A Sensitivity Analysis Approach to Identifying Drivers of Streamflow Hysteresis

We study the critical interplay between streamflow hysteresis and local hydro-morphologic conditions. Hysteresis strength, which can lead to over 60% errors in discharge estimation with widely accepted monitoring methods, is primarily influenced by local characteristics such as bed slope, roughness, and event wave intensity. Our study used a 1D numerical model to highlight the hysteresis response to local hydro-morphological changes acting in isolation or combination. The sensitivity study entails gradual variation of the hydrograph intensity, backwater condition, bed slope (0.0001

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Publication Details

Published
2025-09-28
DOI
https://doi.org/10.31223/x5cj1q
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

A Sensitivity Analysis Approach to Identifying Drivers of Streamflow Hysteresis

Marian Muste, İbrahim Demir, Ehab Meselhe, Kyeongdong Kim et al.
Model Reduction and Neural Networks
article

A Sensitivity Analysis Approach to Identifying Drivers of Streamflow Hysteresis

Marian Muste, İbrahim Demir, Ehab Meselhe, Kyeongdong Kim, Emma House
article en

Abstract

We study the critical interplay between streamflow hysteresis and local hydro-morphologic conditions. Hysteresis strength, which can lead to over 60% errors in discharge estimation with widely accepted monitoring methods, is primarily influenced by local characteristics such as bed slope, roughness, and event wave intensity. Our study used a 1D numerical model to highlight the hysteresis response to local hydro-morphological changes acting in isolation or combination. The sensitivity study entails gradual variation of the hydrograph intensity, backwater condition, bed slope (0.0001

National Science Foundation
Openalex Percentile: Top 79%
Model Reduction and Neural Networks
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