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Optimization of land surface models has been challenging due to the modelcomplexity and uncertainty. In this study, we performed scheme-based modeloptimizations by designing a framework for coupling "the micro-geneticalgorithm" (micro-GA) and "the Noah land surface model with multiple physicsoptions" (Noah-MP). Micro-GA controls the scheme selections among eight different land surfaceparameterization categories, each containing 2–4 schemes, in Noah-MP inorder to extract the optimal scheme combination that achieves the best skillscore. This coupling framework was successfully applied to the optimizationsof evapotranspiration and runoff simulations in terms of surface waterbalance over the Han River basin in Korea, showing outstanding speeds insearching for the optimal scheme combination. Taking advantage of thenatural selection mechanism in micro-GA, we explored the model sensitivityto scheme selections and the scheme interrelationship during the micro-GAevolution process. This information is helpful for better understandingphysical parameterizations and hence it is expected to be effectively usedfor further optimizations with uncertain parameters in a specific set ofschemes

Assessing optimal set of implemented physical parameterization schemes in a multi-physics land surface model using genetic algorithm