Chemical contamination of riverine ecosystems is largely a result of urbanization, industrialization, and agricultural activities occurring on adjacent terrestrial landscapes. Land management activities (e.g., Best Management Practices) are an important tool used to reduce point and non-point sources of pollution. However, the ability to confidently make inferences about the efficacy of land management activities on reducing in-stream chemical concentrations is poorly understood. We estimated regional temporal trends and components of variation for commonly used herbicides (atrazine and metolachlor), total estrogenicity, and riverine sediment concentrations of total PCBs for rivers in the Chesapeake Bay Watershed, USA. We then used the estimated variance components to perform a power analysis and evaluated the statistical power to detect regional temporal trends under different monitoring scenarios. Scenarios included varying the magnitude of the annual contaminant decline, the number of sites sampled each year, the number of years sampled, and sampling frequency. Monitoring for short time periods (e.g., 5 years) was inadequate for detecting regional temporal trends, regardless of the number of sites sampled or the magnitude of the annual declines. Even when monitoring over a 20-year period, sampling a relatively large number of sites each year was required (e.g., > 50 sites) to achieve adequate statistical power for smaller trend magnitudes (declines of 5 – 7%/year). Annual sampling frequency had little impact on power for any monitoring scenario. All sampling scenarios were underpowered for sediment total PCBs. Power was greatest for total estrogenicity, suggesting that this aggregate measure of estrogenic activity may be a useful indicator. This study provides information that can be used to help (1) guide the development of monitoring programs aimed at detecting regional declines in riverine chemical contaminant concentrations in response to land management actions, and (2) set expectations for the ability to detect changes over time.