Wireless Sensor Networks for Improved Snow Water Equivalent and Runoff Estimates
We leverage the frontiers of the Internet of Things technology in a recently developed end-to-end wireless sensor network (WSN) system that samples, collects, stores, and displays mountain hydrology measurements in near real-time. At the core of the system lies an ultra-low power, radio channel-hopi...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8630935/ |
| _version_ | 1818427931128496128 |
|---|---|
| author | Sami A. Malek Steven D. Glaser Roger C. Bales |
| author_facet | Sami A. Malek Steven D. Glaser Roger C. Bales |
| author_sort | Sami A. Malek |
| collection | DOAJ |
| description | We leverage the frontiers of the Internet of Things technology in a recently developed end-to-end wireless sensor network (WSN) system that samples, collects, stores, and displays mountain hydrology measurements in near real-time. At the core of the system lies an ultra-low power, radio channel-hoping, and self-organizing mesh that allows for remote autonomous sampling of snow. Such properties, combined with a rugged weather-sealed design of the devices and multi-level data replication, provides reliable real-time data at spatial and temporal scales previously impractical to achieve in mountain environments. The system was deployed at three 1 km<sup>2</sup> sites across the North Fork of the Feather River basin with a cluster of 12 sensor nodes for each location. Measurements show that existing operational autonomous systems are non-representative spatially, with biases that can reach up to 50%. A comparison between a wet and dry year showed that snow depths exhibit strong multi-scale inter-year spatial stationarity with major rank conservation. Temporally dense analysis using elastic net regression shows that dominant features at the sub-km<sup>2</sup> scale are site-dependent and differ from the watershed scale. Newly introduced explanatory variables, based on the nearest neighbor with a Landsat assimilated historical product, consistently explained up to 90% of the variance in the watershed-scale SWE for both years. At two WSN sites, lagged cross-correlation of snowmelt with stream flow measurements showed a significant improvement of up to 100% compared with existing systems, suggesting that WSNs can be instrumental in improving runoff forecasting and water management. |
| first_indexed | 2024-12-14T14:53:34Z |
| format | Article |
| id | doaj.art-c5ad294879ee4a01b7fea7e924040355 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T14:53:34Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c5ad294879ee4a01b7fea7e9240403552022-12-21T22:57:04ZengIEEEIEEE Access2169-35362019-01-017184201843610.1109/ACCESS.2019.28953978630935Wireless Sensor Networks for Improved Snow Water Equivalent and Runoff EstimatesSami A. Malek0https://orcid.org/0000-0002-7059-1462Steven D. Glaser1Roger C. Bales2Department of Civil and Environmental Engineering, University of California at Berkeley, Berkeley, CA, USADepartment of Civil and Environmental Engineering, University of California at Berkeley, Berkeley, CA, USASierra Nevada Research Institute, University of California at Merced, Merced, CA, USAWe leverage the frontiers of the Internet of Things technology in a recently developed end-to-end wireless sensor network (WSN) system that samples, collects, stores, and displays mountain hydrology measurements in near real-time. At the core of the system lies an ultra-low power, radio channel-hoping, and self-organizing mesh that allows for remote autonomous sampling of snow. Such properties, combined with a rugged weather-sealed design of the devices and multi-level data replication, provides reliable real-time data at spatial and temporal scales previously impractical to achieve in mountain environments. The system was deployed at three 1 km<sup>2</sup> sites across the North Fork of the Feather River basin with a cluster of 12 sensor nodes for each location. Measurements show that existing operational autonomous systems are non-representative spatially, with biases that can reach up to 50%. A comparison between a wet and dry year showed that snow depths exhibit strong multi-scale inter-year spatial stationarity with major rank conservation. Temporally dense analysis using elastic net regression shows that dominant features at the sub-km<sup>2</sup> scale are site-dependent and differ from the watershed scale. Newly introduced explanatory variables, based on the nearest neighbor with a Landsat assimilated historical product, consistently explained up to 90% of the variance in the watershed-scale SWE for both years. At two WSN sites, lagged cross-correlation of snowmelt with stream flow measurements showed a significant improvement of up to 100% compared with existing systems, suggesting that WSNs can be instrumental in improving runoff forecasting and water management.https://ieeexplore.ieee.org/document/8630935/Elastic netfeature selectionInternet of Thingsrunoffsnow water equivalentwireless sensor networks |
| spellingShingle | Sami A. Malek Steven D. Glaser Roger C. Bales Wireless Sensor Networks for Improved Snow Water Equivalent and Runoff Estimates IEEE Access Elastic net feature selection Internet of Things runoff snow water equivalent wireless sensor networks |
| title | Wireless Sensor Networks for Improved Snow Water Equivalent and Runoff Estimates |
| title_full | Wireless Sensor Networks for Improved Snow Water Equivalent and Runoff Estimates |
| title_fullStr | Wireless Sensor Networks for Improved Snow Water Equivalent and Runoff Estimates |
| title_full_unstemmed | Wireless Sensor Networks for Improved Snow Water Equivalent and Runoff Estimates |
| title_short | Wireless Sensor Networks for Improved Snow Water Equivalent and Runoff Estimates |
| title_sort | wireless sensor networks for improved snow water equivalent and runoff estimates |
| topic | Elastic net feature selection Internet of Things runoff snow water equivalent wireless sensor networks |
| url | https://ieeexplore.ieee.org/document/8630935/ |
| work_keys_str_mv | AT samiamalek wirelesssensornetworksforimprovedsnowwaterequivalentandrunoffestimates AT stevendglaser wirelesssensornetworksforimprovedsnowwaterequivalentandrunoffestimates AT rogercbales wirelesssensornetworksforimprovedsnowwaterequivalentandrunoffestimates |