water Article Data- and Model-Based Discharge Hindcasting over a Subtropical River Basin Khondoker Billah 1 , Tuan B. Le 2 and Hatim O. Sharif 1, *   Citation: Billah, K.; Le, T.B.; Sharif, H.O. Data- and Model-Based Discharge Hindcasting over a Subtropical River Basin. Water 2021, 13, 2560. https://doi.org/10.3390/ w13182560 Academic Editor: Tom Ball Received: 24 August 2021 Accepted: 16 September 2021 Published: 17 September 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Department of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA; khondoker.billah@utsa.edu 2 CPS Energy, 500 McCullough Ave, San Antonio, TX 78215, USA; lebaotuan@gmail.com * Correspondence: hatim.sharif@utsa.edu; Tel.: +1-210-458-6478 Abstract: This study aims to evaluate the performance of the Soil and Water Assessment Tool (SWAT), a simple Auto-Regressive with eXogenous input (ARX) model, and a gene expression programming (GEP)-based model in one-day-ahead discharge prediction for the upper Kentucky River Basin. Calibration of the models were carried out for the period of 2002–2005 using daily flow at a stream gauging station unaffected by the flow regulation. Validation of the calibrated models were executed for the period of 2008–2010 at the same gauging station along with another station 88 km downstream. GEP provided the best calibration (coefficient of determination (R) value 0.94 and Nash-Sutcliffe Efficiency (NSE) value of 0.88) and validation (R values of 0.93 and 0.93, NSE values of 0.87 and 0.87, respectively) results at the two gauging stations. While SWAT performed reasonably well in calibration (R value 0.85 and NSE value 0.72), its performance somewhat degraded in validation (R values of 0.85 and 0.82, NSE values of 0.65 and 0.65, for the two stations). ARX performed very well in calibration (R value 0.92, NSE value 0.82) and reasonably well in validation (R values of 0.88 and 0.92, NSE values of 0.76 and 0.85) at the two stations. Research results suggest that sophisticated hydrological models could be outperformed by simple data-driven models and GEP has the advantage to generate functional relationships that allows investigation of the complex nonlinear interrelationships among the input variables. Keywords: Kentucky River Basin; SWAT; ARX; GEP; discharge simulation 1. Introduction The availability of water at the watershed or basin scales in addition to the spatial and temporal distribution of water are largely affected by climatic and topographic factors [1]. Soil, land cover, and land use characteristics are also considered when studying the move- ment and exchange of water [2]. The recent development of robust hydrologic models and significant advancement in the processing power of computers [3] allow modelers to take these factors into account when trying to solve the complexity of hydrological processes. These models, often embedded within decision support tools, are especially necessary when complete information and observed data on discharge, inflow, climate, soil moisture, or other related factors of a basin are limited [4]. Changes in water budget and fluxes within a watershed can be studied based on the estimation and simulation of the models. Several popular conceptual and physically based watershed models have been used for discharge and water quality modeling in the last three decades including CREAMS (Chemical Runoff and Erosion from Agricultural Management Systems) [5], PRMS (Precipitation Runoff Modeling System) [6], HSPF (Hydrologic Simulation Program—Fortran) [7], SWAT (Soil and Water Assessment Tool) [8], and SWBM (Spatial Water Budget Model) [9]. These mod- els are computationally effective and require relatively small time steps to produce outputs through simulations [10]. Among these models, SWAT can analyze how the changes in land use and land cover impact the runoff and water quality in addition to its ability to quantify the hydrological responses to the variation of hydrometeorological conditions, Water 2021, 13, 2560. https://doi.org/10.3390/w13182560 https://www.mdpi.com/journal/water