Assessing the effect of rating curve uncertainty in streamflow simulation on Kulfo watershed, Southern Ethiopia DOI Creative Commons
Nahom Bekele Mena,

Elias Gebeyehu Ayele,

Henok Gubula Chora

et al.

Journal of Water and Climate Change, Journal Year: 2024, Volume and Issue: 15(9), P. 4199 - 4219

Published: Sept. 1, 2024

ABSTRACT Accurate streamflow simulation and comprehending its associated uncertainty are crucial for effective water resource management. However, the of rating curves from which data is derived remains poorly understood. This study aims to simulate under curve conditions. The bootstrap resampling technique (BSRT) was used establish estimate uncertainty. Furthermore, it integrated with standalone hybrid models (GRU, Bi-LSTM, Conv1D-LSTM), assess effect this on simulation. Different lag times rainfall discharge as input DL models. Despite complexity, Conv1D-LSTM model did not outperform Bi-LSTM model. slightly outperforms GRU Moreover, significantly propagates simulation, particularly in high-flow regions. Consequently, uncertainties related Kulfo River led a about 17.8 m3 s−1, representing 22% at peak discharge. performance evaluated using different metrics (RMSE, MAE, NSE, R2). findings underscore importance considering enhance management practices support informed decision-making area.

Language: Английский

Assessing the effect of rating curve uncertainty in streamflow simulation on Kulfo watershed, Southern Ethiopia DOI Creative Commons
Nahom Bekele Mena,

Elias Gebeyehu Ayele,

Henok Gubula Chora

et al.

Journal of Water and Climate Change, Journal Year: 2024, Volume and Issue: 15(9), P. 4199 - 4219

Published: Sept. 1, 2024

ABSTRACT Accurate streamflow simulation and comprehending its associated uncertainty are crucial for effective water resource management. However, the of rating curves from which data is derived remains poorly understood. This study aims to simulate under curve conditions. The bootstrap resampling technique (BSRT) was used establish estimate uncertainty. Furthermore, it integrated with standalone hybrid models (GRU, Bi-LSTM, Conv1D-LSTM), assess effect this on simulation. Different lag times rainfall discharge as input DL models. Despite complexity, Conv1D-LSTM model did not outperform Bi-LSTM model. slightly outperforms GRU Moreover, significantly propagates simulation, particularly in high-flow regions. Consequently, uncertainties related Kulfo River led a about 17.8 m3 s−1, representing 22% at peak discharge. performance evaluated using different metrics (RMSE, MAE, NSE, R2). findings underscore importance considering enhance management practices support informed decision-making area.

Language: Английский

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