Simulation of N2O emissions from a urine‐affected pasture in New Zealand with the ecosystem model DayCent
Simulation of N2O emissions from a urine‐affected pasture in New Zealand with the ecosystem model DayCent
复制标题
使用生态系统模型 DayCent 模拟新西兰受尿液影响牧场的 N2O 排放
DOI：
10.1029/2003jd004261
复制
发表时间：
2004
影响因子：
--
通讯作者：
C. Müller
中科院分区：
文献类型：
--
作者：
E. Stehfest;C. Müller
[1] 我们使用痕量气体模型 DayCent 来模拟新西兰受尿液影响的牧场的一氧化二氮 (N 2 O) 排放量。该站点的数据集包含全年每日硝化-N 2 O（N 2 O nit ）和反硝化-N 2 O（N 2 O den ）排放量、气象数据、土壤湿度以及至少每周土壤铵（NH 4 +）和硝酸盐（NO - 3）含量数据。蒸散量、土壤温度和大部分土壤湿度数据都得到了很好的体现。观察到的和模拟的土壤 NH4 浓度非常吻合，但 DayCent 低估了 NO - 3 浓度，可能是由于硝化速率不足。模拟的 N 2 O 排放量 (18.4 kg N 2 O-N ha -1 yr -1 ) 显示出类似的模式，但超出观测排放量 (4.4 kg N 2 O-N ha -1 yr -1 ) 3 倍以上。模拟和观测到的 N 2 O 排放主要是施氮和强降雨事件后的峰值，并且在高土壤温度下有利于 N 2 O 排放。除了4周的时间段内充满水的孔隙空间被高估并导致高N 2 O排放量（占模拟年N 2 O排放量的三分之一）外，N 2 O巢穴的贡献模拟得很好。 DayCent 高估了 N 2 O 尼特通量，因为它们是按 NH 4 + 转化为 NO - 3 的固定比例计算的，而数据表明，在不引起显着 N 2 O 排放的情况下，可以发生显着的硝化速率。全面的数据集可以解释模型值和观测值之间的差异。使用详细数据集进行深入的模型验证对于更好地理解内部模型行为和导出可能的模型改进至关重要。
[1] We used the trace gas model DayCent to simulate emissions of nitrous oxide (N 2 O) from a urine-affected pasture in New Zealand. The data set for this site contained year-round daily emissions of nitrification-N 2 O (N 2 O nit ) and denitrification-N 2 O (N 2 O den ), meteorological data, soil moisture, and at least weekly data on soil ammonium (NH 4 +) and nitrate (NO - 3) content. Evapotranspiration, soil temperature, and most of the soil moisture data were reasonably well represented. Observed and simulated soil NH4 concentrations agreed well, but DayCent underestimated the NO - 3 concentrations, due possibly to an insufficient nitrification rate. Modeled N 2 O emissions (18.4 kg N 2 O-N ha -1 yr -1 ) showed a similar pattern but exceeded observed emissions (4.4 kg N 2 O-N ha -1 yr -1 ) by more than 3 times. Modeled and observed N 2 O emissions were dominated by peaks following N-application and heavy rainfall events and were favored under high soil temperatures. The contribution of N 2 O den was simulated well except for a 4-week period when water-filled pore space was overestimated and caused high N 2 O emissions which accounted for one third of the simulated annual N 2 O emissions. N 2 O nit fluxes were overestimated with DayCent because they are calculated as a fixed proportion of NH 4 + converted to NO - 3, while the data suggest that significant rates of nitrification can occur without inducing significant N 2 O emissions. The comprehensive data set made it possible to explain discrepancies between modeled and observed values. In-depth model validations with detailed data sets are essential for a better understanding of the internal model behavior and for deriving possible model improvements.