CREWS - Consortium for Research on Environmental Water Systems (CREWS)
SMART FIRES: Sensors, Machine Learning, and Artificial Intelligence in Real Time Fire Science - SMART FireS
Natural Resource Supply Chain Optimization using Aerial Imagery Interpreted with Machine Learning Methods - NRSCOAIMLM
About Montana NSF EPSCoR
CREWS News
Systems Ecology and Earth Sciences
These data were collected to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) and Consortium for Research in Environmental Water Systems (CREWS) programs.
These data were collected to support monitoring of the Upper Clark Fork River restoration, and data collection was funded by the US NSF Long Term Research in Environmental Biology (LTREB) program and the US NSF EPSCoR funded Montana Consortium for Research on Environmental Water Systems. The LTREB monitoring project consists of monthly or bi-weekly water quality monitoring across a 200-km restoration gradient contaminated by historic mining practices to monitor inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and heavy metal contamination.
The data and R code provided here are the underpinnings of a manuscript in the journal, Biogeochemistry (the manuscript title is parallel to resource title). Nitrogen use efficiency in cultivated agriculture is reduced by denitrification and by leaching of nitrate, which reduces water quality and is subject to denitrification downstream. Denitrification and leaching losses from dryland farming during fallow periods (no crop growing) can play a disproportionately large role in cropping system nitrogen losses.
Data and R code bundled here provide a framework to implement the open source Hydrus-1D soil water model to characterize influence of crop rotation, weather, and soils on root zone water flux in a non-irrigated annual cultivation agricultural system. Simulated water flux is combined with soil nitrate concentrations averaged by 2-year rotational sequence to produce nitrate leaching estimates. This framework and the associated results are the analytical underpinning of a paper in revision at the journal of Agricultural Ecosystems & Environment.
These data were collected between February 2016 and August 2018 during the investigation of seasonal hydrologic connectivity between meteoric water and stream flow generation in Hyalite Canyon, MT (USA). We interpreted seasonal, longitudinal patterns of stable water isotope composition in stream flow as a method of determining stream flow generation mechanisms throughout the year.
This resource provides radon, uranium and strontium isotope data, along with select compositional data, for water samples collected in Hyalite Canyon, Montana. Computations in support of mixing models and monte carlo optimization are included, documenting groundwater contributions to streamflow.
These data were collected to support monitoring of the Upper Clark Fork River (UCFR) restoration. The long term UCFR monitoring project consists of monthly or bi-weekly water quality monitoring across a 200-km restoration gradient contaminated by historic mining practices to monitor inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and heavy metal contamination. The original analytical intent for these data was to assess the response of river dissolved organic carbon to the floodplain restoration.
Supplemental text and figures, analytical code, and full dataset documenting compositional differences and results of incubations for effects on dissolved organic carbon concentration and character.