Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantification framework applied to the Community Land Model

Abstract

[1] Uncertainties in hydrologic parameters could have significant impacts on the simulated water and energy fluxes and land surface states, which will in turn affect atmospheric processes and the carbon cycle. Quantifying such uncertainties is an important step toward better understanding and quantification of uncertainty of integrated earth system models. In this paper, we introduce an uncertainty quantification (UQ) framework to analyze sensitivity of simulated surface fluxes to selected hydrologic parameters in the Community Land Model (CLM4) through forward modeling. Thirteen flux tower footprints spanning a wide range of climate and site conditions were selected to perform sensitivity analyses by perturbing the parameters identified. In the UQ framework, prior information about the parameters was used to quantify the input uncertainty using the Minimum-Relative-Entropy approach. The quasi-Monte Carlo approach was applied to generate samples of parameters on the basis of the prior pdfs. Simulations corresponding to sampled parameter sets were used to generate response curves and response surfaces and statistical tests were used to rank the significance of the parameters for output responses including latent (LH) and sensible heat (SH) fluxes. Overall, the CLM4 simulated LH and SH show the largest sensitivity to subsurface runoff generation parameters. However, study sites with deep root vegetation are also affected by surface runoff parameters, while sites with shallow root zones are also sensitive to the vadose zone soil water parameters. Generally, sites with finer soil texture and shallower rooting systems tend to have larger sensitivity of outputs to the parameters. Our results suggest the necessity of and possible ways for parameter inversion/calibration using available measurements of latent/sensible heat fluxes to obtain the optimal parameter set for CLM4. This study also provided guidance on reduction of parameter set dimensionality and parameter calibration framework design for CLM4 and other land surface models under different hydrologic and climatic regimes.Citation: Hou, Z., M. Huang, L. R. Leung, G. Lin, and D. M. Ricciuto (2012), Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantification framework applied to the Community Land Model, J. Geophys.

Streamflow sensitivity to water storage changes across Europe

“…Other modeling approaches often need additional data and longer time series to calibrate the model [Melsen et al, 2014]. Predictions of low flow conditions are often based on multimodel assessments [e.g., Prudhomme et al, 2014] that have large uncertainty in parameters related to subsurface runoff generation [Hou et al, 2012;Huang et al, 2013] and do not provide efficient guidance in understanding regional landscape differences.…”

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

“…The third is the appropriateness of the model parameter specification. How to estimate model parameters has received increasing attention from the hydrology and land surface modeling community over recent years (Franks and Beven, 1997;Gupta et al, 1999;Duan et al, 2001Duan et al, , 2003Jackson et al, 2003;Liu et al, 2005;Hou et al, 2012).…”

“…The relative efficiency and effectiveness of several SA methods have been analyzed and compared. Hou et al (2012) introduced an uncertainty quantification framework to analyze the sensitivity of 10 hydrologic parameters in CLM4SP (Community Land Model Version 4 with satellite phenology) with a generalized linear model (GLM) method. They found that the simulation of sensible heat and latent heat is sensitive to subsurface runoff generation parameters.…”

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.…”