Process-based crop models have faced rapid development over the last years, and many modelling platforms are now available and can be used in a wide range of conditions. Whilst the selection of a model should be suited to the purpose of its application, very few studies focused on the impact of choosing different model structures and data details on the simulation outputs. One important aspect is the soil water dynamics, which can be simulated at different levels of details in terms of data and approaches. In this study, we investigated the impact of model structure and data detail on simulations of sugarcane growth and irrigation scheduling. Three different soil water routines (Standalone, Tipping-Bucket, SWAP) were coupled with the SAMUCA model and calibrated with a comprehensive field experiment dataset. We also tested the influence of using simplified homogeneous (SL) and detailed (DL) soil profile information in model performance. The model framework was evaluated against independent field experiments across Brazil and used to simulate long-term sugarcane growth and irrigation scheduling. After calibration, the SWAP-DL showed the highest accuracy in soil moisture predictions, with a 6 % error (RRMSE), but the difference from TippingBucket-DL was small (8 %). While the performance of stalk dry mass, LAI and water-use efficiency simulations were within the range found in literature, comprehensive field experiments showing significant impacts of drought on sugarcane growth are still lacking for a more rigorous evaluation. Both SWAP and tipping-bucket approaches showed higher robustness to soil data detail as compared to the Standalone method, which should be avoided when soil water is critical for sugarcane growth. The use of tipping-bucket method may still be preferred when the research goal is focused on crop growth and soil parameters are limited. SWAP-SAMUCA may provide an extended ability to represent agrohydrological processes in sugarcane plantations and process understanding.

中文翻译：

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模型结构和土壤数据细节对于模拟作物生长和用水的重要性：甘蔗案例研究


基于过程的作物模型在过去几年中得到了快速发展，现在有许多建模平台可用并且可以在广泛的条件下使用。虽然模型的选择应适合其应用目的，但很少有研究关注选择不同模型结构和数据细节对仿真输出的影响。一个重要的方面是土壤水动态，可以在数据和方法方面以不同的细节水平进行模拟。在这项研究中，我们研究了模型结构和数据细节对甘蔗生长和灌溉调度模拟的影响。将三种不同的土壤水程序（独立、翻斗、SWAP）与 SAMUCA 模型相结合，并使用综合现场实验数据集进行校准。我们还测试了使用简化均质（SL）和详细（DL）土壤剖面信息对模型性能的影响。该模型框架根据巴西各地的独立田间实验进行了评估，并用于模拟长期甘蔗生长和灌溉调度。校准后，SWAP-DL 的土壤湿度预测精度最高，误差为 6% (RRMSE)，但与 TippingBucket-DL 的差异很小（8%）。虽然茎干质量、LAI 和水分利用效率模拟的表现在文献中找到的范围内，但仍缺乏显示干旱对甘蔗生长显着影响的综合田间实验，无法进行更严格的评估。与独立方法相比，SWAP 和翻斗方法对土壤数据细节都表现出更高的鲁棒性，当土壤水对甘蔗生长至关重要时，应避免使用独立方法。 当研究目标集中于作物生长且土壤参数有限时，仍然优选使用翻斗法。 SWAP-SAMUCA 可以提供扩展的能力来表示甘蔗种植园的农业水文过程和过程理解。