Errors in runoff prediction caused by input data errors are analyzed by treating precipitation-runoff models as regression (conditional expectation) models. Independent variables of the regression consist of precipitation and other input measurements; the dependent variable is runoff. In models using erroneous input data, prediction errors are inflated and estimates of expected storm runoff for given observed input variables are biased. A case study using a set of data from Turtle Creek near Dallas, Texas illustrates the problems of model input errors. -from Author
Additional publication details
An analysis of input errors in precipitation- runoff models using regression with errors in the independent variables ( Turtle Creek, Dallas, Texas).