2023 CSDMS meeting-048


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Browse  abstracts

Global Sensitivity Analysis of the AquaCrop-OSPy Model for Corn Yield and Irrigation Under Different Water Treatments (Sheridan, KS)

Wayne Ndlovu, University of Kansas Lawrence , United States. wndlovu@ku.edu
Sam Zipper, Kansas Geological Survey Lawrence Kansas, United States. samzipper@ku.edu
Timothy Foster, University of Manchester Manchester , United Kingdom. timothy.foster@manchester.ac.uk

Crop models are used to simulate crop development, yield and irrigation requirements, but their performance can be influenced by environmental and management conditions such as climate and irrigation strategies. Hence, performing a sensitivity analysis on these models is crucial to identifying influential parameters which informs model calibration. Here, we performed a global sensitivity analysis (Morris Screening method) on crop yield and irrigation on 34 crop parameters using the AquaCrop-OSPy model. This analysis is done for corn in Sheridan, KS under different water treatments (irrigated and rainfed) for varying meteorological scenarios represented by past years annual precipitation (normal-2021, wet-2019 and dry-2002). Thresholds of 0.3t/ha and 20mm are used for yield and irrigation respectively to identify influential parameters. Overall, parameter importance varies for yield and irrigation: parameters related to biomass and yield, root and canopy development, and irrigation strategy are the most influential for yield while those related to irrigation strategy, and root and canopy development are the most influential for irrigation. In general, yield was responsive to fewer parameters in rainfed conditions and simulations with drier meteorological conditions. The normal and wet scenarios have similar influential parameters with varying order of influence for yield under irrigated conditions. However, under rainfed conditions, the normal scenario only has two influential parameters (minimum effective rooting depth and the excess of potential fruits, a parameter related to biomass and yield), while 8 parameters related to biomass and yield production, water stress, and root development are influential during the wet scenario. Yield under irrigated conditions during the wetter years (receiving normal and high precipitation) tends to be impacted by water and temperature stress parameters. The influential parameters will further be analyzed using the Sobol method to calculate each parameter's influence on the output’s variance and interaction with other parameters, and ultimately used to guide model calibration.