1. PBO H2O Water Cycle Studies Using The Plate Boundary Observatory

2. PBO H2O Water Cycle Studies Using The Plate Boundary Observatory
Eric Small, Kristine Larson, John Braun, Felipe Nievinski, Clara Chew, Sarah Evans, Maria Rocco, Praveen Vikram, Ethan Gutmann
Geological Sciences and Aerospace Engineering Sciences, CU
UCAR, NCAR
Funding and support
NSF: Climate and Large-Scale Dynamics, Instrumentation and Facilities, EarthScope, Hydrologic Sciences
NASA: Terrestrial Hydrology, SENH, ESS Grad. Fellowship
UNAVCO
Sevilleta and Niwot LTERs

3. Goal: Use multipath measured by PBO network to estimate terrestrial hydrologic variables
Outline
Terrestrial Water Cycle
GPS Reflectometry
Soil moisture
Vegetation
Snow
Hard
Easy
Advantages: 1. instrumentation exists and is maintained data is archived. 3. same freq as SMAP
Disadvantages: 1. antenna gain pattern. 2. many sites bad. 3.

4. New technique to monitor water stored on land in:
transpiration
New technique to monitor water stored on land in:
Soil
Plants
Snow
Terrestrial and atmospheric components of the water cycle  (source: ESA/AOES Medialab)
Small arrows from one from
Bigger blue arrows from

5. Global Terrestrial Observing System Essential Climate Variables (ECVs)
ECVs: “environmental parameters considered to be the most important for understanding, detecting, monitoring, and assessing the impact of climate change.”
We study three:
1. Soil moisture: land-atmosphere interactions; runoff and infiltration; plant productivity
2. Snow: Timing and amount of runoff; influences climate
3. Above-ground biomass: global carbon budget; influences climate

6. Studying the hydrological cycle 1. Models 2. In situ data 3
Studying the hydrological cycle 1. Models 2. In situ data 3. remote sensing data
Products derived from GPS reflections are a hybrid of in situ and remotely sensed data
Validation of satellite products
Quantify water cycle stores and fluxes
Incorporate into modeling

7. In situ data Positives High frequency
Kurc and Small, 2007
In situ data
Positives
High frequency
Environmental conditions constrained
Monitor at all depths
SCAN soil moisture network
Negatives
Small sampling area
Networks not homogenous
Networks are sparse

8. SMAP slide NASA SMAP (2014) Soil Moisture
Active: high resolution, L-band
Passive: low resolution radiometer
SMAP slide
Positives
Global coverage
Negatives
Course resolution
Frequency = ~3 days
Signal ‘incomplete’
‘surface’ soil moisture
Snow depth, not SWE
Veg greenness, not amount
1.26 GHz for SMAP
1.22 for L2c GPS

9. Using GPS-reflectometry for terrestrial hydrology studies
Geodetic-quality
GPS antenna/receiver
“ignore” direct signal
This is the key transition slide
Focus on the multipath

10. How can you quantify the effects of multipath?
Signal-to-Noise Ratio (SNR)
Marshall, CO L2 SNR Trimble NetRS
direct
multipath
NetRS - Block IIR-M satellites only
dB-Hz
dB(hertz) – bandwidth relative to 1 Hz. E.g., 20 dB-Hz corresponds to a bandwidth of 100 Hz. Commonly used in link budget calculations. Also used in carrier-to-noise-density ratio (not to be confused with carrier-to-noise ratio, in dB).

11. Use interference pattern to estimate hydrologic variables
Detrended multipath signal
Soil moisture  phase shift
Snow depth  frequency
Vegetation  amplitude & MP1

12. GPS reflection footprint
for “standard” geodetic antenna (2 m high)
Sensing Footprint
~1000 m2

13. Use data from PBO network to make snow, vegetation and soil moisture products
Positives
Extensive network exists
Data freely available
Same frequency as satellites
Footprint intermediate in scale
Negatives
Antenna not designed for reflections
Site conditions vary

14. Challenge #1: antenna suppresses reflections
Gain pattern: Choke ring antenna (L2)
Multipath
RHCP
Gain (dB)
LHCP
red = right hand
blue = left hand
antenna is L2 Choke antenna 50
100
150
180
Angle from zenith (degrees)

15. Challenge #2: Sites not chosen for sensing terrestrial hydrologic variables
P015, Show Low, AZ
P013, Paradise Valley, NV
red = right hand
blue = left hand
antenna is L2 Choke antenna
BAD: Buildings, roads, complex topography, cows
Good: natural ecosystems, undisturbed surface, simple topography

16. Algorithm Development: 10 test sites with identical GPS and hydrology infrastructure
SMAP ISST-C

17. P041 (CO)
Soil Moisture volumetric water content is linearly related to phase of interference pattern
Need good example here

18. Soil moisture validation in situ probes gravimetric sampling
GPS
Marshall, CO (co-located with P041)

19. NASA’s SMAP in situ sensor test bed GPS phase shift and in situ soil moisture
Correlation stronger with soil moisture at 2.5 cm
2.5 cm
7.5 cm
Vol. Soil Moisture (x100)
Vol. Soil Moisture (x100)
GPS PHI
GPS PHI

20. Vegetation complex effects on SNR: phase, amplitude and frequency
Experiments: Isolate effects of vegetation on GPS reflections
1. measure vegetation water content (kg/m2) and plant height weekly
2. Compare to SNR data

21. Vegetation MP1: L1 pseudorange multipath intensity (produced by teqc)
Vegetation MP1: L1 pseudorange multipath intensity (produced by teqc). We use MP1rms as a proxy for water in vegetation

22. Validation of vegetation product Compare MP1rms fluctuations to measurements of vegetation water content at PBO sites
2011 PBO Survey: UT, ID, MT
Field Sampling

23. P101 (Utah)
Snow Depth estimate reflector height from frequency of interference pattern

24. snow depth validation hand measurements SNOTEL cameras and poles ultrasonic range sensors laser

25. New snow depth map (posted every day at 2 A.M.)

26. Continued efforts validation using surveys at PBO sites
modeling for retrieval of hydrologic variables
Snow: estimate snow density, convert depth to SWE
Veg: water content time series from MP1rms
Soil moisture: product for SMAP validation

27. Partnerships and Outreach
National and international collaboration with GPS network operators.
e.g., pilot project with NOAA-NGS to convert the DoT Minnesota network for snow sensing.
Public code for users to produce water cycle products.
SMAP Cal-Val
SNOTEL, NSIDC
Outreach
Working with UNAVCO to develop webtools for data distribution.
Web-based outreach