ABSTRACT:

This site provides the Python code we used to modify SWAT model input files for the Spatial Factor Substitution (SFS) method of Jepsen and Harmon (in press). In the SFS method, categorical factors are transferred from a "source" subbasin to a "target" subbasin of a watershed according to a prescribed space-for-time substitution scenario. Categorical factors handled in this Python code are precipitation time series, air temperature time series, land cover, soil type, and slope. We developed the SFS method to resolve landscape contributions to an elevational gradient in long-term ET, and the influence of the "covariance-stationarity" assumption of a typical space-for-time substitution model of climate warming (Jepsen and Harmon, in press).