Location: Water Management and Conservation ResearchTitle: Novel methodology to evaluate and compare evapotranspiration algorithms in an agroecosystem model
Submitted to: Journal of Environmental Modeling and Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/20/2019
Publication Date: 6/23/2019
Citation: Thorp, K.R., Marek, G.W., DeJonge, K.C., Evett, S.R., Lascano, R.J. 2019. Novel methodology to evaluate and compare evapotranspiration algorithms in an agroecosystem model. Journal of Environmental Modeling and Software. 199:214-227. https://doi.org/10.1016/j.envsoft.2019.06.007.
Interpretive Summary: Evapotranspiration (ET) is commonly the greatest pathway of water loss from crop production systems. Accurate simulation tools that calculate ET are critical for improving management of fresh water resources for crop production. The objective of this study was to evaluate and compare ET algorithms within the Cotton2K simulation tool. The methodology details a new approach for comparing different ET algorithms, which is based on state-of-the-art analytical methods and high performance computing. The results showed that different ET algorithms led to better performance for simulating certain agroecosystem processes; however, none of the tested algorithms could consistently perform better in simulating all processes collectively. The results revealed directions for further improvement of the Cotton2K model. The research provides valuable assessments of the Cotton2K simulation tool, which has potential to improve field management decisions, assist breeding selection, inform policy decisions, and conduct lifecycle analysis for the cotton industry.
Technical Abstract: Efforts to improve agroecosystem models require methods for unbiased comparisons among different simulation algorithms. With particular focus on evapotranspiration (ET) calculations in the Cotton2K model, the objectives of this study were to develop a novel methodology for evaluating model parameterization options and to compare model performance using three ET algorithms. The analysis used data from cotton field studies that tested fully irrigated, deficit irrigated, and dryland cotton production in 2000, 2001, and 2008 at Bushland, Texas. Measurements of ET were available from large weighing lysimeters at the Bushland field site. Other measured data included leaf area index, plant component dry matter, canopy height, counts of bolls and mainstem nodes, water content in 10 soil layers, and cotton fiber and seed yield. A Fortran-based version of Cotton2K was updated to include recently standardized ET methods, in addition to two native Penman approaches that required either daily or hourly weather input data. Using high performance computing, a Sobol global sensitivity analysis was conducted to evaluate 72 model input parameters with respect to 22 model outputs. Several model outputs were often highly influenced by cotton variety parameters that control the effect of plant density on growth, leaf growth at prefruiting nodes, prefruit node development, and probability of boll abscission. A multiobjective optimization approach based on calculation of Pareto optimal parameter sets was developed to identify feasible parameterization options for further model evaluation. Statistical tests demonstrated that the three ET methods led to differences in simulation accuracy for ET, soil water contents, and several plant growth metrics (p < 0.05). However, no ET method could consistently outperform the other two methods when considering the simulation outputs collectively. Regardless of the ET method used, Cotton2K tended to underestimate ET as compared to measurements from lysimetry, and simulations of two important plant metrics, leaf area index and seed cotton yield, were not simulated with root mean squared errors better than 66% and 38%, respectively. The simulation approach was useful for unbiased comparison of three ET methods in Cotton2K and suggested that techniques for simulating surface soil water flux and for linking water use with crop growth need improvement.