| 引用本文: | 侯靳锦,孙筱璐,王碧胜,等.不同水分条件下APEX模型参数的敏感性评价和不确定性分析[J].灌溉排水学报,2023,42(8):34-40. |
| HOU Jinjin,SUN Xiaolu,WANG Bisheng,et al.不同水分条件下APEX模型参数的敏感性评价和不确定性分析[J].灌溉排水学报,2023,42(8):34-40. |
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| 摘要: |
| 【目的】评估不同水分条件下APEX（Agricultural Policy/Environmental eXtender）模型参数的敏感性和不确定性。【方法】基于冬小麦水分控制试验，基于Sobol法、Morris法和FAST法分析了不同水分条件下APEX模型参数的敏感性，探讨了灌溉管理、降水年型及地下水位对模型参数敏感性的影响。【结果】在地下水位为1.25 m的条件下，影响冬小麦生物量、产量及蒸散量的最敏感参数均为RDMX（最大根深度），影响叶面积指数的最敏感的参数为DMLA（最大潜在叶面积指数）；当地下水位为5 m时，影响蒸散量、产量和生物量的敏感性参数为PARM38（水分胁迫计算权重系数）和RWPC1（萌发期根系生物量占比）。随着灌水量的增加，RDMX参数敏感性总体降低，而DMLA、DLAI（叶面积生长达到最高点的生育时期）和WA（潜在光能利用率）参数敏感性总体升高。RDMX在干旱年份的敏感性指数高于湿润年份。模型不确定性分析表明，冬小麦生物量、产量和蒸散量观测值均分布在模拟值的5%~95%置信区间内，模拟结果具有较高的可信度。【结论】3种分析方法筛选的最敏感参数具有较好的一致性，但其敏感性在不同灌溉、降水年型或地下水位条件下差异明显，考虑到计算效率和准确性，Morris法更适用于APEX模型的参数敏感性分析。 |
| 关键词: APEX模型；敏感性分析；水分胁迫；Sobol法；Morris法；FAST法；不确定性分析 |
| DOI：10.13522/j.cnki.ggps.2022514 |
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| Sensitivity and Uncertainty Analysis of the APEX Model to Water Status |
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HOU Jinjin, SUN Xiaolu, WANG Bisheng, YANG Xiaohui, XU Mengjie, FANG Quanxiao
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Qingdao Agricultural University, Qingdao 266109, China
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| Abstract: |
| 【Background and Objective】 The APEX model is a comprehensive watershed-scale model for simulating the effects of management practices on agricultural systems and their impacts on water quality, soil erosion, and nutrient cycling. This paper analyzes the sensitivity of its parameters to water status in soil. 【Method】 The analysis is based on data measured from 2016 to 2019 from an irrigation experiment conducted in Jiaodong in Shandong province. Winter wheat was used as the model plant; the Sobol, Morris, and FAST methods were used to analyze the sensitivities of the APEX model parameters associated with crop growth and water stress. We considered the influences of groundwater depth, rainfall and irrigation.【Result】 When groundwater depth was 1.25 m, the maximum root depth (RDMX) was the most sensitive parameter affecting evapotranspiration, biomass, and yield, while the maximum potential leaf area index (DMLA) was the most sensitive parameter impacting leaf area index (LAI). When the groundwater depth was increased to 5 m, the sensitive parameters influencing crop evapotranspiration and yield differed, with PARM38 (weight coefficient of water stress calculation) and RWPC1 (proportion of root biomass during germination) becoming the most sensitive parameters. Results calculated from all three methods indicated that as irrigation water increased, the sensitivity of RDMX decreased, while the sensitivities of DMLA, DLAI (peak point in growth season), and WA (potential light energy utilization) increased. The sensitivity of RDMX was significantly higher in dry years than in humid years, as opposed to the sensitivity of DMLA. Uncertainty analysis demonstrated that wheat biomass, yield, and evapotranspiration fell within the 5% to 95% confidence interval of the simulated data. 【Conclusion】The most sensitive parameters identified by the Sobol, Morris, and FAST methods were consistent, although their sensitivity indexes varied with irrigation treatments, rainfall patterns, and groundwater depth. Considering computational efficiency and accuracy, the Morris method is more suitable for parameter sensitivity analysis of the APEX model. These findings provide valuable insights into the application of the APEX to analyze the impact of environmental conditions on crops. |
| Key words: APEX model; sensitivity analysis; water stress; Sobol; Morris; FAST; uncertainty analysis |
