Ecosystem Scale Measurements of Water Using Cosmic-Ray Neutrons

Ecosystem Scale Measurements of Water Using Cosmic-ray Neutrons

Trenton Franz CUASHI Spring Seminar, March 29th, 2013

Department of Hydrology and Water Resources, University of Arizona

With acknowledgements to COSMOS project Members and Collaborators: M. Zreda, TPA Ferré, C. Zweck, R. Rosolem, W.J. Shuttleworth, X. Zeng, S. Stillman, A. Karczynski, B. Chrisman, D. Desilets, S. Papuga, H. Adams, T. Kolb, B. Hornbuckle, S. Irvin

NSF, Hydroinnova, Questa Instruments, Dualem, Acclima, Santa Rita Experimental Range Motivation

Grand Challenge Issued to Global Land Surface Modeling Community (Wood et al., 2011)

Land surface models at 1 km scale (currently 10-100 km)!

Ability to monitor and predict Earth’s terrestrial water, energy, and biogeochemical cycles

2 Motivation

Grand Challenge Issued to Global Land Surface Modeling Community (Wood et al., 2011)

Land surface models at 1 km scale (currently 10-100 km)!

Ability to monitor and predict Earth’s terrestrial water, energy, and biogeochemical cycles

Critical for: Global food production Water resources suitability Flood and droughts Climate change prediction

Photograph: Central Kenya July 2006 3 How can we do it?

Computational needs:

§ Massively parallel computers currently capable of solving up to 109 unknowns

4 How can we do it?

Computational needs:

§ Massively parallel computers currently capable of solving up to 109 unknowns

Data needs:

§ More limiting than computational requirements § High resolution spatiotemporal forcing data available (NCEP reanalysis, statistical downscaling etc.) § Remote sensing products for biomass (NDVI 1km) § Clearly data needs for biogeochemical cycling

5 Outline

1. Introduce the new cosmic-ray neutron probe and COSMOS project

2. Area-averaged soil moisture measurements in the near surface

3. Recent work on separating pools of water and mobile measurements

Robinson et al. 2008 6 Ecosystem Measurements

§ Energy, Water, and Carbon fluxes measured at intermediate scales with eddy covariance techniques

Tonzi Ranch, CA June 2011

7 Ecosystem Measurements

§ Energy, Water, and Carbon fluxes measured at intermediate scales with eddy covariance techniques

§ Point measurements of soil moisture not necessarily representative of footprint

Tonzi Ranch, CA June 2011

8 Ecosystem Measurements

§ Energy, Water, and Carbon fluxes measured at intermediate scales with eddy covariance techniques

§ Point measurements of soil moisture not necessarily representative of footprint

§ Direct soil moisture measurements at spatial scale time consuming and difficult

Tonzi Ranch, CA June 2011

9 Ecosystem Measurements

§ Energy, Water, and Carbon fluxes measured at intermediate scales with eddy covariance techniques

§ Point measurements of soil moisture not necessarily representative of footprint

§ Direct soil moisture measurements at spatial scale time consuming and difficult

§ Critical link between water and energy balance is latent energy flux Tonzi Ranch, CA June 2011

10 Variations in Soil Moisture Collected over 200 m radius

Mana Road Iowa 17 June 2010 September 2010

5 5

10 10

15 Depth,cm Depth,cm 20

20 25

30 25 5 10 15 20 25 30 35 20 25 30 35 40 45 50 Water content, vol. % Soil moisture, vol. % 11 Variations in Soil Moisture

4 10 16 22 vol. % 20 30 40 vol. % 5 15 25 35 vol. % 15 25 35 vol. % 5 10 15 20 vol. % 5 10 15 20 vol. % 2 4 6 8 vol. % 0 Sterling SMAP-OK 16 Sep 10 SMAP-OK 16 Sep 10 10 20 Jul 10 ARM-1 SMAP-OK Santa Rita 22 Jul 10 23 Jul 10 10 Oct 10

Depth, cm 20 Iowa Sep 10 30

5 15 25 35 vol. % 10 20 30 vol. % 2 4 6 8 wt. % 5 15 25 vol. % 15 30 45 vol. % 3 6 9 vol. % 6 9 12 15 wt. % 0 Toulouse Island 2 Mar 11 Mana Dairy Manitou Road 10 15 Jun 10 26 Jul 10 17 Jun 10

