AUGMENTING WATERSHED MODEL CALIBRATION WITH INCORPORATION OF ANCILLARY DATA SOURCES AND QUALITATIVE SOFT DATA SOURCES

Watershed simulation models such as the Soil & Water Assessment Tool (SWAT) can be calibrated using "hard data" such as temporal streamflow observations; however, users may find upon examination of model outputs, that the calibrated models may not reflect actual watershed behavior. Thus, it is often advantageous to use "soft data" (i.e., qualitative knowledge such as expected denitrification rates that observed time series do not typically exist) to ensure that the calibrated model is representative of the real world. The primary objective of this study is to evaluate the efficacy of coupling SWAT-Check (a post-evaluation framework for SWAT outputs) and IPEAT-SD (Integrated Parameter Estimation and Uncertainty Analysis Tool-Soft & hard Data evaluation) to constrain the bounds of soft data during SWAT auto-calibration. IPEAT-SD integrates 59 soft data variables to ensure SWAT does not violate physical processes known to occur in watersheds. IPEAT-SD was evaluated for two case studies where soft data such as denitrification rate, nitrate attributed from subsurface flow to total discharge ratio, and total sediment loading were used to conduct model calibration. Results indicated that SWAT model outputs may not satisfy reasonable soft data responses without providing predefined bounds. IPEAT-SD provides an efficient and rigorous framework for users to conduct future studies while considering both soft data and traditional hard information measures in watershed modeling.