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PBO H2O Water Cycle Studies Using The Plate Boundary Observatory.

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Presentation on theme: "PBO H2O Water Cycle Studies Using The Plate Boundary Observatory."— Presentation transcript:

1 PBO H2O Water Cycle Studies Using The Plate Boundary Observatory

2 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

4 New technique to monitor water stored on land in: Soil Plants Snow transpiration

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

7 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

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

9 Using GPS-reflectometry for terrestrial hydrology studies “ignore” direct signal Focus on the multipath Geodetic-quality GPS antenna/receiver

10 Marshall, CO L2 SNR Trimble NetRS direct multipath How can you quantify the effects of multipath? Signal-to-Noise Ratio (SNR)

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

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

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) RHCP LHCP Gain (dB) Angle from zenith (degrees) Multipath

15 Challenge #2: Sites not chosen for sensing terrestrial hydrologic variables P013, Paradise Valley, NV P015, Show Low, AZ 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 Soil Moisture volumetric water content is linearly related to phase of interference pattern P041 (CO) Need good example here

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

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

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

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

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


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