Erlangen, Germany – January 22, 1981 in Munich, Germany) was a German meteorologist and climatologist.

Challenges/Objectives It’s en vogue to measure environmental state variables Sensors are cheap, easy to replicate Sensors Must have Good and Representative Exposure and Implementation and Known and Stable Calibrations Measuring State Variables Should Not be the Means to an End Changes in State Variables reflect Fluxes Eddy Covariance Flux measurements requires a system, 3-D Sonic Anemometer and Trace Gas Sensor Sensor performance is stringent—Sensors must respond up to 10 Hz Flux Gradient Requires Resolving Very Small Gradients in Well-Mixed Fluids Above Vegetation Flux-Gradient Theory is Error Prone in Shear Zones, like Canopy-Atmosphere Interface

++ Thermometer is Shielded and Aspirated Sensors Must Be Positioned Not to cause Artifacts ++ Thermometer is Shielded and Aspirated -- Anemometer in Lee of Rain Gauge -- Pyranometer Shaded by Trees, affecting light Quality and Quantity --Rain Gauge May Under Count due to its height and wind New Weather Station on West Circle

Conceptual Diagram of Changes in Atmospheric State Variables in the Planetary Boundary Layer

ESPM 228 Adv Topics Micromet & Biomet Mixed Layer Budget Eq. Flux in from the top Time rate of change of Cm Growth – subsidence of PBL, h Flux in the bottom ESPM 228 Adv Topics Micromet & Biomet

Question 1 Is Wireless Network Technology Ready for Flux-Gradient Measurements? Yes, if Fine Vertical Gradients in Temperature, Wind and Humidity can be measured Yes, if Flux-Gradient Theory is Applied Correctly

Restrictions for Application K-Theory infers Fluxes; it does not measure them directly. Gradients are Measured within the Constant Flux Layer, but above Vegetation Sensors are Best placed Outside the Vegetation Canopy and Roughness Sub-layers Extensive, Upwind Fetch must Exist Steady-State Conditions Should Hold ESPM 228 Adv Topic Micromet & Biomet

ESPM 228 Adv Topic Micromet & Biomet Flux-Gradient Theory Momentum Flux Latent Heat Flux (kg m-2 s-1) (J m-2 s-1) Sensible Heat Flux Trace Gas Flux (J m-2 s-1) (mole m-2 s-1) ESPM 228 Adv Topic Micromet & Biomet

ESPM 228 Adv Topic Micromet & Biomet K Theory Aerodynamic Technique: Assessed with Wind Velocity Profiles http://pages.unibas.ch/geo/mcr/Projects/EBEX/index_profile.en.htm ESPM 228 Adv Topic Micromet & Biomet 12

Measure Atmospheric State at Several Levels Above the Canopy Comments/Recommendations Measure Atmospheric State at Several Levels Above the Canopy Measurements at One point Above the Canopy and Several within the Canopy Is Useless!!—though commonly observed Better Precision is Obtained with Differential Measurements Thermocouples resolve 1/40th C per millivolt and 1/80th C per millivolt if wired differentially

Flux Density: mol m-2 s-1 or J m-2 s-1 Eddy Covariance, Flux Density: mol m-2 s-1 or J m-2 s-1 ESPM 228 Adv Topics Micromet & Biomet

ESPM 228 Adv Topics Micromet & Biomet Eddy Covariance Direct Measure of the Trace Gas Flux Density between the atmosphere and biosphere, mole m-2 s-1 In situ Quasi-continuous Integrative of a Broad Area, 100s m2 Introduces No artifacts, like chambers ESPM 228 Adv Topics Micromet & Biomet

Flux Covariance is Determined by Resolving the Contribution across a Spectrum of Slow to Fast Fluctuations in Wind Velocity and Scalar Mixing Ratio CoSpectral Density Wavenumber

ESPM 228 Adv Topic Micromet & Biomet Real-time Sampling Sample instruments at 10 to 20 Hz; fsample = 2 times fcutoff (f=nz/U) Store real-time data on hard disk, 10-30Mb/day Calibrate Periodically Process and Compute Means, Variances, Covariances, Skewness and Kurtosis. Merge turbulence and meteorological data Apply calibration coefficients and gas law corrections to compute unit-correct flux densities and statistics Compute power spectra and co-spectra; examine instrument response and interference effects ESPM 228 Adv Topic Micromet & Biomet

Question 2 Is Wireless Network Technology Ready for Eddy Covariance? Yes, if Systems can Ingest High Input Information (10 Hz) from Flux Covariance Systems, operating off Solar Panels Yes, if Low Power, 3 Dimensional Sonic Anemometer can be Coupled to Low Power Infrared Spectrometer. Yes, if ample data storage (10-30 Mb/day) is available

New Low Power Methane and CO2/H2O Flux System Power Consumption: <24 W or 2 amp at 12vDC CH4: 8 W H2O/CO2: 10 W Data logger, Met and Sonic Anemometer: < 1 W

Past and Current Biomet Study Sites Crops: soybeans, alfalfa, wheat, corn, rice Grassland and peatland pastures Boreal Conifer Forest Deciduous Forest Savanna Woodland

Annual Time Series of Trace Gas Exchange Xu and Baldocchi, AgForMet, 2004

What is the State of the Flux Footprint?

