Projected changes to rainfall patterns may exacerbate existing risks posed by flooding. Furthermore, increased surface runoff from agricultural land increases pollution through nutrient losses. Agricultural systems are complex because they are managed in individual fields, and it is impractical to provide resources to monitor their water fluxes. In this respect, modelling provides an inexpensive tool for simulating fluxes. At the field-scale, a daily time-step is used routinely. However, it was hypothesised that a finer time-step will provide more accurate identification of peak fluxes. To investigate this, the process-based SPACSYS model that simulates water fluxes, soil carbon and nitrogen cycling, as well as plant growth, with a daily time-step was adapted to provide sub-daily simulations. As a case study, the water flux simulations were checked against a 15-minute measured water flux dataset from April 2013 to February 2016 from a pasture within a monitored grassland research farm, where the data were up-scaled to hourly, 6-hourly and daily. Analyses were conducted with respect to model performance for: (a) each of the four data resolutions, separately (15-minute measured versus 15-minute simulated; hourly measured versus hourly simulated; etc.); and (b) at the daily resolution only, where 15-minute, hourly and 6-hourly simulations were each aggregated to the daily scale. Comparison between measured and simulated fluxes at the four resolutions revealed that hourly simulations provided the smallest misclassification rate for identifying water flux peaks. Conversely, aggregating to the daily scale using either 15-minute or hourly simulations increased accuracy, both in prediction of general trends and identification of peak fluxes. For the latter investigation, the improved identification of extremes resulted in 9 out of 11 peak flow events being correctly identified with only 2 false positives, compared with 5 peaks being identified with 4 false positives of the usual daily simulations. Increased peak flow detection accuracy has the potential to provide clear field management benefits in reducing nutrient losses to water.

降雨模式的预计变化可能会加剧洪水造成的现有风险。此外，农业用地的地表径流增加会因养分流失而增加污染。农业系统是复杂的，因为它们是在各个领域进行管理的，提供资源来监测它们的水通量是不切实际的。在这方面，建模为模拟通量提供了一种廉价的工具。在田间尺度上，通常使用每日时间步长。然而，假设更精细的时间步长将提供更准确的峰值通量识别。为了对此进行研究，基于过程的 SPACSYS 模型可以模拟水通量、土壤碳和氮循环以及植物生长，并采用每日时间步长进行调整，以提供次日模拟。作为案例研究，水通量模拟与 2013 年 4 月至 2016 年 2 月从受监控的草地研究农场内的一个牧场的 15 分钟实测水通量数据集进行对比，数据被放大到每小时、每 6 小时和每天一次。对模型性能进行了以下分析：(a) 四个数据分辨率中的每一个，分别（15 分钟测量与 15 分钟模拟；每小时测量与每小时模拟；等等）；(b) 仅以每日分辨率计算，其中 15 分钟、每小时和 6 小时的模拟分别汇总到每日规模。在四种分辨率下测量的和模拟的通量之间的比较表明，每小时模拟为识别水通量峰值提供了最小的误分类率。反过来，使用 15 分钟或每小时模拟汇总到每日规模，提高了预测总体趋势和识别峰值流量的准确性。对于后一项调查，改进的极端识别导致 11 个峰值流​​量事件中有 9 个被正确识别，只有 2 个误报，而在通常的日常模拟中，5 个峰值被识别为 4 个误报。提高峰值流量检测精度有可能在减少营养损失到水中提供明显的现场管理优势。与通常的日常模拟的 5 个峰值与 4 个误报相比。提高峰值流量检测精度有可能在减少营养损失到水中提供明显的现场管理优势。与通常的日常模拟的 5 个峰值与 4 个误报相比。提高峰值流量检测精度有可能在减少营养损失到水中提供明显的现场管理优势。