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 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 http://xenon.colorado.edu/reflections

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. 2. data is archived. 3. same freq as SMAP Disadvantages: 1. antenna gain pattern. 2. many sites bad. 3.

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 http://knol.google.com Bigger blue arrows from http://www.seafriends.org.nz/oceano/current2.htm

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. 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

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

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

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

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).

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

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

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) Multipath RHCP Gain (dB) LHCP red = right hand blue = left hand antenna is L2 Choke antenna 50 100 150 180 Angle from zenith (degrees)

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

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

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

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

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

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

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

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

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

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

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