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Title: Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five yearsmore » of yield, drain flow, and NO 3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.« less
 [1] ;  [2] ;  [3] ;  [4] ;  [5]
  1. Wuxi Inst. of technology, Wuxi (China)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Climate Change Science Inst., Computer Science and Mathematics Division
  3. Chinese Academy of Sciences (CAS), Urumqi (China). Xinjiang Inst. of Ecology and Geography, Cele National Station of Observation & Research for Desert Grassland Ecosystem
  4. McGill Univ., Sainte-Anne-de-Bellevue, QC (Canada). Dept of Bioresource Engineering
  5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division
Publication Date:
Grant/Contract Number:
AC05-00OR22725; 1620027
Accepted Manuscript
Journal Name:
Journal of Hydrology
Additional Journal Information:
Journal Volume: 544; Journal Issue: C; Journal ID: ISSN 0022-1694
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Office of Science (SC); National Science Foundation (NSF); National Natural Science Foundation of China (NNSFC)
Country of Publication:
United States
60 APPLIED LIFE SCIENCES; RZWQM2; Sparse grid; Surrogate model; QPSO algorithm; Global optimization
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1411306