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A Remote Snow/Water Equivalent Monitoring System Christian Skalka (UVM) Joint work with Jeff Frolik, Beverly Wemple, Tom Neumann (UVM)

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Presentation on theme: "A Remote Snow/Water Equivalent Monitoring System Christian Skalka (UVM) Joint work with Jeff Frolik, Beverly Wemple, Tom Neumann (UVM)"— Presentation transcript:

1 A Remote Snow/Water Equivalent Monitoring System Christian Skalka (UVM) Joint work with Jeff Frolik, Beverly Wemple, Tom Neumann (UVM)

2 How Much Snow is Out There? Snow/Water Equivalent (SWE): measurement of water content in snowpack  Not the same as snow height.

3 How Much Snow is Out There? Real world measurement is complicated  Terrain irregularities  Precipitation irregularities  Wind, forest canopies, elevation  Snow itself!

4 The Importance of SWE  Regional snowpack profiles are critically important: Natural resource planning  Areas such as CA rely on snowmelt for water supply Flood predictions Avalanche forecasting  Accurate SWE monitoring system is a “holy grail” of remote monitoring Snow is dynamic, strange characteristics that make SWE measurement difficult

5 NOAA National SWE Estimate

6 SWE Measurement Techniques Considerable research has yielded a variety of measurement technologies  LIDAR, ground penetrating radar Air based, ground based  Significant cost, low measurement frequencies  Air based techniques complicated by forest canopies  Augmented by national network of static ground- based sensors

7 SNOTEL Sites Multiple SNOTEL sites form a national network of ground-based SWE datapoints SNOTEL sites comprise:  Snow pillows for SWE measurement  Other weather sensing devices  On-site datalogging  Satellite uplink for remote data acquisition (usually)

8 SNOTEL Sites

9 SNOTEL Sites: Drawbacks SNOTEL sites are based on old technology that has significant drawbacks:  High Cost (~$15,000/site)  Expensive, “dumb” data acquisition technology  Environmentally hazardous, unreliable SWE sensing technology  Severe deployment difficulties: mass, construction, not adaptable to terrain

10 SNOTEL Sites: Data Acquisition SNOTEL sites use traditional dataloggers (e.g. CR1000)  ~$1500 per unit  Standalone  Heavy  Not programmable  Power hungry  No intelligent processing of data

11 SNOTEL Sites: Snow Pillows Predominant SWE sensing technology for SNOTEL sites is snow pillow  200 gallon bags of antifreeze, measure snow loads  Must be large to accommodate snow bridging  Must be installed in a large, flat area.

12 SNOTEL Sites: Snow Pillows

13 A New Approach We advocate a new approach to static ground-based SWE monitoring  Fundamental idea: use modern technology for data acquisition and retrieval  Fundamental idea: measure SWE via attenuation of electromagnetic radiation, not load sensing Lightweight, low cost, robust, adaptable Improved spatial resolution

14 Attenuation of Electromagnetic Radiation Basic idea:  Electromagnetic waves of certain types are attenuated by snow/ice/water  Attenuation is measurable  Amount of attenuating medium can be extrapolated from attenuation factor Note well: this approach is robust to terrain and snow bridging effects

15 Attenuation of Electromagnetic Radiation Candidate waveforms: Gamma radiation  Free source (the Universe)  Previous work (UC Berkeley) illustrates effectiveness Microwave radiation  Cheap off-the-shelf sources and detectors

16 Attenuation of Electromagnetic Radiation

17 WSN Computational Platform Our computational platform based on Wireless Sensor Networks (WSNs)  Comprised of motes: low cost (~$50/unit), low powered, extremely lightweight  Networked  Reprogrammable  Easily integrated with a variety of sensors

18 Better Data Acquisition and Retrieval WSNs are highly adaptable:  Algorithms for in-network processing, system power control, communications  Can interoperate with variety of remote data recovery methods Radio, cell, satellite modems Multiple sites networked to single gateway Mote-based data muling

19 Data Muling Demo

20 Prototype Deployment on Mt. Mansfield An experimental network was deployed on Mt. Mansfield in Winter 2007-2008.  Test software, system robustness, power requirements  Sensing accuracy not a priority Project developed with the help of UVM undergraduates  Charley Robinson (CS), Matt Casari (EE), Christopher Henwood (EE)

21 Site Layout USGS stream gage site (Ranch Camp) Three site network  Two in clearing  One in the forest Air temperature (x3) Snow height (x3) Snow weight (rudimentary load sensors)

22 Ranch Camp Prototype Deployment

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24 Deployment Effort The measurement system is transportable by backpack  3 backpacks for 3 sites When the system was installed at the Ranch Camp Site the packs were carried in for a ½ hour hike Pack Requirements:  (2) External Frame Backpacks  (1) Standard Large Camping Style Pack

25 Frame 2-5’ lengths of ¾” metal tube Threaded ends Allows many configurations with simple adapter changes Wires are ran through the pipe for protection Design allows ease of installation and modification

26 Power System 12V 12Ah Battery  Powers snow weight sensors 4xAA Battery -> Upgraded to D cell  Powers mote & height sensor Predicted life of 150 days  Prototype experimentation illustrated that this is a significant overestimate due to cold weather

27 Site Costs Initial Proposal: Cost of all Sites: $3678 Cost for entire project (four sites): $4000

28 Future Work Develop sensor technologies  Currently in initial phases of testing microwave and gamma ray attenuation  Hardware and software issues Develop communication technologies  Time synchronization for networked sites  Remote data retrieval (cell modem interface)

29 Future Work “Ruggedize” system hardware  Current work by Jeff Frolik and Matt Seekins Solve power supply issues  Move to wet-cell batteries exclusively Future deployments and testing  Mt. Mansfield, Central Sierra Snow Laboratory Readily available ground truthing

30 Conclusion SWE data is critically important to society Static ground-based measurement essential to large-scale SWE estimates Our proposed technology significantly advances state-of-the-art  Low cost, easily deployed, better spatial resolution  Highly adaptable, many applications  Key ideas: WSN computational infrastructure, new robust sensing technology

31 Conclusion For more info go to: http://www.cems.uvm.edu/research/cems/snow/swe.php


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