Geology ReportsSearch

USGS · tm4F3

TracerLPM (Version 1): An Excel® workbook for interpreting groundwater age distributions from environmental tracer data

Abstract

TracerLPM is an interactive Excel® (2007 or later) workbook program for evaluating groundwater age distributions from environmental tracer data by using lumped parameter models (LPMs). Lumped parameter models are mathematical models of transport based on simplified aquifer geometry and flow configurations that account for effects of hydrodynamic dispersion or mixing within the aquifer, well bore, or discharge area. Five primary LPMs are included in the workbook: piston-flow model (PFM), exponential mixing model (EMM), exponential piston-flow model (EPM), partial exponential model (PEM), and dispersion model (DM). Binary mixing models (BMM) can be created by combining primary LPMs in various combinations. Travel time through the unsaturated zone can be included as an additional parameter. TracerLPM also allows users to enter age distributions determined from other methods, such as particle tracking results from numerical groundwater-flow models or from other LPMs not included in this program. Tracers of both young groundwater (anthropogenic atmospheric gases and isotopic substances indicating post-1940s recharge) and much older groundwater (carbon-14 and helium-4) can be interpreted simultaneously so that estimates of the groundwater age distribution for samples with a wide range of ages can be constrained. TracerLPM is organized to permit a comprehensive interpretive approach consisting of hydrogeologic conceptualization, visual examination of data and models, and best-fit parameter estimation. Groundwater age distributions can be evaluated by comparing measured and modeled tracer concentrations in two ways: (1) multiple tracers analyzed simultaneously can be evaluated against each other for concordance with modeled concentrations (tracer-tracer application) or (2) tracer time-series data can be evaluated for concordance with modeled trends (tracer-time application). Groundwater-age estimates can also be obtained for samples with a single tracer measurement at one point in time; however, prior knowledge of an appropriate LPM is required because the mean age is often non-unique. LPM output concentrations depend on model parameters and sample date. All of the LPMs have a parameter for mean age. The EPM, PEM, and DM have an additional parameter that characterizes the degree of age mixing in the sample. BMMs have a parameter for the fraction of the first component in the mixture. An LPM, together with its parameter values, provides a description of the age distribution or the fractional contribution of water for every age of recharge contained within a sample. For the PFM, the age distribution is a unit pulse at one distinct age. For the other LPMs, the age distribution can be much broader and span decades, centuries, millennia, or more. For a sample with a mixture of groundwater ages, the reported interpretation of tracer data includes the LPM name, the mean age, and the values of any other independent model parameters. TracerLPM also can be used for simulating the responses of wells, springs, streams, or other groundwater discharge receptors to nonpoint-source contaminants that are introduced in recharge, such as nitrate. This is done by combining an LPM or user-defined age distribution with information on contaminant loading at the water table. Information on historic contaminant loading can be used to help evaluate a model's ability to match real world conditions and understand observed contaminant trends, while information on future contaminant loading scenarios can be used to forecast potential contaminant trends.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bryant C. Jurgens, J.K. Böhlke, Sandra M. Eberts. 2012. TracerLPM (Version 1): An Excel® workbook for interpreting groundwater age distributions from environmental tracer data. https://doi.org/10.3133/tm4f3

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

dMODELS, a MATLAB software package for modeling crustal deformation near volcanic centers and active faults using Global Navigation Satellite System data—User guide

dMODELS is a MATLAB software package that implements the most common analytical models used to interpret deformation measurements near faults and active volcanic centers. This manual focuses on inversion of deformation data from the Global Navigation Satellite System (GNSS). The included case studies emphasize the GNSS inversion component of the software. Source models include pressurized spherical, spheroidal, and horizontal sill (penny-crack) magma reservoirs in a homogeneous, elastic, isotropic, flat half-space. A topography correction is available for the spherical source. Dikes and faults are described following the mathematical notation for the rectangular dislocations in a homogeneous, elastic, flat half-space. Equations have been reviewed for typographical errors present in the original literature and verified against finite-element method numerical models. GNSS data from the 2006 eruption at Augustine Volcano, Alaska; the 1998–2000 unrest at Taal Volcano, Philippines; and the 2009 earthquake in L’Aquila, Italy, are used to demonstrate the application of the software package.

Techniques and Methods

Aspergillosis (Avian) case definition for wildlife

Diagnostic laboratories receive carcasses and samples for diagnostic evaluation and pathogen/toxin detection. Case definitions bring clarity and consistency to the evaluation process. Their use within and between organizations allows more uniform reporting of diseases and etiologic agents. The intent of a case definition is to provide scientifically based criteria for determining: (a) if an individual carcass has a specific disease and degree of confidence in that diagnosis and (b) if there is evidence of a pathogen or toxin in a carcass or sample (for example, swab, tissue sample, skin scraping, blood/serum sample, environmental sample, or other). This case definition is specific to aspergillosis and applies to all avian species.

Techniques and Methods

Field sampling guidelines for developing and verifying satellite remote sensing chlorophyll a concentration and fluorescence models in inland waters

Harmful algal blooms are increasing in frequency in inland waters across the United States, resulting in a need to monitor phytoplankton bloom events to track ecosystem health and productivity. Remote sensing of chlorophyll a values offers a cost-effective and powerful method for early detection and characterization of bloom events and serves as an overall indicator of water quality and trophic state, with regular, repeated sampling of landscape-wide, high spatial resolution measurements. Field measurements are necessary for developing and verifying chlorophyll a retrieval models. For model verification, chlorophyll a concentration or fluorescence and light attenuation measurements are needed; for model development, turbidity and colored dissolved organic matter concentration measurements are additionally needed; and for model development and verification, radiometric measurements, taxonomic identification of phytoplankton, inherent optical properties, and cyanotoxin concentration are further measurements that can provide context. This report outlines detailed methods and priority considerations for collecting high-quality field data in inland waters (defined as rivers, lakes, reservoirs, estuaries, streams, and wetlands). The described methods include best practices for collecting and preparing discretely collected water samples and for calibration, maintenance, and quality assurance and quality control of field sensors. Whereas the priorities will vary between applications, some general guidelines are to collect field samples (1) as close in time to a satellite overpass as possible, (2) from representative areas of the waterbody to capture the range of spatial variability, and (3) near the surface to match remote sensing reflectance data.

Techniques and Methods