Depth, cm 20 Rancho no tengo Santa Rita Kendall 22 Aug 10 6 Jan 11 29 Aug 10

5 10 15 20 wt. % 20 30 40 vol. % 5 10 15 20 vol. % 20 30 40 vol. % 30 40 50 vol. % 30 35 40 45 vol. % 20 30 40 50 vol. % 0 Riet- Desert holz- Chaparral bach 10 8 Mar 11 11 Apr 11 Marshall Mozark Morgan 23 Oct 09 Coastal 18 Apr 11 Monroe Depth, cm 20 Sage 24 Mar 11 9 Mar 11 Neb 3 23 Apr 11 30

0 2 4 6 wt. % 10 20 30 wt. % 5 10 15 20 wt. % 2 4 6 8 wt. % 5 15 wt. % 25 5 15 25 vol. % 5 10 15 20 vol. % 0 San Pedro San Pedro San Pedro 3 Apr 10 7 Jul 07 12 Nov 09 10

San Pedro San Pedro Depth, cm 20 9 Aug 07 6 Dec 08 San Pedro San Pedro 12 Feb 10 5 Mar 10 30

5 15 25 wt. % 5 10 15 20 vol. % 15 20 25 30 wt. % 20 30 40 50 vol. % 10 20 30 40 vol. % 20 30 40 vol. % 35 40 45 vol. % 0

Harvard 10 Park Falls 2 May 11 Metolius 20 Jul 11 Tonzi 14 Jun 11 Bondville 11 May 11 Chestnut 25 Mar 11 Howland

Depth, cm 21 Mar 11 20 4 May 11

30 12 Measurements of Soil Moisture

TDR Sensor Array

1 year

1 month

1 day

1 hour

1 minute

1 m 100 m 10 km 1000 km

Adapted from Robinson et al. 2008 13 Measurements of Soil Moisture

TDR Sensor Array Satellite Remote Sensing

1 year

1 month

1 day

Airborne Remote Sensing 1 hour

1 minute

1 m 100 m 10 km 1000 km

Adapted from Robinson et al. 2008 14 Measurements of Soil Moisture

TDR Sensor Array Satellite Remote Sensing Mobile TDR & EM

1 year

1 month

1 day

Airborne Remote Sensing 1 hour

1 minute

1 m 100 m 10 km 1000 km

Adapted from Robinson et al. 2008 15 Measurements of Soil Moisture

TDR Sensor Array Satellite Remote Sensing Mobile TDR & EM

1 year

1 month

1 day

Airborne Remote Sensing 1 hour

Cosmic-ray Probe and Rover 1 minute

1 m 100 m 10 km 1000 km

Adapted from Robinson et al. 2008 16 COSMOS Project COsmic-ray Soil Moisture Observing System (COSMOS) Phase I: NSF project 2009-2013, ~50 US Probes Phase II: Expansion to 500 probes

17 COSMOS Project COsmic-ray Soil Moisture Observing System (COSMOS) Phase I: NSF project 2009-2013, ~50 US Probes Phase II: Expansion to 500 probes Science Priorities: § Soil moisture controls: § weather and climate models § ecological processes and phenomena § hydrological flow processes in catchments § Water storage on/in vegetation canopies § Frozen precipitation § Remotely sensed measurements of soil moisture 18 COSMOS Project Status § COSMOS data freely available at http://cosmos.hwr.arizona.edu/, some quality control, usually co-located with eddy covariance towers

§ Probes: 60 COSMOS, 60 Independent networks around globe (CosmOz, TERENO, etc.), ~100 more to come online soon (1 yr)

19 Cosmic-ray Neutrons Above the Surface

600 July - August 1964 April - May 1965 500

400

dry earth 300

water 200 Height in air (meters) Height

100

0 1 10 Hendrick and Edge, 1966 Neutrons (10-7 cm-2 sec-1 eV-1) 20 Production of Secondary Particles

200 ) -2 400

600 z coordinate (g cm (g z coordinate 800

1000 -600 -400 -200 0 200 400 x coordinate (g cm-2)

Secondary cosmic-ray particles Cascade initiated by a 10 GeV primary. All produced in copper plates in a trajectories above 1 MeV are shown. large cloud chamber. (Simulations courtesy of D. Desilets, Skobeltzyn, 1927 Sandia National Laboratories) 21 Cosmic-rays on Earth

