Accuracy of travel time distribution (TTD) models as affected by TTD complexity, observation errors, and model and tracer selection
Analytical models of the travel time distribution (TTD) from a source area to a sample location are often used to estimate groundwater ages and solute concentration trends. The accuracies of these models are not well known for geologically complex aquifers. In this study, synthetic data sets were us...
Saved in:
| Published in: | Water resources research 2014-07, Vol.50 (7), p.6191-6213 |
|---|---|
| Main Authors: | , , , , |
| Format: | Article |
| Language: | eng |
| Subjects: | |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Analytical models of the travel time distribution (TTD) from a source area to a sample location are often used to estimate groundwater ages and solute concentration trends. The accuracies of these models are not well known for geologically complex aquifers. In this study, synthetic data sets were used to quantify the accuracy of four analytical TTD models as affected by TTD complexity, observation errors, model selection, and tracer selection. Synthetic TTDs and tracer data were generated from existing numerical models with complex hydrofacies distributions for 1 public‐supply well and 14 monitoring wells in the Central Valley, California. Analytical TTD models were calibrated to synthetic tracer data, and prediction errors were determined for estimates of TTDs and conservative tracer (
NO3−) concentrations. Analytical models included a new, scale‐dependent dispersivity model (SDM) for two‐dimensional transport from the water table to a well and three other established analytical models. The relative influence of the error sources (TTD complexity, observation error, model selection, and tracer selection) depended on the type of prediction. Geological complexity gave rise to complex TTDs in monitoring wells that strongly affected errors of the estimated TTDs. However, prediction errors for
NO3− and median age depended more on tracer concentration errors. The SDM tended to give the most accurate estimates of the vertical velocity and other predictions, although TTD model selection had minor effects overall. Adding tracers improved predictions if the new tracers had different input histories. Studies using TTD models should focus on the factors that most strongly affect the desired predictions.
Key Points
We quantified travel time distribution (TTD) model prediction errors using synthetic data
TTD complexity, tracer errors, and tracer selection strongly affected prediction errors
A scale‐dependent dispersion model improved accuracy of some predictions |
|---|---|
| ISSN: | 0043-1397 1944-7973 |