Parameter optimization of sediment runoff simulation employing acoustic bedload observation

We developed an automatic parameter optimization system that calibrates sediment runoff simulations to match bedload transport rates measured by A Japanese pipe-microphone in a mountainous catchment in Japan. The sediment runoff simulation incorporates both rainfall-runoff processes and sediment production from off-channel sources (i.e., sediment from hillslopes), combining bedload and suspended load transport modeling. The optimization was conducted in two stages. First, rainfall-runoff parameters, such as soil layer thickness and hydraulic conductivity, were optimized to reproduce observed water discharge. Subsequently, sediment transport-related parameters, including the spatial distribution of riverbed grain size, sediment supply volume from hillslopes, grain size distribution of supplied sediment, and channel width, were optimized to improve the accuracy of sediment discharge predictions. Using the optimized parameter sets, the simulation achieved high predictive performance. The Nash-Sutcliffe Efficiency (NSE) for the water discharge was 0.82, and the NSE for sediment discharge was 0.54, indicating that the model successfully captured both the hydrological and sediment transport dynamics of the target basin. This study demonstrates the feasibility of combining surrogate sediment monitoring with process-based sediment runoff modeling to achieve efficient and accurate parameter calibration, even in complex mountainous environments where sediment production from off-channel sources plays a significant role.

Authors

Publication Details

Journal
mediaTUM (Technical University of Munich)
Published
2026-10-15
DOI
https://doi.org/10.14459/2026md1861063
Primary Topic
Soil erosion and sediment transport
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Parameter optimization of sediment runoff simulation employing acoustic bedload observation

Kazuki YAMANOI, Tomoya IWAI, Shusuke Miyata
mediaTUM (Technical University of Munich)
Soil erosion and sediment transport
article

Parameter optimization of sediment runoff simulation employing acoustic bedload observation

Kazuki YAMANOI, Tomoya IWAI, Shusuke Miyata
article en

Abstract

We developed an automatic parameter optimization system that calibrates sediment runoff simulations to match bedload transport rates measured by A Japanese pipe-microphone in a mountainous catchment in Japan. The sediment runoff simulation incorporates both rainfall-runoff processes and sediment production from off-channel sources (i.e., sediment from hillslopes), combining bedload and suspended load transport modeling. The optimization was conducted in two stages. First, rainfall-runoff parameters, such as soil layer thickness and hydraulic conductivity, were optimized to reproduce observed water discharge. Subsequently, sediment transport-related parameters, including the spatial distribution of riverbed grain size, sediment supply volume from hillslopes, grain size distribution of supplied sediment, and channel width, were optimized to improve the accuracy of sediment discharge predictions. Using the optimized parameter sets, the simulation achieved high predictive performance. The Nash-Sutcliffe Efficiency (NSE) for the water discharge was 0.82, and the NSE for sediment discharge was 0.54, indicating that the model successfully captured both the hydrological and sediment transport dynamics of the target basin. This study demonstrates the feasibility of combining surrogate sediment monitoring with process-based sediment runoff modeling to achieve efficient and accurate parameter calibration, even in complex mountainous environments where sediment production from off-channel sources plays a significant role.

mediaTUM (Technical University of Munich)
Openalex Percentile: Top 15%
Soil erosion and sediment transport
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.