Space: • Primary - mostly protons and alphas incoming high- • Interact with magnetic field energy cosmic-ray proton - intensity depends on geomagnetic latitude • Interact with atmospheric nuclei Atmosphere: • Produce secondary particles - cascade generation of - intensity depends on barometric secondary cosmic pressure rays • Produce fast neutrons - slowing down by elastic collisions - leads to thermalization - and then absorption Ground:

scattering The last three processes depend on the thermalization chemical composition of the medium, in absorption particular on its hydrogen content

Summarized in Zreda et al., 2012 22 Elements: What We See

Nucleus size

23 Elements: What Neutrons See

Scattering cross-section

24 Elements: What Slows Neutrons

Logarithmic energy decrement per collision

25 Elements: What Stops Neutrons

Stopping power Stopping Element Power H 22.016 C 0.875 O 0.508 Fe 0.411 Mg 0.297 Na 0.277 Si 0.151 Ca 0.139 Al 0.109 K 0.099

26 Neutron Response to Soil Moisture

5000 SiO , N = 1000 cph 4500 2 S 4000 3500 3000 2500 2000 1500

Modeled Neutron Counts (cph) 1000 0 5 10 15 20 25 30 35 40 45 Mean Soil Moisture (Vol. %)

27 Summary of Key Neutron Properties

1. 8.5:1 difference in fast neutron intensity between dry soil and water (Hendrick and Edge, 1966), see ~3:1 different for natural soil moisture variations

Desilets, 2011 28 Summary of Key Neutron Properties

1. 8.5:1 difference in fast neutron intensity between dry soil and water (Hendrick and Edge, 1966), see ~3:1 different for natural soil moisture variations

2. Hydrogen has stopping power 25 times greater than other major elements present (Zreda et al., 2008 & 2012)

Desilets, 2011 29 Summary of Key Neutron Properties

1. 8.5:1 difference in fast neutron intensity between dry soil and water (Hendrick and Edge, 1966), see ~3:1 different for natural soil moisture variations

2. Hydrogen has stopping power 25 times greater than other major elements present (Zreda et al., 2008 & 2012)

3. Neutron average jump length in air ~30 m, average between 20 to 60 collisions over energy range (~107 to 10 eV), neutron velocity > 10 km s-1 (Desilets, 2011 and Glasstone, 1952)

Desilets, 2011 30 Key Assumption

Therefore assume well-mixed neutron density in air where nature performs averaging and we can sample system at a point!

31 Cosmic-ray Probe

32 Cosmic-ray Probe in the Field

Marshall Lake, CO, Oct 2009, D. Desilets of Hydroinnova LLC (http://hydroinnova.com/main.html, ~$14-26k) 33 Neutron Response to Soil Moisture

5000 SiO , N = 1000 cph 4500 2 S 4000 3500 3000 2500 2000 1500

Modeled Neutron Counts (cph) 1000 0 5 10 15 20 25 30 35 40 45 Mean Soil Moisture (Vol. %)

34 Neutron Response to Soil Moisture

5000 SiO , N = 1000 cph 4500 2 S 4000 Probe calibration function, need at least 1 known water content and neutron count rate 3500 (Desilets et al., 2010) 3000 0.0808 θ(N) = − 0.115! # N & 2500 % ( − 0.372 $ N0 ' 2000 1500 € Modeled Neutron Counts (cph) 1000 0 5 10 15 20 25 30 35 40 45 Mean Soil Moisture (Vol. %)

35 Probe Calibration

36 Probe Calibration

18 locations, 6 depths (0-30 cm)

Key to get spatial s.e.m less than 0.5% Vol. or 0.005 m3 m-3

37 Poisson Counting Statistics

2 µN = σ N

Integration Time vs. Uncertainty 80 1 hr 70 2 hr 6 hr

5000 12 hr SiO , N = 1000 cph 60 4500 2 S 24 hr 4000 72 hr 3500 50 3000 2500 40 2000 1500 30 Modeled Neutron Counts (cph) 1000 0 5 10 15 20 25 30 35 40 45 Mean Soil Moisture (Vol. %) 20 Neutron Count Uncertainty (cph) 10

0 1000 1500 2000 2500 3000 3500 4000 4500 5000 Neutron Count (cph) 38 Defining the Support Volume

86% of neutrons from within 335 m radius in dry air at sea level

Weak dependence on soil moisture, water vapor (~7-10% max reduction)

