169 results — topic: Data Science & Modeling

Dataset

NOAA PSL Soil Moisture and Surface Temperature Probe Data for SPLASH

This dataset contains measurements from a hand-held FieldScout TDR Soil Moisture Meter within the 0-10 cm soil depth of: Time (UTC), GPS locations, Electrical Conductivity (EC), compensated percent volumetric water content (VWC), soil surface temperature (T), and rod length (inches) obtained during

Intrieri, Janet, Jackson, Darren, de Boer, Gijs2023DOI: 10.5281/zenodo.10080897
Dataset

BST/NOAA PSL Level 2 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH

This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) cam

Elston, Jack, Jackson, Darren, Stachura, Maciej2023DOI: 10.5281/zenodo.10205099
Dataset

Measurement of particulated matter (PM1, PM2.5, PM10) using a PM sensor during the SAIL campaign in Gothic, CO and Mt. Crested Butte, CO

We deployed another PM sensor (Modulair-PM, QuantAQ) to measure the mass concentration of particulate matter (PM) for three size cuts at both the main site (M1) and supplementary site (S2) of the SAIL from 14 June 2022 to 14 June 2023. The instruments provide the mass concentration of PM1, PM2.5 and

Shawon, Abu Sayeed Md, Aiken, Allison, Benedict, Katherine2023DOI: 10.5439/2229368
Dataset

BST/NOAA PSL Level 2 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH

This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) cam

Elston, Jack, Jackson, Darren, Stachura, Maciej2023DOI: 10.5281/zenodo.10380444
Dataset

Groundwater and Surface Water Flow (GSFLOW) model files for the East River, Colorado

The data package contains model input files and executables for the East River, Colorado (750 km2) located in the headwaters of the Upper Colorado River Basin. The code applied is the U.S. Geological Survey (USGS) Groundwater and Surface Water Flow (GSFLOW) model. The model contains a 100-m grid res

Rosemary Carroll, Richard Niswonger, Craig Ulrich2023DOI: 10.15485/1998576
Dataset

Dissolved Oxygen and Temperature Data from the Hyporheic Zone of the East River Watershed July 2017 to October 2018

Dissolved oxygen (DO) is critical for aquatic ecosystems. Our focus is on the long-term DO dynamics in hyporheic zone of rivers, which are a function of both transport (hydrologic exchange between river and hyporheic zone) and uptake by biogeochemical reactions or respiration. The study site is the

Ruby Ghosh, Charles McIntire, Michael Freeman2023DOI: 10.15485/1972219Cited 1 times
Dataset

Snowpack Persistence Day of Year Standard Deviation (1993-2022)

This dataset represents an estimate of interannual variability in the day of year (i.e., "Julian Day") of the persistence of the seasonal snowpack. Specifically these are estimates of the first day of bare ground derived from long-term time-series of Landsat, and OLI imagery starting in 1993. These

Ian Breckheimer2023
Dataset

Snowpack Persistence Day of Year Mean (1993 - 2022)

This dataset represents an estimate of the mean day of year (i.e., "Julian Day") of the persistence of the seasonal snowpack from 1993 - 2022. Specifically these are estimates of the first day of bare ground derived from long-term time-series of Landsat, and OLI imagery starting in 1993. These maps

Ian Breckheimer2023
Dataset

Snowpack Onset Day of Year Standard Deviation (1993-2022)

This dataset represents an estimate of interannual variability in the day of year (i.e., "Julian Day") of the onset of the seasonal snowpack. Specifically these are estimates of the last day of bare ground derived from long-term time-series of Landsat, and OLI imagery starting in 1993. To facilitate

Ian Breckheimer2023
Dataset

Snowpack Onset Day of Year Mean (1993 - 2022)

This dataset represents an estimate of the mean day of year (i.e., "Julian Day") of the onset of the seasonal snowpack. Specifically these are estimates of the last day of bare ground derived from long-term time-series of Landsat TM, ETM, and OLI imagery starting in 1993. To facilitate computation o

Ian Breckheimer2023
Dataset

Snowpack Duration Standard Deviation (Water Year 1993 - 2022)

This dataset represents an estimate of interannual variability in the number of days of continuous seasonal snowpack from 1993 - 2022. This map is derived from estimates of the first and last day of bare ground derived from long-term time-series of Landsat TM, ETM, and OLI imagery starting in 1993.

Ian Breckheimer2023
Dataset

Snowpack Duration Mean (Water Year 1993 - 2022)

This dataset represents an estimate of the mean number of days of continuous seasonal snowpack from 1993 - 2022. This map is derived from estimates of the first and last day of bare ground derived from long-term time-series of Landsat TM, ETM, and OLI imagery starting in 1993. These maps combine mon

Ian Breckheimer2023
Dataset

Maximum 2m Air Temperature Monthly Timeseries

These are maps of monthly averages of daily maximum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and microclimate sensor data using Bayesian regression-kriging, incorporating topographic and vegetation structure covariates. These da

Ian Breckheimer2023
Dataset

Minimum 2m Air Temperature Monthly Timeseries

These are maps of monthly averages of daily minimum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and microclimate sensor data using Bayesian regression-kriging, incorporating topographic and vegetation structure covariates. These da

Ian Breckheimer2023
Dataset

Average 2m Air Temperature Monthly Timeseries

These are maps of monthly averages of daily average air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and microclimate sensor data using Bayesian regression-kriging, incorporating topographic and vegetation structure covariates. These da

Ian Breckheimer2023
Dataset

Minimum 2m Air Temperature Daily Timeseries

These are maps of daily minimum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and microclimate sensor data using Bayesian regression-kriging, incorporating topographic and vegetation structure covariates. To reduce storage space, ori

Ian Breckheimer2023
Dataset

Maximum 2m Air Temperature Daily Timeseries

These are maps of daily maximum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and microclimate sensor data using Bayesian regression-kriging, incorporating topographic and vegetation structure covariates. To reduce storage space, ori

Ian Breckheimer2023
Dataset

Snowpack Duration Yearly Timeseries

These maps represent annual estimates of the number of days of continuous seasonal snowpack from 1993 - 2022. The maps are derived from estimates of the first and last day of bare ground derived from long-term time-series of Landsat, and OLI imagery starting in 1993. Annual estimates represent the n

Ian Breckheimer2023
Thesis

Advancements in Measuring and Modeling the Mechanical and Hydrological Properties of Snow and Firn: Multi-sensor Analysis, Integration, and Algorithm Development

Estimating snow mechanical properties – such as elastic modulus, stiffness, and strength – is important for understanding how effectively a vehicle can travel over snow-covered terrain. Vehicle instrumentation data and observations of the snowpack are valuable for improving the estimates of winter v

Meehan Tate2022DOI: 10.18122/td.1979.boisestateCited 3 times
Article

Imputation of contiguous gaps and extremes of subhourly groundwater time series using random forests

Machine learning can provide sustainable solutions to gap-fill groundwater (GW) data needed to adequately constrain watershed models. However, imputing missing extremes is more challenging than other parts of a hydrograph. To impute missing subhourly data, including extremes, within GW time-series d

Dwivedi D., Mital U., Faybishenko B.2022Journal of Machine Learning for Modeling and ComputingDOI: 10.1615/jmachlearnmodelcomput.2021038774Cited 31 times