Decision Support System to Guide Land Use Fingerprinting Using Stable Isotopes under Hydrologic Uncertainty

A Decision Support System (DSS) has been developed to guide a land use fingerprinting method rooted in the Bayesian Markov Chain Monte Carlo (MCMC) simulation framework of Fox and Papanicolaou (2008). The attempt to create a Visual Basic DSS is to assist the end users to tackle an otherwise complex mathematical algorithm under an a 3-year multi-disciplinary and multi-institutional USDA-CBG collaborative applied research agenda. The future goal is to link this DSS to a rainfall disaggregation model in space and time from global circulation models that would complement the water erosion simulation model, WEPP, of USDA. The poster presents the collaborative strength leading to knowledge dissemination and capacity enhancement through utilization of science, mathematics and engineering topics seamlessly. In this study, soil loss from rainfall-runoff are being monitored on a 150 square kilometer urban watershed in Houston, Texas, using natural and simulated rainfall events, total organic carbon/nitrogen concentration (TOC/TN) and stable isotope ratio (δ13C, δ15N) measurements (Ahmed et. al., 2013a,b). The stable isotope composition guided fingerprinting (source and quantity) of SOC by considering the common erosion processes in a watershed allows the quantification of percentage of soil (thereby soil organic carbon) lost from various land-use types. The DSS has become an essential tool to accomplish various goals.