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Highlights

Automated parameterization of DRAINMOD soil inputs would increase the model‘s use and application.

The authors have paid for open access for this article. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License https://creative‌commons.org/licenses/by-nc-nd/4.0/

Submitted for review on 14 July 2023 as manuscript number NRES 15735; approved for publication as a Research Article by Associate Editor Dr. Debasmita Misra and Community Editor Dr. Kati Migliaccio of the Natural Resources & Environmental Systems Community of ASABE on 7 January 2024.

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Abstract. Implementing hydrologic and water quality (HWQ) models in decision-support tools (DSTs) is essential to increasing their adoption by a wide user base. However, the performance of HWQ models and, consequently, DSTs in predicting site hydrology are highly dependent on the proper representation of site-specific soil properties. The objective of this study was to assess the accuracy of DRAINMOD predictions of subsurface drainage discharge using uncalibrated SSURGO- and ROSETTA3-based soil data. First, the model performance was examined by comparing predicted discharge using uncalibrated soil input data to measured discharge from three sites across the Midwest (Vermillion, IL; Delaware, OH; and Clayton, MI) with a total of 15 site-years of data. A second evaluation of the model performance was conducted by comparing predicted discharge using uncalibrated soil parameters to the model predictions using calibrated soil input parameters. The model performance (i.e., using uncalibrated soil parameters) in predicting drainage discharge compared to measured data ranged from good to excellent, with daily mean Nash-Sutcliffe efficiencies (NSEs) of 0.67 at the Vermillion site, 0.60 at the Delaware site, and 0.82 at the Clayton site. The use of calibrated soil input parameters resulted in better goodness-of-fit between measured and predicted discharge (i.e., monthly NSE range: 0.76 – 0.87) than the uncalibrated soil parameters scenario (i.e., monthly NSE range: 0.65 – 0.86) but was not significantly different (i.e., t-test, p range: 0.59 – 0.97> 0.05). Our results suggest that using SSURGO and ROSETTA3-based soil input data in DRAINMOD is an acceptable approach for representing site-specific hydrologic conditions when soil inputs are unavailable, thereby, presenting a potential for implementing the model into DSTs.