ESPM Adv Topics Micromet & Biomet 2D-Footprint Model of Detto-Hsieh ESPM Adv Topics Micromet & Biomet

Methane Flux Footprint of a Peatland Pasture Detto et al. 2011 AgForMet

ESPM 228 Adv Topic Micromet & Biomet Representative Sampling of Energy Fluxes is Needed to Attain Better Energy Balance Closure ESPM 228 Adv Topic Micromet & Biomet

FLUXNET: From Sea to Shining Sea 500+ Sites, circa 2011

Attributes of an Effective Network--People You Need Boots on the Ground – e.g. Technicians, Students/Postdocs, Scientists--to Turn Measurements into Great Data Integrated Data-Base is Needed to Process, Share, Distribute, Archive and Query Data Scientific Community Needs to Access the Data to turn it into Information and Knowledge

Networks Widen the Number and Span of Climates, Biomes and Treatments (e.g. Biophysical Attributes and Traits, Disturbance, Land Use, and Management) Baldocchi, Austral J Botany 2008

Net Ecosystem Carbon Exchange Scales with Length of Growing Season Baldocchi, Austral J Botany, 2008

Question 3 How Do We Measure the Environmental Conditions of a Site in a Representative Way, that are Used to Interpret Fluxes?

Temperature/Humidity Sensor ESPM 129 Biometeorology

Temperature Microclimate Canopy Ht 3m Sample: 100 W m-2/(1.2 * 1005 * 0.4 * 3 * .3) = 0.23 C/m 20 m Sample: 3m canopy: 100 W m-2/(1.2 * 1005 * 0.4 * 20 * .3) = 0.0345 C/m

Temperature and Humidity Sensors Must be Shielded from the Sun and Aspirated!—Hence, Power is Required At 20 C, the T difference is 0.611 C

Biases between Aspirated and Unaspirated Thermometers are too Large, Compared to Gradients and Treatment Differences One Seeks to Quantify in Nature

Transects of PAR under a forest canopy Radiation Field is Bi-Modal, Non-Gaussian and Possesses a High Coefficient of Variation (> 50%) ESPM 129 Biometeorology

A statistical estimate of the number of sensors needed to define the light environment CV, coefficient of variation (per cent) n, number of samples (within 10% of the population mean) n, number of samples (within 5% of the population mean) 150 609 865 100 270 382 50 68 96 25 17 24 10 3 4 ESPM 129 Biometeorology

Tram with radiation sensors traversing in the understory of a savanna woodland ESPM 129 Biometeorology

Radiation Tram System

Quantum Sensors are Not Ideal Substitutes for Pyranometers If Energy Flux Density (J m-2 s-1) is preferred to Quantum Flux Density (moles of quanta m-2 s-1)

Vegetation Selectively Filters Light Energy in Visible, And Enhances Scattering in NIR

Circuit and Power Supply LEDs as Sensors for Measuring Reflected Light in Narrow Wavebands   LED components Incoming Light Data Logger Circuit and Power Supply LED Casing Figure by Mirabel Jaquez, Future UCB Engineering Graduate Student

Multi-Band LED Reflectance Sensor to Assess Canopy Structure and Function

Multi-Band LED Reflectance Sensor

Links between NDVI and Canopy Photosynthesis

Upward Looking Digital Cameras Assess Canopy Structure and Phenology Promise for Cheap Networks of CCD/Cameras

Digital Cameras Produce Continuous Measure of Canopy Structure Ryu et al.

CO2 Microclimate

Probes for Studying CO2 in the Soil

Don’t‘ Trust Manufacturers Specs and Calibrate; Calibrate with Trusted Standards tied to NOAA ESRL

Each Sensor Has Unique Calibration, which can cause +/-100 ppm differences in Readings Among Sensors

Soil CO2 Gradients and Strong and Amenable for Flux Computations

Tests Prove Adequate

Continuous Measurements of Soil Respiration with CO2 Gradient System is Possible

Time Domain Reflectometers are Expensive to Replicate, Soil Moisture Time Domain Reflectometers are Expensive to Replicate, Good for Measuring Time Series ESPM 129 Biometeorology

Wireless Soil Moisture Network To Measure Volumetric Water Content +/- 2% with 95% C.I. 4-19 Sensors for Area < 900 m2 44-80 sensors for Area: 900-6000 m2 Robinson et al 2008 Vadose Zone Journal

Soil Moisture Fields with ElectroMagnetic Inductance, EMI Complex Spatial Fields Exist and Merit Scrutiny Analysis by Trenton Franz, Arizona

ET and Soil Water Deficits: Root-Weighted Soil Moisture Baldocchi et al., 2004 AgForMet

Concluding Points Fluxes Diagnose the State of the Atmosphere; State Variables Don’t Infer Fluxes Well Make sure you Measure the State of the Environment, Not the State of the Sensor Calibrate, Calibrate, Calibrate New Challenges are Needed to Develop Flux Systems that can be Networked Over Space to Measure Advective Fluxes and Spatial Heterogeneity Wireless Network of LED sensors, Digital Cameras, Soil probes for moisture, Revolution in New Trace Gas Sensors with Tunable Diode Laser Spectroscopy