This study evaluates the simulation of water balance components at half-hourly time steps from the Canadian Land Surface Scheme (CLASS) when driven by a 500-year stochastic meteorological data set produced by the Advanced WEather GENerator (AWE-GEN) at two boreal sites with contrasting water availability. The CLASS was driven by ERA5 reanalysis data (CLASS-CTL) over 39 years and its output was used as a surrogate for land surface observations. At both sites, the mean monthly and annual values of all meteorological variables used to drive CLASS, including precipitation, are well captured by AWE-GEN, but their variability is, sometimes, biased. In general, CLASS driven by stochastic data (CLASS-WG) tends to produce higher evapotranspiration compared to values simulated by CLASS-CTL, especially during spring and summer at the wet site. The interannual evapotranspiration-precipitation and runoff-precipitation relationships derived from CLASS-WG and those derived from CLASS-CTL were very similar to each other at the dry site; they both indicate that evapotranspiration and runoff are limited by water availability. At the wet site, however, CLASS-WG only captured well the interannual runoff-precipitation relationship. The sensitivity analysis shows that CLASS water fluxes are particularly affected by the replacement of physically consistent input time series of incoming short-wave radiation, precipitation, temperature, and specific humidity. In conclusion, the results show that even though a weather generator can produce coherent climate time series, the use of this synthetic data as meteorological forcing in a physically based land surface model does not necessarily reproduce the complex surface processes, such as the surface water fluxes. More studies are encouraged to further analyze the constraints of this framework.

本研究评估了由高级天气发生器 (AWE-GEN) 在两个北方站点生成的 500 年随机气象数据集驱动的加拿大陆地表面计划 (CLASS) 半小时时间步长的水平衡分量模拟与水的可用性形成鲜明对比。CLASS 由 ERA5 再分析数据 (CLASS-CTL) 驱动超过 39 年，其输出用作地表观测的替代品。在这两个站点，用于驱动 CLASS 的所有气象变量（包括降水）的月平均和年均值被 AWE-GEN 很好地捕获，但它们的变异性有时是有偏差的。一般而言，与 CLASS-CTL 模拟的值相比，由随机数据驱动的 CLASS (CLASS-WG) 往往会产生更高的蒸散量，尤其是在春季和夏季湿地。CLASS-WG和CLASS-CTL的年际蒸散量-降水量和径流-降水量关系在旱地非常相似；它们都表明蒸散和径流受到可用水量的限制。然而，在湿地，CLASS-WG 只能很好地捕获年际径流-降水关系。敏感性分析表明，CLASS 水通量特别受输入短波辐射、降水、温度和特定湿度的物理一致输入时间序列的替换的影响。总之，结果表明，即使天气发生器可以产生连贯的气候时间序列，在基于物理的地表模型中使用这种合成数据作为气象强迫并不一定能重现复杂的地表过程，例如地表水通量。鼓励更多的研究来进一步分析这个框架的限制。