Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis

Li, Jianduo; Duan, Qingyun; Gong, Wei; Ye, Aizhong; Dai, Yongjiu; Miao, Chiyuan; Di, Zhenhua; Tong, Charles; Sun, Yunwei

doi:10.5194/hess-17-3279-2013

Cited by 74 publications

(65 citation statements)

References 57 publications

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“…It also implements glacier, lake, wetland and dynamic vegetation processes. Similar to previous research presented in Li et al (2013), we select 40 adjustable parameters from CoLM. The parameter names, physical meanings and value ranges are shown in Table 1.…”

Section: Model and Parametersmentioning

confidence: 99%

“…According to statistical learning theory, such a build-prune strategy can extract information from training data and meanwhile avoid the influence of noise (Hastie et al, 2009). Because of its pruning and fitting ability, MARS method can be used as parameter screening method (Gan et al, 2014;Li et al, 2013;Shahsavani et al, 2010), and also surrogate modeling method (Razavi et al, 2012;Song et al, 2012;Zhan et al, 2013).…”

Section: A1 Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

“…The study area and associated data sets are from the Heihe river basin, the same as in Li et al (2013 The forcing data used include downward shortwave and long-wave radiation, precipitation, air temperature, relative humidity, air pressure and wind speed (Hu et al, 2003); the observation data used to validate the simulation of CoLM include: sensible heat, latent heat, upward long-wave radiation, net radiation, soil temperature and soil moisture. The soil temperature and moisture were measured at depth 10, 20, 40 and 80 cm.…”

Section: Study Area and Data Setsmentioning

confidence: 99%

“…Since every sample set of each size was independently generated, we use the 2000 points set to test 50, 100, 200, 400, 800 and 1200 points set, and use the 1200 one to test the 2000 one. For each output variable, we only construct surrogate models for the most sensitive parameters based on the screening results obtained by Li (2012) and Li et al (2013) (see Table 2). …”

Section: Comparison Of Surrogate Modelsmentioning

confidence: 99%

“…After the sensitive parameters are identified using global sensitivity methods (see Li et al, 2013), the next step is to calibrate the sensitive parameters through multi-objective optimization. Since the calibration of CoLM in real-world applications can be very expensive, we aim to establish a surrogate model to represent the response surface of the dynamic CoLM.…”

Section: Comparison Of Surrogate Modelsmentioning

confidence: 99%

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

Gong

Duan

et al. 2015

Hydrol. Earth Syst. Sci.

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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.

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Section: Model and Parametersmentioning

confidence: 99%

Section: A1 Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

Section: Study Area and Data Setsmentioning

confidence: 99%

Section: Comparison Of Surrogate Modelsmentioning

confidence: 99%

Section: Comparison Of Surrogate Modelsmentioning

confidence: 99%

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

Gong

Duan

et al. 2015

Hydrol. Earth Syst. Sci.

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Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models

Rakovec¹,

et al. 2014

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[1] This paper presents a hybrid local-global sensitivity analysis method termed the Distributed Evaluation of Local Sensitivity Analysis (DELSA), which is used here to identify important and unimportant parameters and evaluate how model parameter importance changes as parameter values change. DELSA uses derivative-based ''local'' methods to obtain the distribution of parameter sensitivity across the parameter space, which promotes consideration of sensitivity analysis results in the context of simulated dynamics. This work presents DELSA, discusses how it relates to existing methods, and uses two hydrologic test cases to compare its performance with the popular global, variance-based Sobol' method. The first test case is a simple nonlinear reservoir model with two parameters. The second test case involves five alternative ''bucket-style'' hydrologic models with up to 14 parameters applied to a medium-sized catchment (200 km 2 ) in the Belgian Ardennes. Results show that in both examples, Sobol' and DELSA identify similar important and unimportant parameters, with DELSA enabling more detailed insight at much lower computational cost. For example, in the real-world problem the time delay in runoff is the most important parameter in all models, but DELSA shows that for about 20% of parameter sets it is not important at all and alternative mechanisms and parameters dominate. Moreover, the time delay was identified as important in regions producing poor model fits, whereas other parameters were identified as more important in regions of the parameter space producing better model fits. The ability to understand how parameter importance varies through parameter space is critical to inform decisions about, for example, additional data collection and model development. The ability to perform such analyses with modest computational requirements provides exciting opportunities to evaluate complicated models as well as many alternative models.

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Assessing WRF model parameter sensitivity: A case study with 5 day summer precipitation forecasting in the Greater Beijing Area

Duan

Gong

et al. 2015

Geophysical Research Letters

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A global sensitivity analysis method was used to identify the parameters of the Weather Research and Forecasting (WRF) model that exert the most influence on precipitation forecasting. Twenty-three adjustable parameters were selected from seven physical components of the WRF model. The sensitivity was evaluated based on skill scores calculated over nine 5 day precipitation forecasts during the summer seasons from 2008 to 2010 in the Greater Beijing Area in China. We found that eight parameters are more sensitive than others. Storm type seems to have no impact on the list of sensitive parameters but does influence the degree of sensitivity. We also examined the physical interpretation of parameter sensitivity. This analysis is useful for further optimization of the WRF model parameters to improve precipitation forecasting.

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Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis

Cited by 74 publications

References 57 publications

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

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

Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models

Assessing WRF model parameter sensitivity: A case study with 5 day summer precipitation forecasting in the Greater Beijing Area

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