Can we replace observed forcing with weather generator in land surface modeling? Insights from long-term simulations at two contrasting boreal sites

Abstract

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.