A set of backcast and forecast land use maps of the Ohio River Basin (ORB) was developed that could be used to assess the spatial-temporal patterns of land use/land cover (LULC) change in this important basin. This approach was taken to facilitate assessment of integrated sustainable watershed management (SIWM) planning in the ORB at various spatial scales by providing information on historical LU patterns, future LU trends, and LU legacy maps illustrating spatial and temporal changes in LULC in relation to groundwater travel time. The latter information, combined with water resource-related information on water quality, quantity and ecosystem service values, is expected to provide a quantitative basis for scenario exploration and optimization in support of SIWM over short and longer periods of time. Interest into SIWM on a watershed scale, and supporting research, has increased recently within EPA and other organizations active in monitoring water quality and quantity, water use, and watershed management planning. The overarching purpose of this study was to develop a set of backcast and forecast land use maps for the ORB that could be used to assess the spatial-temporal patterns of LUC in this basin. The Land Transformation Model (LTM), an artificial neural network and GIS-based tool, was used to conduct this study. This tool has been designed to forecast LU changes into the future and simulate LU patterns in the past. The USGSÃ¢â‚¬â„¢s National Land Cover Database (NLCD) was used to develop a forecast and backcast set of GIS maps at 30-m resolution. Simulations back in time included the transformation of land into and out of agriculture, and the loss of urban LU. Backcast LU maps were generated using a training of two time periods (NLCD 2001 and 1992) with the amount of agriculture and urban change scaled to data from the USDA Land In Farms database and the US Census BureauÃ¢â‚¬â„¢s decade Year Built statistic as reported in the 2000 housing census. A recent version of the LTM (2012) was ported to a super computer and recoded to perform the backcast simulation for the ORB. A GIS was used to create spatial inputs for both models. A separate urbanization model was merged with the backcast models. Model simulations at 3-km spatial resolution were considered acceptable.