The total maximum daily load (TMDL) process is ineffective and inappropriate for improving stream water quality in the rural areas of the northern Great Basin, and likely in many areas throughout the country. Important pollutants (e.g., sediment and nutrients) often come from the stream systems rather than external point or nonpoint sources where TMDL focuses. Water quality indicators lag behind ecosystem functions, and monitoring water quality fails to identify causes of, or recovery from, degraded water quality and loss of fish habitat, the most sensitive beneficial use. Ambient monitoring programs should identify risk and recovery, focusing resources toward effective land and water management strategies. To illustrate, we elucidate the connections between various water quality attributes and the seventeen items of the interagency riparian proper functioning condition (PFC) assessment for lotic (running water) riparian systems. We conducted PFC assessment for relevant parts of the Maggie Creek Watershed, and developed hypotheses of improved water quality from improved management and riparian conditions. We then tested these hypotheses using a far more intensive water quality monitoring data set than is generally available to rangeland, rural land, or water quality managers. The Maggie Creek, NV, case study demonstrates that changes in grazing management (timing and duration) resulted in improved stream functionality, leading to reduced sediment and phosphate, increased dissolved oxygen, and improved aquatic habitat. It also demonstrates that monitoring for water quality by monitoring water chemistry requires unaffordable frequency and generates highly variable data that obscures relevant issues while it fails to monitor drivers of system collapse or recovery. Thus water chemistry monitoring fails to timely inform management of impairment risk or the trend from management actions. We suggest that published protocols for monitoring multiple indicators of riparian functions are more relevant, timely, and less expensive.