Mo et al. (2026) Runoff simulation in a typical karst basin based on a Robust Dynamic Merging Algorithm for multi-source satellite precipitation and an improved Xin'anjiang hydrological model

Identification

• Journal: Environmental Modelling & Software
• Year: 2026
• Date: 2026-09-29
• Authors: Chongxun Mo, Xiaojun Qin, Changhao Jiang, Na Li, Wu Zhong, 毕建春 BI Jianchun
• DOI: 10.1016/j.envsoft.2026.107185

Research Groups

College of Architecture and Civil Engineering, Guangxi University, Nanning, China
Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, College of Civil Engineering and Architecture, Guangxi University, Nanning, China

Short Summary

This study presents an integrated modeling framework combining the Robust Dynamic Merging Algorithm (RDMA) with the improved Xin'anjiang (KXAJ) model to simulate runoff in karst basins. The framework outperforms single-source satellite precipitation products and demonstrates strong structural robustness for data-scarce karst basins.

Objective

• Investigate the effectiveness of an integrated modeling framework combining RDMA and KXAJ models for simulating runoff in karst basins.
• Evaluate the performance of the framework using multi-source uncertainty analysis and error attribution.

Study Configuration

• Spatial Scale: Chengbi River Basin, a typical karst basin in China.
• Temporal Scale: Daily time step from 2015 to 2020.

Methodology and Data

• Models used: Robust Dynamic Merging Algorithm (RDMA) for multi-source satellite precipitation merging and improved Xin'anjiang (KXAJ) hydrological model.
• Data sources: Five satellite precipitation products (IMERG, CHIRPS, CMORPH, PERSIANN, ERA5 reanalysis), and in-situ observations.

Main Results

• The merged product (RDMAP) outperforms the best single-source product, improving Kling-Gupta Efficiency (KGE) by 10.96% and reducing Mean Absolute Error (MAE) by 34.83%.
• During validation, the framework improves Nash-Sutcliffe Efficiency (NSE) by 5.56% and decreases Root Mean Square Error (RMSE) by 7.48%.

Contributions

• The integrated modeling framework demonstrates strong structural robustness for data-scarce karst basins.
• RDMA is effective in enhancing temporal consistency of precipitation inputs.

Funding

• This research was supported by the National Natural Science Foundation of China (Grant No. 51909237) and the Guangxi University Research Fund (Grant No. XGZ202104).

Citation

@article{Mo2026Runoff,
  author = {Mo, Chongxun and Qin, Xiaojun and Jiang, Changhao and Li, Na and Zhong, Wu and Jianchun, 毕建春 BI},
  title = {Runoff simulation in a typical karst basin based on a Robust Dynamic Merging Algorithm for multi-source satellite precipitation and an improved Xin'anjiang hydrological model},
  journal = {Environmental Modelling & Software},
  year = {2026},
  doi = {10.1016/j.envsoft.2026.107185},
  url = {https://doi.org/10.1016/j.envsoft.2026.107185}
}