Smillie, Gary M., authorSanders, Thomas G., advisorWard, Robert C., committee memberLoftis, Jim C., committee member2021-09-072021-09-071982https://hdl.handle.net/10217/233880Federal legislation in recent years has required the states to develop water quality management programs which include stream standards and river monitoring. Water quality data, routinely collected by state and federal agencies, has often been of little use in directly determining stream standards compliance. This problem is due to the discrepancy between the statistical nature of water quality sampling and nonstatistically expressed stream standards. However, the use of probability and statistical models in water quality analysis may pro-vide useful assessments of river water quality with stream standards. This research consists of the development and testing of five statistical procedures which allow river water quality to be assessed from available, routinely collected data. The procedures include: 1) probability density function modeling of water quality variables, 2) multiple linear regression modeling of water quality variables, 3) conditional probability modeling of stream standard violations given known river conditions, 4) a water quality index indicating changes in water quality, and 5) a water quality index indicating compliance/non- compliance of water quality variables with stream standards. The utility of each procedure is illustrated with a case study.masters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.Water quality -- MeasurementWater quality managementText