For estimating suspended sediment concentration (SSC) in rivers, turbidity is generally a much better predictor than water discharge. Although it is now possible to collect continuous turbidity data even at remote sites, sediment sampling and load estimation are still conventionally based on discharge. With frequent calibration the relation of turbidity to SSC could be used to estimate suspended loads more efficiently. In the proposed system a programmable data logger signals a pumping sampler to collect SSC specimens at specific turbidity thresholds. Sampling of dense field records of SSC and turbidity is simulated to investigate the feasibility and efficiency of turbidity-controlled sampling for estimating sediment loads during runoff events. Measurements of SSC and turbidity were collected at 10-min intervals from five storm events in a small mountainous watershed that exports predominantly fine sediment. In the simulations, samples containing a mean of 4 to 11 specimens, depending on storm magnitude, were selected from each storm's record, and event loads were estimated by predicting SSC from regressions on turbidity. Using simple linear regression, the five loads were estimated with root mean square errors between 1.9 and 7.7%, compared to errors of 8.8 to 23.2% for sediment rating curve estimates based on the same samples. An estimator for the variance of the load estimate is imprecise for small sample sizes and sensitive to violations in regression model assumptions. The sampling method has potential for estimating the load of any water quality constituent that has a better correlate, measurable in situ, than discharge.