Search research articles

Search research articles

Related Experiment Videos

Spatial interpolation methods for nonstationary plume data.

Patrick M Reed¹, Timothy R Ellsworth, Barbara S Minsker

¹Department of Civil and Environmental Engineering, The Pennsylvania State University, 215B Sackett Building, University Park, PA 16802, USA. preed@engr.psu.edu

|March 24, 2004

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Research to Confront Climate Change Complexity: Intersectionality, Integration, and Innovative Governance.

Earth's future·2024

Same author

Identifying robust adaptive irrigation operating policies to balance deeply uncertain economic food production and groundwater sustainability trade-offs.

Journal of environmental management·2023

Same author

Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States.

Scientific data·2023

Same author

Low cost satellite constellations for nearly continuous global coverage.

Nature communications·2020

Same author

Skill (or lack thereof) of data-model fusion techniques to provide an early warning signal for an approaching tipping point.

PloS one·2018

Same author

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management.

Risk analysis : an official publication of the Society for Risk Analysis·2017

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Quantile kriging is the most reliable method for interpolating groundwater contaminant plumes, outperforming other techniques by minimizing bias from sample variability and preferential sampling. This research guides practitioners in selecting effective plume interpolation strategies.

Area of Science:

Environmental Science
Hydrogeology
Geostatistics

Background:

Groundwater plume interpolation estimates contaminant concentrations at unsampled sites, crucial for understanding contaminant distribution.
Challenges include sparse data, irregular monitoring networks, and the heterogeneous, anisotropic, and nonstationary nature of contaminant fields.
Accurate plume interpolation is vital for effective environmental remediation and risk assessment.

Purpose of the Study:

To comprehensively analyze and compare the performance of six different interpolation methods for scatter-point contaminant concentration data.
To evaluate interpolation accuracy under varying plume complexities and data availability scenarios.
To provide guidance for selecting optimal plume interpolation techniques in practical hydrogeological assessments.

Related Experiment Videos

Main Methods:

Six interpolation methods were evaluated, including intrinsic kriging and inverse-distance weighting.
High-resolution simulation data of perchloroethylene (PCE) contamination in a heterogeneous alluvial aquifer were utilized.
Three test cases were generated, varying plume size, complexity, and data density.

Main Results:

The performance of interpolation methods was significantly influenced by sample variability and preferential sampling.
Quantile kriging demonstrated the highest robustness, exhibiting the least bias across different test scenarios.
Other methods showed varying degrees of effectiveness depending on the specific characteristics of the contaminant plume and data.

Conclusions:

Quantile kriging is recommended as a robust and reliable method for groundwater plume interpolation.
The study highlights the importance of considering data limitations and contaminant field characteristics when choosing an interpolation technique.
Findings assist practitioners in balancing theoretical considerations, ease of implementation, and effectiveness for plume interpolation.