Performance of datasets in hydrological simulation in various basin scales：An investigation on hydrological lumped effect in the Lancang-Mekong region
Year: 2025
Type: article
Abstract: Laos area in the Lancang-Mekong River Basin (LMRB). Choosing appropriate models and precipitation to capture hydrological conditions and manage water resources in various basin scales is crucial. However, when dealing with smaller-scale basins, the suitability of different datasets remains a challenge, particularly in regions characterized by high spatial heterogeneity. In this study, we utilized ERA5-Land data within LMRB and rain gauge data within Laos to calibrate seven mainstream stations and eleven tributary stations from 1981 to 2000 using the Xin'anjiang (XAJ) model. Subsequently, we evaluated the performance of four datasets from 2001 to 2009, including rain gauge data and three gridded datasets (ERA5-Land, IMERG-Final, and APHRODITE). Our analysis revealed that ERA5-Land data outperformed overall among the four datasets, with mainstream stations achieving an average Nash-Sutcliffe Efficiency (NSE) of 0.86 and a 19 % Mean Absolute Relative Error (MARE), while tributary stations exhibited an average NSE of 0.64 with a 59 % MARE. During the validation, the hydrological simulations based on the three gridded precipitation datasets outperformed the simulations using rain gauge data at the eight tributary stations in Laos. Furthermore, a discernible trend is observed among these stations: as the basin scales increase, simulation outcomes improve, emphasizing a distinct hydrological lumped effect. This study can provide a crucial reference for hydrological simulations and water resource management in Laos. • Evaluation of gridded precipitation and Xin’anjiang model in Laos for LMRB. • ERA5-Land surpassed rain gauge data in hydrological simulation in the tributary area. • The larger the basin scale, the more favorable the simulation outcomes in the LMRB.
Authors: Hui Liu, Zhiqiang Dong, Baiyin Baoligao, Vannaphone Phetpaseuth, Thongthip Chandalasane, Wenxue Chen, Xiangpeng Mu, Dengfeng Liu, Lajiao Chen, Xiaochen Li, Hongchang Hu, Jie Wen
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,600
OpenAlex ID: W7082977048