Regression models for estimating urban storm-runoff quality and quantity in the United States

Urban planners and managers need information about the local quantity of precipitation and the quality and quantity of storm runoff if they are to plan adequately for the effects of storm runoff from urban areas. As a result of this need, linear regression models were developed for the estimation of storm-runoff loads and volumes from physical, land-use, and climatic characteristics of urban watersheds throughout the United States. Three statistically different regions were delineated, based on mean annual rainfall, to improve linear regression models. One use of these models is to estimate storm-runoff loads and volumes at gaged and ungaged urban watersheds. The most significant explanatory variables in all linear regression models were total storm rainfall and total contributing drainage area. Impervious area, land-use, and mean annual climatic characteristics were also significant explanatory variables in some linear regression models. Models for dissolved solids, total nitrogen, and total ammonia plus organic nitrogen as nitrogen were the most accurate models for most areas, whereas models for suspended solids were the least accurate. The most accurate models were those for more arid western United States, and the least accurate models were those for areas that had large quantities of mean annual rainfall.Linear regression models were developed for the estimation of storm-runoff loads and volumes from physical, land-use, and climatic characteristics of urban watersheds throughout the United States. Three statistically different regions were delineated, based on mean annual rainfall, to improve linear regression models. One use of these models is to estimate storm-runoff loads and volumes at gaged and ungaged urban watersheds. The most significant explanatory variables in all linear regression models were total storm rainfall and total contributing drainage area. Impervious area, land-use, and mean annual climatic characteristics were also significant explanatory variables in some linear regression models. Models for dissolved solids, total nitrogen, and total ammonia plus organic nitrogen as nitrogen were the most accurate models for most areas, whereas models for suspended solids were the least accurate. The most accurate models were those for the more arid western United States.