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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.15300681 |
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Table of Contents:
- <p><span lang="EN-GB">This study assesses the potential for integrating hydrological modeling outputs with gauge-based observational datasets to enhance anomaly detection within aquatic sensor records. Using the Advanced Terrestrial Simulator (ATS), a hydrological simulation was performed for the Franklin sub-watershed of the Logan River watershed. Publicly available datasets supported detailed surface and subsurface configurations, including Daymet meteorological forcing, SSURGO soils, GLHYMPS geology, and NLCD land cover. Although convergence issues restricted simulation beyond the first year, the modeled results captured the general seasonal trends in streamflow dynamics, including timing and magnitude of peak flows related to snowmelt and precipitation. Comparisons between observed and simulated discharges demonstrated the ability of the model to fill observational gaps and validate spikes, suggesting its utility in distinguishing hydrologic events from sensor-induced anomalies. Flow duration curve comparisons revealed that the model replicates the overall flow regime structure, with systematic overestimations at mid- and high-flow ranges, partially attributed to simplified subsurface representations that do not account for the karstic characteristics of the watershed. Quantitative performance metrics, including R², NSE, RMSE, PBIAS, and KGE, indicated that the simulation would require further calibration for precise forecasting; however, even with a moderate statistical agreement, the model verified valuable for supporting quality control processes. The results show that hydrological models can operate as a physically based benchmark for validating sensor data and improving the reliability of observational datasets critical for hydrologic forecasting and management.</span></p>