Water quality assessment is crucial in investigating impairment within agricultural watersheds. Seasonal and spatial variations on land can directly affect the adjoining riverine systems. Studies have revealed that agricultural activities are often major contributors to altering water quality of surface waters. A common means of addressing this issue is through the establishment and monitoring the health of riparian vegetation buffers along those areas of stream channels that would be most susceptible to the threat. Remote sensing and Geographic Information Systems (GIS) offer a means by which impaired areas can be identified, so that subsequent action toward the establishment of riparian zones can be taken. Modeling the size and rate of land use and land cover (LULC) change is an effective method of projecting localized impairment. This study presents an integrated model utilizing Analytical Hierarchical Process (AHP), Markov Chain Monte Carlo (MCMC) simulations, and geospatial analyses to address areas of impairment within the Pipestem Creek watershed, a part of the Missouri Watershed James Sub-region of North Dakota, USA. The rate and direction of LULC change was analyzed through this model and its impact on the ambient water and soil quality was studied. Tasseled Cap Greenness Index (TCGI) was used to determine the loss of forested land within the watershed from 1976 to 2015. Research results validated temporal and spatial relations of LULC dynamics to nutrient concentrations especially those that would be noted at the mouth of the watershed. It was found that the levels of Total Dissolved Solids (TDS) were much higher for the years 2014 to 2016 with a discernible increased localized alkalizing effect within the watershed. Fallow areas were seen to produce significant amounts of sediment loads from the sub-watershed. LULC distribution from 2007 to 2015 show that it is possible to project future land use change patterns. About 89.90% likelihood of increment in agricultural land leading to a 77.44% likelihood of decrement in forested land in the area was noted for years 2007 to 2015. TCGI generated higher values for years 1976 to 2000 and it gradually reduced for 2000 to 2011 indicating loss of forested land.