Interactive Drivers of Land Cover and Land Use Change in the Upper Mississippi River Basin

Manoj Jha, Iowa State University, manoj@iastate.edu
Eugene S Takle, Iowa State University, gstakle@iastate.edu
Eric Lu, Iowa State University, elu@iastate.edu
Philip W Gassman, Iowa State University, pwgassma@iastate.edu
Catherine L Kling, Iowa State University, ckling@iastate.edu

Nonpoint source pollution has resulted in extensive degradation of soil and water quality in the Upper Mississippi River Basin (UMRB). An effort has been made to examine the environmental and economic impacts of the conservation policy driver, the climate change driver, and how these drivers affect land managers decisions towards land cover and land use change. Some preliminary results including the development of an integrated modeling framework and their validation results were presented at the NASA joint annual meetings in 2006 and 2007. This poster focuses on eliminating errors/biases caused by climate models in predicting meteorological inputs to the hydrologic model and thus increasing the confidence in the outcome of the integrated modeling system. Outputs from an ensemble of ten global climate models (GCMs) were used to drive Soil and Water Assessment Tool (SWAT) model to examine streamflow in the UMRB. Examination of the role of resolution of driving meteorological inputs revealed that low-resolution data generally leads to lower streamflow throughout the year. Errors due to biases in GCMs lead SWAT to produce large errors in streamflow and related water quality components. However, when these biases are eliminated, simulations of streamflow improve dramatically, especially in the cool season (first half of the year). The GCMs used in this analysis showed very little change in precipitation due to increase in greenhouse gases (future climate), and hence very little change in streamflow. Ongoing work is testing an optimization tool, genetic algorithm, in assessing cost-effective allocation of conservation practices for pollution reductions.