The hydroinformatic approach consists of data mining, computational modeling, and uncertainty analysis for interactions of different hydrologic processes in a hydrologic cycle. I developed the innovative geospatial data assimilation technique using Bayesian data conditioning and Information Theory formulation to merge various hydrologic and geologic data from field testing, geophysical surveys, GIS, and laboratory experiments into stochastic or deterministic hydrologic models to predict the most probable state or event of hydrogeologic processes in the past, the present, and the future.
Lee, J., Reeves, H.W., and Dowding, C.H. "The nodal failure index approach to ground-water remediation design," (in press, ASCE Journal of Geotechnical and Geoenvironmental Engineering)
Lee, J., Graettinger, A. J., Moylan, J., and Reeves, H.W., “Directed site exploration for permeable reactive barrier design,” (under review Journal of Hazardous Materials)
Lee, J., **Shah, S.T., Lee, Y., and Geller, J., 2007, “HIS-KCWater: Context-aware geospatial data and service integration,” Proceedings, 2007 Association for Computing Machinery Symposium on Applied Computing, Seoul, Korea.
Graettinger, A.J., Lee, J., Reeves, H.W., and Dethan, D., 2006, “Quantitative methods to direct exploration based on hydrogeologic information,” Journal of Hydroinformatics,Vol.8(2): 77-90.
Glasgow, S.R., Fortney M.D., Lee, J., Graettinger, A.J. and Reeves, H.W., 2003, “MODFLOW-2000 Head uncertainty, A first-order second moment method,” Ground Water, Vol.41 (3): 342-350.
