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A stochastic deep-learning-based approach for improved streamflow simulation

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Abstract

Post-processing using deep learning algorithms can be conducted to improve accuracy of hydrologic predictions and quantify their uncertainty. In this paper, a revised version of the Local uncertainty estimation model (LUEM-R) and the circular block bootstrap (CBB) method have been used to improve the accuracy of the Variable Infiltration Capacity’s (VIC) streamflow simulation and quantify its uncertainty. We used the simulated and observed streamflow at the gauge located in North CAPE Fear basin, USA from the Dayflow dataset. In the LUEM-R method, a combination of Gaussian Mixture Model and Long Short-Term Memory (LSTM) networks, which are able to capture the dependencies in time series, were used to construct the upper and lower prediction limits (PLs) for the 90% confidence level. In the CBB method, a circular block resampling technique was used to account for the dependencies in the time series (CBB-LSTM). The improved streamflow, calculated as the mean of the LSTM outputs for 200 bootstrap realizations, showed a very high correlation with observations, with coefficient of determination values of 0.97 and 0.87 for the training and testing periods, compared to the 0.77 and 0.74 values for the initial VIC simulations. For the CBB-LSTM method, the 90% PLs were constructed by fitting the best distribution at each time step, accordingly. The PLs bracketed 90% and 70% of observations in the training and testing periods, while being significantly narrower than the LUEM-R bands, which contained 92 and 91 percent of observations in the training and testing periods. Ordinary bootstrapping was also conducted using the Random Forest model (OB-RF). Comparison of the results indicates the superiority of CBB-LSTM method to the OB-RF in improving the accuracy and quantifying the uncertainties in hydrological model simulations. Overall, the CBB-LSTM was successful in improving the deterministic accuracy of VIC model simulations and quantifying its uncertainty. Also, the LUEM-R method could be efficiently utilized in quantification of uncertainty of VIC model simulations.

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    Neda Dolatabadi & Banafsheh Zahraie

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Conceptualization: [ND]; Methodology: [ND, BZ]; Formal analysis and investigation: [ND], Writing: original draft preparation: [ND]; Writing: review and editing: [BZ]; All authors read and approved the final manuscript.

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Correspondence to Banafsheh Zahraie.

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Appendix

Appendix

Sensitive parameters of the VIC model and the corresponding limits are presented in Table A1.

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Table 10 Parameters of the VIC model and their feasible bounds
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Dolatabadi, N., Zahraie, B. A stochastic deep-learning-based approach for improved streamflow simulation. Stoch Environ Res Risk Assess 38, 107–126 (2024). https://doi.org/10.1007/s00477-023-02567-1

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