A dynamic prediction model for time‐to‐peak

Langridge, Mistaya; McBean, Edward A.; Bonakdari, Hossein; Gharabaghi, Bahram

doi:10.1002/hyp.14032

Cited by 4 publications

(6 citation statements)

References 26 publications

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“…Thus, shallow flows resulting from relatively small runoff events will be slow. This may be why the equation of Langridge et al (2021), which includes representations of the climate and the peak discharge, performed relatively well in this study. Cen et al (2022) found that unit discharge was the most important factor in determining the transition from laminar to transitional flows, in flume experiments using synthetic vegetation.…”

Section: Darcy-weisbach Roughness Coefficient Fmentioning

confidence: 66%

“…According to Langridge et al (2021), "wet" basins have R c values greater than or equal to 0.7; basins having R c values less than 0.5 are classified as "dry". Values of C 1 and C 2 are provided for "wet", "average" and "dry" basins in seasons which are assumed to be "Wet" (December through March), "Dry" (June through September) and "Average" (April, May, October, and November).…”

Section: Response Times From Existing Empirical Equationsmentioning

confidence: 99%

“…The areas of the basins used by Sheridan (1994) overlap those of the study basins, but the original basin areas in ponds and lakes ranged between 0.11 and 2.34%, which are much smaller than in many Prairie basins. The areas of the basins used by Langridge et al (2021) are not known, but the climates of their basins are far wetter than the Canadian Prairies.…”

Section: Response Times From Existing Empirical Equationsmentioning

confidence: 99%

“…The relatively good agreement between t pL and the observed t p values is interesting because many of the equation parameters (L, S, DA) are also used by the other empirical equations which fared much worse. It is worth noting that unlike the other equations, Langridge et al (2021) includes the effects of climate (though the constants C 1 and C 2 ) and the peak discharge.…”

Section: Observed Times To Peakmentioning

confidence: 99%

“…Langridge et al (2021) developed a modified version of the model first presented inLangridge et al (2020). The revised model replaced coefficients defining the basin wetness, which required values rarely measured in North America, with coefficients whose values are more easily determined.…”

mentioning

confidence: 99%

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Estimating response times, flow velocities and roughness coefficients of Canadian Prairie basins

Shook

Whitfield

Spence

et al. 2023

Preprint

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Abstract. The hydrology and hydrography of the Canadian Prairies are complex and difficult to represent in hydrological models. Recent studies suggest that runoff velocities in the Canadian Prairies may be much smaller than are generally assumed. Times to peak, basin-scale flow velocities and roughnesses were derived from hourly streamflow hydrographs from 23 basins in the central Alberta Prairies. The estimated velocities were much smaller than would be estimated from most commonly used empirical equations suggesting that many existing methods are not suitable for estimating time to peak or lag times in these basins. Basin area was found to be a poor predictor of basin-scale rainfall-runoff flow velocity. Estimated velocities generally increased with basin scale, indicating that slow basin response at small scales could be related to predominance of overland and/or shallow subsurface flow over the very level topography. Basin-scale Manning’s roughness parameters, commonly used in hydrological models, were found to be orders of magnitude greater than values commonly used for streams in other parts of the world. The very large values of roughness call into question whether the Manning equation should be used for modelling runoff on the Prairies. These results have important implications for modelling rainfall-runoff in this region since using widely published values of roughness will result in poor model performance. It is likely that the Darcy-Weisbach equation, which is applicable to all flow regimes, may perform better in hydrological models of this region. Further modelling and field research will be required to determine the physical causes of these very small basin-scale velocities.

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Section: Darcy-weisbach Roughness Coefficient Fmentioning

confidence: 66%

Section: Response Times From Existing Empirical Equationsmentioning

confidence: 99%

Section: Response Times From Existing Empirical Equationsmentioning

confidence: 99%

Section: Observed Times To Peakmentioning

confidence: 99%

mentioning

confidence: 99%

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Estimating response times, flow velocities and roughness coefficients of Canadian Prairie basins

Shook

Whitfield

Spence

et al. 2023

Preprint

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Early detection of riverine flooding events using the group method of data handling for the Bow River, Alberta, Canada

Elkurdy

Binns

Bonakdari

et al. 2021

International Journal of River Basin Management

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Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing

Zhang,

Zhou,

Van Griensven Thé

et al. 2023

Water

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Climate change and urbanization have increased the frequency of floods worldwide, resulting in substantial casualties and property loss. Accurate flood forecasting can offer governments early warnings about impending flood disasters, giving them a chance to evacuate and save lives. Deep learning is used in flood forecasting to improve the timeliness and accuracy of flood water level predictions. While various deep learning models similar to Long Short-Term Memory (LSTM) have achieved notable results, they have complex structures with low computational efficiency, and often lack generalizability and stability. This study applies a spatiotemporal Attention Gated Recurrent Unit (STA-GRU) model for flood prediction to increase the models’ computing efficiency. Another salient feature of our methodology is the incorporation of lag time during data preprocessing before the training of the model. Notably, for 12-h forecasting, the STA-GRU model’s R-squared (R2) value increased from 0.8125 to 0.9215. Concurrently, the model manifested reduced root mean squared error (RMSE) and mean absolute error (MAE) metrics. For a more extended 24-h forecasting, the R2 value of the STA-GRU model improved from 0.6181 to 0.7283, accompanied by diminishing RMSE and MAE values. Seven typical deep learning models—the LSTM, the Convolutional Neural Networks LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the spatiotemporal Attention Long Short-Term Memory (STA-LSTM), the GRU, the Convolutional Neural Networks GRU (CNNGRU), and the STA-GRU—are compared for water level prediction. Comparative analysis delineated that the use of the STA-GRU model and the application of the lag time pre-processing method significantly improved the reliability and accuracy of flood forecasting.

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A dynamic prediction model for time‐to‐peak

Cited by 4 publications

References 26 publications

Estimating response times, flow velocities and roughness coefficients of Canadian Prairie basins

Estimating response times, flow velocities and roughness coefficients of Canadian Prairie basins

Early detection of riverine flooding events using the group method of data handling for the Bow River, Alberta, Canada

Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing

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