In the Mun River Basin, the Mekong's largest tributary, researchers developed a sediment-rating-curve regionalisation framework for sparse monitoring conditions. Flood, normal and drought years were separated before comparing regression and catchment-similarity approaches. Sediment-discharge behaviour varied by hydrological condition, while the preferred method depended on slope and erosion-related catchment attributes, arguing against a single static curve for every place and year.
Key findings
- Rating-curve parameters differed across flood, normal and drought years; the model indicated that interannual flow variability influenced sediment response more strongly than basin-wide land-use change.
- Regression was more suitable for gentler catchments with S_c below 6.0% and K_USLE×C_USLE above 0.05.
- Catchment similarity was more suitable for steeper catchments with S_c above 6.0% and K_USLE×C_USLE below 0.05, where sediment processes were more spatially heterogeneous.
Why this matters globally
The framework may improve sediment-load estimation where monitoring is sparse, informing reservoirs, water quality and flood planning in the Mekong system. Hydrological stratification could transfer elsewhere, but Mun-derived thresholds need external validation.
Thai researcher contribution
Nantawoot Inseeyong, Pavisorn Chuenchum and Jeerapong Laonamsai of Chulalongkorn University, with Kwanchai Pakoksung of Khon Kaen University, developed the Thai Mun Basin framework with Mengzhen Xu of China.
Limitations to consider
Sparse monitoring motivated the work, while the abstract omits station count, observation period, validation accuracy, external testing and uncertainty intervals. Numerical superiority should not be claimed, and Mun-derived thresholds may not transfer directly.