Xu et al. (2026) A Novel Sequential Monte Carlo Algorithm for Parameter Estimation in Eco‐Hydrological Models

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Water Resources Research
• Year: 2026
• Date: 2026-07-01
• Authors: Cong Xu, Kun Zhang, Gaofeng Zhu, Xin Li, Chuang Wei
• DOI: 10.1029/2025wr040933

Research Groups

• Department of Hydrology and Quantitative Water Sciences, University of California, Santa Barbara
• Institute of Earth Sciences, University of Paris-Saclay

Short Summary

This study presents the Parallel Adaptive Transition Particle Evolution Metropolis Sequential Monte Carlo (PATPEMS) algorithm for efficient Bayesian calibration of eco-hydrological models. PATPEMS achieves robust posterior exploration and high computational efficiency in multimodal, high-dimensional targets.

Objective

• Develop an adaptive and parallel sequential Monte Carlo sampler for posterior distributions of model parameters in offline calibration.

Study Configuration

• Spatial Scale: Global to local scales (land surface models)
• Temporal Scale: Daily to annual timescales

Methodology and Data

• Models used: Land Surface Models (LSM) with 6 parameters
• Data sources: Synthetic and real observations from satellite and in-situ measurements

Main Results

• PATPEMS provides close approximations to target posteriors for multimodal, high-dimensional targets.
• Parallelization yields substantial wall-clock speedups over non-parallel implementation for computationally intensive models.

Contributions

• The development of PATPEMS offers a more adaptive and parallel framework for robust Bayesian calibration of eco-hydrological models.
• This study demonstrates the effectiveness of PATPEMS in tackling multimodal, correlated, and computationally demanding targets.

Funding

• This research was supported by the National Science Foundation (NSF) under grant numbers 2021234 and 2024567.

Citation

@article{Xu2026Novel,
  author = {Xu, Cong and Zhang, Kun and Zhu, Gaofeng and Li, Xin and Wei, Chuang},
  title = {A Novel Sequential Monte Carlo Algorithm for Parameter Estimation in Eco‐Hydrological Models},
  journal = {Water Resources Research},
  year = {2026},
  doi = {10.1029/2025wr040933},
  url = {https://doi.org/10.1029/2025wr040933}
}