Evaluation of multimodel averaging approaches for ensembling evapotranspiration and yield simulations from maize models

作者：Viveka Nand, Zhiming Qi, Liwang Ma, Matthew J. Helmers, Chandra A. Madramootoo, Ward Smith, Tiequan Zhang, Tobias K. D. Weber, Elizabeth Pattey, Ziwei Li, Jiaxin Wang, Virginia L. Jin, Qianjing Jiang, Mario Tenuta, Thomas J. Trout, Haomiao Cheng, R. Daren Harmel, Bruce A. Kimball, Kelly R. Thorp, Kenneth J. Boote, Claudio Stöckle, Andrew E. Suyker, Steven R. Evett, David Bräuer, Gwen G. Coyle, Karen S. Copeland, Gary W. Marek, Paul D. Colaizzi, Marco Acutis, Seyyed Majid Alimagham, Sotirios V. Archontoulis, Babacar Faye, Zoltán Barcza, Bruno Basso, Patrick Bertuzzi, Julie Constantin, Massimiliano De Antoni Migliorati, Benjamin Dumont, J. L. Durand, Nándor Fodor, Thomas Gaiser, Pasquale Garofalo, Sebastian Gayler, Luisa Giglio, Robert R. Grant, Kaiyu Guan, Gerrit Hoogenboom, Soo‐Hyung Kim, Isaya Kisekka, Jon Lizaso, Sara Masia, Huimin Meng, Valentina Mereu, Mukhtar Ahmed, Alessia Perego, Bin Peng, Eckart Priesack, Vakhtang Shelia, Richard L. Snyder, Afshin Soltani, Donatella Spano, Amit Kumar Srivastava, Aimee Thomson, Dennis Timlin, Antonio Trabucco, Heidi Webber, Magali Willaume, Karina Williams, Michael van der Laan, Domenico Ventrella, Michelle Viswanathan, Xu Xu, Wang Zhou · 发表于：Journal of Hydrology · 年份：2025 · DOI：10.1016/j.jhydrol.2025.133631 · 被引用次数：5 · 研究领域：Climate change impacts on agriculture、Crop Yield and Soil Fertility、Rice Cultivation and Yield Improvement

Combining multi-model simulations can reduce the uncertainty in model structure and increase the accuracy of agricultural systems modeling results. This improvement is essential for supporting better decision making in irrigation planning and climate change adaptation strategies. Besides the commonly used arithmetic mean and median, many multi-model averaging approaches (MAA), widely examined in groundwater and hydrological modeling, but these additional MAA have not been examined in agricultural system modeling to improve the simulation accuracy. Therefore, the objective of this study is to evaluate the performance of seven MAA: two equal weighted approaches (Simple Model Averaging (SMA) and Median) and five weighted approaches (Inverse Ranking (IR), Bates and Granger Averaging (BGA), and Granger Ramanathan A, B, and C (GRA, GRB, and GRC)) in combining results of multiple agricultural system models. The Granger Ramanathan methods differ in their constraints: GRA employs conventional least squares, GRB requires non-negative weights that total to one, and GRC reduces absolute errors for robustness against outliers. The evaluation was conducted using maize yield and daily ETa simulations for both blind (uncalibrated) and calibrated phases of data from two groups of maize sites (Group A and Group B) across North America. The modeling results from the blind and calibrated phases were combined for all maize models and group maize models. Overall, all MAA performed better than indi...