Watershed monitoring programs generally do not have perfect data collection success rates due to a variety of field and laboratory factors. A major source of error in many stream-gaging records is lost or missing data caused by malfunctioning stream-side equipment. Studies estimate that between 5 and 20 percent of stream-gaging data may be marked as missing for one reason or another. Reconstructing or infilling missing data methods generate larger sets of data. These larger data sets generally generate better estimates of the sampled parameter and permit practical applications of the data in hydrologic or water quality calculations. This study utilizes data from a watershed monitoring program operating in the Northern Virginia area to: (1) identify and summarize the major reasons for the occurrence of missing data; (2) provide recommendations for reducing the occurrence of missing data; (3) describe methods for infilling missing chemical data; (4) develop and evaluate methods for infilling values to replace missing chemical data; and (5) recommend different infilling methods for various conditions. An evaluation of different infilling methods for chemical data over a variety of factors (e.g., amount of annual rainfall, whether the missing chemical parameter is strongly correlated with flow, amount of missing data) is performed using Monte Carlo modeling. Using the results of the Monte Carlo modeling, a Decision Support System (DSS) is developed for easy application of the most appropriate infilling method. / Master of Science