Increases with increasing altitude (decreasing pressure)

Zreda et al., 2008 39 Defining the Support Volume

86% of neutrons from within 335 m radius in dry 86% of neutrons from within a depth of 70 cm air at sea level (dry) Weak dependence on soil moisture, water vapor Depth decreases to 12 cm in wet soils (~7-10% max reduction) Independent of altitude (and pressure) Increases with increasing altitude (decreasing pressure)

Zreda et al., 2008 40 Vertical Heterogeneity WATER RESOURCES Derived a simple framework for RESEARCH calculating effective sensor depth

and uncertainty during natural wetting and drying cycles in soils

PUBLISHED BY THE AMERICAN GEOPHYSICAL UNION

Franz et al. 2012a 41 Santa Rita Experimental Range

§ 35 km south of Tucson, AZ § ~400 mm annual rainfall § 50% of rainfall in July-Sep. § >35oC Daily Temp. in July- Sep. § 30-40% Fractional cover, mixed shrub and cactus § Deep sandy loams w/ gravel

(Cavanaugh et al., 2011) 700 m

42 Validation of Mean Soil Moisture § Installed June 2011

§ Paired study: Open vs. Canopy

§ 18 Locations: 180 Acclima TDT sensors, 18 rain gages (chosen by equal COSMOS sensitivity) TDT Profile Locations EC, Cosmic-ray Probe § TDT probes inserted horizontally at 10, 20, 30, 50, 70 cm, 30 minute data (added 5 cm probe Jan. 2012)

§ ~160 TDT probes worked consistently

Franz et al., 2012b 4343 SRER Cosmic-ray Probe

Santa Rita Experimental Range July 2011 to July 2012 ) 3 − m 3 0.2

0.1

0 Soil Moisture (m Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul

Effective Depth (cm) 0 Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul

Daily Rainfall (mm) 0 Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul

Franz et al., 2012b 44 Validation of Mean Soil Moisture

TDT Wt. Average

) 0.2 Cosmic−ray 3 3 −3 − Difference RMSE = 0.0108 m m m

3 0.15

0.1

0.05

Soil Moisture (m 0

−0.05 Jul 11 Aug 11 Sep 11 Oct 11 Nov 11 Dec 11 Jan 12

Franz et al., 2012b 45 Cosmic-ray Probe Water Balance

Control Volume Mass Balance

Rainfall, P Evapotranspiration, ET Δθ Runoff, Ro P > 0 z = Peff Ro = P − Peff Δt z Soil Moisture, θ (t) Δθ P = 0 z = ET + L Δt Leakage, L

46 Comparison of Daily Net Flux

) SRER ET Comparison, 2011−2012 1 − 8

6 EC data provided by S. Papuga 4 and Z. Sanchez Mejia

Eddy Covar., ET (mm day Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul ) 1 − 8

4 Mostly ET in summer, small L in 2 ray, ET + L (mm day winter − 0 Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Cosmic

Rainfall (mm) 10

0 Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul

47 Additional Pools of Water (Hydrogen)

Classic soil neutron probe

http://www.ars.usda.gov/is/graphics/photos/jan11/ d100-17.jpg

48 Hydrogen Sources in Support Volume

Transient 1. Water Vapor Quasi-static Static 3. Vegetation 2. Built-up 5. Surface Water 4. Intercepted

6. Layer of Water

7. Soil Moisture 8. Lattice Water 9. Soil Carbon Compounds

49 A General Framework

1. Quantify the probe support volume

2. Identify the sources of hydrogen in the support volume

3. Quantify the flux of hydrogen atoms into and out of the support volume

Franz et al. 2013a 50 A General Framework

1. Quantify the probe support volume

2. Identify the sources of hydrogen in the support volume

3. Quantify the flux of hydrogen atoms into and out of the support volume

The support volume is a function of the amount and distribution of hydrogen in the system!

