The Environmental Protection Agency (USEPA) is required to assess the conditions of surface waters nationwide. Using statistically selected sites and observed physical, chemical, and biological indicators of water quality (WQI), collected uniformly at all sites, the USEPA developed estimates of the synoptic conditions of the entire nation’s waters. The estimates were not spatially explicit; and here we used mathematical models to make one dataset more useful for management. Together, and for the first time, we combined four national datasets to predict the spatial distribution of these WQI. 1- The EPA’s Wadeable Stream Assessment (WSA) dataset estimated the nation’s waters in 1392 probability sites; 2- The U.S. Natural Resources Conservation Service Dataset (STATSGO, and SSURGO) described the watersheds of these sites by their soil characteristics (SC); 3- The U.S. Geological Survey’s National Land Cover Database (NLCD) further defined the land-use (LC) of the watersheds; and 4- The USEPA’s Ecological Regions provided a spatial application of synoptic view of water quality.  The SC and LC were independent predictors, and as many as 14 WQI were co-predictors of three jointly estimated WQI. A squared distance metrics of SC and LC calculated, for 900 Level IV ecoregions, the statistical nearest neighbors of the co-predictors, and the model calculated the errors. Consistent with the spatial variability of the observed data, the prediction errors of NO3, total P, suspended solid, and turbidity, approached 80%, and the errors of remaining14 WQI, were greater than 30%. This ecoregional synoptic view provided a useful management tool for estimating the expected water quality locally and regionally in the conterminous U.S.