A GIS-based geomorphological framework for estimating river reaeration coefficients and hypoxia risk screening in data-scarce basins

This study presents and validates a geomorphological-based framework for estimating the river reaeration coefficient (K r ), specifically addressing the challenges of data-scarce basins where conventional hydraulic measurements are unavailable. K r is a critical parameter governing dissolved oxygen recovery, self-purification capacity, and hypoxia risk, yet conventional estimation methods depend on transient and data-intensive field observations. Here, a GIS-based approach is proposed that utilizes only static geomorphological predictors, including the sinuosity index (SI), cumulative distance from source (CDS), and channel slope (S). As a proof-of-concept framework, the proposed geomorphological models were developed using K r values calculated from established empirical reaeration equations and subsequently fitted through nonlinear power-law regression for the Simineh River basin, Iran. Model performance and robustness were evaluated through Monte Carlo uncertainty simulations, sensitivity analysis, and Bland-Altman evaluation. Results demonstrate that hydraulic geometry-based formulations can be effectively represented using SI and CDS, achieving strong predictive performance (R 2 =0.781 and 0.606). For stream-power-based formulations, the inclusion of channel slope markedly improves model accuracy (R 2 > 0.94 and p < 0.001), with an estimated slope exponent of 0.49, closely aligning with the theoretical expectation (0.5), while SI becomes statistically insignificant. Monte Carlo simulations reveal a consistent downstream decline in K r , and sensitivity analysis identifies CDS and S as the dominant controls on K r variability, depending on model structure. The proposed framework serves as a rapid and low-cost screening tool for estimating reaeration potential and assessing hypoxia risk in semi-arid and ungauged river systems, providing a foundation for future validation and refinement using direct in-situ measurements of K r .