High-Rate GPS: Earthquakes, Ice Sheets, and Volcanoes

PBO H2O Water Cycle Studies Using The Plate Boundary Observatory 1PBO 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, CUUCAR, NCARFunding and supportNSF: Climate and Large-Scale Dynamics, Instrumentation and Facilities, EarthScope, Hydrologic SciencesNASA: Terrestrial Hydrology, SENH, ESS Grad. FellowshipUNAVCOSevilleta and Niwot LTERshttp://xenon.colorado.edu/reflections2

Goal: Use multipath measured by PBO network to estimate terrestrial hydrologic variablesOutline Terrestrial Water CycleGPS Reflectometry Soil moistureVegetationSnow

Hard

Easy3Advantages: 1. instrumentation exists and is maintained. 2. data is archived. 3. same freq as SMAPDisadvantages: 1. antenna gain pattern. 2. many sites bad. 3.

New technique to monitor water stored on land in:

SoilPlantsSnowtranspirationTerrestrial and atmospheric components of the water cycle(source: ESA/AOES Medialab)Small arrows from one from http://knol.google.comBigger blue arrows from http://www.seafriends.org.nz/oceano/current2.htm4Global 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 climateStudying 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 dataValidation of satellite productsQuantify water cycle stores and fluxesIncorporate into modeling

6

Kurc and Small, 2007In situ data

PositivesHigh frequencyEnvironmental conditions constrainedMonitor at all depths

NegativesSmall sampling areaNetworks not homogenousNetworks are sparse

SCAN soil moisture networkSMAP slide

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

NegativesCourse resolutionFrequency = ~3 daysSignal incompletesurface soil moistureSnow depth, not SWEVeg greenness, not amount

1.26 GHz for SMAP1.22 for L2c GPS8

Using GPS-reflectometry forterrestrial hydrology studiesignore direct signalFocus on the multipathGeodetic-quality GPS antenna/receiver9This is the key transition slide

Marshall, CO L2 SNR Trimble NetRS directmultipathHow can you quantify the effects of multipath?Signal-to-Noise Ratio (SNR)10NetRS - Block IIR-M satellites onlydB-HzdB(hertz) bandwidth relative to 1 Hz. E.g., 20 dB-Hz corresponds to a bandwidth of 100 Hz. Commonly used inlink budgetcalculations. Also used incarrier-to-noise-density ratio(not to be confused withcarrier-to-noise ratio, in dB).

Use interference pattern to estimate hydrologic variables

Detrended multipath signal

Soil moisture phase shiftSnow depth frequencyVegetation amplitude & MP1

Sensing Footprint~1000 m2GPS reflection footprintfor standard geodetic antenna (2 m high)

Use data from PBO network to make snow, vegetation and soil moisture productsPositivesExtensive network exists Data freely available Same frequency as satellitesFootprint intermediate in scale

NegativesAntenna not designed for reflectionsSite conditions vary

13

Challenge #1: antenna suppresses reflections Gain pattern: Choke ring antenna (L2)RHCPLHCPGain (dB)Angle from zenith (degrees)Multipath050100150180red = right handblue = left handantenna is L2 Choke antenna

14Challenge #2: Sites not chosen for sensing terrestrial hydrologic variables

P013, Paradise Valley, NV

P015, Show Low, AZBAD: Buildings, roads, complex topography, cowsGood: natural ecosystems, undisturbed surface, simple topographyred = right handblue = left handantenna is L2 Choke antenna

15Algorithm Development: 10 test sites with identical GPS and hydrology infrastructure

SMAP ISST-C16

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

P041 (CO)

Need good example here

Soil moisture validationin situ probes gravimetric sampling

In situGPSMarshall, CO (co-located with P041)NASAs SMAP in situ sensor test bed GPS phase shift and in situ soil moisture GPS PHIVol. Soil Moisture (x100)7.5 cm

2.5 cm GPS PHIVol. Soil Moisture (x100)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 dataVegetationMP1: 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 SamplingSnow 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 effortsvalidation using surveys at PBO sitesmodeling for retrieval of hydrologic variablesSnow: estimate snow density, convert depth to SWEVeg: water content time series from MP1rmsSoil moisture: product for SMAP validation

Partnerships and OutreachNational 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, NSIDCOutreachWorking with UNAVCO to develop webtools for data distribution.Web-based outreach