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Modeling crop water use in an irrigated maize cropland using a biophysical process-based model

Ding, Risheng, Kang, Shaozhong, Du, Taisheng, Hao, Xinmei, Tong, Ling
Journal of hydrology 2015 v.529 pp. 276-286
C4 plants, canopy, corn, crop models, cropland, eddy covariance, evaporation, evapotranspiration, hydrologic cycle, irrigation, leaf area index, microclimate, photosynthesis, soil quality, soil water, soil water deficit, stomatal conductance, stomatal movement, stress response, water stress, water uptake, water use efficiency, China
Accurate modeling of crop water use or evapotranspiration (ET) is needed to understand the hydrologic cycle and improve water use efficiency. Biophysical process-based multilayer models can capture details of the nonlinear interaction between microclimate and physiology within the canopy and thus accurately simulate ET. In this study, we extended a process-based multilayer model, ACASA, which explicitly simulated many of the nonlinear biophysical processes within each of ten crop canopy sublayers and then integrated to represent the complete crop canopy. Based on the original ACASA model, we made the improved modifications including four added modules (C4 crop photosynthesis, water stress response of stomatal conductance, crop morphological changes, and heterogeneous root water uptake), and two adjusted calculation procedures (soil evaporation resistance and hydraulic characteristic parameters). Key processes were parameterized for the improved ACASA model using observations. The simulated canopy ET was validated using eddy covariance measurements over an irrigated maize field in an arid inland region of northwest China. The improved ACASA model predicted maize ET for both half-hourly and daily time-scales. The improved model also predicted the reduction in maize ET under the condition of soil water deficit. Soil evaporation, an important component of maize ET, was also satisfactorily simulated in the improved model. Compared to the original ACASA model, the improved model yielded an improved estimation of maize ET. Using the improved model, we found that maize ET was nonlinearly affected by changes in leaf area index and photosynthetic capacity through canopy conductance. In general, the improved ACASA model, a biophysical process-based multilayer model, can be used to diagnose and predict crop ET, and draw some insights into the nonlinear interactions between crop canopy and ambient environment.