Hydrological Stability and Sensitivity Analysis of the Cahaba River Basin: A Combined Review and Simulation Study
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water-18-00894.pdf
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4.27 MB
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Checksum (MD5)
21c23e67b3fe85e0046b42b522d76f62
Author(s)
Preetha, Pooja
Tyrrell, Brian
Moore, Autumn
Date Issued
April 8, 2026
Journal
Water
Publisher
MDPI
Citation
Preetha, P.; Tyrrell, B.; Moore, A. Hydrological Stability and Sensitivity Analysis of the Cahaba River Basin: A Combined Review and Simulation Study. Water 2026, 18, 894.
Version
Final published version
Abstract
A continuous integration framework and methodology for hydrological modeling is proposed that integrates model sensitivity analysis with real-time sensor tasking to prioritize data collection in regions and periods of high hydrological variability and drive model refinement. The Cahaba River Watershed in central Alabama serves as a case study to develop this approach. To this end, a benchmark Soil and Water Assessment Tool (SWAT) model (30 m DEM) was refined with high-resolution spatial datasets in QGIS, including 1 m DEMs, NLCD land cover, and SSURGO soil data. The refined model significantly enhanced subbasin delineation, increasing granularity from 8 to 99 subbasins, thereby improving representation of slope, runoff, and storage variability across heterogeneous landscapes. Sensitivity analyses were performed to evaluate the influence of DEM resolution and curve number (CN) perturbations on hydrologic responses, including retention, flow partitioning, and dominant flow direction. High-resolution DEMs (≤5 m) captured microtopographic features that strongly affect infiltration and surface runoff, while coarser DEMs (≥20 m) systematically underestimated retention and smoothed hydrologic gradients. The higher-resolution DEMs can be used to selectively improve the model at certain hotspots/areas of higher sensitivity. Localized flow simulations demonstrated that fine-scale terrain data substantially improve model realism, with up to 58% greater retention captured in 10 m DEMs compared to 30 m DEMs. The results confirm that aligning sensor placement and model refinement with spatially explicit sensitivity zones enhances both predictive accuracy and computational efficiency. The proposed continuous integration approach provides a scalable pathway for coupling high-resolution modeling with adaptive sensing in watershed management and supports future integration of real-time data assimilation for continuous model improvement.
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DOI of Published Version
https://doi.org/10.3390/w18080894
