Skip to main content
ARS Home » Plains Area » Temple, Texas » Grassland Soil and Water Research Laboratory » Research » Publications at this Location » Publication #319796

Title: Augmenting watershed model calibration with incorporation of ancillary data sources and qualitative soft data sources

item YEN, HAW - Texas A&M Agrilife
item White, Michael
item Ascough Ii, James
item Smith, Douglas
item Arnold, Jeffrey

Submitted to: Journal of the American Water Resources Association
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/21/2016
Publication Date: 6/1/2016
Publication URL:
Citation: Yen, H., White, M.J., Ascough II, J.C., Smith, D.R., Arnold, J.G. 2016. Augmenting watershed model calibration with incorporation of ancillary data sources and qualitative soft data sources. Journal of the American Water Resources Association. 52(3):788-798. doi:10.1111/1752-1688.12428.

Interpretive Summary: Watershed models are calibrated with monitoring data to increase the accuracy and reliability of their predictions. Often there are limited data within any particular watershed to calibrate all the important processes within the model. In these cases, “soft” data from other watersheds may be used to guide the calibration process in a more general way. In this research, an autocalibration tool is augmented with “soft” data ranges to constrain model calibration. Two case studies are presented where the use of this augmented calibration tool yielded superior model calibrations as compared to traditional autocalibration.

Technical Abstract: Watershed simulation models can be calibrated using “hard data” such as temporal streamflow observations; however, users may find upon examination of detailed outputs that some of the calibrated models may not reflect summative actual watershed behavior. Thus, it is necessary to use “soft data” (i.e., qualitative knowledge such as anticipated denitrification rates, that doesn’t have temporal measurements) to ensure the calibrated model is more representative of the real world. The objective in manuscript is to state the importance of coupling SWAT-Check postevaluation framework for SWAT outputs) and IPEAT-SD (Integrated Parameter Estimation and Uncertainty Analysis Tool – Soft & hard Data evaluation) to constrict the bounds of soft data During auto-calibration. IPEAT-SD integrates constraints 59 soft data variables during Calibration to ensure that the model does not violate physical processes known to occur in watersheds. IPEAT-SD was conducted in 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 model outputs may not satisfy reasonable soft data responses without providing pre-defined bounds. IPEAT-SD has provided an efficient and rigorous framework for potential users to conduct extending studies while considering soft data relative to traditional hard information measures for watershed modeling topics.