54 datasets found
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Federal
Arroyo de los Pinos Sediment Monitoring Research Station One Minute Stream flow Time Series Data
Department of the Interior —
Water discharge calculated using Pinos rating curve -
Federal
Arroyo de los Pinos Sediment Monitoring Research Station One Minute Shear Stress Time Series Data
Department of the Interior —
Depth-slope product of shear stress (rho-g-R-S) -
Federal
Arroyo de los Pinos Sediment Monitoring Research Station One Minute Average Stage Time Series Data
Department of the Interior —
Average stage measured at the monitored cross-section -
Federal
Arroyo de los Pinos Sediment Monitoring Research Station One Minute Bedload Flux Time Series Data
Department of the Interior —
Average bedload flux measured by three Reid-type slot samplers -
Federal
Arroyo de los Pinos - Right Sampler One Minute Reid Sampler Pillow Response Time
Department of the Interior —
Raw pillow output (pre calibration) from the right sampler -
Federal
Arroyo de los Pinos - Center Sampler One Minute Acoustic Impact Pulse 2x Time Series Data
Department of the Interior —
Channel 1 (2x) amplification from the center impact sensor -
Federal
Arroyo de los Pinos - Center Sampler One Minute Acoustic Impact Pulse 32x Time Series Data
Department of the Interior —
Channel 5 (32x) amplification from the center impact sensor -
Federal
Arroyo de los Pinos - Left Sampler One Minute Acoustic Impact Pulse 1024x Time Series Data
Department of the Interior —
Channel 10 (1024x) amplification from the left impact sensor -
Federal
Arroyo de los Pinos - Right Sampler One Minute Bedload Flux Time Series Data
Department of the Interior —
Calculated sediment flux per unit width of channel into the right sampler box -
Federal
Arroyo de los Pinos - Right Sampler One Minute Acoustic Impact Pulse 2x Time Series Data
Department of the Interior —
Channel 1 (2x) amplification from the right impact sensor -
Federal
Arroyo de los Pinos - Right Sampler One Minute Acoustic Impact Pulse 64x Time Series Data
Department of the Interior —
Channel 6 (64x) amplification from the right impact sensor -
Federal
Arroyo de los Pinos - Right Sampler One Minute Acoustic Impact Pulse 256x Time Series Data
Department of the Interior —
Channel 8 (256x) amplification from the right impact sensor -
Federal
Arroyo de los Pinos - Center Sampler One Minute Acoustic Impact Pulse 4x Time Series Data
Department of the Interior —
Channel 2 (4x) amplification from the Center impact sensor -
Federal
Arroyo de los Pinos - Center Sampler One Minute Bedload Flux Time Series Data
Department of the Interior —
Calculated sediment flux per unit width of channel into the center sampler box -
Federal
Arroyo de los Pinos - Left Sampler One Minute Acoustic Impact Pulse 32x Time Series Data
Department of the Interior —
Channel 5 (32x) amplification from the left impact sensor -
Federal
S&T Project Number 1871 Final Report:Monitoring Sediment Transport in an Ephemeral Stream
Department of the Interior —
The research focuses on physical measurements of sediment flux and surrogate techniques in order to establish a relationship between the two in an ephemeral stream... -
Federal
Arroyo de los Pinos - Left Sampler One Minute Bedload Flux Time Series Data
Department of the Interior —
Calculated sediment flux per unit width of channel into the left sampler box -
Federal
Extreme gradient boosting machine learning models, suspended sediment, bedload, streamflow, and geospatial data, Minnesota, 2007-2019
Department of the Interior —
A series of machine learning (ML) models were developed for Minnesota. The ML models were trained and tested using suspended sediment, bedload, streamflow, and... -
Federal
Extreme gradient boosting machine learning models, suspended sediment, bedload, streamflow, and geospatial data, Minnesota, 2007-2019
Department of the Interior —
A series of machine learning (ML) models were developed for Minnesota. The ML models were trained and tested using suspended sediment, bedload, streamflow, and... -
Federal
Extreme gradient boosting machine learning models, suspended sediment, bedload, streamflow, and geospatial data, Minnesota, 2007-2019
Department of the Interior —
A series of machine learning (ML) models were developed for Minnesota. The ML models were trained and tested using suspended sediment, bedload, streamflow, and...
54 datasets found