ABSTRACT:

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. Included are all data necessary to run the simulations, code to run the simulations (Hydrus 1D software required - see Related Resources for archived version used), code to aggregate/plot the results, and all of the results.

ABSTRACT:

HYDRUS 1D is an open source, Windows-based software package for simulation of of one-dimensional water movement through variably saturated porous media. The model solves Richards equation for increments of soil depth with thickness and hydrologic characteristics specified by the user. Soil hydrologic characteristics can be assigned based on van Genuchten parameters provided within HYDRUS based on soil texture, or can be specified by the user. The model incorporates a sink term for plant water uptake and provides different options for specifying rooting depth and growth rate. The model provides water content and flux values for observation points at depths specified by the user. The program has a graphical user interface for modifying inputs and visualizing model results, but it is also possible to modify model inputs and visualize outputs by working with the HYDRUS 1D text files, using a program such as R. Using R to modify model input files allows for running many model scenarios in a loop, which streamlines analysis on complex sets of model runs.

ABSTRACT:

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. This work combines nitrogen mass balance with δ15N mass balance to estimate denitrification rates in soil relative to groundwater and streams.

Data includes solute concentrations and isotopic composition of nitrate and water in water samples collected from soil, groundwater and surface water. Soil solution chemistry was characterized in samples from tension lysimeters installed in two non-irrigated fields operated by cooperating farmers. Groundwater and surface water sampling between 2012 and 2017 included two wells, five springs, and three stream sites. Solute concentration and water isotope analysis was conducted in the Montana State University Environmental Analytical Laboratory. Nitrate isotope analyses were conducted at Woods Hole Oceanographic Institution. For detailed analytical methods, see the main manuscript.

ABSTRACT:

This resource contains data collected by the Whitefish Lake Institute (WLI) as well as R code used to compile and conduct quality assurance on the data. This resource reflects joint publication efforts between WLI and the Montana State University Extension Water Quality (MSUEWQ) program. All data included here was uploaded to the National Water Quality Portal (WQX) in 2022. It is the intention of WLI to upload all future data to WQX and this HydroShare resource may also be updated in the future with data for 2022 and forward.

Data Purpose:
The ‘Data’ folder of this resource holds the final data products for the extensive dataset collected by WLI between 2007 and 2021. This folder is likely of interest to users who want data for research and analysis purposes. This dataset contains physical water parameter field data collected by Hydrolab MS5 and DS5 loggers, including water temperature, specific conductance, dissolved oxygen concentration and saturation, barometric pressure, and turbidity. Additional field data that needs further quality assurance prior to use includes chlorophyll a, ORP, pH, and PAR. This dataset also contains water chemistry data analyzed at certified laboratories including total nitrogen, total phosphorus, nitrate, orthophosphate, total suspended solids, organic carbon, and chlorophyll a. The data folder includes R scripts with code for examples of data visualization. This dataset can provide insight to water quality trends in lakes and streams of northwestern Montana over time.
Data Summary:
During the time-period, WLI collected water quality data for 63 lake sites and 17 stream and river sites in northwestern Montana under two separate monitoring projects. The Northwest Montana Lakes Network (NMLN) project currently visits 41 lake sites in Northwestern Montana once per summer. Field data from Hydrolabs are collected at discrete depths throughout a lake's profile, and depth integrated water chemistry samples are collected as well. The Whitefish Water Quality Monitoring Project (WWQMP) currently visits two sites on Whitefish Lake, one site on Tally Lake, and 11 stream and river sites in the Whitefish Lake and Upper Whitefish River watersheds monthly between April and November. Field data is collected at one depth for streams and many depths throughout the lake profiles, and water chemistry samples are collected at discrete depths for Whitefish Lake and streams. The final dataset for both programs includes over 112,000 datapoints of data passing quality assurance assessment and an additional 72,000 datapoints that would need further quality assurance before use.

Workflow Purpose:
The ‘Workflow’ folder of this resource contains the raw data, folder structure, and R code used during this data compilation and upload process. This folder is likely of interest to users who have similar datasets and are interested in code for automating data compilation or upload processes. The R scripts included here have code to stitch together many individual Hydrolab MS5 and DS5 logger files as well as lab electronic data deliverables (EDDs), which may be useful for users who are interested in compiling one or multiple seasons' worth of data into a single file. Reformatting scripts format data to match the multi-sheet excel workbook format required by the Montana Department of Environmental Quality for uploads to WQX, and may be useful to others hoping to automate database uploads.
Workflow Summary:
Compilation code in the workflow folder compiles data from its most original forms, including Hydrolab sonde export files and lab EDDs. This compilation process includes extracting dates and times from comment fields and producing a single file from many input files. Formatting code then reformats the data to match WQX upload requirements, which includes generating unique activity IDs for data collected at the same site, date, and time then linking these activity IDs with results across worksheets in an excel workbook. Code for generating all quality assurance figures used in the decision-making process outlined in the Quality Assurance Document and resulting data removal decisions are included here as well. Finally, this folder includes code for combining data from the separate program uploads for WQX to the more user-friendly structure for analysis provided in the 'Data' file for this HydroShare resource.

ABSTRACT:

The data and R code provided here are the underpinnings of a manuscript in the journal, Citizen Science: Theory and Practice (Volunteer Accuracy in a Benthic Macroinvertebrate Participatory Science Project). Volunteer-derived aquatic macroinvertebrate identifications and resulting water quality metrics are compared to results from a professional entomologist. The assessment included a total of 357 benthic macroinvertebrate quality control (QC) samples collected by volunteers using leaf packs, kick nets, and visual assessments between 2011 and 2016 for the Environmental Quality Institute (EQI) in North Carolina, USA. Of the 357 total samples, 284 were of sufficient quality to be used in the analysis. Data incudes counts of organisms conducted by volunteers on each sample and counts conducted by an entomologist. Macroinvertebrate index values are calculated based on volunteer and entomologist counts and are compared using linear regression and Bray-Curtis dissimilarity methods.

ABSTRACT:

Gallatin County, Montana has been experiencing rapid residential development with extensive reliance on onsite (septic) wastewater systems. Groundwater in the alluvial valley of the county surrounding the Gallatin River and tributaries is relatively shallow and vulnerable to increasing concentrations of nitrate and potentially other contaminants from anthropogenic sources. This data resource includes groundwater nitrate concentrations from three different sources combined and made publicly available for the first time. The resource includes tabulated nitrate concentration point data as well as a geospatial component including the mapped concentrations and spatially interpolated nitrate concentrations. The resource also includes static water levels measured at the time of new well drilling and spatially interpolated depth to groundwater based on those data. Groundwater nitrate data continues to become available through new testing and curation of past records, so parallel future data resources will include more information.
The purpose of the spatially interpolated nitrate concentration map is to identify locations where nitrate concentrations are elevated over a relatively continuous area, where multiple proximal sample locations show concentrations above 3 mg/L. Nitrate concentrations can be highly variable across space and depth within aquifers. The identified areas are not the only places in the Gallatin Valley where nitrate concentrations are above 3 mg/L, nor are all groundwaters within the designated areas above 3 mg/L. These results are a simplified summary of nitrate data compiled as of July 25th, 2024 and this analysis will continue to be refined over time.