Model selection for parameter identifiability problem in Bayesian inference of building energy model

Deep Learning Integrating Scale Conversion and Pedo‐Transfer Function to Avoid Potential Errors in Cross‐Scale Transfer

“…Once out of this scenario, the inverse method will estimate functionally equivalent K sat values that are significantly different from the “true” values. It is the common issue named “the issue of parameter identifiability” (Beven, 2006; Yi & Park, 2021), which is elusive in real‐world cases but may significantly affect model performance. For example, the wrong parameters significantly deteriorate the CNN‐based integrated model estimation in step two of the two‐step strategy (“CNN (two‐step)”).…”

“…Besides, the end‐to‐end strategy (“CNN (end‐to‐end)”) outperforms the inverse method (“Inverse”) for temporal generalization, which is different from the synthetic cases. Scatter plots between the SMAP‐derived and model‐simulated soil moisture data (Figure S11 in Supporting Information ) show that more outliers by the inverse method (“Inverse”) and two‐step strategy (“CNN (two‐step)”) than the end‐to‐end strategy (“CNN (end‐to‐end)”) are far away from the 45‐degree line, especially during temporal and spatio‐temporal generalization, which indicates that the inverse method (“Inverse”) and two‐step strategy (“CNN (two‐step)”) may capture fewer “true” parameters due to the issue of parameter identifiability (Beven, 2006; Yi & Park, 2021) as discussed in the synthetic cases.…”

“…Runoff simulations using parameters estimated by the end‐to‐end strategy (“CNN (end‐to‐end)”) are generally closer to the runoff values from ERA5‐Land than the runoff simulations using parameters derived by the other two methods, particularly for the metrics RMSE and NRMSE . Although the soil‐water model used in this study was not particularly developed for the runoff simulation, it also shows the effects of using worse parameter estimates from the inverse method and two‐step strategy, as well as the impact of the issue of parameter identifiability (Beven, 2006; Yi & Park, 2021).…”

“…2. Parameters estimated by the inverse method (the first step in the two-step strategy) provide reasonable soil moisture estimates at training grids, but generally encounter the issue of parameter identifiability (Beven, 2006;Yi & Park, 2021) and large parameter deviations. After regularizing by the CNN-based integrated model, the parameter estimation improves globally, while the soil moisture simulation deteriorates.…”

“…Establishing an additional buffer distance for the static attributes, including the grid cells outside each model grid, may allow us to consider such effects (Xu et al, 2023). Also, future improvements on the CNN-based integrated model can involve novel structures like ConvLSTM (Li et al, 2021;Shi et al, 2015) that can enhance the utilization of the spatio-temporally varying predictors (e.g., dynamic landscape features), adopting more advanced loss functions like Kling-Gupta efficiency (Gupta et al, 2009), and applying multi-objective optimization (e.g., soil moisture, terrestrial water storage, and runoff) to further avoid the issue of parameter identifiability (Beven, 2006;Yi & Park, 2021). Our approach can be flexibly transferred to other regions, improving the regionalization of the model and increasing our understanding of the natural environment.…”