PBO H2O Water Cycle Studies Using The Plate Boundary Observatory

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 supportNSF: Climate and Large-Scale Dynamics, Instrumentation and Facilities, EarthScope, Hydrologic SciencesNASA: Terrestrial Hydrology, SENH, ESS Grad. Fellowship

UNAVCO

Sevilleta and Niwot LTERs

http://xenon.colorado.edu/reflections

Goal: Use multipath measured by PBO network to estimate terrestrial hydrologic variables

Outline

Terrestrial Water CycleGPS Reflectometry Soil moistureVegetationSnow

Hard

Easy

New technique to monitor water stored on land in:

SoilPlantsSnow

transpiration

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

Studying the hydrological cycle

1. Models2. In situ data3. remote sensing data

Products derived from GPS reflections are a hybrid of in situ and remotely sensed data1. Validation of satellite products2. Quantify water cycle stores and fluxes3. Incorporate into modeling

Kurc and Small, 2007

In situ data

Positives• High frequency• Environmental conditions

constrained• Monitor at all depths

Negatives• Small sampling area• Networks not homogenous• Networks are sparse

SCAN soil moisture network

SMAP slide

NASA SMAP (2014)Soil MoistureActive: high resolution, L-bandPassive: low resolution radiometerPositives

• Global coverage

Negatives• Course resolution• Frequency = ~3 days• Signal ‘incomplete’

‘surface’ soil moistureSnow depth, not SWEVeg greenness, not amount

Using GPS-reflectometry forterrestrial hydrology studies

“ignore” direct signal

Focus on the multipath

Geodetic-quality GPS antenna/receiver

Marshall, CO L2 SNR Trimble NetRS

direct

multipath

How can you quantify the effects of multipath?Signal-to-Noise Ratio (SNR)

Use interference pattern to estimate hydrologic variables

Detrended multipath signal

Soil moisture phase shift

Snow depth frequency

Vegetation amplitude & MP1

Sensing Footprint

~1000 m2

GPS reflection footprintfor “standard” geodetic antenna (2 m high)

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

Challenge #1: antenna suppresses reflections

Gain pattern: Choke ring antenna (L2)

RHCP

LHCPGa

in (

dB

)

Angle from zenith (degrees)

Multipath

0 50 100 150 180

Challenge #2: Sites not chosen for sensing terrestrial hydrologic variables

P013, Paradise Valley, NVP015, Show Low, AZ

BAD: Buildings, roads, complex topography, cows

Good: natural ecosystems, undisturbed surface, simple topography

Algorithm Development: 10 test sites with identical GPS and hydrology infrastructure

SMAP ISST-C

Soil Moisturevolumetric water content is linearly related to phase of interference pattern

P041 (CO)

SNRAcos( f sinE )

Need good example here

Soil moisture validationin situ probes gravimetric sampling

In situ GPS

Marshall, CO (co-located with P041)

NASA’s SMAP in situ sensor test bed GPS phase shift and in situ soil moisture

GPS PHI

Vol

. Soi

l Moi

stur

e (x

100)

7.5 cm 2.5 cm

GPS PHI

Vol

. Soi

l Moi

stur

e (x

100)

Correlation stronger with soil moisture at 2.5 cm

Vegetationcomplex effects on SNR:phase, amplitude and frequency

Experiments: Isolate effects of vegetation on GPS reflections1. measure vegetation water content (kg/m2) and plant height weekly2. Compare to SNR data

VegetationMP1: L1 pseudorange multipath intensity (produced by teqc). We use MP1rms as a proxy for water in vegetation

Validation of vegetation productCompare MP1rms fluctuations to measurements of vegetation water content at PBO sites

2011 PBO Survey: UT, ID, MT Field Sampling

Snow Depth estimate reflector heightfrom frequency of interference pattern

P101 (Utah)

snow depth validationhand measurementsSNOTELcameras and polesultrasonic range sensorslaser

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

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

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