Franz et al. 2013a 51 Soil Lattice Water

Lattice water: function of soil formation and weathering Generally higher lattice water with clay content but need to sample locally

52 Water Vapor

Integrated Water Vapor [0 to 335m] (mm) at Sea Level 100

90 20 mm 80

70 15 60

50 10 40

30 Relative Humidity (%) 20 5

0 0 0 5 10 15 20 25 30 35 40 o Temperature ( C) 5353 Water Vapor

Contribution of WatorWater Vapor to Total H (mol) 16 20% Can remove water vapor portion of signal with surface measurements of air pressure, 14 temperature, relative humidity and assuming 15% standard atmosphere (Rosolem et al., 2013) 12

10 10%

6 5% 4

2 1% Integrated Water Vapor [0 to 335m] (mm) 0 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 3 −3 (cm cm ) [3% Lattice Water] 5454 Biomass

Contribution of Vegetation to Total H (mol) 50 Amazon 60% Can quantify with allometry and Rain 45 gravimetric sampling of biomass 50% Forest 40 )

2 35 40% −

30 West Coast US 25 30% Forests 20

Eastern 15 20% US Wet Biomass (kg m 10 Forests 10% 5 Corn 5% 0 1% 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 3 −3 (cm cm ) [3% Lattice Water]

55 Isolating the Biomass Signal

50 Total Soil Water = 0.05 m3/m3 45 0.15 m3/m3 40 0.25 m3/m3 35 0.35 m3/m3 30 0.45 m3/m3 25 20 15 10

Biomass Water Equivalent (mm) 5

0 1200 1400 1600 1800 2000 2200 2400 2600 2800 Fast Neutrons (cph)

Equations summarized in Franz et al. 2013a 56 Biomass of Forests

~ 100

Wildfire

Ponderosa Pine Nearby Wildfire Site

Pine

Monsoon ~ 3 km

H. Adams Los Alamos, T. Kolb NAU q The reduction of ~100 counts is due to the forest canopy and corresponds to a biomass water equivalent of 27.6 ± 0.8 mm

q Three different allometric estimates give biomass water equivalents in the range 22-32 mm

Franz et al. 2013b 57 2 Iowa Validation Site

2.1 Qualitative Evaluation Daily measured precipitation is shown in Figure 1 and it can be seen that the COSMOS sensor does react to precipitation. On July 10 there was about 20 mm of precipitation in two days. A soil moisture spike of about 3% can be seen a few days later in the COSMOS data. There is also a great example of this in early August when there was over 40 mm of rain one day. The COSMOS sensor showed a soil moisture increase of about 15% a few days later. The soil moisture value for this spike, however, is not very realistic. It shows a soil moisture of nearly 100%, which is not realistic as 100% moisture would mean there would be just water present and no soil. Also, with soil moisture usually being around the range of 20-30%, the parts of the plot that Biomassare above 40% and upof to 60%Maize are not very realistic.

Figure 2: COSMOS sensor (center dot) and validating points. Upper right and lower right circles are wet spots outside the footprint of the COSMOS sensor. Lower left circle is planted opposite of the rest of the ﬁeld, again outside of the COSMOS sensor’s footprint.

The COSMOS sensor at the Iowa Validation Site sits in an area with high organic matter in the soil. This is important to know because hydrogen

Hornbuckle et al., 2012 and Franz et al. 2013b Iowa St. Univ. B. Hornbuckle and S. Irvin 58 Figure 5: Change in mass over time of Mf , Md and Mw. account for this. As expected, there is no mass of any kind present on May 19th, after planting the seeds and before plant emergence. As the season progresses, it can be seen that the fresh mass of the plants (Mf ) increases up until a point, at which they start to senesce and the mass decreases. The water present in the plants (Mw) also can be seen to behave as expected. It initially comprises a majority of the entire plant mass during the early stages of growth. The amount of water then starts to become less of an inﬂuence in the plants as they begin to senesce. The dry matter (Md) can be seen to steadily increase as the season progresses and around the time of senescence,

Date Mf Md Mw May 19 0 0 0 June 24 0.92 0.11 0.81 July 2 2.67 0.28 2.39 Aug. 18 6.44 2.04 4.40 Sept. 13 5.77 3.05 2.72 Nov. 14 0 0 0

2 Table 4: This table shows the values for Figure 5. All units are kg m .

11 Biomass of Maize

Crop Harvest Soil Tilled

Franz et al. 2013b 59 Pools of Hydrogen in Biomass

Franz et al. 2013b 60 COSMOS Rover

Standard (US) version

European version

Figures courtesy of D. Desilets, B. Chrisman 61 COSMOS Rover: Tucson Basin

Tucson Basin, AZ, monthly surveys in dry season, weekly in wet season

1 minute data 7 minute averages

m3/m3

2 Figure 1. Left: Satellite image of the Tucson Basin. Middle: The 1-minute soil moisture data

3 from the July 27th 2012 survey in m3 m-3. Right: The 7-minute smoothed soil moisture data. The B. Chrisman (MS, 2013) 62 4 cosmic-ray over signal is represented by a swath with a width of 700 m. The 7-minute smoothing

5 reducing the counting error to 2%, but increases the footprint size by a factor of 7.

