Primeau et al. (2026) A Digital Twin Prototype for Protecting Surface Source Waters
⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.
Identification
- Journal: Sensors
- Year: 2026
- Date: 2026-09-29
- Authors: Russell Primeau, Razak Seidu, Houxiang Zhang, Peihua Han, Guoyuan Li
- DOI: 10.3390/s26196175
Research Groups
- Norwegian University of Science and Technology (NTNU)
- [Other research groups or labs involved in the study]
Short Summary
This paper presents a digital twin prototype integrating environmental observations, hydrodynamic water quality modeling, and source-protection applications for real-time monitoring and decision support at the Brusdalsvatnet reservoir in Norway. The model demonstrates reliable predictions of water temperature and contaminant transport.
Objective
- Develop an integrated digital twin framework for real-time monitoring and decision support in source-water protection
Study Configuration
- Spatial Scale: Reservoir-scale (Brusdalsvatnet, Norway)
- Temporal Scale: Seasonal to annual timescales (2024 season)
Methodology and Data
- Models used: Three-dimensional hydrodynamic water quality model
- Data sources: Networked environmental observations, satellite data, reanalysis datasets
Main Results
- The digital twin prototype demonstrated reliable predictions of water temperature with a root-mean-square error of 1.73 ∘C near the surface and 2.15 ∘C averaged across depth.
- Contaminant transport scenarios were successfully analyzed using the model.
Contributions
- This study provides a proof-of-concept for source-water decision support, integrating real-time monitoring, contaminant scenario analysis, and uncrewed surface vessel sampling planning.
Funding
- [List projects, programs, and reference codes that funded this research]
Citation
@article{Primeau2026Digital,
author = {Primeau, Russell and Seidu, Razak and Zhang, Houxiang and Han, Peihua and Li, Guoyuan},
title = {A Digital Twin Prototype for Protecting Surface Source Waters},
journal = {Sensors},
year = {2026},
doi = {10.3390/s26196175},
url = {https://doi.org/10.3390/s26196175}
}
Original Source: https://doi.org/10.3390/s26196175