Scheme-Based Optimization of Land Surface Model Using a Micro-Genetic Algorithm: Assessment of Its Performance and Usability for Regional Applications

Abstract

Typical parameter calibration techniques in land surface models often limit to solve the spatiotemporal discrepancy of modeling performances due to high heterogeneity of land surface, especially for regional applications. We evaluated a coupling system of micro genetic algorithm (micro-GA) and the Noah land surface model with multi-physics options (Noah-MP) for its usability for regional applications. Four different regions having different climatic characteristics over East Asia were selected, and for each region Noah-MP provides two to four scheme options in eight scheme categories each of which represents different land surface processes, and the model was optimized through searching the best scheme combination by micro-GA. The optimization focused on the surface water balance, comparing model simulations of evapotranspiration and runoff with the European Centre for Medium-Range Weather Forecasts ERA-Interim land products. The optimizing process was controlled by micro-GA using natural selection and evolution techniques. This study demonstrated that the coupling system assures not only the effectiveness of the scheme-based optimization but also the skill of the used model diagnosis in quantifying model performance during the micro-GA evolution process. Since each region has its own advantageous scheme combination, multiple scheme-combinations are a possible solution for the spatiotemporal discrepancy of modeling performance.