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Enhancing Model Calibration and Uncertainty Estimation through Multi-Objective Optimization with SUFI-2 Algorithm

A. K. Shukla1 *, I. Ahmad1, S. K. Jain2, and M. K. Verma1

  1. Department of Civil Engineering, National Institute of Technology Raipur, G.E. Road, Raipur, Chhattisgarh 492010, India
  2. Water Resources System, Division, National Institute of Hydrology, Roorkee, Uttrakahnd 247667, India

*Corresponding author. Tel.: +941-590-3447. E-mail address: (A. K. Shukla).


The research aimed to quantify the uncertainty in hydrological modeling results, especially in data-scarce areas like the Teesta River. The developed grid-based model from Texas Agricultural and Management University (TAMU) with the help of simulation plugin Quantum Soil and Water Assessment Tools (QSWAT) and calibration (CAL) & validation (VAL) using SWAT-Calibration Uncertainty Program (CUP). Multiple algorithms Minimize & Maximize (MIN & MAX), specifically with Sequential Uncertainty Fitting Version-2 (SUFI-2) algorithm (Best fitted simulation), were used as the popular for the many ideal simulations as possible that are within 95% of the true predictions. The research investigated how eleven different objective functions (OBJF) influenced the CAL results, parameterization, and surface water estimation for the stream flow of the Teesta River. While several OBJF performed well, eight of them, namely Chi-square (Chi2), Multiplicative (Multi), Modified Nash-Sutcliffe efficiency factor (MNSE), Nash-Sutcliffe efficiency (NSE), summation (SUM), Coefficient of determination (R2), Modified coefficient of determination (bR2), and Relative Squared Error (RSR), consistently yielded very similar results because of mostly used MIN methods. For instance, NSE resulted in a CAL & VAL score of 0.85 and 0.88, while R2, MNSE, and bR2 had scores of 0.88 and 0.89 during both CAL & VAL periods. All results from NSE, R2 and bR2 show a correlation between 0.84 to 0.90. However, other OBJF provided slightly different sensitivity values. The Percentage Bias (PBIAS) scored 0.76 for CAL and 0.77 for VAL. The Kling-Gupta efficiency (KGE) scored 0.79 during both the CAL & VAL periods. Additionally, the sum of squares (SSQR) obtained shallow values for both CAL & VAL processes. The aforementioned research shows that varied OBJF can affect CAL findings and hydrological modelling parameter values, emphasizing the need to use several OBJF in uncertainty analysis for more accurate water resource estimation the calibrated model and its outputs aid hydrologists and water resource administrators in agricultural and water assessment. They also support research on climate change's effects on water supply and quality, droughts, and food. The unique idea and flexible techniques make them suitable for most countries.

Keywords: Teesta River Basin, hydrological modelling, multiple OBJF, QSWAT, SWAT-CUP, uncertainty analysis, SUFI-2, and hydrograph component

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