Potential predictability of suspended sediment concentration in the data constrained regions of the Mahanadi River basin, Eastern India

Abstract

ABSTRACT The study proposes an efficient method to evaluate the suspended sediment concentration (SSC) relative to the traditional sediment rating curves (SRC) for gauged stations and subsequently to predict the SSC in ungauged stations of a major river basin. Multiple environmental control parameters were collected from 16 stations along the Mahanadi River basin (MRB) during the monsoon season. The hysteresis behaviour of SSC is assessed and therefore considered for modelling SSC using linear mixed-effects modelling (LMM). A basin-scale rating model is proposed using principal component analysis and stepwise multiple linear regression for estimating the unmeasured SSC. The findings show that the MRB acts differently in terms of hysteresis, with distinct dilution and flushing regimes in SRC. LMM outscored SRC by nearly doubling the mean covariance and notably reducing the percent bias between observed and predicted data across stations. However, unlike LMM, SRC could not correctly estimate low and high SSCs of ≤ 0.05 g/l and ≥ 1.5 g/l, respectively. The error metrics of the proposed rating model are within acceptable limits for all ungauged stations. Nevertheless, its efficiency varies due to smaller catchment areas, non-linearity in sediment transport with respect to catchment area, and other sampling issues. As a result, compared to other known models applied on the MRB, this model has the lowest error and seems to be the best in predicting monthly SSC.