Improved Land Evapotranspiration Simulation of the Community Land Model Using a Surrogate-Based Automatic Parameter Optimization Method
Abstract
:1. Introduction
2. Data and Methodology
2.1. Data
2.2. Systematic Parameter Optimization Framework
2.2.1. Good Lattice Points (GLP) Uniform Sampling Method
2.2.2. Multivariate Adaptive Regression Splines (MARS) Sensitivity Analysis Method
2.2.3. Adaptive Surrogate Modeling-Based Optimization (ASMO) Parameter Optimization Method
- The perturbed parameter samples were obtained by sampling the sensitivity parameter adjustable ranges using the GLP sampling method. Then, these samples were put into the physical model (e.g., CLM4) instead of the default parameters, to obtain either the required model outputs (e.g., ET) or the output errors compared with observations. The perturbed parameters and their simulated outputs constituted the initial sample set.
- Based on the initial sample set, a statistical surrogate model was built between parameters and model outputs using the MARS regression method. Then, the traditional parameter optimization (e.g., the shuffled complex evolution (SCE-UA) global optimization method [23]) was used to search the optimal parameter values of the surrogate model.
- The optimal parameter values of the surrogate model were put into the physical model to obtain a new model output. As a new sample point, the optimal parameters of the surrogate model and their physical model output were added into the initial sample set.
- Steps 2 and 3 were repeatedly conducted until the convergence criterion was met. In this study, the convergence criterion was that the local optimal values remain unchanged after a number of searches equal to five or ten times the number of parameters.
2.3. Model Setup
3. Results
3.1. Sensitivity Analysis Results
3.2. Sensitivity Parameter Optimization Results
3.3. Comparison Analyses of Optimization Results
3.4. Validation Analyses of Community Land Model Version 4.0 Optimal Parameters
3.5. Comparisons between Default and Optimal Parameters
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Index | Parameter | Default | Range | Description |
---|---|---|---|---|
P2 | fdrai | 2.5 | (0.1, 5) | Decay factor of subsurface flow (m−1) |
P4 | Sy | 0.2 | (0.02, 0.27) | Fraction of water volume drained by gravity in an unconfined aquifer |
P6 | poro_b | 0.489 | (0.4401, 0.5379) | The intercept of mineral soil porosity pedotransfer function |
P10 | suc_b | 1.88 | (1.692, 2.068) | The intercept of pedotransfer function of saturated mineral soil matric potential |
P16 | z0mr | 1 | (0.7, 1.3) | Ratio of momentum roughness length to canopy top height |
P32 | rholnir | 1 | (0.7, 1.3) | Leaf reflectance: near-infrared radiation |
P36 | taulnir | 1 | (0.7, 1.3) | Leaf transmittance: near-infrared radiation |
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Zhang, C.; Di, Z.; Duan, Q.; Xie, Z.; Gong, W. Improved Land Evapotranspiration Simulation of the Community Land Model Using a Surrogate-Based Automatic Parameter Optimization Method. Water 2020, 12, 943. https://doi.org/10.3390/w12040943
Zhang C, Di Z, Duan Q, Xie Z, Gong W. Improved Land Evapotranspiration Simulation of the Community Land Model Using a Surrogate-Based Automatic Parameter Optimization Method. Water. 2020; 12(4):943. https://doi.org/10.3390/w12040943
Chicago/Turabian StyleZhang, Chong, Zhenhua Di, Qingyun Duan, Zhenghui Xie, and Wei Gong. 2020. "Improved Land Evapotranspiration Simulation of the Community Land Model Using a Surrogate-Based Automatic Parameter Optimization Method" Water 12, no. 4: 943. https://doi.org/10.3390/w12040943
APA StyleZhang, C., Di, Z., Duan, Q., Xie, Z., & Gong, W. (2020). Improved Land Evapotranspiration Simulation of the Community Land Model Using a Surrogate-Based Automatic Parameter Optimization Method. Water, 12(4), 943. https://doi.org/10.3390/w12040943