Atmospheric vapor and precipitation are not in isotopic equilibrium in a continental mountain environment

A supplement to these data and resource is available at: https://wetlandscapes.gitlab.io/mountain-vapor-isotopes/.

General data description

This README describes some of the core inputs used for, and results generated from, the paper Atmospheric vapor and precipitation are not in isotopic equilibrium in a continental mountain environment. For the full data context, including all raw and intermediate data, code, etc., please visit the research compendium associated with these files: https://osf.io/7he4x/; the data presented here are just a synthesis of some of the core data and results.

Isotope and environmental data were collected in the Medicine Bow National Forest (US Forest Service): https://www.fs.usda.gov/mbr. In particular, an emphasis was placed on collecting data within the Glacier Lakes Ecosystem Experiments Site: https://www.fs.usda.gov/rmrs/experimental-forests-and-ranges/glees-glacier-lakes-ecosystem-experiments-site.

These data were generated via the R script Scripts/015_OutputGeneration.R using both raw and intermediate data (again, see the compendium for more on these files).

This README contains descriptions of the following data sets included in this resource:

• vapor_isotopes.xlsx
• precipitation_isotopes.xlsx
• high_resolution_environmental.csv
• low_resolution_environmental.csv
• expanded_posteriors.csv
• core_posteriors.csv
• environmental_posteriors.csv

Additional files used for reference:

• WI_Template.xlsx

Information about specific data files

File: vapor_isotopes.xlsx

Vapor data measured at GLEES.

Column definitions

These data were formatted for submission to waterisotopes.org. To satisfy the requirements of submitting to that site, we used their submission template: WI_Template.xlsx. The template includes a complete set of column definitions, by sheet.

File: precipitation_isotopes.xlsx

Precipitation data collected at several sites throughout the Snowy Range of the Medicine Bow National Forest.

Column definitions

These data were formatted for submission to waterisotopes.org. To satisfy the requirements of submitting to that site, we used their submission template: WI_Template.xlsx. The template includes a complete set of column definitions, by sheet.

File: high_resolution_environmental.csv

Half-hour environmental/weather data measured at the US-GLE AmeriFlux site, but relevant to this study. For more about these data: https://ameriflux.lbl.gov/sites/siteinfo/US-GLE.

Column definitions

• date_time: The date and time a particular observation was made. Data is half-hourly, thus the date and time represent the end of a half-hour measurement.
• format: yyyy-mm-dd hh:mm:ss
• air_pressure: Air pressure.
• unit: kPa
• air_temperature: Air temperature.
• unit: degrees C
• relative_humidity: Relative humidity.
• unit: %
• wind_direction: Wind direction.
• unit: degrees
• wind_speed: Wind speed.
• unit: m/s
• mixing_ratio: Mixing or humidity ratio.
• unit: ppm
• latitude: Latitude.
• unit: decimal degrees
• datum: WGS 84 (EPSG:4326)
• longitude: Longitude.
• unit: decimal degrees
• datum: WGS 84 (EPSG:4326)
• elevation: Elevation above mean sea level.
• unit: m
• datum: WGS 84 (EPSG:4326)

File: low_resolution_environmental.csv

Daily precipitation data measured at or near the WY95 National Atmospheric Deposition Program site. More about the site: https://nadp.slh.wisc.edu/data/sites/siteDetails.aspx?net=NTN&id=WY95.

Column definitions

• date: Date of an observation.
• format: yyyy-mm-dd
• precipitation: Amount of precipitation recorded.
• unit: mm
• latitude: Latitude.
• unit: decimal degrees
• datum: WGS 84 (EPSG:4326)
• longitude: Longitude.
• unit: decimal degrees
• datum: WGS 84 (EPSG:4326)
• elevation: Elevation above mean sea level.
• unit: m
• datum: WGS 84 (EPSG:4326)

File: expanded_posteriors.csv

This file contains estimates of the parameters determined in all 576 processing scenarios (the "expanded" scenarios; see Table 1 in the manuscript). That is, rather that provide the full set of posterior draws for all scenarios, just the point estimates are highlighted for their use in future estimation efforts.

The basic form of the model used for each scenario was:

$$ \delta_{A} \sim \mathcal{N}(\mu_{eq} = \delta_{A,eq}, \ \sigma_{eq} \sim \mathcal{N}(\mu, \sigma)) $$

Such that:

• $\delta_{A}$: The measured vapor at US-GLE, given some scenario.
• $\mathcal{N}$: Symbolic representation of the normal distribution, with two parameters: mean ($\mu$) and standard deviation ($\sigma$),
• $\mu_{eq}$: Mean for a given equilibrium scenario.
• $\delta_{A, eq}$: The expected equilibrium value, given a scenario.
• $\sigma_{eq}$: The standard deviation in the uncertainty/error for a given equilibrium scenario. The parameter indicated in the parameter column.
• $\mu$: Mean uncertainty; the value estimated in the mean column.
• $\sigma$: Standard deviation of the uncertainty; the value estimated in the sd column.

(Note: This equation is a slightly more explicit form from that used in the manuscript. This version does not include uncertainty in the vapor measurements, $\delta_A$, which was included in the analysis).

