Parameter sensitivity analysis and optimization of Noah land surface model with field measurements from Huaihe River Basin, China

Hou, Ting Ting; Zhu, Yonghua; Lü, Haishen; Sudicky, Edward A.; Yu, Zhongbo; Fen, Ouyang

doi:10.1007/s00477-015-1033-5

Cited by 12 publications

(10 citation statements)

References 43 publications

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“…The quantitative Sobol′ method further confirms that the qualitative SA results are reasonable (Figure and Table ). For the Sobol′ sensitivity index, we set the threshold for ruling out insensitive parameters to 0.1, as recommended by Hou et al () and Tang et al (). The sensitive parameters identified by the Sobol′ method are consistent with the qualitative SA results for 64% of catchments.…”

Section: Resultsmentioning

confidence: 99%

Sensitivity Analysis‐Based Automatic Parameter Calibration of the VIC Model for Streamflow Simulations Over China

Gou

Miao

Duan

et al. 2020

Water Resources Research

128

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Model parameter calibration is a fundamentally important stage that must be completed before applying a model to address practical problems. In this study, we describe an automatic calibration framework that combines sensitivity analysis (SA) and an adaptive surrogate modeling-based optimization (ASMO) algorithm. We use this framework to calibrate catchment-specific sensitive parameters for streamflow simulation in the variable infiltration capacity (VIC) model with a 0.25°spatial resolution over 10 major river basins of China from 1960 to 1979. We found that three parameters-the infiltration parameter (B) and two of the soil depth parameters (D 1 , D 2 )-are highly sensitive in most basins, while other parameter sensitivities are strongly related to the dynamic environment of the basin. Compared with directly calibrating the seven parameters recommended for the default calibration procedure, our framework not only reduced the computing time by two thirds through opting out of insensitive parameters (type I error) but also improved the Nash-Sutcliffe model efficiency coefficient (NSE) for optimized results when it identified a missing sensitive parameter (type II error) in the case study river basins. Results show that the SA-based ASMO framework is an effective and efficient model-optimization technique for matching simulated streamflow with observations across China. The NSE for monthly streamflow ranged from 0.75 to 0.97 and from 0.71 to 0.97 during the validation and calibration periods, respectively. The calibrated parameters can be applied directly in streamflow simulations across China, and the proposed calibration framework holds important implications for relevant simulation studies in other regions.

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Section: Resultsmentioning

confidence: 99%

Sensitivity Analysis‐Based Automatic Parameter Calibration of the VIC Model for Streamflow Simulations Over China

Gou

Miao

Duan

et al. 2020

Water Resources Research

128

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“…The results from the Sobol sensitivity analysis in section 4.1 show that the evapotranspiration module in the Noah land surface model is only sensitive to a subset of the model parameters described in Table . These results coincide with previous studies that suggest that r s ,min , C zil , and fx exp are the most sensitive parameters in the Noah land surface model [ Rosero et al ., ; Hou et al ., ]. The large number of eddy covariance towers used in our study provides evidence that these results extend to multiple climates and land cover types.…”

Section: Discussionmentioning

confidence: 99%

“…To determine the sensitivity of the model parameters, we use the Sobol sensitivity analysis. Prior studies have successfully used this technique to discern the role of the Noah model parameters at a limited number of sites [ Rosero et al ., ; Hou et al ., ].…”

Section: Methodsmentioning

confidence: 99%

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Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning

Chaney

Herman

et al. 2016

JGR Atmospheres

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With their origins in numerical weather prediction and climate modeling, land surface models aim to accurately partition the surface energy balance. An overlooked challenge in these schemes is the role of model parameter uncertainty, particularly at unmonitored sites. This study provides global parameter estimates for the Noah land surface model using 85 eddy covariance sites in the global FLUXNET network. The at‐site parameters are first calibrated using a Latin Hypercube‐based ensemble of the most sensitive parameters, determined by the Sobol method, to be the minimum stomatal resistance (rs,min), the Zilitinkevich empirical constant (Czil), and the bare soil evaporation exponent (fxexp). Calibration leads to an increase in the mean Kling‐Gupta Efficiency performance metric from 0.54 to 0.71. These calibrated parameter sets are then related to local environmental characteristics using the Extra‐Trees machine learning algorithm. The fitted Extra‐Trees model is used to map the optimal parameter sets over the globe at a 5 km spatial resolution. The leave‐one‐out cross validation of the mapped parameters using the Noah land surface model suggests that there is the potential to skillfully relate calibrated model parameter sets to local environmental characteristics. The results demonstrate the potential to use FLUXNET to tune the parameterizations of surface fluxes in land surface models and to provide improved parameter estimates over the globe.

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“…The steady state groundwater model is calibrated against available observations of e.g. groundwater heads with PEST SSMC resulting in several plausible solutions, which is documented in a wide range of literature (Burrows and Doherty 2015;Doherty 2011;Fienen et al 2013;Hou et al 2015;Rossi et al 2014;Woodward et al 2016). After the SSMC procedure, solutions that are not able to fulfill a calibration criterion despite the re-estimation are removed.…”

Section: Groundwater Modelmentioning

confidence: 99%

Comprehensive risk assessment of groundwater drawdown induced subsidence

Sundell

Haaf

Tornborg

et al. 2019

Stoch Environ Res Risk Assess

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We present a method for risk assessment of groundwater drawdown induced land subsidence when planning for subsurface infrastructure. Since groundwater drawdown and related subsidence can occur at large distances from the points of inflow, the large spatial extent often implies heterogeneous geological conditions that cannot be described in complete detail. This calls for estimation of uncertainties in all components of the cause-effect chain with probabilistic methods. In this study, we couple four probabilistic methods into a comprehensive model for economic risk quantification: a geostatistical soil-stratification model, an inverse calibrated groundwater model, an elasto-plastic subsidence model, and a model describing the resulting damages and costs on individual buildings and constructions. Groundwater head measurements, hydraulic tests, statistical analyses of stratification and soil properties and an inventory of buildings are inputs to the models. In the coupled method, different design alternatives for risk reduction measures are evaluated. Integration of probabilities and damage costs result in an economic risk estimate for each alternative. Compared with the risk for a reference alternative, the best prior alternative is identified as the alternative with the highest expected net benefit. The results include spatial probabilistic risk estimates for each alternative where areas with significant risk are distinguished from low-risk areas. The efficiency and usefulness of this modelling approach as a tool for communication to stakeholders, decision support for prioritization of risk reducing measures, and identification of the need for further investigations and monitoring are demonstrated with a case study of a planned railway tunnel in Varberg, Sweden.

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Parameter sensitivity analysis and optimization of Noah land surface model with field measurements from Huaihe River Basin, China

Cited by 12 publications

References 43 publications

Sensitivity Analysis‐Based Automatic Parameter Calibration of the VIC Model for Streamflow Simulations Over China

Sensitivity Analysis‐Based Automatic Parameter Calibration of the VIC Model for Streamflow Simulations Over China

Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning

Comprehensive risk assessment of groundwater drawdown induced subsidence

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