Multi-objective parameter optimization of common land model using adaptive surrogate modeling

Abstract

Abstract. Parameter specification usually has significant influence on the performance of land surface models (LSMs). However, estimating the parameters properly is a challenging task due to the following reasons: (1) LSMs usually have too many adjustable parameters (20 to 100 or even more), leading to the curse of dimensionality in the parameter input space; (2) LSMs usually have many output variables involving water/energy/carbon cycles, so that calibrating LSMs is actually a multi-objective optimization problem; (3) Regional LSMs are expensive to run, while conventional multi-objective optimization methods need a large number of model runs (typically ∼ 10 5 -10 6 ). It makes parameter optimization computationally prohibitive. An uncertainty quantification framework was developed to meet the aforementioned challenges, which include the following steps: (1) using parameter screening to reduce the number of adjustable parameters, (2) using surrogate models to emulate the responses of dynamic models to the variation of adjustable parameters, (3) using an adaptive strategy to improve the efficiency of surrogate modeling-based optimization; (4) using a weighting function to transfer multi-objective optimization to single-objective optimization. In this study, we demonstrate the uncertainty quantification framework on a single column application of a LSM -the Common Land Model (CoLM), and evaluate the effectiveness and efficiency of the proposed framework. The result indicate that this framework can efficiently achieve optimal parameters in a more effective way. Moreover, this result implies the possibility of calibrating other large complex dynamic models, such as regionalscale LSMs, atmospheric models and climate models.

Quantification and attribution of errors in the simulated annual gross primary production and latent heat fluxes by two global land surface models

“…This is supported by a number of other studies [Xia et al, 2002;Rosolem et al, 2013;Gong et al, 2015]. However parameter tuning at PFT level does not reduce model errors uniformly across all land points within a PFT, as found in this study.…”

Active subspaces for sensitivity analysis and dimension reduction of an integrated hydrologic model

“…Calibration of input parameters using observations and the simplified system is possible if the simulation setup matches observation locations. Recent research using CLM suggests that reduced-form models can be used to reduce the number of simulations required for parameter optimization (Gong et al, 2015), adjust parameter values used to predict methane emission from wetlands (Müller et al, 2015) and calibrate parameters identified as important for latent heat estimates (Ray et al, 2015).…”

The Possibility of Applying the EM-PCA Procedure to Lake Water

“…Other similar satisfactory optimization results were determined [54] by his research on hydrological parameter classifi cation. [54] has modeled in his studies on study [55][56][57][58], showing that the values of parameters on PCA diagrams can be presented as tables correlating between investigated parameters. At the same time, in order to identify the missing data and their uncertainties in complex computational models, a reliable approach to analysis is needed.…”