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Enhancing Hydrologic Parameter Calibration in CLM Using Surface Flux and Runoff Observations

This study investigates the calibration of hydrologic parameters in the Community Land Model (CLM) using surface flux and runoff data to enhance model accuracy. By employing stochastic inversion methods, particularly Bayesian inference and the Metropolis-Hasting sampling technique, the research systematically evaluates factors influencing calibration effectiveness, such as probability distribution choices and data characteristics. Results indicate that MCMC-Bayesian calibration significantly improves CLM simulations of heat and water fluxes, enabling reliable parameter estimates across varying climate conditions, while addressing computational and transferability challenges.

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Enhancing Hydrologic Parameter Calibration in CLM Using Surface Flux and Runoff Observations

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  1. Inverse Modeling of Hydrologic Parameters using Surface Flux and Runoff Observations in the Community Land Model (CLM) Objective • Explore the feasibility of calibrating hydrologic parameters using surface flux and runoff observations to improve the accuracy of CLM Approach • Use stochastic inversion/calibration approaches (e.g., Bayesian inference) to describe the input/output uncertainties in a probabilistic manner • Use Metropolis–Hasting sampling method to draw samples from the joint posterior distribution functions • Systematically analyze various factors, including the choices of probability distribution, acceptance probability, site conditions, data type, and spatiotemporal resolution, on the effectiveness of calibration MCMC-Bayesian calibrated parameters can significantly improve CLM simulation of heat flux and runoff Impact • Improved CLM simulations of water and energy fluxes can be achieved through inverse modeling of the hydrologic parameters • Reliable estimates of model parameters under different climate and environmental conditions can be effectively obtained with the Markov-Chain Monte Carlo-Bayesian inversion approach • Challenges of applying the method over a region or globally, including computational requirements, model parameter transferability, and possibility of building surrogates, are being addressed in follow-up studies Sun Y, Z Hou, M Huang, F Tian, and LR Leung. 2013. “Inverse Modeling of Hydrologic Parameters using Surface Flux and Runoff Observations in the Community Land Model.” Hydrology and Earth System Sciences 17:4995-5011. DOI:10.5194/hess-17-4995-2013.

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