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Objective

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

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Objective

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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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