Parameter Estimation

Where soil differs with depth, models that carry water-holding properties inferred at the surface down the whole column bias the moisture reaching crop roots. We applied depth-varying properties to a hyper-resolution land surface model resolving individual agricultural fields at 30 meters. Against ground measurements, systematic surface soil moisture biases dropped by about 14%, with error spread and timing unchanged, correcting the mean state rather than daily dynamics.

Land surface models, which simulate vegetation, soil, water and carbon within Earth system models, depend on parameters that often cannot be measured directly. Varying one carbon flux parameter within its uncertainty range can shift projected atmospheric CO2 in 2100 more than the choice of emissions scenario. This review presents two decades of work on adjusting parameters to match observations is consolidated into seven recurring obstacles, each paired with a machine learning opportunity.

In this study we show how a surrogate model can accurately predict the spatial structure of Land Surface Model’s (LSM) hydrological output fields. For a target level of complexity, the optimal LSM tile configuration was determined using a multi-objective Pareto efficiency analysis. We found that approximately 100 tiles can effectively reproduce a quasi-fully distributed LSM setup with 1% of the computational cost. This method provides a path forward to efficiently determine the optimal tile configurations for LSMs while simultaneously considering the spatial heterogeneity and spatial accuracy of hydrologic processes.