Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations

Journal Article · Wed Dec 20 00:00:00 EST 2023 · Hydrology and Earth System Sciences (Online)

DOI:https://doi.org/10.5194/hess-27-4529-2023· OSTI ID:2246985

Zhang, Yuhang; Ye, Aizhong; Analui, Bita; Nguyen, Phu; Sorooshian, Soroosh; Hsu, Kuolin; Wang, Yuxuan

Abstract. Deep learning (DL) and machine learning (ML) are widely used in hydrological modelling, which plays a critical role in improving the accuracy of hydrological predictions. However, the trade-off between model performance and computational cost has always been a challenge for hydrologists when selecting a suitable model, particularly for probabilistic post-processing with large ensemble members. This study aims to systematically compare the quantile regression forest (QRF) model and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) model as hydrological probabilistic post-processors. Specifically, we evaluate their ability in dealing with biased streamflow simulations driven by three satellite precipitation products across 522 nested sub-basins of the Yalong River basin in China. Model performance is comprehensively assessed using a series of scoring metrics from both probabilistic and deterministic perspectives. Our results show that the QRF model and the CMAL-LSTM model are comparable in terms of probabilistic prediction, and their performances are closely related to the flow accumulation area (FAA) of the sub-basin. The QRF model outperforms the CMAL-LSTM model in most sub-basins with smaller FAA, while the CMAL-LSTM model has an undebatable advantage in sub-basins with FAA larger than 60 000 km2 in the Yalong River basin. In terms of deterministic predictions, the CMAL-LSTM model is preferred, especially when the raw streamflow is poorly simulated and used as input. However, setting aside the differences in model performance, the QRF model with 100-member quantiles demonstrates a noteworthy advantage by exhibiting a 50 % reduction in computation time compared to the CMAL-LSTM model with the same ensemble members in all experiments. As a result, this study provides insights into model selection in hydrological post-processing and the trade-offs between model performance and computational efficiency. The findings highlight the importance of considering the specific application scenario, such as the catchment size and the required accuracy level, when selecting a suitable model for hydrological post-processing.

• Research Organization: Univ. of California, Oakland, CA (United States)
• Sponsoring Organization: USDOE; USDOE Office of International Affairs (IA)
• Grant/Contract Number: IA0000018
• OSTI ID: 2246985
• Alternate ID(s): OSTI ID: 2580422
• Journal Information: Hydrology and Earth System Sciences (Online), Journal Name: Hydrology and Earth System Sciences (Online) Journal Issue: 24 Vol. 27; ISSN 1607-7938
• Publisher: Copernicus GmbH
• Country of Publication: Germany
• Language: English

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