COSMOS Rover: Tucson Basin

1 B. Chrisman2 (MS,Figure 2 .2013) 20 interpolated and smoothed soil moisture maps from 2012 in m3 m-3. The urban area 63

3 and agricultural fields have anomalously high soil moisture values that are consistent in time,

4 meaning the existence of temporal stability in the soil moisture distribution at this scale.

COSMOS Rover: Tucson Basin

Modeling continuous basin soil moisture with depth Basin-scale water mass balance (mm)

1 1 Figure 8. The time-interpolated soil moisture generated from a regression model of the 2 Top: 2 Figure 10. Left: Cumulative distribution plots of the mass balance in mm. Potential 3 cosmic-ray survey values, the Santa Rita reference COSMOS probe, and atmospheric 3 evapotranspiration (PET), precipitation (P), evapotranspiration + leakage (ET+L), Pike equation 4 information (precipitation and max temperature). Bottom: The soil moisture profiles estimated 4 estimate of evaporation (Epike eq.). Right: The cumulative distribution plot of the change in storage 5 from the 3 inputs; SMOS satellite (~ 1 cm), cosmic-ray rover (~10 cm), and an estimated value at -1 6 depth (40 cm). 5 term in mm day .

7 B. Chrisman (MS, 2013) 6 64

Summary

§ Cosmic-ray probe capable of filling gap between point measurements and remote sensing of soil moisture, standardized global datasets

§ Probe sees all hydrogen, slight complication for soil moisture but exciting advance in terrestrial hydrometeorology and ecology

§ General framework for isolating any hydrogen signal from neutron measurements including: soil moisture, water vapor, above-ground biomass, below-ground biomass, …?

65 We Need Your Help

§ Abundance of cosmic-ray probe data and study sites now with more to come, need more field studies quantifying all pools of hydrogen and deconstructing integrated signal

§ Can we quantify canopy interception and its temporal dynamics (liquid and solid)?

§ Can we quantify and partition the change in above- ground biomass, below-ground biomass, and soil carbon compounds?

§ How to fully utilize thermal and fast neutron measurements, 2 independent knowns to solve for 2 unknown hydrogen pools?

66 References

Cavanaugh, M. L., S. A. Kurc, and R. L. Scott (2011), Evapotranspiration partitioning in semiarid shrubland ecosystems: a two-site evaluation of soil moisture control on transpiration, Ecohydrology, 4(5), 671-681. doi:10.1002/eco.157.

Chrisman, B. (May 2013), Quantifying basin-scale soil moisture using the cosmic-ray rover, Master of Science Thesis. Department of Hydrology and Water Resources, University of Arizona.

Desilets, D., M. Zreda, and T. P. A. Ferre (2010), Nature's neutron probe: Land surface hydrology at an elusive scale with cosmic rays, Water Resources Research, 46. doi:W11505 10.1029/2009wr008726.

Desilets, D. (2011), Sandia Report: SAND2011-1101, Radius of influence for a cosmic-ray soil moisture probe: Theory and Monte Carlo simulations, Sandia National Laboratories, Albuquerque, New Mexico 87185 and Livermore, California 94550.

Franz, T. E., M. Zreda, P. A. Ferre, R. Rosolem, C. Zweck, S. Stillman, X. Zeng, and W. J. Shuttleworth (2012a), Measurement depth of the cosmic-ray soil moisture probe affected by hydrogen from various sources, Water Resources Research, 48. doi:10.1029/2012WR011871.

Franz, T. E., M. Zreda, R. Rosolem, and P. A. Ferre (2012b), Field validation of cosmic-ray soil moisture sensor using a distributed sensor network, Vadose Zone Journal, 11(4). doi:10.2136/ vzj2012.0046.

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Franz, T. E., M. Zreda, R. Rosolem, B. Hornbuckle, S. Irvin, H. Adams, T. Kolb, C. Zweck, and W. J. Shuttleworth (2013b), Ecosystem scale measurements of biomass water using cosmic-ray neutrons, Geophysical Research Letters. In Review.

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