Hydrology and Earth System Sciences www.hydrol-earth-syst-sci.net 1027-5606 1607-7938 13 8 2009 10.5194/hess-13-1467-2009 http://www.hydrol-earth-syst-sci.net/13/1467/2009/ http://www.hydrol-earth-syst-sci.net/13/1467/2009/hess-13-1467-2009.html http://www.hydrol-earth-syst-sci.net/13/1467/2009/hess-13-1467-2009.pdf 1467 1483 2009-08-14 Calibration of a crop model to irrigated water use using a genetic algorithm T. Bulatewicz W. Jin S. Staggenborg S. Lauwo M. Miller S. Das D. Andresen J. Peterson D. R. Steward S. M. Welch welchsm@ksu.edu Kansas State University, Department of Computing and Information Sciences, USA Kansas State University, Department of Agronomy, USA Kansas State University, Department of Civil Engineering, USA Kansas State University, Department of Electrical and Computer Engineering, USA Kansas State University, Department of Agricultural Economics, USA Near-term consumption of groundwater for irrigated agriculture in the High Plains Aquifer supports a dynamic bio-socio-economic system, all parts of which will be impacted by a future transition to sustainable usage that matches natural recharge rates. Plants are the foundation of this system and so generic plant models suitable for coupling to representations of other component processes (hydrologic, economic, etc.) are key elements of needed stakeholder decision support systems. This study explores utilization of the Environmental Policy Integrated Climate (EPIC) model to serve in this role. Calibration required many facilities of a fully deployed decision support system: geo-referenced databases of crop (corn, sorghum, alfalfa, and soybean), soil, weather, and water-use data (4931 well-years), interfacing heterogeneous software components, and massively parallel processing (3.8×10⁹ model runs). Bootstrap probability distributions for ten model parameters were obtained for each crop by entropy maximization via the genetic algorithm. The relative errors in yield and water estimates based on the parameters are analyzed by crop, the level of aggregation (county- or well-level), and the degree of independence between the data set used for estimation and the data being predicted. Aigner,~D J. and Goldfeld,~S M.: Estimation and prediction from aggregate data when aggregates are measured more accurately than their components, Econometrica, 42, 113–134, 1974. 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