Stochastic Sediment Connectivity Model for Mekong River Tributaries

Rafael Schmitt from UC Berkeley presents a stochastic model ensemble for sand connectivity in the Se Kong, Se San, and Sre Pok tributaries of the Mekong River. The model uses a Monte Carlo approach with the CASCADE framework to quantify uncertainty in sediment sources and upscale point observations to the entire network. The inverse stochastic approximation partitions sand deliveries, estimating fluxes of 1.9 Mt/yr from Se Kong, 5.3 Mt/yr from Se San, and 11 Mt/yr from Sre Pok.

Use Cases

• Quantifying uncertainty in network sediment connectivity based on unknown source properties
• Partitioning sediment contributions from different tributaries to a main river system
• Identifying transport capacity bottlenecks that control sediment flux magnitude
• Upscaling point observations of sediment transport to an entire river network

Strengths

• Model ensemble quantifies specific sediment fluxes: 1.9 Mt/yr (Se Kong), 5.3 Mt/yr (Se San), and 11 Mt/yr (Sre Pok).
• Provides median grain size estimates for each tributary: 0.4 mm (Se Kong), 0.22 mm (Se San), and 0.19 mm (Sre Pok).
• Applies a stochastic Monte Carlo approach to address uncertainty in sparsely monitored systems.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

University of California, Berkeley

Collection Method

Stochastic modeling using the CASCADE framework with Monte Carlo random initializations.

Geography

Se Kong, Se San, and Sre Pok tributaries of the Mekong River