Sediment rating curves (SRCs) have been applied to estimate daily-suspended sediment load (Qs) worldwide because of its simplicity. In this method, current Qs was estimated by a power function of a sole variable, a current daily water discharge at the same measurement station. However, many studies found that its accuracy is not very high. In this study, we developed a new approach to estimate Qs using multivariate hydrological data at the same station and other upstream stations. Using correlation analysis, the additional variables were selected such as upstream water discharges, rainfall at the current or antecedent day. Therefore, spatial and temporal variability was simply considered in our new approach. Then, five methods, a multiple linear regression (MLR), a multiple nonlinear regression (SLR, QLR, and PLR) and an artificial neural network model (ANNs), were applied. The comparison between the SRC method and our new five methods were done using the Qs data at three measurement stations in three basins of Thailand. The results showed that our new approach for all three-study areas (PLR, and ANNs) gave better results with the observed data than the traditional SRC method except MLR, SLR, and QLR. ANNs estimated Qs with the highest accuracy at P1 (EI = 0.96) while PLR gave results similar ANNs at W4A. For Y14 the result of QLR (EI=0.94) better than ANNs Thus, the more complexity of the model structure and the consideration of the spatial and temporal variability can provide a higher accurate estimation of Qs.