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

Abstract

Abstract. Optimization of land surface models has been challenging due to the model complexity and uncertainty. In this study, we performed scheme-based model optimizations by designing a framework for coupling "the micro-genetic algorithm" (micro-GA) and "the Noah land surface model with multiple physics options" (Noah-MP). Micro-GA controls the scheme selections among eight different land surface parameterization categories, each containing 2-4 schemes, in Noah-MP in order to extract the optimal scheme combination that achieves the best skill score. This coupling framework was successfully applied to the optimizations of evapotranspiration and runoff simulations in terms of surface water balance over the Han River basin in Korea, showing outstanding speeds in searching for the optimal scheme combination. Taking advantage of the natural selection mechanism in micro-GA, we explored the model sensitivity to scheme selections and the scheme interrelationship during the micro-GA evolution process. This information is helpful for better understanding physical parameterizations and hence it is expected to be effectively used for further optimizations with uncertain parameters in a specific set of schemes.

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

“…Separate Micro-GA experiments for ET and Runoff based on the normal NSE and those based on mNSE for both variables showed repercussion effects in all regions as shown in the previous study (Hong et al 2014). For example, while the NSE ET −based optimization for RE2 showed the best result with 0.82 of NSE ET , that simulation from the same scheme combination resulted in 0.06 of the NSE RUN that is much lowered one, compared with 0.22 of NSE RUN from the NSE RUN −based optimization.…”

“…The previous study, Hong et al 2014, used a new version of the Noah LSM with multiple physics options (hereafter Noah-MP) for the scheme-based model optimization. Developed from Noah LSM 3.0v, Noah-MP provides multiple options for the representation of different land surface processes (Niu et al 2011).…”

“…The total possible number of scheme combination becomes 1,728. More detailed information including equations and parameter settings are referred to in the previous studies (Hong et al 2014;Niu et al 2011).…”

“…In addition, this previous study brings a conclusion that LSMs have limited applicability especially to large areas because they should deal with regional surface heterogeneity. Hong et al (2014) designed a coupling system of a LSM with multi-scheme selections and a GA, enabling scheme based optimization (optimization through seeking the best scheme combination). Through the optimization process, this system allows to diagnose the simulating performance of each implemented scheme not only in its own capability but also in the interrelationships with the other schemes.…”

Climate change over the high-mountain versus plain areas: Effects on the land surface hydrologic budget in the Alpine area and northern Italy

“…Recent studies 10 demonstrate that the land surface processes diagnosed by land surface models are sensitive to vegetation dynamics and variations, and that their accuracy can be further improved by considering various aspects of vegetation effects in the subgrid-scale parameterizations (e.g., Park and Park, 2016;Gim et al, 2017). Moreover, the model uncertainties can be significantly reduced by optimal estimation of the parameter values in the schemes (e.g., Lee et al, 2006;Yu et al, 2013) and/or seeking for an optimized set among multiple-physics optional schemes (e.g., Hong et al, 2014Hong et al, , 2015. By applying these methods, the details of 15 model-generated spatial/temporal changes in the future energy and hydrologic budgets can be different from the current results;…”