Column definitions

• isotope: The isotope to which a given scenario applies.
• values: H2, O18
• resolution: The temporal resolution at which a set of measurements or equilibrium estimates were aggregated.
• values: daily, weekly, monthly
• season: The season over which a scenario was run.
• values: annual, summer, winter
• note: annual (Jan-Dec), summer (Jun-Oct), winter (Nov-May)
• equation: The different equations (see the manuscript) used to estimate $\delta_{eq}$.
• values: Equation (3), Equation (4), Equation (5)
• flux_weighting: Whether or not the equilibrium estimate was flux weighted using the relative masses of precipitation associated with a precipitation isotope sample. No flux weighting was associated with the daily resolution data, as we did not have higher-resolution precipitation samples.
• values: unweighted, weighted
• temporal_lag: The temporal lag used to match a set of vapor measurements and equilibrium estimates.
• values: 0, 1, 2, 3, 4, 5, 6, 7
• note: 0 applies to: days, weeks, months; 1 applies to: day, week, month; 2 applies to: days; 3 applies to: days; 4 applies to: days; 5 applies to: days; 6 applies to: days; 7 applies to: days.
• temperature: Whether or not air temperature or an estimate of cloud temperature (hydrometeor) was used to calculate the equilibrium vapor value.
• values: air, hydrometeor
• parameter: The parameter estimated. In this case, sigma ($\sigma_{eq}$) was the value determined.
• mean: The estimated mean uncertainty ($\mu$).
• sd: The estimated standard deviation in the uncertainty ($\sigma$).

File: core_posteriors.csv

The posterior point estimates for the "core" scenarios that investigate the importance of apparent disequilibrium (separation) between the observed/measured vapor value and the equilibrium vapor value, given a processing scenario.

$$ \delta_A \sim \mathcal{N}(\mu_{eq} = \delta_{A,eq} - \Delta, \ \sigma_{eq} \sim \mathcal{N}(\mu, \sigma)) $$

Where:

$$ \Delta \sim \mathcal{N} (\mu_{\Delta}, \sigma_{\Delta}) $$

Such that:

• $\Delta$: Estimated apparent disequilibrium (separation).
• $\mu_{\Delta}$: Mean apparent disequilibrium.
• $\sigma_{\Delta}$: Standard deviation in the estimate of apparent disequilibrium.

All other parameters are similar to those defined for the file expanded_posteriors.csv. (Note: This equation is a slightly more explicit form from that used in the manuscript.)

Some of the columns below contain similar information to the expanded_posteriors.csv data, so some details are omitted for the sake of brevity.

Column definitions

• isotope: The isotope to which a given scenario applies.
• values: H2, O18
• resolution: The temporal resolution at which a set of measurements or equilibrium estimates were aggregated.
• values: daily, weekly, monthly
• season: The season over which a scenario was run.
• values: annual, summer, winter
• equation: The different equations (see the manuscript) used to estimate $\delta_{eq}$.
• values: Equation (3), Equation (4), Equation (5)
• parameter: The parameter of interest, given the model identified above.
• values: apparent disequilibrium ($\mu_{\Delta}$; $\Delta$), sigma ($\sigma_{\Delta}$)
• mean: The estimated mean uncertainty ($\mu$).
• sd: The estimated standard deviation in the uncertainty ($\sigma$).

File: environmental_posteriors.csv

The posterior estimates for the environmental covariate model. This includes scenarios in which no environmental covariates were found to be predictive (model contains only an intercept and sigma term) and others in which the covariates were predictive.

When no environmental covariates were found predictive, the model took on the form highlighted for the core_posteriors.csv data.

When there were predictive covariates then the model looked something like:

$$ \delta_A \sim \mathcal{N} (\mu_{eq} = \delta_{A,eq} - E \vec \beta, \sigma_{eq}) $$

Where each element, $i$, in the vector $\vec \beta$ is distributed as:

$$ \beta_i \sim \mathcal{N} (\mu_i, \sigma_i) $$

Such that:

• $E$: The design matrix (matrix containing all the environmental covariate data).
• $\vec \beta$: A vector of slope parameters, including an intercept. In this context, the intercept takes on the function of the apparent disequilibrium value.

Some of the columns below contain similar information to the expanded_posteriors.csv data, so some details are omitted for the sake of brevity.

Column definitions

• isotope: The isotope to which a given scenario applies.
• values: H2, O18
• resolution: The resolution at which a set of measurements or equilibrium estimates were aggregated.
• values: daily, weekly, monthly
• season: The season over which a scenario was run.
• values: annual, summer, winter
• equation: The different equations (see the manuscript) used to estimate $\delta_{eq}$.
• values: Equation (3), Equation (4), Equation (5)
• parameter
• values: intercept, air_temperature, air_pressure, snow, vapor_gradient, wind_speed, sigma
  • air_temperature, air_pressure, snow, vapor_gradient, and wind_speed represent slopes for the linear model indicated above. In that context, the mean column represents the mean slope value for a given environmental covariate, while the sd column represents the standard deviation in that slope parameter.
  • The intercept parameter can be similarly interpreted as apparent disequilibrium, accounting for the linear influence of environmental covariates (if found predictive). In the absence of predictive covariates, then the intercept parameter is equal to apparent disequilibrium.
• mean: The estimated mean uncertainty ($\mu$).
• sd: The estimated standard deviation for a parameter ($\sigma$).