Sediment supply to rivers, subsequent fluvial transport, and the resulting sediment connectivity on network scales are often sparsely monitored and subject to major uncertainty. We propose to approach that uncertainty by adopting a stochastic method for modeling network sediment connectivity, which we present for the Se Kong, Se San, and Sre Pok (3S) tributaries of the Mekong. We quantify how unknown properties of sand sources translate into uncertainty regarding network connectivity by running the CASCADE (CAtchment Sediment Connectivity And DElivery) modeling framework in a Monte Carlo approach for 7,500 random realizations. Only a small ensemble of realizations reproduces downstream observations of sand transport. This ensemble presents an inverse stochastic approximation of the magnitude and variability of transport capacity, sediment flux, and grain size distribution of the sediment transported in the network (i.e., upscaling point observations to the entire network). The approximated magnitude of sand delivered from each tributary to the Mekong is controlled by reaches of low transport capacity (“bottlenecks”). These bottlenecks limit the ability to predict transport in the upper parts of the catchment through inverse stochastic approximation, a limitation that could be addressed by targeted monitoring upstream of identified bottlenecks. Nonetheless, bottlenecks also allow a clear partitioning of natural sand deliveries from the 3S to the Mekong, with the Se Kong delivering less (1.9 Mt/yr) and coarser (median grain size: 0.4 mm) sand than the Se San (5.3 Mt/yr, 0.22 mm) and Sre Pok (11 Mt/yr, 0.19 mm).

Stochastic Modeling of Sediment Connectivity for Reconstructing Sand Fluxes and Origins in the Unmonitored Se Kong, Se San, and Sre Pok Tributaries of the Mekong River

Schmitt, R. J. P.;Bizzi, S.;Castelletti, A. F.;
2018-01-01

Abstract

Sediment supply to rivers, subsequent fluvial transport, and the resulting sediment connectivity on network scales are often sparsely monitored and subject to major uncertainty. We propose to approach that uncertainty by adopting a stochastic method for modeling network sediment connectivity, which we present for the Se Kong, Se San, and Sre Pok (3S) tributaries of the Mekong. We quantify how unknown properties of sand sources translate into uncertainty regarding network connectivity by running the CASCADE (CAtchment Sediment Connectivity And DElivery) modeling framework in a Monte Carlo approach for 7,500 random realizations. Only a small ensemble of realizations reproduces downstream observations of sand transport. This ensemble presents an inverse stochastic approximation of the magnitude and variability of transport capacity, sediment flux, and grain size distribution of the sediment transported in the network (i.e., upscaling point observations to the entire network). The approximated magnitude of sand delivered from each tributary to the Mekong is controlled by reaches of low transport capacity (“bottlenecks”). These bottlenecks limit the ability to predict transport in the upper parts of the catchment through inverse stochastic approximation, a limitation that could be addressed by targeted monitoring upstream of identified bottlenecks. Nonetheless, bottlenecks also allow a clear partitioning of natural sand deliveries from the 3S to the Mekong, with the Se Kong delivering less (1.9 Mt/yr) and coarser (median grain size: 0.4 mm) sand than the Se San (5.3 Mt/yr, 0.22 mm) and Sre Pok (11 Mt/yr, 0.19 mm).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1042362
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